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

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 r2 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.