Predicting SNP priors for Cancer GWASs

Dartmouth College, Hanover NH

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Abstract

ABSTRACT Current genotyping platforms for GWASs include millions SNPs. Genotyping of a large number of SNPs is beneficial because of higher chances of direct genotyping of causal variant. Hovewer, genotyping multiple SNPs elevates chances of false discoveries because of multiple testing. Identification of genetic control of cancer risk susceptibility was a hot research area for more than decade. Several hundred GWASs have been conducted and many risk associated SNPs were identified. Now it is time to learn from already published cancer GWASs to build a more efficient targeted strategy to identify cancer risk associated SNPs. Based on ours and others data we propose three-fold hypothesis: (i) Cancer risk associated SNPs share sets of common characteristics; (ii) Those characteristics can be used to identify SNPs with high propensity to be associated with cancer risk; (iii) Combining several GWASs enriched by existing knowledge on the cancer-risk gene predictors may substantially improve statistical power to identify novel cancer-associated SNPs. We will conduct targeted analyses of existing genotyping data for the five most common cancers: breast, colon, lung, ovarian, and prostate. Our preliminary results indicate that we will be able to identify SNPs with smaller effects on cancer risk. We will use dbGAP data for discovery and OncoArray data for validation. We propose 3 specific aims: Aim 1. To develop cancer type specific as well as pan-cancer models for predicting SNPs' probabilities to be associated with cancer risk. Aim 2. To conduct targeted analysis of gray zone SNPs with highest predicted probability to be cancer risk associated. Aim 3. To perform validation of the top most significant SNPs from Aim 2 using independent samples. Primary goal of this project is to identify SNPs with highest prior probabilities to be cancer risk associated and use them to perform targeted in silico GWASs for the five most common cancers. We will identify novel risk associated SNPs that in combination with already detected risk associated SNPs will better explain genetic control of cancer susceptibility. The proposed approach represents a cost- effective alternative of traditional GWASs.