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
基本信息
- 批准号:7849472
- 负责人:
- 金额:$ 26.1万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:Alzheimer&aposs DiseaseAmishAnimalsArchitectureAsthmaBlood PressureBone DensityChromosome MappingComplexComputing MethodologiesDataData SetDiabetes MellitusDiseaseGenesGeneticGenotypeHaplotypesHumanKnowledgeLinkLinkage DisequilibriumMapsMethodologyMethodsMilkModelingPerformancePersonsPhasePopulationProbabilityProductionQuantitative Trait LociResearchSingle Nucleotide PolymorphismWorkbasegenetic linkage analysisgenetic pedigreehutteriteimprovedsoftware developmenttrait
项目摘要
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):谱系中的统计基因图谱增进了我们对人类和人类复杂疾病(例如糖尿病、阿尔茨海默氏病、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。动物种群。不幸的是,大多数观察到的谱系数据并不完整,并且存在数据缺失。通过使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),有效推断单倍型配置并计算具有大量连锁基因座和缺失标记数据的复杂谱系的血统同一性(IBD)概率是关键组成部分,并且仍然存在统计基因图谱具有挑战性。拟议工作的广泛、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图谱分析。具体目标是1)将我们目前使用完整谱系数据的条件计数单倍型方法扩展到具有缺失标记数据的谱系,然后改进该方法,使其能够处理标记之间的连锁不平衡(LD); 2) 开发一种计算有效的方法来估计具有大量连锁基因座和缺失标记数据的大型谱系中的 IBD 概率,通过对密集(SNP)标记之间的 LD 信息进行建模来开发精细作图方法,并评估 IBD 概率的性能连锁分析和精细定位中数量性状位点(QTL)作图精度的估计方法; 3)将所提出的方法应用于两个大型真实人类谱系数据集(1623人的哈特派谱系和1412人的阿米什谱系)的连锁分析和精细映射; 4) 开发计算机软件来实现目标 1 和 2。我们实现这些目标的方法是基于计算阶段未知标记处可能有序基因型的条件概率以及计算单倍型配置的可能性。通过设置阶段未知标记处有序基因型的条件概率的阈值和单倍型配置的条件概率的阈值,所提出的单倍型分析方法识别出谱系具有最高可能性的单倍型配置的子集。 IBD 概率基于单倍型配置的这个子集进行估计,然后用作大谱系中基于方差分量的 QTL 作图方法的输入。本研究开发的方法将增强我们绘制人类和动物群体 QTL 和复杂疾病基因图谱的能力。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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{{ truncateString('Guimin Gao', 18)}}的其他基金
Transcriptome-wide association studies and genetic risk prediction for breast cancer integrating RNA splicing and gene expression from multiple tissues
整合来自多个组织的 RNA 剪接和基因表达的乳腺癌全转录组关联研究和遗传风险预测
- 批准号:
10456122 - 财政年份:2019
- 资助金额:
$ 26.1万 - 项目类别:
Transcriptome-wide association studies and genetic risk prediction for breast cancer integrating RNA splicing and gene expression from multiple tissues
整合来自多个组织的 RNA 剪接和基因表达的乳腺癌全转录组关联研究和遗传风险预测
- 批准号:
10017927 - 财政年份:2019
- 资助金额:
$ 26.1万 - 项目类别:
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:
7429818 - 财政年份:2007
- 资助金额:
$ 26.1万 - 项目类别:
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:
7991432 - 财政年份:2007
- 资助金额:
$ 26.1万 - 项目类别:
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 - 财政年份:2007
- 资助金额:
$ 26.1万 - 项目类别:
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:
8072710 - 财政年份:2007
- 资助金额:
$ 26.1万 - 项目类别:
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 - 财政年份:2007
- 资助金额:
$ 26.1万 - 项目类别:
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