Simulation algorithms for genome-wide data and application to admixed data

全基因组数据的模拟算法及其在混合数据中的应用

• 批准号: 7653629
• 负责人: JEFFREY D WALL
• 金额: $ 30.17万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-16 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7653629
• 关键词: Address; Admixture; Affect; African American; Age related macular degeneration; Algorithms; California; Chromosomes; Complex; Computer Simulation; Computer software; DNA Resequencing; Data; Data Analyses; Data Set; Diabetic Retinopathy; Disease; Disease susceptibility; European; Experimental Designs; Eye; Funding; Future; Gene Conversion; Genetic; Genetic Recombination; Genome; Genotype; Glaucoma; Goals; Hispanics; Human; Human Genetics; Individual; Latino; Linkage Disequilibrium; Los Angeles; Maps; Methodology; Methods; Modeling; Native Americans; Navajo; Pattern; Phenotype; Population; Population Control; Population Growth; Predisposition; Property; Research; Research Design; Research Personnel; Resources; Sample Size; Scanning; Simulate; Stratification; Structure; Testing; Time; Universities; Variant; Vision; Work; abstracting; analytical method; base; cost; density; design; genetic variant; genome wide association study; genome-wide; interest; method development; novel; simulation; tool; user friendly software

DESCRIPTION (Proposal abstract): One of the main goals of human genetics is to identify the genetic variants that affect susceptibility to complex, non-Mendelian diseases. A common approach is association mapping, whereby researchers genotype many markers to find those correlated with the phenotype of interest. These markers may not affect disease susceptibility themselves, but are likely to be in strong linkage disequilibrium (LD) with causative markers. One essential tool in the planning and analysis of association studies is computer simulation. Simulations help researchers compare competing experimental designs, and aid in the interpretation of any associations that are found. Despite this importance, there is a lack of proven simulation methods that are appropriate for the genome-wide data sets now being produced. For those methods that do exist, no attempt has been made to test whether the data produced accurately reflects the properties of observed data, or whether their use for power studies introduces a bias in terms of the final estimates of power. In this proposal, we focus on developing methods for simulating whole chromosome genetic data and for analyzing whole-genome association study data. We will test the accuracy of these methods on publicly available data as well as on genotype data collected by our collaborators at the University of Southern California. We will concentrate on how to analyze data from admixed populations such as Latinos, where population stratification makes most existing analytical methods inappropriate. This work will also help us determine the marker density and sample size needed for future association studies.

描述(建议摘要)：人类遗传学的主要目标之一是确定影响对复杂的非孟德尔疾病易感性的遗传变异。一种常见的方法是关联作图，研究人员通过对许多标记进行基因分型来找到与感兴趣的表型相关的标记。这些标记本身可能不影响疾病易感性，但很可能与致病标记处于强连锁不平衡(LD)。规划和分析关联性研究的一个重要工具是计算机模拟。模拟帮助研究人员比较相互竞争的实验设计，并帮助解释发现的任何关联。尽管这很重要，但目前还缺乏适用于目前正在生产的全基因组数据集的经过验证的模拟方法。对于那些确实存在的方法，没有人试图测试产生的数据是否准确地反映了观测数据的性质，或者它们用于功率研究是否在最终估计功率方面引入了偏差。在这个建议中，我们专注于开发模拟全染色体遗传数据和分析全基因组关联研究数据的方法。我们将在公开可用的数据以及我们在南加州大学的合作者收集的基因数据上测试这些方法的准确性。我们将专注于如何分析来自混合人口的数据，例如拉丁裔，那里的人口分层使大多数现有的分析方法不合适。这项工作还将帮助我们确定未来相关性研究所需的标记密度和样本量。