Open Access

An efficient variance component approach implementing an average information REML suitable for combined LD and linkage mapping with a general complex pedigree

Genetics Selection Evolution200638:25

https://doi.org/10.1186/1297-9686-38-1-25

Received: 20 April 2005

Accepted: 5 October 2005

Published: 15 January 2006

Abstract

Variance component (VC) approaches based on restricted maximum likelihood (REML) have been used as an attractive method for positioning of quantitative trait loci (QTL). Linkage disequilibrium (LD) information can be easily implemented in the covariance structure among QTL effects (e.g. genotype relationship matrix) and mapping resolution appears to be high. Because of the use of LD information, the covariance structure becomes much richer and denser compared to the use of linkage information alone. This makes an average information (AI) REML algorithm based on mixed model equations and sparse matrix techniques less useful. In addition, (near-) singularity problems often occur with high marker densities, which is common in fine-mapping, causing numerical problems in AIREML based on mixed model equations. The present study investigates the direct use of the variance covariance matrix of all observations in AIREML for LD mapping with a general complex pedigree. The method presented is more efficient than the usual approach based on mixed model equations and robust to numerical problems caused by near-singularity due to closely linked markers. It is also feasible to fit multiple QTL simultaneously in the proposed method whereas this would drastically increase computing time when using mixed model equation-based methods.

Keywords

quantitative trait loci fine-mapping linkage disequilibrium average information genotype relationships matrix

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Authors’ Affiliations

(1)
School of Rural Science and Agriculture, UNE

Copyright

© INRA, EDP Sciences 2006

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