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STATISTICAL METHODS IN GENETIC ASSOCIATION

ZHANG, GE
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1196099744

Abstract Details

2007, PhD, University of Cincinnati, Medicine : Environmental Health.
Association studies offer great promise in dissecting the genetic basic of human complex diseases. The rapid expansion of genomic information and the cost-effective genotyping technologies have enabled us to systematically interrogate the role of human genetic variation in common diseases by genome-wide association (GWA) mapping. However, the scale and complexity of such studies will raise significant challenges in study design and data analysis. In this dissertation, we investigated several statistical problems that relevant to population-based association studies and the fine-scale mapping of genetic variants that influence susceptibility to complex diseases. First, we developed a variance-based effect size estimator for the locus-specific genetic effect. Comparing to the traditional measures, the proposed estimator is less sensitive to the risk allele frequency and the population prevalence of the disease. We demonstrated the sample size requirement would be considerable large to obtain an accurate estimate on moderate genetic effect and the sample size will increase exponentially with increased demand for precision. We next compared the power of different association test statistics. We observed that the genotype based single-locus tests is generally more powerful than the multi-locus or haplotype based statistics, especially for risk alleles far from additive; and the power of genotype based tests can be uniformly improved by applying the ordered restriction on genotypic risks. Finally, we tested different GWA strategies and explored the factors that may influence the power of GWA studies by extensive simulations using empirical genotype data from the HapMap ENCODE Project. Our results indicate that current commercial genome-wide typing products are capable of capturing most of the common risk variants; however, their power in detecting rare risk variants or variants within recombination hot spots is not satisfactory. We also showed that the properties of the risk variants (e.g. allele frequency, local recombination rate, and functional category) have significant impacts on the power of GWA. The results generated from this comprehensive exercise would be helpful for developing efficient GWA studies.
Ranajit Chakraborty (Advisor)
283 p.
Biology, Genetics
Linkage disequilibrium; Haplotype; Genetic association; Genome-wide association; Association test; ;

Recommended Citations

Citations

  • ZHANG, G. (2007). STATISTICAL METHODS IN GENETIC ASSOCIATION [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1196099744

    APA Style (7th edition)

  • ZHANG, GE. STATISTICAL METHODS IN GENETIC ASSOCIATION. 2007. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1196099744.

    MLA Style (8th edition)

  • ZHANG, GE. "STATISTICAL METHODS IN GENETIC ASSOCIATION." Doctoral dissertation, University of Cincinnati, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1196099744

    Chicago Manual of Style (17th edition)

ucin1196099744
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© 2007, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.