bUniv. Bordeaux, UMR1202, BIOGECO, Talence F-33170, France
cDepartment of Statistics, North Carolina State University, Raleigh, NC, USA
dINRA, UR0588, AGPF, 45075 Orléans, France
Received 22 March 2015. Revised 4 August 2015. Accepted 13 August 2015. Available online 18 August 2015.
Highlights
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First genomic selection study in a maritime breeding population (n = 661).
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The overall intra-chromosomal linkage disequilibrium was low (r2 = 0.01).
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The predictive ability of markers for growth and stem quality ranged from 0.43 to 0.49.
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GBLUP, Bayesian Ridge, Bayesian LASSO regression models had similar predictive power.
Abstract
A two-generation maritime pine (Pinus pinaster Ait.) breeding population (n = 661) was genotyped using 2500 SNP markers. The extent of linkage disequilibrium and utility of genomic selection for growth and stem straightness improvement were investigated. The overall intra-chromosomal linkage disequilibrium was r2 = 0.01. Linkage disequilibrium corrected for genomic relationships derived from markers was smaller ( ). Genomic BLUP, Bayesian ridge regression and Bayesian LASSO regression statistical models were used to obtain genomic estimated breeding values. Two validation methods (random sampling 50% of the population and 10% of the progeny generation as validation sets) were used with 100 replications. The average predictive ability across statistical models and validation methods was about 0.49 for stem sweep, and 0.47 and 0.43 for total height and tree diameter, respectively. The sensitivity analysis suggested that prior densities (variance explained by markers) had little or no discernible effect on posterior means (residual variance) in Bayesian prediction models. Sampling from the progeny generation for model validation increased the predictive ability of markers for tree diameter and stem sweep but not for total height. The results are promising despite low linkage disequilibrium and low marker coverage of the genome (∼1.39 markers/cM).
). Genomic BLUP, Bayesian ridge regression and Bayesian LASSO regression statistical models were used to obtain genomic estimated breeding values. Two validation methods (random sampling 50% of the population and 10% of the progeny generation as validation sets) were used with 100 replications. The average predictive ability across statistical models and validation methods was about 0.49 for stem sweep, and 0.47 and 0.43 for total height and tree diameter, respectively. The sensitivity analysis suggested that prior densities (variance explained by markers) had little or no discernible effect on posterior means (residual variance) in Bayesian prediction models. Sampling from the progeny generation for model validation increased the predictive ability of markers for tree diameter and stem sweep but not for total height. The results are promising despite low linkage disequilibrium and low marker coverage of the genome (∼1.39 markers/cM).
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