Genetic association analyses have successfully identified thousands of genetic variants contributing to complex disease susceptibility. However, these discoveries do not explain the full heritability of many diseases, due to the limited statistical power to detect loci with small effects, especially in regions with rare variants. The development of new and powerful methods is necessary to fully characterize the underlying genetic basis of complex diseases. Family history (FH) contains information on the disease status of un-genotyped relatives, which is related to the genotypes of probands at disease loci. Exploiting available FH in relatives could potentially enhance the ability to identify associations by increasing sample size. Many studies have very low power for genetic research in late-onset diseases because younger participants do not contribute a sufficient number of cases and older patients are more likely deceased without genotypes. Genetic association studies relying on cases and controls need to progress by incorporating additional information from FH to expand genetic research.
This dissertation overcomes these challenges and opens up a new paradigm in genetic research. The first chapter summarizes relevant methods used in this dissertation. In the second chapter, we develop novel methods to exploit the availability of FH in aggregation unit-based test, which have greater power than other existing methods that do not incorporate FH, while maintaining a correct type I error. In the third chapter, we develop methods to exploit FH while adjusting for relatedness using the generalized linear mixed effect models. Such adjustment allows the methods to have well-controlled type I error and maintain the highest sample size because there is no need to restrict the analysis to an unrelated subset in family studies. We demonstrate the flexibility and validity of the methods to incorporate FH from various relatives. The methods presented in the fourth chapter overcome the issue of inflated type I error caused by extremely unbalanced case-control ratio. We propose robust versions of the methods developed in the second and third chapters, which can provide more accurate results for unbalanced study designs. Availability of these novel methods will facilitate the identification of rare variants associated with complex traits.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/44024 |
| Date | 14 March 2022 |
| Creators | Wang, Yanbing |
| Contributors | Dupuis, Josée |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution-ShareAlike 4.0 International, http://creativecommons.org/licenses/by-sa/4.0/ |
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