Novel measures of linkage disequilibrium that correct the bias due to population structure and relatedness
2012
Mangin, Brigitte | Siberchicot, Aurélie | Nicolas, S. | Doligez, Agnes | This, Patrice | Cierco-Ayrolles, Christine | Unité de Biométrie et Intelligence Artificielle (ancêtre de MIAT) (UBIA) ; Institut National de la Recherche Agronomique (INRA) | Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)
UMR 1334 AGAP : Equipe AFEF ‘Architecture et Fonctionnement des Espèces fruitières’ ; Team AFFS ‘Architecture and Functioning of Fruit Species’
显示更多 [+] 显示较少 [-]英语. Among the several linkage disequilibrium measures known to capture different features of the non-independence between alleles at different loci, the most commonly used for diallelic loci is the r(2) measure. In the present study, we tackled the problem of the bias of r(2) estimate, which results from the sample structure and/or the relatedness between genotyped individuals. We derived two novel linkage disequilibrium measures for diallelic loci that are both extensions of the usual r(2) measure. The first one, r(S)(2), uses the population structure matrix, which consists of information about the origins of each individual and the admixture proportions of each individual genome. The second one, r(V)(2), includes the kinship matrix into the calculation. These two corrections can be applied together in order to correct for both biases and are defined either on phased or unphased genotypes. We proved that these novel measures are linked to the power of association tests under the mixed linear model including structure and kinship corrections. We validated them on simulated data and applied them to real data sets collected on Vitis vinifera plants. Our results clearly showed the usefulness of the two corrected r(2) measures, which actually captured 'true' linkage disequilibrium unlike the usual r(2) measure.
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