Novel insights into allele-specific expression and translation in human lymphoblastoid cell lines through integrative analysis of transcriptomic and ribosome profiling data

The rate of mRNA translation makes an important contribution to determining protein abundance in cells and genetic variants that affect the rate at which a protein is synthesized may give rise to human genetic diseases and phenotypes. Cis-acting regulatory elements affecting translation can be revealed from the imbalances in translation rates between the two alternative alleles of a gene in a heterozygous diploid individual. The work presented in this thesis represents the first attempt to infer allelic differences in the rate of protein translation, referred to as allele-specific translation (AST), and develops a mixture model to estimate the prevalence of AST in human lymphoblastoid cell lines (LCLs), using an integrative analysis of transcriptomic and ribosome profiling sequencing data. We applied the pipeline to identify AST in data from both the HeLa and 63 LCLs and found that AST is widespread (a median of 31% of genes are affected), with broader and stronger effect sizes than are found for allele- specific expression across human LCLs. Variants associated with AST are enriched in the 5′ leaders of mRNAs, suggesting that translational control is mostly regulated at the initiation step. We found several cancer- and disease-associated genes that exhibit strong AST signals in HeLa (e.g. NQO1) and multiple LCL samples (e.g. RPLP2 and SREBF2) and validated one candidate causal AST variant experimentally (in the NQO1 gene). The dysregulation of expression of these genes is observed in several cancers as well as other diseases and complex phenotypes, indicating the potential for clinically relevant impacts of genetic variants in these genes. Further investigation of the phenotypic effects of these genetic variations during the pathophysiological development of the diseases might help to uncover the mechanisms underlying the etiology of these diseases. Thus, our method contributes to better understanding of human translational biology