Integrated statistical and pathway approach to next-generation sequencing analysis: a family-based study of hypertension

Abstract

Genome wide association studies (GWAS) have been used to search for associations between genetic variants and a phenotypic trait of interest. New technologies, such as next-generation sequencing, hold the potential to revolutionize GWAS. However, millions of polymorphisms are identified with next-generation sequencing technology. Consequently, researchers must be careful when performing such a large number of statistical tests, and corrections are typically made to account for multiple testing. Additionally, for typical GWAS, the p value cutoff is set quite low (approximately <10(-8)). As a result of this p value stringency, it is likely that there are many true associations that do not meet this threshold. To account for this we have incorporated a priori biological knowledge to help identify true associations that may not have reached statistical significance. We propose the application of a pipelined series of statistical and bioinformatic methods, to enable the assessment of the association of genetic polymorphisms with a disease phenotype--here, hypertension--as well as the identification of statistically significant pathways of genes that may play a role in the disease process.

Figure 1

Genome wide association scans for 5 different phenotypes related to hypertension. For each of the 5 phenotypes, the -log₁₀ of the p value associated with each SNV in (or near) the coding regions. Data from only odd-number chromosomes was provided as part of the GAW18 project; consequently, there is no information for any of the even-number chromosomes, which appear as blank regions on the plots. HTN, hypertension.

Figure 2

Venn diagram of top genes for each phenotype. A list of all SNVs that had p values <0.01 was analyzed. All the genes in these regions were tabulated and a Venn diagram was constructed to identify which genes existed on each of the 5 different lists. DDBP, change in DBP overtime; HTN, hypertension; DSBP, change in SBP overtime

Figure 3

Pathways generated by lists of genes from the association analysis. A, Pathway analysis of top 40 statistically significant genes (p <3.5 × 10⁻⁵) derived from the union of the genes across all 5 phenotypes, M_IPA = 70. Pathway score = 77, corresponding to p = 10⁻⁷⁷. This was the only statistically significant pathway obtained for this set of genes. Molecules in red correspond to genes (N = 30, 75%) from the input list of 40. Functional annotations for this pathway include cell death and survival, gastrointestinal disease, and inflammatory disease. B, Pathway analysis of the top 38 statistically significant genes (p <1.0 × 10⁻⁴) derived from the hypertension phenotype, M_IPA = 70. Pathway score = 62, corresponding to p = 10⁻⁶². This was the only statistically significant pathway obtained for this set of genes. Molecules in red correspond to genes (N = 25, 66%) from the input list of 38. Functional annotations for this pathway include cellular assembly and organization, cellular function and maintenance, and cell death and survival.