Abstract
We propose a novel multifactor dimensionality reduction method for epistasis detection in small or extended pedigrees, FAM-MDR. It combines features of the Genome-wide Rapid Association using Mixed Model And Regression approach (GRAMMAR) with Model-Based MDR (MB-MDR). We focus on continuous traits, although the method is general and can be used for outcomes of any type, including binary and censored traits. When comparing FAM-MDR with Pedigree-based Generalized MDR (PGMDR), which is a generalization of Multifactor Dimensionality Reduction (MDR) to continuous traits and related individuals, FAM-MDR was found to outperform PGMDR in terms of power, in most of the considered simulated scenarios. Additional simulations revealed that PGMDR does not appropriately deal with multiple testing and consequently gives rise to overly optimistic results. FAM-MDR adequately deals with multiple testing in epistasis screens and is in contrast rather conservative, by construction. Furthermore, simulations show that correcting for lower order (main) effects is of utmost importance when claiming epistasis. As Type 2 Diabetes Mellitus (T2DM) is a complex phenotype likely influenced by gene-gene interactions, we applied FAM-MDR to examine data on glucose area-under-the-curve (GAUC), an endophenotype of T2DM for which multiple independent genetic associations have been observed, in the Amish Family Diabetes Study (AFDS). This application reveals that FAM-MDR makes more efficient use of the available data than PGMDR and can deal with multi-generational pedigrees more easily. In conclusion, we have validated FAM-MDR and compared it to PGMDR, the current state-of-the-art MDR method for family data, using both simulations and a practical dataset. FAM-MDR is found to outperform PGMDR in that it handles the multiple testing issue more correctly, has increased power, and efficiently uses all available information.
Highlights
The International HapMap Project [1] was designed to create a genome-wide database of human genetic variation, with the expectation that these data would be useful for genetic association studies of common diseases
We examined genotype data for 25 SNPs in five diabetes candidate genes, adipocnectin receptors 1 and 2 (ADIPOR1, ADIPOR2), adiponectin (APM1, known as ADIPOQ), calsequestrin 1 (CASQ1), and hepatocyte nuclear factor 4A (HNF4A), in 1427 individuals from a single large multi-generational pedigree subdivided into 243 independent families for analysis and 25 SNPs
The objectives of this paper were to introduce a new familybased and flexible epistasis detection analysis method, FAMMDR, which is based on multifactor dimensionality reduction of multi-locus genotypes, and to compare it to the current state-of-the art Multifactor Dimensionality Reduction (MDR) methodology for families, Pedigree-based Generalized MDR (PGMDR)
Summary
The International HapMap Project [1] was designed to create a genome-wide database of human genetic variation, with the expectation that these data would be useful for genetic association studies of common diseases This expectation has been fulfilled with just the initial output of genome-wide association analyses, identifying nearly 500 loci for over 80 common diseases and traits [2,3]. For inflammatory bowel disease (IBD), 32 loci significantly impact disease but they explain only 10% of disease risk and 20% of genetic risk [4] This may be attributed to the fact that recent findings show many types of genetic associations for various traits, with subtle effects: nonadditive genetic effects, non-SNP polymorphisms, epigenetic effects, and gene-environment and gene-gene interactions [5]. These reasons have made epistasis an increasingly accepted characteristic of the genetic architecture of common, complex disorders [21,22,23,24]
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