Biology subjects

Beaumont, R. N.

Publications and source records attributed to Beaumont, R. N..

At least 19 recordsLinked to original sources

bioRxiv2026

Federated cross-biobank conditional analysis identifies LDL-C lowering effects of DNAJC13 haploinsufficiency and LDLR regulation

Whole genome sequencing in diverse population-scale biobanks offers new insights into the genetic architecture of complex traits from rare and non-coding variants. However, rare single variant and aggregate associations are often confounded by linkage disequilibrium and haplotype structure, resulting in large numbers of false-positive associations. Previous methods that rely on reference panels or linkage disequilibrium-matrices to determine conditional independence in meta-analyses do not scale to very rare variants, which may be observed in only one biobank and can exhibit long-range haplotypes. Here, we implement a federated approach to perform iterative conditional meta-analysis on individual-level genotype and phenotype data across biobanks while adhering to data sharing policies. We applied our methodology to a meta-analysis of LDL-C in 614,375 individuals from UK Biobank and All of Us, encompassing six genetic ancestry groups. After conditioning, only 4.3% of significantly associated rare single variants and 6.9% of aggregates remained statistically independent. The proportion of significant aggregates that remained independent after conditioning was higher for coding-based tests than non-coding. We further validate that our approach effectively suppresses false-positive associations using simulations centred on the LDLR locus. We identify allelic series of variants associated with reduced LDL-C, including loss-of-function variants in DNAJC13 and variants in the 3-prime untranslated region of LDLR. Our results highlight that federated conditioning can distinguish independent rare variant signals from linkage and haplotype structure artifacts in multi-ancestry meta-analyses across separate biobanks.

genetics↗

bioRxiv2025

Genotype-level quality control substantially reduces error rates in population-scale whole-genome sequencing

Population-scale whole-genome sequencing data will contain many individual-level genotype errors, even after allele-level quality control (QC). We establish the need for genotype-level QC using UK Biobank (N=490,726) and All of Us v8 (N=414,830), where we remove up to 100 million ([~]9%) additional low-quality variants. We demonstrate reduced false positive rate in downstream genetic association studies, highlight the power of parent-offspring trios for QC, and illustrate the need for sex-specific X-chromosome filtering. We provide a QCed All of Us v8 dataset in plink-pgen format, and an efficient pipeline for QC and conversion from VCF to plink-pgen for UK Biobank.

genetics↗

bioRxiv2025

Whole-genome sequencing analysis of anthropometric traits in 672,976 individuals reveals convergence between rare and common genetic associations

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 5UTR 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.

genetics↗

bioRxiv2023

Whole genome association testing in 333,100 individuals across three biobanks identifies rare non-coding single variant and genomic aggregate associations with height

The role of rare non-coding variation in complex human phenotypes is still largely unknown. To elucidate the impact of rare variants in regulatory elements, we performed a whole-genome sequencing association analysis for height using 333,100 individuals from three datasets: UK Biobank (N=200,003), TOPMed (N=87,652) and All of Us (N=45,445). We performed rare (<0.1% minor-allele-frequency) single-variant and aggregate testing of non-coding variants in regulatory regions based on proximal, intergenic and deep-intronic annotation. We observed 29 independent variants associated with height at P < 6 x 10-10 after conditioning on previously reported variants, with effect sizes ranging from -7cm to +4.7cm. We also identified and replicated non-coding aggregate-based associations proximal to HMGA1 containing variants associated with a 5cm taller height and of highly-conserved variants in MIR497HG on chromosome 17. We have developed a novel approach for identifying non-coding rare variants in regulatory regions with large effects from whole-genome sequencing data associated with complex traits.

genetics↗

bioRxiv2023

Whole genome sequencing analysis identifies rare, large-effect non-coding variants and regions associated with circulating protein levels

The role of non-coding rare variation in common phenotypes is largely unknown, due to a lack of whole-genome sequence data, and the difficulty of categorising non-coding variants into biologically meaningful regulatory units. To begin addressing these challenges, we performed a cis association analysis using whole-genome sequence data, consisting of 391 million variants and 1,450 circulating protein levels in [~]20,000 UK Biobank participants. We identified 777 independent rare non-coding single variants associated with circulating protein levels (P<1x10-9), after conditioning on protein-coding and common associated variants. Rare non-coding aggregate testing identified 108 conditionally independent regulatory regions. Unlike protein-coding variation, rare non-coding genetic variation was almost as likely to increase as decrease protein levels. The regions we identified overlapped predicted tissue-specific enhancers more than promoters, suggesting they represent tissue-specific regulatory regions. Our results have important implications for the identification, and role, of rare non-coding variation associated with common human phenotypes.

genetics↗

bioRxiv2023

Identification and analysis of individuals who deviate from their genetically-predicted phenotype

Findings from genome-wide association studies have facilitated the generation of genetic predictors for many common human phenotypes. Stratifying individuals misaligned to a genetic predictor based on common variants may be important for follow-up studies that aim to identify alternative causal factors. Using genome-wide imputed genetic data, we aimed to classify 158,951 unrelated individuals from the UK Biobank as either concordant or deviating from two well-measured phenotypes. We first applied our methods to standing height: our primary analysis classified 244 individuals (0.15%) as misaligned to their genetically predicted height. We show that these individuals are enriched for self-reporting being shorter or taller than average at age 10, diagnosed congenital malformations, and rare loss-of-function variants in genes previously catalogued as causal for growth disorders. Secondly, we apply our methods to LDL cholesterol. We classified 156 (0.12%) individuals as misaligned to their genetically predicted LDL cholesterol and show that these individuals were enriched for both clinically actionable cardiovascular risk factors and rare genetic variants in genes previously shown to be involved in metabolic processes. Individuals whose LDL-C was higher than expected based on the genetic predictor were also at higher risk of developing coronary artery disease and type-two diabetes, even after adjustment for measured LDL-C, BMI and age, suggesting upward deviation from genetically predicted LDL-C is indicative of generally poor health. Our results remained broadly consistent when performing sensitivity analysis based on a variety of parametric and non-parametric methods to define individuals deviating from polygenic expectation. Our analyses demonstrate the potential importance of quantitatively identifying individuals for further follow-up based on deviation from genetic predictions. Author SummaryHuman genetics is becoming increasingly useful to help predict human traits across a population owing to findings from large-scale genetic association studies and advances in the power of genetic predictors. This provides an opportunity to potentially identify individuals that deviate from genetic predictions for a common phenotype under investigation. For example, an individual may be genetically predicted to be tall, but be shorter than expected. It is potentially important to identify individuals who deviate from genetic predictions as this can facilitate further follow-up to assess likely causes. Using 158,951 unrelated individuals from the UK Biobank, with height and LDL cholesterol, as exemplar traits, we demonstrate that approximately 0.15% & 0.12% of individuals deviate from their genetically predicted phenotypes respectively. We observed these individuals to be enriched for a range of rare clinical diagnoses, as well as rare genetic factors that may be causal. Our analyses also demonstrate several methods for detecting individuals who deviate from genetic predictions that can be applied to a range of continuous human phenotypes.

genetics↗

bioRxiv2020

Fetal alleles predisposing to metabolically favourable adiposity are associated with higher birth weight

BackgroundHigher birth weight is associated with higher adult body mass index (BMI). If genetic variants can be identified with alleles that predispose to both greater fetal growth and to greater adult adiposity, such shared genetic effects might indicate biological processes important in the early patterning of adiposity. However, variants identified in genome-wide association studies of adult BMI have overall been only weakly associated with birth weight. Genetic variants have recently been identified where one allele is associated with higher adult body fat percentage, but lower risk of metabolic disease, likely due to a favourable body fat distribution. The effect of these adult metabolically favourable adiposity alleles on an individuals own birth weight is unknown. AimWe aimed to test the effect on birth weight of a fetal genetic predisposition to higher metabolically favourable adult adiposity and to compare this with the effects of a fetal genetic predisposition to higher adult BMI. We also aimed to examine the effects of a genetic predisposition to higher metabolically favourable adult adiposity or BMI on other birth anthropometric traits (length, ponderal index, head circumference and skinfold thickness) and on cord-blood insulin, leptin and adiponectin. MethodsWe used published GWAS data from up to 406,063 individuals to estimate the fetal effects on birth weight of alleles that are robustly associated with higher metabolically favourable adult adiposity or BMI. We additionally used 9,350 mother-child pairs from four cohorts to test the effects of the same alleles on other birth anthropometric traits and cord-blood markers. In all analyses, we adjusted for potential confounding due to the maternal genotype. We used inverse-variance weighted meta-analyses to combine summary data across SNPs. ResultsFetal genetic predisposition to higher metabolically favourable adult adiposity was associated with higher birth weight (10 grams (95% CI: 7 to 13) higher mean birth weight per 1 SD pooled "genetic score"). Fetal genetic predisposition to higher adult BMI was also associated with higher birth weight, but with a smaller magnitude of effect (4 grams (95% CI: 0 to 8) higher mean birth weight per 1 SD pooled "genetic score") and with higher heterogeneity across SNPs. Effects on other birth anthropometric outcomes were consistent with the effect on birth weight but with wider confidence intervals. There was no strong evidence for an effect on cord-blood markers. ConclusionsSome genetic variants previously linked to adult adiposity influence birth weight. Alleles that predispose to higher metabolically favourable adult adiposity collectively have a stronger effect on birth weight than those predisposing to higher BMI. This suggests that the early accumulation of a metabolically favourable fat distribution might underlie part of the observed association between higher birth weight and higher adult BMI. Larger samples are needed to clarify the effects on other birth anthropometric measures and cord-blood markers.

genetics↗

bioRxiv2020

Common genetic variants with fetal effects on birth weight are enriched for proximity to genes implicated in rare developmental disorders

Birth weight is an important factor in newborn and infant survival, and both low and high birth weights are associated with adverse later life health outcomes. Genome-wide association studies (GWAS) have identified 190 loci associated with either maternal or fetal effects on birth weight. Knowledge of the underlying causal genes and pathways is crucial to understand how these loci influence birth weight, and the links between infant and adult morbidity. Numerous monogenic developmental syndromes are associated with birth weights at the extreme upper or lower ends of the normal distribution, and genes implicated in those syndromes may provide valuable information to help prioritise candidate genes at GWAS loci. We examined the proximity of genes implicated in developmental disorders to birth weight GWAS loci at which a fetal effect is either likely or cannot be ruled out. We used simulations to test whether those genes fall disproportionately close to the GWAS loci. We found that birth weight GWAS single nucleotide polymorphisms (SNPs) fall closer to such genes than expected by chance. This is the case both when the developmental disorder gene is the nearest gene to the birth weight SNP and also when examining all genes within 258kb of the SNP. This enrichment was driven by genes that cause monogenic developmental disorders with dominant modes of inheritance. We found several examples of SNPs located in the intron of one gene that mark plausible effects via different nearby genes implicated in monogenic short stature, highlighting the closest gene to the SNP not necessarily being the functionally relevant gene. This is the first application of this approach to birth weight loci, which has helped identify GWAS loci likely to have direct fetal effects on birth weight which could not previously be classified as fetal or maternal due to insufficient statistical power.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genetics↗

bioRxiv2020

Common maternal and fetal genetic variants show expected polygenic effects on the probability of being born small- or large-for-gestational-age (SGA or LGA), except in the smallest 3% of babies

Babies born clinically Small- or Large-for-Gestational-Age (SGA or LGA; sex- and gestational age-adjusted birth weight (BW) <10th or >90th percentile, respectively), are at higher risks of complications. SGA and LGA include babies who have experienced growth-restriction or overgrowth, respectively, and babies who are naturally small or large. However, the relative proportions within each group are unclear. We aimed to assess the extent to which the genetics of normal variation in birth weight influence the probability of SGA/LGA. We calculated independent fetal and maternal genetic scores (GS) for BW in 12,125 babies and 5,187 mothers. These scores capture the direct fetal and indirect maternal (via intrauterine environment) genetic contributions to BW, respectively. We also calculated maternal fasting glucose (FG) and systolic blood pressure (SBP) GS. We tested associations between each GS and probability of SGA or LGA. For the BW GS, we used simulations to assess evidence of deviation from an expected polygenic model. Higher BW GS were strongly associated with lower odds of SGA and higher odds of LGA (ORfetal=0.65 (0.60,0.71) and 1.47 (1.36,1.59); ORmaternal=0.80 (0.76,0.87) and 1.23 (1.15,1.31), respectively per 1 decile higher GS). Associations were in accordance with a polygenic model except in the smallest 3% of babies (Pfetal=0.0034, Pmaternal=0.023). Higher maternal GS for FG and SBP were associated with higher odds of LGA and SGA respectively (both P<0.01). While lower maternal FG and SBP are generally considered healthy in pregnancy, we found some evidence of association with higher odds of SGA (P=0.015) and LGA (P=0.14) respectively. We conclude that common genetic variants contribute to risk of SGA and LGA, but that additional factors become more important for risk of SGA in the smallest 3% of babies. Naturally low maternal glucose and blood pressure levels may additionally contribute to risk of SGA and LGA, respectively. Author SummaryBabies in the lowest or highest 10% of the population distribution of birth weight (BW) for a given gestational age are referred to as Small- or Large-for-Gestational-Age (SGA or LGA) respectively. These babies have higher risks of complications compared to babies with BW closer to the mean. SGA and LGA babies may have experienced growth restriction or overgrowth, respectively, but may alternatively just be at the tail ends of the normal growth distribution. The relative proportions of normal vs. sub-optimal growth within these groups is unclear. To examine the role of common genetic variation in SGA and LGA, we tested their associations with a fetal genetic score (GS) for BW in 12,125 European-ancestry individuals. We also tested associations with maternal GS (5,187 mothers) for offspring BW, fasting glucose and systolic blood pressure, each of which influences fetal growth via the in utero environment. We found all fetal and maternal GS were associated with SGA and LGA, supporting strong maternal and fetal genetic contributions to birth weight in both tails of the distribution. However, within the smallest 3% of babies, the maternal and fetal GS for BW were higher than expected, suggesting factors additional to common genetic variation are more important in determining birth weight in these very small babies.

genetics↗

bioRxiv2020

Genetic predictors of participation in optional components of UK Biobank

Large studies (e.g. UK Biobank) are increasingly used for GWAS and Mendelian randomization (MR) studies. Selection into and dropout from studies may bias genetic and phenotypic associations. We examine genetic factors affecting participation in four optional components in up to 451,306 UK Biobank participants. We used GWAS to identify genetic variants associated with participation, MR to estimate effects of phenotypes on participation, and genetic correlations to compare participation bias across different studies. 32 variants were associated with participation in one of the optional components (P<6x10-9), including loci with known links to intelligence and Alzheimers disease. Genetic correlations demonstrated that participation bias was common across studies. MR showed that longer educational duration, older menarche and taller stature increased participation, whilst higher levels of adiposity, dyslipidaemia, neuroticism, Alzheimers and schizophrenia reduced participation. Our effect estimates can be used for sensitivity analysis to account for selective participation biases in genetic or non-genetic analyses.

genetics↗

bioRxiv2020

Investigating the causal effect of maternal vitamin B12 and folate levels on offspring birthweight

Lower maternal serum vitamin B12 (B12) and folate levels have been associated with lower offspring birthweight in observational studies. The aim of this study was to investigate whether this relationship is causal. We performed two-sample Mendelian Randomization (MR) using summary data on associations between genotype-B12 (10 genetic variants) or genotype-folate (4 genetic variants) levels from a genome-wide association study of 45,576 individuals (sample 1) and maternal-specific genetic effects on offspring birthweight from the latest EGG consortium meta-analysis with 297,356 individuals reporting their own birthweight and 210,248 women reporting their offsprings birthweight (sample 2). To investigate the effect of offsprings own B12 or folate levels on their own birthweight, we performed two-sample MR using the fetal-specific genetic effects from the latest EGG consortium meta-analysis. We used the inverse variance weighted method, and sensitivity analyses to account for pleiotropy, in addition to sensitivity analyses excluding a potentially pleiotropic variant in the FUT2 gene for B12. We did not find evidence for a causal effect of maternal B12 on offspring birthweight, nor evidence for an effect of offspring B12 on their own birthweight using the fetal-specific genetic effect. The results were consistent across the different methods and in sensitivity analyses excluding the FUT2 variant. We found a positive effect of maternal folate on offspring birthweight (0.146 [0.065, 0.227], which corresponds to an increase in birthweight of 71g per 1SD higher folate). We found some evidence for a small inverse effect of fetal folate on their own birthweight (-0.051 [-0.100, -0.003]). In conclusion, our results are consistent with evidence from randomized controlled trials that increased maternal folate levels increase offspring birthweight. We did not find evidence for a causal effect of B12 on offspring birthweight, suggesting previous observational studies may have been due to confounding.

genetics↗

bioRxiv2019

Is disrupted sleep a risk factor for Alzheimers disease? Evidence from a two-sample Mendelian randomization analysis

INTRODUCTIONIt is established that Alzheimers disease (AD) patients experience sleep disruption. However, it remains unknown whether disruption in the quantity, quality or timing of sleep is a risk factor for the onset of AD.\n\nMETHODSMendelian randomization (MR) was used to estimate the causal effect of self-reported and accelerometer-measured sleep parameters (chronotype, duration, fragmentation, insomnia, daytime napping and daytime sleepiness) on AD risk.\n\nRESULTSOverall, there was little evidence that sleep traits affect the risk of AD. There was some evidence to suggest that self-reported daytime napping was associated with lower AD risk (odds ratio [OR]: 0.70, 95% confidence interval [CI]: 0.50 to 0.99). Some other sleep traits (accelerometer-measured eveningness and sleep duration, and self-reported daytime sleepiness) had ORs for AD risk of a similar magnitude to daytime napping, but were less precisely estimated.\n\nDISCUSSONOur findings provide tentative evidence that daytime napping may reduce AD risk. However, findings should be replicated using independent samples.

epidemiology↗

bioRxiv2018

Variants in the fetal genome near pro-inflammatory cytokine genes on 2q13 are associated with gestational duration

The duration of pregnancy is influenced by fetal and maternal genetic and non-genetic factors. We conducted a fetal genome-wide association meta-analysis of gestational duration, and early preterm, preterm, and postterm birth in 84,689 infants. One locus on chromosome 2q13 was associated with gestational duration; the association was replicated in 9,291 additional infants (combined P = 3.96 x 10-14). Analysis of 15,536 mother-child pairs showed that the association was driven by fetal rather than maternal genotype. Functional experiments showed that the lead SNP, rs7594852, alters the binding of the HIC1 transcriptional repressor. Genes at the locus include several interleukin 1 family members with roles in pro-inflammatory pathways that are central to the process of parturition. Further understanding of the underlying mechanisms will be of great public health importance, since giving birth either before or after the window of term gestation is associated with increased morbidity and mortality.

genetics↗

bioRxiv2018

Assessing the pathogenicity, penetrance and expressivity of putative disease-causing variants in a population setting

Over 100,000 genetic variants are classified as disease-causing in public databases. However, the true penetrance of many of these rare alleles is uncertain and may be over-estimated by clinical ascertainment. As more people undergo genome sequencing there is an increasing need to assess the true penetrance of alleles. Until recently, this was not possible in a population-based setting. Here, we use data from 388,714 UK Biobank (UKB) participants of European ancestry to assess the pathogenicity and penetrance of putatively clinically important rare variants.\n\nAlthough rare variants are harder to genotype accurately than common variants, we were able to classify 1,244 of 4,585 (27%) putatively clinically relevant rare variants genotyped on the UKB microarray as high-quality. We defined \"rare\" as variants with a minor allele frequency of <0.01, and \"clinically relevant\" as variants that were either classified as pathogenic/likely pathogenic in ClinVar or are in genes known to cause two specific monogenic diseases in which we have some expertise: Maturity-Onset Diabetes of the Young (MODY) and severe developmental disorders (DD). We assessed the penetrance and pathogenicity of these high-quality variants by testing their association with 401 clinically-relevant traits available in UKB.\n\nWe identified 27 putatively clinically relevant rare variants associated with a UKB trait but that exhibited reduced penetrance or variable expressivity compared with their associated disease. For example, the P415A PER3 variant that has been reported to cause familial advanced sleep phase syndrome is present at 0.5% frequency in the population and associated with an odds ratio of 1.38 for being a morning person (P=2x10-18). We also observed novel associations with relevant traits for heterozygous carriers of some rare recessive conditions, e.g. heterozygous carriers of the R799W ERCC4 variant that causes Xeroderma pigmentosum were more susceptible to sunburn (one extra sunburn episode reported, P=2x10-8). Within our two disease subsets, we were able to refine the penetrance estimate for the R114W HNF4A variant in diabetes (only ~10% by age 40yrs) and refute the previous disease-association of RNF135 in developmental disorders.\n\nIn conclusion, this study shows that very large population-based studies will help refine the penetrance estimates of rare variants. This information will be important for anyone receiving information about their health based on putatively pathogenic variants.

genetics↗

bioRxiv2018

GWAS in 446,118 European adults identifies 78 genetic loci for self-reported habitual sleep duration supported by accelerometer-derived estimates

Sleep is an essential homeostatically-regulated state of decreased activity and alertness conserved across animal species, and both short and long sleep duration associate with chronic disease and all-cause mortality1,2. Defining genetic contributions to sleep duration could point to regulatory mechanisms and clarify causal disease relationships. Through genome-wide association analyses in 446,118 participants of European ancestry from the UK Biobank, we discover 78 loci for self-reported sleep duration that further impact accelerometer-derived measures of sleep duration, daytime inactivity duration, sleep efficiency and number of sleep bouts in a subgroup (n=85,499) with up to 7-day accelerometry. Associations are enriched for genes expressed in several brain regions, and for pathways including striatum and subpallium development, mechanosensory response, dopamine binding, synaptic neurotransmission, catecholamine production, synaptic plasticity, and unsaturated fatty acid metabolism. Genetic correlation analysis indicates shared biological links between sleep duration and psychiatric, cognitive, anthropometric and metabolic traits and Mendelian randomization highlights a causal link of longer sleep with schizophrenia.

genetics↗

bioRxiv2018

Genetic studies of accelerometer-based sleep measures in 85,670 individuals yield new insights into human sleep behaviour

Sleep is an essential human function but its regulation is poorly understood. Identifying genetic variants associated with quality, quantity and timing of sleep will provide biological insights into the regulation of sleep and potential links with disease. Using accelerometer data from 85,670 individuals in the UK Biobank, we performed a genome-wide association study of 8 accelerometer-derived sleep traits, 5 of which are not accessible through self-report alone. We identified 47 genetic associations across the sleep traits (P<5x10-8) and replicated our findings in 5,819 individuals from 3 independent studies. These included 26 novel associations for sleep quality and 10 for nocturnal sleep duration. The majority of newly identified variants were associated with a single sleep trait, except for variants previously associated with restless legs syndrome that were associated with multiple sleep traits. Of the new associated and replicated sleep duration loci, we were able to fine-map a missense variant (p.Tyr727Cys) in PDE11A, a dual-specificity 3,5-cyclic nucleotide phosphodiesterase expressed in the hippocampus, as the likely causal variant. As a group, sleep quality loci were enriched for serotonin processing genes and all sleep traits were enriched for cerebellar-expressed genes. These findings provide new biological insights into sleep characteristics.

genetics↗

bioRxiv2018

Genome-wide association analyses of chronotype in 697,828 individuals provides new insights into circadian rhythms in humans and links to disease

Using genome-wide data from 697,828 research participants from 23andMe and UK Biobank, we increase the number of identified loci associated with being a morning person, a behavioural indicator of a persons underlying circadian rhythm, from 24 to 351. Using data from 85,760 individuals with activity-monitor derived measures of sleep timing we show that the chronotype loci influence sleep timing: the mean sleep timing of the 5% of individuals carrying the most "morningness" alleles was 25 minutes earlier than the 5% carrying the fewest. The loci were enriched for genes involved in circadian regulation, cAMP, glutamate and insulin signalling pathways, and those expressed in the retina, hindbrain, hypothalamus, and pituitary. We provide evidence that being a morning person is causally associated with better mental health but does not appear to affect BMI or Type 2 diabetes. This study offers new insights into the biology of circadian rhythms and links to disease in humans.

genetics↗

bioRxiv2018

GWAS identifies novel risk locus for erectile dysfunction and implicates hypothalamic neurobiology and diabetes in etiology

GWAS of erectile dysfunction (ED) in 6,175 cases among 223,805 European men identified one new locus at 6q16.3 (lead variant rs57989773, OR 1.20 per C-allele; p = 5.71x10-14), located between MCHR2 and SIM1. In-silico analysis suggests SIM1 to confer ED risk through hypothalamic dysregulation; Mendelian randomization indicates genetic risk of type 2 diabetes causes ED. Our findings provide novel insights into the biological underpinnings of ED.

genomics↗