A Network-Based Kernel Machine Test for the Identification of Risk Pathways in Genome-Wide Association Studies

Abstract

Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). In this study, the kernel converts the genomic information of 2 individuals into a quantitative value reflecting their genetic similarity. With the selection of the kernel, one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for the topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are crucial in the context of GWAS, because genes associated with a disease often interact. Thus, we propose a novel kernel that incorporates the topology of pathways and information on interactions. Using simulation studies, we demonstrate that the proposed method maintains the type I error correctly and can be more effective in the identification of pathways associated with a disease than non-network-based methods. We apply our approach to genome-wide association case-control data on lung cancer and rheumatoid arthritis. We identify some promising new pathways associated with these diseases, which may improve our current understanding of the genetic mechanisms.

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies

“…The applied transformation is given by$\begin{array}{c}T1\rho \mathbf{N}+\left(\mathrm{1}-\rho \right)\mathbf{I},\end{array}$where I denotes the identity matrix and ρ is a weight based on the smallest eigenvalue of N . For more details, see [13]. …”

Powerful Genetic Association Analysis for Common or Rare Variants with High-Dimensional Structured Traits

“…The second approach typically loses power when traits are highly or even modestly correlated with each other (Wu and Pankow 2016). Furthermore, both approaches fail to capture any complicated structures within traits (e.g., inherent regulatory network structure within transcriptomic pathway expressions), which can further lead to power loss (Freytag et al 2014). To address this issue, we propose the DKAT approach in the following section to allow for testing association between high-dimensional, possibly structured, traits and one or more genetic variants.…”

“…A key aspect of DKAT is the kernels, which appropriately summarize the phenotypic and genotypic similarities between pairwise subjects; although DKAT is statistically valid in protecting the correct type I error, irrespective of the kernels being used. However, good choice of kernels, which better reflect the unique data features, can improve the test power (Freytag et al 2014;Zhao et al 2015). In this section, we first briefly review some genotype kernels widely used in existing kernelbased association tests and some kernels that could potentially be used for phenotypes.…”

“…Besides the robustness to highdimensional traits, another major concern of this article is to address the network-type traits, such as expression of genes belonging to the same pathway. For such gene pathway data, a network-based kernel has been proposed of the form K Y ¼ YNY9 (Freytag et al 2014), where N is the undirected adjacency matrix, N ij ¼ 1 represents that gene i and gene j interact with each other in an activating fashion, and N ij ¼ 2 1 represents an inhibition pattern.…”

“…More complicated multivariate-regression modeling strategies often become unstable or computationally intractable when dimensionality of traits increases (Maity et al 2012;Wu and Pankow 2016). None of the methods directly consider the issue of incorporating high-dimensional structured traits, which leads to potential power loss of detecting existing associations (Freytag et al 2014). Thus, new methods are necessary.…”