Dataset Information

Genetic Analysis of Functional Rare Germline Variants across Nine Cancer Types from an Electronic Health Record Linked Biobank.

ABSTRACT:

Background

Rare variants play an essential role in the etiology of cancer. In this study, we aim to characterize rare germline variants that impact the risk of cancer.

Methods

We performed a genome-wide rare variant analysis using germline whole exome sequencing (WES) data derived from the Geisinger MyCode initiative to discover cancer predisposition variants. The case-control association analysis was conducted by binning variants in 5,538 patients with cancer and 7,286 matched controls in a discovery set and 1,991 patients with cancer and 2,504 matched controls in a validation set across nine cancer types. Further, The Cancer Genome Atlas (TCGA) germline data were used to replicate the findings.

Results

We identified 133 significant pathway-cancer pairs (85 replicated) and 90 significant gene-cancer pairs (12 replicated). In addition, we identified 18 genes and 3 pathways that were associated with survival outcome across cancers (Bonferroni P < 0.05).

Conclusions

In this study, we identified potential predisposition genes and pathways based on rare variants in nine cancers.

Impact

This work adds to the knowledge base and progress being made in precision medicine.

SUBMITTER: Shivakumar M

PROVIDER: S-EPMC8419143 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Publications

Genetic Analysis of Functional Rare Germline Variants across Nine Cancer Types from an Electronic Health Record Linked Biobank.

Shivakumar Manu M Miller Jason E JE Dasari Venkata Ramesh VR Zhang Yanfei Y Lee Ming Ta Michael MTM Carey David J DJ Gogoi Radhika R Kim Dokyoon D

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20210708 9

<h4>Background</h4>Rare variants play an essential role in the etiology of cancer. In this study, we aim to characterize rare germline variants that impact the risk of cancer.<h4>Methods</h4>We performed a genome-wide rare variant analysis using germline whole exome sequencing (WES) data derived from the Geisinger MyCode initiative to discover cancer predisposition variants. The case-control association analysis was conducted by binning variants in 5,538 patients with cancer and 7,286 matched co ...[more]

PMID: 34244158

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Project description:BackgroundLarge medical centers in urban areas, like Los Angeles, care for a diverse patient population and offer the potential to study the interplay between genetic ancestry and social determinants of health. Here, we explore the implications of genetic ancestry within the University of California, Los Angeles (UCLA) ATLAS Community Health Initiative-an ancestrally diverse biobank of genomic data linked with de-identified electronic health records (EHRs) of UCLA Health patients (N=36,736).MethodsWe quantify the extensive continental and subcontinental genetic diversity within the ATLAS data through principal component analysis, identity-by-descent, and genetic admixture. We assess the relationship between genetically inferred ancestry (GIA) and >1500 EHR-derived phenotypes (phecodes). Finally, we demonstrate the utility of genetic data linked with EHR to perform ancestry-specific and multi-ancestry genome and phenome-wide scans across a broad set of disease phenotypes.ResultsWe identify 5 continental-scale GIA clusters including European American (EA), African American (AA), Hispanic Latino American (HL), South Asian American (SAA) and East Asian American (EAA) individuals and 7 subcontinental GIA clusters within the EAA GIA corresponding to Chinese American, Vietnamese American, and Japanese American individuals. Although we broadly find that self-identified race/ethnicity (SIRE) is highly correlated with GIA, we still observe marked differences between the two, emphasizing that the populations defined by these two criteria are not analogous. We find a total of 259 significant associations between continental GIA and phecodes even after accounting for individuals' SIRE, demonstrating that for some phenotypes, GIA provides information not already captured by SIRE. GWAS identifies significant associations for liver disease in the 22q13.31 locus across the HL and EAA GIA groups (HL p-value=2.32×10-16, EAA p-value=6.73×10-11). A subsequent PheWAS at the top SNP reveals significant associations with neurologic and neoplastic phenotypes specifically within the HL GIA group.ConclusionsOverall, our results explore the interplay between SIRE and GIA within a disease context and underscore the utility of studying the genomes of diverse individuals through biobank-scale genotyping linked with EHR-based phenotyping.

Dataset Information

Genetic Analysis of Functional Rare Germline Variants across Nine Cancer Types from an Electronic Health Record Linked Biobank.

Background

Methods

Results

Conclusions

Impact

Publications

Genetic Analysis of Functional Rare Germline Variants across Nine Cancer Types from an Electronic Health Record Linked Biobank.

Similar Datasets

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