Biology subjects

Barbeira, A.

Publications and source records attributed to Barbeira, A..

4 recordsLinked to original sources

bioRxiv2020

Pleiotropy-guided transcriptome imputation from normal and tumor tissues identifies new candidate susceptibility genes for breast and ovarian cancer

Familial, genome-wide association (GWAS), and sequencing studies and genetic correlation analyses have progressively unraveled the shared or pleiotropic germline genetics of breast and ovarian cancer. In this study, we aimed to leverage this shared germline genetics to improve the power of transcriptome-wide association studies (TWAS) to identify candidate breast cancer and ovarian cancer susceptibility genes. We built gene expression prediction models using the PrediXcan method in 681 breast and 295 ovarian tumors from The Cancer Genome Atlas and 211 breast and 99 ovarian normal tissue samples from the Genotype-Tissue Expression project and integrated these with GWAS meta-analysis data from the Breast Cancer Association Consortium (122,977 cases/105,974 controls) and the Ovarian Cancer Association Consortium (22,406 cases/40,941 controls). The integration was achieved through novel application of a pleiotropy-guided conditional/conjunction false discovery rate approach for the first time in the setting of a TWAS. This identified 14 new candidate breast cancer susceptibility genes spanning 11 genomic regions and 8 new candidate ovarian cancer susceptibility genes spanning 5 genomic regions at conjunction FDR < 0.05 that were > 1 Mb away from known breast and/or ovarian cancer susceptibility loci. We also identified 38 candidate breast cancer susceptibility genes and 17 candidate ovarian cancer susceptibility genes at conjunction FDR < 0.05 at known breast and/or ovarian susceptibility loci. Overlaying candidate causal risk variants identified by GWAS fine mapping onto expression prediction models for genes at known loci suggested that the association for 55% of these genes was driven by the underlying GWAS signal. SignificanceThe 22 new genes identified by our cross-cancer analysis represent promising candidates that further elucidate the role of the transcriptome in mediating germline breast and ovarian cancer risk.

genetics↗

bioRxiv2019

Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx

BackgroundPopulation structure among study subjects may confound genetic association studies, and lack of proper correction can lead to spurious findings. The Genotype-Tissue Expression (GTEx) project largely contains individuals of European ancestry, but the final release (v8) also includes up to 15% of individuals of non-European ancestry. Assessing ancestry-based adjustments in GTEx provides an opportunity to improve portability of this research across populations and to further measure the impact of population structure on GWAS colocalization.\n\nResultsHere, we identify a subset of 117 individuals in GTEx (v8) with a high degree of population admixture and estimate genome-wide local ancestry. We perform genome-wide cis-eQTL mapping using admixed samples in six tissues, adjusted by either global or local ancestry. Consistent with previous work, we observe improved power with local ancestry adjustment. At loci where the two adjustments produce different lead variants, we observe only 0.8% of tests with GWAS colocalization posterior probabilities that change by 10% or more. Notably, both adjustments produce similar numbers of significant colocalizations. Finally, we identify a small subset of GTEx v8 eQTL-associated variants highly correlated with local ancestry (R2 > 0.7), providing a resource to enhance functional follow-up.\n\nConclusionsWe provide a local ancestry map for admixed individuals in the final GTEx release and describe the impact of ancestry and admixture on gene expression, eQTLs, and GWAS colocalization. While the majority of results are concordant between local and global ancestry-based adjustments, we identify distinct advantages and disadvantages to each approach.

genetics↗

bioRxiv2019

Investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis

Transcriptome-wide association studies (TWAS), an integrative framework using expression quantitative trait loci (eQTLs) to construct proxies for gene expression, have emerged as a promising method to investigate the biological mechanisms underlying associations between genotypes and complex traits. However, challenges remain in interpreting TWAS results, especially regarding their causality implications. In this paper, we describe a new computational framework, probabilistic TWAS (PTWAS), to detect associations and investigate causal relationships between gene expression and complex traits. We use established concepts and principles from instrumental variables (IV) analysis to delineate and address the unique challenges that arise in TWAS. PTWAS utilizes probabilistic eQTL annotations derived from multi-variant Bayesian fine-mapping analysis conferring higher power to detect TWAS associations than existing methods. Additionally, PTWAS provides novel functionalities to evaluate the causal assumptions and estimate tissue- or cell-type specific causal effects of gene expression on complex traits. These features make PTWAS uniquely suited for in-depth investigations of the biological mechanisms that contribute to complex trait variation. Using eQTL data across 49 tissues from GTEx v8, we apply PTWAS to analyze 114 complex traits using GWAS summary statistics from several large-scale projects, including the UK Biobank. Our analysis reveals an abundance of genes with strong evidence of eQTL-mediated causal effects on complex traits and highlights the heterogeneity and tissue-relevance of these effects across complex traits. We distribute software and eQTL annotations to enable users performing rigorous TWAS analysis by leveraging the full potentials of the latest GTEx multi-tissue eQTL data.

genetics↗

bioRxiv2019

Cell type specific genetic regulation of gene expression across human tissues

The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci (cis-QTLs) for the majority of genes across a wide range of human tissues. However, the interpretation of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. Here, we map interactions between computational estimates of cell type abundance and genotype to identify cell type interaction QTLs for seven cell types and show that cell type interaction eQTLs provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations to 87 complex traits show a contribution from cell type interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.\n\nOne Sentence SummaryEstimated cell type abundances from bulk RNA-seq across tissues reveal the cellular specificity of quantitative trait loci.

genomics↗