American journal of human genetics最新文献

Circulating metabolite levels partly reflect the state of human health and diseases and can be impacted by genetic determinants. Hundreds of loci associated with circulating metabolites have been identified; however, most findings focus on predominantly European ancestry or single-study analyses. Leveraging the rich metabolomics resources generated by the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) Program, we harmonized and accessibly cataloged 1,729 circulating metabolites among 25,058 ancestrally diverse samples. From our comparison of multiple methods, we provided a set of reasonable strategies for outlier and imputation handling to process metabolite data and show that inverse normalization by study and half-minimum imputation provide mostly similar results for pooled or meta-analysis. Following the practical analysis framework, we further performed a genome-wide association analysis on 1,135 selected metabolites using whole-genome sequencing data from 16,359 individuals passing the quality-control filters and discovered 1,775 independent loci associated with 667 metabolites. Among 160 unreported locus-metabolite pairs, we identified associations with loci locating within previously implicated metabolite-associated genes, as well as associations with loci locating in genes such as GAB3 and VSIG4 (located on the X chromosome) that may play a role in metabolic regulation. In the sex-stratified analysis, we revealed 85 independent locus-metabolite pairs with evidence of sexual dimorphism, which were located in well-known metabolic genes such as FADS2, D2HGDH, SUGP1, and UGT2B17, strongly supporting the importance of exploring sex difference in the human metabolome. Taken together, our study depicted the genetic contribution to circulating metabolite levels, providing additional insight into the understanding of human health.

RNA sequencing (RNA-seq) has emerged as a powerful tool for resolving variants of uncertain significance (VUSs), particularly those affecting gene expression and splicing. However, most reference datasets and diagnostic protocols employ relatively modest sequencing depths (∼50-150 million reads), which may fail to detect low-abundance transcripts and rare splicing events critical for accurate diagnosis. We evaluated the diagnostic and translational utility of ultra-high-depth (up to ∼1 billion unique reads) RNA-seq in four clinically accessible tissues using the Ultima sequencing platform. After validating the performance of Ultima RNA-seq, we investigated how increasing sequencing depth affects gene and isoform detection, splicing variant discovery, and clinical interpretation of VUSs. Deep RNA-seq substantially improved sensitivity for detecting lowly expressed genes and isoforms, achieving near saturation for detection at 1 billion reads. In two probands with VUSs, pathogenic splicing abnormalities were undetectable at 50 million reads but emerged at 200 million reads, becoming even more pronounced at 1 billion reads. Using deep RNA-seq data, we constructed a resource, MRSD-deep, to estimate the minimum required sequencing depth to achieve desired coverage thresholds. MRSD-deep provided gene- and junction-level guidelines, helping labs select appropriate coverage targets for specific applications. Leveraging deep RNA-seq data on fibroblasts, we also built an expanded splicing-variation reference that successfully identified low-abundance splicing events missed by standard-depth data. Our findings underscore the diagnostic and research benefits of deep RNA-seq for Mendelian disease investigations.