Methods for multi-ancestry and multi-trait fine-mapping and genetic risk prediction

遗传流行病学的两个基本目标是识别遗传变异 导致疾病（精细映射）和多基因风险评分（PRS）的发展，预测个体- 利用遗传信息来评估疾病风险。随着基因数据集的扩大，这些目标变得越来越重要。 现实然而，大多数遗传数据集过度代表欧洲人群，限制了 科学发现，因果变异的发现，以及PRS在非欧洲人群中的准确性。如果 如果不加以解决，减贫战略准确性的差异将扩大基于血统的健康差距。中的大多数方法 遗传流行病学一次只考虑一个祖先和一种疾病。这项研究提出了因果关系的方法， 变异识别和遗传风险预测，共享跨祖先和疾病的信息。 第一个目标是开发一种方法，使用来自多个祖先群体的数据进行精细映射。因果 变异识别提供了对疾病病因的深入了解，并帮助研究人员确定药物靶点。之和 单效应模型（SuSiE）是一种在单一群体中进行精细定位的有效方法。结合 由于模式中基于祖先的差异， 变异之间的相关性，以及在某些但不是所有祖先中存在具有因果效应的变异。 在这个目标中，MultiSuSiE，一个受SuSiE启发的多种群精细映射方法将被开发， 应用。SuSiE在速度、功率和可解释性方面提供了与其他 精细映射方法MultiSuSiE将把最先进的精细映射技术带到多祖先环境中。 第二个目标是开发和应用ssCTPR，这是一种基于汇总统计的PRS方法， 利用跨疾病的共享信息。PRS显示出告知医疗的巨大希望 决策和疾病筛查干预。最近的一种方法，交叉特征惩罚回归（CTPR）， 通过利用疾病之间共享的遗传基础来提高预测准确性，但需要难以获得的 个人数据。在这个目标中，ssCTPR，一种基于CTPR的多性状总结方法， 开发和应用。ssCTPR在统计方法上具有创新性：ssCTPR将联合建模变异 和疾病，使用惩罚回归，并使用Laplacian二次曲线在性状之间共享信息。 在多种疾病环境中有效的惩罚，但尚未使用汇总统计进行调查。 第三个目标是开发一种方法，利用目标1和目标2的方法学进步， 非欧洲人群的PRS预测。非欧洲人群中的PRS预测准确性非常高， 低于欧洲人口。随着减贫战略进入临床，健康结果不公平的人群将 未能从最新的精准医学创新中受益。在这个目标中，MultiPolyPred，一种建模方法， 将开发和应用使用多祖先精细绘图和多疾病PRS的个人风险。我们 方法将是唯一一种利用多祖先精细定位的非欧洲PRS方法。

Project Summary: Two fundamental goals in genetic epidemiology are the identification of genetic variants that cause disease (fine-mapping) and the development of polygenic risk scores (PRS) that predict individual- level disease risk using genetic information. As genetic datasets expand, these goals become increasingly realistic. However, most genetic datasets overrepresent European populations, limiting the generalizability of scientific findings, the discovery of causal variants, and the accuracy of PRS in non-European populations. If unaddressed, differences in PRS accuracy will widen ancestry-based health disparities. Most methods in genetic epidemiology consider one ancestry and disease at a time. This research proposes methods for causal variant identification and genetic risk prediction that share information across ancestries and diseases. The first aim is to develop a method for fine-mapping using data from multiple ancestry groups. Causal variant identification provides insight into disease etiology and helps researchers identify drug targets. The sum of single effects (SuSiE) model is a powerful approach for fine-mapping in a single population. Incorporating data from multiple populations can greatly improve fine-mapping due to ancestry-based differences in patterns of correlation between variants and the presence of variants with causal effects in some, but not all ancestries. In this aim, MultiSuSiE, a multi-population fine-mapping method motivated by SuSiE will be developed and applied. SuSiE provides substantial benefits in terms of speed, power, and interpretability compared to other fine-mapping methods. MultiSuSiE will bring the state-of-the-art in fine-mapping to the multi-ancestry context. The second aim is to develop and apply ssCTPR, a summary statistic based PRS method that leverages shared information across diseases. PRS show great promise for informing medical treatment decisions and disease screening interventions. A recent method, cross-trait penalized regression (CTPR), boosts prediction accuracy by leveraging shared genetic bases across diseases but requires difficult-to-obtain individual-level data. In this aim, ssCTPR, a multi-trait summary statistic-based method motivated by CTPR will be developed and applied. ssCTPR is innovative in its statistical approach: ssCTPR will jointly model variants and diseases, use penalized regression, and share information across traits using a Laplacian quadratic penalty that is effective in the multi-disease setting, but has not been investigated using summary statistics. The third aim is to develop a method that uses the methodological advances of aims 1 and 2 to improve PRS prediction in non-European populations. PRS prediction accuracy in non-European populations is much lower than in European populations. As PRS enter the clinic, populations with inequitable health outcomes will fail to benefit from the latest in precision medicine innovation. In this aim, MultiPolyPred, a method that models individual risk using multi-ancestry fine-mapping and a multi-disease PRS will be developed and applied. Our method will be the only non-European PRS method to leverage multi-ancestry fine-mapping.