Haplotyping and QTL Mapping in Pedigrees with Missing Data

缺失数据谱系的单倍型分析和 QTL 定位

• 批准号: 7259849
• 负责人: Guimin Gao
• 金额: $ 26.1万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-01 至 2012-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7259849
• 关键词: Alzheimer' s Disease; Amish; Animals; Architecture; Asthma; Blood Pressure; Bone Density; Chromosome Mapping; Complex; Computing Methodologies; Data; Data Set; Diabetes Mellitus; Disease; Genes; Genetic; Genotype; Haplotypes; Human; Knowledge; Link; Linkage Disequilibrium; Maps; Methodology; Methods; Milk; Modeling; Numbers; Performance; Persons; Phase; Population; Probability; Production; Quantitative Trait Loci; Research; Single Nucleotide Polymorphism; Work; base; genetic linkage analysis; genetic pedigree; hutterite; improved; software development; trait

DESCRIPTION (provided by applicant): Statistical gene mapping in pedigrees advances our knowledge of the genetic architecture of complex diseases (e.g., diabetes, Alzheimer's disease, asthma) and quantitative traits (e.g., bone density, blood pressure, milk production) in human and animal populations. Unfortunately most observed pedigree data are not complete and have missing data. Efficiently inferring haplotype configurations and calculating identity-by-descent (IBD) probabilities for complex pedigrees with large numbers of linked loci and missing marker data by using the observed genotype data (especially dense single nucleotide polymorphism (SNP) markers) are critical components and remain challenging in statistical gene mapping. The broad, long-term objectives of the proposed work are to develop efficient statistical and computational methods for haplotyping and gene mapping in large pedigrees with missing and phase unknown marker data. The specific aims are to 1) extend our conditional enumeration haplotyping method that currently works with complete pedigree data to pedigrees with missing marker data, and then improve the method so that it can handle linkage disequilibrium (LD) between markers; 2) develop a computationally efficient method for estimating IBD probabilities in large pedigrees with large numbers of linked loci and with missing marker data, develop a fine mapping method by modeling LD information between dense (SNP) markers, and evaluate the performance of the IBD probability estimation method in terms of quantitative trait loci (QTL) mapping accuracy in linkage analysis and fine mapping; 3) apply the proposed methods to linkage analysis and fine mapping of two large, real human pedigree data sets (a 1623-person Hutterite pedigree and a 1412- person Amish pedigree); and 4) develop computer software to implement aims 1 and 2. Our approach to these aims is based on the computation of conditional probabilities of possible ordered genotypes at phase unknown markers and the calculation of likelihood of haplotype configurations. By setting a threshold value for the conditional probabilities of ordered genotypes at phase unknown markers and a threshold value for the conditional probabilities of haplotype configurations, the proposed haplotyping method identifies a subset of haplotype configurations with the highest likelihoods for a pedigree. IBD probabilities are estimated based on this subset of haplotype configurations, and then are used as input to variance components based QTL mapping methods in large pedigrees. The methodologies developed in this research will enhance our ability to map QTL and complex disease genes in human and animal populations.

描述(申请人提供)：家系中的统计基因图谱提高了我们对人类和动物种群中复杂疾病(例如糖尿病、阿尔茨海默病、哮喘)和数量性状(例如骨密度、血压、产奶量)的遗传结构的了解。遗憾的是，大多数观察到的谱系数据并不完整，缺少数据。利用观察到的基因数据(特别是密集单核苷酸多态(SNP)标记)有效地推断具有大量连锁座位和缺失标记数据的复杂家系的单倍型构型和计算IBD概率是统计基因图谱中的关键组成部分和挑战。这项拟议工作的广泛和长期目标是开发有效的统计和计算方法，用于缺少和阶段未知标记数据的大型家系的单倍型和基因作图。其具体目的是：1)将现有的基于完整家系数据的条件枚举单倍型方法推广到具有缺失标记数据的家系，并对该方法进行改进，使其能够处理标记间的连锁不平衡；2)发展一种计算高效的方法来估计大量连锁座位和缺失标记数据的大家系的IBD概率，通过模拟密集(SNP)标记之间的连锁不平衡信息来开发一种精细作图方法，并从连锁分析和精细作图中数量性状座位(QTL)作图的准确性来评价IBD概率估计方法的性能；3)将所提出的方法应用于两个大的真实人类家系数据集(1623人的Hutterite家系和1412人的Amish家系)的连锁分析和精细定位；以及4)开发计算机软件来实现AIMS 1和2。我们的方法是基于在阶段未知标记上可能有序的基因型的条件概率的计算和单倍型配置的可能性的计算。该方法通过设置有序基因在未知标记阶段的条件概率阈值和单倍型配置条件概率的阈值，识别出系谱中概率最高的单倍型配置子集。IBD概率是基于这个单倍型配置的子集估计的，然后被用作大型家系中基于方差分量的QTL定位方法的输入。这项研究中开发的方法学将增强我们在人类和动物群体中定位QTL和复杂疾病基因的能力。