Biology subjects

Gleason, K. J.

Publications and source records attributed to Gleason, K. J..

4 recordsLinked to original sources

bioRxiv2020

A robust two-sample Mendelian Randomization method integrating GWAS with multi-tissue eQTL summary statistics

In the post-genome-wide association era, two-sample Mendelian Randomization (MR) methods have been applied to detect genetically-regulated risk factors for complex diseases. Two-sample MR considers single nucleotide polymorphisms (SNPs) associated with a putative exposure as instrumental variables (IVs) to assess the effect of the exposure on an outcome by leveraging two sets of summary statistics: IV-to-exposure and IV-to-outcome statistics from existing GWASs. Traditional MR methods impose strong assumptions on the validity of IVs, and recent literature has relaxed the assumptions allowing some IVs to be invalid but generally requiring a large number of nearly independent IVs. When treating expression-quantitative-trait-loci (eQTLs) as IVs to detect gene expression levels affecting diseases, existing methods are limited in applicability since the numbers of independent eQTLs for most genes in the genome are limited. To address those challenges, we propose a robust two-sample MR framework that requires fewer IVs and allows moderate IV correlations and some IVs to be invalid. This is achieved by leveraging existing multi-tissue eQTL summary statistics (multiple sets of IV-to-exposure statistics) and GWAS statistics in a mixed model framework. We conducted simulation studies to evaluate the performance of the proposed method and apply it to detect putative causal genes for schizophrenia.

genetics↗

bioRxiv2019

CCmed: cross-condition mediation analysis for identifying robust trans-eQTLs and assessing their effects on human traits

Trans-eQTLs collectively explain a substantial proportion of expression variation, yet are challenging to detect and replicate since their effects are individually weak. Many trans-effects are mediated by cis-gene expression and some of those effects are shared across tissue types/conditions. To detect robust cis-mediated trans-associations at the gene-level and for specific single nucleotide polymorphisms (SNPs), we proposed two Cross-Condition Mediation methods - CCmedgene and CCmedGWAS, respectively. We analyzed data from 13 brain tissue types from the Genotype-Tissue Expression (GTEx) project, and identified trios with cis-eQTLs of a cis-gene having associations with a trans-gene, many of which show evidence of replication in other datasets. By applying CCmedGWAS, we identified trans-genes associated with known schizophrenia susceptibility loci. We further conducted validation analyses assessing the schizophrenia-risk-associations of the identified trans-genes, by harnessing GWAS summary statistics from the Psychiatric Genomics Consortium and multitissue eQTL statistics from GTEx.

genetics↗

bioRxiv2019

Primo: integration of multiple GWAS and omics QTL summary statistics for elucidation of molecular mechanisms of trait-associated SNPs and detection of pleiotropy in complex traits

To provide a comprehensive mechanistic interpretation of how known trait-associated SNPs affect complex traits, we propose a method - Primo - for integrative analysis of GWAS summary statistics with multiple sets of omics QTL summary statistics from different cellular conditions or studies. Primo examines SNPs association patterns to complex and omics traits. In gene regions harboring known susceptibility loci, Primo performs conditional association analysis to account for linkage disequilibrium. Primo allows for unknown study heterogeneity and sample correlations. We show two applications using Primo to examine the molecular mechanisms of known susceptibility loci and to detect and interpret pleiotropic effects.

genomics↗

bioRxiv2018

Insights into impact of DNA copy number alteration and methylation on the proteogenomic landscape of human ovarian cancer via a multi-omics integrative analysis

In this work, we propose iProFun, an integrative analysis tool to screen for Proteogenomic Functional traits perturbed by DNA copy number alterations (CNA) and DNA methylations. The goal is to characterize functional consequences of DNA copy number and methylation alterations in tumors and to facilitate screening for cancer drivers contributing to tumor initiation and progression. Specifically, we consider three functional molecular quantitative traits: mRNA expression levels, global protein abundances, and phosphoprotein abundances. We aim to identify those genes whose CNAs and/or DNA methylations have cis-associations with either some or all three types of molecular traits. In comparison with analyzing each molecular trait separately, the joint modeling of multi-omics data enjoys several benefits: iProFun experienced enhanced power for detecting significant cis-associations shared across different omics data types; and it also achieved better accuracy in inferring cis-associations unique to certain type(s) of molecular trait(s). For example, unique associations of CNA/methylations to global/phospho protein abundances may imply post-translational regulations.\n\nWe applied iProFun to ovarian high-grade serous carcinoma tumor data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium, and identified CNAs and methylations of 500 and 122 genes, respectively, affecting the cis-functional molecular quantitative traits of the corresponding genes. We observed substantial power gain via the joint analysis of iProFun. For example, iProFun identified 130 genes whose CNAs were associated with phosphoprotein abundances by leveraging mRNA expression levels and global protein abundances. By comparison, analyses based on phosphoprotein data alone identified none. A group of these 130 genes clustered in a small region on Chromosome 14q, harboring the known oncogene, AKT1. In addition, iProFun identified one gene, CANX, whose DNA methylation has a cis-association with its global protein abundances but not its mRNA expression levels These and other genes identified by iProFun could serve as potential drug targets for ovarian cancer.

bioinformatics↗