ABI Innovation: An Integrative Approach to Identifying Highly Heritable Subtypes of Complex Phenotypes

识别复杂表型背后的遗传变异有助于理解它们的生物学。以多种特征为特征的复杂表型通常与大量的表型变异相关。目前的统计方法无法解决这种表型异质性，因此缺乏将遗传变异与表型相关联的能力。该项目旨在设计新的算法，区分复杂表型的同质亚型，这些亚型在遗传分析中信息量最大，并识别与亚型相关但不能被未分化表型检测到的遗传变异。亚型的有效性将在多个尺度上得到证明，包括来自基因组结构和表型特征的证据。新算法将在农业重要动植物复杂性状的遗传选择领域得到验证。该项目为培养研究生在涉及计算机科学和生物学的多学科方法，并允许他们在各种生物领域应用的方法。将为高年级本科生开发生物信息学领域的新课程。该项目还将开发高中教材，教育高中生如何对生物数据进行数学建模，从而解决生物学问题。该项目将基于数量遗传学理论和机器学习理论推导出新颖的分析方法，以优化复杂的表型，从而增强基因型-表型相关性的发现。使用经验和统计上严格的方法，该项目将获得复合性状，作为多个表型特征的函数，这些特征在狭义遗传力方面进行了优化，并且容易映射到特定的基因组区域。两个统计模型估计狭义遗传力将被考虑：一个基于样本系谱，和其他直接使用全基因组标记。为了识别以复合性状为特征的亚型的多尺度证据，将导出一个新的机器学习框架来联合分析基因型和表型。通过对大规模生物数据库的分析测试，新算法将获得奶牛饲料效率的高遗传性复合性状和大豆的适应性性状，以改善其遗传选择。该项目中开发的算法还将通过定义和解决新的研究问题来推进机器学习领域，例如使用来自多个来源的数据的联合模型推断，基于矩阵分解的概率聚类，以及遗传力估计的二次优化。该项目将产生用户友好的软件工具，可以广泛部署到研究复杂表型遗传学的生物研究领域。经确认的方法和软件将通过PI？的实验室网站http://www.labhealthinfo.uconn.edu/home/，该网站提供了该项目的更多信息。

Identifying genetic variation underlying complex phenotypes aids the understanding of their biology. Complex phenotypes characterized by a variety of features are often associated with substantial phenotypic variation. Current statistical methods are ineffective to address this phenotypic heterogeneity, and hence lack of power to associate genetic variants with the phenotype. This project aims to design new algorithms that differentiate homogenous subtypes of a complex phenotype that are most informative in genetic analysis, and identify genetic variants that are associated with the subtypes but cannot be detected by the non-differentiated phenotype. The validity of the subtypes will be proved in multiple scales including the evidence from genomic structure and phenotypic features. The new algorithms will be validated in the areas of genetic selection for complex traits of agriculturally-important animals and plants. This project serves a vehicle to train graduate students in the multidisciplinary methods involving computer science and biology, and allow them to apply the methods in a variety of biological fields. A new course in the bioinformatics field will be developed for senior undergraduate students. High school educational materials will also be developed to educate high school students about how to mathematically model biological data so it solves biological problems.This project will derive novel analytics based on quantitative genetics theory and machine learning theory to refine complex phenotypes for enhanced discovery of genotype-phenotype correlations. Using empirical and statistically rigorous methods, this project will derive composite traits, as functions of multiple phenotypic features, that are optimized with respect to narrow-sense heritability, and that map readily to specific genomic regions. Two statistical models for estimating narrow-sense heritability will be considered: one based on sample pedigrees, and the other directly uses the whole-genome markers. To identify multi-scale evidence of a subtype that is characterized by a composite trait, a new machine learning framework will be derived to jointly analyze genotypes and phenotypes. By testing the algorithms in the analysis of large-scale biological databases, the new algorithms will derive highly heritable composite traits for feed efficiency of dairy cattle and adaptive traits of soybean to improve their genetic selection. The algorithms developed in this project will also advance the machine learning field by defining and addressing new research problems, such as the joint model inference using data from multiple sources, probabilistic clustering based on matrix decomposition, and quadratic optimization for heritability estimation. This project will yield user-friendly software tools that can be broadly deployed to biological research areas that study genetics of complex phenotypes. The validated methods and software will be disseminated through the PI?s laboratory website http://www.labhealthinfo.uconn.edu/home/ which provides more information of this project.