Methods for Human Genetic Mapping

描述(由申请人提供)： 常见、复杂的疾病共同构成了美国医疗保健负担的很大一部分，对这些特征的基因分析仍然是生物医学研究人员面临的主要挑战之一。高通量技术的最新进展导致大规模遗传序列信息和其他相关生物数据集的可用性越来越高。如果开发出强大、强大的统计和计算方法和工具来分析这些数据，那么就可以在识别和表征复杂疾病的遗传成分方面取得进展。反过来，这又有可能(1)导致对此类疾病的生物学更好的理解，(2)澄清环境风险因素的作用， 这可能是具有成本效益的治疗和预防战略的目标，以及(3)导致个性化医疗护理的改善。我们的目标是开发强大、强大的性状关联数据分析方法，在全范围的研究设计中，从具有种群结构的无关样本到复杂的近亲交配谱系中的个体样本，对各种遗传和其他“组学”预测因子有用。具体地说，我们建议开发新的方法来进行二进制、定量和纵向性状定位，并使用一系列预测因子，如基因、基因组序列、转录组、表达、代谢、甲基化、蛋白质组或其他数据，其中这些方法结合了相关的协变量并考虑了样本中个体的群体结构和/或相关性。

DESCRIPTION (provided by applicant): Common, complex diseases together account for a large portion of the health care burden in the United States, and genetic analysis of these traits remains one of the major challenges facing biomedical researchers. Recent advances in high-throughput technologies have led to increasing availability of large-scale genetic sequence information and other related biological data sets. If robust, powerful statistical and computational methods and tools are developed to analyze these data, then progress can be made on identifying and characterizing the genetic components of complex disorders. This, in turn, has the potential to (1) lead to better understanding of the biology of such disorders, (2) clarify the role of environmental risk factors, which could be targets of cost-effective treatment and prevention strategies, and (3) lead to improvements in personalized medical care. Our goal is development of robust, powerful trait-association data analysis methods that will be useful for a wide variety of genetic and other "omics" predictors in a full spectrum of study designs, ranging from unrelated samples with population structure to individuals sampled from a complex, inbred pedigree. Specifically, we propose development of novel methods for binary, quantitative, and longitudinal trait mapping with a range of predictors, such as genotype, genomic sequence, transcriptome, expression, metabolomic, methylation, proteomic or other data, where these methods incorporate relevant covariates and account for population structure and/or relatedness of individuals in the sample.