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Skeptics have questioned the value of genomewide association studies. Dr. Joel Hirschhorn writes that the main goal of these studies is not prediction of individual risk but rather discovery of biologic pathways underlying polygenic diseases and traits. Human geneticists seek to understand the inherited basis of human biology and disease, aiming either to gain insights that could eventually improve treatment or to produce useful diagnostic or predictive tests. As recently as 2004, few genetic variants were known to reproducibly influence common polygenic diseases (including cancer, coronary artery disease, and diabetes) or quantitative phenotypes (including lipid levels and blood pressure). This relative ignorance limited potential insights into the pathophysiology of common diseases. The completion of the human genome sequence in 2005 and the provision of an initial catalogue of human genetic variation and a haplotype map (known as . . .

Unhealthful dietary habits are leading risk factors for life-altering diseases and mortality. Large-scale biobanks now enable genetic analysis of traits with modest heritability, such as diet. We perform a genomewide association on 85 single food intake and 85 principal component-derived dietary patterns from food frequency questionnaires in UK Biobank. We identify 814 associated loci, including olfactory receptor associations with fruit and tea intake; 136 associations are only identified using dietary patterns. Mendelian randomization suggests our top healthful dietary pattern driven by wholemeal vs. white bread consumption is causally influenced by factors correlated with education but is not strongly causal for coronary artery disease or type 2 diabetes. Overall, we demonstrate the value in complementary phenotyping approaches to complex dietary datasets, and the utility of genomic analysis to understand the relationships between diet and human health. The choice of food intake is at least partially influenced by genetics, even though the effect sizes appear rather modest. Here, Cole et al. perform GWAS for food intake (85 individual food items and 85 derived dietary patterns) and test potential causal relationships with cardiometabolic traits using Mendelian randomization.

Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. Using a large evaluation set of genes with fine-mapped coding variants, we show that PoPS and the closest gene individually outperform other gene prioritization methods, but observe the best overall performance by combining PoPS with orthogonal methods. Using this combined approach, we prioritize 10,642 unique gene–trait pairs across 113 complex traits and diseases with high precision, finding not only well-established gene–trait relationships but nominating new genes at unresolved loci, such as LGR4 for estimated glomerular filtration rate and CCR7 for deep vein thrombosis. Overall, we demonstrate that PoPS provides a powerful addition to the gene prioritization toolbox. Polygenic Priority Score (PoPS) prioritizes candidate effector genes at complex trait loci by integrating genome-wide association summary statistics with other data types. Combining PoPS with methods that leverage local genetic signals further improves the performance.

Compelling evidence shows that brown and beige adipose tissue are protective against metabolic diseases 1 , 2 . PR domain-containing 16 (PRDM16) is a dominant activator of the biogenesis of beige adipocytes by forming a complex with transcriptional and epigenetic factors and is therefore an attractive target for improving metabolic health 3 – 8 . However, a lack of knowledge surrounding the regulation of PRDM16 protein expression hampered us from selectively targeting this transcriptional pathway. Here we identify CUL2–APPBP2 as the ubiquitin E3 ligase that determines PRDM16 protein stability by catalysing its polyubiquitination. Inhibition of CUL2–APPBP2 sufficiently extended the half-life of PRDM16 protein and promoted beige adipocyte biogenesis. By contrast, elevated CUL2–APPBP2 expression was found in aged adipose tissues and repressed adipocyte thermogenesis by degrading PRDM16 protein. Importantly, extended PRDM16 protein stability by adipocyte-specific deletion of CUL2–APPBP2 counteracted diet-induced obesity, glucose intolerance, insulin resistance and dyslipidaemia in mice. These results offer a cell-autonomous route to selectively activate the PRDM16 pathway in adipose tissues. The ubiquitin E3 ligase CUL2–APPBP2 determines PRDM16 protein stability by catalysing PRDM16 polyubiquitination in beige fat.

Key Points Genome-wide association studies are rapidly becoming feasible as an approach for identifying the genes that underlie common diseases and related quantitative traits. This strategy combines a comprehensive and unbiased survey of the genome with the power to detect common alleles with modest phenotypic effects. Sets of markers for genome-wide association studies can be chosen using various criteria, but the degree to which a particular marker set actually surveys the genome should be evaluated if the label “genome-wide association” is to be applied. Empirical assessments of linkage disequilibrium patterns, such as those that are being performed in the HapMap project, will enable the selection of efficient sets of markers and the evaluation of the comprehensiveness of a given marker set. Study design and interpretation of results must include appropriate statistical thresholds that take multiple-hypothesis testing into account, as can be achieved, for example, by permutation testing. Balancing the need for power to detect modest effects with the cost of genotyping large numbers of markers will probably require a multi-stage design. False-positive results that arise due to population stratification might outnumber true associations, and population stratification should be assessed and corrected for, if needed. Alternatively, family-based designs can be used, but high-quality data are needed to avoid artifacts that are specific to these designs. Gene–gene and gene–environment interactions might be common in complex traits, but unbounded searches for such interactions are unlikely to retain adequate power in studies of hundreds of thousands of markers. Either new methods will be required, or, alternatively, markers with individual effects will need to be identified first, followed by focused searches for interactions. Genome-wide association studies are likely to become a reality in the near future. Care will be required in their design, performance, analysis and interpretation, and well-conceived pilot studies might be valuable for understanding and minimizing the pitfalls of this approach. Nevertheless, genome-wide association studies have the potential to identify many genes for common diseases and quantitative traits. Genetic factors strongly affect susceptibility to common diseases and also influence disease-related quantitative traits. Identifying the relevant genes has been difficult, in part because each causal gene only makes a small contribution to overall heritability. Genetic association studies offer a potentially powerful approach for mapping causal genes with modest effects, but are limited because only a small number of genes can be studied at a time. Genome-wide association studies will soon become possible, and could open new frontiers in our understanding and treatment of disease. However, the execution and analysis of such studies will require great care.

The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes. Identifying which genes and pathways explain genetic associations is challenging. Here, the authors present DEPICT, a tool for gene prioritization, pathway analysis and tissue/cell-type enrichment analysis that can be used to generate testable hypotheses from genetic association studies.

Peter Visscher and colleagues report a new method for approximate conditional and joint association analysis that makes use of summary statistics from meta-analysis of GWAS. They apply this to meta-analysis summary data for height, body mass index and type 2 diabetes. We present an approximate conditional and joint association analysis that can use summary-level statistics from a meta-analysis of genome-wide association studies (GWAS) and estimated linkage disequilibrium (LD) from a reference sample with individual-level genotype data. Using this method, we analyzed meta-analysis summary data from the GIANT Consortium for height and body mass index (BMI), with the LD structure estimated from genotype data in two independent cohorts. We identified 36 loci with multiple associated variants for height (38 leading and 49 additional SNPs, 87 in total) via a genome-wide SNP selection procedure. The 49 new SNPs explain approximately 1.3% of variance, nearly doubling the heritability explained at the 36 loci. We did not find any locus showing multiple associated SNPs for BMI. The method we present is computationally fast and is also applicable to case-control data, which we demonstrate in an example from meta-analysis of type 2 diabetes by the DIAGRAM Consortium.

Widespread linkage disequilibrium and incomplete annotation of cell-to-cell state variation represent substantial challenges to elucidating mechanisms of trait-associated genetic variation. Here we perform genetic fine-mapping for blood cell traits in the UK Biobank to identify putative causal variants. These variants are enriched in genes encoding proteins in trait-relevant biological pathways and in accessible chromatin of hematopoietic progenitors. For regulatory variants, we explore patterns of developmental enhancer activity, predict molecular mechanisms, and identify likely target genes. In several instances, we localize multiple independent variants to the same regulatory element or gene. We further observe that variants with pleiotropic effects preferentially act in common progenitor populations to direct the production of distinct lineages. Finally, we leverage fine-mapped variants in conjunction with continuous epigenomic annotations to identify trait–cell type enrichments within closely related populations and in single cells. Our study provides a comprehensive framework for single-variant and single-cell analyses of genetic associations. Fine-mapping of blood cell traits in the UK Biobank identifies putative causal variants and enrichment of fine-mapped variants in accessible chromatin of hematopoietic progenitor cells. The study provides an analytical framework for single-variant and single-cell analyses of genetic associations.

Key Points Genome-wide association studies are systematic, well-powered surveys to explore the relationships between sites of common genome sequence variation and disease predisposition on a genome-wide scale. The capacity to undertake genome-wide association studies has resulted in spectacular advances in the understanding of the genetic basis of common phenotypes of biomedical importance, such as diabetes, asthma and some cancers. Application of this approach to large, well-characterized data sets has revealed over 50 disease-susceptibility loci and has provided valuable insights into the allelic architecture of multifactorial traits. The implementation of such studies requires meticulous attention to all stages of the experimental process, from the ascertainment of the samples through to analysis and interpretation of the findings. There is considerable potential for a wide variety of errors and biases to result in spurious associations if precautions are not taken. Extensive replication of positive findings remains the best guarantee against erroneous claims of association. The demand for large-scale replication is leading to extensive international collaborations between groups. Nonetheless, substantial challenges remain as researchers seek more complete descriptions of the susceptibility architecture of traits of interest, and to translate the information gathered into improvements in clinical management. Genome-wide association studies have led to an improved understanding of the genetic basis of common diseases. Following the first wave of such studies, this Review takes a critical look at progress so far and considers how future studies can be optimized. The past year has witnessed substantial advances in understanding the genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.

To evaluate the coverage and accuracy of whole-exome sequencing (WES) across vendors. Blood samples from three trios underwent WES at three vendors. Relative performance of the three WES services was measured for breadth and depth of coverage. The false-negative rates (FNRs) were estimated using the segregation pattern within each trio. Mean depth of coverage for all genes was 189.0, 124.9, and 38.3 for the three vendor services. Fifty-five of the American College of Medical Genetics and Genomics 56 genes, but only 56 of 63 pharmacogenes, were 100% covered at 10 × in at least one of the nine individuals for all vendors; however, there was substantial interindividual variability. For the two vendors with mean depth of coverage >120 ×, analytic positive predictive values (aPPVs) exceeded 99.1% for single-nucleotide variants and homozygous indels, and sensitivities were 98.9–99.9%; however, heterozygous indels showed lower accuracy and sensitivity. Among the trios, FNRs in the offspring were 0.07–0.62% at well-covered variants concordantly called in both parents. The current standard of 120 × coverage for clinical WES may be insufficient for consistent breadth of coverage across the exome. Ordering clinicians and researchers would benefit from vendors’ reports that estimate sensitivity and aPPV, including depth of coverage across the exome.