Trees Assembling Mann‐Whitney Approach for Detecting Genome‐Wide Joint Association Among Low‐Marginal‐Effect Loci

Abstract

Common complex diseases are likely influenced by the interplay of hundreds, or even thousands, of genetic variants. Converging evidence shows that genetic variants with low-marginal-effects (LME) play an important role in disease development. Despite their potential significance, discovering LME genetic variants and assessing their joint association on high-dimensional data (e.g., genome-wide association studies) remain a great challenge. To facilitate joint association analysis among a large ensemble of LME genetic variants, we proposed a computationally efficient and powerful approach, which we call Trees Assembling Mann-Whitney (TAMW). Through simulation studies and an empirical data application, we found that TAMW outperformed multifactor dimensionality reduction (MDR) and the likelihood-ratio-based Mann-Whitney approach (LRMW) when the underlying complex disease involves multiple LME loci and their interactions. For instance, in a simulation with 20 interacting LME loci, TAMW attained a higher power (power=0.931) than both MDR (power=0.599) and LRMW (power=0.704). In an empirical study of 29 known Crohn’s disease (CD) loci, TAMW also identified a stronger joint association with CD than those detected by MDR and LRMW. Finally, we applied TAMW to Wellcome Trust CD GWAS to conduct a genome-wide analysis. The analysis of 459K single nucleotide polymorphisms was completed in 40 hours using parallel computing, and revealed a joint association predisposing to CD (p-value=2.763e-19). Further analysis of the newly discovered association suggested that 13 genes, such as ATG16L1 and LACC1, may play an important role in CD pathophysiological and etiological processes.

HiSeeker: Detecting High-Order SNP Interactions Based on Pairwise SNP Combinations

“…To assess the performance of HiSeeker, we perform extensive simulation experiments using six different disease models and compare its power with four representative approaches: AntEpiSeeker [23], EDCF [29], DCHE [30] and TAMW [28]. We adopt the same measure of power proposed by Wan et al [13] as follows:$Power=\frac{S}{N_D}$ where S is the number of datasets in which true interaction loci are successfully identified among all generated $N_D$ datasets.…”

GWGGI: software for genome-wide gene-gene interaction analysis

“…Based on this definition, we can calculate the variance, and build the test statistic, , which follows a standard normal distribution under null hypothesis. The p-value can thus be calculated to evaluate joint association of identified genetic variants with diseases, considering possible interactions [ 6 , 7 ].…”

“…For complex diseases influenced by the interplay of hundreds genetic variants, LRMW can only identified those most significant interactions. To consider interactions among hundreds or even thousands genetic variants, most of which have low marginal effects, we also developed TAMW [ 6 ]. TAMW uses an ensemble algorithm to combine many de-correlated tree models so as to consider a large ensemble of genetic variants with low marginal effects.…”

“…Both LRMW and TAMW involve model selection procedure, so the p-value should be obtained by using permutation test. Alternatively, we can split the data to training dataset and testing dataset, and obtain the p-value on testing dataset [ 6 ]. Specifically, we first build the model on the training dataset and then apply the model to testing dataset by assigning LR values to the individuals according to their genotypes.…”

A survey about methods dedicated to epistasis detection

“…Then for each individual, all likelihood ratios are assembled into a unique one by averaging the total number of trees. Finally, a U -statistic is constructed with comparisons between assembled likelihood ratios of cases vs. controls in order to evaluate the joint association of the selected SNPs with the phenotype (Wei et al, 2013 ). The U -statistic is calculated in the following way: .…”