Influence of parental sample sizes on the estimating genetic parameters in cultured clam <i>Meretrix meretrix</i> based on factorial mating designs

LIANG Bingbing; YUE Xin; WANG Hongxia; LIU Baozhong

doi:10.1007/s13131-016-0875-0

因子交配设计中文蛤亲本数目对遗传参数估计的影响

doi: 10.1007/s13131-016-0875-0

1.
实验海洋生物学重点实验室, 中国科学院海洋研究所, 青岛 266071;中国科学院大学, 北京 100049
2.
实验海洋生物学重点实验室, 中国科学院海洋研究所, 青岛 266071;青岛海洋科学与技术国家实验室, 青岛 266200

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- 收稿日期: 2015-03-31
- 修回日期: 2015-09-14

Influence of parental sample sizes on the estimating genetic parameters in cultured clam Meretrix meretrix based on factorial mating designs

1.
Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China;University of Chinese Academy of Sciences, Beijing 100049, China
2.
Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China;Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266200, China

摘要

摘要: 在人工育种中,遗传参数的精确估计是制定有效选育策略的前提和基础。贝类重要经济性状遗传力的估计已有诸多文献报道,然而不同家系结构对遗传参数估计准确性影响的研究却鲜有报道。因此,本研究采用REML算法和Bayesian推论对文蛤不同规模因子设计的家系进行生长性状遗传参数的估计,探究不同亲本数对参数估计准确性的影响。结果显示,当时用REML算法对四个性状进行遗传力估计时,9个和16个全同胞家系文蛤生长性状的遗传力在0.23-0.32之间,25个全同胞家系生长性状的遗传力在0.19-0.22之间。当使用Bayesian推论时,9个和16个全同胞家系文蛤生长性状的遗传力在0.11-0.12之间,25个全同胞家系生长性状的遗传力在0.13-0.16之间。与REML算法相比,根据Bayesian推论得到的遗传力值略低,但是仍处于中等遗传力的水平。当亲本数目由6增加到10时,采用REML算法得出的遗传力估计值接近于0.20,而采用Bayesian算法得出的估计值接近于0.12。生长性状之间的遗传相关呈现显著的正相关,且不同规模的因子设计之间并无显著差异。当全同胞家系数目由9个增加到25个时,育种值的准确性呈现增加趋势。本文研究结果可为文蛤的人工选育工作提供可靠的理论指导。
- 文蛤 /
- 亲本数目 /
- 遗传参数 /
- 因子设计 /
- 限制性最大似然估计法 /
- 贝叶斯推论
Abstract: The precise and accurate knowledge of genetic parameters is a prerequisite for making efficient selection strategies in breeding programs. A number of estimators of heritability about important economic traits in many marine mollusks are available in the literature, however very few research have evaluated about the accuracy of genetic parameters estimated with different family structures. Thus, in the present study, the effect of parent sample size for estimating the precision of genetic parameters of four growth traits in clam M. meretrix by factorial designs were analyzed through restricted maximum likelihood (REML) and Bayesian. The results showed that the average estimated heritabilities of growth traits obtained from REML were 0.23-0.32 for 9 and 16 full-sib families and 0.19-0.22 for 25 full-sib families. When using Bayesian inference, the average estimated heritabilities were 0.11-0.12 for 9 and 16 full-sib families and 0.13-0.16 for 25 full-sib families. Compared with REML, Bayesian got lower heritabilities, but still remained at a medium level. When the number of parents increased from 6 to 10, the estimated heritabilities were more closed to 0.20 in REML and 0.12 in Bayesian inference. Genetic correlations among traits were positive and high and had no significant difference between different sizes of designs. The accuracies of estimated breeding values from the 9 and 16 families were less precise than those from 25 families. Our results provide a basic genetic evaluation for growth traits and should be useful for the design and operation of a practical selective breeding program in the clam M. meretrix.
- Meretrix meretrix /
- parental sample sizes /
- genetic parameter /
- factorial design /
- restricted maximum likelihood /
- Bayesian inference

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参考文献(27)

Blanc J M. 2003. Comparison of experimental designs for estimating quantitative genetic parameters in fish. Aquaculture Research, 34(12): 1099-1105

Bolker B M, Brooks M E, Clark C J, et al. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology & Evolution, 24(3): 127-135

Busack C, Knudsen C M. 2007. Using factorial mating designs to in-crease the effective number of breeders in fish hatcheries. Aquaculture, 273(1): 24-32

Dégremont L, Ernande B, Bédier E, et al. 2007. Summer mortality of hatchery-produced Pacific oyster spat (Crassostrea gigas): I. Es-timation of genetic parameters for survival and growth. Aquaculture, 262(1): 41-53

Dupont-Nivet M, Vandeputte M, Chevassus B. 2002. Optimization of factorial mating designs for inference on heritability in fish spe-cies. Aquaculture, 204(3-4): 361-370

Dupont-Nivet M, Vandeputte M, Haffray P, et al. 2006. Effect of differ-ent mating designs on inbreeding, genetic variance and re-sponse to selection when applying individual selection in fish breeding programs. Aquaculture, 252(2-4): 161-170

Engstr.m G, McMillan I, McKay L, et al. 1996. Comparison of mating systems in a fish population: a simulation study. Journal of An-imal Breeding and Genetics, 113(1-6): 559-566

Falconer D S, Mackay T F C. 1996. Introduction to Quantitative Ge-netics. 4th ed. Harlow: Longman, 464

Gilmour A R, Gogel B J, Cullis B R, et al. 2009. ASReml User Guide Re-lease 3.0. Hemel Hempstead, UK: VSN International Ltd Hadfield J D. 2010. MCMC methods for multi-response generalized

linear mixed models: the MCMCglmm R package. Journal of Statistic Software, 33(2): 1-22

Henderson C R. 1974. General flexibility of linear model techniques for sire evaluation. Journal of Dairy Science, 57(8): 963-972

Lewis T W, Blott S C, Woolliams J A. 2013. Comparative analyses of genetic trends and prospects for selection against hip and el-bow dysplasia in 15 UK dog breeds. BMC Genetics, 14(1): 1471-2156

Littell R C. 2002. Analysis of unbalanced mixed model data: a case study comparison of ANOVA versus REML/GLS. Journal of Ag-ricultural, Biological, and Environmental Statistics, 7(4): 472-490

Liu Baozhong, Dong Bo, Tang Baojun, et al. 2006. Effect of stocking density on growth, settlement and survival of clam larvae, Meretrix meretrix. Aquaculture, 258(1-4): 344-349

Mallet A L, Freeman K R, Dickie L M. 1986. The genetics of produc-tion characters in the blue mussel Mytilus edulis: I. A prelimin-ary analysis. Aquaculture, 57(1-4): 133-140

Mathew B, Bauer A M, Koistinen P, et al. 2012. Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters. Heredity, 109(4): 235-245

Nguyen T T T, Hayes B J, Guthridge K, et al. 2011. Use of a microsatel-lite-based pedigree in estimation of heritabilities for economic traits in Australian blue mussel, Mytilus galloprovincialis. Journal of Animal Breeding and Genetics, 128(6): 482-490

Patterson H D, Thompson R. 1971. Recovery of inter-block informa-tion when block sizes are unequal. Biometrika, 58(3): 545-554

Schenkel F S, Schaeffer L R, Boettcher P J. 2002. Comparison between estimation of breeding values and fixed effects using Bayesian and empirical BLUP estimation under selection on parents and missing pedigree information. Genetics Selection Evolution, 34(1): 41-59

Silva F F E, Viana J M S, Faria V R, et al. 2013. Bayesian inference of mixed models in quantitative genetics of crop species. Theoret-ical and Applied Genetics, 126(7): 1749-1761

Str.mgren T, Nielsen M V. 1989. Heritability of growth in larvae and juveniles of Mytilus edulis. Aquaculture, 80(1-2): 1-6

Thompson R, Brotherstone S, White I M S. 2005. Estimation of quant-itative genetic parameters. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1459): 1469-1477

Toro J E, Alcapán A C, Vergara A M, et al. 2004. Heritability estimates of larval and spat shell height in the Chilean blue mussel (Mytilus chilensis Hupe 1854) produced under controlled labor-atory conditions. Aquaculture Research, 35(1): 56-61

Waldmann P, Ericsson T. 2006. Comparison of REML and Gibbs sampling estimates of multi-trait genetic parameters in Scots pine. Theoretical and Applied Genetics, 112(8): 1441-1451

Wang Hongxia, Chai Xueliang, Liu Baozhong. 2011. Estimation of ge-netic parameters for growth traits in cultured clam Meretrix meretrix (Bivalvia: Veneridae) using the Bayesian method based on Gibbs sampling. Aquaculture Research, 42(2): 240-247

Wilson A J, Réale D, Clements M N, et al. 2010. An ecologist's guide to the animal model. Journal of Animal Ecology, 79(1): 13-26

Yan Xiwu, Huo Zhongming, Yang Feng, et al. 2014. Heritability of lar-val and juvenile growth for two stocks of Manila clam Ruditapes philippinarum. Aquaculture Research, 45(3): 484-490

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文章访问数: 1159
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因子交配设计中文蛤亲本数目对遗传参数估计的影响

doi: 10.1007/s13131-016-0875-0

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Influence of parental sample sizes on the estimating genetic parameters in cultured clam Meretrix meretrix based on factorial mating designs

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