Genome-wide fine mapping was conducted by linkage disequilibrium mapping, testing for the significance of association between expression levels and inferred haplotype descent at each locus, as previously described (Solberg Woods et al. 2010, 2012).
Association mapping tests for an association between a set of genome-wide single-nucleotide polymorphisms (SNPs) and a specified phenotype, with the expectation that one or more of these SNPs may be linked to the causal variant(s).
Of the six atom mapping algorithms tested for atom mapping of elementally balanced reactions, most had an impressive prediction accuracy of 91% or higher, e.g. the DREAM, CLCA, MWED, ICMAP and RDT algorithms.
Additional markers from the LepR3 map were tested for polymorphism in the 'Topas' x 'Glacier' population and integrated into the Rlm2 map.
Prior to generating the consensus female RBT map, we tested for significant differences in recombination distances between adjacent loci between the two female RBT maps for each linkage group.
Regions of the map were tested for enrichment in genes from particular ancestral linkage groups with Fisher's Exact Test, and breakpoints in ancestral linkage group composition were identified using a hidden Markov model, as previously described [ 6, 7].
The accuracy of the AL8/78-RH maps was tested for both chromosome assignment and marker order by comparing with previously published genetic and RH maps [ 5, 14, 21, 23].
Group statistical parametric maps were tested for significance using cluster-level inference (cluster-defining threshold, P<0.001; cluster probability of P<0.05, family wise error-corrected for multiple comparisons).
Regulatory polymorphisms can then be mapped and tested for association with disease.
We mapped SNPs tested for interactions to promoter and enhancer regions in order to investigate if SNPs involved in epistatic interactions were enriched for promoter or enhancer mappings.
