Finally, our rankings and determination of interesting SNPs was based on an adjusted P-value obtained using a method that accounts for multiple correlated tests when first determining the genetic model and then across all SNPs within each candidate gene region. This resulting adjusted P-value is less conservative than a uniform adjustment across the number of SNPs that assumes independence (i.e. Bonferroni correction), but more stringent than simply ignoring the evaluation of multiple SNPs. This analysis does not yield evidence for an independent effect for each SNP. Here, we used both haplotype and joint regression modeling. However, we do view this adjusted P-value to be the evidence for both a particular SNP and for the gene since the SNPs were originally chosen to capture the common variation within the gene. These adjusted P-values may then be compared with the appropriate significance level for the type of hypothesis of interest. Since the genes were chosen to capture the important components within the entire system, we use a conservative significance α-level of 0.0009 across the 54 gene regions to indicate significance at the
