Knowledge-driven analysis identifies a gene-gene interaction affecting high-density lipoprotein cholesterol levels in multi-ethnic populations

A PLSPM-based test statistic for detecting gene-gene co-association in genome-wide association study with case-control design

For genome-wide association data analysis, two genes in any pathway, two SNPs in the two linked gene regions respectively or in the two linked exons respectively within one gene are often correlated with each other. We therefore proposed the concept of gene-gene co-association, which refers to the effects not only due to the traditional interaction under nearly independent condition but the correlation between two genes. Furthermore, we constructed a novel statistic for detecting gene-gene co-association based on Partial Least Squares Path Modeling (PLSPM). Through simulation, the relationship between traditional interaction and co-association was highlighted under three different types of co-association. Both simulation and real data analysis demonstrated that the proposed PLSPM-based statistic has better performance than single SNP-based logistic model, PCA-based logistic model, and other gene-based methods.

Gene-based testing of interactions in association studies of quantitative traits

Various methods have been developed for identifying gene–gene interactions in genome-wide association studies (GWAS). However, most methods focus on individual markers as the testing unit, and the large number of such tests drastically erodes statistical power. In this study, we propose novel interaction tests of quantitative traits that are gene-based and that confer advantage in both statistical power and biological interpretation. The framework of gene-based gene–gene interaction (GGG) tests combine marker-based interaction tests between all pairs of markers in two genes to produce a gene-level test for interaction between the two. The tests are based on an analytical formula we derive for the correlation between marker-based interaction tests due to linkage disequilibrium. We propose four GGG tests that extend the following P value combining methods: minimum P value, extended Simes procedure, truncated tail strength, and truncated P value product. Extensive simulations point to correct type I error rates of all tests and show that the two truncated tests are more powerful than the other tests in cases of markers involved in the underlying interaction not being directly genotyped and in cases of multiple underlying interactions. We applied our tests to pairs of genes that exhibit a protein–protein interaction to test for gene-level interactions underlying lipid levels using genotype data from the Atherosclerosis Risk in Communities study. We identified five novel interactions that are not evident from marker-based interaction testing and successfully replicated one of these interactions, between SMAD3 and NEDD9, in an independent sample from the Multi-Ethnic Study of Atherosclerosis. We conclude that our GGG tests show improved power to identify gene-level interactions in existing, as well as emerging, association studies.

Epistasis is likely to play a significant role in complex diseases or traits and is one of the many possible explanations for “missing heritability.” However, epistatic interactions have been difficult to detect in genome-wide association studies (GWAS) due to the limited power caused by the multiple-testing correction from the large number of tests conducted. Gene-based gene–gene interaction (GGG) tests might hold the key to relaxing the multiple-testing correction burden and increasing the power for identifying epistatic interactions in GWAS. Here, we developed GGG tests of quantitative traits by extending four P value combining methods and evaluated their type I error rates and power using extensive simulations. All four GGG tests are more powerful than a principal component-based test. We also applied our GGG tests to data from the Atherosclerosis Risk in Communities study and found five gene-level interactions associated with the levels of total cholesterol and high-density lipoprotein cholesterol (HDL-C). One interaction between SMAD3 and NEDD9 on HDL-C was further replicated in an independent sample from the Multi-Ethnic Study of Atherosclerosis.

Replication of endometriosis-associated single-nucleotide polymorphisms from genome-wide association studies in a Caucasian population

STUDY QUESTION

Is it possible to replicate the previously identified genetic association of four single-nucleotide polymorphisms (SNPs), rs12700667, rs7798431, rs1250248 and rs7521902, with endometriosis in a Caucasian population?

SUMMARY ANSWER

A borderline association was observed for rs1250248 and endometriosis (P = 0.049). However, we could not replicate the other previously identified endometriosis-associated SNPs (rs12700667, rs7798431 and rs7521902) in the same population.

WHAT IS KNOWN ALREADY

Endometriosis is considered a complex disease, influenced by several genetic and environmental factors, as well as interactions between them. Previous studies have found genetic associations with endometriosis for SNPs at the 7p15 and 2q35 loci in a Caucasian population.

STUDY DESIGN, SIZE, DURATION

Allele frequencies of SNPs were investigated in patients with endometriosis and controls.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Blood samples and peritoneal biopsies were taken from a Caucasian female population consisting of 1129 patients with endometriosis and 831 controls. DNA was extracted for genotyping. The study was performed at a University hospital and research laboratories.

MAIN RESULTS AND THE ROLE OF CHANCE

A weak association with endometriosis (all stages) was observed for rs1250248 (P = 0.049). No significant associations were observed for the SNPs rs12700667, rs7798431 and rs7521902. A non-significant trend towards the association of rs1250248 with moderate/severe endometriosis was observed (odds ratio 1.18, 95% confidence interval 0.97-1.44).

LIMITATIONS, REASONS FOR CAUTION

The inability to confirm all previous findings may result from differences between populations and type II errors.

WIDER IMPLICATIONS OF THE FINDINGS

Our result demonstrates the difficulty of identifying common genetic variants in complex diseases.

STUDY FUNDING/COMPETING INTEREST(S)

This study was supported by grants from the Karolinska Institutet and Stockholm City County/Karolinska Institutet (ALF), Stockholm, Sweden, Swedish Medical Research Council (K2007-54X-14212-06-3, K2010-54X-14212-09-3), Stockholm, Sweden, Leuven University Research Council (Onderzoeksraad KU Leuven), the Leuven University Hospitals Clinical Research Foundation (Klinisch onderzoeksfonds) and by the National Scientific Foundation (Fonds voor Wetenschappelijk Onderzoek, FWO). The authors have no conflict of interest.

ATHENA: a tool for meta-dimensional analysis applied to genotypes and gene expression data to predict HDL cholesterol levels

Technology is driving the field of human genetics research with advances in techniques to generate high-throughput data that interrogate various levels of biological regulation. With this massive amount of data comes the important task of using powerful bioinformatics techniques to sift through the noise to find true signals that predict various human traits. A popular analytical method thus far has been the genome-wide association study (GWAS), which assesses the association of single nucleotide polymorphisms (SNPs) with the trait of interest. Unfortunately, GWAS has not been able to explain a substantial proportion of the estimated heritability for most complex traits. Due to the inherently complex nature of biology, this phenomenon could be a factor of the simplistic study design. A more powerful analysis may be a systems biology approach that integrates different types of data, or a meta-dimensional analysis. For this study we used the Analysis Tool for Heritable and Environmental Network Associations (ATHENA) to integrate high-throughput SNPs and gene expression variables (EVs) to predict high-density lipoprotein cholesterol (HDL-C) levels. We generated multivariable models that consisted of SNPs only, EVs only, and SNPs + EVs with testing r-squared values of 0.16, 0.11, and 0.18, respectively. Additionally, using just the SNPs and EVs from the best models, we generated a model with a testing r-squared of 0.32. A linear regression model with the same variables resulted in an adjusted r-squared of 0.23. With this systems biology approach, we were able to integrate different types of high-throughput data to generate meta-dimensional models that are predictive for the HDL-C in our data set. Additionally, our modeling method was able to capture more of the HDL-C variation than a linear regression model that included the same variables.

Interaction between SNPs in the RXRA and near ANGPTL3 gene region inhibits apoB reduction after statin-fenofibric acid therapy in individuals with mixed dyslipidemia

The mixed dyslipidemia phenotype is characterized by elevated triglycerides (TG), low HDL cholesterol (HDL-C), increased ApoB levels, and premature coronary atherosclerosis. Fibrate-statin combination therapy reduces ApoB levels and coronary events in the mixed dyslipidemia population. We sought to identify gene-gene interactions that affect ApoB response to statin-fenofibric acid therapy in the mixed dyslipidemia population. Using a predefined subset of single-nucleotide polymorphisms (SNPs) that were previously associated with TG, VLDL, or HDL-C, we applied gene-gene interaction testing in a randomized, double-blind, clinical trial examining the response to fenofibric acid (FNA) and its combination with statin in 1,865 individuals with mixed dyslipidemia. Of 11,783 possible SNP pairs examined, we detected a single significant interaction between rs12130333, located within the ANGPTL3 gene region, and rs4240705, within the RXRA gene, on ApoB reduction after statin-FNA therapy (P = 4.0 × 10(-6)). ApoB response to therapy gradually reduced with the increasing number of T alleles in the rs12130333 but only in the presence of the GG genotype of rs4240705. Individuals doubly homozygous for the minor alleles at rs12130333 and rs4240705 showed a paradoxical increase of 1.8% in ApoB levels after FNA-statin combination therapy. No gene-gene interaction was identified other than an interaction between SNPs in the ANGPTL3 and RXRA regions, which results in the inhibition of ApoB reduction in response to statin-FNA therapy. Further study is required to examine the clinical applicability of this genetic interaction and its effect on coronary events.

Integration of biological networks and pathways with genetic association studies

Millions of genetic variants have been assessed for their effects on the trait of interest in genome-wide association studies (GWAS). The complex traits are affected by a set of inter-related genes. However, the typical GWAS only examine the association of a single genetic variant at a time. The individual effects of a complex trait are usually small, and the simple sum of these individual effects may not reflect the holistic effect of the genetic system. High-throughput methods enable genomic studies to produce a large amount of data to expand the knowledge base of the biological systems. Biological networks and pathways are built to represent the functional or physical connectivity among genes. Integrated with GWAS data, the network- and pathway-based methods complement the approach of single genetic variant analysis, and may improve the power to identify trait-associated genes. Taking advantage of the biological knowledge, these approaches are valuable to interpret the functional role of the genetic variants, and to further understand the molecular mechanism influencing the traits. The network- and pathway-based methods have demonstrated their utilities, and will be increasingly important to address a number of challenges facing the mainstream GWAS.