We evaluated ancestry effects on mutation rates, DNA methylation, and mRNA and miRNA expression among 10,678 patients across 33 cancer types from The Cancer Genome Atlas. We demonstrated that cancer ...subtypes and ancestry-related technical artifacts are important confounders that have been insufficiently accounted for. Once accounted for, ancestry-associated differences spanned all molecular features and hundreds of genes. Biologically significant differences were usually tissue specific but not specific to cancer. However, admixture and pathway analyses suggested some of these differences are causally related to cancer. Specific findings included increased FBXW7 mutations in patients of African origin, decreased VHL and PBRM1 mutations in renal cancer patients of African origin, and decreased immune activity in bladder cancer patients of East Asian origin.
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•This large analysis identified ancestry correlates in cancer•Ancestry-associated artifacts and confounders were identified•Ancestry effects are profoundly tissue specific•Rates of FBXW7, VHL, and PBRM1 mutations and immune activity vary by ancestry
Analyzing mutation rates, gene and miRNA expression, and DNA methylation across tumor types, Carrot-Zhang et al. separate confounders and identify ancestry-related effects that potentially explain cancer etiology and treatment.
Differential mRNA expression between ancestry groups can be explained by both genetic and environmental factors. We outline a computational workflow to determine the extent to which germline genetic ...variation explains cancer-specific molecular differences across ancestry groups. Using multi-omics datasets from The Cancer Genome Atlas (TCGA), we enumerate ancestry-informative markers colocalized with cancer-type-specific expression quantitative trait loci (e-QTLs) at ancestry-associated genes. This approach is generalizable to other settings with paired germline genotyping and mRNA expression data for a multi-ethnic cohort.
For complete details on the use and execution of this protocol, please refer to Carrot-Zhang et al. (2020), Robertson et al. (2021), and Sayaman et al. (2021).
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•Protocol for obtaining controlled access TCGA datasets•Protocols for quality control analysis and genotype imputation of TCGA germline data•Statistical analysis for determining ancestry-associated SNPs•Determination of ancestry-associated germline genetic variation driving mRNA expression
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
Differential mRNA expression between ancestry groups can be explained by both genetic and environmental factors. We outline a computational workflow to determine the extent to which germline genetic variation explains cancer-specific molecular differences across ancestry groups. Using multi-omics datasets from The Cancer Genome Atlas (TCGA), we enumerate ancestry-informative markers colocalized with cancer-type-specific expression quantitative trait loci (e-QTLs) at ancestry-associated genes. This approach is generalizable to other settings with paired germline genotyping and mRNA expression data for a multi-ethnic cohort.
People of different ancestries vary in cancer risk and outcome, and their molecular differences may indicate sources of these variations. Determining the “local” ancestry composition at each genetic ...locus across ancestry-admixed populations can suggest causal associations. We present a protocol to identify local ancestry and detect the associated molecular changes, using data from the Cancer Genome Atlas. This workflow can be applied to cancer cohorts with matched tumor and normal data from admixed patients to examine germline contributions to cancer.
For complete details on the use and execution of this protocol, please refer to Carrot-Zhang et al. (2020).
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•Protocols for local ancestry identification using the TCGA data•Detecting local ancestry associated with cancer risk•Statistical analysis associating molecular changes with local ancestry•Understanding the contribution of genetic ancestry in admixed patients
People of different ancestries vary in cancer risk and outcome, and their molecular differences may indicate sources of these variations. Determining the “local” ancestry composition at each genetic locus across ancestry-admixed populations can suggest causal associations. We present a protocol to identify local ancestry and detect the associated molecular changes, using data from the Cancer Genome Atlas. This workflow can be applied to cancer cohorts with matched tumor and normal data from admixed patients to examine germline contributions to cancer.
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