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一个更新的多基因指数库:扩展的表型、新的队列和改进的因果推断。

An Updated Polygenic Index Repository: Expanded Phenotypes, New Cohorts, and Improved Causal Inference.

作者信息

Alemu Robel, Terskaya Anastasia, Howell Matthew, Guan Junming, Sands Harry, Kleinman Aaron, Bann David, Morris Tim, Ploubidis George B, Fitzsimons Emla, Harris Kathleen Mullan, Caspi Avshalom, Corcoran David L, Moffitt Terrie E, Poulton Richie, Sugden Karen, Williams Benjamin S, Steptoe Andrew, Ajnakina Olesya, Vainik Uku, Esko Tõnu, Campbell Archie, Hayward Caroline, Iacono William G, McGue Matt, Krueger Robert F, Docherty Anna R, Shabalin Andrey A, Hertwig Ralph, Koellinger Philipp, Richter David, Goebel Jan, Ahlskog Rafael, Oskarsson Sven, Magnusson Patrik K E, Harden K Paige, Tucker-Drob Elliot M, Pahnke Charlotte K L, Maj Carlo, Spinath Frank M, Herd Pamela, Freese Jeremy, Laibson David, Meyer Michelle N, Jala Jonathan, Cesarini David, Young Alexander Strudwick, Turley Patrick, Benjamin Daniel J, Okbay Aysu

机构信息

UCLA Anderson School of Management, Los Angeles, CA, USA.

Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

出版信息

bioRxiv. 2025 May 18:2025.05.14.653986. doi: 10.1101/2025.05.14.653986.

Abstract

Polygenic indexes (PGIs) - DNA-based phenotype predictors for individual phenotypes - have become essential tools across the biomedical and social sciences. We introduce Version 2 of the Polygenic Index Repository, which expands the number of phenotypes from 47 to 61, increases the number of participating datasets from 11 to 20, and adopts a more consistent and improved methodology for PGI construction. For 16 phenotypes, we leverage summary statistics from an updated GWAS meta-analysis with greater statistical power compared to the original release, thereby improving the PGI's predictive power. To improve power for family-based analyses, we provide imputed parental PGIs in all datasets with first-degree relatives and we provide a framework for interpreting results from analyses that control for parental PGIs. Together, the updates improve predictive accuracy, expand coverage to new cohorts and phenotypes, and introduce novel tools that reduce confounding bias and improve interpretability.

摘要

多基因指数(PGIs)——基于DNA的个体表型预测指标——已成为生物医学和社会科学领域的重要工具。我们推出了多基因指数库的第2版,该版本将表型数量从47个增加到61个,参与数据集的数量从11个增加到20个,并采用了更一致、更完善的多基因指数构建方法。对于16种表型,我们利用更新后的全基因组关联研究(GWAS)荟萃分析的汇总统计数据,与原始版本相比,其统计效力更强,从而提高了多基因指数的预测能力。为提高基于家系分析的效力,我们在所有包含一级亲属的数据集中提供了推算的父母多基因指数,并提供了一个框架,用于解释控制父母多基因指数的分析结果。这些更新共同提高了预测准确性,将覆盖范围扩展到新的队列和表型,并引入了新工具,减少混杂偏倚并提高可解释性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a16/12132466/ae02ad9387f2/nihpp-2025.05.14.653986v1-f0001.jpg

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