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: 7628936
• 负责人: Guimin Gao
• 金额: $ 7.97万
• 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 2009-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7628936
• 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; 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（由申请人提供）：谱系中的统计基因定位提高了我们对人类和动物群体中复杂疾病（如糖尿病、阿尔茨海默病、哮喘）和数量性状（如骨密度、血压、产奶量）的遗传结构的认识。不幸的是，大多数观察到的系谱数据是不完整的，有缺失的数据。利用观察到的基因型数据（特别是密集的单核苷酸多态性（SNP）标记）有效地推断具有大量连锁位点和缺失标记数据的复杂家系的单倍型配置和计算血统识别（IBD）概率是统计基因定位的关键组成部分，也是目前的挑战。所提出的工作的广泛，长期目标是开发有效的统计和计算方法，用于在具有缺失和阶段未知标记数据的大型谱系中进行单倍型和基因定位。具体目标是：1)将目前适用于完整家系数据的条件枚举单倍型方法扩展到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（LD）；2)开发具有大量连锁位点和缺失标记数据的大型家系IBD概率估计方法，开发密集（SNP）标记间LD信息建模的精细作图方法，并从连锁分析和精细作图的QTL作图精度两方面评价IBD概率估计方法的性能；3)将提出的方法应用于两个大型真实人类系谱数据集（1623人的赫特人系谱和1412人的阿米什人系谱）的连锁分析和精细映射；4)开发计算机软件以实现目标1和目标2。我们实现这些目标的方法是基于在阶段未知标记上可能有序基因型的条件概率的计算和单倍型配置的可能性的计算。通过设置阶段未知标记有序基因型条件概率的阈值和单倍型配置条件概率的阈值，所提出的单倍型方法确定了谱系中具有最高可能性的单倍型配置子集。基于该单倍型配置子集估计IBD概率，然后将其用作大型谱系中基于方差成分的QTL定位方法的输入。本研究开发的方法将增强我们在人类和动物群体中绘制QTL和复杂疾病基因的能力。