American journal of human genetics最新文献

Variants of uncertain significance (VUSs) limit the actionability of genetic testing. A prominent example is MUTYH, a DNA repair factor underlying colorectal cancer with a pathogenic variant carrier rate of ∼1:50. To systematically interrogate MUTYH variant function, we coupled deep mutational scanning to DNA repair reporters containing its lesion substrate, 8OG:A. Our variant-to-function map covers 96.6% of possible MUTYH point variants (n = 10,941) and achieves 100% accuracy on known clinical variants (n = 247). Leveraging a large clinical registry, we observe significant associations with colorectal polyps and cancer, with more severely impaired missense variants conferring greater risk. We recapitulate functional differences between pathogenic founder alleles and highlight sites of complete missense intolerance, including residues that intercalate DNA and coordinate essential Zn²⁺ or Fe-S clusters. This map provides a resource to resolve the >1,100 existing missense VUSs in MUTYH and demonstrates a scalable strategy to interrogate other clinically relevant DNA repair factors.

The lack of computational methods capable of detecting epistasis in biobanks has led to uncertainty about the role of non-additive genetic effects on complex trait variation. The marginal epistasis framework is a powerful approach because it estimates the likelihood of a SNP being involved in any interaction, thereby reducing the multiple testing burden. Current implementations of this approach have failed to scale genome wide in large human studies. To address this, we present the sparse marginal epistasis (SME) test, which concentrates the scans for epistasis to regions of the genome that have known functional enrichment for a quantitative trait of interest. By leveraging the sparse nature of this modeling setup, we develop a statistical algorithm that allows SME to run 10-90 times faster than state-of-the-art epistatic mapping methods. In a study of complex traits measured in 349,411 individuals from the UK Biobank, we show that reducing searches of epistasis to variants in functionally enriched regions facilitates the identification of genetic interactions associated with regulatory genomic elements.

A lack of representation in genomic research and limited access to computational training create barriers for many researchers seeking to analyze large-scale genetic datasets. The All of Us Research Program provides an unprecedented opportunity to address these gaps by offering genomic data from a broad range of participants, but its impact depends on equipping researchers with the necessary skills to use it effectively. The All of Us Biomedical Researcher (BR) Scholars Program at Baylor College of Medicine aims to break down these barriers by providing early-career researchers with hands-on training in computational genomics through the All of Us Evenings with Genetics Research Program. The year-long program begins with the faculty summit, an in-person computational boot camp that introduces scholars to foundational skills for using the All of Us dataset via a cloud-based research environment. The genomics tutorials focus on genome-wide association studies (GWASs), utilizing Jupyter Notebooks and the Hail computing framework to provide an accessible and scalable approach to large-scale data analysis. Scholars engage in hands-on exercises covering data preparation, quality control, association testing, and result interpretation. By the end of the summit, participants will have successfully conducted a GWAS, visualized key findings, and gained confidence in computational resource management. This initiative expands access to genomic research by equipping early-career researchers from a variety of backgrounds with the tools and knowledge to analyze All of Us data. By lowering barriers to entry and promoting the study of representative populations, the program fosters innovation in precision medicine and advances equity in genomic research.

Strong sex differences exist in sleep phenotypes and also cardiovascular diseases (CVDs). However, sex-specific causal effects of sleep phenotypes on CVD-related outcomes have not been thoroughly examined. Mendelian randomization (MR) analysis is a useful approach for estimating the causal effect of a risk factor on an outcome of interest when interventional studies are not available. We first conducted sex-specific genome-wide association studies (GWASs) for suboptimal-sleep phenotypes (insomnia, obstructive sleep apnea [OSA], short and long sleep durations, and excessive daytime sleepiness) utilizing the Million Veteran Program (MVP) dataset. We then developed a semi-empirical Bayesian framework that (1) calibrates variant-phenotype effect estimates by leveraging information across sex groups and (2) applies shrinkage sex-specific effect estimates in MR analysis to alleviate weak instrumental bias when sex groups are analyzed in isolation. Simulation studies demonstrate that the causal effect estimates derived from our framework are substantially more efficient than those obtained through conventional methods. We estimated the causal effects of sleep phenotypes on CVD-related outcomes using sex-specific GWAS data from the MVP and All of Us. Significant sex differences in causal effects were observed, particularly between OSA and chronic kidney disease, as well as long sleep duration on several CVD-related outcomes. By applying shrinkage estimates for instrumental variable selection, we identified multiple sex-specific significant causal relationships between OSA and CVD-related phenotypes. The method is generalizable and can be used to improve power and alleviate weak instrument bias when only a small sample is available for a specific condition or group.

Striking disparities in lung cancer exist, with Black/African American individuals disproportionately affected by lung cancer, yet the genetic architecture in African ancestry individuals is poorly understood. We aimed to address this by performing a comprehensive genetic association study of lung cancer, incorporating local ancestry, across 6,490 African ancestry individuals (2,390 individuals with lung cancer and 4,100 control subjects). We identified a single genome-wide significant (p < 5 × 10^-8) locus, 15q25.1 (lead SNP rs17486278, OR [95% CI] = 1.34 [1.23-1.45], p = 4.52 × 10^-12), that has consistently shown a strong association with lung cancer across populations. Additionally, we identified nine suggestive (p < 1 × 10^-6) loci. Four of these loci (3p12.1, 8q22.2, 14q11.2, and 18q22.3) have no prior reported associations with lung cancer. We performed a multi-ancestry lung cancer meta-analysis using prior large-scale summary statistics from European and Asian ancestry populations, incorporating our African ancestry results. The meta-analysis identified 17 genome-wide significant loci, including an association with locus 4q35.2 (p = 1.22 × 10^-8), a genomic region that has been previously linked to forced expiratory volume. Genome-wide SNP-based heritability for lung cancer was 16% among African ancestry individuals. Follow-up in silico functional analyses identified genetically regulated gene expression (GReX) of nine genes (AC012184.3, ADK, CCDC12, CHRNA3, EML4, PSMA4, SNRNP200, TMEM50A, and ZYG11A) associated with lung cancer risk and biological pathways relevant to cancer and lung function. Cumulatively, these findings further elucidate the genetic architecture of lung cancer in African ancestry individuals, confirming prior loci and revealing new loci.