Consensus genetic structuring and typological value of markers using multiple co-inertia analysis.
Working with weakly congruent markers means that consensus genetic structuring of populations requires methods explicitly devoted to this purpose. The method, which is presented here, belongs to the multivariate analyses. This method consists of different steps. First, single-marker analyses were performed using a version of principal component analysis, which is designed for allelic frequencies (%PCA). Drawing confidence ellipses around the population positions enhances %PCA plots. Second, a multiple co-inertia analysis (MCOA) was performed, which reveals the common features of single-marker analyses, builds a reference structure and makes it possible to compare single-marker structures with this reference through graphical tools. Finally, a typological value is provided for each marker. The typological value measures the efficiency of a marker to structure populations in the same way as other markers. In this study, we evaluate the interest and the efficiency of this method applied to a European and African bovine microsatellite data set. The typological value differs among markers, indicating that some markers are more efficient in displaying a consensus typology than others. Moreover, efficient markers in one collection of populations do not remain efficient in others. The number of markers used in a study is not a sufficient criterion to judge its reliability. "Quantity is not quality".
Laloë
Denis
D
Station de génétique quantitative et appliquée UR337, INRA, 78352 Jouy-en-Josas, France. denis.laloe@jouy.inra.fr
Jombart
Thibaut
T
Dufour
Anne-Béatrice
AB
Moazami-Goudarzi
Katayoun
K
D000354:Africa, Western; D000483:Alleles; D000818:Animals; D044822:Biodiversity; D001947:Breeding; D002417:Cattle / Q000235:genetics; D005602:France; D005787:Gene Frequency; D005819:Genetic Markers; D005828:Genetics, Population; D018895:Microsatellite Repeats; D008957:Models, Genetic; D015999:Multivariate Analysis; D025341:Principal Component Analysis
D005819:Genetic Markers