Genomic Selection for Growth Traits in Pacific Oyster (Crassostrea gigas): Potential of Low-Density Marker Panels for Breeding Value Prediction.
Genomic Selection for Growth Traits in Pacific Oyster (Crassostrea gigas): Potential of Low-Density Marker Panels for Breeding Value Prediction.
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DOI：
10.3389/fgene.2018.00391
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发表时间：
2018
影响因子：
3.7
通讯作者：
Houston RD
中科院分区：
文献类型：
--
作者：
Gutierrez AP;Matika O;Bean TP;Houston RD
太平洋牡蛎是全球重要的水产养殖物种，通过选择性育种进行遗传改良是一个主要目标。基因组选择有可能加快育种计划中关键目标性状的遗传增益，但尚未在牡蛎中进行评估。太平洋牡蛎（Crassostrea gigas）SNP芯片的发展为多基因性状的基因组选择策略提供了新的研究机会。在这项研究中，人口的820牡蛎（包括23个全同胞家庭）的基因型使用中密度SNP阵列（23 K信息SNP），和生长相关性状的遗传结构[壳高（SH），壳长（SL），和湿重（WW）]进行了评估。3个性状的遗传力均为中等水平（SH为0.26 ± 0.06，SL为0.23 ± 0.06，WW为0.35 ± 0.05），GWAS分析结果表明其遗传结构为多基因遗传。基因组预测方法被用来估计生长的育种值，并与基于系谱的方法进行比较。基因组预测模型（GBLUP）的准确性优于传统的系谱法（PBLUP）的SL和WW的25%，SH的30%。此外，SNP标记密度的降低对预测准确性几乎没有影响，即使当密度降低到几百个SNP时。这些结果表明，基因组选择在牡蛎育种中的使用可以提供有益的选择育种候选人，以改善复杂的经济性状，在相对较低的成本。
Pacific oysters are a key aquaculture species globally, and genetic improvement via selective breeding is a major target. Genomic selection has the potential to expedite genetic gain for key target traits of a breeding program, but has not yet been evaluated in oyster. The recent development of SNP arrays for Pacific oyster (Crassostrea gigas) raises the opportunity to test genomic selection strategies for polygenic traits. In this study, a population of 820 oysters (comprising 23 full-sibling families) were genotyped using a medium density SNP array (23 K informative SNPs), and the genetic architecture of growth-related traits [shell height (SH), shell length (SL), and wet weight (WW)] was evaluated. Heritability was estimated to be moderate for the three traits (0.26 ± 0.06 for SH, 0.23 ± 0.06 for SL and 0.35 ± 0.05 for WW), and results of a GWAS indicated that the underlying genetic architecture was polygenic. Genomic prediction approaches were used to estimate breeding values for growth, and compared to pedigree based approaches. The accuracy of the genomic prediction models (GBLUP) outperformed the traditional pedigree approach (PBLUP) by ∼25% for SL and WW, and ∼30% for SH. Further, reduction in SNP marker density had little impact on prediction accuracy, even when density was reduced to a few hundred SNPs. These results suggest that the use of genomic selection in oyster breeding could offer benefits for the selection of breeding candidates to improve complex economic traits at relatively modest cost.