III-CXT-Small: Algorithmic strategies for genotype-phenotype correlations

随着新的测序和基因分型技术的发展，现在正在进行全基因组研究，以了解表型的遗传基础。许多测量基因型-表型关系的基本原理，以及计算相关的群体遗传参数，相对来说已经很好地理解了。然而，即将到来的技术极大地改变了这些研究的规模和范围，这些研究已经涵盖了全基因组区域的数万个人。对这些数据的分析需要新的算法和统计技术。这个项目的重点是在一个典型的全基因组关联研究中可能出现的问题的一个子集。这些包括：(a)使用重叠序列数据将基因型分型为单倍型，并将该算法应用于人类个体序列的分型；高覆盖率的长序列数据的可用性将使这种方法在不久的将来成为分阶段的选择方法。(b)对相互作用影响表型的基因座对进行快速过滤，并将其应用于常见疾病表型的多基因座检测。所提出的工作减少了多位点测试的计算瓶颈。(c)平衡选择下的区域检测。现有的测试主要集中在检测正选择区域。拟议的研究寻找基因组中平衡选择的证据，特别关注与双相情感障碍相关的基因。(d)利用遗传变异和基因表达之间的联系重建调控途径。根据大学政策，本研究的所有软件都可以作为源代码或网络工具免费提供，用于学术、研究和非商业目的。有关该项目的进一步信息可在该项目的网站上找到：http://bix.ucsd.edu/algen

In the wake of new sequencing and genotyping technologies, whole genome studies are now being undertaken to understand the genetic basis of phenotypes. Many of the principles underlying the measurement of genotype-phenotype relationships, as well as computing related population genetic parameters, are relatively well understood. However, the upcoming technologies dramatically change the scale and scope of these studies, which already encompass tens of thousands of individuals over a genome-wide region. The analysis of this data requires novel algorithmic and statistical techniques. This project focuseson a subset of the problems that could arise in a typical whole-genome based association study. These include:(a) Phasing of genotypes into haplotypes using overlapping sequence data, and the application of this algorithm to phasing individual human sequences; the availability of high coverage long sequence data will make this approach the method of choice for phasing in the near future. (b) Fast filtering for pairs of loci that interactively influence a phenotype and its application to multiple-locus testing of common disease phenotypes. The proposed work reduces the computational bottleneck in multiple locus testing.(c) Detection of regions under balancing selection. Available tests are focused on detection of regions under positive selection. The proposed research looks for evidence of balancing selection in the genome, with specific attention on genes associated with bipolar disorder.(d) Reconstruction of regulatory pathways using associations between genetic variation and gene-expression.All software from this research is freely available as source-code, or as web-tools for academic, research and non-commercial purposes in accordance with University policy. Further information on the project may be found at the project web site: http://bix.ucsd.edu/algen