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The method generates a putative transcriptional network that contains groups of candidate genes that we predict will co-express and function coherently with the TGG in producing a common cellular response.
When the different groups of candidate loci were compared, the distribution of mean selection coefficients was clearly different.
The MKPRF analysis presented here clearly demonstrates a difference between these disease candidate loci and other groups of candidate loci, supporting this assumption and further motivating a population genetic approach to disease candidate gene selection and identification.
There were three groups of candidate models used in this study: nitrogen-dependent population growth, nitrogen-dependent population growth with a positive feedback effect of decaying plant biomass on nitrogen cycling and nitrogen independent population growth with the same feedback effect.
Nonetheless, many of these studies have relied on small samples of loci or groups of candidate genes, neither of which is likely sufficient to capture patterns of genome-wide variation or provide insight into the relative roles of demographic history and selection.
Additionally, we asked whether disease candidate loci show a trend for selection that is distinct from other groups of candidate loci previously published [14], [15], since a central assumption for this work is that disease response loci are under stronger selection than most other loci in the genome.
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Targeted mass spectrometry, using multiple reaction monitoring (MRM) technology, enables the simultaneous assessment of groups of candidates for improved detection performance.
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