Computing effective population size from molecular data: The case of three rare Spanish ruminant populations

Abstract

The aim of this study was to compare different methodologies to compute the effective size when the genealogies are not available (or are shallow) in three rare Spanish ruminant populations. For this purpose the authors used molecular information from three Spanish local ruminant populations (the Pajuna cattle, Payoya goat and Merino de Grazalema sheep populations). Several methods based on molecular or pedigree data were applied to estimate the effective population size in the three studied populations. Estimates based on increase in molecular coancestry (N efm) in Pajuna (8.5) and Payoya (16.7) populations were 2- and 3-fold lower than those obtained using the linkage disequilibrium method. However, N efm in Merino de Grazalema population reached a higher value (110.5 vs 86.2). Regarding the effective size using temporal methods (F statistics and coalescence theory), the results for Pajuna were very similar across methodologies with values ranging from 6.0 to 7.8. In the Payoya goat, the results obtained ranged from 15.0 to 33.4. For Merino de Grazalema was not possible to estimate the N e using temporal methods. Regarding the genealogical methods, pedigrees highly compatible with molecular information were generated from the genotypes of the individuals, the correlations between the molecular and the genealogical coancestry matrix were high from 0.82 to 0.94. The effective population sizes based on individual increase in inbreeding were similar for Pajuna (17.0) and Payoya (18.1) and for Merino de Grazalema was 24.2. The N e based on an increase in coancestry (N ec) was higher in all cases ranging from 20.2 for Pajuna to 38.3 for Merino de Grazalema. The N ec for Payoya was 27.1. We can conclude that there is no single value of molecular-based N e for each population, because high ranges for effective size where found across methodologies. However, the assessed ranking was steady: the Pajuna is the most endangered population, followed by Payoya and Merino de Grazalema. When the priority for conservation is of concern, all methods seem to be useful, but it is not possible to combine them. It is recommendable to use the same method across populations to define the risk status of the list of populations. Moreover, if a precise value of N e is needed, for example, to define the size of sampled animals to be genotyped under a genomic selection scenario, different methodologies would lead to different conclusions. Further research seems to be needed on this issue.