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Gene-environment interactions of novel variants associated with head and neck cancer.

ABSTRACT: A genome-wide association study for upper aerodigestive tract cancers identified 19 candidate single-nucleotide polymorphisms (SNPs). We used these SNPs to investigate the potential gene-gene and gene-environment interactions in head and neck squamous cell carcinoma (HNSCC) risk.The 19 variants were genotyped using Taqman assays among 575 cases and 676 controls in our population-based case-control study.A restricted cubic spline model suggested both ADH1B and HEL308 modified the association between smoking pack-years and HNSCC. Classification and regression tree analysis demonstrated a higher-order interaction between smoking status, ADH1B, FLJ13089, and FLJ35784 in HNSCC risk. Compared with ever smokers carrying ADH1B T/C+T/T genotypes, smokers carrying ADH1B C/C genotype and FLJ13089 A/G+A/A genotypes had the highest risk of HNSCC (odds ratio = 1.84).Our results suggest that the risk associated with these variants may be specifically important among specific exposure groups.

SUBMITTER: Liang C

PROVIDER: S-EPMC3662053 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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Gene-environment interactions of novel variants associated with head and neck cancer.

Liang Caihua C Marsit Carmen J CJ Houseman E Andres EA Butler Rondi R Nelson Heather H HH McClean Michael D MD Kelsey Karl T KT

Head & neck 20111102 8

<h4>Background</h4>A genome-wide association study for upper aerodigestive tract cancers identified 19 candidate single-nucleotide polymorphisms (SNPs). We used these SNPs to investigate the potential gene-gene and gene-environment interactions in head and neck squamous cell carcinoma (HNSCC) risk.<h4>Methods</h4>The 19 variants were genotyped using Taqman assays among 575 cases and 676 controls in our population-based case-control study.<h4>Results</h4>A restricted cubic spline model suggested ...[more]

PMID: 22052802

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Project description:Summary Gene-by-environment (GxE) interactions, in which a genetic variant’s phenotypic effect is condition specific, are fundamental for understanding fitness landscapes and evolution but have been difficult to identify at the single-nucleotide level. Although many condition-specific quantitative trait loci (QTLs) have been mapped, these typically contain numerous inconsequential variants in linkage, precluding understanding of the causal GxE variants. Here, we introduce BARcoded Cas9 retron precise parallel editing via homology (CRISPEY-BAR), a high-throughput precision genome editing strategy, and use it to map GxE interactions of naturally occurring genetic polymorphisms impacting yeast growth. We identified hundreds of GxE variants within condition-specific QTLs, revealing unexpected genetic complexity. Moreover, we found that 93.7% of non-neutral natural variants within ergosterol biosynthesis pathway genes showed GxE interactions, including many impacting antifungal drug resistance through diverse molecular mechanisms. In sum, our results suggest an extremely complex, context-dependent fitness landscape characterized by pervasive GxE interactions while also demonstrating massively parallel genome editing as an effective means for investigating this complexity. Graphical abstract Highlights • CRISPEY-BAR precisely measures effects of single genetic variants in high throughput• Pooled competition of barcoded strains maps condition-specific (GxE) fitness effects• Editing more than 4,000 natural genetic variants reveals pervasive GxE and molecular mechanisms• Many natural GxE variants in the ergosterol pathway lead to antifungal drug resistance Chen et al. introduce CRISPEY-BAR, a method to precisely measure individual genetic variant effects through massively parallel genome editing. Editing more than 4,000 natural genetic variants in yeast, they found pervasive condition-specific effects known as gene-by-environment (GxE) interactions. The single-nucleotide resolution revealed many GxE variants underlying antifungal drug resistance and their molecular mechanisms.

Dataset Information

Gene-environment interactions of novel variants associated with head and neck cancer.

Publications

Gene-environment interactions of novel variants associated with head and neck cancer.

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