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A recent study has identified novel African-derived haplotypes, previously unreported in white people, which predispose to or are protective against type 1 diabetes mellitus (T1DM) in African Americans. These findings could have important implications for T1DM disease risk and prognosis assessment in African Americans.

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.

Birthweight has been found to associate with later-life health outcomes. Here the authors perform a meta-analysis of epigenome-wide association studies of 8,825 neonates from 24 birth cohorts in the Pregnancy And Childhood Epigenetics Consortium, identifying differentially methylated CpGs in neonatal blood that associate with birthweight.

A large-scale genetic analysis of type 1 diabetes identifies new susceptibility variants, highlights potential regulatory mechanisms and provides genetic support for therapeutic targets for immune intervention.

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.

Felton et al. conduct a systematic review to determine the utility of islet autoantibodies as biomarkers of type 1 diabetes heterogeneity. They find that islet autoantibodies are most likely to be useful for patient stratification prior to clinical diagnosis.

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.

Felton et al. conducted a systematic review to evaluate studies testing disease-modifying therapies and features linked to treatment response for type 1 diabetes prevention. While the quality of prevention and intervention trials is found to be high, precision analyses on factors associated with treatment response are of poorer quality.

A systematic review of evidence, across the key pillars of prevention, diagnosis, treatment and prognosis, outlines milestones that need to be met to enable the broad clinical implementation of precision medicine in diabetes care.

Jacobsen, Sherr et al. evaluate the utility of novel technologies in the treatment of type 1 diabetes. Their systematic review finds technologies such as continuous glucose monitoring, insulin pumps, and decision support tools improve important measures (e.g., HbA1c, time in range, quality of life) allowing precision-directed uptake of technology.

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.

Type 1 diabetes mellitus is an autoimmune disease that is characterized by insulin deficiency due to pancreatic β-cell loss and results in hyperglycaemia. This Primer by Lernmark and colleagues describes this chronic disorder, which requires lifelong insulin administration and affects >500,000 children worldwide.

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.

Naylor et al. systematically review the efficacy of treatments for beta-cell monogenic diabetes. Limited evidence from the mostly non-randomized, small studies supports no treatment in glucokinase-related hyperglycemia and sulfonylureas for HNF1A-diabetes; further evidence is needed on the optimum treatments in these and other monogenic subtypes.

Takele et al. conduct a systematic review and meta-analysis of the effects of intervention characteristics on preventing gestational diabetes. Group or healthcare facility-based physical activity interventions are found to be more effective in preventing gestational diabetes than community-based interventions.

Ahmad, Lim, Morieri, Tam et al perform a systematic review and meta-analysis of biomarkers, genetic markers, and risk scores for prediction of cardiovascular outcomes in Type 2 diabetes. A few prognostic markers are identified that provide incremental predictive utility beyond established cardiovascular risk factors.

Semnani-Azad et al. review the evidence on prognostic factors that predict cardiovascular disease and type 2 diabetes for women, and cardiometabolic profile in offspring subsequent to gestational diabetes. The evidence was of low quality, but some maternal characteristics were predictive of unfavourable outcomes in women and their offspring.