Genome-Wide Association Study (GWAS) has recently been proposed as a powerful strategy for detecting the many subtle genetic variants that underlie phenotypic variation of complex polygenic traits in population-based samples. One of the main obstacles to successfully using the linkage disequilibrium based methods is knowledge of any underlying population structure. The presence of subgroups within a population can result in spurious association. A robust statistical method is developed to remove the population structure interference in GWAS by incorporating single control marker into testing for significance of genetic association of a polymorphic marker (SNP) with phenotypic variance of a complex trait. The novel approach avoids the need of structure prediction which could be infeasible or inadequate in practice and accounts properly for a varying effect of population stratification on different regions of the genome under study. Both intensive computer simulation study and eQTL analysis in genetically divergent human populations show that the new method confers an improved statistical power for detecting genuine genetic association in subpopulations and an effective control of spurious associations stemmed from population structure.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:567793 |
| Date | January 2013 |
| Creators | Jiang, Ning |
| Publisher | University of Birmingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.bham.ac.uk//id/eprint/4031/ |
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