Dataset Information

Two-step hypothesis testing to detect gene-environment interactions in a genome-wide scan with a survival endpoint.

ABSTRACT: Defined by their genetic profile, individuals may exhibit differential clinical outcomes due to an environmental exposure. Identifying subgroups based on specific exposure-modifying genes can lead to targeted interventions and focused studies. Genome-wide interaction scans (GWIS) can be performed to identify such genes, but these scans typically suffer from low power due to the large multiple testing burden. We provide a novel framework for powerful two-step hypothesis tests for GWIS with a time-to-event endpoint under the Cox proportional hazards model. In the Cox regression setting, we develop an approach that prioritizes genes for Step-2 G×E testing based on a carefully constructed Step-1 screening procedure. Simulation results demonstrate this two-step approach can lead to substantially higher power for identifying gene-environment ( G×E ) interactions compared to the standard GWIS while preserving the family wise error rate over a range of scenarios. In a taxane-anthracycline chemotherapy study for breast cancer patients, the two-step approach identifies several gene expression by treatment interactions that would not be detected using the standard GWIS.

SUBMITTER: Kawaguchi ES

PROVIDER: S-EPMC9007892 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Two-step hypothesis testing to detect gene-environment interactions in a genome-wide scan with a survival endpoint.

Kawaguchi Eric S ES Li Gang G Lewinger Juan Pablo JP Gauderman W James WJ

Statistics in medicine 20220124 9

Defined by their genetic profile, individuals may exhibit differential clinical outcomes due to an environmental exposure. Identifying subgroups based on specific exposure-modifying genes can lead to targeted interventions and focused studies. Genome-wide interaction scans (GWIS) can be performed to identify such genes, but these scans typically suffer from low power due to the large multiple testing burden. We provide a novel framework for powerful two-step hypothesis tests for GWIS with a time ...[more]

PMID: 35075649

Similar Datasets

Project description:BackgroundMost men diagnosed with prostate cancer have low-risk cancers. How to predict prostate cancer progression at the time of diagnosis remains challenging.ObjectiveTo identify single nucleotide polymorphisms (SNPs) associated with death from prostate cancer.Design, setting, and participantsBlood samples from 11 506 men in Sweden were collected during 1991-1996. Of these, 1053 men were diagnosed with prostate cancer and 245 died from the disease. Stage and grade at diagnosis and outcome information were obtained, and DNA from all cases was genotyped.Outcome measurements and statistical analysisA total of 6 126 633 SNPs were tested for association with prostate-cancer-specific survival time using a Cox proportional hazard model, adjusted for age, stage, and grade at diagnosis. A value of 1×10-6 was used as suggestive significance threshold. Positive candidate SNPs were tested for association with gene expression using expression quantitative trait locus analysis.Results and limitationsWe found 12 SNPs at seven independent loci associated with prostate-cancer-specific survival time. One of 6 126 633 SNPs tested reached genome-wide significance (p<5×10-8) and replicated in an independent cohort: rs73055188 (p=5.27×10-9, per-allele hazard ratio [HR]=2.27, 95% confidence interval [CI] 1.72-2.98) in the AOX1 gene. A second SNP reached a suggestive level of significance (p<1×10-6) and replicated in an independent cohort: rs2702185 (p=7.1×10-7, per-allele HR=2.55, 95% CI=1.76-3.69) in the SMG7 gene. The SNP rs73055188 is correlated with AOX1 expression levels, which is associated with biochemical recurrence of prostate cancer in independent cohorts. This association is yet to be validated in other ethnic groups.ConclusionsThe SNP rs73055188 at the AOX1 locus is associated with prostate-cancer-specific survival time, and AOX1 gene expression level is correlated with biochemical recurrence of prostate cancer.Patient summaryWe identify two genetic markers that are associated with prostate-cancer-specific survival time.

Dataset Information

Two-step hypothesis testing to detect gene-environment interactions in a genome-wide scan with a survival endpoint.

Publications

Two-step hypothesis testing to detect gene-environment interactions in a genome-wide scan with a survival endpoint.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets