In the future, we believe that models that consider population size and fitness in a unified framework will be necessary to fully understand signatures that adaptation leaves in populations of variable size.
Of the three current criteria for metabolic syndrome (IDF, NCEP, and WHO), we used the NCEP criteria because they are more appropriate to apply here to populations of variable geographical origin rather than the IDF criteria, for simplicity, and the WHO criteria, which include insulin resistance and microalbuminuria (6).
The procedure starts by generating a random population of variable clusters.
We accomplished this by using the frequency of questions in the population of variable clusters as the criterion for inclusion in our predictive modeling strategies.
Second, another assumption applied in the current paper was the possibility to offer interventions to a population of variable size, by varying the budget spent on each intervention.
Thus, and following Sassaman [ 11] we consider some T. c. cancriformis populations to be androdioecious, as populations consist of variable proportions of hermaphrodites and males, with some populations being made solely of selfing hermaphrodites.
These were selected from pools and populations of highly variable germplasm, including landraces from all over Latin America, and some germplasm from temperate populations mixed in as well.
This is expected in populations of highly variable RNA viruses and implies that non-synonymous substitutions can be highly deleterious [68], [69].
The reduced model included only the dependent variable (BRDC binary case control status) and any potentially confounding covariates (i.e., the covariates sex, age, or population of origin variable in the combined NM + CA cohort).
Likewise, for our analysis of the combined cohort (NM + CA), both sex and age were again treated as covariates, but we also included a population of origin variable as a third and final covariate (Additional file 2: Figure S1).
For all FvR regression analyses, we used the following covariates: NM (sex, age), CA (sex, age), Combined cohort NM + CA (sex, age, population of origin variable) (Additional file 3: Figure S2).
