Genome Analysis: Data Accuracy, Haplotyping, and Mapping

描述(申请人提供)：长期目标：开发用于解释和分析人类遗传变异的定量方法和软件。这些方法将根据大规模序列变异研究的具体需要而定制，特别是那些试图了解复杂疾病的遗传基础的研究。其目的是为参与这类研究的科学家提供一套集成的工具，以a)监控和改进数据质量，b)设计有效的研究，以及c)执行强大的数据分析，最终降低开发针对常见疾病的有效医疗方法的成本。主要具体目标：1.开发自动方法，从序列跟踪数据中调用基因类型，并为每个基因调用分配一个“质量分数”，量化调用是正确的概率，允许仔细监控数据的准确性。2.扩展现有的从群体基因数据推断单倍型的统计方法，使其能够归因于缺失的基因类型，识别潜在的基因分型错误，并使其更适用于更大(基因组)规模的数据。3.开发方法，从人口数据中推断重组率，并确定潜在的重组“热点”或“冷点”(有助于设计有效的图谱研究，以定位影响疾病易感性的变异)。4.开发连锁不平衡作图方法，同时有效利用来自多个SNP标记的数据，从而降低作图研究的成本，增加成功的机会。 目标1将通过对分析员称为基因类型的相关序列跟踪特征的统计分析来实现。AIMS 2-4将利用群体遗传学模型，预测自然群体中预期的单倍型变异模式，以及连锁不平衡模式将如何受到局部重组率变化的影响。在实施这些方法时，将广泛使用计算统计方法，如马尔科夫链蒙特卡罗。这些方法将在真实数据和模拟数据上进行测试。将开发、记录、分发和支持用户友好的软件。

DESCRIPTION (provided by applicant): Long-term objective: to develop quantitative methods and software for the interpretation and analysis of human genetic variation. The methods will be tailored to the specific needs of large-scale studies of sequence variation, particularly those attempting to understand the genetic basis of complex diseases. The aim will be to supply scientists involved in such studies with an integrated set of tools to a) monitor and improve data quality, b) design effective studies, and c) perform powerful data analyses, ultimately reducing the cost of developing effective medical treatments for common diseases.Major specific aims: 1. To develop automatic methods for calling genotypes from sequence trace data, and for assigning each genotype call a "quality score", quantifying the probability that the call is correct, allowing data accuracy to be carefully monitored. 2. To extend an existing statistical method for inferring haplotypes from population genotype data to allow it to impute missing genotypes, identify potential genotyping errors, and make it more applicable to data on a larger (genomic) scale. 3. To develop methods to infer recombination rates, and identify potential recombination "hotspots" or "coldspots", from population data (information that will aid in the design of effective mapping studies aiming to locate variants affecting disease susceptibility). 4. To develop methods for linkage disequilibrium mapping that make efficient use of data from many SNP markers simultaneously, thus reducing the costs, and increasing the chances of success, of mapping studies. Aim 1 will be achieved through a statistical analysis of pertinent sequence trace features for analyst-called genotypes. Aims 2-4 will exploit population genetics models that make predictions about patterns of haplotypic variation expected in natural populations, and how patterns of linkage disequilibrium will be affected by variations in local recombination rate. Computational statistical methods, such as Markov chain Monte Carlo, will be used extensively in implementing these methods. The methods will be tested on real and simulated data. User-friendly software will be developed, documented, distributed and supported.