Clique-Finding for Heterogeneity and Multidimensionality in Biomarker Epidemiology Research: The CHAMBER Algorithm

Commonly-occurring disease etiology may involve complex combinations of genes and exposures resulting in etiologic heterogeneity. We present a computational algorithm that employs clique-finding for heterogeneity and multidimensionality in biomedical and epidemiological research (the "CHAMBER" algorithm).This algorithm uses graph-building to (1) identify genetic variants that influence disease risk and (2) predict individuals at risk for disease based on inherited genotype. We use a set-covering algorithm to identify optimal cliques and a Boolean function that identifies etiologically heterogeneous groups of individuals. We evaluated this approach using simulated case-control genotype-disease associations involving two- and four-gene patterns. The CHAMBER algorithm correctly identified these simulated etiologies. We also used two population-based case-control studies of breast and endometrial cancer in African American and Caucasian women considering data on genotypes involved in steroid hormone metabolism. We identified novel patterns in both cancer sites that involved genes that sulfate or glucuronidate estrogens or catecholestrogens. These associations were consistent with the hypothesized biological functions of these genes. We also identified cliques representing the joint effect of multiple candidate genes in all groups, suggesting the existence of biologically plausible combinations of hormone metabolism genes in both breast and endometrial cancer in both races.The CHAMBER algorithm may have utility in exploring the multifactorial etiology and etiologic heterogeneity in complex disease

Lack of association between polymorphisms of the IL18R1 and IL18RAP genes and cardiovascular risk: the MORGAM Project

<p>Abstract</p> <p>Background</p> <p>Interleukin-18 is a pro-inflammatory cytokine suspected to be associated with atherosclerosis and its complications. We had previously shown that one single nucleotide polymorphism (SNP) of the <it>IL18 </it>gene was associated with cardiovascular disease (CVD) through an interaction with smoking. As a further step for elucidating the contribution of the IL-18 pathway to the etiology of CVD, we here investigated the association between the genetic variability of two IL-18 receptor genes, <it>IL18R1 </it>and <it>IL18RAP</it>, with the risk of developing CVD.</p> <p>Methods</p> <p>Eleven tagging SNPs, 5 in <it>IL18R1 </it>and 6 in <it>IL18RAP</it>, characterizing the haplotypic variability of the corresponding genes; were genotyped in 5 European prospective CVD cohorts including 1416 cases and 1772 non-cases, as part of the MORGAM project. Both single-locus and haplotypes analyses were carried out to investigate the association of these SNPs with CVD.</p> <p>Results</p> <p>We did not find any significant differences in allele, genotype and haplotype frequencies between cases and non-cases for either of the two genes. Moreover, the search for interactions between SNPs located in different genes, including 5 <it>IL18 </it>SNPs previously studied in the MORGAM project, and between SNPs and environmental factors remained unfruitful.</p> <p>Conclusion</p> <p>Our analysis suggests that the variability of <it>IL18R1 </it>and <it>IL18RAP </it>genes are unlikely to contribute to modulate the risk of CVD.</p

Meta-analysis of four new genome scans for lipid parameters and analysis of positional candidates in positive linkage regions

Lipid levels in plasma strongly influence the risk for coronary heart disease. To localise and subsequently identify genes affecting lipid levels, we performed four genome-wide linkage scans followed by combined linkage/association analysis. Genome-scans were performed in 701 dizygotic twin pairs from four samples with data on plasma levels of HDL- and LDL-cholesterol and their major protein constituents, apolipoprotein AI (ApoAI) and Apolipoprotein B (ApoB). To maximise power, the genome scans were analysed simultaneously using a well-established meta-analysis method that was newly applied to linkage analysis. Overall LOD scores were estimated using the means of the sample-specific quantitative trait locus (QTL) effects inversely weighted by the standard errors obtained using an inverse regression method. Possible heterogeneity was accounted for with a random effects model. Suggestive linkage for HDL-C was observed on 8p23.1 and 12q21.2 and for ApoAI on 1q21.3. For LDL-C and ApoB, linkage regions frequently coincided (2p24.1, 2q32.1, 19p13.2 and 19q13.31). Six of the putative QTLs replicated previous findings. After fine mapping, three maximum LOD scores mapped within 1cM of major candidate genes, namely APOB (LOD =2.1), LDLR (LOD =1.9) and APOE (LOD =1.7). APOB haplotypes explained 27% of the QTL effect observed for LDL-C on 2p24.1 and reduced the LOD-score by 0.82. Accounting for the effect of the LDLR and APOE haplotypes did not change the LOD score close to the LDLR gene but abolished the linkage signal at the APOE gene. In conclusion, application of a new meta-analysis approach maximised the power to detect QTLs for lipid levels and improved the precision of their location estimate. © 2005 Nature Publishing Group. All rights reserved

An information-theoretic analysis of genetics, gender and age in cancer patients

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