Chen, Mengyuan; (2024) The application of a hidden Markov random field model in genome-wide association studies. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Genome-wide association studies (GWASs) are widely used to detect single nucleotide polymorphisms (SNPs) associated with diseases. Commonly, we use hypothesis testing to identify associations. When analysing multiple SNPs, people usually use multivariate analysis methods which are usually based on individual genotype data or meta analysis methods which integrate summary statistics from single SNP analysis. However, individual genotype data are difficult to obtain due to privacy and most meta analysis methods do not consider and utilize correlations between SNPs. All these multiple SNPs analysis methods can only test whether multiple SNPs are associated with one disease and cannot identify specific SNPs associated with disease within multiple SNPs. In this thesis, we study how to leverage linkage disequilibrium (LD) information, which summarises the degree of association between different SNPs, and use summary statistics to discover SNPs associated with disease. We propose to use a hidden Markov random field model (HMRF) to model the correlation structure between SNPs and FDR control procedure to identify the association. Simulation experiments show that our method is better than other methods in terms of controlling false discovery rate and the power of discovering true associated SNPs. Then the proposed method is extended into gene association analysis. Simulation studies demonstrate that our approach outperforms other methodologies concerning the control of false discovery rate and the efficacy in detecting associated genes.

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