Confounding of linkage disequilibrium patterns in large scale DNA based gene-gene interaction studies

Abstract

Background In Genome-Wide Association Studies (GWAS), the concept of linkage disequilibrium is important as it allows identifying genetic markers that tag the actual causal variants. In Genome-Wide Association Interaction Studies (GWAIS), similar principles hold for pairs of causal variants. However, Linkage Disequilibrium (LD) may also interfere with the detection of genuine epistasis signals in that there may be complete confounding between Gametic Phase Disequilibrium (GPD) and interaction. GPD may involve unlinked genetic markers, even residing on different chromosomes. Often GPD is eliminated in GWAIS, via feature selection schemes or so-called pruning algorithms, to obtain unconfounded epistasis results. However, little is known about the optimal degree of GPD/LD-pruning that gives a balance between false positive control and sufficient power of epistasis detection statistics. Here, we focus on Model-Based Multifactor Dimensionality Reduction as one large-scale epistasis detection tool. Its performance has been thoroughly investigated in terms of false positive control and power, under a variety of scenarios involving different trait types and study designs, as well as error-free and noisy data, but never with respect to multicollinear SNPs. Results Using real-life human LD patterns from a homogeneous subpopulation of British ancestry, we investigated the impact of LD-pruning on the statistical sensitivity of MB-MDR. We considered three different non-fully penetrant epistasis models with varying effect sizes. There is a clear advantage in pre-analysis pruning using sliding windows at r 2 of 0.75 or lower, but using a threshold of 0.20 has a detrimental effect on the power to detect a functional interactive SNP pair (power < 25 % ). Signal sensitivity, directly using LD-block information to determine whether an epistasis signal is present or not, benefits from LD-pruning as well (average power across scenarios: 87%), but is largely hampered by functional loci residing at the boundaries of an LD-block. Conclusions Our results confirm that LD patterns and the position of causal variants in LD blocks do have an impact on epistasis detection, and that pruning strategies and LD-blocks definitions combined need careful attention, if we wish to maximize the power of large-scale epistasis screenings.

A Single-Step Genome Wide Association Study on Body Size Traits Using Imputation-Based Whole-Genome Sequence Data in Yorkshire Pigs

“…Moreover, among the 110 non-repeating loci screened by interpretation variance, 101 are new loci after imputation, which indicates that imputed WGS data adds a lot of useful information. Increasing marker density could lead to high linkage disequilibrium (LD) to improve the resolution of gene mapping, although it may also be a burden (Joiret et al, 2019). Too high LD between markers will cause noise and increase false positive rates (Wang et al, 2010).…”

Understanding interactions between risk factors, and assessing the utility of the additive and multiplicative models through simulations

“…We did not model linkage disequilibrium (LD) in our simulations, as the type of simulations we used do not accommodate LD, but we tested our observations on a real GWAS of ACPA-positive RA (anti-citrullinated protein antibody positive rheumatoid arthritis). It may be advisable to prune for LD when addressing interactions in genome-wide data sets [ 40 ].…”

Recommendations for Choosing the Genotyping Method and Best Practices for Quality Control in Crop Genome-Wide Association Studies

“…However, it should be noticed that parametric methods are based on several genetic assumptions, including those of linkage equilibrium (LE) among markers and HWE within sub-populations. Approximate LE from the original SNP dataset can be obtained by removing markers through LD pruning algorithms (Joiret et al, 2019); on the other hand, HWE may not be met even in populations of open-pollinating crops, due to displacements, breeding activities, and clonal propagation (Campoy et al, 2016).…”