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The authors present a multiomics study of type 2 diabetes and quantitative blood lipid and lipoprotein traits in Hispanic/Latino populations. They demonstrate how integrating GWAS with omics characterization can advance precision medicine.

A large genome-wide association study of more than 5 million individuals reveals that 12,111 single-nucleotide polymorphisms account for nearly all the heritability of height attributable to common genetic variants.

A genome-wide association meta-analysis study of blood lipid levels in roughly 1.6 million individuals demonstrates the gain of power attained when diverse ancestries are included to improve fine-mapping and polygenic score generation, with gains in locus discovery related to sample size.

Inbreeding depression has been observed in many different species, but in humans a systematic analysis has been difficult so far. Here, analysing more than 1.3 million individuals, the authors show that a genomic inbreeding coefficient (FROH) is associated with disadvantageous outcomes in 32 out of 100 traits tested.

A meta-analysis of genome-wide association studies of type 2 diabetes (T2D) identifies more than 600 T2D-associated loci; integrating physiological trait and single-cell chromatin accessibility data at these loci sheds light on heterogeneity within the T2D phenotype.

Genome-wide association and fine-mapping analyses in ancestrally diverse populations implicate candidate causal genes and mechanisms underlying type 2 diabetes. Trans-ancestry genetic risk scores enhance transferability across populations.

Nationwide genomic biobank in Mexico unravels demographic history and complex trait architecture from 6,057 individuals.

Penetrance of variants in monogenic disease and clinical utility of common polygenic variation has not been well explored on a large-scale. Here, the authors use exome sequencing data from 77,184 individuals to generate penetrance estimates and assess the utility of polygenic variation in risk prediction of monogenic variants.

Exome-sequencing analyses of a large cohort of patients with type 2 diabetes and control individuals without diabetes from five ancestries are used to identify gene-level associations of rare variants that are associated with type 2 diabetes.

A strategy for inferring phase for rare variant pairs is applied to exome sequencing data for 125,748 individuals from the Genome Aggregation Database (gnomAD). This resource will aid interpretation of rare co-occurring variants in the context of recessive disease.

A genomic constraint map for the human genome constructed using data from 76,156 human genomes from the Genome Aggregation Database shows that non-coding constrained regions are enriched for regulatory elements and variants associated with complex diseases and traits.

Dyslipidemia and obesity have a high prevalence in populations with Amerindian backgrounds, such as Mexican–Americans. Here, the authors design an approach to identify Amerindian risk genes in Mexicans and identify five genomic loci, which include RORA and SIK3that may contribute to the risk of dyslipidemia and obesity in Amerindian populations.

A risk haplotype for type 2 diabetes is identified with four amino acid substitutions in SLC16A11, which is present at ∼50% frequency in Native American samples and ∼10% in east Asian samples, but is rare in European and African samples; SLC16A11 may alter hepatic lipid metabolism, causing an increase in triacylglycerol levels.

Genetic variation in ANGPTL4 is associated with lipid traits. Here, the authors find that predicted loss-of-function variants in ANGPTL4 are associated with glucose homeostasis and reduced risk of type 2 diabetes and that Angptl4^−/− mice on a high-fat diet show improved insulin sensitivity.

Systemic lupus erythematosus (SLE) is an autoimmune disease with a strong ethnic and gender bias. In a transancestral genetic association study, Langefeldet al. identify 24 novel regions associated with risk to lupus and propose a cumulative hits hypothesis for loci conferring risk to SLE.

Here, Imamuraet al. conduct meta-analysis of genome-wide association studies to identify novel susceptibility loci for type 2 diabetes (T2D) in the Japanese population. By doing so, this study shows that both ethnicity-specific and ethnically-shared genetic loci can contribute to T2D risk.

Multi-nucleotide variants (MNV) are genetic variants in close proximity of each other on the same haplotype whose functional impact is difficult to predict if they reside in the same codon. Here, Wang et al. use the gnomAD dataset to assemble a catalogue of MNVs and estimate their global mutation rate.

Upstream open reading frames (uORFs), located in 5’ untranslated regions, are regulators of downstream protein translation. Here, Whiffin et al. use the genomes of 15,708 individuals in the Genome Aggregation Database (gnomAD) to systematically assess the deleteriousness of variants creating or disrupting uORFs.

A large empirical assessment of sequence-resolved structural variants from 14,891 genomes across diverse global populations in the Genome Aggregation Database (gnomAD) provides a reference map for disease-association studies, population genetics, and diagnostic screening.

A novel variant annotation metric that quantifies the level of expression of genetic variants across tissues is validated in the Genome Aggregation Database (gnomAD) and is shown to improve rare variant interpretation.

A catalogue of predicted loss-of-function variants in 125,748 whole-exome and 15,708 whole-genome sequencing datasets from the Genome Aggregation Database (gnomAD) reveals the spectrum of mutational constraints that affect these human protein-coding genes.

A survey of genetic variation in Native American and Siberian populations reveals that Native Americans are descended from at least three streams of gene flow from Asia: after the initial peopling of the continent there was a southward expansion facilitated by the coast, with sequential population splits and little gene flow after divergence, especially in South America.

Analysis of predicted loss-of-function variants from 125,748 human exomes and 15,708 whole genomes in the Genome Aggregation Database (gnomAD) provides a roadmap for human ‘knockout’ studies and a guide for future research into disease biology and drug-target selection.

Analysis of large genomic datasets, including gnomAD, reveals that partial LRRK2 loss of function is not strongly associated with diseases, serving as an example of how human genetics can be leveraged for target validation in drug discovery.