Statistical, population genetics and genetic epidemiolog

统计、群体遗传学和遗传流行病学

• 批准号: 7330690
• 负责人: dmitri v zaykin
• 金额: --
• 依托单位: NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7330690
• 关键词: Statistical; population; genetics; genetic; epidemiolog

This project is concerned with statistical genetics problems, particularly in the area of developing and evaluating methods for mapping human trait variation. We have been investigating multi-locus approaches that provide increased efficiency across models of phenotype, and robustness with respect to the random mating assumption. We have developed a novel approach to statistical mapping of genetic variants predictive of traits such as disease susceptibility and adverse reactions to drugs. The approach is based on an observation that in a region of genetic association, the extent of correlation between alleles (linkage disequilibrium, LD) can be different between the case and the control groups. The extent and the pattern of LD can be compared between samples by a visual inspection of LD "color plots". However, patterns conveyed by such plots are subject to random variation, and a statistical assessment of the LD difference is necessary. A difficulty in constructing an "LD contrast" test is that the deviations from the Hardy-Weinberg (random mating) proportions are likely to manifest in phenotype-based samples in the presence of genetic association. The deviations from random pairing of haplotypes are likely to occur even if the underlying population is in Hardy-Weinberg equilibrium. Thus, standard techniques of phasing haplotypes are unreliable. The LD contrast method deals with this issue. The method is found especially efficient when high susceptibilities are linked to a pair of "yin yang haplotypes", i.e. haplotypes carrying different alleles at many SNP sites. Dr. Kyoko Shibata (Research Fellow) has been working in the direction of improving the likelihood-based statistical methods for association of haplotypes and diplotypes with continuous traits. Dr. Shibata has been applying her methods to study genetic components of pain sensitivity.

该项目涉及统计遗传学问题，特别是在开发和评估绘制人类性状变异的方法方面。我们一直在研究多位点方法，这些方法可以提高表型模型的效率以及随机交配假设的稳健性。我们开发了一种新方法来对遗传变异进行统计绘图，以预测疾病易感性和药物不良反应等特征。该方法基于这样的观察：在遗传关联区域中，等位基因之间的相关程度（连锁不平衡，LD）在病例组和对照组之间可能不同。可以通过目视检查 LD“彩色图”来比较样品之间的 LD 范围和模式。然而，此类图所传达的模式会受到随机变化的影响，因此有必要对 LD 差异进行统计评估。构建“LD对比”测试的困难在于，在存在遗传关联的情况下，与Hardy-Weinberg（随机交配）比例的偏差可能会在基于表型的样本中显现出来。即使潜在群体处于哈迪-温伯格平衡状态，单倍型随机配对的偏差也可能发生。因此，定相单倍型的标准技术是不可靠的。 LD 对比法解决了这个问题。当高敏感性与一对“阴阳单倍型”（即在许多 SNP 位点携带不同等位基因的单倍型）相关时，该方法特别有效。 Kyoko Shibata 博士（研究员）一直致力于改进基于可能性的统计方法，用于单倍型和双倍型与连续性状的关联。柴田博士一直在运用她的方法来研究疼痛敏感性的遗传成分。