Identifying regulatory relationships among genomic loci, biological pathways, and disease

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Elucidating genetic factors of complex diseases is one of the most important challenges in biomedical research. Recently, a genetical genomics approach of mapping genotype to transcripts has been used in complex disease analysis. This approach treats messenger ribonucleic acid (mRNA) expression as a quantitative trait and identifies putative regulatory loci for the expression of an individual gene. However, the single-gene approach could not detect single nucleotide polymorphisms (SNP's) associated with the concerted activity of multiple genes. Since complex diseases result from the interactions of multiple genes, it is important to consider associations between genotype and multiple gene expression. We developed the differential allelic co-expression (DACE) that identifies regulatory loci that affect the inter-correlation structure of multiple genes or a gene set. We applied DACE to two benchmark datasets: the normal human lymphoblastoid cell dataset and the glioblastoma dataset. These datasets consist of both SNPs and mRNA expression profiles for each sample. When analyzing the lymphoblastoid cell dataset, principal component analysis (PCA) was compared with the DACE test. While PCA identified associations found by single-gene analysis, the DACE test detected associations not identified by the single-gene approach. Using the DACE test, seven putative regulatory loci of immune-related pathways were identified in lymphoblastoid cells after controlling for family-wise error rate. In the glioblastoma dataset, DACE identified 4582 SNPs associated with six pathways. In 231 of the 4582 SNPs, patient survival length was correlated significantly with the SNP genotype. This finding suggests that our integrative approach may provide a biological explanation for the putative relationship between sequence level variation and disease outcome, via expression of a functional pathway. The DACE test shows promise for finding regulatory relationships between a genomic locus and sets of genes which may be related to disease outcome.