Scaling up coalescent linkage disequilibrium mapping

描述(由申请人提供)：家族图研究通常能够在5厘米的区域内定位疾病基因，但这样的区域可能包含50+基因。基于群体数据中的连锁不平衡(LD)的方法有可能精确定位疾病相关基因。这项建议从现有的LD映射算法开始，该算法在计算上限制在0.5厘米或更小的区域，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)预先计算基因组某一区域的古代谱系关系(其中一个ENCODE区域将用作概念验证)。基本上，所有现代样本都将共享其深层谱系的一部分；预计算将极大地减少在同一染色体区域寻找疾病基因的不同群体所做的多余工作。这一建议将把一个功能强大但计算代价高昂的映射算法转化为实用算法。寻找导致疾病发生的特定基因对于诊断、理解和治疗非常重要。建立在疾病基因位置粗略概念基础上的诊断测试通常只在为其开发的种族中有效；由实际致病基因或多个基因提供信息的测试在所有人群中都可以有效。对致病基因的了解也可以阐明疾病的机制，并为治疗设计提供靶点。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。基于家庭的研究通常确定一个包含50+基因的大染色体区域；需要基于群体的研究来进一步缩小该位置。这项提议将扩展基于人口数据的精细基因定位算法，以便它可以用于更大规模的研究和更广泛的不确定领域。

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.