Biology subjects

Barbeira, A. N.

Publications and source records attributed to Barbeira, A. N..

3 recordsLinked to original sources

bioRxiv2018

Cancer eQTLs can be determined from heterogeneous tumor gene expression data by modeling variation in tumor purity

Expression quantitative trait loci (eQTLs) identified using tumor gene expression data could affect gene expression in cancer cells, tumor-associated normal cells, or both. Here, we demonstrate a method to identify eQTLs affecting expression in cancer cells by modeling the statistical interaction between genotype and tumor purity. Only one-third of breast cancer risk variants, identified as eQTLs from a conventional analysis, could be confidently attributed to cancer cells. The remaining variants could affect cells of the tumor microenvironment, such as immune cells and fibroblasts. Deconvolution of tumor eQTLs will help determine how inherited polymorphisms influence cancer risk, development, and treatment response.

genomics↗

bioRxiv2018

Integrating Predicted Transcriptome From Multiple Tissues Improves Association Detection

Integration of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is needed to improve our understanding of the biological mechanisms underlying GWAS hits, and our ability to identify therapeutic targets. Gene-level association test methods such as PrediXcan can prioritize candidate targets. However, limited eQTL sample sizes and absence of relevant developmental and disease context restricts our ability to detect associations. Here we propose an efficient statistical method that leverages the substantial sharing of eQTLs across tissues and contexts to improve our ability to identify potential target genes: MulTiXcan. MulTiXcan integrates evidence across multiple panels while taking into account their correlation. We apply our method to a broad set of complex traits available from the UK Biobank and show that we can detect a larger set of significantly associated genes than using each panel separately. To improve applicability, we developed an extension to work on summary statistics: S-MulTiXcan, which we show yields highly concordant results with the individual level version. Results from our analysis as well as software and necessary resources to apply our method are publicly available.

genomics↗

bioRxiv2017

Integrative cross tissue analysis of gene expression identifies novel type 2 diabetes genes

To understand the mechanistic underpinnings of type 2 diabetes (T2D) loci mapped through GWAS, we performed a tissue-specific gene association study in a cohort of over 100K individuals (ncases {approx} 26K,ncontrols {approx} 84K) across 44 human tissues using MetaXcan, a summary statistics extension of PrediXcan. We found that 90 genes significantly (FDR < 0.05) associated with T2D, of which 24 are previously reported T2D genes, 29 are novel in established T2D loci, and 37 are novel genes in novel loci. Of these, 13 reported genes, 15 novel genes in known loci, and 6 genes in novel loci replicated (FDRrep < 0.05) in an independent study (ncases {approx} 10K, ncontrols {approx} 62K). We also found enrichment of significant associations in expected tissues such as liver, pancreas, adipose, and muscle but also in tibial nerve, fibroblasts, and breast. Finally, we found that monogenic diabetes genes are enriched in T2D genes from our analysis suggesting that moderate alterations in monogenic (severe) diabetes genes may promote milder and later onset type 2 diabetes.

genetics↗