Imputation of the Rare HOXB13 G84E Mutation and Cancer Risk in a Large Population-Based Cohort

Abstract

An efficient approach to characterizing the disease burden of rare genetic variants is to impute them into large well-phenotyped cohorts with existing genome-wide genotype data using large sequenced referenced panels. The success of this approach hinges on the accuracy of rare variant imputation, which remains controversial. For example, a recent study suggested that one cannot adequately impute the HOXB13 G84E mutation associated with prostate cancer risk (carrier frequency of 0.0034 in European ancestry participants in the 1000 Genomes Project). We show that by utilizing the 1000 Genomes Project data plus an enriched reference panel of mutation carriers we were able to accurately impute the G84E mutation into a large cohort of 83,285 non-Hispanic White participants from the Kaiser Permanente Research Program on Genes, Environment and Health Genetic Epidemiology Research on Adult Health and Aging cohort. Imputation authenticity was confirmed via a novel classification and regression tree method, and then empirically validated analyzing a subset of these subjects plus an additional 1,789 men from Kaiser specifically genotyped for the G84E mutation (r2 = 0.57, 95% CI = 0.37−0.77). We then show the value of this approach by using the imputed data to investigate the impact of the G84E mutation on age-specific prostate cancer risk and on risk of fourteen other cancers in the cohort. The age-specific risk of prostate cancer among G84E mutation carriers was higher than among non-carriers. Risk estimates from Kaplan-Meier curves were 36.7% versus 13.6% by age 72, and 64.2% versus 24.2% by age 80, for G84E mutation carriers and non-carriers, respectively (p = 3.4×10−12). The G84E mutation was also associated with an increase in risk for the fourteen other most common cancers considered collectively (p = 5.8×10−4) and more so in cases diagnosed with multiple cancer types, both those including and not including prostate cancer, strongly suggesting pleiotropic effects.

A Large Multiethnic Genome-Wide Association Study of Prostate Cancer Identifies Novel Risk Variants and Substantial Ethnic Differences

“…RPGEH participants included men in the RPGEH Genetic Epidemiology Research on Aging (GERA) cohort (dbGaP phs000674.v1.p1), as well as PCa cases with a DNA sample in the RPGEH biorepository but who were not part of GERA. These studies have been previously described (29–31,41) (dbGaP phs000674.v1.p1). Briefly, the ProHealth study focused on ascertaining KP Northern California African-American PCa cases.…”

Computational Modeling of complete HOXB13 protein for predicting the functional effect of SNPs and the associated role in hereditary prostate cancer

“…UNR is a cytoplasmic protein known to function as an RNA chaperone and is found to be crucial in the control of cell proliferation and death4849. The protein mainly destabilizes the c-fos mRNA and helps in the initiation and activation of cap-independent translation via the IRES for various transcripts, especially the proto-oncogene c-myc, rhinovirus, poliovirus, the cell cycle PISTLRE kinase, and pro-apoptotic factor (Apaf-1)5051. The SNP rs543028086 was predicted to result in the disruption of the UNR Binding Site (UNR_BS) motif in the 3′UTR of human HOXB13 gene, which results in the deregulation of pro-apoptotic factor (Apaf-1), which might have a negative effect on the control of cell death.…”

Strategies for Imputing and Analyzing Rare Variants in Association Studies

“…Follow-up genotyping on the variant, at least on a subset of your individuals, can confirm if the variant imputed correctly. Ideally the correlation of the imputed genotype and the actual genotype will have a correlation similar to the estimated r 2 from imputation (e.g., the r info 2 estimate) [26]. For rare variants, it may be very useful to confirm the genotype by enriching the number of individuals who are imputed to have the rare variant, though this may slightly invent inflate the true r 2 because of enrichment.…”

“…However, another study found an ancestral haplotype containing the mutation [31], suggesting that imputation might be possible. By using the hybrid imputation approach and combining 1000 Genomes Project data with an enriched set of prostate cancer cases who carried the mutation, Hoffmann and colleagues were able to successfully impute the G84E mutation into a large cohort with r 2 =0.57 (computed from a genotyped subset) [26]. Why one study was able to impute this mutation while another was not reflects the difficulty in imputing rare variants [16].…”

“…Why one study was able to impute this mutation while another was not reflects the difficulty in imputing rare variants [16]. Possible explanations here include differences in the original GWAS array [26] and the imputation approach used, as well as potential difficulty in accurately estimating r 2 , which is less accurate for rare variants [14]. Note also that Hoffmann et al used the imputed data to test for a pleiotropic effect of the HOXB13 mutation across multiple different cancers [26]; this highlights the value of using imputation of discovered risk variants into cohort studies to test for cross-phenotype effects.…”