Individuals with lower socio-economic status ( ) are at increased risk of physical and mental illnesses and tend to die at an earlier age. Explanations for the association between and health typically focus on factors that are environmental in origin. However, common single nucleotide polymorphisms (SNPs) have been found collectively to explain around 18% (SE = 5%) of the phenotypic variance of an area-based social deprivation measure of . Molecular genetic studies have also shown that physical and psychiatric diseases are at least partly heritable. It is possible, therefore, that phenotypic associations between SES and health arise partly due to a shared genetic etiology.
We conducted a genome-wide association study ( ) on social deprivation and on household income using the 112,151 participants of UK Biobank. We find that common SNPs explain 21% (SE = 0.5%) of the variation in social deprivation and 11% (SE = 0.7%) in household income. 2 independent SNPs attained genome-wide statistical-significance for household income, rs187848990 on chromosome 2, and rs8100891 on chromosome 19. Genes in the regions of these SNPs have been associated with intellectual disabilities, schizophrenia, and synaptic plasticity. Extensive genetic correlations were found between both measures of socioeconomic status and illnesses, anthropometric variables, psychiatric disorders, and cognitive ability.
These findings show that some SNPs associated with are involved in the brain and central nervous system. The genetic associations with are probably mediated via other partly-heritable variables, including cognitive ability, education, personality, and health.
2016-robinson.pdf: “Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population”, Elise B. Robinson, Beate St Pourcain, Verneri Anttila, Jack A. Kosmicki, Brendan Bulik-Sullivan, Jakob Grove, Julian Maller, Kaitlin E. Samocha, Stephan J. Sanders, Stephan Ripke, Joanna Martin, Mads V. Hollegaard, Thomas Werge, David M. Hougaard, Benjamin M. Neale, David M. Evans, David Skuse, Preben Bo Mortensen, Anders D. Børglum, Angelica Ronald, George Davey Smith, Mark J. Daly
Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17–29% of the in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and and major depressive disorder (0.32 ± 0.07 s.e.), low between and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn’s disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.
2015-yang.pdf: “Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index”, (2015-08-31; ):
We propose a method (GREML-LDMS) to estimate heritability for human complex traits in unrelated individuals using whole-genome sequencing data. We demonstrate using simulations based on whole-genome sequencing data that ~97% and ~68% of variation at common and rare variants, respectively, can be captured by imputation. Using the GREML-LDMS method, we estimate from 44,126 unrelated individuals that all ~17 million imputed variants explain 56% (standard error (s.e.) = 2.3%) of for height and 27% (s.e. = 2.5%) of for body mass index ( ), and we find evidence that height-associated and -associated variants have been under natural selection. Considering the imperfect tagging of imputation and potential overestimation of heritability from previous family-based studies, heritability is likely to be 60–70% for height and 30–40% for . Therefore, the missing heritability is small for both traits. For further discovery of genes associated with complex traits, a study design with SNP arrays followed by imputation is more cost-effective than whole-genome sequencing at current prices.
Higher paternal age at offspring conception increases de novo genetic mutations (Kong et al., 2012). Based on evolutionary genetic theory we predicted that the offspring of older fathers would be less likely to survive and reproduce, i.e. have lower fitness. In a sibling control study, we find clear support for negative paternal age effects on offspring survival, mating and reproductive success across four large populations with an aggregate N > 1.3 million in main analyses. Compared to a sibling born when the father was 10 years younger, individuals had 4–13% fewer surviving children in the four populations. Three populations were pre-industrial (1670-1850) Western populations and showed a pattern of paternal age effects across the offspring’s lifespan. In 20th-century Sweden, we found no negative paternal age effects on child survival or marriage odds. Effects survived tests for competing explanations, including maternal age and parental loss. To the extent that we succeeded in isolating a mutation-driven effect of paternal age, our results can be understood to show that de novo mutations reduce offspring fitness across populations and time. We can use this understanding to predict the effect of increasingly delayed reproduction on offspring genetic load, mortality and fertility.
Neural network architectures with memory and attention mechanisms exhibit certain reasoning capabilities required for question answering. One such architecture, the dynamic memory network (DMN), obtained high accuracy on a variety of language tasks. However, it was not shown whether the architecture achieves strong results for question answering when supporting facts are not marked during training or whether it could be applied to other modalities such as images. Based on an analysis of the DMN, we propose several improvements to its memory and input modules. Together with these changes we introduce a novel input module for images in order to be able to answer visual questions. Our new DMN+ model improves the state of the art on both the Visual Question Answering dataset and the -10k text question-answering dataset without supporting fact supervision.
We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we trained a large convolutional neural network to predict the probability that task-space motion of the gripper will result in successful grasps, using only monocular camera images and independently of camera calibration or the current robot pose. This requires the network to observe the spatial relationship between the gripper and objects in the scene, thus learning hand-eye coordination. We then use this network to servo the gripper in real time to achieve successful grasps. To train our network, we collected over 800,000 grasp attempts over the course of two months, using between 6 and 14 robotic manipulators at any given time, with differences in camera placement and hardware. Our experimental evaluation demonstrates that our method achieves effective real-time control, can successfully grasp novel objects, and corrects mistakes by continuous servoing.
“Generating images with recurrent adversarial networks”, (2016-02-16):
Gatys et al 2015 showed that optimizing pixels to match features in a convolutional network with respect reference image features is a way to render images of high visual quality.
We show that unrolling this gradient-based optimization yields a recurrent computation that creates images by incrementally adding onto a visual “canvas”. We propose a recurrent generative model inspired by this view, and show that it can be trained using adversarial training to generate very good image samples. We also propose a way to quantitatively compare adversarial networks by having the generators and discriminators of these networks compete against each other.
“Deep Exploration via Bootstrapped DQN”, (2016-02-15):
Efficient exploration in complex environments remains a major challenge for reinforcement learning. We propose bootstrapped DQN, a simple algorithm that explores in a computationally and statistically efficient manner through use of randomized value functions. Unlike dithering strategies such as epsilon-greedy exploration, DQN carries out temporally-extended (or deep) exploration; this can lead to exponentially faster learning. We demonstrate these benefits in complex stochastic MDPs and in the large-scale Arcade Learning Environment. DQN substantially improves learning times and performance across most Atari games.
2016-lane.pdf: “Is there a publication bias in behavioral intranasal oxytocin research on humans? Opening the file drawer of one lab”, A. Lane, O. Luminet, G. Nave, M. Mikolajczak
2001-ioannidis.pdf: “Comparison of Evidence of Treatment Effects in Randomized and Nonrandomized Studies”, (2001-08-01; ):
Context: There is substantial debate about whether the results of nonrandomized studies are consistent with the results of randomized controlled trials on the same topic.
Objectives: To compare results of randomized and nonrandomized studies that evaluated medical interventions and to examine characteristics that may explain discrepancies between randomized and nonrandomized studies.
Data Sources: MEDLINE (1966–March 2000), the Cochrane Library (Issue 3, 2000), and major journals were searched.
Study Selection: Forty-five diverse topics were identified for which both randomized trials (n = 240) and nonrandomized studies (n = 168) had been performed and had been considered in meta-analyses of binary outcomes.
Data Extraction: Data on events per patient in each study arm and design and characteristics of each study considered in each meta-analysis were extracted and synthesized separately for randomized and nonrandomized studies.
Data Synthesis: Very good correlation was observed between the summary odds ratios of randomized and nonrandomized studies (r = 0.75; p < 0.001); however, nonrandomized studies tended to show larger treatment effects (28 vs 11; p = 0.009). Between-study heterogeneity was frequent among randomized trials alone (23%) and very frequent among nonrandomized studies alone (41%). The summary results of the 2 types of designs differed beyond chance in 7 cases (16%). Discrepancies beyond chance were less common when only prospective studies were considered (8%). Occasional differences in sample size and timing of publication were also noted between discrepant randomized and nonrandomized studies. In 28 cases (62%), the natural logarithm of the odds ratio differed by at least 50%, and in 15 cases (33%), the odds ratio varied at least 2-fold between nonrandomized studies and randomized trials.
Conclusions: Despite good correlation between randomized trials and nonrandomized studies—in particular, prospective studies—discrepancies beyond chance do occur and differences in estimated magnitude of treatment effect are very common.
2015-hofman.pdf: “Evolution of the Human Brain: From Matter to Mind”, (2015; ):
Design principles and operational modes are explored that underlie the information processing capacity of the human brain.
The hypothesis is put forward that in higher organisms, especially in primates, the complexity of the neural circuitry of the cerebral cortex is the neural correlate of the brain’s coherence and predictive power, and, thus, a measure of intelligence. It will be argued that with the evolution of the human brain we have nearly reached the limits of biological intelligence.
[Keywords: biological intelligence, cognition, consciousness, cerebral cortex, primates, information processing, neural networks, cortical design, human brain evolution]
2012-lee.pdf: “Correlation and Causation in the Study of Personality”, (2012-07-26; ):
Personality psychology aims to explain the causes and the consequences of variation in behavioural traits. Because of the observational nature of the pertinent data, this endeavour has provoked many controversies. In recent years, the computer scientist Judea Pearl has used a graphical approach to extend the innovations in causal inference developed by Ronald Fisher and Sewall Wright. Besides shedding much light on the philosophical notion of causality itself, this graphical framework now contains many powerful concepts of relevance to the controversies just mentioned. In this article, some of these concepts are applied to areas of personality research where questions of causation arise, including the analysis of observational data and the genetic sources of individual differences.