Improving Discovery of Causal Variants in Genetic Association Studies

imputation;genetic association;tagging;statistical genetics;population genetics;synthetic association;Genome-wide association

Dickson, Samuel Price ; Greg Gibson, Committee Chair,Marie Davidian, Committee Co-Chair,David Bird, Committee Member,Matt Nelson, Committee Member,Jung-Ying Tzeng, Committee Member,Dickson, Samuel Price ; Greg Gibson ; Committee Chair ; Marie Davidian ; Committee Co-Chair ; David Bird ; Committee Member ; Matt Nelson ; Committee Member ; Jung-Ying Tzeng ; Committee Member

University:North Carolina State University

关键词: imputation;    genetic association;    tagging;    statistical genetics;    population genetics;    synthetic association;    Genome-wide association;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/5237/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

In recent years population-based association studies have been advocated as the most powerful method of discovering genetic loci that are associated with heritable traits, particularly for complex traits that are likely caused by a variety of factors including environmental effects and multiple genetic loci.Genome-wide association studies (GWAS) have already yielded a large number of such associations, but there is growing concern that the results of these studies are not explaining as much genetic variation as they were expected to.Chapter 2 discusses tagging and imputation to leverage the information available on commercial genotyping chips to make inferences about variants found in large reference samples such as those made available by the International HapMap Consortium.Transferability of multi-marker tagging is assessed.Tagging and imputation are compared, and a method of using tagging to select a reduced tag set to be used for imputation.Chapter 3 details how multiple low frequency causal variants can create synthetic associations among more common variants and may be responsible for many of the genome-wide associations that have already been observed.Examples of synthetic associations are demonstrated in congenital deafness and sickle-cell anemia.Chapter 4 examines issues related to combining samples of diverse genetic ancestry for analysis in genetic association studies.Through simulation it is shown that type I error can be controlled and power increased using statistical methods to account for differences in populations.