Dog Cloning For Special Forces: Breed All You Can Breed

Decision analysis of whether cloning the most elite Special Forces dogs is a profitable improvement over standard selection procedures. Unless training is extremely cheap or heritability is extremely low, dog cloning is hypothetically profitable.
genetics, decision-theory, R, bibliography, order-statistics
2018-09-182019-08-24 finished certainty: possible importance: 3


Cloning is widely used in an­i­mal & plant breed­ing de­spite steep costs due to its ad­van­tages; more un­usual re­cent ap­pli­ca­tions in­clude cre­at­ing en­tire polo horse teams and re­ported tri­als of cloning in elite po­lice/Spe­cial Forces war dogs. Given the cost of dog cloning, how­ev­er, can this ever make more sense than stan­dard screen­ing meth­ods for se­lect­ing from work­ing dog breeds, or would the in­crease in suc­cess­ful dog train­ing be too low un­der all rea­son­able mod­els to turn a profit?

I model the ques­tion as one of ex­pected cost per dog with the trait of suc­cess­fully pass­ing train­ing, suc­cess in train­ing be­ing a di­choto­mous li­a­bil­ity thresh­old with a poly­genic ge­netic ar­chi­tec­ture; given the ex­treme level of se­lec­tion pos­si­ble in se­lect­ing the best among al­ready-elite Spe­cial Forces dogs and a range of her­i­tabil­i­ties, this pre­dicts clones’ suc­cess prob­a­bil­i­ties. To ap­prox­i­mate the rel­e­vant pa­ra­me­ters, I look at some re­ported train­ing costs and suc­cess rates for reg­u­lar dog can­di­dates, broad dog her­i­tabil­i­ties, and the few cur­rent dog cloning case stud­ies re­ported in the me­dia.

Since none of the rel­e­vant pa­ra­me­ters are known with con­fi­dence, I run the cost-ben­e­fit equa­tion for many hy­po­thet­i­cal sce­nar­ios, and find that in a large frac­tion of them cov­er­ing most plau­si­ble val­ues, dog cloning would im­prove train­ing yields enough to be profitable (in ad­di­tion to its other ad­van­tages).

As fur­ther il­lus­tra­tion of the use-case of screen­ing for an ex­treme out­come based on a par­tial pre­dic­tor, I con­sider the ques­tion of whether height PGSes could be used to screen the US pop­u­la­tion for peo­ple of NBA height, which turns out to be rea­son­ably doable with cur­rent & fu­ture PGSes.

and are spe­cially trained for their jobs. Only some dogs are up to the task, but like train­ing see­ing-eye guide dogs, it’s diffi­cult to know in ad­vance and many dogs will wash out of train­ing as ex­pen­sive fail­ures, with even fewer be­ing able to han­dle the ex­treme life of a Spe­cial Forces dog; then they may get in­jured on the job, de­velop or can­cer, cut­ting short their ca­reer, and lead­ing to peren­nial short­ages. This is de­spite the best efforts of the (mostly Eu­ro­pean) breed­ers who raise the , , , and pre­ferred for war dogs.

The 7 To­mor­row Dogs/­Top­pies cloned in 2007.

In 2014, Bloomberg re­ported on an in­ter­est­ing as­pect of Sooam Biotech, the fa­mous South Ko­rean dog cloning com­pa­ny: they were cloning a Spe­cial Forces dog. If it’s hard to be a K9, it’s even harder to be a SF dog, able to jump out of air­planes (they have spe­cial para­chute har­ness­es), go on raids, carry cam­eras with them, even (re­port­ed­ly) wear lit­tle dog­gie hoods with in­frared cam­era gog­gles for night work; so valu­able and spe­cial­ized are such dogs that spe­cial $20,000 an­i­ma­tronic dog mod­els like the “K9 Hero-Trauma” are sold to train medics how to treat in­juries like gun­shot wounds or am­pu­ta­tions. If you have a suc­cess­ful SF dog… maybe the clone will be much more likely to suc­ceed than a ran­dom puppy picked from one of the usual breed­ers, and you can make as many clones as nec­es­sary long after the orig­i­nal has gone to Dog Heav­en.

Clones of elite in­di­vid­u­als are in­creas­ingly com­mon in agri­cul­ture; plants, like the myr­i­ads of ap­ple va­ri­eties, have al­ways been prop­a­gated clon­al­ly, but cloning of cat­tle has made ma­jor com­mer­cial in­roads1—not just cloning of cat­tle for beef or cows for milk, but also clones of rodeo bulls (the log­i­cal ex­ten­sion of the highly suc­cess­ful se­lec­tive breed­ing for rodeo bulls). A strik­ing ex­am­ple of this ap­proach is the world polo cham­pion , who is so en­thu­si­as­tic about the ben­e­fits of horse cloning that he has cloned his prized polo horse not once but >10 times, and has rode en­tire teams of clones to re­peated vic­to­ry. On the other hand, dog clones are still ex­tremely ex­pen­sive (~$100,000) and prices have not yet come down to the ~$10,000–$20,000 of cat­tle.

There may be cheaper al­ter­na­tives to im­prov­ing SF dog yield: train­ing is prob­a­bly well-re­fined and can’t be wa­tered down with­out risk­ing lives, but that leaves a place for im­prove­ment of what is trained, the se­lec­tion into train­ing—­bet­ter pre­dic­tion of SF po­ten­tial means fewer dogs wash­ing out means less to­tal money spent to pro­duce a suc­cess­ful SF dog. The pre­dic­tions don’t work well, but the de­scrip­tions of screen­ing sug­gest there’s a lot of room for im­prove­ment: the re­search lit­er­a­ture sup­ports the gen­er­al­iza­tion that dog and cat be­hav­ioral mea­sure­ments are not all that pre­dic­tive. They may be badly de­signed or test­ing the wrong things, or there may be in­her­ent noise which can be fixed by do­ing mul­ti­ple mea­sure­ments. (Even some­thing as ap­par­ently me­chan­i­cal as offer­ing to a cat can have and may have rater-spe­cific effects, per­haps be­cause—“set and set­ting”—the cat is fear­ful and dis­trusts the per­son offer­ing the cat­nip that day, with the anx­i­ety shut­ting down any re­sponse or play.) Many de­scribed mea­sure­ments in the lit­er­a­ture mea­sure a dog on­ce, on one day, by one per­son, for ex­am­ple, mea­sur­ing ag­gres­sive­ness by tak­ing away food and see­ing if the dog snaps at the per­son, and that’s the whole test. Such a test will be hin­dered by day-to-day vari­a­tion (per­haps he is stressed that day), differ­ent lev­els of lik­ing for that par­tic­u­lar food, dis­lik­ing of the per­son tak­ing the food, sheer ran­dom­ness in the par­tic­u­lar split-sec­ond de­ci­sion of whether the dog de­cides to ex­press their ag­gres­sion—­likely would be much sta­bler and pre­dic­tive if they were done mul­ti­ple times in mul­ti­ple ways by mul­ti­ple peo­ple etc (although such ex­tended test­ing would in­crease the cost of test­ing). Of course, that would take more time and would cost a lot more, and it’s un­clear the in­crease in pre­dic­tions is worth it.

How­ev­er, rank­ing for se­lec­tion is eas­ier than pre­dic­tion of all dat­a­points: only the or­der­ing mat­ters, and only the or­der­ing in a par­tic­u­lar re­gion (n­ear the thresh­old) mat­ters. When con­sid­ered in a re­al-world con­text, such pre­dic­tive im­prove­ments do not need to be all that large (a point long made by psy­cho­me­tri­cians & in­dus­trial psy­chol­o­gists eg Tay­lor & Rus­sell 1939//); coun­ter­in­tu­itive­ly, a score or test which cor­re­lates, say, r = 0.10 with an out­come, which in many ar­eas of sci­ence would be dis­missed as a triv­ial cor­re­la­tion of no in­ter­est, can be quite use­ful in screen­ing & —and the rarer the out­come, the larger the ben­e­fit.2 In the case of dog cloning, our ‘score’ is the ex­tent to which a donor’s per­for­mance pre­dicts the per­for­mance of its clones, through their shared genes.

Both ap­proaches could wind up be­ing ex­pen­sive and there’s no a pri­ori an­swer about which one would be more cost-effec­tive. To a cer­tain ex­tent, they are also mu­tu­ally ex­clu­sive ap­proach­es: dog cloning is so ex­pen­sive that un­less it re­sults in high prob­a­bil­ity of suc­cess, it prob­a­bly won’t be cost-effec­tive at all, and if the prob­a­bil­ity is suffi­ciently high, then test­ing is no longer use­ful (be­cause you would save money by sim­ply try­ing to train all clones), so bet­ter test­ing is un­likely to then pay for it­self. Test­ing to gain in­for­ma­tion is only profitable in a cer­tain in­ter­me­di­ate re­gion of prob­a­bil­i­ties & cost­s/ben­e­fits.

So it’s not ab­surd to think that dog cloning could work out well for train­ing SF dogs, and I took a closer look.

Benefits

The ben­e­fits of dog cloning are not lim­ited purely to repli­cat­ing an elite SF dog. The po­ten­tial ben­e­fits of dog clones in­clude:

  1. lower to­tal cost: the pri­mary rea­son for cloning is that since dog clones are more likely to suc­ceed in train­ing given any rea­son­able her­i­tabil­i­ty, they may re­duce washout costs enough to com­pen­sate for the ex­pense of cloning.

    But the to­tal life­time cost of a dog goes be­yond suc­cess or fail­ure in be­com­ing a use­ful dog. Suc­cess­ful dogs can still learn at differ­ent rates, and re­quire more or less in­ten­sive in­ter­ven­tion by train­ers. Dogs can learn mul­ti­ple roles, so a ‘suc­cess’ may only be a par­tial suc­cess, like a dog who is ap­proved for odor de­tec­tion of bombs or drugs, but can’t be used on pa­trol or raids. They can have longer or shorter ca­reers, re­flect­ing lev­els of com­pe­tence and med­ical is­sues (hip dys­pla­sia con­stantly comes up in war dog dis­cus­sions as a dis­abling med­ical prob­lem, and is highly her­i­ta­ble).

    Hence, dis­cussing only suc­cess/­fail­ure in train­ing and the re­duc­tion in av­er­age train­ing cost will se­ri­ously un­der­es­ti­mate the ben­e­fits of cloning the best: clones of the best SF dogs will train faster, with less effort/­time, ex­cel at more roles (more likely to be ac­cept­able for at least one role), be less likely to have crip­pling med­ical is­sues that kill them or end their ca­reers pre­ma­ture­ly, and have longer ca­reers in gen­er­al.

  2. greater scal­a­bil­ity in dogs: there are only a few dog breed­ers, and they have only a rel­a­tive hand­ful of bitches at any time

    Even if de­mand spiked in a war and 1,000 more dogs were needed yes­ter­day, they would­n’t ex­ist—­dogs take a cer­tain amount of time to reach sex­ual ma­tu­ri­ty, have only so big lit­ters, mat­ing in in­bred/­nar­row pedi­grees like Ger­man Shep­herd­s/­Ma­li­nois must be man­aged care­fully to avoid ex­ac­er­bat­ing ex­ist­ing ge­netic is­sues (and eat­ing the seed corn), train­ing takes a while (Rit­land notes that the US Navy takes de­liv­ery of 2-year-old dog can­di­dates), and so on. In read­ing about US war dogs, a peren­nial theme noted by Ham­mer­strom 2005 is that a war hap­pens (WWII, Viet­nam, War on Ter­ror), war dogs be­come in­cred­i­bly use­ful to front­line troops, and dog sup­ply sim­ply can­not keep up.

    Use of cloning can break part of the bot­tle­neck by en­abling sur­ro­gacy in fe­male dogs of other breeds which are not scarce, and by en­abling un­lim­ited re­pro­duc­tion of a par­tic­u­lar dog. (This does­n’t re­quire cloning, since one could cre­ate the nec­es­sary em­bryos with stan­dard IVF, but since the IVF/surrogacy is nec­es­sary, why not use cloning as well?)

    This op­tion is highly valu­able and jus­ti­fies dog cloning on its own; and be­cause this op­tion is avail­able, mil­i­taries can more steeply re­duce war dog num­bers dur­ing peace­time as no ‘re­serve’ is nec­es­sary.3

  3. greater scal­a­bil­ity in fa­cil­i­ties: an­other bot­tle­neck might be not the num­ber of dogs, but the in­fra­struc­ture for hous­ing/­train­ing/test­ing the dogs.

    There might be only so many dog ken­nels and ex­pe­ri­enced dog train­ers at any point, and in­creas­ing the num­ber could take a while. (You prob­a­bly want the train­ers and pro­gram man­age­ment to have SF dog han­dler ex­pe­ri­ence them­selves, but it might take decades for a re­cruit to be­come an ex­pe­ri­enced train­er.) So given the in­elas­tic through­put, here it would be valu­able to im­prove the qual­ity of in­puts, which will in­crease the to­tal yield, sim­ply be­cause it means less di­lu­tion or waste of scarce fixed hous­ing/­train­ing/test­ing slots on dogs less likely to suc­ceed.

  4. greater pre­dictabil­ity:

    • Re­sponse to Train­ing: yield might be in­creased sim­ply by the in­her­ent ho­mo­gene­ity of clones al­low­ing im­proved train­ing by greater ex­pe­ri­ence, rather than any in­creased ge­netic mer­it.

      One of the rea­sons Adolfo Cam­bi­aso gives for in­vest­ing so heav­ily in clones of a sin­gle polo horse is that he has learned from his long ex­pe­ri­ence with the donor horse how best to train them: each new clone can be given per­son­al­ized train­ing which he knows it’ll re­spond best to, be­cause he’s trained many clones be­fore them. If there is some con­sis­tent weak­ness the clones are prone to, he can start ad­dress­ing it be­fore it even shows up. He also has gained long ex­pe­ri­ence with their in­jury propen­si­ties, pref­er­ences, and other be­hav­ior, in­stead of start­ing from scratch with each new colt. Their sim­i­lar­ity avoids the need for learn­ing or wasted ped­a­gogy.

      Dogs pre­sum­ably vary as much as horses do, and train­ing of clones could ben­e­fit from this sort of ho­mo­gene­ity. (S­ince dog train­ers will have never en­coun­tered clones be­fore, and iden­ti­cal twin dogs are van­ish­ingly rare, there’s no way to know how use­ful this would be in prac­tice un­til large num­bers of dog clones have been trained by in­di­vid­ual train­er­s.)

    • Re­duced Vari­ance for Ex­per­i­men­ta­tion or Analy­sis: scales poorly with in­creas­ing vari­ance; rel­a­tively small in­creases in noise can re­quire much larger n to over­come. The most effi­cient ex­per­i­ments are , which avoid com­par­isons be­tween in­di­vid­u­als, but these are often im­pos­si­ble—one could not test im­prove­ments in puppy rear­ing, for ex­am­ple, or most train­ing pro­gram changes. This is true of many things in hu­mans as well; for this rea­son, ex­per­i­ments with iden­ti­cal twins are highly effi­cient (in the , a sam­ple of n > 10,000 chil­dren could have been re­placed with n~300). Iden­ti­cal twins are re­mark­ably pow­er­ful even in the ab­sence of ran­dom­iza­tion for in­fer­ring cau­sa­tion (Turkheimer & Harden 2014) and by con­trol­ling for all ge­net­ics (which in hu­man re­search, de­bunks a large frac­tion of all cor­re­la­tional re­search in psy­chol­o­gy/­so­ci­ol­o­gy), make cor­re­la­tional analy­ses much more likely to de­liver use­ful causal in­sights. As dog iden­ti­cal twins hardly ex­ist, this has hith­erto been en­tirely un­avail­able a re­search de­sign for dog re­searchers, but clones change that.

    • Re­duced Vari­ance For Process Con­trol: given the choice be­tween a small group of clones and a much larger group of reg­u­lar dogs, such that they have os­ten­si­bly iden­ti­cal av­er­age costs & the same num­ber of ex­pected suc­cess­es, which should a breeder or trainer prefer? The small group of clones, of course.

      The large group will, by the law of small num­bers, have larger ab­solute fluc­tu­a­tions due to ran­dom­ness, es­pe­cially with a base rate like 1%. It’ll be ‘feast or famine’. Some­times there will be con­sid­er­ably more, some­times con­sid­er­ably less in ab­solute num­bers. This will com­pli­cate plan­ning great­ly, stress fa­cil­i­ties/­train­ers, risk de­liv­er­ing too few (or too many) dogs each year, and so on. Switch­ing to clones with a higher base rate will make the over­all process more con­trol­lable and pre­dictable, and this is worth some­thing.

  5. use in se­lec­tive breed­ing:

    The ma­jor use of cloning in cat­tle is for ac­cel­er­at­ing breed­ing pro­grams, and not for their im­me­di­ate mar­ginal in­crease in meat or milk yield. While dog breed­ing is not nearly as so­phis­ti­cat­ed, the ben­e­fits of cloning may also be larger for the long-term im­prove­ment in the breed than for its im­me­di­ate ben­e­fits in each cloned dog:

    • clones can im­prove Es­ti­mates Of Ge­netic Merit by pro­vid­ing the most ac­cu­rate pos­si­ble her­i­tabil­ity es­ti­mates (ge­net­i­cally iden­ti­cal in­di­vid­u­als reared in differ­ent en­vi­ron­ments), and cor­rect­ing in­di­vid­ual es­ti­mates of traits, which is vi­tal for plan­ning any kind of breed­ing or se­lec­tion pro­gram
    • a clone can have a Greater Ge­netic Po­ten­tial than the av­er­age SF dog if in­ten­sive se­lec­tion is done among SF dogs: if the best SF dog is se­lected for cloning, it’ll have a higher ge­netic po­ten­tial than the de­fault cal­cu­la­tion of a + on a ran­dom SF dog would im­ply.
    • elite clones can be Heav­ily Used In Breed­ing Pro­grams in al­low­ing par­tic­u­lar in­di­vid­u­als to keep con­tribut­ing ge­net­i­cally long after the orig­i­nal has be­come in­fer­tile or died, or con­tribute far more (as men­tioned be­fore, fe­male dogs are highly lim­ited in re­pro­duc­tive fe­cun­dity com­pared to males, but they could be cloned & born via sur­ro­ga­cy). For ex­am­ple, the first cloned dog, Snup­py, died in 2015, but is , and the record for num­ber of clones ap­pears to be the 49 clones of the world’s tini­est dog, Mir­a­cle Milly.4
  6. Value of In­for­ma­tion: dog cloning may or may not be worth­while, but if it is, the to­tal re­turns from cloning hun­dreds of dogs per year in­defi­nitely (plus the ad­di­tional ben­e­fits) could be large. It would be valu­able to know if it would work.

    Since I have not found any SF/­mil­i­tary-spe­cific her­i­tabil­i­ties re­ported in the sci­en­tific lit­er­a­ture (and the SF dog pro­grams gen­er­ally seem ge­net­i­cally un­so­phis­ti­cated so there may not be any pri­vate or clas­si­fied ones ei­ther), the only way to know is to try it out ex­per­i­men­tal­ly. The clones’ re­al­ized per­for­mance would also pro­vide ad­di­tional valu­able in­for­ma­tion as it would es­ti­mate her­i­tabil­i­ty, which would be use­ful for the reg­u­lar kinds of breed­ing & se­lec­tion as well—be­cause they give an idea of how much one can pre­dict a dog’s per­for­mance based on known rel­a­tives, and how fast a breed­ing pro­gram can/should pro­ceed.

    And since, to be profitable, the suc­cess rate of clones need to be >=9% (which is highly like­ly, see later), this is rea­son­ably easy to es­ti­mate: a sam­ple of ~50 clones would give a rea­son­ably pre­cise es­ti­mate as to the suc­cess rate and en­able bet­ter de­ci­sion-mak­ing as to whether to keep pur­su­ing cloning (in which case more in­for­ma­tion will come in and firm up the de­ci­sion) or drop it as a dead end due to too high costs and/or low her­i­tabil­i­ty. (By the same log­ic, one could treat choice of donor it­self as a mul­ti­-armed ban­dit prob­lem to op­ti­mize the se­lec­tion, since with suc­cess rates likely >50%, the nec­es­sary sam­ple sizes will be not un­rea­son­able and will be reached as clone use ramps up­—­like in South Ko­rea, which has at least 42 clone dogs de­ployed in 2019 and ap­pear to be in­creas­ing clone use as they claim great in­creases in suc­cess rates, de­creases in costs, and net sav­ings, im­ply­ing sub­stan­tial her­i­tabil­i­ty.)

Modeling the SF selection problem

South Korea

How could we es­ti­mate the ben­e­fit of cloning? Given an ac­tive dog cloning pro­gram like Sooam and suffi­cient ex­pe­ri­ence, it can be es­ti­mated di­rect­ly.

Choi et al 2014 re­ports that nor­mal­ly-bred de­tec­tor dogs have a train­ing suc­cess rate of 30% vs 86% for cloned dogs; the 30% ap­pears to be based on the drug de­tec­tion pro­gram, and the 86% is based on their sam­ple of 7 of which 6 passed (ie )5 notes as a fol­lowup

…the Toppy [clones] had the ex­act same ge­netic in­for­ma­tion as the elite drug sniffing dog, whereas the con­trol dogs were the off­spring of sniffer dogs. Sur­pris­ing­ly, all seven Toppy were se­lected with high scores, in con­trast with the con­trol group, of which three of the seven trained dogs were se­lected (Choi et al. 2014). In the 6 months after the seven Toppy clones were added to air­port se­cu­ri­ty, the drug de­tec­tion rate in­creased six­fold, at the same time sav­ing the bud­get for se­lect­ing elite dogs. Thus, out­stand­ing abil­i­ties can be passed on to the next gen­er­a­tion by cloning iden­ti­cal dogs that in­herit iden­ti­cal ge­netic ma­te­r­i­al.

A 2017 Ko­rea Bizwire pro­vides a par­tial cost-ben­e­fit analy­sis in a press re­lease:

Cloning and de­ploy­ment of spe­cial forces dogs be­gan in 2012 as part of an ini­tia­tive by the Rural De­vel­op­ment Ad­min­is­tra­tion (RDA), in an effort to slash spend­ing on po­lice dog train­ing. Spe­cial forces dogs come at a high price. For every dog, an es­ti­mated 1.3 bil­lion won ($112,554) is spent on train­ing them for mul­ti­ple pur­poses such as hu­man res­cue, ex­plo­sive de­tec­tion and cus­tom ser­vice. De­spite the price, only 3 out of 10 dogs [30%] make it through the ex­haus­tive train­ing process to serve on po­lice forces. Clone dogs, on the other hand, have a much higher pass rate of 80%, bring­ing down the train­ing costs to 46 mil­lion won ($39,775). Com­pared to reg­u­lar dogs, they offer sav­ings of 65%.

“Shar­ing a com­pe­tent and well-trained dog is no longer im­pos­si­ble, thanks to cloning”, said Im Gi-soon, a chief an­i­mal biotech­nol­o­gist at the Na­tional In­sti­tute of An­i­mal Sci­ence (NIAS).

It’s un­clear if this 80% es­ti­mate is merely re-re­port­ing Choi et al 2014, but if train­ing each costs = ~$33,000, so go­ing from 30% to 80% suc­cess rate means the clones have a train­ing cost which is = 35% that of the reg­u­lar dogs or $72,000. The costs here clearly ex­clude cloning, but as Vi­a­gen is able to offer con­sumer dog cloning at $50,000 and Sooam has the ad­van­tage of ex­pe­ri­ence & much greater scale (in ad­di­tion to any pa­tri­otic dis­counts), the SK po­lice could be get­ting a sub­stan­tially lower price. But if they pay the full $50,000 any­way, then they are still re­duc­ing the to­tal cost to , sav­ing $8,804. And at the $15k which may be the Vi­a­gen mar­ginal cost, they would save $52,554.

How­ev­er, one might doubt these num­bers or how ap­plic­a­ble they are, and they ap­pear to ex­clude the sub­stan­tial cost of cloning, ren­der­ing the cost-ben­e­fit in­com­plete.

A states:

Ac­cord­ing to the An­i­mal and Plant Quar­an­tine Agen­cy, 42 of its 51 [82%] sniffer dogs were cloned from par­ent an­i­mals as of April, in­di­cat­ing such cloned de­tec­tion dogs are al­ready mak­ing sig­nifi­cant con­tri­bu­tions to the coun­try’s quar­an­tine ac­tiv­i­ties. The num­ber of cloned dogs first out­paced their nat­u­rally born coun­ter­parts in 2014, the agency said. Of the ac­tive cloned dogs, 39 are cur­rently de­ployed at In­cheon In­ter­na­tional Air­port, the coun­try’s main gate­way…While the av­er­age cost of rais­ing one de­tec­tion dog is over 100 mil­lion won (US$85,600), it is less than half that when util­is­ing cloned pup­pies, they said.

The lower price here may re­fer to lower lev­els of se­lec­tiv­i­ty: “de­tec­tion dog” vs “train­ing them for mul­ti­ple pur­poses”. But the word­ing im­plies this refers to to­tal costs, since it states “rais­ing” rather than just “train­ing”, which usu­ally means a to­tal cost from the be­gin­ning. So if train­ing each can­di­date dog costs the im­plied $25,680 and the suc­cess rates con­tinue to be 30% vs 80%, and the clones have a per-suc­cess cost half that of nor­mal dogs, then the im­plied amor­tized cloning cost would ap­pear to be ~$8,560 ().

Cost-benefit in selection problems

How would we ap­proach this prob­lem from first prin­ci­ples?

A SF dog is highly se­lected among can­di­date dogs, and it is ei­ther an ac­cept­able SF dog or not. Be­ing a SF dog re­quires a pack­age of traits, rang­ing from phys­i­cal health to courage to fine­ly-con­trolled ag­gres­sion (at­tack­ing if the han­dler or­ders, im­me­di­ately stop­ping when coun­ter-ordered), which sum up to an over­all qual­i­ty: some­what poorer health can be made up by bet­ter smelling skills, say.

So a nat­ural ap­proach is to treat it as a lo­gis­tic mod­el, or more specifi­cal­ly, a (“Ch25, Thresh­old Char­ac­ters”, Lynch & Walsh 1998): if a bunch of ran­dom vari­ables all sum up to a cer­tain high score, the dog be­comes SF, oth­er­wise, it is a nor­mal dog. These ran­dom vari­ables can be split into ge­netic vari­ables, and every­thing else, ‘en­vi­ron­men­tal’ vari­ables.

Then the ben­e­fit of cloning can be es­ti­mated based on how much the ge­netic vari­ables con­tribute to a high score, how high the ge­netic vari­ables of a cloned SF dog might be (re­mem­ber­ing that they are highly se­lected and thus im­ply re­gres­sion to the mean), and this pro­vides an es­ti­mate for in­creased prob­a­bil­ity that the clones will achieve a high score too. This is effec­tively an ex­treme case of where a sin­gle in­di­vid­ual is used as the ‘par­ent’ of the ‘next gen­er­a­tion’. (This is not a , be­cause the clone is differ­ent from the se­lected donor in­di­vid­u­al, and is a draw from a new dis­tri­b­u­tion.)

Once the prob­a­bil­ity a clone will suc­ceed ver­sus a ran­dom can­di­date dog is cal­cu­lat­ed, then one can get the cost of screen­ing can­di­date dogs for a SF dog ver­sus cloning+screen­ing clone dogs for a SF dog.

So we need to know:

  1. how diffi­cult it is for a reg­u­lar SF dog can­di­date to suc­ceed, and what the im­plied thresh­old for a ‘SF score’ is of a ran­dom SF dog, and of a elite SF dog

    • if pos­si­ble, how much less diffi­cult it is for a cloned SF dog can­di­date to suc­ceed, for the im­plied boost in their av­er­age scores
  2. the cost of train­ing a reg­u­lar SF dog can­di­date

  3. the cost of cloning an elite SF dog

  4. the her­i­tabil­ity of SF suc­cess, or fail­ing that, dog traits in gen­eral as a prior

The an­swers seem to be:

  1. <1% of breeder pup­pies may even­tu­ally make it to suc­cess­ful SF de­ploy­ment; most se­lec­tion hap­pens in the 2 years be­fore han­dover from the breeder to the mil­i­tary, and fail­ure rates are sub­stan­tially lower dur­ing the mil­i­tary train­ing. For more con­ven­tional mil­i­tary or po­lice use, suc­cess rates are much high­er, and from puppy to de­ploy­ment, prob­a­bly more some­thing like 25%.

    Of suc­cess­ful SF dogs, the SF cloning pi­lots ap­pear to be choos­ing from dogs in the top 1% or higher of SF dogs.

  2. the post-han­dover cost of train­ing per SF dog is likely >$50,000, with to­tal life­time cost be­ing high­er; con­ven­tional mil­i­tary/po­lice dogs are again much less strin­gently se­lect­ed/­trained and thus cost much less, per­haps as low as $20,000.

  3. dog cloning costs have dropped steeply since the in 20056 (in large part thanks to con­sumer de­mand for pet cloning), with 2019 list prices at <$50,000 and mar­ginal costs pos­si­bly as low as $16,000 (so cloning at scale could cost only >$16,000)

Base Rates

Dog Success Rates

Frost 1990, in a broad re­view of mil­i­tary work­ing dog train­ing: “How­ev­er, this ‘Eu­ro­pean so­lu­tion’ turned out to be only tem­po­rary, as re­jec­tion rates con­tin­ued to re­main high, and con­tinue to­day in the range of 25 to 50% (An­der­sen, Burke, Craig, Hayter, Mc­Cath­ern, Parks, Thor­ton).”7

Ham­mer­strom 2005, dis­cussing Viet­nam-era war dogs, cites Lem­ish 1996 that there was “a high re­jec­tion rate of 30 to 50% of the po­ten­tial ca­nine re­cruits”.

finds in their US Air Force sam­ple, 21% of dogs failed both types of train­ing, sum­ma­rizes the over­all fail­ure rate as “In many se­lec­tion and train­ing pro­grams for po­lice and de­tec­tion dogs, more than half of the can­di­date dogs are re­jected for be­hav­ioral rea­sons (Wils­son and Sund­gren, 1997b; Slab­bert and Oden­daal, 1999; Mae­jima et al., 2007)”8, and notes that given the costs of a failed can­di­date, “While the im­prove­ments in pre­dic­tion ob­served here were small (2–7%), given the costs of pur­chas­ing, im­port­ing, hous­ing, and train­ing (ap­prox­i­mately $24,291$18,5002010US per dog), this small per­cent­age im­prove­ment re­sults in a sub­stan­tial po­ten­tial sav­ings.”

A 2011 ar­ti­cle on the 341st es­ti­mates that “The suit­abil­ity rate runs around 50%. In other words, to pro­duce 100 ser­vice­able dogs per year, the pro­gram will at­tempt to train about 200.”

The 2014 Bloomberg quotes Badertscher as say­ing “you’re lucky if one or two dogs out of a lit­ter of eight might have the drive and fo­cus to be­come the kind of dogs who can find bombs, take fire, and work in­de­pen­dently on com­mand—let alone jump out of air­planes at night.” A fol­lowup ar­ti­cle quotes a trainer as es­ti­mat­ing “maybe out of a lit­ter of eight only four would be po­lice ser­vice dogs or mil­i­tary dogs”.

Rit­land 2013 de­scribes dogs ap­pro­pri­ate for Navy Seals as be­ing “a one-in-a-t­hou­sand (or more) propo­si­tion…I call them 1 per­center­s…but they are more like one in ten thou­sand.”9

Stripes, re­port­ing on Sooam, states in 2016:

But breed­ing and train­ing pro­grams are costly and often in­effi­cient. For ex­am­ple, the school that trains K-9s for the De­part­ment of De­fense has found that the suit­abil­ity rate runs around 50%, so the pro­gram tries to train about 200 dogs per year to pro­duce 100 that are ser­vice­able.

The afore­men­tioned South Ko­rean news­pa­per ar­ti­cle put reg­u­lar dogs in the sniffer train­ing pro­gram at 30% suc­cess rates.

A trainer at the USDA Na­tional De­tec­tor Dog Train­ing Cen­ter in 2019 de­scribed screen­ing the gen­eral pop­u­la­tion of dogs for can­di­dates: “We could look at 100 dogs and not come back with any…once they go through ini­tial test­ing, the per­cent­age of those dogs that make it is maybe 70%.”

Clone Success Rates

One of the first work­ing dogs cloned was a par­tic­u­larly fa­mous Cana­dian po­lice dog , whose han­dler/owner James Syming­ton, won a Sooam con­test and re­ceived 5 clones of Trakr in 2009, which he be­gan train­ing in search-and-res­cue un­der the aus­pices of his Team Trakr Foun­da­tion (TTF). TTF ap­pears to have gone de­funct some­time be­fore 2014, with its last non­profit fil­ing in 2011, and I am un­able to find any in­for­ma­tion about how the 5 clones worked out. (I have pinged TTF’s con­tact­s.)

South Ko­rean po­lice in 2011 re­ported a 7⁄7 suc­cess rate for the clones vs 3⁄10 for nor­mal dogs.

Aus­tralia was re­ported to be work­ing on a 2011 deal to have up to 10 cloned sniffer dogs by 2013, but as there is no trace of these Aus­tralian dogs else­where, the deal must have fallen through.

The 2014 Bloomberg ar­ti­cle on Sooam (“For $100,000, You Can Clone Your Dog: These two were made to or­der in a South Ko­rean lab. They’re only the be­gin­ning”) re­ported that Sooam had a con­tract for 40 dogs for South Ko­rean clones of which “sev­eral are al­ready in ser­vice” (pre­sum­ably the 7 re­ported be­fore), and also on the birth of 2 clones of a par­tic­u­larly elite SF dog then serv­ing in Afghanistan (name clas­si­fied), “Ghost” and “Echo”, later joined by a third, “Specter”; the Amer­i­can trainer in­volved, Bran­non, praises the re­sults, re­port­ing in 2016 a 3⁄3 suc­cess rate (as op­posed to a more typ­i­cal 4⁄8 es­ti­mate given in the ar­ti­cle):

Bran­non says cloning seems to take the guess work out of nor­mal breed­ing pro­ce­dures. “Mean­ing, you have an ex­cel­lent male an ex­cel­lent fe­male, and maybe out of a lit­ter of eight only four would be po­lice ser­vice dogs or mil­i­tary dogs,” ac­cord­ing to Bran­non. Specter is the third clone that the ken­nel has trained, and the other two are now work­ing with fed­eral SWAT units. “Right now were are three for three and they’re all suc­cess­ful,” said Bran­non.

A New Sci­en­tist re­port in 2016 vis­it­ing Sooam men­tions 4 Ger­man Shep­herds from 1 donor for SK po­lice: “two 9-mon­th-old Ger­man shep­herds, cloned for the na­tional po­lice. Their orig­i­nal was a work­ing dog deemed par­tic­u­larly ca­pa­ble and well-dis­posed…­Fur­ther down is an­other pair of pup­pies cloned from the same donor; these ones are just 2 months old.”, com­ment­ing on how “in­cred­i­bly eerie” it is to see dogs with the same “man­ner­isms” and “perky left ear…­like look­ing at a liv­ing growth chart.” If for­mal po­lice train­ing be­gan at 2 years of age, by 2019 all 4 should be known as suc­cesses or fail­ures.

Stripes re­ported in 2016 that 2 of Bran­non’s clones fin­ished train­ing & were work­ing for ATF and that Bran­non was re­ceiv­ing an­other clone.10

3 Sooam-cloned Ma­li­nois were gifted to Rus­sia in 2016; they re­port­edly badly failed ini­tial test­ing in 2017, which was blamed on their thin fur coats be­ing un­suited to the Yakutsk cold. (I don’t know if this should be con­sid­ered a 0⁄3 ex­am­ple or not, given that there were ap­par­ently ex­ten­u­at­ing cir­cum­stances.)

The afore­men­tioned South Ko­rean news­pa­per ar­ti­cle put clone dogs in the sniffer train­ing pro­gram at 80% (vs 30%).

The first (and as of Au­gust 2019, on­ly?) Chi­nese cloned po­lice dog, Kunx­un, was re­port­edly suc­cess­ful in train­ing & ac­cepted for duty. An­other 6 dog clones be­gan po­lice train­ing in No­vem­ber 2019.

As of late 2019, K9 dog clone suc­cess rates are ap­par­ently high enough to al­low one K9 dog breeder to offer “a bet­ter, a five-year war­ranty in­stead of a sin­gle-year war­ran­ty, which is offered by the other ken­nel”; and an ex-Navy Seal try­ing to launch a busi­ness for pro­vid­ing guard dogs in large vol­ume for schools to guard against mass shoot­ings, Joshua Mor­ton, trains only clones (a team of dog & han­dler is $125,000/year), due to the re­li­a­bil­ity of cloning & train­ing a par­tic­u­lar dog: “Cloning al­lows me to be con­sis­tent. Now, I know that I can tell a client, ‘Hey, I’ll have this dog ready in nine months.’…It’s way more effec­tive, way more effi­cient.” Olof Ols­son of Sooam in Jan­u­ary 2021 is quoted as say­ing “80–90% end up go­ing into ser­vice and we’ve been told mul­ti­ple times that our clones re­spond bet­ter to train­ing.”

Heritability

The con­nec­tion be­tween be­ing a clone and suc­cess prob­a­bil­ity is me­di­ated by the ac­cu­racy of pre­dic­tion from a donor to the clone, and to what ex­tent a high donor ‘score’ pre­dicts a high clone score.

A donor & clone are equiv­a­lent to a pair of iden­ti­cal twins raised apart (MZAs), and this pre­dic­tion is sim­ply the her­i­tabil­ity of the trait (which is the square, so a r = 0.10 is a h2=0.01). In hu­mans, h2s for every­thing av­er­age ~0.50 or r = 0.70; dogs seem to av­er­age lower her­i­tabil­i­ties, with much of the ge­netic vari­ance be­tween dogs be­ing the breed-level differ­ences (which have al­ready been ex­ploited by dog breed­er­s/SF train­ers in their fo­cus on Ma­li­nois etc), but even if the her­i­tabil­ity is a fifth the size, a h2=0.10/r = 0.31 is use­ful.

SF-spe­cific dog her­i­tabil­i­ties should be cal­cu­la­ble us­ing ex­ist­ing pedi­gree records from breed­ers or the oc­ca­sional gov­ern­ment pro­grams, but I did­n’t find any men­tioned in my read­ing.

Dog her­i­tabil­i­ties in gen­eral vary widely and are diffi­cult to sum­ma­rize be­cause of equally widely vary­ing meth­ods, breeds, and analy­ses. Hav­ing been heav­ily se­lec­tively bred, there are large be­tween-breed differ­ences in be­hav­ior due to ge­net­ics (most re­cent­ly: , MacLean et al 201911; , Horschler et al 2019); these group-level her­i­tabil­i­ties are ir­rel­e­vant to this analy­sis as can­di­date dogs are al­ready drawn from the best-suited breeds, and it is the re­main­ing with­in-breed in­di­vid­ual ge­netic differ­ences which mat­ters. This nar­rower her­i­tabil­ity is most fre­quently es­ti­mated <0.50 on mea­sured traits, and the most re­cent meta-analy­sis,Hradecká 2015 finds global mean her­i­tabil­i­ties like 0.15/0.10/0.15/0.09/0.12, which would seem to not bode well for cloning.

How­ev­er, the dog her­i­tabil­ity lit­er­a­ture is plagued with se­ri­ous mea­sure­ment er­ror is­sues: the mea­sured vari­ables are un­sta­ble, un­re­li­able, do not pre­dict within the same dog over long pe­ri­ods of time, and are gen­er­ally psy­cho­me­t­ri­cally in­ad­e­quate. Mea­sure­ment er­ror bi­ases her­i­tabil­ity es­ti­mates to­wards ze­ro: if a mea­sure­ment of ‘tem­pera­ment’ is not mea­sur­ing tem­pera­ment but some­thing like how ag­gres­sive a par­tic­u­lar trainer is, then re­gard­less of how her­i­ta­ble tem­pera­ment truly is, the mea­sure­men­t’s her­i­tabil­ity will be near-ze­ro; but one would be badly mis­taken to then in­fer that tem­pera­ment can­not be affected by breed­ing or that a clone will have a com­pletely differ­ent tem­pera­ment from the donor. In the pa­pers which re­port rel­e­vant as­pects of mea­sure­ment er­ror, the mea­sure­ments are typ­i­cally ex­tremely bad, with r = 0.1–0.2 be­ing com­mon (for com­par­ison, a prop­erly ad­min­is­tered IQ test will be r > 0.8). If one ad­justs a mea­sured her­i­tabil­ity es­ti­mate like 0.09 for such noisy mea­sure­ments, the true her­i­tabil­ity could be eas­ily be 0.66 or high­er. MacLean et al 2019 re­ports a set of be­hav­ioral trait her­i­tabil­i­ties with­in-breed av­er­ag­ing ~0.15 (see also ta­ble 4, Il­ska et al 2017), us­ing the C-BARQ in­ven­to­ry, de­vel­oped with fac­tor analy­sis and which has rea­son­able test-retest re­li­a­bil­ity r~0.5 and load­ing ~57% on the la­tent fac­tors, sug­gest­ing a true mean her­i­tabil­ity >0.24.

An­other is­sue is the in­ter­pre­ta­tion of a low her­i­tabil­ity on in­di­vid­ual be­hav­ioral traits: should SF her­i­tabil­ity be thought of as a sin­gle trait, per­haps the sum of a large num­ber of more atomic be­hav­ioral traits, in which case low her­i­tabil­i­ties mean that an elite SF dog still has only a some­what higher to­tal ge­netic ad­van­tage than a ran­dom SF can­di­date dog? Or should, given the need for long se­quences of cor­rect de­ci­sions & ac­tions draw­ing on many traits with­out a sin­gle mis­take, we see it as more of a trait anal­o­gous to the , in which a small ad­van­tage on each atomic trait (due to strin­gent se­lec­tion + low her­i­tabil­i­ties) nev­er­the­less mul­ti­plies out to a large differ­ence in the fi­nal out­comes—and so a clone will out­per­form much more than one would ex­pect from a low her­i­tabil­ity on each atomic trait?12 Per­haps most rel­e­vant­ly, God­dard and Beil­harz (1982) ex­am­ine guide dogs, and es­ti­mate the her­i­tabil­ity of a “suc­cess” trait rather than in­di­vid­ual traits or sub­tests, which is much higher than the be­hav­ioral mean her­i­tabil­i­ties: 0.44 (higher than 4 of the 5 be­hav­ioral traits they es­ti­mate). A “suc­cess” trait here is an “in­dex score”, by weight­ing cor­re­lated vari­ables ac­cord­ing to their im­por­tance, tend to be op­ti­mal pre­dic­tors with much greater her­i­tabil­i­ty, and to out­per­form in­di­vid­ual vari­ables (Lynch & Walsh 2018: /).

Sooam has pub­lished be­hav­ioral re­search on cloned dogs: Kim et al 2018/Lee et al 2018/Oh et al 2018 re­views. None of the Sooam pa­pers take a be­hav­ioral ge­net­ics ap­proach or at­tempt to es­ti­mate her­i­tabil­i­ty/­ge­netic cor­re­la­tion­s/li­a­bil­ity thresh­old mod­els de­spite those be­ing nec­es­sary for a cor­rect an­swer, so they have to be read close­ly.

Choi et al 2014 has al­ready been re­viewed. Kim et al 2018 states that 4 clones of a can­cer-s­niffing dog were made but “own­er­ship prob­lems” pre­vented more than one from be­ing eval­u­at­ed, which Kim et al 2018 states had sim­i­lar ca­pa­bil­i­ties as the donor (cited to Kim et al 2015 which I am un­able to down­load or read). Oh et al 2016 com­pared 2 clone pup­pies, find­ing them sim­i­lar on the Puppy Ap­ti­tude Test. Shin et al 2016 tested learn­ing/mem­o­ry/­ex­plo­ration in 6 clones ver­sus 4 con­trols, show­ing gen­er­ally lower vari­ance; no vari­ance sta­tis­tics are re­ported (just p-val­ues), but count­ing dots on the plots, the im­plied vari­ance all look >50% to me. Lee et al 2016 mated a cloned de­tec­tor dog with a reg­u­lar fe­male dog and tested the 10 off­spring; the off­spring achieved above av­er­age scores with a pass rate of 60% (which is roughly in­ter­me­di­ate reg­u­lar dogs and cloned dogs, sug­gest­ing high her­i­tabil­ity given a non-de­tec­tor moth­er). Choi et al 2017/Choi 201813 found greater con­sis­tency of be­hav­ioral traits in clone than con­trol pup­pies but did not es­ti­mate di­rect cor­re­la­tion­s/her­i­tabil­i­ties, in­stead us­ing cal­cu­lat­ing s com­par­ing the two groups’ vari­ances; since these are un­re­lated con­trol dogs and a sin­gle group of clone, the re­duc­tion in vari­ance should be equiv­a­lent to her­i­tabil­ity and can be read off from the F sub­scripts, in which case the var­i­ous her­i­tabil­i­ties are 0.20/0.35/0.40/0.23 etc.

Since clones have al­ready been de­ployed in prac­tice, we can try to work back­wards from ob­served suc­cess rates of clones vs nor­mal dogs. The anec­do­tal in­stances im­ply high suc­cess rates, near­ing 100%, vs stan­dard suc­cess rates of <50%, but a tiny to­tal sam­ple size and un­clear de­fi­n­i­tions of suc­cess. More specifi­cal­ly, the South Ko­rean sniffer pro­gram re­port­edly has 30% vs 80% on a com­mon out­come, and the sam­ple size is un­clear but po­ten­tially into the hun­dreds.14

Us­ing the li­a­bil­ity thresh­old mod­el, one could work back from a thresh­old and differ­ence in suc­cess rates to es­ti­mate an im­plied her­i­tabil­i­ty. In this case, the cut­points for 30% and 80% are −0.52SD and +0.84SD, im­ply­ing the clones are +1.36SD above the nor­mals, ig­nor­ing any se­lec­tion be­fore en­roll­ment. That is the mean they re­gressed back to, based on the un­known her­i­tabil­ity (how much back to re­gress) and a cer­tain thresh­old (how high the donor/o­rig­i­nal started off).

The lower the thresh­old, the greater her­i­tabil­ity must be to avoid throw­ing away abil­ity and still match­ing the ob­served suc­cess rate; the higher the thresh­old, the lower her­i­tabil­ity can be while still pro­vid­ing enough abil­i­ty-en­rich­ment in the clones to have that higher suc­cess rate. In this case, we can as­sume a thresh­old like <1%, given that only elites are be­ing cloned and this is con­sis­tent with every­thing else, in which case then the nec­es­sary her­i­tabil­ity turns out to be… ~50%, which is plau­si­ble:

qnorm(0.80) - qnorm(0.30)
# [1] 1.36602175
0.513 * truncNormMean(qnorm(1-0.01))
# [1] 1.3672549

So based on the ex­ist­ing dog lit­er­a­ture and ex­trap­o­lat­ing from the cur­rent ob­served 80–100% dog suc­cess rates, a her­i­tabil­ity of ~50% seems most plau­si­ble to me.

Costs

Training

But that may be worth­while de­pend­ing on how ex­pen­sive it is to train enough dogs to get a suc­cess­ful dog, and how ex­pen­sive cloning is.

For com­par­ison, sim­i­lar high­ly-trained civil­ian dogs, trained in (some­times by train­ers who used to train for Spe­cial Forces), can sell for $51,643$40,0002011$77,465$60,0002011 with Rit­land sell­ing his dogs at $50,000–$100,000, and the best award-win­ning “ex­ec­u­tive pro­tec­tion dogs” sell­ing for up to $296,947$230,0002011. Bloomberg notes “Ca­nines with finely trained noses now fetch $25,000 and up on the open mar­ket, where bor­der pa­trol units, the State De­part­ment, and pri­vate se­cu­rity firms go for ca­nine tal­ent.”

Ham­mer­strom 2005 quotes two cost es­ti­mates of a US mil­i­tary con­trac­tor BSI 1969–1970 at $38,696$10,0001974 (Lem­ish 1996/1999, War Dogs: A His­tory of Loy­alty and Hero­ism) and $58,044$15,0001974 (cited to “Perry Mon­ey, a for­mer Marines Corps han­dler of a BSI dog”), and men­tions a pro­gram to breed dogs for bet­ter health & “su­pe­rior am­bush de­tec­tion” (which failed for un­known rea­son­s); he also quotes US Air Force LTC Ban­nis­ter, com­man­der of the 341st Train­ing Squadron, as es­ti­mat­ing the 120-day “DoD MWD Course” at $70,593$50,0002005 “per trained dog” (un­clear if this refers to av­er­age over all dogs, or per suc­cess­ful ‘trained’ dog)15. Sinn et al 2010 quotes US Air Force train­ing of pa­trol & de­tec­tion dogs at ~$24,291$18,5002010, with a 21% to­tal fail­ure rate im­ply­ing a 1.2x higher cost per suc­cess of ~$30,747$23,4172010. A 2011 NYT ar­ti­cle on Marines notes it is “an ex­pen­sively trained ca­nine (the cost to the Amer­i­can mil­i­tary can be as high as $51,643$40,0002011 per dog)”. Rit­land & Brozek 2013 quotes a US Navy SEAL dog’s in­di­vid­ual cost at >$62,414$50,0002013.16 South Ko­rean po­lice quote a drug sniffer dog at $51,643$40,0002011 for train­ing in 2011. Bloomberg 2017 re­ports “The U.S. mil­i­tary spends up to $283,000 to train a work­ing war dog…Once it has a promis­ing pup, the Pen­ta­gon spends an ad­di­tional $42,000 to train a K9 unit…When all is said and done, a fully trained mil­i­tary dog costs about as much as a small mis­sile.”17 A 2019 Chi­nese source on the first do­mes­ti­cal­ly-cloned po­lice dog (cloned from a donor po­lice dog who is “one in a thou­sand”) cites a stan­dard po­lice dog train­ing cost of $75,000 over 5 years; the clone was re­port­edly suc­cess­ful in train­ing. A 2019 Wired notes “Highly trained bomb- and dis­ease-s­niffing dogs are in short sup­ply and ex­pen­sive, as much as $25,000 per pooch.”

These es­ti­mates vary con­sid­er­ably, and seem to re­flect het­ero­gene­ity in what cost is be­ing cal­cu­lat­ed, how se­lec­tive a role or fa­cil­ity is due to diffi­culty of role (po­lice dogs cost less than sin­gle-role mil­i­tary dogs who cost less than mul­ti­-role dogs who cost less than Spe­cial Forces dogs). But it’s hard to see what is dri­ving some of these differ­ences, like the 6x differ­ence be­tween Rit­land/Ban­nis­ter and Bloomberg’s es­ti­mate, as a Navy SEAL dog is pre­sum­ably a “work­ing war dog”. It seems to me that the Rit­land/Ban­nis­ter fig­ure is re­fer­ring to the cost to train a sin­gle dog who hap­pened to be suc­cess­ful (plau­si­ble since Ban­nis­ter is pro­vid­ing a 2005 es­ti­mate from the dog train­ing fa­cil­i­ties he over­sees), while the lat­ter refers to the to­tal cost to get one suc­cess­ful dog out of an un­spec­i­fied num­ber of can­di­dates; if they are re­fer­ring to the same dogs, then the im­plied suc­cess rate is 1⁄6. Rit­land guessti­mates that the fail­ure rate among his SF dogs, after they have passed all of the thresh­olds which lead to for­mal ac­qui­si­tion by the US Navy (which Rit­land em­pha­sizes rep­re­sents most of the se­lec­tion done on the dogs), is “more like 3 or 4 in 10 in­stead of 7.5 out of 10”, which, com­bined with some ad­di­tional fail­ures or ex­penses else­where in the process, seems rea­son­ably con­sis­tent: if each dog costs >$62,414$50,0002013, then even at face-value that fail­ure es­ti­mate im­plies sub­stan­tially higher price-tags. Al­ter­nate­ly, the Bloomberg es­ti­mate might be a ‘to­tal life­time cost’ es­ti­mate of some sort, in­clud­ing all of the main­te­nance and equip­ment and costs dur­ing de­ploy­ment and re­tire­ment, while Rit­land/Ban­nis­ter is re­fer­ring only to the up­front train­ing cost.

It is worth not­ing, given how much se­lec­tion takes place be­fore sale to SF train­ers, that these costs em­bed the cost of fail­ure be­fore­hand: breed­ers are not char­i­ties, so how­ever much it costs on net to raise, train, and test the dogs which don’t get bought for train­ing, the sale price of a can­di­date SF dog must cover the washouts as well (after re­coup­ing what­ever is pos­si­ble by washouts’ al­ter­nate us­es, per­haps in less se­lec­tive roles), or else the breeder would go out of busi­ness.

Cloning

Dog cloning, on the other hand, cost $100,000 list price in 2015 from Sooam, down from the orig­i­nal $194,004$150,0002008 in 2008, when Bernann McK­in­ney re­ceived 5 cloned pit bulls from Sooam.18 Vi­a­gen in 2018 re­port­edly offers a $50,000 plan; the pro­filed pet own­er, Amy Vange­mert, re­ceived 3 cloned pup­pies (one of which was adopted out) and in­ci­den­tally in­tends to do it again as nec­es­sary. (Vi­a­gen’s price re­mained the same in 2019.) The Chi­nese firm Sino­gene, which cloned a Kun­ming wolf­dog in 2019, re­port­edly charges $53,000–$56,000 for a dog clone, and claims to have done >40 to­tal as of Sep­tem­ber 2019. A No­vem­ber 2019 anec­dote in­volv­ing a bil­lion­aire’s cloned dog stop­ping a drone men­tions “Mini Vader, the $144,000 (£111,563) pup that sourced from Dr Olof Olsson’s Ko­re­an-based cloning lab Swis­sX, earned his jaw-drop­ping price tag with a mid-air res­cue that un­for­tu­nately left his owner with a nas­ti­ly-gashed hand.” Sooam ap­par­ently offers a guar­an­tee, and Vi­a­gen will re­fund if not suc­cess­ful, so that price should be firm. (Why Sooam is able to charge twice as much as Vi­a­gen, or why Swis­sX—­bet­ter known for mar­i­jua­na-re­lated ac­tiv­i­ties—­costs 2–3x as much as any­one else, I do not know.)

For com­par­ison, the other kind of com­mer­cial­ly-offered pet cloning is for cats. The first com­mer­cial cat clone, re­port­edly cost $73,275$50,0002004 in 2004; more re­cent­ly, cat clones from Vi­a­gen cost $35,000 in 2016 and $25,000 in 2019 (although the ar­ti­cle notes that “Cloning a dog costs $50,000 while a cat is now $35,000—the com­pany re­cently in­creased the fee by $10,000 to cover ris­ing costs.”). Over­seas, the first Chi­nese cat clone from Sino­gene Biotech­nol­ogy Com­pany in July 2019 as the start of a com­mer­cial ser­vice quotes Sino­gene as say­ing it “is ex­pected to cost 250,000 yuan ($35,400) each. Zhao told the Global Times that sev­eral cat own­ers had al­ready booked the ser­vice, hint­ing that the fu­ture mar­ket could be huge. The com­pany also offers a dog cloning ser­vice, cost­ing 380,000 yuan [$54,400].”

The real price per dog may be low­er. Their prac­tice seems to be to en­gage in overkill by im­plant­ing mul­ti­ple clone em­bryos to en­sure the min­i­mum spec­i­fied num­ber of healthy clones, and offer all re­sult­ing healthy live-births to the cus­tomer—­for ex­am­ple, Vange­mert is not men­tioned as be­ing charged $150k in­stead of $50k for the 3 pup­pies, as would be the case if Vi­a­gen charged $50k for each suc­cess and she re­quested 2 and got 3. So the per-clone cost ap­pears to have be­come sur­pris­ingly rea­son­able: $50k–$100k for 1 dog, and po­ten­tially <$16k (if a pur­chase re­sults in 3 pup­pies as in Vange­mert’s case), sug­gest­ing that the price has a large fixed cost to it and the mar­ginal costs might be quite small, which is not help­ful in the pet-re­place­ment sce­nario but would be im­por­tant to large-s­cale cloning of spe­cific an­i­mals like elite drug sniffers. For com­par­ison, cat­tle and horse cloning have be­come in­dus­tri­al­ized at ~$10–15k; dog cloning is ap­par­ently more diffi­cult (harder to con­trol es­trous, Vi­a­gen notes), so that may be a lower bound for the fore­see­able fu­ture.

Liability threshold model

This re­quires us to es­ti­mate two things: the thresh­old and the her­i­tabil­ity on the li­a­bil­ity scale.

For com­mon po­lice dogs and other work­ing dogs, train­ing ap­pears to be not that hard, and es­ti­mates of 30–50% are seen. This gives a thresh­old of 50%, or in stan­dard de­vi­a­tions, 0SD.

A SF dog is much more se­lec­tive, and the only spe­cific es­ti­mate given is <1% by Mike Rit­land, which in stan­dard de­vi­a­tions, means each dog would be >=2.33SD, and the ac­tual mean cre­ated by this se­lec­tion effect is +2.66SD. (If this is con­fus­ing imag­ine a thresh­old like 50%: is the mean of every­one over 50% equal to 50%? No, it has to be high­er, and the mean of every­one >=0S­D/>=50% is ac­tu­ally more like 0.8S­D/75%—not 0S­D/50%!—and we need to use the to get it right.)

The clone of the SF dog shares only ge­net­ics with it, it does­n’t ben­e­fit from the unique luck and en­vi­ron­ment that the orig­i­nal did which helped it achieve it suc­cess, so it will regress to the mean. If ge­net­ics de­ter­mined 100% of the out­come, then the clones would al­ways be +2.66SD just like the donor, and hence make the 1%/+2.33SD cut­off 100% of the time, as they have the same ge­netic po­ten­tial and zero en­vi­ron­men­tal in­put (although that is ex­tremely un­likely a sce­nar­io, due to mea­sure­ment er­ror in the test­ing if noth­ing else). While if ge­net­ics con­tributed 0% to the out­come and did not mat­ter, then the clones will make the 1% cut­off just as often as if they were a ran­dom dog sam­pled from their breed­ers ie. 1%. And in be­tween, in be­tween.

Un­der a more plau­si­ble case like ge­net­ics de­ter­min­ing 50% of the vari­abil­ity (a com­mon level of her­i­tabil­ity for bet­ter-s­tud­ied hu­man trait­s), then that is equiv­a­lent to a per­fect ge­netic pre­dic­tor cor­re­lat­ing r = 0.7; the r, re­mem­ber, is equiv­a­lent to ‘for each 1 SD in­crease in the in­de­pen­dent vari­able, ex­pect +r SDs in the de­pen­dent vari­able’, so since the clone donor is +2.66SD, the clones will only be SD above the mean. If the clones are dis­trib­uted around a mean of +1.86SD thanks to their genes, what’s the prob­a­bil­ity they will then reach up to a to­tal of +2.33SD (the thresh­old) with help from the en­vi­ron­ment & luck? Half the vari­ance is used up, and the en­vi­ron­ment has to con­tribute an­other = +0.47SD, de­spite caus­ing differ­ences of only 0.7SD on av­er­age. In that case, the clones will have ~26% chance of be­ing suc­cess­ful—which is a re­mark­able 26x greater than a ran­dom dog, but also far from guar­an­teed.

But one can do bet­ter, since it is not nec­es­sary to se­lect a ran­dom SF dog (with their im­plied av­er­age of +2.66SD) but one can se­lect the best SF dog and clone this elite spec­i­men in­stead. Mul­ti­-stage se­lec­tion is al­ways more effi­cient than sin­gle-stage se­lec­tion, par­tic­u­larly when we are in­ter­ested in ex­tremes/­tails, due to the ‘thin tails’ of the nor­mal dis­tri­b­u­tion. At any time there are thou­sands of SF dogs world­wide, and more in re­tire­ment (and per­haps more if tis­sue sam­ples have been pre­served from ear­lier gen­er­a­tions), so the gain from an ad­di­tional se­lec­tion step is po­ten­tially large (e­spe­cially when we con­sider tail effect­s), and since only 1 dog is nec­es­sary for cloning, why set­tle for any­thing less than the best? If one can se­lect at least the best SF dog out of 100019, then the new ‘thresh­old’ is +4.26SD and the ex­pec­ta­tion for our elite dog is +4.47SD, and like­wise, the clones at 50% her­i­tabil­ity would be +3.16SD, which is con­sid­er­ably above the orig­i­nal SF thresh­old of 2.33SD, and now fully 88% of the clones would be ex­pected to suc­ceed at SF train­ing.

Source code defin­ing the trun­cated nor­mal dis­tri­b­u­tion, the cloning process, and a Monte Carlo im­ple­men­ta­tion20:

## exact mean for the truncated normal distribution:
truncNormMean <- function(a, mu=0, sigma=1, b=Inf) {
        phi <- dnorm
        erf <- function(x) 2 * pnorm(x * sqrt(2)) - 1
        Phi <- function(x) { 0.5 * (1 + erf(x/sqrt(2))) }
        Z <- function(beta, alpha) { Phi(beta) - Phi(alpha) }

        alpha = (a-mu)/sigma; beta = (b-mu)/sigma

        return( (phi(alpha) - phi(beta)) / Z(beta, alpha) ) }
## If we select the top percentile, the cutoff is +2.32SD, but the mean is higher, +2.66SD:
qnorm(0.99)
# [1] 2.32634787
truncNormMean(qnorm(0.99))
# [1] 2.66521422
truncNormMean(qnorm(1-0.01^2))
# [1] 3.95847967

cloningBoost <- function(successP=0.01, preThreshold=0.01, heritability=0.5,
    verbose=FALSE) {
  threshold <- qnorm(1-preThreshold)
  successThreshold <- qnorm(1-successP)

  originalMean <- truncNormMean(threshold)
  cloneMean <- 0 + (sqrt(heritability) * originalMean) ## regress to mean
  regression <- originalMean - cloneMean

  cloneP <- pnorm(cloneMean - successThreshold, sd=sqrt(1-heritability))

  if (verbose) { print(round(digits=3, c(threshold, successThreshold, originalMean,
                                         cloneMean, regression, cloneP))) }
  return(cloneP) }
## Alternative Monte Carlo implementation to check:
cloningBoostMC <- function(successP=0.01, preThreshold=0.01, heritability=0.5,
    verbose=FALSE, iters1=10000000, iters2=1000) {
  threshold <- qnorm(1-preThreshold)
  successThreshold <- qnorm(1-successP)

  r     <- sqrt(  heritability)
  r_env <- sqrt(1-heritability)

  ## NOTE: this is a brute-force approach chosen for simplicity. If runtime is
  ## a concern, one can sample from the extremes directly using the beta-transform trick:
  ## https://www.gwern.net/Order-statistics#sampling-gompertz-distribution-extremes
  population  <- rnorm(iters1, mean=0, sd=1)
  eliteDonors <- population[population>=threshold]

  clones <- as.vector(sapply(eliteDonors, function(d) {
    rnorm(iters2, ## sample _n_ clones per donor
        ## regress back to mean for true genetic mean:
        mean=d*r,
        ## left-over non-genetic variance affecting clones:
        sd=r_env) }))

  successes <- clones>=successThreshold
  cloneP    <- mean(successes)

  if (verbose) { library(skimr)
    print(skim(population)); print(skim(eliteDonors)); print(skim(successes)) }

  return(cloneP)
}

## Varying heritabilities, 0-1:
cloningBoost(successP=0.01, heritability=1.0, verbose=TRUE)
# [1] 2.326 2.326 2.665 2.665 0.000 1.000
# [1] 1
cloningBoost(successP=0.01, heritability=0.8, verbose=TRUE)
# [1] 2.326 2.326 2.665 2.384 0.281 0.551
# [1] 0.551145688
cloningBoost(successP=0.01, heritability=0.5, verbose=TRUE)
# [1] 2.326 2.326 2.665 1.885 0.781 0.266
# [1] 0.266071352
cloningBoost(successP=0.01, heritability=0.2, verbose=TRUE)
# [1] 2.326 2.326 2.665 1.192 1.473 0.102
# [1] 0.102340263
cloningBoost(successP=0.01, heritability=0.0, verbose=TRUE)
# [1] 2.326 2.326 2.665 0.000 2.665 0.010
# [1] 0.01

## Enriched selection by selecting elites rather than random:
cloningBoost(successP=0.01, preThreshold=0.01 * (1/1000), heritability=0.5, verbose=TRUE)
# [1] 4.265 2.326 4.479 3.167 1.312 0.883
# [1] 0.882736927

For in­sight, we can look at how fi­nal suc­cess prob­a­bil­ity in­creases with differ­ent her­i­tabil­i­ties/_r_s, in the sin­gle-step se­lec­tion sce­nario (cor­re­spond­ing to a ran­dom se­lec­tion of SF dogs for cloning) and for the dou­ble-step se­lec­tion (s­e­lect­ing a top 1% SF dog for cloning):

## Plotting the increase in subsequent probability given various correlations:
df1 <- data.frame(PriorP=numeric(), R=numeric(), Success.Rate=numeric())
for (p in c(0.01, seq(0.05, 0.95, by=0.05), 0.99)) {
 for (r in seq(0,1, by=0.01)) {
   df1 <- rbind(df1, data.frame(PriorP=p, R=r,
                    Success.Rate=cloningBoost(successP=p, heritability=r^2)))
 }
}

library(ggplot2); library(gridExtra)
p1 <- qplot(R, Success.Rate, color=as.ordered(PriorP), data=df1) +
    geom_line() + theme(legend.title=element_blank())
p2 <- qplot(R, log(Success.Rate), color=as.ordered(PriorP), data=df1) +
    geom_line() + theme(legend.title=element_blank())
grid.arrange(p1, p2, ncol=1)

## Double-step selection:
df2 <- data.frame(PriorP=numeric(), R=numeric(), Success.Rate=numeric())
for (p in c(0.01, seq(0.05, 0.95, by=0.05), 0.99)) {
 for (r in seq(0.01,1, by=0.01)) {
   df2 <- rbind(df2, data.frame(PriorP=p, preThreshold=p * 0.01, R=r,
                    Success.Rate=cloningBoost(successP=p, preThreshold=p * 0.01, heritability=r^2)))
 }
}

library(ggplot2); library(gridExtra)
p1 <- qplot(R, Success.Rate, color=as.ordered(PriorP), data=df2) +
    geom_line() + theme(legend.title=element_blank())
p2 <- qplot(R, log(Success.Rate), color=as.ordered(PriorP), data=df2) +
    geom_line() + theme(legend.title=element_blank())
grid.arrange(p1, p2, ncol=1)
How the prob­a­bil­ity of post-s­e­lec­tion suc­cess in­creases given a prior base rate and a pre­dic­tor of r power for sin­gle-step se­lec­tion; ab­solute prob­a­bil­i­ties, and log-trans­formed.
Like­wise, but with an ad­di­tional se­lec­tion step prior to cloning to fur­ther se­lect the best one.

Cost-benefit

Does cloning min­i­mize loss? My cost-ben­e­fit be­low takes the cost per fi­nal dog with­out cloning, com­putes the im­plied per-dog-can­di­date cost, and then com­putes the in­creased suc­cess rate for a given thresh­old+her­i­tabil­i­ty, and sees if the ex­pected cloning+­train­ing cost is less than the orig­i­nal to­tal cost.

dogCloningCB <- function(successP, heritability, totalTrainingCost, marginalCloningCost, verbose=FALSE) {
    normalLoss           <- totalTrainingCost
    marginalTrainingCost <-  totalTrainingCost / (1/successP)

    cloningP    <- cloningBoost(successP=successP, heritability=heritability)
    cloningLoss <- ((1/cloningP) * (marginalTrainingCost + marginalCloningCost))

    if(verbose) {return(list(Boost=cloningP, Cost.normal=normalLoss, Cost.marginal=marginalTrainingCost, Cost.clone=cloningLoss,
                  Profitable=normalLoss>cloningLoss, Profit=normalLoss-cloningLoss)) }
    return(normalLoss-cloningLoss) }

## Example: 30% success rate, 50% heritability, $85k per-dog training cost, $15k per-clone cost
dogCloningCB(0.30, 0.5, 85600, 15000, verbose=TRUE)
# $Boost
# [1] 0.972797623
#
# $Cost.normal
# [1] 85600
#
# $Cost.marginal
# [1] 25680
#
# $Cost.clone
# [1] 41817.5364
#
# $Profitable
# [1] TRUE
#
# $Profit
# [1] 43782.4636

Scenarios

As the key her­i­tabil­ity trait is al­most com­pletely un­known and her­i­tabil­i­ties of dog be­hav­ioral traits are all over the map and seem to suffer from se­vere mea­sure­ment er­ror is­sues, we might as well con­sider a wide range of sce­nar­ios to get an idea of what it would take. For suc­cess/thresh­old, we con­tinue with 1%; for her­i­tabil­i­ty, we’ll con­sider the most plau­si­ble range, 0–90%; for train­ing cost, we’ll do the full $50k–$283k range since while it’s un­clear what these num­bers mean, treat­ing them as a to­tal per-dog cost is be­ing con­ser­v­a­tive and makes it harder for cloning to be profitable, and for cloning costs we’ll con­sider the Vange­mert case up to Vi­a­gen’s list price of $50k (s­ince there does­n’t seem to be any good rea­son to pay twice as much to Sooam).

scenarios <- expand.grid(SuccessP=0.01, Heritability=seq(0, 0.9, by=0.10), trainingCost=seq(50000, 283000, by=10000), cloningCost=seq(15000, 50000, by=10000))
scenarios$Profit <- round(unlist(Map(dogCloningCB, scenarios[,1], scenarios[,2], scenarios[,3], scenarios[,4])))

## Plot relationships among profitable scenarios:
scenariosProfitable <- scenarios[scenarios$Profit>0,]
library(ggplot2); library(gridExtra)
p1 <- qplot(cloningCost,  Profit, color=Heritability, data=scenariosProfitable) + geom_hline(yintercept=0, color="red")
p2 <- qplot(trainingCost, Profit, color=Heritability, data=scenariosProfitable) + geom_hline(yintercept=0, color="red")
grid.arrange(p1, p2, ncol=1)

## All profitable scenarios:
scenariosProfitable
# ...
The SF dog cloning sce­nar­ios show­ing profit vs pos­si­ble cloning & train­ing costs, col­ored by her­i­tabil­i­ties.
The sub­set of profitable sce­nar­ios for SF dog cloning (typ­i­cally re­quir­ing high her­i­tabil­i­ties, and higher train­ing costs / lower cloning cost­s).
Suc­cess Prob­a­bil­ity Her­i­tabil­ity Train­ing cost Cloning Cost Profit
0.01 0.6 50000 15000 4333
0.01 0.7 50000 15000 13962
0.01 0.8 50000 15000 21877
0.01 0.9 50000 15000 29015
0.01 0.5 60000 15000 1369
0.01 0.6 60000 15000 14038
0.01 0.7 60000 15000 23729
0.01 0.8 60000 15000 31695
0.01 0.9 60000 15000 38879
0.01 0.5 70000 15000 10993
0.01 0.6 70000 15000 23743
0.01 0.7 70000 15000 33497
0.01 0.8 70000 15000 41514
0.01 0.9 70000 15000 48744
0.01 0.4 80000 15000 2576
0.01 0.5 80000 15000 20617
0.01 0.6 80000 15000 33449
0.01 0.7 80000 15000 43264
0.01 0.8 80000 15000 51332
0.01 0.9 80000 15000 58609
0.01 0.4 90000 15000 12086
0.01 0.5 90000 15000 30242
0.01 0.6 90000 15000 43154
0.01 0.7 90000 15000 53032
0.01 0.8 90000 15000 61151
0.01 0.9 90000 15000 68473
0.01 0.4 100000 15000 21596
0.01 0.5 100000 15000 39866
0.01 0.6 100000 15000 52859
0.01 0.7 100000 15000 62799
0.01 0.8 100000 15000 70970
0.01 0.9 100000 15000 78338
0.01 0.3 110000 15000 2785
0.01 0.4 110000 15000 31106
0.01 0.5 110000 15000 49490
0.01 0.6 110000 15000 62565
0.01 0.7 110000 15000 72567
0.01 0.8 110000 15000 80788
0.01 0.9 110000 15000 88202
0.01 0.3 120000 15000 12119
0.01 0.4 120000 15000 40616
0.01 0.5 120000 15000 59114
0.01 0.6 120000 15000 72270
0.01 0.7 120000 15000 82334
0.01 0.8 120000 15000 90607
0.01 0.9 120000 15000 98067
0.01 0.3 130000 15000 21453
0.01 0.4 130000 15000 50126
0.01 0.5 130000 15000 68738
0.01 0.6 130000 15000 81976
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0.01 0.9 50000 35000 1937
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0.01 0.6 110000 35000 3639
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0.01 0.9 70000 45000 8128
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0.01 0.5 210000 45000 32980
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0.01 0.5 220000 45000 42604
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0.01 0.5 230000 45000 52228
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0.01 0.4 240000 45000 7729
0.01 0.5 240000 45000 61852
0.01 0.6 240000 45000 100346
0.01 0.7 240000 45000 129792
0.01 0.8 240000 45000 153997
0.01 0.9 240000 45000 175826
0.01 0.4 250000 45000 17239
0.01 0.5 250000 45000 71476
0.01 0.6 250000 45000 110052
0.01 0.7 250000 45000 139560
0.01 0.8 250000 45000 163816
0.01 0.9 250000 45000 185691
0.01 0.4 260000 45000 26749
0.01 0.5 260000 45000 81101
0.01 0.6 260000 45000 119757
0.01 0.7 260000 45000 149327
0.01 0.8 260000 45000 173634
0.01 0.9 260000 45000 195555
0.01 0.4 270000 45000 36259
0.01 0.5 270000 45000 90725
0.01 0.6 270000 45000 129462
0.01 0.7 270000 45000 159095
0.01 0.8 270000 45000 183453
0.01 0.9 270000 45000 205420
0.01 0.4 280000 45000 45769
0.01 0.5 280000 45000 100349
0.01 0.6 280000 45000 139168
0.01 0.7 280000 45000 168862
0.01 0.8 280000 45000 193272
0.01 0.9 280000 45000 215284

Conclusion

Un­der the most plau­si­ble sce­nar­ios cor­re­spond­ing to US & South Ko­rea costs & suc­cess rates, and the high her­i­tabil­i­ties in­di­cated by avail­able ev­i­dence, dog cloning is profitable in the­o­ry. This is con­sis­tent with the re­ports from South Ko­rea that dog cloning is profitable in prac­tice.

So dog cloning for po­lice/­mil­i­tary use is profitable in both the­ory & prac­tice.

The ben­e­fits of cloning or us­ing par­tial pre­dic­tors of ex­treme out­liers is gen­er­al, and will be ap­plic­a­ble to many other ar­eas, es­pe­cially where screen­ing/­train­ing is lengthy & ex­pen­sive.

See Also

Appendix

Dog heritabilities

Notes on read­ing re­views & meta-analy­ses on the psy­cho­me­t­ric prop­er­ties & her­i­tabil­i­ties of dog be­hav­ioral traits, par­tic­u­larly for work­ing dogs. Dog her­i­tabil­i­ties might be ex­pected to be low in the con­text of con­sid­er­ing dogs of the same breed (as would be rel­e­vant to a breed­ing or train­ing con­tex­t): heavy se­lec­tive breed­ing would tend to re­duce with­in-breed her­i­tabil­i­ties (while in­creas­ing group her­i­tabil­i­ty).

Over­all, her­i­tabil­i­ties ap­pear to differ by breed and be quite low (say, closer to 25% than to the ) but the psy­cho­me­t­ric prop­er­ties of dog be­hav­ioral tests also ap­pear to be poor, with low item counts, re­li­a­bil­i­ties, test-retests, and pre­dic­tive pow­er, rater/­judge effects, and lit­tle use of la­tent fac­tors to ex­tract more re­li­able mea­sures, sug­gest­ing con­sid­er­able to­tal mea­sure­ment er­ror and thus con­sid­er­able un­der­es­ti­ma­tion of pre­dic­tion/her­i­tabil­i­ties. Pos­si­bly dog her­i­tabil­i­ties are much closer to hu­man her­i­tabil­i­ties than they seem.

On mea­sure­ment er­ror and her­i­tabil­i­ty:

  • Wils­son & Sund­gren 1997, “The use of a be­hav­iour test for se­lec­tion of dogs for ser­vice and breed­ing. II. Her­i­tabil­ity for tested pa­ra­me­ters and effect of se­lec­tion based on ser­vice dog char­ac­ter­is­tics” notes that their use of fac­tor analy­sis on mul­ti­ple tests to de­rive an in­dex value yields higher her­i­tabil­ity es­ti­mates than the raw tests (com­pare Ta­ble 1 with Ta­ble 3):

    It is re­mark­able that the her­i­tabil­ity for the cal­cu­lated in­dex value and for the four fac­tors from the fac­tor analy­sis is com­par­a­tively high (Ta­bles 2 and 3). This is nor­mally ex­pected to hold true for sin­gle well-de­fined char­ac­ter­is­tics. This study, how­ev­er, shows a higher her­i­tabil­ity for com­plex be­hav­iour sys­tems. The more com­plex pa­ra­me­ters, in­dex val­ues and the four fac­tors from the fac­tor analy­sis show a higher her­i­tabil­ity than most of the sin­gle char­ac­ter­is­tics that they are based on. One pos­si­ble ex­pla­na­tion is that the eval­u­ated char­ac­ter­is­tics over­lap and a higher de­gree of con­fi­dence can be achieved if the in­for­ma­tion from the eval­u­ated char­ac­ter­is­tics are pooled. The prob­a­bil­ity of this ex­pla­na­tion is fur­ther en­hanced by the rel­a­tively high pos­i­tive phe­no­typic cor­re­la­tion main­tained be­tween the char­ac­ter­is­tics (Wils­son and Sund­gren, 1996). God­dard and Beil­harz (1982) show a her­i­tabil­ity as high as 0.44 to pre­dict a dog’s abil­ity to be­come a guide dog for the blind. The char­ac­ter­is­tic used was de­fined as “suc­cess”. Macken­zie et al. (1985) cal­cu­lated the her­i­tabil­ity of “tem­pera­ment” to be 0.51 in 575 mil­i­tary dogs. In both cases the high her­i­tabil­ity fig­ures were cal­cu­lated on a char­ac­ter­is­tic that sum­marises com­plex be­hav­iour sys­tems. With re­gards to this, it should be pointed out that the char­ac­ter­is­tic “tem­pera­ment” in the study of Macken­zie et al. (1985) is de­fined as a mil­i­tary dog’s suit­abil­ity for pro­tec­tion and track­ing and must not be con­sid­ered syn­ony­mous with the de­fi­n­i­tion of tem­pera­ment used in this study.

  • Jones & Gosling 2005, “Tem­pera­ment and per­son­al­ity in dogs (Ca­nis fa­mil­iaris): a re­view and eval­u­a­tion of past re­search”

    If tem­pera­ment tests are to be of any val­ue, they must be shown to be both re­li­able and valid. Re­li­a­bil­ity is a pre­req­ui­site for va­lid­i­ty, and so we re­view the ev­i­dence for re­li­a­bil­ity first.The first thing to con­clude about re­li­a­bil­ity is that with the few ex­cep­tions we will dis­cuss in more de­tail, re­searchers have rarely re­ported re­li­a­bil­ity of any kind.

    …Table 3 is di­vided into two types of re­li­a­bil­i­ty: in­ter-ob­server agree­ment and test–retest re­li­a­bil­i­ty. The stud­ies us­ing in­ter-ob­server agree­ment used the tra­di­tional method of analy­sis in which each vari­able is an­a­lyzed across sub­jects (in­stead of com­put­ing re­li­a­bil­ity within sub­ject­s). The cor­re­la­tions sug­gest that in­ter-judge agree­ment varies greatly across stud­ies and traits. Al­though strong agree­ment is pos­si­ble, it is by no means guar­an­teed; the sam­ple-weighted mean agree­ment cor­re­la­tion was .60, but the agree­ment cor­re­la­tions ranged from .00 to .86…Two stud­ies ap­pear in the test–retest re­li­a­bil­ity cat­e­go­ry, listed in the lower sec­tion of Ta­ble 3, ex­am­in­ing the cor­re­la­tion be­tween scores when dogs were tested twice. One of these stud­ies, by God­dard and Beil­harz (1986), re­veals Ac­tiv­ity level is re­li­able from test to test, but that this re­li­a­bil­ity de­creases as pup­pies age. The other study, by Netto and Planta (1997), shows a strong mean cor­re­la­tion, but also in­cluded many in­signifi­cant cor­re­la­tions. Closer ex­am­i­na­tion re­veals that many of the Kappa co­effi­cients re­ported are ze­ro,indi­cat­ing no re­li­a­bil­i­ty. How­ev­er, this is par­tially an ar­ti­fact of the test­ing sit­u­a­tion be­cause the sub­tests were not in­tended to elicit Ag­gres­sion, so it makes lit­tle sense to as­sess the re­li­a­bil­ity with which they elicited ag­gres­sion. Of the sub­tests in this study which were in­tended to elicit ag­gres­sion, the low­est Kappa co­effi­cient is -.03 for re­ac­tion to an ar­ti­fi­cial hand tak­ing away food, and re­ac­tion to a stranger be­ing mildly threat­en­ing when meet­ing the dog’s han­dler. How­ev­er, Netto and Plan­ta’s study should be com­mended for fully re­port­ing their re­li­a­bil­ity data; when in­ter­preted against an un­der­stand­ing of the test­ing sit­u­a­tions, these are data are very valu­able. Ta­ble 4 sum­ma­rizes all the in­ter­nal con­sis­tency es­ti­mates re­ported in the stud­ies re­viewed. In­ter­nal con­sis­tency mea­sures es­ti­mate the de­gree to which items on a scale as­sess the same con­struct. In hu­man per­son­al­ity re­search, they are often used fol­low­ing fac­tor analy­ses to de­ter­mine the in­ter­nal co­her­ence of the de­rived fac­tors. Of the 16 stud­ies in our re­view to fo­cus on fac­tor analy­sis, only three re­ported in­ter­nal con­sis­ten­cy. Two of these stud­ies (Hsu and Ser­pell, 2003; Ser­pell and Hsu, 2001) gath­ered data us­ing ques­tion­naires with 5-point fre­quency (Lik­ert) scales; the third (Sek­sel et al., 1999) used a 100-point scale. One ad­di­tional study that did not fo­cus on fac­tor analy­sis also re­ported in­ter­nal con­sis­tency (Gosling et al., 2003a) and is in­cluded in Ta­ble 4. In­ter­nal con­sis­tency var­ied greatly across stud­ies and fac­tors, rang­ing from .42 for “Han­dling”, to .93 for “Stranger-di­rected Ag­gres­sion”. Al­though high con­sis­tency is pos­si­ble, it is by no means guar­an­teed. Nonethe­less, the in­ter­nal con­sis­tency mea­sures had a weighted mean of .76, well within the lim­its ac­cept­able in most hu­man per­son­al­ity re­search (John and Benet-Martinez, 2000)

    Over­all, the ev­i­dence for con­ver­gent va­lid­ity is rea­son­ably promis­ing, with the var­i­ous es­ti­mates av­er­ag­ing about .40 across the nine di­men­sions ex­am­ined here.

  • Cau­choix et al 2018, “The re­peata­bil­ity of cog­ni­tive per­for­mance: a meta-analy­sis” finds across 25 an­i­mal species “mean R es­ti­mates rang­ing be­tween 0.15 and 0.28.”

  • “Per­son­al­ity and per­for­mance in mil­i­tary work­ing dogs: Re­li­a­bil­ity and pre­dic­tive va­lid­ity of be­hav­ioral tests”, Sinn et al 2010: finds con­sid­er­able in­ter-rater dis­agree­ment, and low long-term test-retest re­li­a­bil­ity

  • Hradecká 2015, “Her­i­tabil­ity of be­hav­ioural traits in do­mes­tic dogs: A meta-analy­sis”, meta-an­a­lyzes global her­i­tabil­i­ties across var­i­ous do­mains as 0.15/0.10/0.15/0.09/0.12; nar­row­ing down to the “Psy­chi­cal” do­main of traits which seem to be most key to SF train­ing, and the breeds which are most often em­ployed: Bel­gian Shep­herd Dog, 0.13; Ger­man Shep­herd Dog: 0.12; Labrador Re­triev­er: 0.07. These are low but Hradecká 2015 com­ments on the high un­re­li­a­bil­ity of the mea­sure­ments be­ing used in most dog her­i­tabil­ity stud­ies, which will have the effect of ex­tremely re­duc­ing her­i­tabil­ity es­ti­mates:

    Mul­ti­fac­to­r­ial analy­sis re­vealed that val­ues of her­i­tabil­ity of be­hav­ioural traits were affected not only by bi­otic fac­tors such as age and sex, sug­gest­ing im­por­tance of ex­pe­ri­ence, train­ing, and learn­ing (e.g., Kar­jalainen et al., 1996; Meyer et al., 2012), but also by abi­otic fac­tors such as test­ing mon­th, weather dur­ing the test­ing, place of test­ing, judges, etc. This ques­tioned the meth­ods of eval­u­at­ing her­i­tabil­i­ty…e­val­u­a­tions of the be­hav­ioural traits are often diffi­cult due to the lack of test­ing re­peata­bil­ity be­tween and also within judges. Per­for­mance test­ing is usu­ally sub­jec­tive as sig­nifi­cantly differ­ent scores are given by the judges as shown, for ex­am­ple, in Finnish Spitz (Kar­jalainen et al., 1996).

    For per­spec­tive, if we as­sume a test-retest as much as 0.20, and we cor­rect the Bel­gian Shep­herd Dog mean psy­chi­cal her­i­tabil­ity of 0.13 for the test-retest alone (which is only one form of mea­sure­ment er­ror), the Spear­man cor­rec­tion yields a true her­i­tabil­ity of .

Re­views:

  • van den Berg 2017, “Ge­net­ics of dog be­hav­ior”

    The dog ge­netic stud­ies re­viewed in this chap­ter used more sub­jec­tive phe­no­typic mea­sures. Most her­i­tabil­ity stud­ies used phe­no­types based on the be­hav­ior of dogs in test bat­ter­ies. Jones and Gosling (2005) have re­viewed stud­ies of ca­nine per­son­al­ity and noted that, “In the­o­ry, test bat­ter­ies were the clos­est to achiev­ing ob­jec­tiv­i­ty, but in prac­tice the lev­els of ob­jec­tiv­ity ac­tu­ally at­tained var­ied sub­stan­tial­ly.” The mol­e­c­u­lar ge­netic stud­ies mostly used even more sub­jec­tive mea­sures such as own­er-re­port ques­tion­naires and ex­pert rat­ings (ex­perts be­ing vet­eri­nar­i­ans, train­ers, or dog obe­di­ence judges). Owner and ex­pert rat­ings may be in­flu­enced by a va­ri­ety of fac­tors other than the be­hav­ior of the dog, e.g. owner per­son­al­ity and ex­pec­ta­tions of typ­i­cal dog be­hav­ior. In­tu­itive­ly, the use of spe­cific and ob­jec­tive met­rics in ge­netic stud­ies seems prefer­able. How­ev­er, be­hav­ior of dogs in a test bat­tery may not be rep­re­sen­ta­tive of their be­hav­ior in every­day life and it is often un­clear what ex­actly is be­ing mea­sured. Van den Berg and col­leagues used three meth­ods for mea­sur­ing ca­nine ag­gres­sive be­hav­ior: a be­hav­ioral test of the dog (van den Berg et al ., 2003), a ques­tion­naire for the dog owner (van den Berg et al ., 2006), and a per­sonal in­ter­view with the dog owner (van den Berg et al ., 2003 , 2006). The most promis­ing her­i­tabil­ity es­ti­mates (i.e. high her­i­tabil­ity with low stan­dard er­rors) were ob­tained for the owner im­pres­sions col­lected dur­ing the per­sonal in­ter­view (Li­inamo et al ., 2007). This is rather sur­pris­ing be­cause of the sub­jec­tiv­ity of these phe­no­types. Large co­or­di­nated pro­jects, such as the Eu­ro­pean LUPA con­sor­tium, make an effort to clar­ify dog be­hav­ioral phe­no­types by fol­low­ing stan­dard pro­ce­dures to de­scribe dog be­hav­ior (Le­quarré et al ., 2011). This is of great value for progress in ca­nine be­hav­ioral ge­net­ics.

  • “Ca­nine Be­hav­ioral Ge­net­ic­s—A Re­view”, Macken­zie 1986

    Vari­able Pro­por­tion
    Pos­ture in Pavlov stand 0.43
    In­ves­tiga­tive be­hav­ior in Pavlov stand 0.46
    Es­cape at­tempts while in Pavlov stand 0.56
    Hu­man avoid­ance and vo­cal­iza­tion at 5 weeks 0.59
    Play­ful fight­ing at 13–15 weeks 0.42
    Leash fight­ing 0.77
    Docil­ity dur­ing sit-train­ing 0.48
    Run­ning time for long bar­rier 0.78
    Vo­cal­iza­tion on U-shaped bar­rier 0.47

    Ta­ble 2: Pro­por­tion of to­tal vari­ance due to breed differ­ences be­tween Basen­jis and Cocker Spaniels (after Scott and Fuller, 1965)

    …G. Geiger in­ves­ti­gated the breed­ing-book of Dachs­hunds in Ger­many in 1973 and found the scores bet­ter dis­trib­uted than the data stud­ied by Sacher, per­haps due to the 12-point sys­tem used as op­posed to the 4-point sys­tem used in the pointer prize class­es. He con­ducted a three­-level nested 379 analy­sis of vari­ance on 1463 full- and half-sib prog­eny of 21 sires. In con­trast to the ear­lier find­ings of Humphrey and Warner (1934), King (1954) and Mahut (1958), his re­sults showed ma­ter­nal effects but no effect due to sex. The her­i­tabil­i­ties are shown in Ta­ble III (Geiger, 1973, cited in Pflei­der­er-Hogn­er, 1979).

    Trait Sire Dam
    Hare track­ing 0.03 0.46
    Nose 0.01 0.39
    Seek 0.00 0.41
    Obe­di­ence 0.01 0.19

    Ta­ble 3: Her­i­tabil­ity es­ti­mates in Dachs­hunds (after Geiger, 1973)

    A sec­ond study of ad­di­tive ge­netic vari­a­tion in 1973 came from the Army Dog Train­ing Cen­ter in Solleft­ea, Swe­den. C. Reuter­wall and N. Ry­man re­ported on their study of 958 Ger­man Shep­herds from 29 sires. The 8 be­hav­ioral traits stud­ied were la­beled A-H:

    • Trait A was termed “Affa­bil­ity” (tested by hav­ing an un­known per­son con front the dog);
    • Trait B was termed “Dis­po­si­tion for Self De­fense” (tested by hav­ing an un­known per­son at­tack the dog);
    • Trait C was termed “Dis­po­si­tion for Self De­fense and De­fense of Han­dler” (tested by hav­ing an un­known per­son at­tack the dog and han­dler);
    • Trait D was termed “Dis­po­si­tion for Fight­ing in a Play­ful Man­ner” (tested by ask­ing the dog to fight for a sleeve or stick);
    • Trait E was termed “Courage” (tested by hav­ing a man-shaped fig­ure ap­proach the dog);
    • Trait F was termed “Abil­ity to Meet with Sud­den Strong Au­di­tory Dis­tur­bance” (tested by fir­ing shots at some dis­tance and mak­ing a noise with tin cans just be­hind the dog);
    • Trait G was termed “Dis­po­si­tion for For­get­ting Un­pleas­ant In­ci­dents” (tested by scar­ing the dog at a cer­tain place and then ask­ing the dog to pass the place again);
    • Trait H was termed “Adap­tive­ness to Differ­ent Sit­u­a­tions and En­vi­ron­ments” (tested by ob­ser­va­tions dur­ing the other parts of the test).

    In con­trast to Geiger’s find­ings, Reuter­wall and Ry­man re­ported sig­nifi­cant differ­ences be­tween the sex­es, males han­dling noise (Trait F) bet­ter and ex­hibit­ing more con­trolled de­fense (part of Trait C) and play­ful fight­ing (Trait D). Sex differ­ences had also been noted by Humphrey and Warner (1934), King (1954) and Mahut (1958). Reuter­wall and Ry­man noted that, in all 380 the traits stud­ied, the ad­di­tive ge­netic vari­a­tion was small (Reuter­wall and Ry­man, 1973). The her­i­tabil­ity es­ti­mates listed in Ta­ble IV were re­ported by Willis based on the in­for­ma­tion found in Reuter­wall and Ry­man (Willis, 1977). It should be noted that the scores used by Reuter­wall and Ry­man were trans­formed and ex­tremely com­plex. Some work­ers in Swe­den to­day, work­ing on the ge­net­ics of the breed­ing pro­gram at the Statens Hund­sko­la, feel that the find­ings of Reuter­wall and Ry­man’s study are based on scores too com­plex to have much mean­ing (L. Falt, per­sonal com­mu­ni­ca­tion, 1982).

    Trait Males Fe­males
    A [Affa­bil­i­ty] 0.17 0.09
    B [Dis­po­si­tion for self­-de­fense] 0.11 0.26
    C [Dis­po­si­tion for self­-de­fense and de­fense of han­dler] 0.04 0.16
    D [Dis­po­si­tion for fight­ing in a play­ful man­ner] 0.16 0.21
    E [Courage] 0.05 0.13
    F [A­bil­ity to meet with sud­den strong au­di­tory dis­tur­bance] −0.04 0.15
    G [Dis­po­si­tion for for­get­ting un­pleas­ant in­ci­dents] 0.10 0.17
    H [Adap­tive­ness to differ­ent sit­u­a­tions and en­vi­ron­ments ] 0.00 0.04

    Ta­ble 4: Her­i­tabil­i­ties in Ger­man Shep­herds (after Reuter­wall and Ry­man, 1973)

    The next year, M.E. God­dard and R.G. Beil­harz stated their be­lief that fear­ful­ness and dog dis­trac­tion were her­i­ta­ble in Aus­tralian guide dogs (God­dard and Beil­harz, 1974). In 1982, God­dard and Beil­harz re­ported fur­ther on the ge­net­ics of Aus­tralian guide dogs…Fear­ful­ness emerged as the most im­por­tant and most highly her­i­ta­ble com­po­nent of suc­cess. Es­ti­mates of her­i­tabil­i­ties based on scores of 394 Labrador Re­triev­ers com­puted from sire com­po­nents, dam com­po­nents and the two com­bined are listed in Ta­ble V (God­dard and Beil­harz, 1982). In con­trast to re­ports by Scott and Bielfelt (1976), Geiger (1973) and Scott and Fuller (1965), no strong ma­ter­nal effects were ev­i­dent (God­dard and Beil­harz, 1982)

    Trait Sire Dam Com­bined
    Suc­cess 0.46 0.42 0.44
    Fear 0.67 0.25 0.46
    Dog dis­trac­tion −0.04 0.23 0.09
    Ex­citabil­ity 0.00 0.17 0.09
    Health 0.40 0.10 0.25
    Hip dys­pla­sia 0.08 0.20 0.14

    Ta­ble 5: Her­i­tabil­ity es­ti­mates in Aus­tralian Labradors (after God­dard and Beil­harz, 1982)

    …Es­ti­mates of her­i­tabil­i­ties based on scores of 249 Labrador Re­triev­ers, cal­cu­lated from com­bined sire and dam com­po­nents, are listed in Ta­ble VI (God­dard and Beil­harz, 1983). Ner­vous­ness had the high­est her­i­tabil­ity and was the only trait with a sig­nifi­cant sire com­po­nent. Es­ti­mates of ge­netic cor­re­la­tions be­tween the traits are listed in Ta­ble VII (God­dard and Beil­harz, 1983). In con­trast to other work­ers (Castle­berry et al., 1976; Bartlett, 1976; Ros­berg and Olaus­son, 1976), God­dard and Beil­harz (1983) found no neg­a­tive cor­re­la­tions be­tween im­por­tant traits. How­ev­er, they did not list cor­re­la­tions for hip dys­pla­sia. They also noted the im­por­tance of sex; fe­males be­ing more fear­ful and dis­tracted by scents but less ag­gres­sive and dis­tracted by dogs than males. Sex differ­ences were also noted by Humphrey and Warner (1934), King {1954), Mahut (1958), Reuter­wall and Ry­man (1973) and Pflei­der­er-Hogner {1979). G. Quein­nec, B. Quein­nec and R. Darre re­ported on their work with French rac­ing grey­hounds (Quein­nec et al., 1974). Breed­ing val­ues for grey­hounds were based 40% on the an­i­mal’s own per­for­mance and 60% on the per­for­mance of its prog­eny, both over 3 rac­ing sea­sons to ac­count for re­peata­bil­ity

    Trait Her­i­tabil­ity
    Ner­vous­ness (N) 0.58
    Sus­pi­cion (S) 0.10
    Con­cen­tra­tion (C) 0.28
    Will­ing­ness (W) 0.22
    Dis­trac­tion (D) 0.08
    Dog dis­trac­tion (DD) 0.27
    Nose dis­trac­tion (ND) 0.00
    Sound-shy (SS) 0.14
    Hear­ing sen­si­tiv­ity (HS) 0.00
    Body sen­si­tiv­ity (BS) 0.30

    Ta­ble 6: Her­i­tabil­ity es­ti­mates in Aus­tralian Labradors (after God­dard and Beil­harz, 1983)

    In 1975, the U.S. Army Biosen­sor Project re­ported a her­i­tabil­ity es­ti­mate of 0.70 for their in­ter­me­di­ate tem­pera­ment eval­u­a­tions. They also stated their in­ten­tion to use her­i­tabil­ity es­ti­mates of both hip dys­pla­sia (pre­vi­ously es­ti­mated in their colony as 0.22) and tem­pera­ment in the cal­cu­la­tion of breed­ing val­ues (Castle­berry et al., 1975). The fol­low­ing year, they re­ported the first known es­ti­mate of the ge­netic cor­re­la­tion be­tween tem­pera­ment and hip dys­pla­sia (con­sid­ered by many to be the two ma­jor prob­lems in breed­ing dogs for mil­i­tary or po­lice work). Be­fore list­ing the es­ti­mate, they noted that pre­vi­ous dys­plasi­a-free lit­ters had shown un­de­sir­able tem­pera­ments. Their es­ti­mate of the phe­no­typic cor­re­la­tion be­tween the two traits was −0.25 and that of the ge­netic cor­re­la­tion was −0.35 (Castle­berry et al., 1976). In 1976, C.R. Bartlett re­ported her­i­tabil­i­ties and ge­netic cor­re­la­tions be­tween traits stud­ied in Amer­i­can guide dogs. The traits listed were hip dys­plasia, body sen­si­tiv­ity (judged by how hard a jerk on the choke-chain leash the new dog could tol­er­ate; low scores in­di­cat­ing a lack of sen­si­tiv­i­ty), ear sen­si­tiv­ity (judged by how loud a vo­cal cor­rec­tion the new dog re­quired; low scores in­di­cat­ing lack of sen­si­tiv­i­ty), nose (ol­fac­tory acu­ity lead­ing to dis­trac­tion prob­lems for all but the best train­ers; low scores in­di­cat­ing great­est use of the nose), in­tel­li­gence (the abil­ity of the dog to un­der­stand things from its own view­point, not im­ply­ing a will­ing­ness to obey; low scores in­di­cat­ing great in­tel­li­gence, which may be a prob­lem to all but the best train­er­s), will­ing­ness {will­ing­ness to do what the dog’s mas­ter asks of it, re­gard­less of dis­trac­tions; low scores in­di­cat­ing the most will­ing dogs), en­ergy (ac­tiv­ity ver­sus lazi­ness; low scores in­di­cat­ing ac­tive, en­er­getic dogs), self right (the be­lief of the dog that it has a right to be where it is; neg­a­tive scores in­di­cat­ing a ten­dency to give way to an­oth­er), con­fi­dence (con­fi­dence shown with strange peo­ple or in strange en­vi­ron­ments; low scores in­di­cat­ing more con­fi­dent dogs), fight­ing in­stinct (ten­dency to fight; low pos­i­tive scores in­di­cat­ing the ten­dency to avoid fights, neg­a­tive scores in­di­cat­ing even less ten­dency to fight, pass­ing into sub­mis­sion) and pro­tec­tive in­stinct (a de­sire of the dog to pro­tect its own; low pos­i­tive scores in­di­cat­ing a dog which will speak if a stranger ap­proaches its mas­ter with men­ace, but will not fight to pro­tect the mas­ter). Her­i­tabil­ity es­ti­mates of these traits, based on over 700 records for males and over 1000 records for fe­males, both cal­cu­lated by pa­ter­nal half-sib analy­sis, are listed in Ta­ble VIII (Bartlett, 1976)

    Trait Males Fe­males Com­bined
    Hips 0.72 0.46 0.54
    Body sen­si­tiv­ity 0.26 0.05 0.10
    Ear sen­si­tiv­ity 0.49 0.14 0.25
    Nose 0.30 0.05 0.12
    In­tel­li­gence 0.17 −0.07 −0.06
    Will­ing­ness −0.14 −0.04 −0.03
    En­ergy −0.03 0.06 0.05
    Self­-right 0.15 0.25 0.22
    Con­fi­dence 0.04 0.26 0.16
    Fight­ing in­stinct −0.05 −0.08 −0.04
    Pro­tec­tive in­stinct −0.21 −0.13 −0.12

    Ta­ble 8: Her­i­tabil­ity es­ti­mates in Amer­i­can guide dogs (after Bartlett, 1976)

    Ros­berg and Olaus­son re­ported low her­i­tabil­ity es­ti­mates for men­tal traits in the dogs at the Swedish Army Dog Cen­ter in Solleft­ea, Swe­den. All dogs in­cluded in the study were Ger­man Shep­herds. Phe­no­typic cor­re­la­tions be­tween the men­tal traits they were study­ing and hip dys­pla­sia were small, but neg­a­tive. Ge­netic cor­re­la­tions were neg­a­tive, rang­ing up to −0.55, but the au­thors felt they were un­re­li­able due to prob­lems with the ma­te­r­ial stud­ied (Ros­berg and Olaus­son, 1976). A study of the ge­net­ics of Amer­i­can guide dogs was com­pleted in 1976 by C.J. Pfaffen­berg­er, J.P. Scott, J.L. Fuller, B.E. Gins­burg and S.W. Bielfelt. They fol­lowed up Scott and Fuller’s (1965) work in be­hav­ior and ob­tained es­ti­mates of her­i­tabil­ity for their puppy tests. The traits re­ported by Scott and Bielfelt (1976} in their chap­ter on analy­sis of the pup­py-test­ing pro­gram in­cluded the fol­low­ing: sit (three rep­e­ti­tions of a forced sit with a vo­cal com­mand}; come (five rep­e­ti­tions of the han­dler mov­ing away, kneel­ing down, call­ing the puppy by name, fol­lowed by the com­mand “come” while clap­ping the hand­s); fetch (three rep­e­ti­tions of play­ful re­triev­ing with vo­cal com­mand); trained re­sponse (a com­plex score, in­di­cat­ing if the puppy was afraid of the tester or not, was over-ex­cited or co­op­er­ated calm­ly, did or did not pay at­ten­tion to mov­ing ob­jects, ad­justed slowly or read­ily to the new en­vi­ron­ment, showed no cu­rios­ity or was cu­ri­ous about new ob­jects and peo­ple, did or did not re­mem­ber pre­vi­ous ex­pe­ri­ence, tried to do what the tester wanted or not, and showed per­sis­tence or not in per­form­ing a task); will­ing in train­ing (also a com­plex score, in­di­cat­ing if the puppy was fear­ful or at ease, afraid to move or moved freely, was in­differ­ent or friendly to the tester, was un­re­spon­sive or re­spon­sive to en­cour­age­ment, uri­nated or was con­ti­nent, was up­set by the new sit­u­a­tion or was con­fi­dent, and was ob­sti­nate or will­ing in its re­spons­es); body sen­si­tiv­ity (an­other com­plex score, in­di­cat­ing if the puppy stood erect or cow­ered, turned head away or not, looked at or away from the tester, showed pain by ac­tion or not, came back after pain or at­tempted to es­cape, tucked in the tail or not, wagged tail or not after pain, and growled or not when in pain); ear sen­si­tiv­ity (sim­i­lar to body sen­si­tiv­i­ty, ex­cept in re­la­tion to sound in­stead of pain); new-ex­pe­ri­ence re­sponse (sim­i­lar to trained re­spon­se, but this time an emo­tional re­sponse to novel stim­uli, not train­ing); will­ing in new ex­pe­ri­ence (sim­i­lar to will­ing in train­ing, ex­cept re­lated to novel stim­uli in­stead of train­ing); traffic (indi­cates if puppy can avoid a mov­ing and sta­tion­ary cart with­out be­com­ing fear­ful); foot­ing-cross­ing (indi­cates if puppy no­ticed differ­ences in foot­ing be­tween curbs and metal patches in the side­walk); close­ness {how close the puppy passed to ob­struc­tion­s); heel (how well the puppy ac­cepted leash train­ing). Eleven of the 13 traits, whose her­i­tabil­ity es­ti­mates are listed in Ta­ble XI, had dam com­po­nents much larger than the sire com­po­nents, in­di­cat­ing strong ma­ter­nal effects (S­cott and Bielfelt, 1976). This agrees with the find­ings of Scott and Fuller {1965) and Geiger {1973). As part of the same study, J.L. Fuller ex­am­ined the re­la­tion­ship be­tween phys­i­cal mea­sure­ments and be­hav­ior. Once again, no sub­stan­tial cor­re­la­tions were found (Fuller, 1976).

    Trait Her­i­tabil­ity
    Sit 0.06
    Come 0.14
    Fetch 0.24
    Trained re­sponse 0.08
    Will­ing in train­ing 0.12
    Body sen­si­tiv­ity 0.16
    Ear sen­si­tiv­ity 0.00
    New-ex­pe­ri­ence re­sponse 0.06
    Will­ing new ex­pe­ri­ence 0.24
    Traffic 0.12
    Foot­ing-cross­ing 0.06
    Close­ness 0.04
    Heel 0.10

    Ta­ble 11: Her­i­tabil­ity es­ti­mates for Cal­i­for­nia guide dogs (after Scott and Bielfelt, 1976)

    Com­par­ing Scott and Fuller’s 1965 es­ti­mates with those of the U.S. Army Biosen­sor project (Castle­berry et al., 1975), it seems pos­si­ble that cer­tain com­po­nents of be­hav­ior may be highly her­i­ta­ble. The fail­ure of other work­ers to find high es­ti­mates may in­di­cate that such es­ti­mates are quite sen­si­tive to the qual­ity of the tests, size of the sam­ples and sta­tis­ti­cal method­ol­o­gy.

    In 1979, M. Pflei­der­er-Hogner es­ti­mated her­i­tabil­i­ties of Schutzhund scores in Ger­many. She an­a­lyzed 2046 test re­sults in 1291 Ger­man Shep­herds from 37 sires, all tested an­i­mals be­ing born in 1973. The four cri­te­ria stud­ied were track­ing, obe­di­ence, man-work and char­ac­ter. She found sex and the num­ber of dogs com­pet­ing in a given trial to be sig­nifi­cant, but not age or month of tri­al. Sex differ­ences were pre­vi­ously noted by Humphrey and Warner (1934), King (1954), Mahut (1958) and Reuter­wall and Ry­man (1973). Es­ti­mates of her­i­tabil­i­ties from sire com­po­nents, dam com­po­nents and their com­bi­na­tion are listed in Ta­ble XII (Pflei­der­er-Hogn­er, 1979).

    Trait Sire Dam Com­bined
    Track­ing 0.01 0.20 0.10
    Obe­di­ence 0.04 0.13 0.09
    Man-work 0.04 0.07 0.06
    Char­ac­ter 0.05 0.17 0.12

    Ta­ble 12: Her­i­tabil­ity es­ti­mates for Ger­man Schutzhund scores (after Pflei­der­er-Hogn­er, 1979)

    In 1982, L. Falt, L. Swen­son and E. Wils­son re­ported their un­pub­lished work on her­i­tabil­ity es­ti­mates for be­hav­ioral traits stud­ied at the Na­tional Dog School (S­tatens Hund­sko­la) in Solleft­ea, Swe­den. [Falt, L., Swen­son, L. and Wilsson, E., 1982. “Men­talbeskrivn­ing av val­par. Bat­tre Tjanste­hun­dar, Pro­jek­trap­port II”. Statens Hund­sko­la, Sveriges Lant­bruk­suni­ver­sitet and Stock­holms Uni­ver­sitet. Un­pub­lished.] The traits stud­ied in 8-week-old Ger­man Shep­herd pup­pies in­clud­ed: yelp (time from first sep­a­ra­tion from lit­ter to first dis­tress cal­l); shriek (time from the same sep­a­ra­tion to the first se­ri­ous, em­phatic dis­tress cal­l); con­tact 1 (ten­dency to ap­proach a strange per­son in a strange place after sep­a­ra­tion); fetch (pur­sue a ball and pick it up in the mouth); re­trieve (bring­ing the ball back after pick­ing it up); 389 re­ac­tion (to a strange ob­ject in a strange place); so­cial com­pe­ti­tion (ac­tu­ally a form of tug-of-war); ac­tiv­ity (num­ber of squares en­tered when left in a marked are­na); con­tact 2 (time spent near a strange per­son sit­ting pas­sively in a chair in the mid­dle of the marked are­na); ex­ploratory be­hav­ior (num­ber of vis­its to strange ob­jects placed in the cor­ners of the marked are­na). Es­ti­mates of her­i­tabil­i­ties for the traits, cal­cu­lated from sire com­po­nents and dam com­po­nents sep­a­rate­ly, are listed in Ta­ble XIII (Falt et al., 1982). Al­though some spe­cific be­hav­iors had low her­i­tabil­ity es­ti­mates, oth­ers had quite high es­ti­mates.

    Trait Sire Dam
    Yelp 0.66 0.73
    Shriek 0.22 0.71
    Con­tact 1 0.77 1.01
    Fetch 0.73 0.10
    Re­trieve 0.19 0.51
    Re­ac­tion 0.09 1.06
    So­cial com­pe­ti­tion 0.11 0.76
    Ac­tiv­ity 0.43 0.76
    Con­tact 2 0.05 1.11
    Ex­ploratory be­hav­ior 0.31 0.83

    Ta­ble 13: Her­i­tabil­ity es­ti­mates for Swedish Ger­man Shep­herds (after Felt et at., 1982)

    …They felt that im­proved train­ing and up­bring­ing were as im­por­tant as ge­net­ics in pro­duc­ing good be­hav­ior. Since the first-gen­er­a­tion hy­brids per­formed bet­ter than ei­ther of their pure-bred par­ents in prob­lem-solv­ing sit­u­a­tions, Scott and Fuller rec­om­mended that cross-breds be con­sid­ered as work­ing dogs, pro­vided that the pure-bred lines were prop­erly main­tained. Main­te­nance of the pure-bred lines seems im­por­tant since they stated that the het­ero­sis (hy­brid vig­or) lasted only for one gen­er­a­tion. Con­se­quent­ly, in­ter-breed­ing of the hy­brids should not re­sult in any im­prove­ment in prob­lem-solv­ing abil­i­ty. They also rec­om­mended against breed­ing one cham­pion sire to many bitch­es, since they felt that good breed­ing pro­grams need to con­sider mul­ti­ple cri­te­ria to be effec­tive (S­cott and Fuller, 1965).

Fur­ther read­ing:

NBA Screening Scenario

Anal­o­gous to the dog cloning sce­nar­io, I con­sider the case of se­lect­ing for ex­tremes on PGSes, mo­ti­vated by a sce­nario of scout­ing tall men for the NBA.

Set­ting up the NBA se­lec­tion prob­lem as a li­a­bil­ity thresh­old model with cur­rent height PGSes as a noisy pre­dic­tor, height se­lec­tion can be mod­eled as se­lect­ing for ex­tremes on a PGS which is re­gressed back to the mean to yield ex­pected adult height, and prob­a­bil­ity of be­ing tall enough to con­sider a NBA ca­reer.

Fill­ing in rea­son­able val­ues, non­triv­ial num­bers of tall peo­ple can be found by ge­nomic screen­ing with a cur­rent PGS, and as PGSes ap­proach their pre­dic­tive up­per bound (derived from whole-genome-based her­i­tabil­ity es­ti­mates of height), se­lec­tion is ca­pa­ble of se­lect­ing al­most all tall peo­ple by tak­ing the top PGS per­centile.

The se­lec­tion prob­lem above is fairly gener­ic. The topic of rank­ing & se­lec­tion based on a noisy pre­dic­tor can be il­lus­trated by con­sid­er­ing a sim­i­lar sce­nario.

Genomic Prediction of Height

Can height be pre­dict­ed? Yes: one of the best-per­form­ing PGSes for a com­mon hu­man com­plex traits as of 2019 is height PGSes, which has leapt from ~20% in to pre­dict­ing ~40–42% of vari­ance or r = 0.65 in 2018 (Qian et al 2019//). Nor have cur­rent height PGSes have not hit a ceil­ing yet. Whole genome data in­di­cates (), us­ing GCTA-like her­i­tabil­ity tech­niques, that the pedi­gree-based her­i­tabil­ity es­ti­mates of height are sub­stan­tially cor­rect and that whole genome se­quenc­ing will en­able GWASes to even­tu­ally pre­dict up to 79% vari­ance or r = 0.89 for height.21

Can ex­treme height be pre­dict­ed? Also yes—while the ex­tremes of hu­man height can be caused by dis­eases, and can be affected by rare mu­ta­tions, par­tic­u­larly for short­ness, for the most part, it and tall­ness in par­tic­u­lar are caused by com­mon ge­netic vari­ants and thus ex­tremely tall peo­ple have el­e­vated height PGS scores (, Liu et al 2013, Sex­ton et al 2018; the last pro­vides the spe­cific case of , who is +4.2SD on the 2014 height PGS). So, one can pre­dict with con­sid­er­able prob­a­bil­ity in­di­vid­u­als of ex­treme height by look­ing for suffi­ciently ex­treme height PGSes.

Can ex­treme height of every­one be pre­dict­ed? Yes, be­cause even­tu­al­ly, every­one will be ge­net­i­cally se­quenced. 23andMe and UK BioBank and Japan Biobank and are only the start. The cost of SNP geno­typ­ing as of 2019 is far too low to not, as it would cost per­haps $20 in bulk (about what the UKBB paid years ago), and it is profitable con­sid­er­ing only (, Chan­f­reau-Coffinier et al 2019) and treat­ment of rare mono­genic dis­eases (only par­tially solved by the uni­ver­sal use of 22) and screen­ing new­borns (, Far­naes et al 2018, Cey­han-Bir­soy et al 2019, Sharp et al 2019), never mind the ben­e­fits to re­search (which will drive fur­ther progress & make it even more cost-effec­tive through effects and bet­ter PGSes) or life­long uses such as CAD/T2D/IBD/breast can­cer/stroke (Torka­mani et al 2019, Khera et al 2018, , Mavad­dat 2019, , , , ) pre­dic­tion & pre­ven­tion. So height will be pre­dictable for every­one (in­clud­ing the de­ceased).

Height in NBA Basketball

“I’ll check up on any­one over 7 feet that’s breath­ing.”

Ryan Blake, NBA scout23

Height is a guinea of hu­man ge­net­ics be­cause it is so vis­i­ble, eas­ily mea­sured, highly her­i­ta­ble, and yet not a sim­ple Mendelian trait but highly poly­genic. Height is also in­ter­est­ing be­cause a ma­jor pro­fes­sional sport, bas­ket­ball, de­pends on the spe­cific trait of height to an un­usual ex­ten­t–no other ma­jor sport (like soc­cer, base­ball, foot­ball, or crick­et) de­pends on a sin­gle phys­i­cal high­ly-her­i­ta­ble high­ly-pre­dictable trait the way bas­ket­ball does. NBA play­ers as of 2018 av­er­age 6 foot 7 inch (2.01 m), after a his­tor­i­cal growth spurt which saw player heights grow enor­mously (stag­nat­ing in the past 2 decades, per­haps be­cause they ran out of tall peo­ple and have had to em­pha­size ath­leti­cism & speed more than big men, al­though taller play­ers are still paid more). The short­est player in the en­tire NBA as of 2018 is at 5 foot 9 inch (1.75 m), and the short­est player ever was , 5 foot 3 inch (1980s–1990s). (Graph­ing the cur­rent dis­tri­b­u­tion of NBA player heights, it is fairly nor­mal look­ing, but ap­pears trun­cated at 6 foot and a bit right-skewed.) Amus­ing­ly, many NBA play­ers are re­lated.

The im­por­tance of height to NBA en­trance is demon­strated by the ex­treme rar­ity of NBA-like height. The US adult male pop­u­la­tion has a mean height of 69.2 (2.98SD) inches24, while NBA play­ers are al­most a foot higher at a mean height of 79 inch­es, putting the av­er­age NBA player at fully +3.29SD in height25. There are per­haps <80,000 men in the USA >=3.29SD. (S­ports jour­nal­ist has fa­mously ar­gued that “while the prob­a­bil­ity of, say, an Amer­i­can be­tween 6’6” and 6’8" be­ing an NBA player to­day stands at a mere 0.07%, it’s a stag­ger­ing 17% for some­one 7 feet or taller.") And there were 3,853,472 ba­bies to­tal born in the USA in 2018 or ~1926736 male ba­bies, so sim­i­lar­ly, there are per­haps ~1000 male ba­bies each year who will grow up to be >=3.29SD or the top 0.05% in height26, and per­haps ~25 with a PGS as ex­treme as Shawn Bradley27. Some­what more re­al­is­tic would be to ask what thresh­old a mean of +3.29 is; by the trun­cated nor­mal28, that’s a thresh­old of +3.01SD or the top 0.13%, im­ply­ing more like 2500 male ba­bies per year, still a fairly small num­ber.

Height as Screening Problem

A NBA or col­lege bas­ket­ball re­cruiter might be quite in­ter­ested in know­ing who those 2500 are—height is cer­tainly not the only de­ter­mi­nant of suc­cess, one has to at least want to play, but such a height would be a huge help in be­com­ing a NBA play­er. Get­ting to po­ten­tial re­cruits as early as pos­si­ble could help de­velop an in­ter­est in bas­ket­ball, ac­cel­er­ate their ca­reer, deal with rough patch­es, or just make them more at­tached to a par­tic­u­lar col­lege or team.

Re­gard­less of how plau­si­ble this par­tic­u­lar sce­nario is, is it at least sta­tis­ti­cally pos­si­ble? Can ex­tremes in adult height, given the base rates & the <100% her­i­tabil­ity of height & r = 0.65 PGS, be pre­dicted ac­cu­rately enough to be plau­si­bly use­ful for screen­ing?

So con­cep­tu­ally the model is: a large sam­ple of nor­mal vari­ables are gen­er­ated (the PGS), then the top n% are se­lect­ed; this cre­ates a new dis­tri­b­u­tion of trun­cated nor­mal vari­ables with a much higher mean, yield­ing a cer­tain boost in SD (like +4S­D), but then to pre­dict the cor­re­lated vari­able (adult height), it must then be re­gressed back to the mean due to the r < 1 cor­re­la­tion of the 2 vari­ables (er­ror in the PGS), yield­ing a smaller out­come boost on the cor­re­lated vari­able (like +2S­D); with the cor­rect boost es­ti­mat­ed, the prob­a­bil­ity any of the top n% will pass an ad­di­tional thresh­old (eg NBA height thresh­olds at +3.01SD) can then be cal­cu­lat­ed. (Trick­i­ly, the dis­tri­b­u­tion of sam­ples after se­lec­tion is not merely a nor­mal dis­tri­b­u­tion shifted high­er, but also has a differ­ent, small­er, SD, so that must be ad­justed for as well as the higher mean.)

With the prob­a­bil­ity of suc­cess con­di­tional on se­lect­ing the top n% with a PGS es­ti­mat­ed, the to­tal num­ber of suc­cess­ful se­lected can­di­dates be in­ferred from the to­tal pop­u­la­tion and com­pared with the es­ti­mated num­ber of all suc­cess­ful can­di­dates to give an idea of screen­ing effi­cien­cy.

The dog cloning ap­proach can be par­tially reused here: the ‘her­i­tabil­ity’ is 0.42 (vari­ance of PGS with adult height), the global suc­cess rate is set by +3.01SD, and we want to know what pre-screen­ing must be ap­plied for a rea­son­able prob­a­bil­ity of a can­di­date suc­ceed­ing. A rea­son­able value here might be 10%: a re­cruiter is­n’t in­vest­ing that much time in each pos­si­ble re­cruit, but at 1% they’d be wast­ing a lot of their time, but 10% seems like a rea­son­able value to look at.

Model

Im­ple­ment­ing the nec­es­sary trun­cated nor­mal ap­pa­ra­tus (ex­act im­ple­men­ta­tion & a Monte Carlo im­ple­men­ta­tion to check):

## can check with Monte Carlo and against `etruncnorm` & `vtruncnorm` in
## 'truncnorm' package: https://cran.r-project.org/web/packages/truncnorm/truncnorm.pdf
truncNormMean <- function(a, mu=0, sigma=1, b=Inf) {
        phi <- dnorm
        erf <- function(x) 2 * pnorm(x * sqrt(2)) - 1
        Phi <- function(x) { 0.5 * (1 + erf(x/sqrt(2))) }
        Z <- function(beta, alpha) { Phi(beta) - Phi(alpha) }

        alpha = (a-mu)/sigma; beta = (b-mu)/sigma

        return( (phi(alpha) - phi(beta)) / Z(beta, alpha) ) }
truncNormMeanMC <- function(a, mu=0, sigma=1, b=Inf, iters=1000000) {
    mean(Filter(function(x){x>a && x<b}, rnorm(iters, mean=mu, sd=sigma))) }

truncNormMean(1)
# [1] 1.52513528
truncNormMeanMC(1)
# [1] 1.52510301
library(truncnorm)
etruncnorm(1)
# [1] 1.52513528

truncNormSD <- function(a, mu=0, sigma=1, b=Inf) {
        phi <- dnorm
        erf <- function(x) 2 * pnorm(x * sqrt(2)) - 1
        Phi <- function(x) { 0.5 * (1 + erf(x/sqrt(2))) }
        Z <- function(beta, alpha) { Phi(beta) - Phi(alpha) }

       alpha = (a-mu)/sigma; beta = (b-mu)/sigma

       sqrt(1 +
           ((alpha * phi(alpha)) - phi(beta))/Z(beta,alpha) -
               (((phi(alpha)-phi(beta))/Z(beta,alpha))^2))
}
truncNormSDMC <- function(a, mu=0, sigma=1, b=Inf, iters=1000000) {
    sd(Filter(function(x){ x>=a & x <=b },  rnorm(iters, mean=mu, sd=sigma))) }

truncNormSD(1)
# [1] 0.446203614
truncNormSDMC(1)
# [1] 0.445727302
sqrt(vtruncnorm(a=1))
# [1] 0.446203614

Model the se­lec­tion prob­lem:

doubleSelection <- function(successP=0.01, preThreshold=0.01, heritability=0.42, verbose=FALSE) {
    r <- sqrt(heritability)
    threshold <- qnorm(1-preThreshold)

    meanPost <- truncNormMean(threshold) * r
    sdPost <- sqrt(truncNormSD(threshold))

    successThreshold <- qnorm(1-successP)

    p <- pnorm(meanPost - successThreshold, sd=sdPost)

    if (verbose) { print(c(r, threshold, meanPost, sdPost, successThreshold, p)) }
    p
    }
doubleSelectionMC <- function(successP=0.01, preThreshold=0.01, heritability=0.42,
                              iters=100000000, verbose=FALSE) {

    threshold <- qnorm(1-preThreshold)
    successThreshold <- qnorm(1-successP)
    r <- sqrt(heritability)

    library(MASS)
    data = mvrnorm(n=iters, mu=c(0, 0), Sigma=matrix(c(1, r, r, 1), nrow=2))

    screen     <- data[data[,1] >= threshold,]
    successful <- screen[screen[,2] >= successThreshold,]

    p <- nrow(successful) / nrow(screen)

    ## Skim to visualize non-normal post-selection distributions:
    if (verbose) { library(skimr);
        print(skim(screen[,1])); print(skim(screen[,2]))
        print(skim(successful[,2])); }
    p
}

doubleSelection(successP=(1-pnorm(3.01)), preThreshold=0.001, heritability=0.79, verbose=TRUE)
# [1] 0.888819442 3.090232306 2.992735123 0.510269172 3.010000000 0.486504425
# [1] 0.486504425
doubleSelectionMC(successP=(1-pnorm(3.01)), preThreshold=0.001, heritability=0.79, verbose=TRUE)
#     variable missing complete      n mean   sd   p0  p25  p50  p75 p100     hist
#  screen[, 1]       0   100076 100076 3.37 0.26 3.09 3.17 3.29 3.48 5.57 ▇▃▁▁▁▁▁▁
#  screen[, 2]       0   100076 100076 2.99 0.52 0.85 2.64 2.98 3.33  5.7 ▁▁▅▇▅▁▁▁
#  successful[, 2]       0    47745 47745 3.42 0.32 3.01 3.17 3.35 3.6  5.7 ▇▅▂▁▁▁▁▁
# [1] 0.477087414

Also of in­ter­est is cal­cu­lat­ing how many suc­cess­ful tall adults will be found in the screen, by ap­ply­ing the pre-s­e­lec­tion & post-s­e­lec­tion prob­a­bil­ity to the to­tal sam­ple:

net <- function(pre, h2) {
    p <- doubleSelection(successP=(1-pnorm(3.01)), preThreshold=pre, heritability=h2)
    population <- 1926736
    selected   <- population * pre
    successful <- selected * p
    print(round(digits=4, p)); print(round(c(selected, successful)))
    return(c(p, successful)) }

## Example: with the top 1%, we will get ~207 tall adults (out of the 2500):
net(0.01, 0.42)
# [1] 0.0107
# [1] 19267   207

## The tradeoff between spreading a wide net and catching as many as possible: as we spread a wider net,
## we catch more total, but each becomes much less likely to succeed. eg AUC/ROC curves
round(digits=3, sapply(1/seq(from=1, to=100000, by=1000), function(pre) { net(pre, 0.42)}[1]))
#   [1]   NaN 0.052 0.079 0.098 0.114 0.127 0.139 0.150 0.159 0.168 0.176 0.184 0.191 0.197 0.203 0.209 0.215 0.220 0.226 0.231 0.235 0.240 0.244 0.248 0.252 0.256
#  [27] 0.260 0.264 0.268 0.271 0.274 0.278 0.281 0.284 0.287 0.290 0.293 0.296 0.299 0.301 0.304 0.307 0.309 0.312 0.314 0.317 0.319 0.321 0.324 0.326 0.328 0.330
#  [53] 0.332 0.334 0.336 0.338 0.340 0.342 0.344 0.346 0.348 0.350 0.352 0.353 0.355 0.357 0.359 0.360 0.362 0.364 0.365 0.367 0.368 0.370 0.372 0.373 0.375 0.376
#  [79] 0.378 0.379 0.381 0.382 0.383 0.385 0.386 0.387 0.389 0.390 0.391 0.393 0.394 0.395 0.397 0.398 0.399 0.400 0.402 0.403 0.404 0.405
round(sapply(1/seq(from=1, to=100000, by=1000), function(pre) { net(pre, 0.42)}[2]))
#   [1] NaN 101  76  63  55  49  45  41  38  36  34  32  31  29  28  27  26  25  24  23  23  22  21  21  20  20  19  19  18  18  18  17  17  17  16  16  16  15  15  15
#  [41]  15  14  14  14  14  14  13  13  13  13  13  12  12  12  12  12  12  12  11  11  11  11  11  11  11  11  10  10  10  10  10  10  10  10  10  10   9   9   9   9
#  [81]   9   9   9   9   9   9   9   9   9   9   8   8   8   8   8   8   8   8   8   8

Scenarios

It turns out that ex­am­in­ing the top 0.3% (n = 600) by PGS2018 is enough to en­rich can­di­dates to a 10% prob­a­bil­ity of be­ing tall enough for the NBA; com­bined, that im­plies ~60 tall peo­ple per year, and broad­en­ing to n = 10,000 will se­lect ~3x more, 175 tall peo­ple:

net(0.0003, 0.42)
# [1] 0.1036
# [1] 578  60

net(10000 / 1926736, 0.42)
# [1] 0.0175
# [1] 10000   175

Screen­ing 10,000 peo­ple is not un­re­al­is­tic, and a pay­off of 175 tall peo­ple is a po­ten­tially worth­while one.

Con­sid­er­ing po­ten­tial fur­ther im­prove­ments, as the PGS ap­proaches the WGS up­per bound of 79%, with the top 0.3%, the yield boosts to ~408:

net(0.0003, 0.79)
# [1] 0.7064
# [1] 578 408

With such a pre­dic­tor, one might want to cast a wider net; go­ing back to a 10% suc­cess prob­a­bil­i­ty, with the op­ti­mal pre­dic­tor, one would be able to re­cover es­sen­tially all tall peo­ple per year by tak­ing the top 1.3% (n = 25,000):

net(0.013, 0.79)
# [1] 0.1008
# [1] 25048 2525

(At that point, un­caught tall peo­ple would be the ex­cep­tional cas­es: those not in­cluded in the screen to be­gin with, those who are tall be­cause of dis­eases/en­vi­ron­men­tal fac­tors, those with novel de novo mu­ta­tions not pre­vi­ously iden­ti­fied, etc.)

If a se­lec­tion sam­ple of n = 25,000 is too large de­spite be­ing com­pre­hen­sive, a sam­ple 10x smaller (n = 2,500) will still re­cover about half the tall peo­ple by tak­ing the top 0.13%:

net(2500 / 1926736, 0.79)
# [1] 0.4367
# [1] 2500 1092

  1. In 2015, plans were an­nounced for a aim­ing for 100,000 clones a year—while its present sta­tus is un­clear and as of 2017 it “seems to be well be­hind sched­ule”, it shows the am­bi­tions.↩︎

  2. For ex­am­ple, in ap­ple breed­ing, there are so many seedlings and so few ap­ple tasters, and the goal is to se­lect ap­ples so su­pe­rior that they can po­ten­tially com­pete with ex­ist­ing com­mer­cial va­ri­eties (which have been se­lected out of count­less mil­lions of ap­ple trees, on net, over the past few cen­turies), that is a sin­gle bite of a sin­gle ap­ple (which ) from a sin­gle seedling for sev­eral years in a row with no sec­ond chances—and only then can .↩︎

  3. The new bot­tle­neck pre­sum­ably be­comes the hu­man train­ers, but they can give more in­ten­sive train­ing or par­tic­i­pate in re­search pro­grams in peace­time to keep their skills sharp & train the next gen­er­a­tion of hu­man train­ers, and given the pop­u­lar­ity of ‘Schutzhund’ and ‘ex­ec­u­tive pro­tec­tion dogs’ among civil­ians, there may be enough civil­ian de­mand for trained dogs to main­tain a re­serve of train­ers. This is prob­a­bly a moot point for the USA, as the global ‘War on Ter­ror’ and de­mand for SF dogs shows lit­tle sign of slack­en­ing soon.↩︎

  4. "“We made 49 be­cause we were cu­ri­ous about the small­ness,” ex­plains Jeong, the head re­searcher. “Would it trans­fer?” He shakes his head. “It did­n’t—the clones turned out big­ger.”" The owner sued H Bion/­Sooam in March 2019, claim­ing that only 10 clones were sup­posed to be made and she has ac­cused them of ly­ing about the re­sults to reuse them in mi­cro-pigs and other projects with­out roy­al­ties (case #6:2019cv00425; com­plaint).↩︎

  5. Choi et al 2013:

    Six cloned dogs that fin­ished the train­ing course were eval­u­ated by a fi­nal drug-de­tec­tion dog se­lec­tion test and all of them passed. The pass level was a score of 60. [The cloned dog] To-Tue was graded as Ex­cel­lent (s­core 90) and the re­main­ing five dogs were eval­u­ated as Good. In age matched-con­trols, seven pup­pies fin­ished the train­ing course and one of them passed the test. One of the eight pup­pies died be­fore the train­ing course was over. The pass rate of cloned dogs was 86% since six pup­pies passed among seven cloned ones. That of con­trols was 13% in the ag­gre­gate since one passed among eight con­trol ones. This value was lower than gen­er­ally found as 30% () or 50% (Weiss and Green­berg 1997).

    ↩︎
  6. In­ter­est­ing­ly, there was a pre­vi­ous suc­cess­ful cloned dog, in 2000, by the project (funded by a do­na­tion to Texas A&M Uni­ver­sity of $4,263,096$2,300,0001998)—but it was still­born and not widely re­ported.↩︎

  7. The ci­ta­tions are to in­ter­views Frost con­ducted & sum­ma­rized:

    • An­der­sen, Gary, LTC. Tele­phone in­ter­view, 1969-11-28. LTC An­der­sen stated that the re­jec­tion rate at pro­cure­ment of MWDs is ap­prox­i­mately 50%, due to med­ical prob­lems (pri­mar­ily hip dys­plasi­a). The washout rate for a pa­trol dog is about 16%. He also men­tioned that this rate used to be 5% in the 70’s. He did not know why the re­jec­tion rates were higher to­day, since the qual­ity of the dogs is bet­ter. He said that it could be that not enough time is spent try­ing to get the ‘slower’ dogs to pass the train­ing course (Frost, Park­s). He said he felt the high re­jec­tion rate was one rea­son for the large back­log of req­ui­si­tions. He also said there are ad­e­quate num­bers of train­ers, but there is no ap­par­ent for­mal­ized cer­ti­fi­ca­tion process. When asked about com­mand and con­trol of the MWD Pro­gram, LTC An­der­sen said he un­der­stood it, but it was very con­fus­ing to peo­ple out­side the pro­gram, and that it is largely in­effec­tive in func­tion since there is no one cen­tral man­ager for all facets of the MWD Pro­gram (Bur­well, Parks, Stam­p).
    • Craig, Dan. Tele­phone in­ter­view, 1989-12-05. Dr. Craig said that all dogs pro­cured for the MWD Pro­gram are bought for de­tec­tor dogs, and those that wash out are trained as pa­trol dogs…He said that the re­jec­tion rate for Pa­trol/­Ex­plo­sive Dogs is 43%, while that for Pa­trol/­Drug Dogs is 29% (An­der­sen; Bur­well; Mc­Cath­ern, Tele­phone in­ter­view; Tay­lor, E.).
    • Mc­Cath­ern, Marge. Tele­phone in­ter­view, 1989-12-01. While dis­cussing re­jec­tion rates in the MWD Pro­gram, Ms. Mc­Cath­ern said the re­jec­tion rate for the new ex­plo­sive course was 83%, and that it differs for each course. She also said the re­jec­tion rate at pro­cure­ment was around 50%. She ad­vised the au­thor to talk to Dr. Craig for the av­er­age costs in­volved in train­ing each spe­cialty of MWD.
    • Thor­ton, William H., LTC. “The Role of Mil­i­tary Work­ing Dogs in Low In­ten­sity Con­flict”. Army-Air Force Cen­ter for Low In­ten­sity Con­flict. This pa­per dis­cusses the his­tor­i­cal and cur­rent roles of the MWD, and presents rea­son­ing for the need to ex­pand the role of the MWD. Such rea­son­ing cen­ters around econ­omy of force, low tech­nol­o­gy, high ca­pa­bil­i­ty, op­er­a­tional flex­i­bil­ity of the MWD, and the need for wider use of the MWDs ca­pa­bil­i­ties other than as a law en­force­ment as­set. Prob­lems with the cur­rent MWD sys­tem are pre­sent­ed. The pa­per states that 98% of all dogs pro­cured by DODDC come from Eu­rope, that 45% are re­jected after train­ing, and that there is a back­log of 430 req­ui­si­tions of MWDs (Tay­lor, E.).
    ↩︎
  8. The ci­ta­tions from Sinn et al 2010:

    ↩︎
  9. Tri­dent K9 War­riors, Rit­land & Brozek 2013:

    …As rare as it is to find a dog with the kind of prey drive that we seek, it is equally diffi­cult to find a dog with the kind of nose that will help it suc­ceed as a work­ing dog with the SEAL Teams. Find­ing a dog with both those qual­i­ties is truly a one-in-a-t­hou­sand (or more) propo­si­tion. That’s where good breed­ing comes in, of course, and se­lect­ing for both those traits will in­vari­ably pro­duce dogs that are stronger in one area over an­oth­er…The last qual­ity that I look for is diffi­cult to de­scribe in del­i­cate terms. A dog has to have a big set of nuts on him—metaphor­i­cally speak­ing. Most dogs, even among those se­lected from the elite breed­ers from around the world, don’t have the kind of dom­i­nance and true for­ward ag­gres­sion that is need­ed. Dogs have been do­mes­ti­cated and bred for so long that the type of dog that is will­ing to stand up to and fight a hu­man—a hu­man that is not fright­ened by that dog and phys­i­cally ca­pa­ble of dis­abling that dog—is a very, very rare an­i­mal. I call them the 1 per­centers (this was be­fore the term had a po­lit­i­cal con­no­ta­tion), but they are more like one in ten thou­sand.

    …It is also im­por­tant to un­der­stand that when I ac­quire a dog from a breeder of Ma­li­nois, I’m not get­ting a very young puppy who has­n’t been trained at all. The two-to-three­-year-old dogs have al­ready gone through rig­or­ous train­ing; some even have be­come what is re­ferred to as a “ti­tled dog.” That means that they’ve been trained and have earned a cer­ti­fi­ca­tion in one of sev­eral differ­ent Eu­ro­pean dog sports. One of the more com­mon types of those is Schutzhund, a dog sport pop­u­lar in Ger­many. When this sport was first or­ga­nized and the com­pe­ti­tions for­mal­ized, a dog that had com­pleted Schutzhund train­ing and be­came cer­ti­fied in the sport was also es­sen­tially qual­i­fied to be a Ger­man po­lice dog. That was the orig­i­nal in­tent of the pro­gram, but be­tween pol­i­tics and hurt feel­ings, the dogs that earn the “ti­tle” don’t nec­es­sar­ily have the com­pe­tency to be­come ac­tual work­ing po­lice dogs. The sport is so pop­u­lar that other breeds of dogs now can en­ter into the com­pe­ti­tions.

    …A­gain, that com­par­i­son be­tween the hu­man mem­bers of the SEAL Teams and their ca­nine co-work­ers ap­plies. No one goes into the SEAL Teams with­out first com­plet­ing ba­sic train­ing and then one ad­di­tional level be­fore start­ing BUD/S train­ing. While there is a 75% at­tri­tion rate among those en­ter­ing BUD/S, we don’t have that great a fail­ure rate among the dogs. I haven’t kept sta­tis­tics to track that rate among the dogs we ac­quire, but it is more like three or four in ten in­stead of seven and a half out of ten.

    Part of the rea­son for that is that the early weed­ing-out process among the dogs is more vig­or­ous than it is among the sailors. As I stated ear­lier, I felt the first test I had to pass to qual­ify as a SEAL Team can­di­date was­n’t very hard at all. When I’m eval­u­at­ing prospec­tive team dogs, my stan­dards are much high­er. In ad­di­tion, when we se­lect sires and bitches for breed­ing, we al­ready have in mind the kinds of work that these dogs will be asked to do. As a con­se­quence, we breed for those qual­i­ties, and from the mo­ment those dogs are born—to be more pre­cise, in the first sev­eral days of their lives—I’m al­ready be­gin­ning their train­ing.

    ↩︎
  10. "“They’ve been per­form­ing ex­cel­lent. They’re ex­actly like the orig­i­nal one,” he said in a tele­phone in­ter­view. “I can say it ab­solutely does work, and we have been able to cre­ate the same dog with the same qual­i­ties.”…Bran­non, who also trains dogs for po­lice de­part­ments around the U.S. as well as the mil­i­tary, said he was skep­ti­cal about cloning in the be­gin­ning but is now con­vinced it is more effi­cient than nat­u­ral-breed­ing pro­grams. He’s ex­pect­ing an­other clone next year—this one the twin of a dog that has helped agents find mil­lions of dol­lars in nar­cotics and ap­pre­hend many sus­pect­s."↩︎

  11. MacLean et al 2019:

    We as­sessed the her­i­tabil­i­ty(h2) of 14 be­hav­ioral traits (Fig 1) mea­sured by the Ca­nine Be­hav­ioral As­sess­ment and Re­search Ques­tion­naire (C-BARQ), a well-val­i­dated in­stru­ment for quan­ti­fy­ing di­verse as­pects of dog be­hav­ior (17, 18, 19, ), in­clud­ing ag­gres­sion, fear, train­abil­i­ty, at­tach­ment, and preda­tory chas­ing be­hav­iors. We com­bined be­hav­ioral data from 14,020 in­di­vid­ual dogs with breed-level ge­netic iden­ti­ty-by-s­tate (IBS) es­ti­mates…Us­ing a mixed-effects mod­el­ing ap­proach (Effi­cient Mixed-Model As­so­ci­a­tion; EMMA) to con­trol for re­lat­ed­ness be­tween breeds, we found that a large pro­por­tion of vari­ance in dog be­hav­ior is at­trib­ut­able to ge­netic fac­tors (Fig 1). The mean her­i­tabil­ity was 0.51 ± 0.12 (SD) across all 14 traits (range: h2 0.27–0.77), and sig­nifi­cantly higher than the null ex­pec­ta­tion in all cases (per­mu­ta­tion tests, p < 0.001).

    “Fig 1. Her­i­tabil­ity es­ti­mates, breed-level be­hav­ioral data, and clus­ter­ing based on be­hav­ioral and ge­netic da­ta. A) Her­i­tabil­ity (h2) es­ti­mates (pro­por­tion of vari­ance at­trib­ut­able to ge­netic fac­tors) for 14 be­hav­ioral traits. Geno­typic vari­a­tion ac­counts for five times more vari­ance in analy­ses across vs. within breeds (with­in-breed es­ti­mates com­piled from Il­ska et al., 2017). Points for Hay­ward et al. and Parker et al. re­flect the re­sults of analy­ses with in­de­pen­dent ge­netic datasets. Er­ror bars re­flect the 95% con­fi­dence in­ter­vals.”

    These es­ti­mates are also sig­nifi­cantly higher than those iden­ti­fied in pre­vi­ous stud­ies as­sess­ing her­i­tabil­ity of these traits in large with­in-breed sam­ples (t13 = −12.25, p < 0.001; 22, but see 23). Es­ti­mat­ing be­tween-breed vari­ance thus yields h2 es­ti­mates that are on av­er­age, five times higher (range= 1.3–25.5 times high­er), which is likely due to more vari­ance among, com­pared to within breeds. In­ter­est­ing­ly, the traits with the high­est her­i­tabil­ity were train­abil­ity (h2 = 0.73), stranger-di­rected ag­gres­sion (h2 = 0.68), chas­ing (h2 = 0.62) and at­tach­ment and at­ten­tion seek­ing (h2 = 0.56), which is con­sis­tent with the hy­poth­e­sis that these be­hav­iors have been im­por­tant tar­gets of se­lec­tion dur­ing the cul­ti­va­tion of mod­ern breeds.

    ↩︎
  12. pro­vides an anal­o­gous ex­am­ple: sci­en­tific pro­duc­tiv­i­ty.

    Some re­searchers are or­ders of mag­ni­tude more pro­lific and suc­cess­ful than oth­ers. Un­der a nor­mal dis­tri­b­u­tion con­cep­tu­al­iza­tion of sci­en­tific tal­ent, this would be odd & re­quire them to be many stan­dard de­vi­a­tions be­yond the norm on some ‘out­put’ vari­able. Shock­ley sug­gests that this is­n’t so sur­pris­ing if we imag­ine sci­en­tific re­search as more of a ‘pipeline’: a sci­en­tist has ideas, which feeds into back­ground re­search, which feeds into a se­ries of ex­per­i­ments, which feeds into writ­ing up pa­pers, then get­ting them pub­lished, then in­flu­enc­ing other sci­en­tists, then back to get­ting ideas.

    Each step is a differ­ent skill, which is plau­si­bly nor­mal­ly-dis­trib­ut­ed, but each step re­lies on the out­put of a pre­vi­ous step: you can’t ex­per­i­ment on non-ex­is­tent ideas, and you can only pub­lish on that which you ex­per­i­mented on, etc. Few peo­ple have an im­pact by sim­ply hav­ing a fab­u­lous idea if they can’t be both­ered to write it down. (Con­sider how much more im­pact Claude Shan­non, Euler, Ra­manu­jan, or Gauss would have had if they had pub­lished more than they did.) So if one re­searcher is merely some­what bet­ter than av­er­age at each step, they may wind up hav­ing a far larger out­put of im­por­tant work than a re­searcher who is ex­actly av­er­age at each step.

    If SF dogs are sim­i­lar, then there could be dogs which are or­ders of mag­ni­tude bet­ter than oth­ers, and this could stem from small ad­van­tages over com­peti­tors at each step; so small her­i­tabil­i­ties pro­duc­ing small gains could still pro­duce large out­put gains as long as many steps are be­ing im­proved si­mul­ta­ne­ous­ly.↩︎

  13. Choi 2018 does­n’t seem to be us­ing the same dataset as Choi et al 2014, be­cause in Choi et al 2014, there were 8 clones and in Choi 2018 there were 6 clones; and in the lat­ter, 3⁄4 con­trol pup­pies passed quar­an­tine train­ing.↩︎

  14. Num­bers >500 often come up in Sooam/po­lice ar­ti­cles, and the sniffer ar­ti­cle spec­i­fies that it has 42 cur­rently and that 50% were clones by 2014 from the ini­tial po­lice clones some­where ~2008; given sub­stan­tial turnover in dogs with ca­reers <10 years and an 80% suc­cess rate, that im­plies at least hun­dreds have en­tered sniffer train­ing to­tal.↩︎

  15. Ham­mer­strom 2005:

    “The DoD MWD Train­er/­Su­per­vi­sor Course pro­vides ken­nel mas­ters and train­ers with the skills to en­hance their MWD pro­gram. The course in­cludes in­struc­tion in ken­nel man­age­ment, ad­min­is­tra­tion, dog team train­ing, and con­tem­po­rary em­ploy­ment con­cepts” (Briefing by LTC Ban­nis­ter the com­man­der of the 341st Train­ing Squadron, on Sep­tem­ber 7, 2005).

    The DoD MWD Course [which pro­duces the trained dogs] pro­vides both pa­trol and dual cer­ti­fied pa­trol/de­tec­tor dogs [Cost is about $50,000 per trained dog.] The course is 120-days long. The dogs are trained in ei­ther drug or ex­plo­sive de­tec­tion. The dogs are trained to de­tect mar­i­jua­na, hashish, hero­in, and co­caine and must meet a 90% ac­cu­racy stan­dard to cer­ti­fy. Ex­plo­sive de­tec­tor dogs are trained to de­tect seven ex­plo­sive sub­stances (smoke­less pow­der, ni­tro dy­na­mite, am­mo­nia dy­na­mite, TNT, C-4, wa­ter gel, and ) and two chem­i­cal com­pounds (sodium and potas­sium chlo­rate) and must meet a strict 95% stan­dard (briefing by LTC Ban­nis­ter, com­man­der of the 341st Train­ing Squadron, on Sep­tem­ber 7, 2005).

    The 341st is a ma­jor train­ing lo­ca­tion as of 2011:

    With a sec­ond ken­nel fa­cil­ity lo­cated on Med­ina An­nex about a mile away, Lack­land AFB has ap­prox­i­mately 900 dogs at any given time. The squadron’s school trains about 270 mul­ti­pur­pose dogs a year, ac­cord­ing to school offi­cials. Not only does the school train new dogs, but it trains han­dlers and su­per­vi­sors as well… To keep up with the de­mand for trained dogs, the school uses a va­ri­ety of pro­cure­ment meth­ods, in­clud­ing its own breed­ing pro­gram. The suit­abil­ity rate runs around 50 per­cent. In other words, to pro­duce 100 ser­vice­able dogs per year, the pro­gram will at­tempt to train about 200.

    ↩︎
  16. Tri­dent K9 War­riors, Rit­land & Brozek 2013:

    …No mat­ter that the navy had in­vested more than $62,414$50,0002013 in the ac­qui­si­tion, train­ing, and care of Duco be­fore Seth spent that year in our pro­gram pre-de­ploy­ment, Duco was still “his.” That was as it should be; un­for­tu­nate­ly, it is­n’t al­ways. I’ve trained hun­dreds of dogs for a va­ri­ety of pur­pos­es, and it’s not al­ways easy to let them go to an­other home, es­pe­cially a qual­ity dog like Duco. Train­ing dogs to be of ser­vice to us is my job, and it’s also my pas­sion. See­ing how a pair like Seth and Duco con­tinue to op­er­ate does my heart good.

    ↩︎
  17. “The Dogs of War Are in High De­mand: After send­ing hun­dreds of ca­nines to post Sept. 11 bat­tle­fields, the Pen­ta­gon is buy­ing ro­bot pooches to help train medics.” (2017):

    The U.S. mil­i­tary spends up to $283,000 to train a work­ing war dog.

    Once it has a promis­ing pup, the Pen­ta­gon spends an ad­di­tional $42,000 to train a K9 unit, a process that starts with obe­di­ence and drug and/or bomb de­tec­tion at Lack­land Air Force Base in San An­to­nio, Texas. Some of the dogs get a sec­ond round of train­ing in how to pa­trol, de­tain an en­emy and at­tack. A “du­al-pur­pose” dog spends about 120 days com­plet­ing both train­ing cy­cles.

    When all is said and done, a fully trained mil­i­tary dog costs about as much as a small mis­sile. Keep­ing them in the field as long as pos­si­ble is in­creas­ingly good busi­ness. (The Air Force de­clined to dis­cuss ca­nine ca­su­alty rates.)

    ↩︎
  18. pg37, Dog, In­c.: The Un­canny In­side Story of Cloning Man’s Best Friend, Woes­tendiek 2010.↩︎

  19. Per­haps by com­bin­ing noisy rat­ings like train­ing records & com­pleted ser­vice records & han­dler rat­ings & gen­eral sur­veys, and se­lect­ing the best out of 10,000+ can­di­dates; since there’s no avail­able data for even spec­u­lat­ing about what are plau­si­ble r val­ues for such a pro­ce­dure or what the to­tal num­ber of can­di­dates might be if a gov­ern­ment like the US fed­eral gov­ern­ment made a se­ri­ous effort to screen all of its & avail­able al­lies’ SF dogs, let’s con­sider ‘1 of 1000’ as some­thing of a lower bound­—there are surely tens of thou­sands of SF dogs avail­able now & in decades to come, and a SF cloning pro­gram should be able to at least se­lect from the top few hun­dred of those SF dogs. (Bloomberg 2017, of the US alone: “At the mo­ment, roughly 1,600 Mil­i­tary War Dogs (MWDs) are ei­ther in the field or help­ing re­cu­per­at­ing vet­er­ans.”; NYT 2011, 2700.)↩︎

  20. The MC in­di­cates that the ex­act im­ple­men­ta­tion is slightly wrong, off by a rel­a­tive −1%; I have not been able to fig­ure out why the ex­act im­ple­men­ta­tion is con­ser­v­a­tive but it prob­a­bly has some­thing to do with the vari­ance of se­lected in­di­vid­u­als be­ing too small. So it is slightly bi­ased against cloning effi­ca­cy.↩︎

  21. Reach­ing such a pre­dic­tor will be ex­tremely diffi­cult in the near-term, but it’s worth re­mem­ber­ing that pedi­gree & GWAS are not mu­tu­ally ex­clu­sive ap­proach­es, and pre­dict­ing solely from SNPs is not the only nor the best pos­si­ble way to do ge­nomic pre­dic­tion. GWASes can be ex­tended to the mixed model ap­proach, where ge­netic re­lat­ed­ness to other in­di­vid­u­als of known phe­no­type is used for pre­dic­tion. Ex­plic­itly mod­el­ing re­lat­ed­ness of in­di­vid­u­als is a pow­er­ful method of pre­dic­tion (if some­one is more ge­net­i­cally sim­i­lar to their tall pa­ter­nal grand­fa­ther than their tall ma­ter­nal grand­moth­er, you can pre­dict they will be taller with­out know­ing any spe­cific SNPs), and can be com­bined with PGS-based ap­proaches based on in­di­vid­ual SNPs. The frame­work, for ex­am­ple, is widely used in agri­cul­ture for much bet­ter pre­dic­tions than pos­si­ble with merely in­di­vid­ual SNPs (; in GWAS, eg , ). Height is typ­i­cally mea­sured by biobanks, so as they get larg­er, use of SNP or WGS data for in­fer­ring ge­netic re­lat­ed­ness will be­come more fea­si­ble.↩︎

  22. Which, in­ci­den­tal­ly, are often kept in stor­age and would al­low overnight se­quenc­ing of a large frac­tion of the pop­u­la­tion. Every baby in Cal­i­for­nia since 1983 has blood spots stored, which could be used for genome se­quenc­ing.↩︎

  23. As quoted by Pablo S. Tor­re, 2011.↩︎

  24. Ac­cord­ing to Ta­ble 12 of “An­thro­po­met­ric Ref­er­ence Data for Chil­dren and Adults: United States, 2011—2014”, so:

    • all races: 69.2 inches
    • 95th per­centile: 74.1 inches
    • 95th per­centile = +1.64 SD
    • SD = 2.98
    ↩︎
  25. NBA mean of 6 foot 7 inches = 79 inches = = +3.29SD.↩︎

  26. (1-pnorm(3.29))*1926736↩︎

  27. 1926736 * (1 - pnorm(4.2))↩︎

  28. truncNormMean(3.01) → 3.923↩︎