Login Register Cart 0
Generic placeholder image

Current Chinese Science

Editor-in-Chief

ISSN (Print): 2210-2981
ISSN (Online): 2210-2914

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
Research Article Section: Marine Sciences

Simulation Analysis on Genomic Selection of Grouper (Epinephelus coioides) Breeding for Categorical Traits

Author(s): Zhiyuan Ma and Xinxin You*

Volume 1, Issue 1, 2021

Published on: 09 September, 2020

Page: [87 - 97] Pages: 11

DOI: 10.2174/2210298101999200909111243

Abstract

Background: The basic principle of genome selection (GS) is to establish a model of genome estimated breeding value (GEBV) by using single-nucleotide polymorphisms (SNPs) covering the entire genome. Despite the decreasing cost of high-throughput genotyping, the GS strategy remains expensive due to the need for phenotyping and genotyping for a large number of samples. Simulation analysis of genome selection is a popular, lower-cost method to determine an optimal breeding program of GS.

Objective: To evaluate the utility of simulation data to study the influence of different factors on algorithms. This could be helpful for developing genome selection breeding strategies, especially for stress and resistance traits of fish.

Methods: Real data of orange-spotted grouper (Epinephelus coioides) were obtained from a previous genome-wide association study. Ammonia tolerance, different population sizes, SNP density, QTL number, kinship (base mutation rate), and heritability were considered. All of the phenotypes and genotypes were generated by AlphaSimR simulation software. Four genome selection algorithms (gBLUP, rrBLUP, BayesA, and BayesC) were tested to derive GEBV, and their accuracies (area under the curve, AUC) were compared.

Results: In different scenarios, the AUC ranges from 0.4237 to 0.6895 for BayesA, 0.4282 to 0.6878 for BayesC, 0.4278 to 0.6798 for gBLUP, and 0.4346 to 0.6834 for rrBLUP. The mean AUC of these four algorithms was not significantly different (0.547–0.548). The accuracies of the four genome selection algorithms were similar but had different predictive performances in specific scenarios. The gBLUP was most stable, and the rrBLUP was slightly better at predicting low heritability traits. When the number of individuals was small, the BayesA and BayesC algorithms were more robust.

Conclusion: A practical GS scheme should be optimized in accordance with marker density, heritability, and reference population size. Adequate preliminary research is necessary. The results provide a framework for the design of genomic selection schemes in E. coioides breeding.

Keywords: gBLUP, rrBLUP, BayesA, BayesC, categorical traits, grouper, genomic selection.

« Previous
Graphical Abstract

[1]
Meuwissen TH, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001; 157(4): 1819-29.
[PMID: 11290733]
[2]
Hayes BJ, Lewin HA, Goddard ME. The future of livestock breeding: genomic selection for efficiency, reduced emissions intensity, and adaptation. Trends Genet 2013; 29(4): 206-14.
[http://dx.doi.org/10.1016/j.tig.2012.11.009] [PMID: 23261029]
[3]
Wiggans GR, Cole JB, Hubbard SM, Sonstegard TS. Genomic selection in dairy cattle: The USDA experience. Annu Rev Anim Biosci 2017; 5(1): 309-27.
[http://dx.doi.org/10.1146/annurev-animal-021815-111422] [PMID: 27860491]
[4]
Khatkar MS. Genomic selection in aquaculture breeding programs.In: Liu Z, Ed Bioinformatics in Aquaculture. Chichester, UK: John Wiley & Sons Ltd. 2017; pp. 380-91.
[http://dx.doi.org/10.1002/9781118782392.ch21]
[5]
Nejati-Javaremi A, Smith C, Gibson JP. Effect of total allelic relationship on accuracy of evaluation and response to selection. J Anim Sci 1997; 75(7): 1738-45.
[http://dx.doi.org/10.2527/1997.7571738x] [PMID: 9222829]
[6]
Gianola D. Priors in whole-genome regression: The bayesian alphabet returns. Genetics 2013; 194(3): 573-96.
[http://dx.doi.org/10.1534/genetics.113.151753] [PMID: 23636739]
[7]
You X, Shan X, Shi Q. Research advances in the genomics and applications for molecular breeding of aquaculture animals. Aquaculture 2020; 526(735357): 1-24.
[http://dx.doi.org/10.1016/j.aquaculture.2020.735357]
[8]
Li B, VanRaden PM, Guduk E, et al. Genomic prediction of residual feed intake in US Holstein dairy cattle. J Dairy Sci 2020; 103(3): 2477-86.
[http://dx.doi.org/10.3168/jds.2019-17332] [PMID: 31954583]
[9]
Rezende FM, Nani JP, Peñagaricano F. Genomic prediction of bull fertility in US Jersey dairy cattle. J Dairy Sci 2019; 102(4): 3230-40.
[http://dx.doi.org/10.3168/jds.2018-15810] [PMID: 30712930]
[10]
Schulman NF, Sahana G, Iso-Touru T, et al. Mapping of fertility traits in Finnish ayrshire by genome-wide association analysis. Anim Genet 2011; 42(3): 263-9.
[http://dx.doi.org/10.1111/j.1365-2052.2010.02149.x] [PMID: 21554346]
[11]
Viitala SM, Schulman NF, De Koning DJ, et al. Quantitative trait loci affecting milk production traits in Finnish ayrshire dairy cattle. J Dairy Sci 2003; 86(5): 1828-36.
[http://dx.doi.org/10.3168/jds.S0022-0302(03)73769-2] [PMID: 12778594]
[12]
Luan T, Woolliams JA, Lien S, Kent M, Svendsen M, Meuwissen THE. The accuracy of genomic selection in Norwegian red cattle assessed by cross-validation. Genetics 2009; 183(3): 1119-26.
[http://dx.doi.org/10.1534/genetics.109.107391] [PMID: 19704013]
[13]
Daetwyler HD, Kemper KE, van der Werf JH, Hayes BJ. Components of the accuracy of genomic prediction in a multi-breed sheep population. J Anim Sci 2012; 90(10): 3375-84.
[http://dx.doi.org/10.2527/jas.2011-4557] [PMID: 23038744]
[14]
Lillehammer M, Sonesson AK, Klemetsdal G, Blichfeldt T, Meuwissen THE. Genomic selection strategies to improve maternal traits in Norwegian white sheep. J Anim Breed Genet 2020; 137(4): 384-94.
[http://dx.doi.org/10.1111/jbg.12475] [PMID: 32236991]
[15]
Lourenco DAL, Tsuruta S, Fragomeni BO, et al. Genetic evaluation using single-step genomic best linear unbiased predictor in American angus. J Anim Sci 2015; 93(6): 2653-62.
[http://dx.doi.org/10.2527/jas.2014-8836] [PMID: 26115253]
[16]
Lillehammer M, Meuwissen THE, Sonesson AK. Genomic selection for maternal traits in pigs. J Anim Sci 2011; 89(12): 3908-16.
[http://dx.doi.org/10.2527/jas.2011-4044] [PMID: 21841086]
[17]
Song H, Ye S, Jiang Y, Zhang Z, Zhang Q, Ding X. Using imputation-based whole-genome sequencing data to improve the accuracy of genomic prediction for combined populations in pigs. Genet Sel Evol 2019; 51(1): 58.
[http://dx.doi.org/10.1186/s12711-019-0500-8] [PMID: 31638889]
[18]
Wolc A, Zhao HH, Arango J, et al. Response and inbreeding from a genomic selection experiment in layer chickens. Genet Sel Evol 2015; 47(59): 59.
[http://dx.doi.org/10.1186/s12711-015-0133-5] [PMID: 26149977]
[19]
Liu X, Wang H, Wang H. Factors affecting genomic selection revealed by empirical evidence in maize. Crop J 2018; 6(4): 341-52.
[http://dx.doi.org/10.1016/j.cj.2018.03.005]
[20]
Edwards SM, Buntjer JB, Jackson R, et al. The effects of training population design on genomic prediction accuracy in wheat. Theor Appl Genet 2019; 132(7): 1943-52.
[http://dx.doi.org/10.1007/s00122-019-03327-y] [PMID: 30888431]
[21]
Sun J, Poland JA, Mondal S, et al. High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage. Theor Appl Genet 2019; 132(6): 1705-20.
[http://dx.doi.org/10.1007/s00122-019-03309-0] [PMID: 30778634]
[22]
Muleta KT, Pressoir G, Morris GP. Optimizing genomic selection for a sorghum breeding program in Haiti: A simulation study. G3 (Bethesda) 2019; 9(2): 391-401.
[http://dx.doi.org/doi.org/10.1534/g3.118.200932] [PMID: 30530641]
[23]
Rice B, Lipka AE. Evaluation of RR-BLUP genomic selection models that incorporate peak genome-wide association study signals in maize and sorghum. Plant Genome 2019; 12(1): 1-14.
[http://dx.doi.org/10.3835/plantgenome2018.07.0052] [PMID: 30951091]
[24]
Dong L, Xiao S, Chen J, Wan L, Wang Z. Genomic selection using extreme phenotypes and pre-selection of SNPs in large yellow croaker (Larimichthys crocea). Mar Biotechnol (NY) 2016; 18(5): 575-83.
[http://dx.doi.org/10.1007/s10126-016-9718-4] [PMID: 27704224]
[25]
Bangera R, Thorland I, Hillestad B, Avitua S, Johansen H, Refstie T. Accuracy of genomic predictions for yield traits, flesh colour and fillet fat in Atlantic salmon (Salmo salar). In: Proceedings of the World Congress on Genetics Applied to Livestock Production. 2018.
[26]
Tsai H-Y, Hamilton A, Tinch AE, et al. Genomic prediction of host resistance to sea lice in farmed Atlantic salmon populations. Genet Sel Evol 2016; 48(1): 47.
[http://dx.doi.org/10.1186/s12711-016-0226-9] [PMID: 27357694]
[27]
Wang Q, Yu Y, Yuan J, et al. Effects of marker density and population structure on the genomic prediction accuracy for growth trait in Pacific white shrimp Litopenaeus vannamei. BMC Genet 2017; 18(1): 45.
[http://dx.doi.org/10.1186/s12863-017-0507-5] [PMID: 28514941]
[28]
Jones DB, Jerry DR, Khatkar MS, et al. A comparative integrated gene-based linkage and locus ordering by linkage disequilibrium map for the Pacific white shrimp, Litopenaeus vannamei. Sci Rep 2017; 7(1): 10360.
[http://dx.doi.org/10.1038/s41598-017-10515-7] [PMID: 28871114]
[29]
Vallejo RL, Leeds TD, Fragomeni BO, et al. Evaluation of genome-enabled selection for bacterial cold water disease resistance using progeny performance data in rainbow trout: insights on genotyping methods and genomic prediction models. Front Genet 2016; 7(96): 96.
[http://dx.doi.org/10.3389/fgene.2016.00096] [PMID: 27303436]
[30]
Vallejo RL, Leeds TD, Gao G, et al. Genomic selection models double the accuracy of predicted breeding values for bacterial cold water disease resistance compared to a traditional pedigree-based model in rainbow trout aquaculture. Genet Sel Evol 2017; 49(1): 17.
[http://dx.doi.org/10.1186/s12711-017-0293-6] [PMID: 28148220]
[31]
Sargolzaei M, Schenkel FS. QMSim: a large-scale genome simulator for livestock. Bioinformatics 2009; 25(5): 680-1.
[http://dx.doi.org/10.1093/bioinformatics/btp045] [PMID: 19176551]
[32]
Zhbannikov IY, Arbeev KG, Yashin AI. cophesim: a comprehensive phenotype simulator for testing novel association methods. F1000 Res 2017; 6: 1294.
[http://dx.doi.org/10.12688/f1000research.11968.1] [PMID: 28979766]
[33]
Böhm S, Engelmann C. xSim: The extreme-scale simulator. IEEE 2011; 280-6.
[34]
Faux A-M, Gorjanc G, Gaynor RC, et al. AlphaSim: software for breeding program simulation. Plant Genome 2016; 9(3): 1-14.
[http://dx.doi.org/10.3835/plantgenome2016.02.0013] [PMID: 27902803]
[35]
Pierre S, Gaillard S, Prévot‐D’Alvise N. Grouper aquaculture: Asian success and Mediterranean trials. Aquat Conserv 2008; 18(3): 297-308.
[http://dx.doi.org/10.1002/aqc.840]
[36]
Rimmer MA, Glamuzina B. A review of grouper (Family Serranidae: Subfamily Epinephelinae) aquaculture from a sustainability science perspective. Rev Aquacult 2017; 11(1): 58-87.
[http://dx.doi.org/10.1111/raq.12226]
[37]
Lu X, Kong J, Meng X, et al. Identification of SNP markers associated with tolerance to ammonia toxicity by selective genotyping from de novo assembled transcriptome in Litopenaeus vannamei. Fish Shellfish Immunol 2018; 73(1): 158-66.
[http://dx.doi.org/10.1016/j.fsi.2017.12.005] [PMID: 29208499]
[38]
You X, Shu L, Li S, et al. Construction of high-density genetic linkage maps for orange-spotted grouper Epinephelus coioides using multiplexed shotgun genotyping. BMC Genet 2013; 14(1): 113.
[http://dx.doi.org/10.1186/1471-2156-14-113] [PMID: 24289265]
[39]
Xu T, Zhang X, Ruan Z. Genome resequencing of the orange-spotted grouper (Epinephelus coioides) for a genome-wide association study on ammonia tolerance. Aquaculture 2019; 512(734332): 1-8.
[http://dx.doi.org/10.1016/j.aquaculture.2019.734332]
[40]
DiBattista JD, Wakefield CB, Moore GI. Genomic and life-history discontinuity reveals a precinctive lineage for a deep-water grouper with gene flow from tropical to temperate waters on the west coast of Australia. Ecol Genet Genom 2018; 9: 23-33.
[http://dx.doi.org/10.1016/j.egg.2018.09.001]
[41]
Yang Y, Wu L, Wu X, et al. Identification of candidate growth-related SNPs and genes using GWAS in brown-marbled grouper (Epinephelus fuscoguttatus). Mar Biotechnol 2020; 22(2): 153-66.
[http://dx.doi.org/10.1007/s10126-019-09940-8] [PMID: 31927644]
[42]
Zhang X, Zhou L, Gui J. Biotechnological innovation in genetic breeding and sustainable green development in Chinese aquaculture. Scientia Sinica Vitae 2019; 49: 1-21.
[http://dx.doi.org/10.1360/SSV-2019-0142]
[43]
Gutierrez AP, Matika O, Bean TP, Houston RD. Genomic selection for growth traits in Pacific oyster (Crassostrea gigas): potential of low-density marker panels for breeding value prediction. Front Genet 2018; 9(391): 391.
[http://dx.doi.org/10.3389/fgene.2018.00391] [PMID: 30283494]
[44]
Liu G, Dong L, Gu L, et al. Evaluation of genomic selection for seven economic traits in yellow drum (Nibea albiflora). Mar Biotechnol 2019; 21(6): 806-12.
[http://dx.doi.org/10.1007/s10126-019-09925-7] [PMID: 31745748]
[45]
Wang Y, Sun G, Zeng Q, et al. Predicting growth traits with genomic selection methods in Zhikong scallop (Chlamys farreri). Mar Biotechnol 2018; 20(6): 769-79.
[http://dx.doi.org/10.1007/s10126-018-9847-z] [PMID: 30116982]
[46]
Li H, Su G, Jiang L, Bao Z. An efficient unified model for genome-wide association studies and genomic selection. Genet Sel Evol 2017; 49(1): 64.
[http://dx.doi.org/10.1186/s12711-017-0338-x] [PMID: 28836943]
[47]
Liu Y, Lu S, Liu F, et al. Genomic selection using BayesCπ and GBLUP for resistance against Edwardsiella tarda in Japanese flounder (Paralichthys olivaceus). Mar Biotechnol (NY) 2018; 20(5): 559-65.
[http://dx.doi.org/10.1007/s10126-018-9839-z] [PMID: 29943315]
[48]
Shirali M, Miraei-Ashtiani SR, Pakdel A, Haley C, Navarro P, Pong-Wong R. A comparison of the sensitivity of the BayesC and genomic best linear unbiased prediction (GBLUP) methods of estimating genomic breeding values under different quantitative trait locus (QTL) model assumptions. Iran J Appl Anim Sci 2015; 5(1): 41-6.
[49]
Sonesson AK, Meuwissen TH. Testing strategies for genomic selection in aquaculture breeding programs. Genet Sel Evol 2009; 41(1): 37.
[http://dx.doi.org/10.1186/1297-9686-41-37] [PMID: 19566932]
[50]
Andonov S, Lourenco DAL, Fragomeni BO, et al. Accuracy of breeding values in small genotyped populations using different sources of external information-A simulation study. J Dairy Sci 2017; 100(1): 395-401.
[http://dx.doi.org/10.3168/jds.2016-11335] [PMID: 28341049]
[51]
Dufflocq P, Pérez-Enciso M, Lhorente JP, Yáñez JM. Accuracy of genomic predictions using different imputation error rates in aquaculture breeding programs: A simulation study. Aquaculture 2018; 503(1): 225-30.
[http://dx.doi.org/10.1016/j.aquaculture.2018.12.061]
[52]
Karimi K, Sargolzaei M, Plastow GS, Wang Z, Miar Y. Opportunities for genomic selection in American mink: A simulation study. PLoS One 2019; 14(3)e0213873
[http://dx.doi.org/10.1371/journal.pone.0213873] [PMID: 30870528]
[53]
Nielsen HM, Sonesson AK, Meuwissen TH. Optimum contribution selection using traditional best linear unbiased prediction and genomic breeding values in aquaculture breeding schemes. J Anim Sci 2011; 89(3): 630-8.
[http://dx.doi.org/10.2527/jas.2009-2731] [PMID: 21036937]
[54]
Odegård J, Moen T, Santi N, Korsvoll SA, Kjøglum S, Meuwissen TH. Genomic prediction in an admixed population of Atlantic salmon (Salmo salar). Front Genet 2014; 5(402): 402.
[http://dx.doi.org/10.3389/fgene.2014.00402] [PMID: 25484890]
[55]
Fang L, Sahana G, Su G, et al. Integrating sequence-based GWAS and RNA-Seq provides novel insights into the genetic basis of mastitis and milk production in dairy cattle. Sci Rep 2017; 7(1): 45560.
[http://dx.doi.org/10.1038/srep45560] [PMID: 28358110]
[56]
Kriaridou C, Tsairidou S, Houston RD, Robledo D. Genomic prediction using low density marker panels in aquaculture: performance across species, traits, and genotyping platforms. Front Genet 2020; 11(124): 124.
[http://dx.doi.org/10.3389/fgene.2020.00124] [PMID: 32174974]
[57]
Gorjanc G, Dumasy J-F, Gonen S, Gaynor RC, Antolin R, Hickey JM. Potential of low-coverage genotyping-by-sequencing and imputation for cost-effective genomic selection in biparental segregating populations. Crop Sci 2017; 57(3): 1404-20.
[http://dx.doi.org/10.2135/cropsci2016.08.0675]
[58]
Tsai H-Y, Matika O, Edwards SM, et al. Genotype imputation to improve the cost-efficiency of genomic selection in farmed Atlantic salmon. G3: Genes, Genomes. G3 (Bethesda) 2017; 7(4): 1377-83.
[http://dx.doi.org/10.1534/g3.117.040717] [PMID: 28250015]
[59]
Yoshida GM, Carvalheiro R, Lhorente JP. Accuracy of genotype imputation and genomic predictions in a two-generation farmed Atlantic salmon population using high-density and low-density SNP panels. Aquaculture 2018; 491: 147-54.
[http://dx.doi.org/10.1016/j.aquaculture.2018.03.004]
[60]
Ogutu JO, Piepho H-P, Schulz-Streeck T. A comparison of random forests, boosting and support vector machines for genomic selection. BMC Proc 2011; 5(S3)(Suppl. 3): S11.
[http://dx.doi.org/10.1186/1753-6561-5-S3-S11] [PMID: 21624167]
[61]
Tsai H-Y, Hamilton A, Tinch AE, et al. Genome wide association and genomic prediction for growth traits in juvenile farmed Atlantic salmon using a high density SNP array. BMC Genomics 2015; 16(969): 969.
[http://dx.doi.org/10.1186/s12864-015-2117-9] [PMID: 26582102]
[62]
Bunlipatanon P. U‐taynapun K. Growth performance and disease resistance against Vibrio vulnificus infection of novel hybrid grouper (Epinephelus lanceolatus × Epinephelus fuscoguttatus). Aquacult Res 2017; 48(4): 1711-23.
[http://dx.doi.org/10.1111/are.13008]
[63]
Zenger KR, Khatkar MS, Jones DB, Khalilisamani N, Jerry DR, Raadsma HW. Genomic selection in aquaculture: Application, limitations and opportunities with special reference to marine shrimp and pearl oysters. Front Genet 2019; 9(693): 693.
[http://dx.doi.org/10.3389/fgene.2018.00693] [PMID: 30728827]
[64]
Mathiassen JR, Misimi E, Toldnes B, Bondø M, Østvik SO. High-speed weight estimation of whole herring (Clupea harengus) using 3D machine vision. J Food Sci 2011; 76(6): E458-64.
[http://dx.doi.org/10.1111/j.1750-3841.2011.02226.x] [PMID: 22417497]
[65]
Saberioon M, Gholizadeh A, Cisar P, Pautsina A, Urban J. Application of machine vision systems in aquaculture with emphasis on fish: state‐of‐the‐art and key issues. Rev Aquacult 2016; 9(4): 369-87.
[http://dx.doi.org/10.1111/raq.12143]
[66]
Zion B. The use of computer vision technologies in aquaculture–a review. Comput Electron Agric 2012; 88: 125-32.
[http://dx.doi.org/10.1016/j.compag.2012.07.010]
[67]
Zenger KR, Khatkar MS, Jerry DR, Raadsma HW. The next wave in selective breeding: implementing genomic selection in aquaculture. Proc Assoc Advmt Anim Breed Genet. Aquaculture 2017; (22): 105-12.
[68]
Rajsic P, Weersink A, Navabi A, Pauls KP. Economics of genomic selection: the role of prediction accuracy and relative genotyping costs. Euphytica 2016; 210(2): 259-76.
[http://dx.doi.org/10.1007/s10681-016-1716-0]
[69]
Riedelsheimer C, Melchinger AE. Optimizing the allocation of resources for genomic selection in one breeding cycle. Theor Appl Genet 2013; 126(11): 2835-48.
[http://dx.doi.org/10.1007/s00122-013-2175-9] [PMID: 23982591]

Rights & Permissions Print Cite
Article Metrics
23
© 2024 Bentham Science Publishers | Privacy Policy