Models of Complex Genetic Systems

描述(申请人提供)：新的DNA测序方法已经从人类和许多其他物种产生了大量的DNA序列，包括现在已经灭绝的物种。拟议的研究将有助于分析和解释DNA序列数据，既可以理解自然选择和重组等基本遗传过程，也可以了解人类历史和遗传病的遗传基础。拟议的研究在四个方面进行：(I)分析尼安德特人骨骼的DNA序列数据，以了解尼安德特人与现代人之间的关系，并了解现代人自尼安德特人分离以来的近代史；(Ii)预测最近经历范围扩大的种群的基因频率模式，特别侧重于欧洲、亚洲和美洲现代人种群过去的范围扩展；(Iii)分析从极端环境样本中获得的细菌物种的DNA序列，目的是量化不同菌株之间的基因重组和分化的程度，(Iv)对精神分裂症、多种癌症以及1型和2型糖尿病等复杂遗传性疾病的遗传基础进行建模，以量化造成高风险的基因之间的相互作用，并设计新的方法来确定与高风险相关的基因。虽然这四个领域的目标略有不同，但每个领域都将使用类似的数学和统计方法。将开发包括自然选择、重组、遗传漂移和迁徙在内的种群遗传力模型，并根据现有数据进行测试。这些模型的计算机模拟将被用来生成用于测试所提议的统计方法的假设数据集。 公共卫生相关性：在美国和其他发达国家，癌症、心脏病和糖尿病等部分遗传但不是由单基因缺陷引起的疾病是导致死亡的主要原因。这项拟议的研究将开发数学和统计工具，通过提高对现代人历史的理解，并通过寻找新的方法来识别导致此类疾病风险更高的基因，从而帮助理解此类疾病的遗传基础。

DESCRIPTION (provided by applicant): New methods of DNA sequencing have produced a great abundance of DNA sequences from humans and many other species, including species now extinct. The proposed research will be intended to help analyze and interpret DNA sequence data both for understanding basic genetic processes such as natural selection and recombination and for understanding the history of humans and the genetic basis of inherited diseases. The proposed research is in four areas: (i) the analysis of DNA sequence data from Neanderthal bones for the purpose of understanding the relationship between Neanderthals and modern humans and for understanding the recent history of modern humans since their separation from Neanderthals, (ii) the prediction of patterns in gene frequencies in populations that have undergone a recent range expansion, with particular emphasis on past range expansions of modern humans populations in Europe, Asia, and the Americas, (iii) the analysis of DNA sequences of bacterial species obtained in samples from extreme environments, for the purpose of quantifying the extent of genetic recombination and differentiation between different strains, (iv) modeling the genetic basis of complex inherited diseases such as schizophrenia, many cancers, and type 1 and 2 diabetes, for the purpose of quantifying the interactions among genes that create higher risk and for devising new methods for identifying genes associated with higher risk. Although the goals of the four areas are somewhat different, similar mathematical and statistical methods will be used in each. Models of population genetic forces, including natural selection, recombination, genetic drift, and migration will be developed and tested against available data. Computer simulations of those models will be used to generate hypothetical data sets for testing proposed statistical methods. PUBLIC HEALTH RELEVANCE: Diseases, such as cancer, heart disease, and diabetes, that are partly inherited yet are not caused by defects in single genes, are the leading cause of mortality in the US and other developed countries. The proposed research will develop mathematical and statistical tools that will help with understanding the genetic basis of such diseases, both by improving the understanding of the history of modern humans and by finding new ways to identify genes causing a higher risk of such diseases.