Dominance on the Human Genome and Non-additive Polygenic Models for Predicting Complex Traits

项目摘要 优势度是遗传学中最基本的概念之一，在种群中有许多关键的含义 遗传学，因为它最终决定了选择如何在种群中表现出来。然而，尽管它无可争辩地 重要性，显性也是遗传学中最不具特征的量之一，特别是在人类中， 主要的挑战是目前的方法不能区分显性和基因组的适合性效应。 变种。这项拟议的K99/R00工作将从双重角度系统地解决这一长期存在的问题- 视角，通过推断人类的优势并定量模拟其在塑造人类表型中的作用 复杂的特征和疾病。具体地说，在Aim1中，我将开发一种基于机器学习的强大方法来 利用古老的基因，推断人类基因组在百万数据库范围内的优势现实分布 在非非洲群体中对基因组区域的显性变异敏感的导入祖先。在……里面 目标2，我将建立占全基因组区域显性的非加性多基因模型来鉴定 在英国生物库中描述的偏离加性模型的复杂性状，提高了表型和 疾病风险预测，并有助于深入了解复杂的性状生物学。最后，在目标3中 (R00阶段)，我将扩展这些方法来推断全球种群的优势变异和 研究显性，结合选择和混合，如何决定复杂的性状表型 不同的人类群体。这项工作的指导阶段将在生态部和 加州大学洛杉矶分校的进化生物学，张博士将在那里获得丰富的培训机会并得到支持 由活跃的科学界主办，包括许多系列研讨会、期刊俱乐部和网络活动。Dr。 Kirk Lohmueller博士(主要导师)和Sriram Sankararaman博士(共同导师)将对张博士进行计算培训 以及人口遗传学、机器学习应用和大规模疾病的统计方法 关联数据分析。K99期间的研究培训、合作和专业发展 阶段将帮助张博士成为人类群体遗传学的独立研究员。

Project Abstract Dominance is one of the most fundamental concepts in genetics and has many key implications in population genetics, as it ultimately determines how selection manifests in a population. However, despite its unarguable importance, dominance is also one of the least characterized quantities in genetics, especially in humans, with the major challenge being current methods cannot distinguish dominance from the fitness effect of genomic variants. This proposed K99/R00 work will systematically address this longstanding problem from a dual- perspectives, by inferring dominance in humans and quantitatively model its role in shaping the phenotypes of complex traits and diseases. Specifically, in Aim1, I will develop a powerful machine learning-based method to infer the realistic distribution of dominance on the human genome in megabase-scale, leveraging archaic introgressed ancestry in non-African populations that is sensitive to dominance variation in genomic regions. In Aim 2, I will develop non-additive polygenic models accounting for dominance in full genomic regions to identify complex traits profiled in UK Biobank that deviate from additive models, improve the accuracy of phenotype and disease risk predictions, and contribute to an in-depth understanding of complex trait biology. Finally, in Aim 3 (R00 phase), I will extend these approaches to infer dominance variation in worldwide populations and investigate how dominance, combined with selection and admixture, determines complex trait phenotypes in diverse human populations. The mentored phase of this work will take place at the Department of Ecology and Evolutionary Biology at UCLA, where Dr. Zhang will have access to rich training opportunities and be supported by active scientific communities, including numerous seminar series, journal clubs, and networking activities. Dr. Kirk Lohmueller (primary mentor) and Dr. Sriram Sankararaman (co-mentor) will train Dr. Zhang in computational and statistical methods in population genetics, machine learning applications, and large-scale disease association data analysis. The research trainings, collaborations, and professional development during the K99 phase will assist Dr. Zhang in becoming an independent investigator in human population genetics.