Trans-ethnic predicted expression genome-wide association analysis identifies a gene for estrogen receptor-negative breast cancer

Genome-wide association studies (GWAS) have identified more than 90 susceptibility loci for breast cancer, but the underlying biology of those associations needs to be further elucidated. More genetic factors for breast cancer are yet to be identified but sample size constraints preclude the identif...

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Bibliographic Details
Published in:PLoS genetics 2017-09, Vol.13 (9), p.e1006727-e1006727
Main Authors: Gao, Guimin, Pierce, Brandon L, Olopade, Olufunmilayo I, Im, Hae Kyung, Huo, Dezheng
Format: Article
Language:English
Subjects:
Association analysis
Biology and Life Sciences
Breast cancer
Breast Neoplasms - genetics
Breast Neoplasms - pathology
Disease susceptibility
Estrogen Receptor alpha - genetics
Estrogen receptors
Estrogens
Female
Gene expression
Gene Expression Regulation, Neoplastic
Gene mapping
Genes
Genetic aspects
Genetic diversity
Genetic factors
Genetic Predisposition to Disease
Genome-wide association studies
Genome-Wide Association Study
Genomes
Haptoglobins - genetics
Health aspects
Health risk assessment
Humans
Medicine and Health Sciences
Nuclear Proteins - genetics
p53 Protein
Physical Sciences
Polymorphism, Single Nucleotide
Quantitative trait loci
Research and Analysis Methods
Single-nucleotide polymorphism
Statistical analysis
Studies
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Summary:Genome-wide association studies (GWAS) have identified more than 90 susceptibility loci for breast cancer, but the underlying biology of those associations needs to be further elucidated. More genetic factors for breast cancer are yet to be identified but sample size constraints preclude the identification of individual genetic variants with weak effects using traditional GWAS methods. To address this challenge, we utilized a gene-level expression-based method, implemented in the MetaXcan software, to predict gene expression levels for 11,536 genes using expression quantitative trait loci and examine the genetically-predicted expression of specific genes for association with overall breast cancer risk and estrogen receptor (ER)-negative breast cancer risk. Using GWAS datasets from a Challenge launched by National Cancer Institute, we identified TP53INP2 (tumor protein p53-inducible nuclear protein 2) at 20q11.22 to be significantly associated with ER-negative breast cancer (Z = -5.013, p = 5.35×10-7, Bonferroni threshold = 4.33×10-6). The association was consistent across four GWAS datasets, representing European, African and Asian ancestry populations. There are 6 single nucleotide polymorphisms (SNPs) included in the prediction of TP53INP2 expression and five of them were associated with estrogen-receptor negative breast cancer, although none of the SNP-level associations reached genome-wide significance. We conducted a replication study using a dataset outside of the Challenge, and found the association between TP53INP2 and ER-negative breast cancer was significant (p = 5.07x10-3). Expression of HP (16q22.2) showed a suggestive association with ER-negative breast cancer in the discovery phase (Z = 4.30, p = 1.70x10-5) although the association was not significant after Bonferroni adjustment. Of the 249 genes that are 250 kb within known breast cancer susceptibility loci identified from previous GWAS, 20 genes (8.0%) were statistically significant associated with ER-negative breast cancer (p
ISSN:1553-7404
1553-7390
1553-7404
DOI:10.1371/journal.pgen.1006727