Scaling up coalescent linkage disequilibrium mapping

扩大合并连锁不平衡图谱

• 批准号: 7895063
• 负责人: Mary K Kuhner
• 金额: $ 43.34万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-20 至 2012-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7895063
• 关键词: Algorithms; Area; Chromosomes; Coin; Computers; DNA Resequencing; Data; Development; Diagnosis; Diagnostic tests; Disease; Ethnic Origin; Event; Face; Family; Genealogy; Genes; Genetic Recombination; Genome; Genomics; Human; Knowledge; Length; Libraries; Link; Linkage Disequilibrium; Linkage Disequilibrium Mapping; Location; Maps; Methods; Modeling; Pattern; Performance; Population; Probability; Recombinants; Recording of previous events; Relative (related person); Research Personnel; Sampling; Side; Site; Structure; Testing; Trees; Uncertainty; Variant; Work; base; human disease; novel; population based; public health relevance; scale up; sound; therapy design; trait

DESCRIPTION (provided by applicant): Family-mapping studies are often able to locate a disease gene within an area of 5 cM, but such areas may contain 50+ genes. Methods based on linkage disequilibrium (LD) in population data have the potential to pinpoint disease-associated genes. This proposal begins with an existing LD mapping algorithm which is computationally limited to areas of 0.5 cM or less, and develops three approaches to making it usable for human disease-mapping studies. (1) Simplify the model of recombination, tracking fewer recombinations by disregarding fine recombinational structure between adjacent SNPs. (2) Construct the map in overlapping windows along the chromosome, rather than attempting to analyze the entire region simultaneously. (3) Pre-compute the ancient genealogical relationships for a region of the genome (one of the ENCODE regions will be used as a proof of concept). Essentially all modern samples will share portions of their deep genealogy; pre-computation will greatly reduce the redundant work done by different groups seeking disease loci in the same chromosomal region. This proposal will transform a powerful but computationally expensive mapping algorithm into one of practical use. Finding the specific genes which contribute to development of a disease is important in diagnosis, understanding, and treatment. Diagnostic tests built on a rough idea of a disease gene's location often work only in the ethnicity for which they were developed; tests informed by the actual causative gene or genes can work in all populations. Knowledge of the causative genes can also illuminate the mechanisms of disease and provide targets for treatment design. Public Health Relevance: Finding the precise gene or genes contributing to a human disease is important for diagnosis, understanding, and treatment. Family-based studies often identify a large chromosomal region containing 50+ genes; population-based studies are needed to narrow the location further. This proposal will extend a fine-scale gene-location algorithm based on population data so that it can be used in larger studies and across wider areas of uncertainty.

描述（由申请人提供）：家族图谱研究通常能够在5 cM的区域内定位疾病基因，但这些区域可能包含50多个基因。基于群体数据中连锁不平衡（LD）的方法具有查明疾病相关基因的潜力。该建议从现有的LD映射算法开始，该算法在计算上限于0.5 cM或更小的区域，并开发了三种方法，使其可用于人类疾病映射研究。(1)简化重组模型，通过忽略相邻SNP之间的精细重组结构来跟踪较少的重组。(2)在沿着染色体的重叠窗口中构建地图，而不是试图同时分析整个区域。(3)预先计算基因组区域的古代系谱关系（其中一个ENCODE区域将用作概念证明）。基本上，所有现代样本都将共享其深层谱系的一部分;预先计算将大大减少不同群体在同一染色体区域寻找疾病基因座所做的冗余工作。这个建议将一个强大的，但计算昂贵的映射算法转化为一个实际使用。发现导致疾病发展的特定基因在诊断、理解和治疗中很重要。建立在疾病基因位置的粗略概念上的诊断测试通常只适用于它们开发的种族;由实际致病基因或基因提供信息的测试可以在所有人群中工作。致病基因的知识也可以阐明疾病的机制，并为治疗设计提供目标。公共卫生相关性：找到导致人类疾病的精确基因对于诊断，理解和治疗非常重要。以家族为基础的研究通常会确定一个包含50多个基因的大染色体区域;需要以人群为基础的研究来进一步缩小位置。该提案将扩展基于人口数据的精细规模基因定位算法，使其可用于更大规模的研究和更广泛的不确定性领域。