American journal of human genetics最新文献

Failing to correct for multiple testing in selection scans can lead to false discoveries of recent genetic adaptations. The scanning statistics in selection studies are often too complicated to theoretically derive a genome-wide significance level or empirically validate control of the family-wise error rate (FWER). By modeling the autocorrelation of identity-by-descent (IBD) rates, we propose a computationally efficient method to determine genome-wide significance levels in an IBD-based scan for recent positive selection. In whole-genome simulations, we show that our method has approximate control of the FWER and can adapt to the spacing of tests along the genome. We also show that these scans can have more than 50% power to reject the null model in hard sweeps with a selection coefficient greater than or equal to 0.01 and a sweeping allele frequency between 25% and 75%. Many human genes and gene complexes have statistically significant excesses of IBD segments in thousands of samples of African, European, and South Asian ancestry groups from the Trans-Omics for Precision Medicine project and the United Kingdom Biobank. Among the significant loci, two excess IBD signals in regions enriched for deletions are shared across ancestry groups.

Triploid and haploid conceptions are not viable and are a common occurrence in humans, where they account for 10% of all pregnancy losses. Despite the parent of origin being important in the etiology of the pregnancy, our knowledge of their causes is limited, especially at the point of conception. Using a dataset of 96,660 biopsies and a validation dataset of 44,324 from human blastocyst embryos generated by intracytoplasmic sperm injection, we estimate that 1.1% of human conceptions (n = 1,063) contain extra or missing chromosome sets in zygotes. In our cohort of intracytoplasmic-sperm-injection-derived embryos, where the risk of polyspermy is inherently lower compared to natural conception, we identify for the first time a maternal age effect, with a 1.046-per-year increased risk in triploidy/haploidy (p < 0.001). In 0.03% of couples, we identified three or more triploid/haploid embryos, suggesting a personal risk effect (p = 0.03). Genotype analysis of 41 triploid embryo biopsies and their parents shows that around one-third of maternal triploid conceptions originate in meiosis I and two-thirds in meiosis II. Seven of these embryos are inferred to have entirely failed to initiate meiotic recombination genome wide, a surprising finding suggesting that human oocytes with pervasive meiotic recombination failure that are formed during fetal development are capable of ovulation in adult life. Finally, we identify a type of genome-wide maternal isodiploidy (two maternal chromosome sets) in 0.05% of embryos (41/74,009). Collectively, our findings shed light on the biology of meiosis and the formation of human oocytes with the number of chromosome sets.

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.