THESIS
2019
xi, 48 pages : illustrations ; 30 cm
Abstract
In genome-wide association studies (GWASs), a large number of trait-associated single-nucleotide
polymorphisms (SNPs) have been detected. Among these associations, not all
SNPs are the causal ones due to the correlation between SNPs. Many statistical fine-mapping
methods have been proposed to identify SNPs that mechanistically affect a
disease/trait. These existing methods need information of all SNPs for the fine-mapping
purpose. Before the whole-genome sequencing technique is widely used in genome-wide
association studies to genotype every SNP in the genome, we need to rely on imputation
methods to obtain genotypes or summary statistics of all SNPs. However, existing
imputation methods assume the effect sizes are all zero in their calculations. This unrealistic
assumption has...[
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In genome-wide association studies (GWASs), a large number of trait-associated single-nucleotide
polymorphisms (SNPs) have been detected. Among these associations, not all
SNPs are the causal ones due to the correlation between SNPs. Many statistical fine-mapping
methods have been proposed to identify SNPs that mechanistically affect a
disease/trait. These existing methods need information of all SNPs for the fine-mapping
purpose. Before the whole-genome sequencing technique is widely used in genome-wide
association studies to genotype every SNP in the genome, we need to rely on imputation
methods to obtain genotypes or summary statistics of all SNPs. However, existing
imputation methods assume the effect sizes are all zero in their calculations. This unrealistic
assumption has introduced some error to the imputation results, and has therefore
affected the accuracy of fine-mapping.
In this thesis, we propose a novel method to carry out fine-mapping and imputation
jointly to avoid the
flawed assumption in current imputation methods. Experiments with
simulation data and real data illustrate that our new method outperforms traditional fine-mapping
methods in both accuracy and speed. For imputation, our method also shows
slightly improvement in accuracy compared with traditional methods.
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