Scaling up coalescent linkage disequilibrium mapping

扩大合并连锁不平衡图谱

• 批准号: 7561283
• 负责人: Mary K Kuhner
• 金额: $ 42.5万
• 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/7561283
• 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）：家庭图研究通常能够在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。基于链接不平衡（LD）的方法中，人口数据中的方法具有查明与疾病相关的基因的潜力。该提案始于现有的LD映射算法，该算法在计算上仅限于0.5 cm或更少的区域，并开发了三种方法，使其可用于人类疾病图研究。 （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更少的重组。 （2）在沿着染色体重叠的窗户中构建地图，而不是尝试同时分析整个区域。 （3）预先计算基因组区域的古代家谱关系（其中一个编码区域将用作概念证明）。从本质上讲，所有现代样本都将分享其深层家谱的一部分。预成立将大大减少在同一染色体区域寻求疾病基因座的不同群体所做的冗余工作。该建议将将一种强大但昂贵的映射算法转化为实际用途之一。找到有助于疾病发展的特定基因在诊断，理解和治疗中很重要。诊断测试以对疾病基因位置的粗略想法建立的，通常仅在开发的种族中起作用；实际的病因基因或基因所告知的测试在所有人群中都可以工作。对病因基因的了解也可以照亮疾病的机制，并为治疗设计提供目标。公共卫生相关性：找到有助于人类疾病的精确基因或基因对于诊断，理解和治疗很重要。基于家庭的研究通常确定一个含有50多个基因的大染色体区域。需要基于人群的研究来进一步缩小位置。该提案将基于人群数据扩展一种基因地点算法，以便在较大的研究和更广泛的不确定性领域中使用。