Genome-wide Complex Trait Analysis Research Articles

Childhood peer network characteristics: genetic influences and links with early mental health trajectories.

Peer relationships are important for children's mental health, yet little is known of their etiological underpinnings. Here, we explore the genetic influences on childhood peer network characteristics in three different networks defined by rejection, acceptance, and prosocial behavior. We further examine the impact of early externalizing and internalizing trajectories on these same peer network characteristics. Participants were 1,288 children from the Dutch 'Generation R' birth cohort. At age 7, we mapped out children's classroom peer networks for peer rejection, acceptance, and prosocial behavior using mutual peer nominations. In each network, genetic influences were estimated for children's degree centrality, closeness centrality and link reciprocity from DNA using Genome-wide Complex Trait Analysis. Preschool externalizing and internalizing trajectories were computed using parental ratings at ages 1.5, 3, and 5years. Of the three network properties examined, closeness centrality emerged as significantly heritable across all networks. Preschool externalizing problems predicted unfavorable positions within peer rejection networks and having fewer mutual friendships. In contrast, children with preschool-internalizing problems were not actively rejected by their peers, but were less well-connected within their social support network. Our finding of significant heritability for closeness centrality should be taken as preliminary evidence that requires replication. Nevertheless, it can orient us to the role of genes in shaping a child's position within peer networks. Additionally, social network perspectives offer rich insights into how early life mental health trajectories impact a child's later functioning within peer networks.

Evidence of Common Genetic Overlap Between Schizophrenia and Cognition.

Cognitive impairment is a core feature of schizophrenia but there is limited understanding of the genetic relationship between cognition in the general population and schizophrenia. We examine how common variants associated with schizophrenia en masse contribute to childhood cognitive ability in a population-based sample, and the extent to which common genetic variants associated with childhood cognition explain variation in schizophrenia. Schizophrenia polygenic risk scores were derived from the Psychiatric Genomics Consortium (n = 69 516) and tested for association with IQ, attention, processing speed, working memory, problem solving, and social cognition in over 5000 children aged 8 from the Avon Longitudinal Study of Parents and Children birth cohort. Polygenic scores for these cognitive domains were tested for association with schizophrenia in a large UK schizophrenia sample (n = 11 853). Bivariate genome-wide complex trait analysis (GCTA) estimated the amount of shared genetic factors between schizophrenia and cognitive domains. Schizophrenia polygenic risk score was associated with lower performance IQ (P = .001) and lower full IQ (P = .013). Polygenic score for performance IQ was associated with increased risk for schizophrenia (P = 3.56E-04). Bivariate GCTA revealed moderate genetic correlation between schizophrenia and both performance IQ (r G = −.379, P = 6.62E-05) and full IQ (r G = −.202, P = 5.00E-03), with approximately 14% of the genetic component of schizophrenia shared with that for performance IQ. Our results support the presence of shared common genetic factors between schizophrenia and childhood cognitive ability. We observe a genetic relationship between schizophrenia and performance IQ but not verbal IQ or other cognitive variables, which may have implications for studies utilizing cognitive endophenotypes for psychosis.

Quantifying the heritability of glioma using genome-wide complex trait analysis.

Genome-wide association studies (GWAS) have successfully identified a number of common single-nucleotide polymorphisms (SNPs) influencing glioma risk. While these SNPs only explain a small proportion of the genetic risk it is unclear how much is left to be detected by other, yet to be identified, common SNPs. Therefore, we applied Genome-Wide Complex Trait Analysis (GCTA) to three GWAS datasets totalling 3,373 cases and 4,571 controls and performed a meta-analysis to estimate the heritability of glioma. Our results identify heritability estimates of 25% (95% CI: 20–31%, P = 1.15 × 10−17) for all forms of glioma - 26% (95% CI: 17–35%, P = 1.05 × 10−8) for glioblastoma multiforme (GBM) and 25% (95% CI: 17–32%, P = 1.26 × 10−10) for non-GBM tumors. This is a substantial increase from the genetic variance identified by the currently identified GWAS risk loci (~6% of common heritability), indicating that most of the heritable risk attributable to common genetic variants remains to be identified.

Genome-wide estimate of the heritability of Multiple System Atrophy

IntroductionMultiple System Atrophy (MSA) is a neurodegenerative disease which presents heterogeneously with symptoms and signs of parkinsonism, ataxia and autonomic dysfunction. Although MSA typically occurs sporadically, rare pathology-proven MSA families following either autosomal recessive or autosomal dominant patterns have been described, indicating a heritable contribution to the pathogenesis. MethodsWe used Genome-Wide Complex Trait Analysis (GCTA) to estimate the heritable component of MSA due to common coding variability in imputed genotype data of 907 MSA cases and 3866 population-matched controls. GCTA only assesses the effect of putative causal variants in linkage disequilibrium (LD) with all common SNPs on the genotyping platform. ResultsWe estimate the heritability among common variants of MSA in pooled cases at 2.09–6.65%, with a wider range of values in geographic and diagnostic subgroups. Meta-analysis of our geographic cohorts reveals high between-group heterogeneity. Contributions of single chromosomes are generally negligible. We suggest that all calculated MSA heritability among common variants could be explained by the presence of misdiagnosed cases in the clinical subgroup based on a Bayesian estimate using literature-derived rates of misdiagnosis. DiscussionMSA is a challenging disease to study due to high rates of misdiagnosis and low prevalence. Given our low estimates of heritability, common genetic variation appears to play a less prominent role in risk for MSA than in other complex neurodegenerative diseases such as Parkinson's disease, Alzheimer's disease, and Amyotrophic Lateral Sclerosis. The success of future gene discovery efforts rests on large pathologically-confirmed case series and an interrogation of both common and rare genetic variants.

Fractal behavior of poly(GC) and poly(TA) DNA segments arranged in quasiperiodic Fibonacci sequence

We used the atomistic molecular mechanics method with a well-known universal force field (UFF), as implemented in forcite module, to investigate the fractal properties of the poly GC and poly TA base pairs diluted in solvent, grown in conformity with the quasiperiodic Fibonacci sequence. It was obtained through simulations, and demonstrated that solvent-accessible surface area and volume of these molecules follow power-law behavior that depends on the chain length with exponent near 1 for the volume, and for the surface. The exponents calculated presented a dependence on the solvent probe radius. It was demonstrated that only in a rigid simple model these exponents converge to unity as the chain length increases to infinity. However the reason for fractionary exponents found here could be just attributed to finite size effect, but in fact, the flexibility plays a central rule in a real molecular system, and could explain the fractionary exponents obtained here. Both classes of macromolecules analyzed present a self-similar characteristic that could assist for understanding of several biological properties, and result in a variety of potential applications.

Estimating cumulative pathway effects on risk for age-related macular degeneration using mixed linear models.

BackgroundAge-related macular degeneration (AMD) is the leading cause of irreversible visual loss in the elderly in developed countries and typically affects more than 10 % of individuals over age 80. AMD has a large genetic component, with heritability estimated to be between 45 % and 70 %. Numerous variants have been identified and implicate various molecular mechanisms and pathways for AMD pathogenesis but those variants only explain a portion of AMD’s heritability. The goal of our study was to estimate the cumulative genetic contribution of common variants on AMD risk for multiple pathways related to the etiology of AMD, including angiogenesis, antioxidant activity, apoptotic signaling, complement activation, inflammatory response, response to nicotine, oxidative phosphorylation, and the tricarboxylic acid cycle. While these mechanisms have been associated with AMD in literature, the overall extent of the contribution to AMD risk for each is unknown.MethodsIn a case–control dataset with 1,813 individuals genotyped for over 600,000 SNPs we used Genome-wide Complex Trait Analysis (GCTA) to estimate the proportion of AMD risk explained by SNPs in genes associated with each pathway. SNPs within a 50 kb region flanking each gene were also assessed, as well as more distant, putatively regulatory SNPs, based on DNaseI hypersensitivity data from ocular tissue in the ENCODE project.ResultsWe found that 19 previously associated AMD risk SNPs contributed to 13.3 % of the risk for AMD in our dataset, while the remaining genotyped SNPs contributed to 36.7 % of AMD risk. Adjusting for the 19 risk SNPs, the complement activation and inflammatory response pathways still explained a statistically significant proportion of additional risk for AMD (9.8 % and 17.9 %, respectively), with other pathways showing no significant effects (0.3 % – 4.4 %).DiscussionOur results show that SNPs associated with complement activation and inflammation significantly contribute to AMD risk, separately from the risk explained by the 19 known risk SNPs. We found that SNPs within 50 kb regions flanking genes explained additional risk beyond genic SNPs, suggesting a potential regulatory role, but that more distant SNPs explained less than 0.5 % additional risk for each pathway.ConclusionsFrom these analyses we find that the impact of complement SNPs on risk for AMD extends beyond the established genome-wide significant SNPs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0760-4) contains supplementary material, which is available to authorized users.

The genetics of alcohol dependence: Twin and SNP-based heritability, and genome-wide association study based on AUDIT scores.

Alcohol dependence (AD) is among the most common and costly public health problems contributing to morbidity and mortality throughout the world. In this study, we investigate the genetic basis of AD in a Dutch population using data from the Netherlands Twin Register (NTR) and the Netherlands Study of Depression and Anxiety (NESDA). The presence of AD was ascertained via the Alcohol Use Disorders Identification Test (AUDIT) applying cut-offs with good specificity and sensitivity in identifying those at risk for AD. Twin-based heritability of AD-AUDIT was estimated using structural equation modeling of data in 7,694 MZ and DZ twin pairs. Variance in AD-AUDIT explained by all SNPs was estimated with genome-wide complex trait analysis (GCTA). A genome-wide association study (GWAS) was performed in 7,842 subjects. GWAS SNP effect concordance analysis was performed between our GWAS and a recent AD GWAS using DSM-IV diagnosis. The twin-based heritability of AD-AUDIT was estimated at 60% (55-69%). GCTA showed that common SNPs jointly capture 33% (SE = 0.12, P = 0.002) of this heritability. In the GWAS, the top hits were positioned within four regions (4q31.1, 2p16.1, 6q25.1, 7p14.1) with the strongest association detected for rs55768019 (P = 7.58 × 10(-7) ). This first GWAS of AD using the AUDIT measure found results consistent with previous genetic studies using DSM diagnosis: concordance in heritability estimates and direction of SNPs effect and overlap with top hits from previous GWAS. Thus, the use of appropriate questionnaires may represent cost-effective strategies to phenotype samples in large-scale biobanks or other population-based datasets.

Meta-analysis of Genome-Wide Association Studies for Extraversion: Findings from the Genetics of Personality Consortium.

Extraversion is a relatively stable and heritable personality trait associated with numerous psychosocial, lifestyle and health outcomes. Despite its substantial heritability, no genetic variants have been detected in previous genome-wide association (GWA) studies, which may be due to relatively small sample sizes of those studies. Here, we report on a large meta-analysis of GWA studies for extraversion in 63,030 subjects in 29 cohorts. Extraversion item data from multiple personality inventories were harmonized across inventories and cohorts. No genome-wide significant associations were found at the single nucleotide polymorphism (SNP) level but there was one significant hit at the gene level for a long non-coding RNA site (LOC101928162). Genome-wide complex trait analysis in two large cohorts showed that the additive variance explained by common SNPs was not significantly different from zero, but polygenic risk scores, weighted using linkage information, significantly predicted extraversion scores in an independent cohort. These results show that extraversion is a highly polygenic personality trait, with an architecture possibly different from other complex human traits, including other personality traits. Future studies are required to further determine which genetic variants, by what modes of gene action, constitute the heritable nature of extraversion.Electronic supplementary materialThe online version of this article (doi:10.1007/s10519-015-9735-5) contains supplementary material, which is available to authorized users.

Quantifying the heritability of testicular germ cell tumour using both population-based and genomic approaches.

A sizable fraction of testicular germ cell tumour (TGCT) risk is expected to be explained by heritable factors. Recent genome-wide association studies (GWAS) have successfully identified a number of common SNPs associated with TGCT. It is however, unclear how much common variation there is left to be accounted for by other, yet to be identified, common SNPs and what contribution common genetic variation makes to the heritable risk of TGCT. We approached this question using two complimentary analytical techniques. We undertook a population-based analysis of the Swedish family-cancer database, through which we estimated that the heritability of TGCT at 48.9% (CI:47.2%–52.3%). We also applied Genome-Wide Complex Trait Analysis to 922 cases and 4,842 controls to estimate the heritability of TGCT. The heritability explained by known common risk SNPs identified by GWAS was 9.1%, whereas the heritability explained by all common SNPs was 37.4% (CI:27.6%–47.2%). These complementary findings indicate that the known TGCT SNPs only explain a small proportion of the heritability and many additional common SNPs remain to be identified. The data also suggests that a fraction of the heritability of TGCT is likely to be explained by other classes of genetic variation, such as rare disease-causing alleles.

A genome-wide analysis of putative functional and exonic variation associated with extremely high intelligence.

Although individual differences in intelligence (general cognitive ability) are highly heritable, molecular genetic analyses to date have had limited success in identifying specific loci responsible for its heritability. This study is the first to investigate exome variation in individuals of extremely high intelligence. Under the quantitative genetic model, sampling from the high extreme of the distribution should provide increased power to detect associations. We therefore performed a case–control association analysis with 1409 individuals drawn from the top 0.0003 (IQ >170) of the population distribution of intelligence and 3253 unselected population-based controls. Our analysis focused on putative functional exonic variants assayed on the Illumina HumanExome BeadChip. We did not observe any individual protein-altering variants that are reproducibly associated with extremely high intelligence and within the entire distribution of intelligence. Moreover, no significant associations were found for multiple rare alleles within individual genes. However, analyses using genome-wide similarity between unrelated individuals (genome-wide complex trait analysis) indicate that the genotyped functional protein-altering variation yields a heritability estimate of 17.4% (s.e. 1.7%) based on a liability model. In addition, investigation of nominally significant associations revealed fewer rare alleles associated with extremely high intelligence than would be expected under the null hypothesis. This observation is consistent with the hypothesis that rare functional alleles are more frequently detrimental than beneficial to intelligence.

Single Nucleotide Polymorphism Heritability of Behavior Problems in Childhood: Genome-Wide Complex Trait Analysis

Genetic factors contribute to individual differences in behavior problems. In children, genome-wide association studies (GWAS) have yielded the first suggestive results when aiming to identify genetic variants that explain heritability, but the proportion of genetic variance that can be attributed to common single nucleotide polymorphisms (SNPs) remains to be determined, as only a few studies have estimated SNP heritability, with diverging results. Genomic-relationship-matrix restricted maximum likelihood (GREML) as implemented in the software Genome-Wide Complex Trait Analysis (GCTA) was used to estimate SNP heritability (SNP h(2)) for multiple phenotypes within 4 broad domains of children's behavioral problems (attention-deficit/hyperactivity symptoms, internalizing, externalizing, and pervasive developmental problems) and cognitive function. We combined phenotype and genotype data from 2 independent, population-based Dutch cohorts, yielding a total number of 1,495 to 3,175 of 3-, 7-, and 9-year-old children. Significant SNP heritability estimates were found for attention-deficit/hyperactivity symptoms (SNP h(2)= 0.37-0.71), externalizing problems (SNP h(2)= 0.44), and total problems (SNP h(2)= 0.18), rated by mother or teacher. Sensitivity analyses with exclusion of extreme cases and quantile normalization of the phenotype data decreased SNP h(2) as expected under genetic inheritance, but they remained statistically significant for most phenotypes. We provide evidence of the influence of common SNPs on child behavior problems in an ethnically homogenous sample. These results support the continuation of large GWAS collaborative efforts to unravel the genetic basis of complex child behaviors.

A genome-wide approach to children's aggressive behavior: The EAGLE consortium.

Individual differences in aggressive behavior emerge in early childhood and predict persisting behavioral problems and disorders. Studies of antisocial and severe aggression in adulthood indicate substantial underlying biology. However, little attention has been given to genome-wide approaches of aggressive behavior in children. We analyzed data from nine population-based studies and assessed aggressive behavior using well-validated parent-reported questionnaires. This is the largest sample exploring children's aggressive behavior to date (N = 18,988), with measures in two developmental stages (N = 15,668 early childhood and N = 16,311 middle childhood/early adolescence). First, we estimated the additive genetic variance of children's aggressive behavior based on genome-wide SNP information, using genome-wide complex trait analysis (GCTA). Second, genetic associations within each study were assessed using a quasi-Poisson regression approach, capturing the highly right-skewed distribution of aggressive behavior. Third, we performed meta-analyses of genome-wide associations for both the total age-mixed sample and the two developmental stages. Finally, we performed a gene-based test using the summary statistics of the total sample. GCTA quantified variance tagged by common SNPs (10-54%). The meta-analysis of the total sample identified one region in chromosome 2 (2p12) at near genome-wide significance (top SNP rs11126630, P = 5.30 × 10(-8) ). The separate meta-analyses of the two developmental stages revealed suggestive evidence of association at the same locus. The gene-based analysis indicated association of variation within AVPR1A with aggressive behavior. We conclude that common variants at 2p12 show suggestive evidence for association with childhood aggression. Replication of these initial findings is needed, and further studies should clarify its biological meaning. © 2015 Wiley Periodicals, Inc.

Genome-wide analysis of attention deficit hyperactivity disorder in Norway.

BackgroundAttention deficit hyperactivity disorder (ADHD) is a highly heritable neuropsychiatric condition, but it has been difficult to identify genes underlying this disorder. This study aimed to explore genetics of ADHD in an ethnically homogeneous Norwegian population by means of a genome-wide association (GWA) analysis followed by examination of candidate loci.Materials and MethodsParticipants were recruited through Norwegian medical and birth registries as well as the general population. Presence of ADHD was defined according to DSM-IV criteria. Genotyping was performed using Illumina Human OmniExpress-12v1 microarrays. Statistical analyses were divided into several steps: (1) genome-wide association in the form of logistic regression in PLINK and follow-up pathway analyses performed in DAPPLE and INRICH softwares, (2) SNP-heritability calculated using genome-wide complex trait analysis (GCTA) tool, (3) gene-based association tests carried out in JAG software, and (4) evaluation of previously reported genome-wide signals and candidate genes of ADHD.ResultsIn total, 1.358 individuals (478 cases and 880 controls) and 598.384 autosomal SNPs were subjected to GWA analysis. No single polymorphism reached genome-wide significance. The strongest signal was observed at rs9949006 in the ENSG00000263745 gene (OR=1.51, 95% CI 1.28–1.79, p=1.38E-06). Pathway analyses of the top SNPs implicated genes involved in the regulation of gene expression, cell adhesion and inflammation. Among previously identified ADHD candidate genes, prominent association signals were observed for SLC9A9 (rs1393072, OR=1.46, 95% CI = 1.21–1.77, p=9.95E-05) and TPH2 (rs17110690, OR = 1.38, 95% CI = 1.14–1.66, p=8.31E-04).ConclusionThis study confirms the complexity and heterogeneity of ADHD etiology. Taken together with previous findings, our results point to a spectrum of biological mechanisms underlying the symptoms of ADHD, providing targets for further genetic exploration of this complex disorder.

Genetic and Environmental Factors Are Associated with Serum 25-Hydroxyvitamin D Concentrations in Older African Americans1–3

Background: Low circulating 25-hydroxyvitamin D [25(OH)D] is prevalent in African Americans, but predictors of vitamin D status are understudied compared to Caucasian populations.Objective: We investigated whether certain environmental and genetic factors are predictors of circulating 25(OH)D in 989 elderly African Americans participating in the Health, Aging, and Body Composition (Health ABC) Study.Methods: Regression analysis estimated the cross-sectional association of nongenetic (environmental) factors with 25(OH)D. Single nucleotide polymorphisms (SNPs) associated with 25(OH)D in Caucasian genome-wide association studies (GWASs) were analyzed for association with serum 25(OH)D, including analyses of all imputed SNPs in identified genomic regions. Genome-wide complex trait analysis (GCTA) evaluated the association of all (genome-wide) genotyped SNPs with serum 25(OH)D in the Health ABC Study with replication in the Multi-Ethnic Study of Atherosclerosis (MESA) cohort.Results: Gender, study site, season of blood draw, body mass index, dietary supplement use, dairy and cereal consumption, Healthy Eating Index score, and walking >180 min/wk were associated with 25(OH)D (P < 0.05), jointly explaining 25% of the variation in circulating 25(OH)D. Multivitamin supplement use was the strongest predictor of circulating 25(OH)D, and supplement users had a 6.3-μg/L higher serum 25(OH)D concentration compared with nonusers. Previous GWAS-identified gene regions were not replicated in African Americans, but the nonsynonymous rs7041 SNP in group-specific component (vitamin D binding protein) was close to significance thresholds (P = 0.08), and there was evidence for an interaction between this SNP and use of multivitamin supplements in relation to serum 25(OH)D concentration (P = 0.04). Twenty-three percent (95% CI: 0%, 52%) of the variation in serum 25(OH)D was explained by total genetic variation in a pooled GCTA of 2087 Health ABC Study and MESA African-American participants, but population substructure effects could not be separated from other genetic influences.Conclusions: Modifiable dietary and lifestyle predictors of serum 25(OH)D were identified in African Americans. GCTA confirms that a proportion of 25(OH)D variability is attributable to genetic variation, but genomic regions associated with the 25(OH)D phenotype identified in prior GWASs of European Americans were not replicated in the Health ABC Study in African Americans.

Genome-wide association study of parity in Bangladeshi women.

Human fertility is a complex trait determined by gene-environment interactions in which genetic factors represent a significant component. To better understand inter-individual variability in fertility, we performed one of the first genome-wide association studies (GWAS) of common fertility phenotypes, lifetime number of pregnancies and number of children in a developing country population. The fertility phenotype data and DNA samples were obtained at baseline recruitment from individuals participating in a large prospective cohort study in Bangladesh. GWAS analyses of fertility phenotypes were conducted among 1,686 married women. One SNP on chromosome 4 was non-significantly associated with number of children at P <10-7 and number of pregnancies at P <10-6. This SNP is located in a region without a gene within 1 Mb. One SNP on chromosome 6 was non-significantly associated with extreme number of children at P <10-6. The closest gene to this SNP is HDGFL1, a hepatoma-derived growth factor. When we excluded hormonal contraceptive users, a SNP on chromosome 5 was non-significantly associated at P <10-5 for number of children and number of pregnancies. This SNP is located near C5orf64, an open reading frame, and ZSWIM6, a zinc ion binding gene. We also estimated the heritability of these phenotypes from our genotype data using GCTA (Genome-wide Complex Trait Analysis) for number of children (hg 2 = 0.149, SE = 0.24, p-value = 0.265) and number of pregnancies (hg 2 = 0.007, SE = 0.22, p-value = 0.487). Our genome-wide association study and heritability estimates of number of pregnancies and number of children in Bangladesh did not confer strong evidence of common variants for parity variation. However, our results suggest that future studies may want to consider the role of 3 notable SNPs in their analysis.

Familiality and SNP heritability of age at onset and episodicity in major depressive disorder.

Strategies to dissect phenotypic and genetic heterogeneity of major depressive disorder (MDD) have mainly relied on subphenotypes, such as age at onset (AAO) and recurrence/episodicity. Yet, evidence on whether these subphenotypes are familial or heritable is scarce. The aims of this study are to investigate the familiality of AAO and episode frequency in MDD and to assess the proportion of their variance explained by common single nucleotide polymorphisms (SNP heritability). For investigating familiality, we used 691 families with 2-5 full siblings with recurrent MDD from the DeNt study. We fitted (square root) AAO and episode count in a linear and a negative binomial mixed model, respectively, with family as random effect and adjusting for sex, age and center. The strength of familiality was assessed with intraclass correlation coefficients (ICC). For estimating SNP heritabilities, we used 3468 unrelated MDD cases from the RADIANT and GSK Munich studies. After similarly adjusting for covariates, derived residuals were used with the GREML method in GCTA (genome-wide complex trait analysis) software. Significant familial clustering was found for both AAO (ICC=0.28) and episodicity (ICC=0.07). We calculated from respective ICC estimates the maximal additive heritability of AAO (0.56) and episodicity (0.15). SNP heritability of AAO was 0.17 (p=0.04); analysis was underpowered for calculating SNP heritability of episodicity. AAO and episodicity aggregate in families to a moderate and small degree, respectively. AAO is under stronger additive genetic control than episodicity. Larger samples are needed to calculate the SNP heritability of episodicity. The described statistical framework could be useful in future analyses.

Massively expedited genome-wide heritability analysis (MEGHA)

The discovery and prioritization of heritable phenotypes is a computational challenge in a variety of settings, including neuroimaging genetics and analyses of the vast phenotypic repositories in electronic health record systems and population-based biobanks. Classical estimates of heritability require twin or pedigree data, which can be costly and difficult to acquire. Genome-wide complex trait analysis is an alternative tool to compute heritability estimates from unrelated individuals, using genome-wide data that are increasingly ubiquitous, but is computationally demanding and becomes difficult to apply in evaluating very large numbers of phenotypes. Here we present a fast and accurate statistical method for high-dimensional heritability analysis using genome-wide SNP data from unrelated individuals, termed massively expedited genome-wide heritability analysis (MEGHA) and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary statistics of interest. MEGHA produces estimates and significance measures of heritability with several orders of magnitude less computational time than existing methods, making heritability-based prioritization of millions of phenotypes based on data from unrelated individuals tractable for the first time to our knowledge. As a demonstration of application, we conducted heritability analyses on global and local morphometric measurements derived from brain structural MRI scans, using genome-wide SNP data from 1,320 unrelated young healthy adults of non-Hispanic European ancestry. We also computed surface maps of heritability for cortical thickness measures and empirically localized cortical regions where thickness measures were significantly heritable. Our analyses demonstrate the unique capability of MEGHA for large-scale heritability-based screening and high-dimensional heritability profile construction.

Genetic contributions to variation in general cognitive function: a meta-analysis of genome-wide association studies in the CHARGE consortium (N=53949).

General cognitive function is substantially heritable across the human life course from adolescence to old age. We investigated the genetic contribution to variation in this important, health- and well-being-related trait in middle-aged and older adults. We conducted a meta-analysis of genome-wide association studies of 31 cohorts (N=53 949) in which the participants had undertaken multiple, diverse cognitive tests. A general cognitive function phenotype was tested for, and created in each cohort by principal component analysis. We report 13 genome-wide significant single-nucleotide polymorphism (SNP) associations in three genomic regions, 6q16.1, 14q12 and 19q13.32 (best SNP and closest gene, respectively: rs10457441, P=3.93 × 10−9, MIR2113; rs17522122, P=2.55 × 10−8, AKAP6; rs10119, P=5.67 × 10−9, APOE/TOMM40). We report one gene-based significant association with the HMGN1 gene located on chromosome 21 (P=1 × 10−6). These genes have previously been associated with neuropsychiatric phenotypes. Meta-analysis results are consistent with a polygenic model of inheritance. To estimate SNP-based heritability, the genome-wide complex trait analysis procedure was applied to two large cohorts, the Atherosclerosis Risk in Communities Study (N=6617) and the Health and Retirement Study (N=5976). The proportion of phenotypic variation accounted for by all genotyped common SNPs was 29% (s.e.=5%) and 28% (s.e.=7%), respectively. Using polygenic prediction analysis, ~1.2% of the variance in general cognitive function was predicted in the Generation Scotland cohort (N=5487; P=1.5 × 10−17). In hypothesis-driven tests, there was significant association between general cognitive function and four genes previously associated with Alzheimer's disease: TOMM40, APOE, ABCG1 and MEF2C.

Shared Genetic Influences Between Attention-Deficit/Hyperactivity Disorder (ADHD) Traits in Children and Clinical ADHD

ObjectiveTwin studies and genome-wide complex trait analysis (GCTA) are not in agreement regarding heritability estimates for behavioral traits in children from the general population. This has sparked a debate on the possible difference in genetic architecture between behavioral traits and psychiatric disorders. In this study, we test whether polygenic risk scores associated with variation in attention-deficit/hyperactivity disorder (ADHD) trait levels in children from the general population predict ADHD diagnostic status and severity in an independent clinical sample.MethodSingle nucleotide polymorphisms (SNPs) with p < .5 from a genome-wide association study of ADHD traits in 4,546 children (mean age, 7 years 7 months) from the Avon Longitudinal Study of Parents and Children (ALSPAC; general population sample) were selected to calculate polygenic risk scores in 508 children with an ADHD diagnosis (independent clinical sample) and 5,081 control participants. Polygenic scores were tested for association with case-control status and severity of disorder in the clinical sample.ResultsIncreased polygenic score for ADHD traits predicted ADHD case-control status (odds ratio = 1.17 [95% CI = 1.08–1.28], p = .0003), higher ADHD symptom severity (β = 0.29 [95% CI = 0.04–0.54], p = 0.02), and symptom domain severity in the clinical sample.ConclusionThis study highlights the relevance of additive genetic variance in ADHD, and provides evidence that shared genetic factors contribute to both behavioral traits in the general population and psychiatric disorders at least in the case of ADHD.

Genetic Architecture of White Matter Hyperintensities Differs in Hypertensive and Nonhypertensive Ischemic Stroke

Epidemiological studies suggest that white matter hyperintensities (WMH) are extremely heritable, but the underlying genetic variants are largely unknown. Pathophysiological heterogeneity is known to reduce the power of genome-wide association studies (GWAS). Hypertensive and nonhypertensive individuals with WMH might have different underlying pathologies. We used GWAS data to calculate the variance in WMH volume (WMHV) explained by common single nucleotide polymorphisms (SNPs) as a measure of heritability (SNP heritability [HSNP]) and tested the hypothesis that WMH heritability differs between hypertensive and nonhypertensive individuals. WMHV was measured on MRI in the stroke-free cerebral hemisphere of 2336 ischemic stroke cases with GWAS data. After adjustment for age and intracranial volume, we determined which cardiovascular risk factors were independent predictors of WMHV. Using the genome-wide complex trait analysis tool to estimate HSNP for WMHV overall and within subgroups stratified by risk factors found to be significant in multivariate analyses. A significant proportion of the variance of WMHV was attributable to common SNPs after adjustment for significant risk factors (HSNP=0.23; P=0.0026). HSNP estimates were higher among hypertensive individuals (HSNP=0.45; P=7.99×10(-5)); this increase was greater than expected by chance (P=0.012). In contrast, estimates were lower, and nonsignificant, in nonhypertensive individuals (HSNP=0.13; P=0.13). A quarter of variance is attributable to common SNPs, but this estimate was greater in hypertensive individuals. These findings suggest that the genetic architecture of WMH in ischemic stroke differs between hypertensives and nonhypertensives. Future WMHV GWAS studies may gain power by accounting for this interaction.