Dataset Information

Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases.

ABSTRACT: Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. Using a large evaluation set of genes with fine-mapped coding variants, we show that PoPS and the closest gene individually outperform other gene prioritization methods, but observe the best overall performance by combining PoPS with orthogonal methods. Using this combined approach, we prioritize 10,642 unique gene-trait pairs across 113 complex traits and diseases with high precision, finding not only well-established gene-trait relationships but nominating new genes at unresolved loci, such as LGR4 for estimated glomerular filtration rate and CCR7 for deep vein thrombosis. Overall, we demonstrate that PoPS provides a powerful addition to the gene prioritization toolbox.

SUBMITTER: Weeks EM

PROVIDER: S-EPMC10836580 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases.

Weeks Elle M EM Ulirsch Jacob C JC Cheng Nathan Y NY Trippe Brian L BL Fine Rebecca S RS Miao Jenkai J Patwardhan Tejal A TA Kanai Masahiro M Nasser Joseph J Fulco Charles P CP Tashman Katherine C KC Aguet Francois F Li Taibo T Ordovas-Montanes Jose J Smillie Christopher S CS Biton Moshe M Shalek Alex K AK Ananthakrishnan Ashwin N AN Xavier Ramnik J RJ Regev Aviv A Gupta Rajat M RM Lage Kasper K Ardlie Kristin G KG Hirschhorn Joel N JN Lander Eric S ES Engreitz Jesse M JM Finucane Hilary K HK

Nature genetics 20230713 8

Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. Using a large evaluation set of genes with fine-mapped coding variants, we show that PoPS and the closest gene i ...[more]

PMID: 37443254

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Project description:Therapy-related pulmonary complications are among the leading causes of morbidity among long-term survivors of childhood cancer. Restrictive ventilatory defects (RVD) are prevalent, with risks increasing after exposures to chest radiotherapy and radiomimetic chemotherapies. Using whole-genome sequencing data from 1,728 childhood cancer survivors in the St. Jude Lifetime Cohort Study, we developed and validated a composite RVD risk prediction model that integrates clinical profiles and polygenic risk scores (PRS), including both published lung phenotype PRSs and a novel survivor-specific pharmaco/radiogenomic PRS (surPRS) for RVD risk reflecting gene-by-treatment (GxT) interaction effects. Overall, this new therapy-specific polygenic risk prediction model showed multiple indicators for superior discriminatory accuracy in an independent data set. The surPRS was significantly associated with RVD risk in both training (OR = 1.60, P = 3.7 × 10-10) and validation (OR = 1.44, P = 8.5 × 10-4) data sets. The composite model featuring the surPRS showed the best discriminatory accuracy (AUC = 0.81; 95% CI, 0.76-0.87), a significant improvement (P = 9.0 × 10-3) over clinical risk scores only (AUC = 0.78; 95% CI: 0.72-0.83). The odds of RVD in survivors in the highest quintile of composite model-predicted risk was ∼20-fold higher than those with median predicted risk or less (OR = 20.01, P = 2.2 × 10-16), exceeding the comparable estimate considering nongenetic risk factors only (OR = 9.20, P = 7.4 × 10-11). Inclusion of genetic predictors also selectively improved risk stratification for pulmonary complications across at-risk primary cancer diagnoses (AUCclinical = 0.72; AUCcomposite = 0.80, P = 0.012). Overall, this PRS approach that leverages GxT interaction effects supports late effects risk prediction among childhood cancer survivors.SignificanceThis study develops a therapy-specific polygenic risk prediction model to more precisely identify childhood cancer survivors at high risk for pulmonary complications, which could help improve risk stratification for other late effects.

Dataset Information

Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases.

Publications

Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases.

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