The complex nature of human cognition has resulted in cognitive genomics lagging behind many other fields in terms of gene discovery using genome-wide association study ( ) methods. In an attempt to overcome these barriers, the current study utilized GWAS meta-analysis to examine the association of common genetic variation (~8M single-nucleotide polymorphisms (SNP) with minor allele frequency ⩾1%) to general cognitive function in a sample of 35 298 healthy individuals of European ancestry across 24 cohorts in the Cognitive Genomics Consortium (COGENT). In addition, we utilized individual lookups and polygenic score analyses to identify genetic overlap with other relevant neurobehavioral phenotypes. Our primary meta-analysis identified two novel SNP loci (top SNPs: rs76114856 in the CENPO gene on chromosome 2 and rs6669072 near LOC105378853 on chromosome 1) associated with cognitive performance at the genome-wide statistical-significance level (p<5 × 10−8). Gene-based analysis identified an additional three Bonferroni-corrected statistically-significant loci at chromosomes 17q21.31, 17p13.1 and 1p13.3. Altogether, common variation across the genome resulted in a conservatively estimated heritability of 21.5% (s.e. = 0.01%) for general cognitive function. Integration with prior of cognitive performance and educational attainment yielded several additional statistically-significant loci. Finally, we found robust polygenic correlations between cognitive performance and educational attainment, several psychiatric disorders, birth length/weight and smoking behavior, as well as a novel genetic association to the personality trait of openness. These data provide new insight into the genetics of neurocognitive function with relevance to understanding the pathophysiology of neuropsychiatric illness.
2017-mcrae.pdf: “Prevalence and architecture of de novo mutations in developmental disorders”, (2017-01-25; ):
The genomes of individuals with severe, undiagnosed developmental disorders are enriched in damaging de novo mutations (DNMs) in developmentally important genes. Here we have sequenced the exomes of 4,293 families containing individuals with developmental disorders, and meta-analysed these data with data from another 3,287 individuals with similar disorders. We show that the most important factors influencing the diagnostic yield of DNMs are the sex of the affected individual, the relatedness of their parents, whether close relatives are affected and the parental ages. We identified 94 genes enriched in damaging DNMs, including 14 that previously lacked compelling evidence of involvement in developmental disorders. We have also characterized the phenotypic diversity among these disorders. We estimate that 42% of our cohort carry pathogenic DNMs in coding sequences; approximately half of these DNMs disrupt gene function and the remainder result in altered protein function. We estimate that developmental disorders caused by DNMs have an average prevalence of 1 in 213 to 1 in 448 births, depending on parental age. Given current global demographics, this equates to almost 400,000 children born per year.
Online media use has become an increasingly important behavioral domain over the past decade. However, studies into the etiology of individual differences in media use have focused primarily on pathological use. Here, for the first time, we test the genetic influences on online media use in a UK representative sample of 16 year old twins, who were assessed on time spent on educational (n = 2,585 twin pairs) and entertainment websites (n = 2,614 twin pairs), time spent gaming online (n = 2,635 twin pairs), and Facebook use (n = 4,333 twin pairs). Heritability was substantial for all forms of online media use, ranging from 34% for educational sites to 37% for entertainment sites and 39% for gaming. Furthermore, genetics accounted for 24% of the variance in Facebook use. Our results support an active model of the environment, where young people choose their online engagements in line with their genetic propensities.
2017-kong.pdf: “Selection against variants in the genome associated with educational attainment”, (2017-01-11; ):
Epidemiological studies suggest that educational attainment is affected by genetic variants. Results from recent genetic studies allow us to construct a score from a person’s genotypes that captures a portion of this genetic component. Using data from Iceland that include a substantial fraction of the population we show that individuals with high scores tend to have fewer children, mainly because they have children later in life. Consequently, the average score has been decreasing over time in the population. The rate of decrease is small per generation but marked on an evolutionary timescale. Another important observation is that the association between the score and fertility remains highly statistically-significant after adjusting for the educational attainment of the individuals.
Epidemiological and genetic association studies show that genetics play an important role in the attainment of education. Here, we investigate the effect of this genetic component on the reproductive history of 109,120 Icelanders and the consequent impact on the gene pool over time. We show that an educational attainment POLYEDU, constructed from results of a recent study is associated with delayed reproduction (p < 10−100) and fewer children overall. The effect is stronger for women and remains highly after adjusting for educational attainment. Based on 129,808 Icelanders born between 1910 and 1990, we find that the average POLYEDU has been declining at a rate of ~0.010 standard units per decade, which is substantial on an evolutionary timescale. Most importantly, because POLYEDU only captures a fraction of the overall underlying genetic component the latter could be declining at a rate that is two to three times faster.,
Mortality selection is a general concern in the social and health sciences. Recently, existing health and social science cohorts have begun to collect genomic data. Causes of selection into a genomic dataset can influence results from genomic analyses. Selective non-participation, which is specific to a particular study and its participants, has received attention in the literature. But mortality selection—the very general phenomenon that genomic data collected at a particular age represents selective participation by only the subset of birth cohort members who have survived to the time of data collection—has been largely ignored. Here we test the hypothesis that such mortality selection may significantly alter estimates in polygenetic association studies of both health and non-health traits. We demonstrate mortality selection into genome-wide Retirement Study (HRS). We then model the selection process. Finally, we test whether mortality selection alters estimates from genetic association studies. We find evidence for mortality selection. Healthier and more socioeconomically advantaged individuals are more likely to survive to be eligible to participate in the genetic sample of the HRS. Mortality selection leads to modest drift in estimating time-varying genetic effects, a drift that is enhanced when estimates are produced from data that has additional mortality selection. There is no general solution for correcting for mortality selection in a birth cohort prior to entry into a longitudinal study. We illustrate how genetic association studies using HRS data can adjust for mortality selection from study entry to time of genetic data collection by including probability weights that account for mortality selection. Mortality selection should be investigated more broadly in genetically-informed samples from other cohort studies.data collection at older ages using the U.S.-based Health and
“Deep Reinforcement Learning: An Overview”, (2017-01-25):
We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications. We start with background of machine learning, deep learning and reinforcement learning. Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration. After that, we discuss important mechanisms for RL, including attention and memory, unsupervised learning, transfer learning, multi-agent RL, hierarchical RL, and learning to learn. Then we discuss various applications of RL, including games, in particular, AlphaGo, robotics, natural language processing, including dialogue systems, machine translation, and text generation, computer vision, neural architecture design, business management, finance, healthcare, Industry 4.0, smart grid, intelligent transportation systems, and computer systems. We mention topics not reviewed yet, and list a collection of RL resources. After presenting a brief summary, we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant update.
“Wasserstein GAN”, (2017-01-26):
We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches. Furthermore, we show that the corresponding optimization problem is sound, and provide extensive theoretical work highlighting the deep connections to other distances between distributions.
With the advent of large labelled datasets and high-capacity models, the performance of machine vision systems has been improving rapidly. However, the technology has still major limitations, starting from the fact that different vision problems are still solved by different models, trained from scratch or fine-tuned on the target data. The human visual system, in stark contrast, learns a universal representation for vision in the early life of an individual. This representation works well for an enormous variety of vision problems, with little or no change, with the major advantage of requiring little training data to solve any of them.
“A Conceptual Introduction to Hamiltonian Monte Carlo”, (2017-01-10):
Hamiltonian Monte Carlo has proven a remarkable empirical success, but only recently have we begun to develop a rigorous understanding of why it performs so well on difficult problems and how it is best applied in practice. Unfortunately, that understanding is confined within the mathematics of differential geometry which has limited its dissemination, especially to the applied communities for which it is particularly important. In this review I provide a comprehensive conceptual account of these theoretical foundations, focusing on developing a principled intuition behind the method and its optimal implementations rather of any exhaustive rigor. Whether a practitioner or a statistician, the dedicated reader will acquire a solid grasp of how Hamiltonian Monte Carlo works, when it succeeds, and, perhaps most importantly, when it fails.
[Memoir of an ex-theoretical-physics grad student at the University of Rochester with Sarada Rajeev who gradually became disillusioned with physics research, burned out, and left to work in finance and is now a writer. Henderson was attracted by the life of the mind and the grandeur of uncovering the mysteries of the universe, only to discover that, after the endless triumphs of the 20th century and predicting enormous swathes of empirical experimental data, theoretical physics has drifted and become a branch of abstract mathematics, exploring ever more recondite, simplified, and implausible models in the hopes of obtaining any insight into physics’ intractable problems; one must be brilliant to even understand the questions being asked by the math and incredibly hardworking to make any progress which hasn’t already been tried by even more brilliant physicists of the past (while living in ignominious poverty and terror of not getting a grant or tenure), but one’s entire career may be spent chasing a useless dead end without one having any clue.]
The next thing I knew I was crouched in a chair in Rajeev’s little office, with a notebook on my knee and focused with everything I had on an impromptu lecture he was giving me on an esoteric aspect of some mathematical subject I’d never heard of before. Zeta functions, or elliptic functions, or something like that. I’d barely introduced myself when he’d started banging out equations on his board. Trying to follow was like learning a new game, with strangely shaped pieces and arbitrary rules. It was a challenge, but I was excited to be talking to a real physicist about his real research, even though there was one big question nagging me that I didn’t dare to ask: What does any of this have to do with physics?
…Even a Theory of Everything, I started to realize, might suffer the same fate of multiple interpretations. The Grail could just be a hall of mirrors, with no clear answer to the “What?” or the “How?”—let alone the “Why?” Plus physics had changed since Big Al bestrode it. Mathematical as opposed to physical intuition had become more central, partly because quantum mechanics was such a strange multi-headed beast that it diminished the role that everyday, or even Einstein-level, intuition could play. So much for my dreams of staring out windows and into the secrets of the universe.
…If I did lose my marbles for a while, this is how it started. With cutting my time outside of Bausch and Lomb down to nine hours a day—just enough to pedal my mountain bike back to my bat cave of an apartment each night, sleep, shower, and pedal back in. With filling my file cabinet with boxes and cans of food, and carting in a coffee maker, mini-fridge, and microwave so that I could maximize the time spent at my desk. With feeling guilty after any day that I didn’t make my 15-hour quota. And with exceeding that quota frequently enough that I regularly circumnavigated the clock: staying later and later each night until I was going home in the morning, then in the afternoon, and finally at night again.
…The longer and harder I worked, the more I realized I didn’t know. Papers that took days or weeks to work through cited dozens more that seemed just as essential to digest; the piles on my desk grew rather than shrunk. I discovered the stark difference between classes and research: With no syllabus to guide me I didn’t know how to keep on a path of profitable inquiry. Getting “wonderfully lost” sounded nice, but the reality of being lost, and of re-living, again and again, that first night in the old woman’s house, with all of its doubts and dead-ends and that horrible hissing voice was … something else. At some point, flipping the lights on in the library no longer filled me with excitement but with dread.
…My mental model building was hitting its limits. I’d sit there in Rajeev’s office with him and his other students, or in a seminar given by some visiting luminary, listening and putting each piece in place, and try to fix in memory what I’d built so far. But at some point I’d lose track of how the green stick connected to the red wheel, or whatever, and I’d realize my picture had diverged from reality. Then I’d try toggling between tracing my steps back in memory to repair my mistake and catching all the new pieces still flying in from the talk. Stray pieces would fall to the ground. My model would start falling down. And I would fall hopelessly behind. A year or so of research with Rajeev, and I found myself frustrated and in a fog, sinking deeper into the quicksand but not knowing why. Was it my lack of mathematical background? My grandiose goals? Was I just not intelligent enough?
…I turned 30 during this time and the milestone hit me hard. I was nearly four years into the Ph.D. program, and while my classmates seemed to be systematically marching toward their degrees, collecting data and writing papers, I had no thesis topic and no clear path to graduation. My engineering friends were becoming managers, getting married, buying houses. And there I was entering my fourth decade of life feeling like a pitiful and penniless mole, aimlessly wandering dark empty tunnels at night, coming home to a creepy crypt each morning with nothing to show for it, and checking my bed for bugs before turning out the lights…As I put the final touches on my thesis, I weighed my options. I was broke, burned out, and doubted my ability to go any further in theoretical physics. But mostly, with The Grail now gone and the physics landscape grown so immense, I thought back to Rajeev’s comment about knowing which problems to solve and realized that I still didn’t know what, for me, they were.
1982-perlis.pdf: “Epigrams on Programming”, (1982-09-01; ):
[130 epigrams on computer science and technology, published in 1982, for ACM’s SIGPLAN journal, by noted computer scientist and programming language researcher Alan Perlis. The epigrams are a series of short, programming-language-neutral, humorous statements about computers and programming, distilling lessons he had learned over his career, which are widely quoted.]
8. A programming language is low level when its programs require attention to the irrelevant….19. A language that doesn’t affect the way you think about programming, is not worth knowing….54. Beware of the Turing tar-pit in which everything is possible but nothing of interest is easy.
15. Everything should be built top-down, except the first time….30. In programming, everything we do is a special case of something more general—and often we know it too quickly….31. Simplicity does not precede complexity, but follows it….58. Fools ignore complexity. Pragmatists suffer it. Some can avoid it. Geniuses remove it….65. Make no mistake about it: Computers process numbers—not symbols. We measure our understanding (and control) by the extent to which we can arithmetize an activity….56. Software is under a constant tension. Being symbolic it is arbitrarily perfectible; but also it is arbitrarily changeable.
1. One man’s constant is another man’s variable. 34. The string is a stark data structure and everywhere it is passed there is much duplication of process. It is a perfect vehicle for hiding information.
36. The use of a program to prove the 4-color theorem will not change mathematics—it merely demonstrates that the theorem, a challenge for a century, is probably not important to mathematics.
39. Re graphics: A picture is worth 10K words—but only those to describe the picture. Hardly any sets of 10K words can be adequately described with pictures.
48. The best book on programming for the layman is Alice in Wonderland; but that’s because it’s the best book on anything for the layman.
77. The cybernetic exchange between man, computer and algorithm is like a game of musical chairs: The frantic search for balance always leaves one of the 3 standing ill at ease….79. A year spent in artificial intelligence is enough to make one believe in God….84. Motto for a research laboratory: What we work on today, others will first think of tomorrow.
91. The computer reminds one of Lon Chaney—it is the machine of a thousand faces.
7. It is easier to write an incorrect program than understand a correct one….93. When someone says “I want a programming language in which I need only say what I wish done”, give him a lollipop….102. One can’t proceed from the informal to the formal by formal means.
100. We will never run out of things to program as long as there is a single program around.
108. Whenever 2 programmers meet to criticize their programs, both are silent….112. Computer Science is embarrassed by the computer….115. Most people find the concept of programming obvious, but the doing impossible. 116. You think you know when you can learn, are more sure when you can write, even more when you can teach, but certain when you can program. 117. It goes against the grain of modern education to teach children to program. What fun is there in making plans, acquiring discipline in organizing thoughts, devoting attention to detail and learning to be self-critical?