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Risk of disease is multifactorial and can be shaped by socio-economic, demographic, cultural, environmental and genetic factors. Our understanding of the genetic determinants of disease risk has greatly advanced with the advent of genome-wide association studies (GWAS), which detect associations between genetic variants and complex traits or diseases by comparing populations of cases and controls. However, much of this discovery has occurred through GWAS of individuals of European ancestry, with limited representation of other populations, including from Africa, The Americas, Asia and Oceania. Population demography, genetic drift and adaptation to environments over thousands of years have led globally to the diversification of populations. This global genomic diversity can provide new opportunities for discovery and translation into therapies, as well as a better understanding of population disease risk. Large-scale multi-ethnic and representative biobanks and population health resources provide unprecedented opportunities to understand the genetic determinants of disease on a global scale.

The human proteome is a crucial intermediate between complex diseases and their genetic and environmental components, and an important source of drug development targets and biomarkers. Here, we comprehensively assess the genetic architecture of 257 circulating protein biomarkers of cardiometabolic relevance through high-depth (22.5×) whole-genome sequencing (WGS) in 1328 individuals. We discover 131 independent sequence variant associations ( P  < 7.45 × 10 −11 ) across the allele frequency spectrum, all of which replicate in an independent cohort ( n  = 1605, 18.4 x WGS). We identify for the first time replicating evidence for rare-variant cis -acting protein quantitative trait loci for five genes, involving both coding and noncoding variation. We construct and validate polygenic scores that explain up to 45% of protein level variation. We find causal links between protein levels and disease risk, identifying high-value biomarkers and drug development targets. The human proteome represents a crucial link between complex disease and genetic/environmental factors. Here, the authors investigate 257 cardiometabolic-relevant protein biomarkers in whole genome sequencing data from 1328 individuals, revealing the genetic architecture underlying biomarker variation.

Congruent, low-friction relative movement between the articulating elements of a synovial joint is an essential pre-requisite for sustained, efficient, function. Where disorders of joint formation or maintenance exist, mechanical overloading and osteoarthritis (OA) follow. The heritable component of OA accounts for ~ 50% of susceptible risk. Although almost 100 genetic risk loci for OA have now been identified, and the epidemiological relationship between joint development, joint shape and osteoarthritis is well established, we still have only a limited understanding of the contribution that genetic variation makes to joint shape and how this modulates OA risk. In this article, a brief overview of synovial joint development and its genetic regulation is followed by a review of current knowledge on the genetic epidemiology of established joint shape disorders and common shape variation. A summary of current genetic epidemiology of OA is also given, together with current evidence on the genetic overlap between shape variation and OA. Finally, the established genetic risk loci for both joint shape and osteoarthritis are discussed.

The genome-wide annotation of variants (GWAVA) software predicts whether noncoding variants are likely to be functional using a classifier trained on a range of genomic and epigenomic annotations. Identifying functionally relevant variants against the background of ubiquitous genetic variation is a major challenge in human genetics. For variants in protein-coding regions, our understanding of the genetic code and splicing allows us to identify likely candidates, but interpreting variants outside genic regions is more difficult. Here we present genome-wide annotation of variants (GWAVA), a tool that supports prioritization of noncoding variants by integrating various genomic and epigenomic annotations.

Whole genome sequencing has enabled new insights into the genetic architecture of complex traits, especially through access to low-frequency and rare variation. This Comment highlights the key contributions from this technology and discusses considerations for its use and future perspectives.

Osteoarthritis causes pain and functional disability for over 500 million people worldwide. To develop disease-stratifying tools and modifying therapies, we need a better understanding of the molecular basis of the disease in relevant tissue and cell types. Here, we study primary cartilage and synovium from 115 patients with osteoarthritis to construct a deep molecular signature map of the disease. By integrating genetics with transcriptomics and proteomics, we discover molecular trait loci in each tissue type and omics level, identify likely effector genes for osteoarthritis-associated genetic signals and highlight high-value targets for drug development and repurposing. These findings provide insights into disease aetiopathology, and offer translational opportunities in response to the global clinical challenge of osteoarthritis. Understanding the molecular effects of disease variants in relevant tissues is essential to understanding and treating disease. Here, the authors discover expression and protein quantitative trait loci in cartilage and synovium from 115 osteoarthritis patients to pinpoint genes of action and potential drug treatments.

Despite the increasing global burden of neurological disorders, there is a lack of effective diagnostic and therapeutic biomarkers. Proteins are often dysregulated in disease and have a strong genetic component. Here, we carry out a protein quantitative trait locus analysis of 184 neurologically-relevant proteins, using whole genome sequencing data from two isolated population-based cohorts ( N  = 2893). In doing so, we elucidate the genetic landscape of the circulating proteome and its connection to neurological disorders. We detect 214 independently-associated variants for 107 proteins, the majority of which (76%) are cis-acting, including 114 variants that have not been previously identified. Using two-sample Mendelian randomisation, we identify causal associations between serum CD33 and Alzheimer’s disease, GPNMB and Parkinson’s disease, and MSR1 and schizophrenia, describing their clinical potential and highlighting drug repurposing opportunities. Serum proteins are easily accessible biomarkers and drug targets. Here, the authors use whole genome sequencing data to describe the genetic architecture of neurologically-relevant serum proteins and establish causal protein-neurological disease relationships.

The variation in weight within a shared environment is largely attributable to genetic factors. Whilst many genes/loci confer susceptibility to obesity, little is known about the genetic architecture of healthy thinness. Here, we characterise the heritability of thinness which we found was comparable to that of severe obesity (h2 = 28.07 vs 32.33% respectively), although with incomplete genetic overlap (r = -0.49, 95% CI [-0.17, -0.82], p = 0.003). In a genome-wide association analysis of thinness (n = 1,471) vs severe obesity (n = 1,456), we identified 10 loci previously associated with obesity, and demonstrate enrichment for established BMI-associated loci (pbinomial = 3.05x10-5). Simulation analyses showed that different association results between the extremes were likely in agreement with additive effects across the BMI distribution, suggesting different effects on thinness and obesity could be due to their different degrees of extremeness. In further analyses, we detected a novel obesity and BMI-associated locus at PKHD1 (rs2784243, obese vs. thin p = 5.99x10-6, obese vs. controls p = 2.13x10-6 pBMI = 2.3x10-13), associations at loci recently discovered with much larger sample sizes (e.g. FAM150B and PRDM6-CEP120), and novel variants driving associations at previously established signals (e.g. rs205262 at the SNRPC/C6orf106 locus and rs112446794 at the PRDM6-CEP120 locus). Our ability to replicate loci found with much larger sample sizes demonstrates the value of clinical extremes and suggest that characterisation of the genetics of thinness may provide a more nuanced understanding of the genetic architecture of body weight regulation and may inform the identification of potential anti-obesity targets.

We have identified a variant in ADCY3 (encoding adenylate cyclase 3) associated with markedly increased risk of obesity and type 2 diabetes in the Greenlandic population. The variant disrupts a splice acceptor site, and carriers have decreased ADCY3 RNA expression. Additionally, we observe an enrichment of rare ADCY3 loss-of-function variants among individuals with type 2 diabetes in trans-ancestry cohorts. These findings provide new information on disease etiology relevant for future treatment strategies. Individuals from a Greenlandic Inuit population with homozygous loss-of-function variants in ADCY3 (adenylate cyclase 3) have increased risk for obesity and type 2 diabetes. Carriers of rare ADCY3 variants in trans-ancestry populations also show increased association with type 2 diabetes.

Next-generation association studies can be empowered by sequence-based imputation and by studying founder populations. Here we report ∼9.5 million variants from whole-genome sequencing (WGS) of a Cretan-isolated population, and show enrichment of rare and low-frequency variants with predicted functional consequences. We use a WGS-based imputation approach utilizing 10,422 reference haplotypes to perform genome-wide association analyses and observe 17 genome-wide significant, independent signals, including replicating evidence for association at eight novel low-frequency variant signals. Two novel cardiometabolic associations are at lead variants unique to the founder population sequences: chr16:70790626 (high-density lipoprotein levels beta −1.71 (SE 0.25), P =1.57 × 10 −11 , effect allele frequency (EAF) 0.006); and rs145556679 (triglycerides levels beta −1.13 (SE 0.17), P =2.53 × 10 −11 , EAF 0.013). Our findings add empirical support to the contribution of low-frequency variants in complex traits, demonstrate the advantage of including population-specific sequences in imputation panels and exemplify the power gains afforded by population isolates. Isolated populations can provide useful information on low-frequency variants for dissecting genetic architecture of complex traits. Here, Zeggini and colleagues show enrichment of rare and low-frequency variants and 8 novel low-frequency variant signals for cardiometabolic traits in two Greek isolated populations