Abstract
Deviation from multiplicativity of genetic risk factors is biologically plausible and might explain why Genome-wide association studies (GWAS) so far could unravel only a portion of disease heritability. Still, evidence for SNP-SNP epistasis has rarely been reported, suggesting that 2-SNP models are overly simplistic. In this context, it was recently proposed that the genetic architecture of complex diseases could follow limiting pathway models. These models are defined by a critical risk allele load and imply multiple high-dimensional interactions. Here, we present a computationally efficient one-degree-of-freedom “supra-multiplicativity-test” (SMT) for SNP sets of size 2 to 500 that is designed to detect risk alleles whose joint effect is fortified when they occur together in the same individual. Via a simulation study we show that the SMT is powerful in the presence of threshold models, even when only about 30–45% of the model SNPs are available. In addition, we demonstrate that the SMT outperforms standard interaction analysis under recessive models involving just a few SNPs. We apply our test to 10 consensus Alzheimer’s disease (AD) susceptibility SNPs that were previously identified by GWAS and obtain evidence for supra-multiplicativity () that is not attributable to either two-way or three-way interaction.
Highlights
Despite of thousands of confirmed disease susceptibility variants [1], the findings from Genome-wide association studies (GWAS) so far explain only a portion of the heritability of complex diseases [2]
In order to explain the phenomenon of missing evidence for interaction, Zuk et al [6] suggested that common diseases may follow so-called limiting pathway liability models (LPLMs)
LPLMs can be viewed as a special case of the larger class of liability models [7,8] which allow that the risk contribution of the involved factors may vary
Summary
Despite of thousands of confirmed disease susceptibility variants [1], the findings from Genome-wide association studies (GWAS) so far explain only a portion of the heritability of complex diseases [2]. Multi-SNP approaches like interaction and pathway analysis were proposed [3] to detect the still unexplained portion of genetic disease risk. In order to explain the phenomenon of missing evidence for interaction, Zuk et al [6] suggested that common diseases may follow so-called limiting pathway liability models (LPLMs). LPLMs can be viewed as a special case of the larger class of liability models [7,8] which allow that the risk contribution of the involved factors may vary. Li et al [8] describe two sources of liability to depression, namely genetic liability for stress sensitivity mediating depression, and genetic liability for depression in general Both sources are shown to be under polygenic control. Further important classes of more complex high-dimensional models have been discussed in [9]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
