Novel measures of linkage disequilibrium that correct the bias due to population structure and relatedness

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...

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Bibliographic Details
Published in:Heredity 2012-03, Vol.108 (3), p.285-291
Main Authors: Mangin, B, Siberchicot, A, Nicolas, S, Doligez, A, This, P, Cierco-Ayrolles, C
Format: Article
Language:English
Subjects:
Alleles
Bias
Biomedical and Life Sciences
Biomedicine
Computer Simulation
Cytogenetics
Disequilibrium
Ecology
Evolutionary Biology
Gene loci
Genetics, Population
Genotype
Genotypes
Human Genetics
Human health and pathology
Life Sciences
Linkage Disequilibrium
Models, Genetic
Original
original-article
Plant Genetics and Genomics
Polymorphism, Single Nucleotide
Population structure
Reproducibility of Results
Vitis - genetics
Vitis vinifera
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Summary: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.
ISSN:0018-067X
1365-2540
0018-067X
DOI:10.1038/hdy.2011.73