American journal of human genetics最新文献

Complex trait-associated genetic variation is highly pleiotropic. This extensive pleiotropy implies that multi-phenotype analyses are informative for characterizing genetic associations, as they facilitate the discovery of trait-shared and trait-specific variants and pathways ("genetic factors"). Previous efforts have estimated genetic factors using matrix factorization (MF) applied to numerous genome-wide association studies (GWASs). However, existing methods are susceptible to spurious factors arising from residual confounding due to sample sharing in biobank GWASs. Furthermore, MF approaches have historically estimated dense factors, loaded on most traits and variants, that are challenging to map onto interpretable biological pathways. To address these shortcomings, we introduce "GWAS latent embeddings accounting for noise and regularization" (GLEANR), an MF method for detection of sparse genetic factors from summary statistics. GLEANR accounts for sample sharing between studies and uses regularization to estimate a data-driven number of interpretable factors. GLEANR is robust to confounding induced by shared samples and improves the replication of genetic factors derived from distinct biobanks. We used GLEANR to evaluate 137 diverse GWASs from the UK Biobank, identifying 58 factors that decompose the genetic architecture of input traits and have distinct signatures of negative selection and degrees of polygenicity. These sparse factors can be interpreted with respect to disease, cell type, and pathway enrichment. We highlight three such factors that captured platelet-measure phenotypes and were enriched for disease-relevant markers corresponding to distinct stages of platelet differentiation. Overall, GLEANR is a powerful tool for discovering both trait-specific and trait-shared pathways underlying complex traits from GWAS summary statistics.

In humans, homologous gene conversions occur at a higher rate than crossovers; however, gene conversion tracts are small and often unobservable. As a result, estimating gene conversion rates is more difficult than estimating crossover rates. We present a method for multi-individual identity-by-descent (IBD) inference that allows for mismatches due to genotype error and gene conversion. We use the inferred IBD to detect alleles that have changed due to gene conversion in the recent past. We analyze data from the TOPMed and UK Biobank studies to estimate autosome-wide maps of gene conversion rates. For 10 kb, 100 kb, and 1 Mb windows, the correlation between our TOPMed gene conversion map and the deCODE sex-averaged crossover map ranges from 0.56 to 0.67. We find that the strongest gene conversion hotspots typically fall back to the baseline gene conversion rate within 1 kb. In 100 kb and 1 Mb windows, our estimated gene conversion map has higher correlation than the deCODE sex-averaged crossover map with PRDM9 binding enrichment (0.34 vs. 0.29 for 100 kb windows and 0.52 vs. 0.34 for 1 Mb windows), suggesting that the effect of PRDM9 binding is greater on gene conversion than on crossover recombination. Our TOPMed gene conversion maps are constructed from 55-fold more observed allele conversions than the recently published deCODE gene conversion maps. Our maps provide sex-averaged estimates for 10 kb, 100 kb, and 1 Mb windows, whereas the deCODE gene conversion maps provide sex-specific estimates for 3 Mb windows.

Classification of genetic variants remains an obstacle to realizing the full potential of clinical genetic sequencing. Because of their ability to interrogate large numbers of variants, multiplexed assays of variant effect and computational tools are viewed as a critical part of the solution to variant classification uncertainty. However, the (joint) performance of these assays and tools on novel variants has not been established. Transformation of the qualitative classification guidelines developed by the American College of Medical Genetics and Genomics (ACMG) into a quantitative Bayesian point system enables empirical validation of strength of evidence assigned to evidence criteria. Here, we derived a maximum-likelihood estimate model that converts frequentist odds ratios calculated from case-control data to proportions pathogenic and applied this model to functional assays, alone and in combination with computational tools across several domains of BRCA1. Furthermore, we defined exceptionally conserved ancestral residues (ECARs) and interrogated the performance of assays and tools at these residues in BRCA1. We found that missense substitutions in BRCA1 that fall at ECARs are disproportionately likely to be pathogenic with effect sizes similar to that of protein-truncating variants. In contrast, for substitutions falling at non-ECAR positions, concordant predictions of pathogenicity from functional assays and computational tools often fail to meet the additive assumptions of strength in ACMG guidelines. Thus, collectively, we conclude that strengths of evidence assigned by expert opinion in the ACMG guidelines are not universally applicable and require empirical validation.

The atopic march lacks early identification methods for high-risk children. In this study, we assessed whether the risk of atopic diseases in infants could be predicted using a polygenic score (PGS) for total immunoglobulin E (IgE) levels. The PGS estimated using the polygenic model generated by PRS-CS was significantly correlated with log-transformed IgE levels (ρ = 0.200, p < 2.2 × 10^-16). Assessment of the risk from birth to 2 years of age in a Japanese birth cohort (n = 17,154) applying the estimated PGS revealed significantly elevated incidence risk ratios in the highest PGS quintile (Q5) compared with those in the reference quintiles (Q1-Q3) for food allergy (1.51-fold; 95% confidence interval: 1.30-1.76), atopic dermatitis (1.30-fold; 1.12-1.51), and both conditions (1.88-fold; 1.46-2.43). These findings address critical gaps in allergy and PGS research among non-European populations, suggesting the contribution of genetic predisposition to IgE production in early-onset allergic diseases and supporting the use of PGS in early intervention.

Sharing diverse genomic and other biomedical datasets is critical to advancing scientific discoveries and their equitable translation to improve human health. However, data sharing remains challenging in the context of legacy datasets, evolving policies, multi-institutional consortium science, and international stakeholders. The NIH-funded Polygenic Risk Methods in Diverse Populations (PRIMED) Consortium was established to improve the performance of polygenic risk estimates for a broad range of health and disease outcomes with global impacts. Improving polygenic risk score performance across genetically diverse populations requires access to large, diverse cohorts. We report on the design and implementation of data-sharing policies and procedures developed in PRIMED to aggregate and analyze data from multiple heterogeneous sources while adhering to pre-existing data-sharing policies for each integrated dataset and respecting participant preferences and informed consent. Specifically, we describe two primary data-sharing mechanisms-coordinated dbGaP applications and a Consortium Data Sharing Agreement-and provide alternatives when individual-level data cannot be shared within the Consortium (e.g., federated analyses). We also describe technical implementation of Consortium data sharing in the NHGRI Analysis Visualization and Informatics Lab-space (AnVIL) cloud platform to share derived individual-level data, genomic summary results, and methods workflows with appropriate permissions. As a consortium making secondary use of pre-existing data sources, we also discuss challenges and propose solutions for release of individual- and summary-level data products to the broader scientific community. We make recommendations for ongoing and future policymaking with the goal of informing future consortia and other research activities.