Andrew R. Wood

Sleep and circadian traits have been widely studied using common variants, but the contribution of rare coding variation remains unclear. We analyzed rare coding variants in 397,065 whole-exome sequenced UK Biobank participants across 36 sleep phenotypes from self-report, diagnoses, sleep medication use and accelerometry, and meta-analyzed results with 171,536 whole-genome sequenced All of Us participants of diverse ancestries, with replication in the Mass General Brigham Biobank (N = 31,275). We identified 260 genes associated with sleep phenotypes, including novel associations with sleep medication use in 29 genes and 24 out of 29 have not previously been reported with any sleep phenotypes. We observed modest but significant rare variant heritability and strong genetic correlations between sleep medication use, insomnia and fatigue. Temporal gene expression trajectory analyses indicate that genes associated with self-reported sleep traits show constant high prenatal expression, whereas genes linked to sleep medication phenotypes exhibit peak expression in the late prenatal period. These findings highlight distinct biological mechanisms captured by different measurement sources of sleep phenotypes and reveal rare-variant-informed targets for therapeutic discovery.

GWAS have generally focused on common variants from genotyping arrays or rare protein-coding variants from exome sequencing. Here, we use whole-genome sequencing data to evaluate the contribution to and architecture of rare non-coding variants for three commonly studied anthropometric traits: height, BMI and waist-hip ratio adjusted for BMI. Analysing 447,461 individuals in the UK Biobank for discovery and 225,515 individuals in All of Us for replication, we identify 90 rare and low-frequency single variant associations, including two independent rare variants upstream of IGF2BP2 that substantially reduce waist-hip ratio adjusted for BMI, but have distinct effects on other adiposity traits. We further identify 135 coding variant aggregates. For example, UBR3 protein-truncating variants are associated with a 2.7 kg/m2 increase in BMI. We additionally identify 51 non-coding variant aggregate associations, including one in the 5’UTR of FGF18 associated with up to 6 cm effects on height. We show that 97% of rare variant associations occur near GWAS-identified loci, demonstrating convergence of rare and common variant associations. Finally, we show that ultra rare variants explain a small fraction of heritability compared to common variants for these traits, that heritability is largely shared across ancestries, and that it concentrates around common variant loci.

Sleep disorders affect over 30% of the U.S population and are linked to increased disease risk and mortality. However, the genetic architecture of sleep disorders and the overlap of sleep disorders with habitual sleep quality, quantity, and timing have been poorly characterized. In addition, it is unknown if clinical sleep disorders are extremes of habitual sleep traits. Here, we systematically investigated the genetic basis of seven clinical sleep disorder traits, and sixteen medications used for sleep problems in 1,600,000 individuals. We identified 590 genetic associations for sleep apnea, insomnia, restless legs syndrome, narcolepsy and a combined sleep disorder phenotype, of which 367 were previously unreported. Additionally, we discovered 142 genetic associations with sleep medication use. While overall genetic architecture was shared across sleep traits, we found unique genetic factors for the different sleep disorders that reflect fundamentally different biological mechanisms including for example autoimmune processes with narcolepsy and skeletal morphology in sleep apnea. Furthermore, sleep genetic factors showed a broad multi-omic impact on gene and protein expression levels. These findings suggest that while clinical sleep disorders share genetic architecture with each other and with variation in sleep patterns within the general population, they are not simply extremes of normal sleep variation but involve unique biological mechanisms. These results advance our understanding of sleep disorders and suggest potential novel therapeutic targets.

Aims/hypothesis: Suboptimal sleep health is linked to higher risks for incident type 2 diabetes. We aimed to assess the clinical utility of adding self-reported sleep traits to a type 2 diabetes prediction model. Methods: In this cohort study, we used UK Biobank data and Cox proportional hazards models to examine how self-reported sleep duration and insomnia symptoms were associated with incident type 2 diabetes risk. Harrell’s C statistic and net reclassification improvement (NRI) were used to assess whether sleep traits improved the incident type 2 diabetes discrimination and predictive utility achieved using QDiabetes variables, with and without including a type 2 diabetes polygenic risk score (PGS). Independent replication was explored in the Nurses’ Health Study, the Nurses’ Health Study II and the Health Professionals Follow-up Study. Results: Extremes of sleep duration and occasional or frequent insomnia symptoms were associated with higher risks for incident type 2 diabetes. In the UK Biobank and replication cohorts, adding sleep traits to the QDiabetes risk score did not improve type 2 diabetes prediction (C statistic: QDiabetes alone 0.8933; QDiabetes + sleep duration 0.8939; QDiabetes + insomnia 0.8931; QDiabetes + sleep traits 0.8935). The corresponding total NRI values were: 0.08 (95% CI −0.18, 0.33), 0.04 (95% CI −0.08, 0.16) and 0.04 (95% CI −0.10, 0.18). Inclusion of PGS data marginally improved the type 2 diabetes risk prediction achieved using The QDiabetes calculator, with or without the inclusion of sleep traits in the model (QDiabetes + PGS: C statistic 0.8945; total NRI 0.20 [95% CI 0.12, 0.28]; QDiabetes + PGS + sleep traits: C statistic 0.8946; total NRI 0.18 [95% CI 0.09, 0.27]). Conclusions/interpretation: While sleep duration and insomnia symptoms were associated with type 2 diabetes risk, they are not useful for improving type 2 diabetes prediction beyond QDiabetes model performance. Inclusion of a type 2 diabetes PGS marginally improved prediction but lacked clear clinical utility. Graphical

We performed ancestry and sex specific Phenome Wide Association Studies (PheWAS) to explore disease related outcomes associated with genetically predicted height. This is the largest PheWAS on genetically predicted height involving up to 840,000 individuals of diverse ancestry. We explored European, African, East Asian ancestries and Hispanic population groups. Increased genetically predicted height is associated with hyperpotassemia and autism in the male cross-ancestry analysis. We report male-only European ancestry associations with anxiety disorders, post-traumatic stress and substance addiction and disorders. We identify a signal with benign neoplasm of other parts of digestive system in females. We report associations with a series of disorders, several with no prior evidence of association with height, involving mental disorders and the endocrine system. Our study suggests that increased genetically predicted height is associated with higher prevalence of many clinically relevant traits which has important implications for epidemiological and clinical disease surveillance and risk stratification.

Genetic association studies have mostly focussed on common variants from genotyping arrays or rare protein-coding variants from exome sequencing. Here, we used whole-genome sequence (WGS) data in 672,976 individuals of diverse ancestry to evaluate the contribution and architecture of rare non-coding variants to three commonly studied anthropometric traits: height, body mass index (BMI) and waist-hip ratio adjusted for BMI (WHRadjBMI). Analysing 447,461 individuals in UK Biobank for discovery and 225,515 individuals in All of Us for replication, we identified 90 novel rare and low-frequency single variant associations. This includes two independent rare variants upstream of IGF2BP2 that both substantially reduce WHRadjBMI, but have distinct effects on other adiposity traits. We identified 135 coding variant aggregates, several of which were missed by exome sequencing studies. For example, UBR3 protein-truncating variants were associated with a 2.7kg/m2 increase in BMI. We additionally identified 51 non-coding variant aggregate associations, including in the 5’UTR of FGF18 (a highly constrained gene with no previously reported coding associations) associated with up to 6cm effects on height. We show that 97% of rare variant associations occur near GWAS loci demonstrating convergence of rare and common variant associations. Finally, we show that ultra rare variants (MAF<0.01%) explain a small fraction of heritability (<10%) compared to common variants for these traits, that heritability is largely shared across ancestries, and that this heritability is concentrated at or near common variant loci. Our work demonstrates the importance of large-scale WGS for fully understanding the genetic architecture of complex traits.

The contribution of rare noncoding genetic variation to common phenotypes is largely unknown, as a result of a historical lack of population-scale whole-genome sequencing data and the difficulty of categorizing noncoding variants into functionally similar groups. To begin addressing these challenges, we performed a cis association analysis using whole-genome sequencing data, consisting of 1.1 billion variants, 123 million noncoding aggregate-based tests and 2,907 circulating protein levels in ~50,000 UK Biobank participants. We identified 604 independent rare noncoding single-variant associations with circulating protein levels. Unlike protein-coding variation, rare noncoding genetic variation was almost as likely to increase or decrease protein levels. Rare noncoding aggregate testing identified 357 conditionally independent associated regions. Of these, 74 (21%) were not detectable by single-variant testing alone. Our findings have important implications for the identification, and role, of rare noncoding genetic variation associated with common human phenotypes, including the importance of testing aggregates of noncoding variants.

Biobank-scale Whole-Genome Sequencing (WGS) studies are increasingly pivotal in unraveling the genetic bases of diverse health outcomes. However, managing and analyzing these datasets’ sheer volume and complexity presents significant challenges. We propose vcf2agds , an all-in-one toolkit that efficiently converts WGS data from Variant Call Format (VCF) format to the annotated Genomic Data Structure (aGDS) format, significantly reducing data size while supporting seamless genomic and functional data integration for comprehensive genetic analyses. The toolkit was applied to the UK Biobank 500k WGS data, resulting in twenty-three aGDS files, one for each chromosome, which collectively compressed 1,473.85 Tebibytes of pVCF data into 1.10 Tebibytes. Utilizing these aGDS files, we conducted a functionally informed rare variant association analysis of total cholesterol employing the STAARpipeline and detected 480 genome-wide significant coding and noncoding associations. Overall, vcf2agds offers a streamlined approach facilitating the efficient management and analysis of biobank-scale WGS data across hundreds of thousands of samples.

Investigating the genetic factors influencing human birth weight may lead to biological insights into fetal growth and long-term health. We report analyses of rare variants that impact birth weight when carried by either fetus or mother, using whole exome sequencing data in up to 234,675 participants. Rare protein-truncating and deleterious missense variants are collapsed to perform gene burden tests. We identify 9 genes; 5 with fetal-only effects on birth weight, 1 with maternal-only effects, 3 with both, and observe directionally concordant associations in an independent sample. Four of the genes were previously implicated by GWAS of birth weight. IGF1R and PAPPA2 (fetal and maternal-acting) have known roles in insulin-like growth factor bioavailability and signalling. PPARG, INHBE and ACVR1C (fetal-acting) are involved in adipose tissue regulation, and the latter two also show associations with favourable adiposity patterns in adults. We highlight the dual role of PPARG (fetal-acting) in adipocyte differentiation and placental angiogenesis. NOS3 (fetal and maternal-acting), NRK (fetal), and ADAMTS8 (maternal-acting) have been implicated in placental function and hypertension. To conclude, our analysis of rare coding variants identifies regulators of fetal adipose tissue and fetoplacental angiogenesis as determinants of birth weight, and further evidence for the role of insulin-like growth factors.