Genome-wide meta-analyses of restless legs syndrome yield insights into genetic architecture, disease biology and risk prediction.

Restless legs syndrome (RLS) affects up to 10% of older adults. Their healthcare is impeded by delayed diagnosis and insufficient treatment. To advance disease prediction and find new entry points for therapy, we performed meta-analyses of genome-wide association studies in 116,647 individuals with RLS (cases) and 1,546,466 controls of European ancestry. The pooled analysis increased the number of risk loci eightfold to 164, including three on chromosome X. Sex-specific meta-analyses revealed largely overlapping genetic predispositions of the sexes (rg = 0.96). Locus annotation prioritized druggable genes such as glutamate receptors 1 and 4, and Mendelian randomization indicated RLS as a causal risk factor for diabetes. Machine learning approaches combining genetic and nongenetic information performed best in risk prediction (area under the curve (AUC) = 0.82-0.91). In summary, we identified targets for drug development and repurposing, prioritized potential causal relationships between RLS and relevant comorbidities and risk factors for follow-up and provided evidence that nonlinear interactions are likely relevant to RLS risk prediction.

DeepNull models non-linear covariate effects to improve phenotypic prediction and association power.

Genome-wide association studies (GWASs) examine the association between genotype and phenotype while adjusting for a set of covariates. Although the covariates may have non-linear or interactive effects, due to the challenge of specifying the model, GWAS often neglect such terms. Here we introduce DeepNull, a method that identifies and adjusts for non-linear and interactive covariate effects using a deep neural network. In analyses of simulated and real data, we demonstrate that DeepNull maintains tight control of the type I error while increasing statistical power by up to 20% in the presence of non-linear and interactive effects. Moreover, in the absence of such effects, DeepNull incurs no loss of power. When applied to 10 phenotypes from the UK Biobank (n = 370K), DeepNull discovered more hits (+6%) and loci (+7%), on average, than conventional association analyses, many of which are biologically plausible or have previously been reported. Finally, DeepNull improves upon linear modeling for phenotypic prediction (+23% on average).

Genome-wide association study in almost 195,000 individuals identifies 50 previously unidentified genetic loci for eye color.

Human eye color is highly heritable, but its genetic architecture is not yet fully understood. We report the results of the largest genome-wide association study for eye color to date, involving up to 192,986 European participants from 10 populations. We identify 124 independent associations arising from 61 discrete genomic regions, including 50 previously unidentified. We find evidence for genes involved in melanin pigmentation, but we also find associations with genes involved in iris morphology and structure. Further analyses in 1636 Asian participants from two populations suggest that iris pigmentation variation in Asians is genetically similar to Europeans, albeit with smaller effect sizes. Our findings collectively explain 53.2% (95% confidence interval, 45.4 to 61.0%) of eye color variation using common single-nucleotide polymorphisms. Overall, our study outcomes demonstrate that the genetic complexity of human eye color considerably exceeds previous knowledge and expectations, highlighting eye color as a genetically highly complex human trait.

Disease risk scores for skin cancers.

We trained and validated risk prediction models for the three major types of skin cancer- basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma-on a cross-sectional and longitudinal dataset of 210,000 consented research participants who responded to an online survey covering personal and family history of skin cancer, skin susceptibility, and UV exposure. We developed a primary disease risk score (DRS) that combined all 32 identified genetic and non-genetic risk factors. Top percentile DRS was associated with an up to 13-fold increase (odds ratio per standard deviation increase >2.5) in the risk of developing skin cancer relative to the middle DRS percentile. To derive lifetime risk trajectories for the three skin cancers, we developed a second and age independent disease score, called DRSA. Using incident cases, we demonstrated that DRSA could be used in early detection programs for identifying high risk asymptotic individuals, and predicting when they are likely to develop skin cancer. High DRSA scores were not only associated with earlier disease diagnosis (by up to 14 years), but also with more severe and recurrent forms of skin cancer.

Direct-to-consumer genetic testing for factor V Leiden and prothrombin 20210G>A: the consumer experience.

BACKGROUND: Clinical genetic testing for inherited predisposition to venous thromboembolism (VTE) is common among patients and their families. However, there is incomplete consensus about which individuals should receive testing, and the relative risks and benefits. METHODS: We assessed outcomes of receiving direct-to-consumer (DTC) results for the two most common genetic risk factors for VTE, factor V Leiden in the F5 gene (FVL) and prothrombin 20210G>A in the F2 gene (PT). Two thousand three hundred fifty-four customers (1244 variant-positive and 1110 variant-negative individuals) of the personal genetics company 23andMe, Inc., who had received results online for F5 and F2 variants, participated in an online survey-based study. Participants responded to questions about perception of VTE risk, discussion of results with healthcare providers (HCPs) and recommendations received, actions taken to control risk, emotional responses to receiving risk results, and perceived value of the information. RESULTS: Most participants (90% of variant-positive individuals, 99% of variant-negative individuals) had not previously been tested for F5 and/or F2 variants. The majority of variant-positive individuals correctly perceived that they were at higher than average risk for developing VTE. These individuals reported moderate rates of discussing results with HCPs (41%); receiving prevention advice from HCPs (31%), and making behavioral changes to control risk (e.g., exercising more, 30%). A minority (36%) of variant-positive individuals worried more after receiving VTE results. Nevertheless, most participants reported that knowing their risk had been an advantage (78% variant-positive and 58% variant-negative) and were satisfied knowing their genetic probability for VTE (81% variant-positive and 67% variant-negative). CONCLUSION: Consumers reported moderate rates of behavioral change and perceived personal benefit from receiving DTC genetic results for VTE risk.

Author Correction: Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection.

In the version of the paper initially published, information on competing interests for author Benjamin M. Neale was missing. The 'Competing interests' statement should have included the sentence 'B.M.N. is on the Scientific Advisory Board of Deep Genomics'.

Author Correction: Multi-trait analysis of genome-wide association summary statistics using MTAG.

In the version of the paper initially published, no competing interests were declared. The 'Competing interests' statement should have stated that B.M.N. is on the Scientific Advisory Board of Deep Genomics. The error has been corrected in the HTML and PDF versions of the article.

Publisher Correction: Genome-wide association meta-analysis of individuals of European ancestry identifies new loci explaining a substantial fraction of hair color variation and heritability.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Publisher Correction: Multi-trait analysis of genome-wide association summary statistics using MTAG.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

R/qtl2: Software for Mapping Quantitative Trait Loci with High-Dimensional Data and Multiparent Populations.

R/qtl2 is an interactive software environment for mapping quantitative trait loci (QTL) in experimental populations. The R/qtl2 software expands the scope of the widely used R/qtl software package to include multiparent populations derived from more than two founder strains, such as the Collaborative Cross and Diversity Outbred mice, heterogeneous stocks, and MAGIC plant populations. R/qtl2 is designed to handle modern high-density genotyping data and high-dimensional molecular phenotypes, including gene expression and proteomics. R/qtl2 includes the ability to perform genome scans using a linear mixed model to account for population structure, and also includes features to impute SNPs based on founder strain genomes and to carry out association mapping. The R/qtl2 software provides all of the basic features needed for QTL mapping, including graphical displays and summary reports, and it can be extended through the creation of add-on packages. R/qtl2, which is free and open source software written in the R and C++ programming languages, comes with a test framework.

Gene expression imputation identifies candidate genes and susceptibility loci associated with cutaneous squamous cell carcinoma.

Cutaneous squamous cell carcinoma (cSCC) is a common skin cancer with genetic susceptibility loci identified in recent genome-wide association studies (GWAS). Transcriptome-wide association studies (TWAS) using imputed gene expression levels can identify additional gene-level associations. Here we impute gene expression levels in 6891 cSCC cases and 54,566 controls in the Kaiser Permanente Genetic Epidemiology Research in Adult Health and Aging (GERA) cohort and 25,558 self-reported cSCC cases and 673,788 controls from 23andMe. In a discovery-validation study, we identify 19 loci containing 33 genes whose imputed expression levels are associated with cSCC at false discovery rate < 10% in the GERA cohort and validate 15 of these candidate genes at Bonferroni significance in the 23andMe dataset, including eight genes in five novel susceptibility loci and seven genes in four previously associated loci. These results suggest genetic mechanisms contributing to cSCC risk and illustrate advantages and disadvantages of TWAS as a supplement to traditional GWAS analyses.

Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals.

Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.

Genome-wide association meta-analysis highlights light-induced signaling as a driver for refractive error.

Refractive errors, including myopia, are the most frequent eye disorders worldwide and an increasingly common cause of blindness. This genome-wide association meta-analysis in 160,420 participants and replication in 95,505 participants increased the number of established independent signals from 37 to 161 and showed high genetic correlation between Europeans and Asians (>0.78). Expression experiments and comprehensive in silico analyses identified retinal cell physiology and light processing as prominent mechanisms, and also identified functional contributions to refractive-error development in all cell types of the neurosensory retina, retinal pigment epithelium, vascular endothelium and extracellular matrix. Newly identified genes implicate novel mechanisms such as rod-and-cone bipolar synaptic neurotransmission, anterior-segment morphology and angiogenesis. Thirty-one loci resided in or near regions transcribing small RNAs, thus suggesting a role for post-transcriptional regulation. Our results support the notion that refractive errors are caused by a light-dependent retina-to-sclera signaling cascade and delineate potential pathobiological molecular drivers.

An Association Mapping Framework To Account for Potential Sex Difference in Genetic Architectures.

Over the past few years, genome-wide association studies have identified many trait-associated loci that have different effects on females and males, which increased attention to the genetic architecture differences between the sexes. The between-sex differences in genetic architectures can cause a variety of phenomena such as differences in the effect sizes at trait-associated loci, differences in the magnitudes of polygenic background effects, and differences in the phenotypic variances. However, current association testing approaches for dealing with sex, such as including sex as a covariate, cannot fully account for these phenomena and can be suboptimal in statistical power. We present a novel association mapping framework, MetaSex, that can comprehensively account for the genetic architecture differences between the sexes. Through simulations and applications to real data, we show that our framework has superior performance than previous approaches in association mapping.

Genome-wide association meta-analysis of individuals of European ancestry identifies new loci explaining a substantial fraction of hair color variation and heritability.

Hair color is one of the most recognizable visual traits in European populations and is under strong genetic control. Here we report the results of a genome-wide association study meta-analysis of almost 300,000 participants of European descent. We identified 123 autosomal and one X-chromosome loci significantly associated with hair color; all but 13 are novel. Collectively, single-nucleotide polymorphisms associated with hair color within these loci explain 34.6% of red hair, 24.8% of blond hair, and 26.1% of black hair heritability in the study populations. These results confirm the polygenic nature of complex phenotypes and improve our understanding of melanin pigment metabolism in humans.

Multi-trait analysis of genome-wide association summary statistics using MTAG.

We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N eff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations.

Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection.

Recent work has hinted at the linkage disequilibrium (LD)-dependent architecture of human complex traits, where SNPs with low levels of LD (LLD) have larger per-SNP heritability. Here we analyzed summary statistics from 56 complex traits (average N = 101,401) by extending stratified LD score regression to continuous annotations. We determined that SNPs with low LLD have significantly larger per-SNP heritability and that roughly half of this effect can be explained by functional annotations negatively correlated with LLD, such as DNase I hypersensitivity sites (DHSs). The remaining signal is largely driven by our finding that more recent common variants tend to have lower LLD and to explain more heritability (P = 2.38 × 10-104); the youngest 20% of common SNPs explain 3.9 times more heritability than the oldest 20%, consistent with the action of negative selection. We also inferred jointly significant effects of other LD-related annotations and confirmed via forward simulations that they jointly predict deleterious effects.

Genome-wide association analysis of pain severity in dysmenorrhea identifies association at chromosome 1p13.2, near the nerve growth factor locus.

Dysmenorrhea is a common chronic pelvic pain syndrome affecting women of childbearing potential. Family studies suggest that genetic background influences the severity of dysmenorrhea, but genetic predisposition and molecular mechanisms underlying dysmenorrhea are not understood. In this study, we conduct the first genome-wide association study to identify genetic factors associated with dysmenorrhea pain severity. A cohort of females of European descent (n = 11,891) aged 18 to 45 years rated their average dysmenorrhea pain severity. We used a linear regression model adjusting for age and body mass index, identifying one genome-wide significant (P < 5 × 10) association (rs7523086, P = 4.1 × 10, effect size 0.1 [95% confidence interval, 0.074-0.126]). This single nucleotide polymorphism is colocalising with NGF, encoding nerve growth factor. The presence of one risk allele corresponds to a predicted 0.1-point increase in pain intensity on a 4-point ordinal pain scale. The putative effects on NGF function and/or expression remain unknown. However, genetic variation colocalises with active epigenetic marks in fat and ovary tissues, and expression levels in aorta tissue of a noncoding RNA flanking NGF correlate. Participants reporting extreme dysmenorrhea pain were more likely to report being positive for endometriosis, polycystic ovarian syndrome, depression, and other psychiatric disorders. Our results indicate that dysmenorrhea pain severity is partly genetically determined. NGF already has an established role in chronic pain disorders, and our findings suggest that NGF may be an important mediator for gynaecological/pelvic pain in the viscera.