Rapid Evolution of Genomic Architecture and Multi-omics Traits

摘要 表型性状的进化不仅对我们理解进化论很重要，而且对 遗传流行病学和统计遗传学。通过这项提议，我将使用大规模的排序和 多组学分析以测试性状进化的快速性。为了检验这一假设，我将提出我们的 了解罕见的变异和突变，精细的种群结构，以及多组学特性和 疾病。 当前项目 1)美洲原住民的进化和健康。在一次与秘鲁国家研究所的合作中，我开始了 最近，我们对来自秘鲁的150名主要是美国原住民血统的人进行了测序 发表在PNAS上，现在正在评估脂肪酸去饱和酶的全球进化动力学 (FADS)基因簇，这是多不饱和脂肪酸调节的关键。 2)TOPMed中罕见的变异。在Trans-Omics for Precision Medicine(TOPMed)项目中，我 开发了一种新的方法来评估不同注释类别之间的密切联系 一群人。我发现功能变异(例如无意义的)也更容易受到种群结构的影响。 3)血统突变。在两个项目中，我测试了不同祖先在突变模式上的差异。在 首先，我证明了不同祖先的癌细胞在体细胞突变率上存在差异。在第二个阶段， 我的研究表明，阿米什人的从头基因突变平均比不是创始人的欧洲人少3个。 未来的项目 1)稀有变种和研究设计。从我们在当前项目3中的分析扩展，我们将扩展这一点 不是按类别比较变异的方法，而是按某些连续值比较的方法 无害性和更广泛的方法学。 2)罕见变异型IBD。我们将开发一种新的方法来识别完全相同的小片段- 通过利用罕见的变异来降低(IBD)。这将是我们如何对基因组关系矩阵进行建模的关键 用于关联模型。 3)突变率因血统而异。在当前项目3的基础上，我们将使用从头开始的突变 我们在TOPMed的三个组中识别的计数作为全基因组关联分析的表型结果。 初步发现一些有希望的结果，我们将在酵母中使用分子分析进行后续研究。 4)快速变化性状的进化系统生物学。使用这个程序，我们将开发一种 用于识别疾病复杂系统生物学模型的近似贝叶斯计算(ABC)框架 由分子表型介导的性状。

Abstract The evolution of phenotypic traits is important both for our understanding of evolutionary theory, but also for genetic epidemiology and statistical genetics. Through this proposal, I will use large scale sequencing and multi-omics profiling to test the rapidness of trait evolution. To test this hypothesis, I will advance our understanding of rare variation and mutation, fine-scale population structure, and multi-omics traits and disease. Current Projects 1) Native American evolution and health. In a collaboration I started with the Peruvian National Institute of Health, we have sequence 150 predominantly Native American ancestry individuals from Peru, recently published in PNAS and now are evaluating the global evolutionary dynamics of the Fatty Acid Desaturase (FADS) gene cluster, which is critical to poly-unsaturated fatty acid regulation. 2) Rare variants in TOPMed. Within the Trans-Omics for Precision Medicine (TOPMed) project, I developed a new means of evaluating different annotation categories of rare variation between closely related cohorts. I find that functional variation (e.g. non-sense) are also more susceptible to population structure. 3) Mutation by ancestry. In two projects, I test for differences in mutational patterns by ancestry. In the first, I demonstrate that cancer cell lines have differences in somatic mutation rates by ancestry. In the second, I show that Amish individuals have on average 3 less de novo mutations than non-Founder Europeans. Future Projects 1) Rare variants and study design. Expanding from our analysis in current project 3, we will extend this methodology to compare variation not by categories, but for some continuous values for in silico predictors of deleteriousness and for a wider range of methodologies. 2) Rare variant IBD. We will develop a new method to identify small segments that are identical-by- descent (IBD) by leveraging rare variation. This will be critical in how we model the genomic relationship matrix for association models. 3) Mutation rate variation by ancestry. Building from current project 3, we will use the de novo mutation counts we identify in trios across TOPMed as a phenotypic outcome for a genome-wide association analysis. Preliminary findings show some promising results that we will follow-up using molecular assays in yeast. 4) Evolutionary systems biology of rapidly changing traits. Using this program, we will develop an Approximate Bayesian Computation (ABC) framework to identify complex systems biology models of disease traits mediated by molecular phenotypes.