A framework for transcriptome-wide association studies in breast cancer in diverse study populations
| Autor: | Bhattacharya, Arjun, García-Closas, Montserrat, Olshan, Andrew F., Perou, Charles M., Troester, Melissa A., Love, Michael I. |
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| Rok vydání: | 2020 |
| Předmět: |
Survival
lcsh:QH426-470 Quantitative Trait Loci Population Breast Neoplasms Genome-wide association study Computational biology Biology Expression quantitative trait loci (eQTL) 03 medical and health sciences Breast cancer 0302 clinical medicine Genotype Genetic variation medicine Humans education Transcriptome-wide analysis (TWAS) lcsh:QH301-705.5 030304 developmental biology Genetic association 0303 health sciences education.field_of_study Polymorphism Genetic Research Reproducibility of Results medicine.disease Human genetics Black or African American lcsh:Genetics Polygenic traits lcsh:Biology (General) Polygene 030220 oncology & carcinogenesis Cohort Expression quantitative trait loci Female Transcriptome Genome-Wide Association Study |
| Zdroj: | Genome Biology, Vol 21, Iss 1, Pp 1-18 (2020) Genome Biology |
| ISSN: | 1474-760X |
| Popis: | BackgroundThe relationship between germline genetic variation and breast cancer survival is largely unknown, especially in understudied minority populations who often have poorer survival. Genome-wide association studies (GWAS) have interrogated breast cancer survival but often are underpowered due to subtype heterogeneity and clinical covariates and detect loci in non-coding regions that are difficult to interpret. Transcriptome-wide association studies (TWAS) show increased power in detecting functionally relevant loci by leveraging expression quantitative trait loci (eQTLs) from external reference panels in relevant tissues. However, ancestry- or race-specific reference panels may be needed to draw correct inference in ancestrally diverse cohorts. Such panels for breast cancer are lacking.ResultsWe provide a framework for TWAS for breast cancer in diverse populations, using data from the Carolina Breast Cancer Study (CBCS), a population-based cohort that oversampled black women. We perform eQTL analysis for 406 breast cancer-related genes to train race-stratified predictive models of tumor expression from germline genotypes. Using these models, we impute expression in independent data from CBCS and TCGA, accounting for sampling variability in assessing performance. These models are not applicable across race, and their predictive performance varies across tumor subtype. Within CBCS (N = 3,828), at a false discovery-adjusted significance of 0.10 and stratifying for race, we identify associations in black women nearAURKA,CAPN13,PIK3CA, andSERPINB5via TWAS that are underpowered in GWAS.ConclusionsWe show that carefully implemented and thoroughly validated TWAS is an efficient approach for understanding the genetics underpinning breast cancer outcomes in diverse populations. |
| Databáze: | OpenAIRE |
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