Development of genomic predictions for harvest and carcass weight in channel catfish

BackgroundCatfish farming is the largest segment of US aquaculture and research is ongoing to improve production efficiency, including genetic selection programs to improve economically important traits. The objectives of this study were to investigate the use of genomic selection to improve breeding value accuracy and to identify major single nucleotide polymorphisms (SNPs) associated with harvest weight and residual carcass weight in a channel catfish population. Phenotypes were available for harvest weight (n = 27,160) and residual carcass weight (n = 6020), and 36,365 pedigree records were available. After quality control, genotypes for 54,837 SNPs were available for 2911 fish. Estimated breeding values (EBV) were obtained with traditional pedigree-based best linear unbiased prediction (BLUP) and genomic (G)EBV were estimated with single-step genomic BLUP (ssGBLUP). EBV and GEBV prediction accuracies were evaluated using different validation strategies. The ability to predict future performance was calculated as the correlation between EBV or GEBV and adjusted phenotypes.ResultsCompared to the pedigree BLUP, ssGBLUP increased predictive ability up to 28% and 36% for harvest weight and residual carcass weight, respectively; and GEBV were superior to EBV for all validation strategies tested. Breeding value inflation was assessed as the regression coefficient of adjusted phenotypes on breeding values, and the results indicated that genomic information reduced breeding value inflation. Genome-wide association studies based on windows of 20 adjacent SNPs indicated that both harvest weight and residual carcass weight have a polygenic architecture with no major SNPs (the largest SNPs explained 0.96 and 1.19% of the additive genetic variation for harvest weight and residual carcass weight respectively).ConclusionsGenomic evaluation improves the ability to predict future performance relative to traditional BLUP and will allow more accurate identification of genetically superior individuals within catfish families.

[1]  M. Goddard,et al.  Prediction of total genetic value using genome-wide dense marker maps. , 2001, Genetics.

[2]  Kyle E. Martin,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 , 2017, Genetics Selection Evolution.

[3]  G. Waldbieser,et al.  A standardized microsatellite marker panel for parentage and kinship analyses in channel catfish, Ictalurus punctatus. , 2013, Animal genetics.

[4]  J. Woolliams,et al.  Inbreeding in genome-wide selection. , 2007, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[5]  Zhanjiang Liu,et al.  Identification of novel genes significantly affecting growth in catfish through GWAS analysis , 2017, Molecular Genetics and Genomics.

[6]  J. Woolliams,et al.  The Impact of Genetic Architecture on Genome-Wide Evaluation Methods , 2010, Genetics.

[7]  K. Gharbi,et al.  Genome wide association and genomic prediction for growth traits in juvenile farmed Atlantic salmon using a high density SNP array , 2015, BMC Genomics.

[8]  Yutaka Masuda,et al.  The Dimensionality of Genomic Information and Its Effect on Genomic Prediction , 2016, Genetics.

[9]  M. DePristo,et al.  A framework for variation discovery and genotyping using next-generation DNA sequencing data , 2011, Nature Genetics.

[10]  J. M. Yáñez,et al.  The use of genomic information increases the accuracy of breeding value predictions for sea louse (Caligus rogercresseyi) resistance in Atlantic salmon (Salmo salar) , 2017, Genetics Selection Evolution.

[11]  K. Gharbi,et al.  Genomic prediction of host resistance to sea lice in farmed Atlantic salmon populations , 2016, Genetics Selection Evolution.

[12]  R M Bourdon,et al.  Method R variance components procedure: application on the simple breeding value model. , 1994, Journal of animal science.

[13]  Ignacy Misztal,et al.  Weighting Strategies for Single-Step Genomic BLUP: An Iterative Approach for Accurate Calculation of GEBV and GWAS , 2016, Front. Genet..

[14]  F. Peñagaricano,et al.  Genome-Wide Association Study for Identifying Loci that Affect Fillet Yield, Carcass, and Body Weight Traits in Rainbow Trout (Oncorhynchus mykiss) , 2016, Front. Genet..

[15]  Michael E. Goddard,et al.  Genomic selection: A paradigm shift in animal breeding , 2016 .

[16]  T A Cooper,et al.  Technical note: adjustment of traditional cow evaluations to improve accuracy of genomic predictions. , 2011, Journal of dairy science.

[17]  R. L. Quaas,et al.  Additive Genetic Model with Groups and Relationships , 1988 .

[18]  W. Muir,et al.  Genome-wide association mapping including phenotypes from relatives without genotypes. , 2012, Genetics research.

[19]  W. Muir,et al.  Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. , 2007, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[20]  M. Lund,et al.  Genomic prediction when some animals are not genotyped , 2010, Genetics Selection Evolution.

[21]  R. Carvalheiro,et al.  Genomic Prediction Accuracy for Resistance Against Piscirickettsia salmonis in Farmed Rainbow Trout , 2017, G3: Genes, Genomes, Genetics.

[22]  G. Wiens,et al.  Accurate genomic predictions for BCWD resistance in rainbow trout are achieved using low‐density SNP panels: Evidence that long‐range LD is a major contributing factor , 2018, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[23]  M. Calus,et al.  The impact of genotyping different groups of animals on accuracy when moving from traditional to genomic selection. , 2012, Journal of dairy science.

[24]  Heng Li Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM , 2013, 1303.3997.

[25]  I Misztal,et al.  Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. , 2010, Journal of dairy science.

[26]  M. Goddard,et al.  Invited review: Genomic selection in dairy cattle: progress and challenges. , 2009, Journal of dairy science.

[27]  T. Meuwissen,et al.  Genomic prediction in an admixed population of Atlantic salmon (Salmo salar) , 2014, Front. Genet..

[28]  Yutaka Masuda,et al.  Dimensionality of genomic information and performance of the Algorithm for Proven and Young for different livestock species , 2016, Genetics Selection Evolution.

[29]  I Misztal,et al.  Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus. , 2015, Journal of animal science.

[30]  Ignacy Misztal,et al.  Single Step, a general approach for genomic selection , 2014 .

[31]  D. Falconer,et al.  Introduction to Quantitative Genetics. , 1962 .

[32]  J. Woolliams,et al.  Evaluation of the linkage-disequilibrium method for the estimation of effective population size when generations overlap: an empirical case , 2015, BMC Genomics.

[33]  S. Koren,et al.  The channel catfish genome sequence provides insights into the evolution of scale formation in teleosts , 2016, Nature Communications.

[34]  Ignacy Misztal,et al.  Accuracy of estimated breeding values with genomic information on males, females, or both: an example on broiler chicken , 2015, Genetics Selection Evolution.

[35]  J. Sved Linkage disequilibrium and homozygosity of chromosome segments in finite populations. , 1971, Theoretical population biology.

[36]  Ignacy Misztal,et al.  Changes in variance explained by top SNP windows over generations for three traits in broiler chicken , 2014, Front. Genet..

[37]  I Misztal,et al.  Implications of SNP weighting on single‐step genomic predictions for different reference population sizes , 2017, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[38]  P. VanRaden,et al.  Efficient methods to compute genomic predictions. , 2008, Journal of dairy science.