开发更具通用性的多基因风险评分的挑战与机遇。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Ying Wang, Kristin Tsuo, Masahiro Kanai, Benjamin M Neale, Alicia R Martin
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引用次数: 0

摘要

多基因风险评分(PRS)通过汇总从全基因组关联研究中发现的多个基因变异的信息,估算个体患复杂性状和疾病的遗传可能性。PRS 可以预测多种疾病,因此被广泛应用于研究领域。一些研究已对 PRS 作为生物标记物在预防医学中的潜在应用进行了调查,但仍需开展大量工作,以明确确定并向患者传达不同人口群体中遗传和可改变风险因素的绝对风险。然而,PRS 目前最大的局限性在于其在不同血统和队列中的通用性较差。目前正在通过方法开发和数据生成计划努力提高其通用性。本综述旨在全面讨论 PRS 目前的开发进展、影响其通用性的因素,以及提高其准确性、可移植性和实施的前景广阔的领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores.

Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants identified from genome-wide association studies. PRS can predict a broad spectrum of diseases and have therefore been widely used in research settings. Some work has investigated their potential applications as biomarkers in preventative medicine, but significant work is still needed to definitively establish and communicate absolute risk to patients for genetic and modifiable risk factors across demographic groups. However, the biggest limitation of PRS currently is that they show poor generalizability across diverse ancestries and cohorts. Major efforts are underway through methodological development and data generation initiatives to improve their generalizability. This review aims to comprehensively discuss current progress on the development of PRS, the factors that affect their generalizability, and promising areas for improving their accuracy, portability, and implementation.

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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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