Consensus genetic structuring and typological value of markers using multiple co-inertia analysis

Abstract

-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".congruence / multiple co-inertia analysis / biodiversity / microsatellite / allelic frequencies

Genetic and morphometric characterization of a local Vietnamese Swamp Buffalo population

“…Genetic analysis of the 11 districts was also done through a multivariate coinertia analysis (MCOA) in order to estimate genetic structure similarity across markers (Laloë et al 2007;Berthouly et al 2008a). This method allows extracting the common information from separate analyses in the setting up of a reference typology, and comparing each separate typology.…”

“…It may detect the efficiency of each locus to participate in the synthetic (i.e. multiloci) population genetic structuring (Laloë et al 2007;Berthouly et al 2008a). Our results exhibited high values of Cos 2 indicating a good congruence of each locus district structuring and the syntheticone.…”

Using molecular markers and multivariate methods to study the genetic diversity of local European and Asian chicken breeds

“…It may detect the efficiency of each locus for breeds’ consensual structuring because it takes into account the shared as well as the non‐shared structure of data tables, i.e. loci, to build an axis (Laloë et al. 2007).…”

“…The efficiency of a marker is assessed by its typological value (Tv; the contribution of the marker to the construction of the reference typology), which is equal to the product of the variance (Var) multiplied by the congruence with the consensus Cos 2 (i.e. the correlation between the scores of individual locus tables and the synthetic variable of the same rank) (Laloë et al. 2007).…”

Multiple Evolutionary Pathways to Decreased Lateral Plate Coverage in Freshwater Threespine Sticklebacks

“…Given the potential nonindependence among markers, we performed a co‐ordinated analysis of allele frequencies via multiple co‐inertia analysis to visualize genotypic differentiation and overlap among the phenotypic clusters (Laloë et al 2007; Jombart et al 2009). This was performed using the “ade4” package (Dray and Dufour 2007), implemented in R. Details of the mathematics underlying multiple co‐inertia analysis (MCoA) and its application to genetic data can be found elsewhere (Laloë et al 2007; Pavoine and Bailly 2007), but in brief, MCoA is an ordination method that seeks to find a common typology among multiple sources of tabulated data pertaining to the same objects, in this instance, allele frequency data for each locus ( k = 7 tables) summarizing each of the four phenotypic clusters. Within the MCoA framework, a separate between‐class PCA is performed for each locus, centering each "population" in multivariate space based on their respective allele frequencies.…”