ASSOCIATION BETWEEN PHENOTYPES AND DISEQUALIBRIATE LOCI

描述（由申请人提供）：已知人类多态性的数量正在迅速增长，确定基因型的时间和金钱成本已大大降低。这一结果可能会推动对影响人类表型的基因的研究，并对理解、避免和治疗疾病产生积极的影响。它还将影响对药物反应的理解和对药物过敏反应的避免。虽然数据收集的速度取得了如此显著的进步，但处理如此庞大和复杂的数据集的统计方法却没有跟上步伐。最紧迫的问题是如何检测表型和大量潜在相关基因座之间的关联。目前的大多数方法都需要多次测试，并遭受相关的统计能力损失。这个问题更加复杂，因为位点和测试不是独立的，这导致了过度校正。

DESCRIPTION (provided by applicant): The number of known human polymorphisms is growing rapidly and the cost, both in time and money, for ascertaining genotypes has decreased substantially. The result can potentially drive forward the search for genes affecting human phenotypes with positive consequences for understanding, avoiding and treating disease. It will also impact the understanding of drug response and the avoidance of allergic reactions to drugs. While the rate of data gathering has advanced so significantly, the statistical methods to deal with such large and complex data sets have not kept pace. The most pressing problem is that of how to test for association between phenotypes and a vast array of potentially associated loci. Most current methods require multiple testing and suffer the associated loss of statistical power. This problem is further complicated as the loci, and hence the tests, are not independent which leads to over correction. Pairwise measures of linkage disequilibrium, haplotype blocks and hot spots inadequately describe the patterns of associations between the allelic states at proximal loci. More sophisticated coalescent and population genetic models have problems of tractability or of fully incorporating the information from haplotype samples. Graphical modeling provides a statistical framework for characterizing precisely this sort of complex stochastic data. This empirical approach can provide concise, accurate and tractable representations of the joint distribution of alleles at proximal loci. This is directly relevant to such problems as detecting association with phenotypic variables and selecting informative subsets of loci. The great potential of this approach is that categorical phenotypes can be included in the same analysis and association with polymorphisms assessed jointly with the inter locus associations. This proposal is to extend graphical modeling methods already developed by the investigators for haploid data to diploid data, larger genomic regions, admixed populations and family data.