Person:

Chu, Jen-Hwa

Background: Graphical models (e.g., Bayesian networks) have been used frequently to describe complex interaction patterns and dependent structures among genes and other phenotypes. Estimation of such networks has been a challenging problem when the genes considered greatly outnumber the samples, and the situation is exacerbated when one wishes to consider the impact of polymorphisms (SNPs) in genes. Results: Here we describe a multistep approach to infer a gene-SNP network from gene expression and genotyped SNP data. Our approach is based on 1) construction of a graphical Gaussian model (GGM) based on small sample estimation of partial correlation and false-discovery rate multiple testing; 2) extraction of a subnetwork of genes directly linked to a target candidate gene of interest; 3) identification of cis-acting regulatory variants for the genes composing the subnetwork; and 4) evaluating the identified cis-acting variants for trans-acting regulatory effects of the target candidate gene. This approach identifies significant gene-gene and gene-SNP associations not solely on the basis of gene co-expression but rather through whole-network modeling. We demonstrate the method by building two complex gene-SNP networks around Interferon Receptor 12B2 (IL12RB2) and Interleukin 1B (IL1B), two biologic candidates in asthma pathogenesis, using 534,290 genotyped variants and gene expression data on 22,177 genes from total RNA derived from peripheral blood CD4+ lymphocytes from 154 asthmatics. Conclusion: Our results suggest that graphical models based on integrative genomic data are computationally efficient, work well with small samples, and can describe complex interactions among genes and polymorphisms that could not be identified by pair-wise association testing.

Background: The investigation of complex disease heterogeneity has been challenging. Here, we introduce a network-based approach, using partial correlations, that analyzes the relationships among multiple disease-related phenotypes. Results: We applied this method to two large, well-characterized studies of chronic obstructive pulmonary disease (COPD). We also examined the associations between these COPD phenotypic networks and other factors, including case-control status, disease severity, and genetic variants. Using these phenotypic networks, we have detected novel relationships between phenotypes that would not have been observed using traditional epidemiological approaches. Conclusion: Phenotypic network analysis of complex diseases could provide novel insights into disease susceptibility, disease severity, and genetic mechanisms.

Purpose: Identification of single nucleotide polymorphisms (SNPs) associated with development of advanced colorectal adenomas.

Experimental Design: Discovery Phase: 1,406 Caucasian patients (139 advanced adenoma cases and 1,267 controls) from the Adenoma Prevention with Celecoxib (APC) trial were included in a genome-wide association study (GWAS) to identify variants associated with post-polypectomy disease recurrence. Genome-wide significance was defined as false discovery rate < 0.05, unadjusted p=7.4×10−7. Validation Phase: Results were further evaluated using 4,175 familial colorectal adenoma or CRC cases and 5,036 controls from patients of European ancestry (COloRectal Gene Identification consortium, Scotland, Australia and VQ58).

Results: Our study identified eight SNPs associated with advanced adenoma risk in the APC trial (rs2837156, rs7278863, rs2837237, rs2837241, rs2837254, rs741864 at 21q22.2, and rs1381392 and rs17651822 at 3p24.1, at p<10–7 level with odds ratio – OR>2). Five variants in strong pairwise linkage disequilbrium (rs7278863, rs2837237, rs741864, rs741864 and rs2837241, r2=0.8–1) are in or near the coding region for the tight junction adhesion protein, IGSF5. An additional variant associated with advanced adenomas, rs1535989 (minor allele frequency 0.11; OR 2.09; 95% confidence interval 1.50–2.91), also predicted CRC development in a validation analysis (p=0.019) using a series of adenoma cases or CRC (CORGI study) and 3 sets of CRC cases and controls (Scotland, VQ58 and Australia, N=9,211).

Conclusions: Our results suggest that common polymorphisms contribute to the risk of developing advanced adenomas and might also contribute to the risk of developing CRC. The variant at rs1535989 may identify patients whose risk for neoplasia warrants increased colonoscopic surveillance.