The origin, the function and the phenotypic impact of human alleles

遗传变异是进化创新的主要来源， 用于表型变异。因此，了解这种变化在 包括基础生物学和进化论，以及最终的孟德尔和复杂疾病。 我们将通过自发突变过程研究遗传变异的起源。 测序数据集的计算分析将揭示机械力 潜在的人类生殖系和体细胞癌症突变。我们将设计新的统计 新突变的模型将在群体遗传学，癌症基因组学， 和神经精神疾病的遗传学。 接下来，我们将改进计算方法，用于解释和预测 分子功能上的突变，包括编码和非编码变异。我们的方法 整合来自进化遗传学和生物物理学的数据，并依靠比较、功能 结构数据。新开发的方法将在医学和生物医学领域得到应用。 群体遗传学 我们将研究等位基因的种群动态，以估计塑造遗传的力量。 种群内的变异。我们将依靠群体遗传学模型来分析 人类表型的进化维持和遗传结构。着迷于 基因型和表型之间的关系，我们将结合联合收割机的理论模型， 大规模测序数据集的统计分析以推断等位基因的性质 复杂特征的结构。我们将设计新的方法来表征和预测 常见疾病风险的遗传成分。!

Genetic variation is the primary source of evolutionary innovation and a major factor responsible for phenotypic variation. Consequently, understanding such variation has great importance in both basic biology and evolution, and ultimately Mendelian and complex disease. We will study the origin of genetic variation through spontaneous mutational processes. Computational analysis of sequencing datasets will shed light on the mechanistic forces underlying germ-line and somatic cancer mutations in human. We will design new statistical models of de novo mutation that will have applications in population genetics, cancer genomics and genetics of neuropsychiatric disease. Next, we will improve computational methods for interpreting and predicting the effect of mutation on molecular function, including both coding and non-coding variation. Our methods integrate data from evolutionary genetics and biophysics and rely on comparative, functional and structural data. The newly developed methods will have applications in both medical and population genetics. We will study the population dynamics of alleles to estimate the forces that shape genetic variation within populations. We will rely on population genetics models to analyze evolutionary maintenance and genetic architecture of human phenotypes. Fascinated by the relationship between genotype and phenotype, we will combine theoretical models and statistical analysis of large-scale sequencing datasets to infer properties of the allelic architecture of complex traits. We will design new approaches to characterize and predict the genetic component of common disease risk. !