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海洋松的基因组选择。

Genomic selection in maritime pine.

机构信息

INRA, UMR1202, BIOGECO, Cestas F-33610, France.

INRA, UMR1202, BIOGECO, Cestas F-33610, France; Univ. Bordeaux, UMR1202, BIOGECO, Talence F-33170, France.

出版信息

Plant Sci. 2016 Jan;242:108-119. doi: 10.1016/j.plantsci.2015.08.006. Epub 2015 Aug 18.

DOI:10.1016/j.plantsci.2015.08.006
PMID:26566829
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 r(2)=0.01. Linkage disequilibrium corrected for genomic relationships derived from markers was smaller (rV(2)=0.006). 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).

摘要

利用 2500 个 SNP 标记对两代海洋松(Pinus pinaster Ait.)育种群体(n=661)进行了基因型分析。研究了连锁不平衡的程度和基因组选择在生长和茎干直度改良方面的应用。总体的染色体内连锁不平衡程度为 r(2)=0.01。考虑到标记的基因组关系,连锁不平衡校正后更大(rV(2)=0.006)。使用基因组 BLUP、贝叶斯岭回归和贝叶斯 LASSO 回归统计模型获得基因组估计育种值。使用两种验证方法(随机抽取种群的 50%和后代的 10%作为验证集),每个方法重复 100 次。在统计模型和验证方法中,平均预测能力约为 0.49,用于茎干扫描,分别为 0.47 和 0.43,用于总高度和树干直径。敏感性分析表明,贝叶斯预测模型中,先验密度(标记解释的方差)对后验均值(剩余方差)几乎没有或没有明显影响。从后代群体中采样进行模型验证可以提高标记对树干直径和茎干扫描的预测能力,但对总高度的预测能力没有提高。尽管连锁不平衡程度低且基因组标记覆盖率低(约 1.39 个标记/cM),但结果还是很有希望的。

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