Cite this paper:
WANG Quanchao, YU Yang, LI Fuhua, ZHANG Xiaojun, XIANG Jianhai. Predictive ability of genomic selection models for breeding value estimation on growth traits of Pacific white shrimp Litopenaeus vannamei[J]. HaiyangYuHuZhao, 2017, 35(5): 1221-1229

Predictive ability of genomic selection models for breeding value estimation on growth traits of Pacific white shrimp Litopenaeus vannamei

WANG Quanchao1,2, YU Yang1, LI Fuhua1,3, ZHANG Xiaojun1, XIANG Jianhai1
1 Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China;
2 University of Chinese Academy of Sciences, Beijing 100049, China;
3 Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China
Genomic selection (GS) can be used to accelerate genetic improvement by shortening the selection interval. The successful application of GS depends largely on the accuracy of the prediction of genomic estimated breeding value (GEBV). This study is a first attempt to understand the practicality of GS in Litopenaeus vannamei and aims to evaluate models for GS on growth traits. The performance of GS models in L. vannamei was evaluated in a population consisting of 205 individuals, which were genotyped for 6 359 single nucleotide polymorphism (SNP) markers by specific length amplified fragment sequencing (SLAF-seq) and phenotyped for body length and body weight. Three GS models (RR-BLUP, BayesA, and Bayesian LASSO) were used to obtain the GEBV, and their predictive ability was assessed by the reliability of the GEBV and the bias of the predicted phenotypes. The mean reliability of the GEBVs for body length and body weight predicted by the different models was 0.296 and 0.411, respectively. For each trait, the performances of the three models were very similar to each other with respect to predictability. The regression coefficients estimated by the three models were close to one, suggesting near to zero bias for the predictions. Therefore, when GS was applied in a L. vannamei population for the studied scenarios, all three models appeared practicable. Further analyses suggested that improved estimation of the genomic prediction could be realized by increasing the size of the training population as well as the density of SNPs.
Key words:    Oncholaimus zhangi sp. nov.|free-living marine nematode|taxonomy   
Received: 2016-02-03   Revised: 2016-03-25
PDF (288 KB) Free
Print this page
Add to favorites
Email this article to others
Articles by WANG Quanchao
Articles by YU Yang
Articles by LI Fuhua
Articles by ZHANG Xiaojun
Articles by XIANG Jianhai
Andriantahina F, Liu X L, Huang H, Xiang J H. 2013. Selection for growth performance of tank-reared Pacific white shrimp, Litopenaeus vannamei. Chinese Journal of Oceanology and Limnology, 31(3):534-541.
Argue B J, Arce S M, Lotz J M, Moss S M. 2002. Selective breeding of Pacific white shrimp (Litopenaeus vannamei) for growth and resistance to Taura syndrome virus.Aquaculture, 204(3-4):447-460.
Aulchenko Y S, Ripke S, Isaacs A, van Duijn C M. 2007.GenABEL:an R library for genome-wide association analysis. Bioinformatics, 23(10):1 294-1 296.
Browning S R, Browning B L. 2007. Rapid and accurate haplotype phasing and missing-data inference for wholegenome association studies by use of localized haplotype clustering. The American Journal of Human Genetics, 81(5):1 084-1 097.
Castillo-Juárez H, Campos-Montes G R, Caballero-Zamora A, Montaldo H H. 2015. Genetic improvement of Pacific white shrimp[Penaeus (Litopenaeus) vannamei]:perspectives for genomic selection. Frontiers in Genetics, 6:93.
Clark S A, Hickey J M, Daetwyler H D, van der Werf J H J. 2012. The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes. Genetics Selection Evolution, 44:4.
Clark S A, Hickey J M, van der Werf J H J. 2011. Different models of genetic variation and their effect on genomic evaluation. Genetics Selection Evolution, 43:18.
Cui Z, Hui M, Liu Y, Song C, Li X, Li Y, Liu L, Shi G, Wang S, Li F, Zhang X, Liu C, Xiang J, Chu K H. 2015. Highdensity linkage mapping aided by transcriptomics documents ZW sex determination system in the Chinese mitten crab Eriocheir sinensis. Heredity, 115(3):206-215.
Daetwyler H D, Calus M P L, Pong-Wong R, de los Campos G, Hickey J M. 2013. Genomic prediction in animals and plants:simulation of data, validation, reporting, and benchmarking. Genetics, 193(2):347-365.
Daetwyler H D, Hickey J M, Henshall J M, Dominik S, Gredler B, van der Werf J H J, Hayes B J. 2010. Accuracy of estimated genomic breeding values for wool and meat traits in a multi-breed sheep population. Animal Production Science, 50(12):1 004-1 010.
de los Campos G, Naya H, Gianola D, Crossa J, Legarra A, Manfredi E, Weigel K, Cotes J M. 2009. Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics, 182(1):375-385.
de los Campos G, Pérez P, Vazquez A I, Crossa J. 2013.Genome-enabled prediction using the BLR (Bayesian Linear Regression) R-package. In:Gondro C, van der Werf J, Hayes B eds. Genome-Wide Association Studies and Genomic Prediction. Springer, New York. p.299-320.
Desta Z A, Ortiz R. 2014. Genomic selection:genome-wide prediction in plant improvement. Trends in Plant Science, 19(9):592-601.
Dou J Z, Li X, Fu Q, Jiao W Q, Li Y P, Li T Q, Wang Y F, Hu X L, Wang S, Bao Z M. 2016. Evaluation of the 2b-RAD method for genomic selection in scallop breeding.Scientific Reports, 6:19 244.
Endelman J B. 2011. Ridge regression and other kernels for genomic selection with R package rrBLUP. The Plant Genome, 4(3):250-255.
Erbe M, Hayes B J, Matukumalli L K, Goswami S, Bowman P J, Reich C M, Mason B A, Goddard M E. 2012. Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. Journal of Dairy Science, 95(7):4 114-4 129.
Gao H, Su G, Janss L, Zhang Y, Lund M S. 2013. Model comparison on genomic predictions using high-density markers for different groups of bulls in the Nordic Holstein population. Journal of Dairy Science, 96(7):4 678-4 687.
Goddard M E, Hayes B J. 2007. Genomic selection. Journal of Animal Breeding and Genetics, 124(6):323-330.
Habier D, Fernando R L, Dekkers J C M. 2007. The impact of genetic relationship information on genome-assisted breeding values. Genetics, 177(4):2 389-2 397.
Hayes B J, Bowman P J, Chamberlain A C, Verbyla K, Goddard M E. 2009b. Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genetics Selection Evolution, 41:51.
Hayes B J, Bowman P J, Chamberlain A J, Goddard M E. 2009a. Invited review:Genomic selection in dairy cattle:progress and challenges. Journal of Dairy Science, 92(2):433-443.
Heslot N, Yang H P, Sorrells M E, Jannink J L. 2012. Genomic selection in plant breeding:a comparison of models. Crop Science, 52:146-160.
Lee S H, van der Werf J H J, Hayes B J, Goddard M E, Visscher P M. 2008. Predicting unobserved phenotypes for complex traits from whole-genome SNP data. PLoS Genetics, 4(10):e1000231.
Liu T F, Qu H, Luo C L, Shu D M, Wang J, Lund M S, Su G S. 2014. Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens. BMC Genetics, 15:110.
Luan S, Luo K, Chai Z, Cao B X, Meng X H, Lu X, Liu N, Xu S Y, Kong J. 2015. An analysis of indirect genetic effects on adult body weight of the Pacific white shrimp Litopenaeus vannamei at low rearing density. Genetics Selection Evolution, 47:95.
Luan T, Woolliams J A, Lien S, Kent M, Svendsen M, Meuwissen T H E. 2009. The accuracy of genomic selection in Norwegian red cattle assessed by crossvalidation. Genetics, 183(3):1 119-1 126.
Meuwissen T H E, Hayes B J, Goddard M E. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4):1 819-1 829.
Meuwissen T. 2003. Genomic selection:the future of marker assisted selection and animal breeding. In:Marker Assisted Selection:A Fast Track to Increase Genetic Gain in Plant and Animal Breeding? Session Ⅱ:MAS in animals. FAO Electronic Forum on Biotechnology in Food and Agriculture:Conference 10.
Meuwissen T. 2009. Accuracy of breeding values of 'unrelated' individuals predicted by dense SNP genotyping. Genetics Selection Evolution, 41:35.
Moser G, Tier B, Crump R E, Khatkar M S, Raadsma H W. 2009. A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers. Genetics Selection Evolution, 41:56.
Neves H H R, Carvalheiro R, O'Brien A M P, Utsunomiya Y T, do Carmo A S, Schenkel F S, Sölkner J, McEwan J C, Van Tassell C P, Cole J B, da Silva M V G B, Queiroz S A, Sonstegard T S, Garcia J F. 2014. Accuracy of genomic predictions in Bos indicus (Nellore) cattle. Genetics Selection Evolution, 46:17.
Nielsen H M, Sonesson A K, Yazdi H, Meuwissen T H E. 2009.Comparison of accuracy of genome-wide and BLUP breeding value estimates in sib based aquaculture breeding schemes. Aquaculture, 289(3-4):259-264.
Nirea K G, Sonesson A K, Woolliams J A, Meuwissen T H E. 2012. Strategies for implementing genomic selection in family-based aquaculture breeding schemes:double haploid sib test populations. Genetics Selection Evolution, 44:30.
Ødegård J, Moen T, Santi N, Korsvoll S A, Kjøglum S, Meuwissen T H E. 2014. Genomic predictions in aquaculture:reliabilities in an admixed Atlantic salmon population. In:Proceedings, 10th World Congress of Genetics Applied to Livestock Production. ASAS (American Society of Animal Science), Vancouver.
Okpala C O R, Bono G. 2016. Investigating the biometric and physicochemical characteristics of freshly harvested Pacific white shrimp (Litopenaeus vannamei):a comparative approach. Journal of the Science of Food and Agriculture, 96(4):1 231-1 240.
Okpala C O R, Choo W S, Dykes G A. 2014. Quality and shelf life assessment of Pacific white shrimp (Litopenaeus vannamei) freshly harvested and stored on ice. LWT-Food Science and Technology, 55(1):110-116.
Ostersen T, Christensen O F, Henryon M, Nielsen B, Su G S, Madsen P. 2011. Deregressed EBV as the response variable yield more reliable genomic predictions than traditional EBV in pure-bred pigs. Genetics Selection Evolution, 43:38.
Park T, Casella G. 2008. The Bayesian lasso. Journal of the American Statistical Association, 103(482):681-686.
Pszczola M, Strabel T, Mulder H A, Calus M P L. 2012.Reliability of direct genomic values for animals with different relationships within and to the reference population. Journal of Dairy Science, 95(1):389-400.
R Development Core Team. 2014. R:a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.
Resende M F R, Muñoz P Jr, Resende M D V, Garrick D J, Fernando R L, Davis J M, Jokela E J, Martin T A, Peter G F, Kirst M. 2012. Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.).Genetics, 190(4):1 503-1 510.
Silva F F E, de Resende M D V, Rocha G S, Duarte D A S, Lopes P S, Brustolini O J B, Thus S, Viana J M S, Guimarães S E F. 2013. Genomic growth curves of an outbred pig population. Genetics and Molecular Biology, 36(4):520-527.
Solberg T R, Sonesson A K, Woolliams J A, Meuwissen T H E. 2008. Genomic selection using different marker types and densities. Journal of Animal Science, 86(10):2 447-2 454.
Sonesson A K, Meuwissen T H E. 2009. Testing strategies for genomic selection in aquaculture breeding programs.Genetics Selection Evolution, 41:37.
Spindel J, Begum H, Akdemir D, Virk P, Collard B, Redoña E, Atlin G, Jannink J L, McCouch S R. 2015. Genomic Selection and association mapping in rice (Oryza sativa):effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLoS Genetics, 11(2):e1004982.
Sui J, Luan S, Luo K, Meng X H, Lu X, Cao B X, Li W J, Chai Z, Liu N, Xu S Y, Kong J. 2015. Genetic parameters and response to selection for harvest body weight of pacific white shrimp, Litopenaeus vannamei. Aquaculture Research, 47(9):2 795-2 803,
Sun X W, Liu D Y, Zhang X F, Li W B, Liu H, Hong W G, Jiang C B, Guan N, Ma C X, Zeng H P, Xu C H, Song J, Huang L, Wang C M, Shi J J, Wang R, Zheng X H, Lu C Y, Wang X W, Zheng H K. 2013. SLAF-seq:an efficient method of large-scale de novo SNP discovery and genotyping using high-throughput sequencing. Plos One, 8(3):e58700.
Tan J, Luan S, Luo K, Guan J T, Li W J, Sui J, Guo Z J, Xu S Y, Kong J. 2016. Heritability and genotype by environment interactions for growth and survival in Litopenaeus vannamei at low and high densities. Aquaculture Research,
Tayeh N, Klein A, Le Paslier M C, Jacquin F, Houtin H, Rond C, Chabert-Martinello M, Magnin-Robert J B, Marget P, Aubert G, Burstin J. 2015. Genomic prediction in pea:effect of marker density and training population size and composition on prediction accuracy. Frontiers in Plant Science, 6:941.
van der Werf J H J, Kinghorn B P, Banks R G. 2010. Design and role of an information nucleus in sheep breeding programs. Animal Production Science, 50(12):998-1 003.
Verbyla K, Hayes B J, Bowman P J, Goddard M E. 2009.Accuracy of genomic selection using stochastic search variable selection in Australian Holstein Friesian dairy cattle. Genetics Research, 91(5):307-311.
Weigel K A, de los Campos G, González-Recio O, Naya H, Wu X L, Long N, Rosa G J M, Gianola D. 2009. Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers. Journal of Dairy Science, 92(10):5 248-5 257.
Yu Y, Wei J K, Zhang X J, Liu J W, Liu C Z, Li F H, Xiang J H. 2014. SNP discovery in the transcriptome of white Pacific shrimp Litopenaeus vannamei by next generation sequencing. PLoS One, 9(1):e87218.
Yu Y, Zhang X J, Yuan J B, Li F H, Chen X H, Zhao Y Z, Huang L, Zheng H K, Xiang J H. 2015. Genome survey and high-density genetic map construction provide genomic and genetic resources for the Pacific White Shrimp Litopenaeus vannamei. Scientific Reports, 5:15 612.
Zhang Z, Zhang Q, Ding X D. 2011. Advances in genomic selection in domestic animals. Chinese Science Bulletin, 56(25):2 655-2 663.