American journal of human genetics最新文献

Polygenic risk scores (PRSs) have demonstrated strong potential for improving prostate cancer risk stratification. However, it is unknown whether the clinical utility of prostate cancer PRS varies by demographic, lifestyle, and socioeconomic factors. We validated a previously developed multi-ancestry PRS of 451 prostate cancer risk variants and evaluated context-dependent effects using genetic and clinical data from the diverse All of Us Research Program, including 7,577 indivisuals with prostate cancer and 90,608 control individuals across six genetic ancestry groups. In ancestry-stratified testing, the PRS showed strong associations with prostate cancer risk, with odds ratios (ORs) per standard deviation (SD) increase ranging from 1.61 (95% confidence interval [CI] = 1.02-2.64, p = 0.05) in Middle Eastern to 2.19 (95% CI = 1.98-2.42, p = 2.2 × 10^-51) in American populations. Age-stratified analyses showed reduced PRS effects with increasing age. Across modifiable lifestyle and healthcare access factors, PRS effects were larger in those with a higher body mass index (OR = 2.15 vs. 1.96 in individuals with obesity and normal weight, respectively, p = 0.03), in never or former smokers vs. current smokers (OR = 2.06, 2.37, and 1.93, respectively, p = 0.06), and in those recently accessing healthcare (OR = 2.21 vs. 1.88, p = 0.05), highlighting important context-specific modifiers. We did not observe context-dependent effects of other socioeconomic factors, such as income, education, and insurance. In a phenome-wide association study (PheWAS), the PRS was associated with 14 clinical outcomes, including known prostate cancer-related conditions. These findings confirm the predictive strength of the multi-ancestry prostate cancer PRS across diverse populations and underscore the importance of accounting for demographic-, lifestyle-, and healthcare-related contexts when applying PRSs in clinical and public health settings.

Reactions between metabolites are catalyzed by enzymes. These biochemical reactions form complex metabolic networks, which are only partially characterized in humans and whose regulation remains poorly understood. Here, we assess human biochemical reactions and regulation using Mendelian randomization (MR), a genetic observational causal inference technique, to understand the methods' strengths and weaknesses in identifying metabolic reactions and regulation. We combine four metabolite and two protein quantitative trait locus (QTL) studies to determine how well MR recovers 945 curated canonical enzyme-substrate/product relationships. Using genetic variants from an enzyme's transcribed (cis) region as instrumental variables, MR-inferred estimates have high precision (35%-47%) but low recall (3.2%-4.6%) for identifying the substrates and products of an enzyme. Testing reverse causality from metabolites to enzymes using genome-wide instruments yields lower precision (1.8%-8.5%) and recall (1.0%-1.9%) due to an increased multiple-testing burden. Literature review of 106 Bonferroni-significant results identifies 45 links (43%) confirmed by different degrees of evidence, including bidirectional links between linoleate and cytochrome P450 3A4 (CYP3A4) levels (p = 8.6 × 10^-32). Eleven enzymes in the 106 links involve drug targets, allowing for an interpretation between N-acetyl putrescine and IL1RAP (p = 2.7 × 10^-7), as IL1RAP is a target of the psoriasis drug spesolimab, and putrescine levels are elevated in psoriatic tissues. This work highlights how MR can be leveraged to explore human metabolic regulation and identify both canonical reactions and previously unknown regulation.

Genome-wide association studies (GWASs) have identified thousands of loci associated with a variety of common, complex human traits. Recent efforts have focused on characterizing chromatin accessibility to discover regulatory elements that modify the expression of nearby genes, suggesting that trait associations are mediated through changes in gene regulation. Genetic variants associated with differences in chromatin accessibility, known as chromatin accessibility quantitative trait loci (caQTLs), are established contributors to gene expression differences, providing mechanistic hypotheses for signals identified by GWAS. Using the assay for transposase-accessible chromatin with sequencing (ATAC-seq), we assessed chromatin accessibility in 189 diverse human liver samples, identifying over 2 million accessible chromatin regions enriched for gene regulatory features and, in 175 of these samples, over 14,000 caQTLs. Focusing subsequently on liver-relevant complex traits, we obtained publicly available blood lipid GWAS data and identified 157 loci where caQTLs, expression quantitative trait loci (eQTLs), and GWAS signals colocalized. This generated specific molecular hypotheses about regulatory elements, affected genes, and, in some cases, implicated transcription factors. Finally, we enumerated the set of blood lipid trait signals that lack an obvious proposed mechanism beyond catalogs of liver caQTLs and eQTLs. After integrating 10 multi-omic quantitative trait locus (QTL) regulatory mechanism datasets while considering limitations in statistical power, we found that approximately 20% of blood lipid GWAS signals lacked a statistical link to a proposed mechanism. Our results demonstrate the value of integrating multiple genomic datasets to improve understanding of GWAS signals while emphasizing the need for additional experimental approaches to fully characterize complex trait associations.