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Title: Statistical methods for rare variant association
Author: Alshahrani, Mohammed Nasser D.
ISNI:       0000 0004 7657 1416
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2018
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Deoxyribonucleic acid (DNA) sequencing allows researchers to conduct more complete assessments of low-frequency and rare genetic variants. In anticipation of the availability of next-generation sequencing data, there is increasing interest in investigating associations between complex traits and rare variants (RVs). In contrast to association studies of common variants (CVs), due to the low frequencies of RVs, common wisdom suggests that existing statistical tests for CVs might not work, motivating the recent development of several new tests that analyze RVs, most of which are based on the idea of pooling/collapsing RVs. Genome-wide association studies (GWAS) based on common SNPs gained more attention in the last few years and have been regularly used to examine complex genetic compositions of diseases and quantitative traits. GWASs have not discovered everything associated with diseases and genetic variations. However, recent empirical evidence has demonstrated that low-frequency and rare variants are, in fact, connected to complex diseases. This thesis will focus on the study of rare variant association. Aggregation tests, where multiple rare variants are analyzed jointly, have incorporated weighting schemes on variants. However, their power is very much dependent on the weighting scheme. I will address three topics in this thesis: the definition of rare variants and their call file (VCF) and a description of the methods that have been used in rare variant analysis. Finally, I will illustrate challenges involved in the analysis of rare variants and propose different weighting schemes for them. Therefore, since the efficiency of rare variant studies might be considerably improved by the application of an appropriate weighting scheme, choosing the proper weighting scheme is the topic of the thesis. In the following chapters, I will propose different weighting schemes, where weights are applied at the level of the variant, the individual or the cell (i.e. the individual genotype call), as well as a weighting scheme that can incorporate quality measures for variants (i.e., a quality score for variant calls) and cells (i.e., genotype quality).
Supervisor: Gusnanto, Arief ; Taylor, Charles ; Barrett, Jenny Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available