Genome-wide Complex Trait Analysis Research Articles

Do our "big data" in genetic analysis need to get bigger?

Individual papers in this special issue might seem disappointing in their lack of discovery of specific genes of potential relevance to mental disorders. Yet, collectively, they yield information that could not be gleaned otherwise. Combining genome-wide complex trait analysis and classic approaches to estimate heritability in the same sample, and supplementing genome-wide association studies of common variants with exome and sequencing analyses, provides an unprecedented opportunity to examine major issues encountered in genetic research of complex traits, in ways not easily done with a series of unrelated studies using different samples, measures, and analytical approaches. Extending molecular genetic approaches to fully multivariate analyses will be an important future direction. These will require bigger analyses of even bigger big data, but will be essential in efforts to redefine psychopathology in the Research Domain Criteria (RDoC) approach promoted in the NIMH strategic plan.

Common variation near ROBO2 is associated with expressive vocabulary in infancy.

Twin studies suggest that expressive vocabulary at ~24 months is modestly heritable. However, the genes influencing this early linguistic phenotype are unknown. Here we conduct a genome-wide screen and follow-up study of expressive vocabulary in toddlers of European descent from up to four studies of the EArly Genetics and Lifecourse Epidemiology consortium, analysing an early (15–18 months, ‘one-word stage’, NTotal=8,889) and a later (24–30 months, ‘two-word stage’, NTotal=10,819) phase of language acquisition. For the early phase, one single-nucleotide polymorphism (rs7642482) at 3p12.3 near ROBO2, encoding a conserved axon-binding receptor, reaches the genome-wide significance level (P=1.3 × 10−8) in the combined sample. This association links language-related common genetic variation in the general population to a potential autism susceptibility locus and a linkage region for dyslexia, speech-sound disorder and reading. The contribution of common genetic influences is, although modest, supported by genome-wide complex trait analysis (meta-GCTA h215–18-months=0.13, meta-GCTA h224–30-months=0.14) and in concordance with additional twin analysis (5,733 pairs of European descent, h224-months=0.20).

Genetics and intelligence differences: five special findings.

Intelligence is a core construct in differential psychology and behavioural genetics, and should be so in cognitive neuroscience. It is one of the best predictors of important life outcomes such as education, occupation, mental and physical health and illness, and mortality. Intelligence is one of the most heritable behavioural traits. Here, we highlight five genetic findings that are special to intelligence differences and that have important implications for its genetic architecture and for gene-hunting expeditions. (i) The heritability of intelligence increases from about 20% in infancy to perhaps 80% in later adulthood. (ii) Intelligence captures genetic effects on diverse cognitive and learning abilities, which correlate phenotypically about 0.30 on average but correlate genetically about 0.60 or higher. (iii) Assortative mating is greater for intelligence (spouse correlations ~0.40) than for other behavioural traits such as personality and psychopathology (~0.10) or physical traits such as height and weight (~0.20). Assortative mating pumps additive genetic variance into the population every generation, contributing to the high narrow heritability (additive genetic variance) of intelligence. (iv) Unlike psychiatric disorders, intelligence is normally distributed with a positive end of exceptional performance that is a model for ‘positive genetics'. (v) Intelligence is associated with education and social class and broadens the causal perspectives on how these three inter-correlated variables contribute to social mobility, and health, illness and mortality differences. These five findings arose primarily from twin studies. They are being confirmed by the first new quantitative genetic technique in a century—Genome-wide Complex Trait Analysis (GCTA)—which estimates genetic influence using genome-wide genotypes in large samples of unrelated individuals. Comparing GCTA results to the results of twin studies reveals important insights into the genetic architecture of intelligence that are relevant to attempts to narrow the ‘missing heritability' gap.

Genome-wide interaction of genotype by erythrocyte n-3 fatty acids contributes to phenotypic variance of diabetes-related traits.

BackgroundLittle is known about the interplay between n-3 fatty acids and genetic variants for diabetes-related traits at the genome-wide level. The present study aimed to examine variance contributions of genotype by environment (GxE) interactions for different erythrocyte n-3 fatty acids and genetic variants for diabetes-related traits at the genome-wide level in a non-Hispanic white population living in the U.S.A. (n = 820). A tool for Genome-wide Complex Trait Analysis (GCTA) was used to estimate the genome-wide GxE variance contribution of four diabetes-related traits: HOMA-Insulin Resistance (HOMA-IR), fasting plasma insulin, glucose and adiponectin. A GxE genome-wide association study (GWAS) was conducted to further elucidate the GCTA results. Replication was conducted in the participants of the Boston Puerto Rican Health Study (BPRHS) without diabetes (n = 716).ResultsIn GOLDN, docosapentaenoic acid (DPA) contributed the most significant GxE variance to the total phenotypic variance of both HOMA-IR (26.5%, P-nominal = 0.034) and fasting insulin (24.3%, P-nominal = 0.042). The ratio of arachidonic acid to eicosapentaenoic acid + docosahexaenoic acid contributed the most significant GxE variance to the total variance of fasting glucose (27.0%, P-nominal = 0.023). GxE variance of the arachidonic acid/eicosapentaenoic acid ratio showed a marginally significant contribution to the adiponectin variance (16.0%, P-nominal = 0.058). None of the GCTA results were significant after Bonferroni correction (P < 0.001). For each trait, the GxE GWAS identified a far larger number of significant single-nucleotide polymorphisms (P-interaction ≤ 10E-5) for the significant E factor (significant GxE variance contributor) than a control E factor (non-significant GxE variance contributor). In the BPRHS, DPA contributed a marginally significant GxE variance to the phenotypic variance of HOMA-IR (12.9%, P-nominal = 0.068) and fasting insulin (18.0%, P-nominal = 0.033).ConclusionErythrocyte n-3 fatty acids contributed a significant GxE variance to diabetes-related traits at the genome-wide level.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-781) contains supplementary material, which is available to authorized users.

Genotype-environment correlation in the era of DNA.

One of John Loehlin’s many contributions to the field of behavioral genetics involves gene-environment (GE) correlation. The empirical base for GE correlation was research showing that environmental measures are nearly as heritable as behavioral measures and that genetic factors mediate correlations between environment and behavior. Attempts to identify genes responsible for these phenomena will come up against the ‘missing heritability’ problem that plagues DNA research on complex traits throughout the life sciences. However, DNA can also be used for quantitative genetic analyses of unrelated individuals (Genome-wide Complex Trait Analysis, GCTA) to investigate genetic influence on environmental measures and their behavioral correlates. A novel feature of GCTA is that it enables genetic analysis of family-level environments (e.g., parental socioeconomic status) and school-level environments (e.g., teaching quality) that cannot be investigated using within-family designs such as the twin method. An important implication of GE correlation is its shift from a passive model of the environment imposed on individuals to an active model in which individuals actively create their own experiences in part on the basis of their genetic propensities.

Genome-wide analysis of the heritability of amyotrophic lateral sclerosis.

Considerable advances have been made in our understanding of the genetics underlying amyotrophic lateral sclerosis (ALS). Nevertheless, for the majority of patients who receive a diagnosis of ALS, the role played by genetics is unclear. Further elucidation of the genetic architecture of this disease will help clarify the role of genetic variation in ALS populations. To estimate the relative importance of genetic factors in a complex disease such as ALS by accurately quantifying heritability using genome-wide data derived from genome-wide association studies. We applied the genome-wide complex trait analysis algorithm to 3 genome-wide association study data sets that were generated from ALS case-control cohorts of European ancestry to estimate the heritability of ALS. Cumulatively, these data sets contained genotype data from 1223 cases and 1591 controls that had been previously generated and are publically available on the National Center for Biotechnology Information database of genotypes and phenotypes website (http://www.ncbi.nlm.nih.gov/gap). The cohorts genotyped as part of these genome-wide association study efforts include the InCHIANTI (aging in the Chianti area) Study, the Piemonte and Valle d'Aosta Register for Amyotrophic Lateral Sclerosis, the National Institute of Neurological Disorders and Stroke Repository, and an ALS specialty clinic in Helsinki, Finland. A linear mixed model was used to account for all known single-nucleotide polymorphisms simultaneously and to quantify the phenotypic variance present in ostensibly outbred individuals. Variance measures were used to estimate heritability. With our meta-analysis, which is based on genome-wide genotyping data, we estimated the overall heritability of ALS to be approximately 21.0% (95% CI, 17.1-24.9) (SE = 2.0%), indicating that additional genetic variation influencing risk of ALS loci remains to be identified. Furthermore, we identified 17 regions of the genome that display significantly high heritability estimates. Eleven of these regions represent novel candidate regions for ALS risk. We found the heritability of ALS to be significantly higher than previously reported. We also identified multiple, novel genomic regions that we hypothesize may contain causative risk variants that influence susceptibility to ALS.

What can genes tell us about the relationship between education and health?

We use genome wide data from respondents of the Health and Retirement Study (HRS) to evaluate the possibility that common genetic influences are associated with education and three health outcomes: depression, self-rated health, and body mass index. We use a total of 1.7 million single nucleotide polymorphisms obtained from the Illumina HumanOmni2.5-4v1 chip from 4233 non-Hispanic white respondents to characterize genetic similarities among unrelated persons in the HRS. We then used the Genome Wide Complex Trait Analysis (GCTA) toolkit, to estimate univariate and bivariate heritability. We provide evidence that education (h2 = 0.33), BMI (h2 = 0.43), depression (h2 = 0.19), and self-rated health (h2 = 0.18) are all moderately heritable phenotypes. We also provide evidence that some of the correlation between depression and education as well as self-rated health and education is due to common genetic factors associated with one or both traits. We find no evidence that the correlation between education and BMI is influenced by common genetic factors.

Resolving the effects of maternal and offspring genotype on dyadic outcomes in genome wide complex trait analysis ("M-GCTA").

Genome wide complex trait analysis (GCTA) is extended to include environmental effects of the maternal genotype on offspring phenotype ("maternal effects", M-GCTA). The model includes parameters for the direct effects of the offspring genotype, maternal effects and the covariance between direct and maternal effects. Analysis of simulated data, conducted in OpenMx, confirmed that model parameters could be recovered by full information maximum likelihood (FIML) and evaluated the biases that arise in conventional GCTA when indirect genetic effects are ignored. Estimates derived from FIML in OpenMx showed very close agreement to those obtained by restricted maximum likelihood using the published algorithm for GCTA. The method was also applied to illustrative perinatal phenotypes from ~4,000 mother-offspring pairs from the Avon Longitudinal Study of Parents and Children. The relative merits of extended GCTA in contrast to quantitative genetic approaches based on analyzing the phenotypic covariance structure of kinships are considered.

The genetic architecture of pediatric cognitive abilities in the Philadelphia Neurodevelopmental Cohort.

The objective of this analysis was to examine the genetic architecture of diverse cognitive abilities in children and adolescents, including the magnitude of common genetic effects and patterns of shared and unique genetic influences. Subjects included 3,689 members of the Philadelphia Neurodevelopmental Cohort, a general population sample of ages 8-21 years who completed an extensive battery of cognitive tests. We used genome-wide complex trait analysis (GCTA) to estimate the SNP-based heritability of each domain, as well as the genetic correlation between all domains that showed significant genetic influence. Several of the individual domains suggested strong influence of common genetic variants (e.g. reading ability, h2g=0.43, p=4e-06; emotion identification, h2g=0.36, p=1e-05; verbal memory, h2g=0.24, p=0.005). The genetic correlations highlighted trait domains that are candidates for joint interrogation in future genetic studies (e.g. language reasoning and spatial reasoning, r(g)=0.72, p=0.007). These results can be used to structure future genetic and neuropsychiatric investigations of diverse cognitive abilities.

A comparative analysis of family-based and population-based association tests using whole genome sequence data.

The revolution in next-generation sequencing has made obtaining both common and rare high-quality sequence variants across the entire genome feasible. Because researchers are now faced with the analytical challenges of handling a massive amount of genetic variant information from sequencing studies, numerous methods have been developed to assess the impact of both common and rare variants on disease traits. In this report, whole genome sequencing data from Genetic Analysis Workshop 18 was used to compare the power of several methods, considering both family-based and population-based designs, to detect association with variants in the MAP4 gene region and on chromosome 3 with blood pressure. To prioritize variants across the genome for testing, variants were first functionally assessed using prediction algorithms and expression quantitative trait loci (eQTLs) data. Four set-based tests in the family-based association tests (FBAT) framework--FBAT-v, FBAT-lmm, FBAT-m, and FBAT-l--were used to analyze 20 pedigrees, and 2 variance component tests, sequence kernel association test (SKAT) and genome-wide complex trait analysis (GCTA), were used with 142 unrelated individuals in the sample. Both set-based and variance-component-based tests had high power and an adequate type I error rate. Of the various FBATs, FBAT-l demonstrated superior performance, indicating the potential for it to be used in rare-variant analysis. The updated FBAT package is available at: http://www.hsph.harvard.edu/fbat/.

Comprehensive Analysis of MRI-Phenotypes Provides New Insights into Lumbar Disc Degeneration for Genetic Studies

Introduction For the past two decades, numerous genetic association studies addressing lumbar disc degeneration (LDD) have been performed, but few of them can be replicated. The possible reasons could be the phenotype definition of LDD was highly variable between studies and level-specific variations were not addressed. As such, this study addressed the relationship between MRI features of the discs at different lumbar levels to explore the etiology of LDD; thereby, providing new insights and measurements for genetic studies. Materials and Methods Sagittal T2-weighted MRI of the lumbar spine was assessed in a population sample of 2,952 Southern Chinese (mean age, 41.1 years; range, 15.0-65.4 years; 40.7% males; 59.3% females). Loss of disc signal intensity, disc bulges/extrusions, Schmorl nodes, high-intensity zones, and bone marrow changes were assessed on imaging. Subject demographics, environmental, and lifestyle factors were also evaluated. Pair-wised polychoric correlations between phenotypes in all five lumbar discs were performed using R v3.01 Heritability was estimated by Genome-wide Complex Trait Analysis (GCTA) using genotype data to identify phenotypes shared common genetic factors. Local regressions were performed between conditions and risk factors, such as age and body mass index, to distinguish the relationship between phenotypes and environmental factors. Phenotypes which were highly correlated and shared common genetic factors were grouped to form a new measurement. Results Analyses suggested distinct genetic etiologies for the upper (L1-L2) versus lower regions (L3-S1). Estimation of heritability showed the upper (L1-L2) and lower regions (L3-S1) of the lumbar spine shared common genetics factors, respectively; but upper and lower regions could present independent genetic features. Regression between phenotype and age denoted that age-related condition was restricted to lower regions while the upper region suggests developmental characters. By combining highly correlated MRI phenotypes in the upper and lower regions separately, two composite scores were generated: a degenerative score (DgS) (represents age-related disc changes) and a developmental score (DvS) (represents congenital variations). Conclusion Based on one of the largest population-based MRI studies of LDD, comprehensive analyses of MRI phenotypes provided new insights into its etiology. Our study proposes a novel phenotype scoring system to assess disc changes on MRI. This scoring system can be used as the basis to promote a standardization of phenotype delineation, maximizing the potential of replication and meta-analysis studies of genetic risk factors for LDD. Disclosure of Interest None declared

From modeling to measurement: developmental trends in genetic influence on adiposity in childhood.

ObjectiveEvidence of increasing heritability of BMI over childhood can seem paradoxical given longer exposure to environmental influences. Genomic data were used to provide direct evidence of developmental increases in genetic influence.MethodsBMI standard deviation scores (BMI-SDS) at ages 4 and 10 were calculated for 2,556 twin pairs in the Twins Early Development Study. Twin analyses estimated heritability of BMI-SDS at each age and the longitudinal genetic correlation. One randomly selected twin per pair was genotyped. Genome-wide complex trait analysis (GCTA) determined DNA-based heritability at each age and the longitudinal genomic correlation. Associations with a polygenic obesity risk score (PRS) using 28 obesity-related single nucleotide polymorphisms (SNPs) were assessed at each age, with bootstrapping to test the significance of the increase in variance explained.ResultsTwin-estimated heritability increased from age 4 (0.43; 95% CI: 0.35-0.53) to 10 (0.82; 0.74-0.88). GCTA-estimated heritability went from non-significant at 4 (0.20; −0.21 to 0.61) to significant at 10 (0.29; 0.01-0.57). Longitudinal genetic correlations derived from twins (0.58) and GCTA (0.66) were similar. The same PRS explained more variance at 10 than 4 years (R2 Δ:0.024; 0.002-0.078).ConclusionsGCTA and PRS findings confirm twin-based results suggesting increasing genetic influence on adiposity during childhood despite substantial genetic stability.

Heritability of variation in glycaemic response to metformin: a genome-wide complex trait analysis

SummaryBackgroundMetformin is a first-line oral agent used in the treatment of type 2 diabetes, but glycaemic response to this drug is highly variable. Understanding the genetic contribution to metformin response might increase the possibility of personalising metformin treatment. We aimed to establish the heritability of glycaemic response to metformin using the genome-wide complex trait analysis (GCTA) method.MethodsIn this GCTA study, we obtained data about HbA1c concentrations before and during metformin treatment from patients in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) study, which includes a cohort of patients with type 2 diabetes and is linked to comprehensive clinical databases and genome-wide association study data. We applied the GCTA method to estimate heritability for four definitions of glycaemic response to metformin: absolute reduction in HbA1c; proportional reduction in HbA1c; adjusted reduction in HbA1c; and whether or not the target on-treatment HbA1c of less than 7% (53 mmol/mol) was achieved, with adjustment for baseline HbA1c and known clinical covariates. Chromosome-wise heritability estimation was used to obtain further information about the genetic architecture.Findings5386 individuals were included in the final dataset, of whom 2085 had enough clinical data to define glycaemic response to metformin. The heritability of glycaemic response to metformin varied by response phenotype, with a heritability of 34% (95% CI 1–68; p=0·022) for the absolute reduction in HbA1c, adjusted for pretreatment HbA1c. Chromosome-wise heritability estimates suggest that the genetic contribution is probably from individual variants scattered across the genome, which each have a small to moderate effect, rather than from a few loci that each have a large effect.InterpretationGlycaemic response to metformin is heritable, thus glycaemic response to metformin is, in part, intrinsic to individual biological variation. Further genetic analysis might enable us to make better predictions for stratified medicine and to unravel new mechanisms of metformin action.FundingWellcome Trust.

Estimating the heritability of colorectal cancer

A sizable fraction of colorectal cancer (CRC) is expected to be explained by heritable factors, with heritability estimates ranging from 12 to 35% twin and family studies. Genome-wide association studies (GWAS) have successfully identified a number of common single-nucleotide polymorphisms (SNPs) associated with CRC risk. Although it has been shown that these CRC susceptibility SNPs only explain a small proportion of the genetic risk, it is not clear how much of the heritability these SNPs explain and how much is left to be detected by other, yet to be identified, common SNPs. Therefore, we estimated the heritability of CRC under different scenarios using Genome-Wide Complex Trait Analysis in the Genetics and Epidemiology of Colorectal Cancer Consortium including 8025 cases and 10 814 controls. We estimated that the heritability explained by known common CRC SNPs identified in GWAS was 0.65% (95% CI:0.3-1%; P = 1.11 × 10-16), whereas the heritability explained by all common SNPs was at least 7.42% (95% CI: 4.71-10.12%; P = 8.13 × 10(-8)), suggesting that many common variants associated with CRC risk remain to be detected. Comparing the heritability explained by the common variants with that from twin and family studies, a fraction of the heritability may be explained by other genetic variants, such as rare variants. In addition, our analysis showed that the gene × smoking interaction explained a significant proportion of the CRC variance (P = 1.26 × 10(-2)). In summary, our results suggest that known CRC SNPs only explain a small proportion of the heritability and more common SNPs have yet to be identified.

A Genome-wide Association Meta-analysis of Preschool Internalizing Problems

Preschool internalizing problems (INT) are highly heritable and moderately genetically stable from childhood into adulthood. Gene-finding studies are scarce. In this study, the influence of genome-wide measured single nucleotide polymorphisms (SNPs) was investigated in 3 cohorts (total N = 4,596 children) in which INT was assessed with the same instrument, the Child Behavior Checklist (CBCL). First, genome-wide association (GWA) results were used for density estimation and genome-wide complex trait analysis (GCTA) to calculate the variance explained by all SNPs. Next, a fixed-effect inverse variance meta-analysis of the 3 GWA analyses was carried out. Finally, the overlap in results with prior GWA studies of childhood and adulthood psychiatric disorders and treatment responses was tested by examining whether SNPs associated with these traits jointly showed a significant signal for INT. Genome-wide SNPs explained 13% to 43% of the total variance. This indicates that the genetic architecture of INT mirrors the polygenic model underlying adult psychiatric traits. The meta-analysis did not yield a genome-wide significant signal but was suggestive for the PCSK2 gene located on chromosome 20p12.1. SNPs associated with other psychiatric disorders appeared to be enriched for signals with INT (λ = 1.26, p < .03). Our study provides evidence that INT is influenced by many common genetic variants, each with a very small effect, and that, even as early as age 3, genetic variants influencing INT overlap with variants that play a role in childhood and adulthood psychiatric disorders.

Regulatory genomic regions active in immune cell types explain a large proportion of the genetic risk of multiple sclerosis

There is little understanding of how genetic variants discovered in recent genome-wide association studies are involved in the pathogenesis of multiple sclerosis (MS). We aimed to investigate which chromatin states and cell types explain genetic risk in MS. We used genotype data from 1854 MS patients and 5164 controls produced by the International Multiple Sclerosis Genetics Consortium and Wellcome Trust Case Control Consortium. We estimated the proportion of phenotypic variance between cases and controls explained by cell-specific chromatin state and DNase I hypersensitivity sites (DHSs) using the Genome-wide Complex Trait Analysis software. A large proportion of variance was explained by single-nucleotide polymorphisms (SNPs) in strong enhancer (SE) elements of immortalized B lymphocytes (5.39%). Three independent SNPs located within SE showed suggestive evidence of association with MS: rs12928822 (odds ratio (OR)=0.81, 95% confidence interval (CI)=0.73-0.89, P=2.48E-05), rs727263 (OR=0.75, 95% CI=0.66-0.85, P=3.26E-06) and rs4674923 (OR=0.85, 95% CI=0.79-0.92, P=1.63E-05). Genetic variants located within DHSs of CD19+ B cells explained the greatest proportion of variance. Genetic variants influencing the risk of MS are located within regulatory elements active in immune cells. This study also identifies a number of immune cell types likely to be involved in the causal cascade and that carry important implications for future studies of therapeutic design.

Polygenic Risk for Alzheimer's Disease is not Associated with Cognitive Ability or Cognitive Aging in Non-Demented Older People

Alzheimer's disease (AD) and non-pathological cognitive aging have phenotypic similarities which may be influenced by an overlapping set of genetic variants. Genome-wide complex trait analysis estimates that common genetic variants account for about 24% of the variation contributing to liability for AD. It is also estimated that 24% of the variance of non-pathological cognitive aging is accounted for by common single nucleotide polymorphisms. However, although the APOE locus is associated with both AD and cognitive aging, it is not known to what extent other common genetic variants, with smaller effect sizes that influence both, overlap. We test the hypothesis that polygenic risk for AD is associated with cognitive ability and cognitive change in about 3,000 non-demented older people (Cognitive Ageing Genetics England and Scotland-CAGES-consortium). We found no significant association of polygenic risk for AD with cognitive ability or cognitive change in CAGES, indicating that the genetic etiologies of AD and non-pathological cognitive decline differ.

Dissection of additive genetic variability for quantitative traits in chickens using SNP markers

The aim of this study was to separate marked additive genetic variability for three quantitative traits in chickens into components associated with classes of minor allele frequency (MAF), individual chromosomes and marker density using the genomewide complex trait analysis (GCTA) approach. Data were from 1351 chickens measured for body weight (BW), ultrasound of breast muscle (BM) and hen house egg production (HHP), each bird with 354 364 SNP genotypes. Estimates of variance components show that SNPs on commercially available genotyping chips marked a large amount of genetic variability for all three traits. The estimated proportion of total variation tagged by all autosomal SNPs was 0.30 (SE 0.04) for BW, 0.33 (SE 0.04) for BM, and 0.19 (SE 0.05) for HHP. We found that a substantial proportion of this variation was explained by low frequency variants (MAF <0.20) for BW and BM, and variants with MAF 0.10-0.30 for HHP. The marked genetic variance explained by each chromosome was linearly related to its length (R(2) = 0.60) for BW and BM. However, for HHP, there was no linear relationship between estimates of variance and length of the chromosome (R(2) = 0.01). Our results suggest that the contribution of SNPs to marked additive genetic variability is dependent on the allele frequency spectrum. For the sample of birds analysed, it was found that increasing marker density beyond 100K SNPs did not capture additional additive genetic variance.

Why do we differ in number sense? Evidence from a genetically sensitive investigation

Basic intellectual abilities of quantity and numerosity estimation have been detected across animal species. Such abilities are referred to as ‘number sense’. For human species, individual differences in number sense are detectable early in life, persist in later development, and relate to general intelligence. The origins of these individual differences are unknown. To address this question, we conducted the first large-scale genetically sensitive investigation of number sense, assessing numerosity discrimination abilities in 837 pairs of monozygotic and 1422 pairs of dizygotic 16-year-old twin pairs. Univariate genetic analysis of the twin data revealed that number sense is modestly heritable (32%), with individual differences being largely explained by non-shared environmental influences (68%) and no contribution from shared environmental factors. Sex-Limitation model fitting revealed no differences between males and females in the etiology of individual differences in number sense abilities. We also carried out Genome-wide Complex Trait Analysis (GCTA) that estimates the population variance explained by additive effects of DNA differences among unrelated individuals. For 1118 unrelated individuals in our sample with genotyping information on 1.7million DNA markers, GCTA estimated zero heritability for number sense, unlike other cognitive abilities in the same twin study where the GCTA heritability estimates were about 25%. The low heritability of number sense, observed in this study, is consistent with the directional selection explanation whereby additive genetic variance for evolutionary important traits is reduced.

Regional heritability advanced complex trait analysis for GPU and traditional parallel architectures

Quantification of the contribution of genetic variation to phenotypic variation for complex traits becomes increasingly computationally demanding with increasing numbers of single-nucleotide polymorphisms and individuals. To meet the challenges in making feasible large-scale studies, we present the REgional heritability advanced complex trait analysis software. Adapted from advanced complex trait analysis (and, in turn, genome-wide complex trait analysis), it is tailored to exploit the parallelism present in modern traditional and graphics processing unit (GPU)-accelerated machines, from workstations to supercomputers. We adapt the genetic relationship matrix estimation algorithm to remove limitations on memory, allowing the analysis of large datasets. We build on this to develop a version of the code able to efficiently exploit GPU-accelerated systems for both the genetic relationship matrix and REstricted maximum likelihood (REML) parts of the analysis, offering substantial speedup over the traditional central processing unit version. We develop the ability to analyze multiple small regions of the genome across multiple compute nodes in parallel, following the 'regional heritability' approach. We demonstrate the new software using 1024 GPUs in parallel on one of the world's fastest supercomputers. The code is freely available at http://www.epcc.ed.ac.uk/software-products CONTACT: a.gray@ed.ac.uk.