Traditional GWAS focus primarily on the most significant genetic markers, often without sufficient power to
detect relatively small effects conferred by most genetic variants. Moreover, the vast majority of variants
identified by GWAS are common proxy SNPs, which have no direct biological relevance to disease. In addition,
there is growing evidence that genes do not work in isolation. Instead, complex molecular networks and
cellular pathways are often involved in disease susceptibility progression. Therefore, pathway analysis, which
jointly considers multiple variants with moderate signals in related genes, may help evaluate the cumulative
contributions of genes within particular biological pathways and identify pathways relevant to the etiology of
disease. Pathway analysis approaches have been successfully applied to various complex diseases, including
basal cell carcinoma (BCC) study conducted by our group. In addition, traditional pathway analyses simply
assign SNPs to nearby genes based on their physical locations. However, most such SNPs do not represent
functional variants of that gene, and multiple testing on these large numbers of SNPs may introduce many
false positive findings. Moreover, some SNPs that are located in a structural gene but regulate the expression
of another gene would be annotated inappropriately. Therefore, defining the expression quantitative trait loci
(eQTLs) and assigning these expression-related SNPs (eSNPs) to the genes they regulate may help
functionally annotate SNPs and define the cumulative contribution of particular genes in a better way. Our
group integrated eQTL information into the BCC GWAS for pathway analysis and successfully identified
several novel disease-related pathways. Here we propose to apply this method to squamous cell carcinoma
(SCC) of the skin to systematically assess the associations of biological pathways with SCC risk. We will use
gene expression data from the MuTHER project and the GTEx Portal to integrate the eQTL information on skin.
Then we plan to take advantage of the previous SCC GWAS by our group in the discovery stage and utilize a
SCC case-control study with GWAS data from the Kaiser Permanente Northern California health care system
for replication. We propose the following specific aims: (1) Use two pathway-based approaches to evaluate the
associations of biological pathways with SCC risk by estimating the effects of skin eSNPs within each pathway
using our previous SCC GWAS data of 2,710 cases and 35,637 controls from the Nurses? Health Study and
Health Professionals Follow-up Study. (2) Validate the associations of potential disease-related loci within the
identified pathways in an external replication study with 6,891 SCC cases and 54,566 controls from the Kaiser
Permanente Northern California health care system. This will be the first well-positioned post-GWAS study
integrating GWAS on SCC and GWAS on gene expression in skin into a pathway analysis. The current study
will utilize GWAS data on SNPs as well as gene expression to a greater extent and help uncover the potential
relations between biological pathways and SCC development.
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