Gradually overestimating/underestimating the recombination rate, decreases the impact of linkage disequilibrium in modeling the effect between neighboring sites.
Genomic selection models exploit this linkage disequilibrium to estimate the effects of the markers and genomic breeding values [ 9].
In addition, the conditional independent effects test of rs2237892, rs2237895 and rs2237897 confirmed that they were in a linkage disequilibrium block, as the effects of rs2237892 and rs2237895 on type 2 diabetes could be equally attributable to rs2237897 (see ESM Table 4).
This linkage disequilibrium results in interference, where the effect of selection on each locus is decreased (Hill and Robertson 1966).
Significant markers could potentially represent causative mutations within previously identified genes, functional RNAs or regulatory regions, or more likely are not causative by themselves but in sufficient linkage disequilibrium (LD) to pick up the effect of the nearby causative factor.
To minimize the effect of linkage disequilibrium in the analysis, we also excluded markers in high linkage disequilibrium (with the indep-pairwise option in Plink) and long-range linkage disequilibrium regions reported previously or detected in our population.
EXPLoRA explicitly models the effect of linkage disequilibrium to explain the dependencies between neighboring sites in the data.
Most studies were not sufficiently powered to examine the effect of linkage disequilibrium within the MHC, including HLA-DQB1 alleles.
The effect of linkage disequilibrium is modeled by the transition probabilities τ between two neighboring marker sites.
