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“Genetic Risk Factors Have a Substantial Impact on Healthy Life Years”, Jukarainen et al 2022

“Genetic risk factors have a substantial impact on healthy life years”⁠, Sakari Jukarainen, Tuomo Kiiskinen, Aki S. Havulinna, Juha Karjalainen, Mattia Cordioli, Joel T. Rämö et al (2022-01-28; ⁠, ; similar):

The impact of genetic variation on overall disease burden has not been comprehensively evaluated. Here we introduce an approach to estimate the effect of different types of genetic risk factors on disease burden quantified through disability-adjusted life years (DALYs, “lost healthy life years”). We use genetic information from 735,748 individuals with registry-based follow-up of up to 48 years. At the individual level, rare variants had higher effects on DALYs than common variants, while common variants were more relevant for population-level disease burden. Among common variants, rs3798220 (LPA) had the strongest effect, with 1.18 DALYs attributable to carrying 1 vs 0 copies of the minor allele. Belonging to top 10% vs bottom 90% of a polygenic score for multisite chronic pain had an effect of 3.63 DALYs. Carrying a deleterious rare variant in LDLR, MYBPC3, or BRCA1/​2 had an effect of around 4.1–13.1 DALYs. The population-level disease burden attributable to some common variants is comparable to the burden from modifiable risk factors such as high sodium intake and low physical activity. Genetic risk factors can explain a sizeable number of healthy life years lost both at the individual and population level, highlighting the importance of incorporating genetic information into public health efforts.

“Validation of Genomic Predictions for a Lifetime Merit Selection Index for the US Dairy Industry”, Fessenden et al 2020

“Validation of genomic predictions for a lifetime merit selection index for the US dairy industry”⁠, Brenda Fessenden, Daniel J. Weigel, Jason Osterstock, David T. Galligan, Fernando Di Croce (2020-11; similar):

Selection indices are a critical component of many breeding programs. A common purpose of a selection index is to predict an animal’s genetic potential for total economic merit.

The objective of this study was to evaluate retrospectively whether a specific selection index comprising genomically-enhanced predicted transmitting abilities had the ability to predict observed lifetime profit in US Holstein animals. The selection index evaluated was dairy wellness profit (DWP$). In total, 2,185 animals were included in this study. Index values were used to rank and assign animals to quartiles (genetic groups: worst 25%, 26–50%, 51–75%, and best 25%). Generalized linear mixed effects models were applied to estimate the associations between index quartile and defined economic outcomes. Similar analyses were conducted to estimate associations between index quartile and observed phenotype to characterize the extent to which profitability outcomes were driven by economically relevant production and health traits.

Differences in lifetime profit and annuity value between the best and worst genetic groups for DWP$ were $811 (±$297) and $232 (±$88), respectively. Statistically-significant differences were also observed between top and bottom quartiles for milk production (8,077 kg), fat production (336 kg), protein production (264 kg), live calves (0.5), time spent in the lactating herd (6.6 mo), and cow mortality (8.4%). Additionally, differences in disease incidence were statistically-significant between the best and worst DWP$ quartiles for metritis (5.2%), mastitis (14.9%), and lameness (15.9%).

The observed results of this study demonstrated the ability of DWP$ predictions to predict lifetime profitability of Holstein animals and its potential utility as a tool to guide selection and breeding programs. Improving DWP$ through genetic selection, when combined with good management practices, provides an opportunity for dairy producers to improve overall herd profitability.

“Multi-trait Genomic Selection Methods for Crop Improvement”, Moeinizade et al 2020

2020-moeinizade.pdf: “Multi-trait Genomic Selection Methods for Crop Improvement”⁠, Saba Moeinizade, Aaron Kusmec, Guiping Hu, Lizhi Wang, Patrick S. Schnable (2020-06-01; similar):

Plant breeders make selection decisions based on multiple traits, such as yield, plant height, flowering time, and disease resistance.

A commonly used approach in multi-trait genomic selection is index selection, which assigns weights to different traits relative to their economic importance. However, classical index selection only optimizes genetic gain in the next generation, requires some experimentation to find weights that lead to desired outcomes, and has difficulty optimizing non-linear breeding objectives. Multi-objective optimization has also been used to identify the Pareto frontier of selection decisions, which represents different trade-offs across multiple traits.

We propose a new approach, which maximizes certain traits while keeping others within desirable ranges. Optimal selection decisions are made using a new version of the look-ahead selection algorithm, which was recently proposed for single trait genomic selection and achieved superior performance with respect to other state-of-the-art selection methods.

To demonstrate the effectiveness of the new method a case study is developed using a realistic data set where our method is compared with conventional index selection. Results suggest that the multi-trait look-ahead selection is more effective at balancing multiple traits compared to index selection.

[Keywords: multi-trait genomic selection, optimization, simulation]

“Plant Breeders Should Be Determining Economic Weights for a Selection Index instead of Using Independent Culling for Choosing Parents in Breeding Programs With Genomic Selection”, Batista et al 2018

“Plant breeders should be determining economic weights for a selection index instead of using independent culling for choosing parents in breeding programs with genomic selection”⁠, Lorena G. Batista, R. Chris Gaynor, Gabriel R. A. Margarido, Tim Byrne, Peter Amer, Gregor Gorjanc, John M. Hickey et al (2018-12-20; similar):

In the context of genomic selection, we evaluated and compared recurrent selection breeding programs using either index selection or independent culling for selection of parents. We simulated a clonally propagated crop breeding program for 20 cycles of selection using either independent culling or an economic selection index with two unfavourably correlated traits under selection. Cycle time from crossing to selection of parents was kept the same for both strategies.

Our results demonstrate that accurate knowledge of the economic importance of traits is essential even when performing independent culling. This is because independent culling achieved its optimum genetic gain when the culling threshold for each trait varied accordingly to the economic importance of the traits. When gains from independent culling were maximized, the efficiency of converting genetic diversity into genetic gain of both selection methods were equivalent.

When the same proportion selected of 10% for each trait was used instead of optimal culling levels, index selection was 10%, 128% and 310% more efficient than independent culling when T2 had a relative economic importance of 1.0, 2.5 and 5.0, respectively. Given the complexity of estimating optimal culling levels and the fact that the gains achieved with independent culling are, at most, equivalent to index selection, the use of an economic selection index is recommended for multi-trait genomic selection.

“Possibilities in an Age of Genomics: The Future of Selection Indices”, Cole & VanRaden 2018

“Possibilities in an age of genomics: The future of selection indices”⁠, J. B. Cole, P. M. VanRaden (2018-04; similar):

Selective breeding has been practiced since domestication, but early breeders commonly selected on appearance (eg. coat color) rather than performance traits (eg. milk yield). A breeding index converts information about several traits into a single number used for selection and to predict an animal’s own performance. Calculation of selection indices is straightforward when phenotype and pedigree data are available. Prediction of economic values 3 to 10 yr in the future, when the offspring of matings planned using the index will be lactating, is more challenging.

The first USDA selection index included only milk and fat yield, whereas the latest version of the lifetime net merit index includes 13 traits and composites (weighted averages of other additional traits). Selection indices are revised to reflect improved knowledge of biology, new sources of data, and changing economic conditions. Single-trait selection often suffers from antagonistic correlations with traits not in the selection objective. Multiple-trait selection avoids those problems at the cost of less-than-maximal progress for individual traits. How many and which traits to include is not simple to determine because traits are not independent. Many countries use indices that reflect the needs of different producers in different environments. Although the emphasis placed on trait groups differs, most indices include yield, fertility, health, and type traits.

Addition of milk composition, feed intake, and other traits is possible, but they are more costly to collect and many are not yet directly rewarded in the marketplace, such as with incentives from milk processing plants. As the number of traits grows, custom selection indices can more closely match genotypes to the environments in which they will perform.

Traditional selection required recording lots of cows across many farms, but genomic selection favors collecting more detailed information from cooperating farms. A similar strategy may be useful in less developed countries. Recording important new traits on a fraction of cows can quickly benefit the whole population through genomics.

[Keywords: breeding program, genetic improvement, selection index]

“Improving Genetic Prediction by Leveraging Genetic Correlations among Human Diseases and Traits”, Maier et al 2018

“Improving genetic prediction by leveraging genetic correlations among human diseases and traits”⁠, Robert M. Maier, Zhihong Zhu, Sang Hong Lee, Maciej Trzaskowski, Douglas M. Ruderfer, Eli A. Stahl, Stephan Ripke et al (2018-03-07; ⁠, ; backlinks; similar):

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes⁠, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait.

“Fifty Years of Pig Breeding in France: Outcomes and Perspectives”, Bidanel et al 2018

2018-bidanel.pdf: “Fifty years of pig breeding in France: outcomes and perspectives”⁠, Jean Pierre Bidanel, Parsaoran Silalahi, Thierry Tribout, Laurianne Canario, Alain Ducos, Hervé Garreau et al (2018-02-01; similar):

This synthesis reviews the main changes that have occurred in the pig breeding sector in France since the 1966 Breeding Act.

It briefly discusses the first 20 years, which were the subject of a review in 1986. It describes subsequent changes in more detail, in particular the March 1994 decree on pig selection and its organisational consequences.

Breeding goals, initially limited to production traits, have then integrated meat quality traits, sow prolificness and maternal abilities. Regarding tools, implementation of genetic evaluation based on the BLUP animal model in the mid-1990s and development of artificial insemination profoundly changed breeders’ work. A new major change, genomic selection, is currently being implemented. Large genetic gains have been obtained since 1970 for the main components of the breeding goal: they have exceeded 200 g/​d for on-test average daily gain, −0.5 points for feed conversion ratio and 12 percentage points for carcass lean content, and approached 6 additional piglets born alive per litter.

These gains have reduced environmental impacts of pig production but also had some detrimental effects: an increase in piglet pre-weaning mortality and greater heterogeneity of performances.

Issues for future breeding goals (eg. inclusion of traits related to welfare, robustness and adaptation), methods and tools (eg. genomic selection, fine phenotyping, genome editing) are then discussed.

[Keywords: animal welfare, artificial insemination, Best Linear Unbiased Prediction, breeding aims, breeding programmes, breeding value, carcasses, environmental impact, feed conversion efficiency, genetic gain, genome analysis, lean, meat quality, performance traits, piglet production, piglets, prolificness, reproductive traits, sows, traits]

“Embryo Biopsies for Genomic Selection”, Mullaart & Wells 2018

2018-mullaart.pdf: “Embryo Biopsies for Genomic Selection”⁠, Erik Mullaart, David Wells (2018-01-01; backlinks)

“Economic Selection Index Development for Beefmaster Cattle I: Terminal Breeding Objective”, Ochsner et al 2017

“Economic selection index development for Beefmaster cattle I: Terminal breeding objective”⁠, K. P. Ochsner, M. D. MacNeil, R. M. Lewis, M. L. Spangler (2017-03-01; similar):

The objective of this study was to develop an economic selection index for Beefmaster cattle in a terminal production system where bulls are mated to mature cows with all resulting progeny harvested.

National average prices from 2010 to 2014 were used to establish income and expenses for the system. Phenotypic and genetic parameter values among the selection criteria and goal traits were obtained from literature. Economic values were estimated by simulating 100,000 animals and approximating the partial derivatives of the profit function by perturbing traits one at a time, by 1 unit, while holding the other traits constant at their respective means.

Relative economic values (REV) for the terminal objective traits HCW, marbling score (MS), ribeye area (REA), 12th-rib fat (FAT), and feed intake (FI) were 91.29, 17.01, 8.38, −7.07, and −29.66, respectively. Consequently, improving the efficiency of beef production is expected to impact profitability greater than improving carcass merit alone. The accuracy of the index lies between 0.338 (phenotypic selection) and 0.503 (breeding values known without error).

The application of this index would aid Beefmaster breeders in their sire selection decisions, facilitating genetic improvement for a terminal breeding objective.

“Genomic Selection in Plant Breeding: Methods, Models, and Perspectives”, Crossa et al 2017

2017-crossa.pdf: “Genomic Selection in Plant Breeding: Methods, Models, and Perspectives”⁠, José Crossa, Paulino Pérez-Rodríguez, Jaime Cuevas, Osval Montesinos-López, Diego Jarquín, Gustavo de los Campos et al (2017-01-01)

“Genomic Selection in Dairy Cattle: The USDA Experience”, Wiggans et al 2017

2017-wiggans.pdf: “Genomic Selection in Dairy Cattle: The USDA Experience”⁠, George R. Wiggans, John B. Cole, Suzanne M. Hubbard, Tad S. Sonstegard (2017; backlinks; similar):

Genomic selection has revolutionized dairy cattle breeding.

Since 2000, assays have been developed to genotype large numbers of single-nucleotide polymorphisms (SNPs) at relatively low cost. The first commercial SNP genotyping chip was released with a set of 54,001 SNPs in December 2007. Over 15,000 genotypes were used to determine which SNPs should be used in genomic evaluation of US dairy cattle. Official USDA genomic evaluations were first released in January 2009 for Holsteins and Jerseys, in August 2009 for Brown Swiss, in April 2013 for Ayrshires, and in April 2016 for Guernseys.

Producers have accepted genomic evaluations as accurate indications of a bull’s eventual daughter-based evaluation. The integration of DNA marker technology and genomics into the traditional evaluation system has doubled the rate of genetic progress for traits of economic importance, decreased generation interval, increased selection accuracy, reduced previous costs of progeny testing, and allowed identification of recessive lethals.

[Keywords: genetic evaluation, single-nucleotide polymorphism, SNP, reliability, imputation⁠, haplotype⁠, genotype]

“Low-dose Paroxetine Exposure Causes Lifetime Declines in Male Mouse Body Weight, Reproduction and Competitive Ability As Measured by the Novel Organismal Performance Assay”, Ruff et al 2015

2015-gaukler.pdf: “Low-dose paroxetine exposure causes lifetime declines in male mouse body weight, reproduction and competitive ability as measured by the novel organismal performance assay”⁠, James S. Ruff, Tessa Galland, Kirstie A. Kandaris, Tristan K. Underwood, Nicole M. Liu, Elizabeth L. Young et al (2015; ⁠, ⁠, ; backlinks; similar):

Paroxetine is a selective serotonin reuptake inhibitor (SSRI) that is currently available on the market and is suspected of causing congenital malformations in babies born to mothers who take the drug during the first trimester of pregnancy.

We utilized organismal performance assays (OPAs), a novel toxicity assessment method, to assess the safety of paroxetine during pregnancy in a rodent model. OPAs utilize genetically diverse wild mice (Mus musculus) to evaluate competitive performance between experimental and control animals as they compete amongst each other for limited resources in semi-natural enclosures. Performance measures included reproductive success, male competitive ability and survivorship.

Paroxetine-exposed males weighed 13% less, had 44% fewer offspring, dominated 53% fewer territories and experienced a 2.5-fold increased trend in mortality, when compared with controls. Paroxetine-exposed females had 65% fewer offspring early in the study, but rebounded at later time points. In cages, paroxetine-exposed breeders took 2.3× longer to produce their first litter and pups of both sexes experienced reduced weight when compared with controls. Low-dose paroxetine-induced health declines detected in this study were undetected in preclinical trials with dose 2.5-8× higher than human therapeutic doses.

These data indicate that OPAs detect phenotypic adversity and provide unique information that could useful towards safety testing during pharmaceutical development.

[Keywords: intraspecific competition, pharmacodynamics, reproductive success, semi-natural enclosures, SSRI, toxicity assessment.]

“Growth, Efficiency, and Yield of Commercial Broilers from 1957, 1978, and 2005”, Zuidhof et al 2014

“Growth, efficiency, and yield of commercial broilers from 1957, 1978, and 2005”⁠, M. J. Zuidhof, B. L. Schneider, V. L. Carney, D. R. Korver, F. E. Robinson (2014-12; backlinks; similar):

The effect of commercial selection on the growth, efficiency, and yield of broilers was studied using 2 University of Alberta Meat Control strains unselected since 1957 and 1978, and a commercial Ross 308 strain (2005). Mixed-sex chicks (n = 180 per strain) were placed into 4 replicate pens per strain, and grown on a current nutritional program to 56 d of age. Weekly front and side profile photographs of 8 birds per strain were collected. Growth rate, feed intake, and measures of feed efficiency including feed conversion ratio, residual feed intake, and residual maintenance energy requirements were characterized. A nonlinear mixed Gompertz growth model was used to predict BW and BW variation, useful for subsequent stochastic growth simulation. Dissections were conducted on 8 birds per strain semiweekly from 21 to 56 d of age to characterize allometric growth of pectoralis muscles, leg meat, abdominal fat pad, liver, gut, and heart. A novel nonlinear analysis of covariance was used to test the hypothesis that allometric growth patterns have changed as a result of commercial selection pressure. From 1957 to 2005, broiler growth increased by over 400%, with a concurrent 50% reduction in feed conversion ratio, corresponding to a compound annual rate of increase in 42 d live BW of 3.30%. Forty-two-day FCR decreased by 2.55% each year over the same 48-yr period. Pectoralis major growth potential increased, whereas abdominal fat decreased due to genetic selection pressure over the same time period. From 1957 to 2005, pectoralis minor yield at 42 d of age was 30% higher in males and 37% higher in females; pectoralis major yield increased by 79% in males and 85% in females. Over almost 50 yr of commercial quantitative genetic selection pressure, intended beneficial changes have been achieved. Unintended changes such as enhanced sexual dimorphism are likely inconsequential, though musculoskeletal, immune function, and parent stock management challenges may require additional attention in future selection programs.

[Keywords: broiler, genetic change, efficiency, yield dynamics]

Age-related changes in size (mixed-sex BW and front view photos) of University of Alberta Meat Control strains unselected since 1957 and 1978, and Ross 308 broilers (2005). Within each strain, images are of the same bird at 0, 28, and 56 d of age.

“Chapter 9. Genetic Influences on the Behavior of Chickens Associated With Welfare and Productivity”, Muir 2014-page-21

2014-muir.pdf#page=21: “Chapter 9. Genetic Influences on the Behavior of Chickens Associated with Welfare and Productivity”⁠, William M. Muir (2014; backlinks; similar):

Many behaviors in poultry can be modified by genetic selection. Selection of laying hens for maximum egg production had the unfortunate side effect of increased rates of beak inflicted damage on other birds. Selective breeding has eliminated broodiness and has either increased or decreased other behaviors, such as hysteria, fearfulness, appetite in broilers, social dominance, ability and damage to other birds. Genetic selection can be used to reduce behaviors that cause welfare problems. However, it must be approached with caution to avoid unintended consequences that would be detrimental to welfare. A calm, docile bird that appears behaviorally calm, may take longer for its heart rate to return to normal after it is frightened. The use of group selection instead of single-bird selection can be effectively used to reduce undesirable behaviors such as feather pecking and to maintain high egg production. An entire group of birds is selected instead of selecting individuals.

[Keywords: feather pecking, group selection, poultry, welfare]

“The Perfect Milk Machine: How Big Data Transformed the Dairy Industry: Dairy Scientists Are the Gregor Mendels of the Genomics Age, Developing New Methods for Understanding the Link between Genes and Living Things, All While Quadrupling the Average Cow's Milk Production Since Your Parents Were Born”, Madrigal 2012

“The Perfect Milk Machine: How Big Data Transformed the Dairy Industry: Dairy scientists are the Gregor Mendels of the genomics age, developing new methods for understanding the link between genes and living things, all while quadrupling the average cow's milk production since your parents were born”⁠, Alexis C. Madrigal (2012-05-01; similar):

…Already, Badger-Bluff Fanny Freddie has 346 daughters who are on the books and thousands more that will be added to his progeny count when they start producing milk. This is quite a career for a young animal: He was only born in 2004.

There is a reason, of course, that the semen that Badger-Bluff Fanny Freddie produces has become such a hot commodity in what one artificial-insemination company calls “today’s fast paced cattle semen market.” In January of 2009, before he had a single daughter producing milk, the United States Department of Agriculture took a look at his lineage and more than 50,000 markers on his genome and declared him the best bull in the land. And, three years and 346 milk-providing and data-providing daughters later, it turns out that they were right. “When Freddie [as he is known] had no daughter records our equations predicted from his DNA that he would be the best bull”, USDA research geneticist Paul VanRaden emailed me with a detectable hint of pride. “Now he is the best progeny tested bull (as predicted).”

Data-driven predictions are responsible for a massive transformation of America’s dairy cows. While other industries are just catching on to this whole “big data” thing, the animal sciences—and dairy breeding in particular—have been using large amounts of data since long before VanRaden was calculating the outsized genetic impact of the most sought-after bulls with a pencil and paper in the 1980s. Dairy breeding is perfect for quantitative analysis. Pedigree records have been assiduously kept; relatively easy artificial insemination has helped centralized genetic information in a small number of key bulls since the 1960s; there are a relatively small and easily measurable number of traits—milk production, fat in the milk, protein in the milk, longevity, udder quality—that breeders want to optimize; each cow works for three or four years, which means that farmers invest thousands of dollars into each animal, so it’s worth it to get the best semen money can buy. The economics push breeders to use the genetics.

The bull market (heh) can be reduced to one key statistic, lifetime net merit, though there are many nuances that the single number cannot capture. Net merit denotes the likely additive value of a bull’s genetics. The number is actually denominated in dollars because it is an estimate of how much a bull’s genetic material will likely improve the revenue from a given cow. A very complicated equation weights all of the factors that go into dairy breeding and—voila—you come out with this single number. For example, a bull that could help a cow make an extra 1000 pounds of milk over her lifetime only gets an increase of $1.3$1.02012 in net merit while a bull who will help that same cow produce a pound more protein will get $4.56$3.412012 more in net merit. An increase of a single month of predicted productive life yields $46.8$35.02012 more.

…In 1942, when my father was born, the average dairy cow produced less than 5,000 pounds of milk in its lifetime. Now, the average cow produces over 21,000 pounds of milk. At the same time, the number of dairy cows has decreased from a high of 25 million around the end of World War II to fewer than nine million today…a mere 70 years of quantitative breeding optimized to suit corporate imperatives quadrupled what all previous civilization had accomplished.

…John Cole, yet another USDA animal improvement scientist, generated an estimate of the perfect bull by choosing the optimal observed genetic sequences and hypothetically combining them. He found that the optimal bull would have a net merit value of $10,239.5$7,515.02011, which absolutely blows any current bull out of the water. In other words, we’re nowhere near creating the perfect milk machine.

“Use of Haplotypes to Estimate Mendelian Sampling Effects and Selection Limits”, Cole & VanRaden 2011

“Use of haplotypes to estimate Mendelian sampling effects and selection limits”⁠, J. B. Cole, P. M. VanRaden (2011; backlinks; similar):

Limits to selection and Mendelian sampling (MS) terms can be calculated using haplotypes by summing the individual additive effects on each chromosome. Haplotypes were imputed for 43 382 single nucleotide polymorphisms (SNP) in 1,455 Brown Swiss, 40,351 Holstein and 4,064 Jersey bulls and cows using the Fortran program findhap.f90, which combines population and pedigree haplotyping methods. Lower and upper bounds of MS variance were calculated for daughter pregnancy rate (a measure of fertility), milk yield, lifetime net merit (a measure of profitability) and protein yield assuming either no or complete linkage among SNP on the same chromosome. Calculated selection limits were greater than the largest direct genomic values observed in all breeds studied. The best chromosomal genotypes generally consisted of two copies of the same haplotype even after adjustment for inbreeding. Selection of animals rather than chromosomes may result in slower progress, but limits may be the same because most chromosomes will become homozygous with either strategy. Selection on functions of MS could be used to change variances in later generations.

Lifetime net merit: Lower selection limits for NM$ with no adjustment for inbreeding were $5,255.3$3,857.02011 (BS), $10,239.5$7,515.02011 (HO) and $6,374.0$4,678.02011 (JE). Adjusted values were slightly smaller and were $5,200.8$3,817.02011 (BS), $10,210.9$7,494.02011 (HO) and $6,275.9$4,606.02011 (JE). Upper bounds had values of $12,453.6$9,140.02011 (BS), $32,139.6$23,588.02011 (HO) and $15,692.4$11,517.02011 (JE) and were not adjusted for inbreeding because they were calculated from individual loci rather than complete haplotypes. The largest DGV among all genotyped animals in each breed were $1,501.5$1,102.02011 (BS), $3,444.5$2,528.02011 (HO) and $2,120.1$1,556.02011 (JE). The top active bulls (AI and foreign bulls with semen distributed in the US that are in or above the 80th percentile, based on NM) in each breed following the August 2010 genetic evaluation had GEBV (Genomic estimated breeding value) for NM$ of +$1,490.6$1,094.02011 (BS: 054BS00374), +$2,163.7$1,588.02011 (HO: 001HO08784) and +$1,760.4$1,292.02011 (JE: 236JE00146).

…If two copies of each of the 30 best haplotypes in the US Holstein population were combined in a single animal (Lower bounds of selection limit/​SLC for NM\$), it would have a GEBV for NM\$ of +$10,239.5$7,515.02011 (Figure 5), ~5× larger than that of the current best Holstein bull in the US, whose GEBV for NM$ are +$2,163.7$1,588.02011.

“Economic Evaluation of Genomic Breeding Programs”, König et al 2009

“Economic evaluation of genomic breeding programs”⁠, S. König, H. Simianer, A. Willam (2009-01; similar):

The objective of this study was to compare a conventional dairy cattle breeding program characterized by a progeny testing scheme with different scenarios of genomic breeding programs. The ultimate economic evaluation criterion was discounted profit reflecting discounted returns minus discounted costs per cow in a balanced breeding goal of production and functionality.

A deterministic approach mainly based on the gene flow method and selection index calculations was used to model a conventional progeny testing program and different scenarios of genomic breeding programs. As a novel idea, the modeling of the genomic breeding program accounted for the proportion of farmers waiting for daughter records of genotyped young bulls before using them for artificial insemination. Technical and biological coefficients for modeling were chosen to correspond to a German breeding organization. The conventional breeding program for 50 test bulls per year within a population of 100,000 cows served as a base scenario. Scenarios of genomic breeding programs considered the variation of costs for genotyping, selection intensity of cow sires, proportion of farmers waiting for daughter records of genotyped young bulls, and different accuracies of genomic indices for bulls and cows.

Given that the accuracies of genomic indices are greater than 0.70, a distinct economic advantage was found for all scenarios of genomic breeding programs up to 2.59×, mainly due to the reduction in generation intervals. Costs for genotyping were negligible when focusing on a population-wide perspective and considering additional costs for herdbook registration, milk recording, or keeping of bulls, especially if there is no need for yearly recalculation of effects of single nucleotide polymorphisms.

Genomic breeding programs generated a higher discounted profit than a conventional progeny testing program for all scenarios where at least 20% of the inseminations were done by genotyped young bulls without daughter records. Evaluation of levels of annual genetic gain for individual traits revealed the same potential for low heritable traits (h2 = 0.05) compared with moderate heritable traits (h2 = 0.30), preconditioning highly accurate genomic indices of 0.90.

The final economic success of genomic breeding programs strongly depends on the complete abdication of any forms of progeny testing to reduce costs and generation intervals, but such a strategy implies the willingness of the participating milk producers.

[Keywords: genomic selection, breeding program, economics, deterministic approach]

“Prediction of Response to Marker-assisted and Genomic Selection Using Selection Index Theory”, Dekkers 2007

2007-dekkers.pdf: “Prediction of response to marker-assisted and genomic selection using selection index theory”⁠, J. C. M. Dekkers (2007-12-07; similar):

Selection index methods can be used for deterministic assessment of the potential benefit of including marker information in genetic improvement programmes using marker-assisted selection (MAS).

By specifying estimates of breeding values derived from marker information (M-EBV) as a correlated trait with heritability equal to 1, it was demonstrated that marker information can be incorporated in standard software for selection index predictions of response and rates of inbreeding, which requires specifying phenotypic traits and their genetic parameters. Path coefficient methods were used to derive genetic and phenotypic correlations between M-EBV and the phenotypic data. Methods were extended to multi-trait selection and to the case when M-EBV are based on high-density marker genotype data, as in genomic selection.

Methods were applied to several example scenarios, which confirmed previous results that MAS substantially increases response to selection but also demonstrated that MAS can result in substantial reductions in the rates of inbreeding.

Although further validation by stochastic simulation is required, the developed methodology provides an easy means of deterministically evaluating the potential benefits of MAS and to optimize selection strategies with availability of marker data.

[Keywords: inbreeding, genomic selection, marker assisted selection, selection index, selection response]

“Long-term Selection With Known Quantitative Trait Loci”, Dekkers & Settar 2004

2004-dekkers.pdf: “Long-term Selection with Known Quantitative Trait Loci”⁠, Jack C. M. Dekkers, Petek Settar (2004-01-01)

“Strategies to Utilize Marker-Quantitative Trait Loci Associations”, Haley & Visscher 1998

1998-haley.pdf: “Strategies to Utilize Marker-Quantitative Trait Loci Associations”⁠, C. S. Haley, P. M. Visscher (1998; ; backlinks; similar):

Marker-assisted selection holds promise because genetic markers provide completely heritable traits that can be measured at any age in either sex and that are potentially correlated with traits of economic value. Theoretical and simulation studies show that the advantage of using marker-assisted selection can be substantial, particularly when marker information is used, because normal selection is less effective, for example, for sex-limited or carcass traits. Assessment of the available information and its most effective use is difficult, but approaches such as crossvalidation may help in this respect. Marker systems are now becoming available that allow the high density of markers required for close associations between marker loci and trait loci. Emerging technologies could allow large numbers of polymorphic sites to be identified, practically guaranteeing that markers will be available that are in complete association with any trait locus. Identifying which polymorphism out of many that is associated with any trait will remain problematic, but multiple-locus disequilibrium measures may allow performance to be associated with unique marker haplotypes⁠. This type of approach, combined with cheap and high density markers, could allow a move from selection based on a combination of “infinitesimal” effects plus individual loci to effective total genomic selection. In such an unified model, each region of the genome would be given its appropriate weight in a breeding program. However, the collection of good quality trait information will remain central to the use of these technologies for the foreseeable future.

[Keywords: markers, breeding, quantitative trait loci⁠, selection]

“Applications of Index Selection”, Walsh & Lynch 1997

“Applications of Index Selection”⁠, Bruce Walsh, Michael Lynch (1997-08-04; ; backlinks; similar):

The first topic, which consists of the bulk of this chapter, is using index selection to improve a single trait. One can have a number of measures of the same trait in either relatives of a focal individual or as multiple measures of the same trait in a single individual, or both. How does one best use this information? We start by developing the general theory for using an index to improve the response in a single trait (which follows as a simplification of the Smith-Hazel index). We then apply these results to several important cases—a general analysis when either phenotypic or genotypic correlations are zero, improving response using repeated measurements of a characters over time, and using information from relatives to improve response with a special focus on combined selection (the optimal weighting of individual and family information, proving many of the details first presented in Chapter 17). As we will see in Chapter 35, the mixed-model power of BLUP provides a better solution to many of these problems, but index selection is both historically important as well as providing clean analytic results. In contrast to the first topic, the final three are essentially independent of each other and we try to present them as such (so that the reader can simply turn to the section of interest without regard to previous material in this chapter). They include selection on a ratio, selection on sex-specific and sexually-dimorphic traits, and finally selection on the environmental variance σ2E when it shows heritable variation (expanding upon results from Chapter 13).

“Theory of Index Selection”, Walsh & Lynch 1997

“Theory of Index Selection”⁠, Bruce Walsh, Michael Lynch (1997-08-04; ; backlinks; similar):

While Chapters 28 and 29 present the basic theory for multivariate response, how, in practice, does one perform artificial selection on multiple traits? One of the commonest schemes is to construct some sort of index, wherein the investigator assigns (either explicitly or implicitly) a weighting scheme to each trait, creating an univariate character that becomes the target of selection. For example, if z is the vector of character values measured in an individual, the most common index is a linear combination Pbizi = bT z and most of our discussion focuses on such linear indices. We start with a general review of the theory of selection on a linear index and then cover in great detail the Smith-Hazel index (the index giving the largest expected response in a specified linear combination of characters) and its extensions. We also discuss a number of other indices for different purposes, such as restricted (constraining changes in specified traits) and desired-gains (specifying how the components, rather than the index, will evolve) indices. We conclude our discussion of index selection by considering how to best handle nonlinear indices. We finish the chapter by examining the other approach for selecting on multiple traits, namely choosing traits sequentially. Tandem selection, focusing on a single trait each generation (where the focal trait changes over generations) is one such approach, while the other is to select different traits at different times within the life span of single individuals (independent culling and multistage index selection).

“Economic Weights and Index Selection of Milk Production Traits When Multiple Production Quotas Apply”, Gibson 1989

1989-gibson.pdf: “Economic weights and index selection of milk production traits when multiple production quotas apply”⁠, J. P. Gibson (1989-10-01; similar):

The generation of profit in dairy production can be approximated by a generalized profit equation, which is a function of the genotype of the animals used. In the absence of legislated quotas on production, the economic weights for traits contributing to profit, for use in a selection index, have been shown to be simple functions of the partial derivatives of profit with respect to output of the traits. These functions reflect the fact that output in most agricultural industries will already be maximized, either because of saturated markets or limitations on total inputs. When a single quota applies, different functions result, which reflect the downward rescaling of enterprise size as output per animal of a trait under quota is increased. Difficulties arise when multiple non-independent quotas apply, such as in the United Kingdom (UK) milk market where quotas are triggered by both milk volume and fat concentration. The functions describing the economic weights are then dependent on the form of the dependency between the quota criteria and on the genetic change resulting from the applied selection index. Economic weights for milk volume, fat, protein and lactose yield applicable to Holstein’Friesian cattle in the UK were found to be –1·6 p/​1, 76·6 p/​kg, 170·0 p/​kg and 7·0 p/​kg, scaled to 1986 prices. These weights would not change much if the quota were changed to fat yield only. Use of appropriate selection indexes should result in genetic increases of milk volume, fat, protein and lactose yield, with gradual increases in fat and protein concentrations and the fat to protein ratio. In most situations, selecting on the combined evaluation for fat plus protein yield would be a simple procedure with high efficiency (0·995 of maximum efficiency).

“Selection Indices for Non-linear Profit Functions”, Goddard 1983

1983-goddard.pdf: “Selection indices for non-linear profit functions”⁠, M. E. Goddard (1983-03-01; similar):

Conventional selection index theory assumes that the total merit or profitability of animals is a linear function of measurable traits. However, in many cases merit may be a non-linear function of these traits.

A linear selection index can still be used in this situation but the optimum index depends on the selection intensity to be used and on the number of generation over which the selection response is to be maximized. Nonlinear selection indices have been suggested but these result in a lower selection response than the best linear index.

Linear selection indices suggested in the past are shown to correspond to the optimum linear index for either a very small selection response or, in the case of restricted indices, a very large selection response.

The economic value of a trait may depend on management decisions taken by the farmer. In this situation the economic values should be calculated assuming that the management decisions taken maximize profit given the present genetic value of the animals.

“Index Selection for Genetic Improvement of Quantitative Characters”, Lin 1978

1978-lin.pdf: “Index selection for genetic improvement of quantitative characters”⁠, C. Y. Lin (1978-03-01; similar):

This paper reviews the basic theory and summarizes various modifications of the selection index. The limitations of selection index are discussed in 4 parts:

  1. changes of parameters due to selection.
  2. sampling errors of parameter estimation.
  3. evaluation of relative economic weights, and
  4. internal deterrents to index selection.

“Multi-stage Index Selection”, Cunningham 1975

1975-cunningham.pdf: “Multi-stage index selection”⁠, E. P. Cunningham (1975; similar):

Selection index theory is extended to cover the case of selection in several stages.

General algebra is given for adjusting in later stages for the effects of selection in earlier stages. In addition a method is developed for the incorporation of an index into an index. This simplifies the reuse of data from earlier stages of selection.

A numerical example is used to illustrate the methods and to compare 3 single-stage and 3 2-stage selection procedures.

“Restricted Selection Indices”, Kempthorne & Nordskog 1959

1959-kempthorne.pdf: “Restricted Selection Indices”⁠, Oscar Kempthorne, Arne W. Nordskog (1959-03-01; similar):

…While use of I will result in best progress in H, the means of the Gi will change in either a positive or negative direction, so that a breeder may well be interested in increasing H as much as possible with a restriction that some Gi or some linear functions of the Gi will not change. For example, a poultry breeder may feel that he should keep mean egg size at a constant intermediate level while using an index to maximize progress in genetic economic value based on egg weight, body weight, and production. It was in fact such a situation which led to the present note.

…The constrained optimization procedure given here [using Lagrange multipliers] permits no genetic change in the chosen attribute, attributes, or linear functions of attributes, and may be unduly restrictive from some points of view. It is not entirely obvious but can be shown that if the restricted index is to keep say attribute 1 constant, then the weight associated with attribute 1 in the genotypic economic value is irrelevant.

Conclusion: The purpose of the present note is to give a derivation and examples of restricted selection indices. A further development of indices requiring some genetic changes to be of particular sign will be presented in a later paper.

“The Optimum Emphasis on Dams' Records When Proving Dairy Sires”, Lush 1944

1944-lush.pdf: “The Optimum Emphasis on Dams' Records When Proving Dairy Sires”⁠, Jay L. Lush (1944-11-01; similar):

Nearly all sire indexes which have been proposed can be described by the general equation

I = a + c (XWY)

in which a, b, and c are constants, X is the average production of the daughters, Y is the average production of their dams and I is the index.

The size of a affects only the general level (the mean) of the indexes. The size of c affects the variability of I but not its accuracy for comparing the breeding values (G) of 2 or more indexed sires. The size of b affects the accuracy of the index as well as its variability.

The main contribution of this paper is in showing that maximum accuracy of the index is attained when

b = (σx / σy) · (rgxrxyrgy)⧸(rgxrgyrxy)

If rGY = zero this optimum value of b becomes simply the regression of X on Y. If rGY has a small positive value (as is possible if breeders whose cows have high records generally try harder than other breeders to get good bulls—and if the extra efforts are partially successful) the optimum value of b is a little less than the regression of X on Y. The regression of X on Y is about 0.5 to 0.6 both for milk and for test in most sets of data actually used for proving dairy sires. The optimum value for b in dairy data will, therefore, be not far from 0.5.

If rGY is zero, selection of sires on the optimum index, as thus defined, will make 1⧸√1−r2 times as much progress as choosing the sires on the average of their daughters alone. The size of this factor, when rGY is very small and rXY has such values as are usually encountered in proving dairy sires, is about 1.12 to 1.20.

The size of rXY or of the regression of X on Y is affected more by the correlation (v) between a daughter’s record and the record of a mate of her sire, other than her own dam, than it is by the correlation (r) between a daughter and her own dam, especially when n is large. The regression of X on Y approaches vu and rXY approaches v⁄√uw as a limit when n becomes extremely large, u being the phenotypic correlation between the mates of the same sire and w being the phenotypic correlation between daughters of a sire.

A sire index can be made as variable as desired by adjusting c. The value 2.0, used for c in the intermediate or equal-parent indexes makes σI generally just a little larger than σD or σO This index can be used rather fairly for comparing proven sires directly with individual cows, as is necessary in evaluating pedigrees. It is, however, more variable than real breeding values. Consequently, if it is to be used directly as the sire’s most probable breeding value, the index needs first to be regressed far toward the breed average (just as cows’ records do) to allow for the average amount of non-genetic variation in such indexes. Approximately this amount of regression would already be accomplished in an index which used for c twice the heritability of differences between the records of individual cows. Rice 1944’s proposed “NEW” index⁠, which uses 1.0 for c, is the equal-parent index regressed half way toward the breed average. It is, therefore, half as variable but has exactly the same accuracy.

“The Genetic Basis For Constructing Selection Indexes”, Hazel 1943b

1943-hazel-2.pdf: “The Genetic Basis For Constructing Selection Indexes”⁠, L. N. Hazel (1943-11-20)

“A Discriminant Function For Plant Selection”, Smith 1936

1936-smith.pdf: “A Discriminant Function For Plant Selection”⁠, H. Fairfield Smith (1936-11; similar):

The characters with which a plant breeder is principally concerned are those known as “quantitative characters”. They present particular difficulty because heritable variations are masked by larger non-heritable variations which make it difficult to determine the genotypic values of individual plants or lines unless we have sufficient seed and facilities to grow replicated plots of each line. In the earlier stages of selection breeders try to select plants in the field on the basis of observable characters which they believe may be associated with the desired character or quality (for example, grain and ear sizes as indices to yielding ability, or flintiness of grain as an index of protein content), but the actual worth to be attributed to each character is usually unknown. The problem may be approached by seeking to determine what “discriminant function” (Fisher 1936) of the observable characters may best indicate the “genetic value” of a plant or line.

…The object of this paper is to suggest how a method for selecting plant lines may be worked out in a logical and systematic manner. The value of a plant may be expressed as a linear function of its characters, then, using Fisher’s concept of “discriminant functions”, we may derive that linear function of observable characters which will be the best available guide to the genetic value of each line.

The expectation of “genetic advance” over the mean of the unselected population for any given selection intensity may also be estimated and used to compare the relative efficiencies of various breeding programmes.

It is shown further that arbitrary ratios, such as the “migration coefficient” or the “tiller survival rate”, are likely to be inefficient as indices to the genetic value of either of the characters whose ratio is observed.

Variance § Sum of correlated variables

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Sum of normally distributed random variables

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Sewall Wright

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Robert Bakewell (farmer)

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Path analysis (statistics)

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Multivariate normal distribution

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Multi-objective optimization

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Linear discriminant analysis

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Jay Laurence Lush

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Index selection

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Index (statistics)

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Index (economics)

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Charles Roy Henderson

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Miscellaneous