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Gorssen W, Winters C, Meyermans R, Chapard L, Hooyberghs K, Janssens S, Huisman A, Peeters K, Mulder H, Buys N. A promising resilience parameter for breeding: the use of weight and feed trajectories in growing pigs. J Anim Sci Biotechnol 2023; 14:101. [PMID: 37525252 PMCID: PMC10391771 DOI: 10.1186/s40104-023-00901-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 05/31/2023] [Indexed: 08/02/2023] Open
Abstract
BACKGROUND Increasing resilience is a priority in modern pig breeding. Recent research shows that general resilience can be quantified via variability in longitudinal data. The collection of such longitudinal data on weight, feed intake and feeding behaviour in pigs has been facilitated by the development of technologies such as automated feeding stations. The goal of this study was to investigate resilience traits, which were estimated as deviations from longitudinal weight, feed intake and feeding behaviour data during the finishing phase. A dataset with 324,207 records between the age of 95 and 155 days on 5,939 Piétrain pigs with known pedigree and genomic information was used. We provided guidelines for a rigid quality control of longitudinal body weight data, as we found that outliers can significantly affect results. Gompertz growth curve analysis, linear modelling and trajectory analyses were used for quantifying resilience traits. RESULTS To our knowledge, this is the first study comparing resilience traits from longitudinal body weight, feed intake and feeding behaviour data in pigs. We demonstrated that the resilience traits are lowly to moderately heritable for deviations in body weight (h2 = 2.9%-20.2%), in feed intake (9.4%-23.3%) and in feeding behaviour (16.2%-28.3%). Additionally, these traits have good predictive abilities in cross-validation analyses. Deviations in individual body weight and feed intake trajectories are highly correlated (rg = 0.78) with low to moderate favourable genetic correlations with feed conversion ratio (rg = 0.39-0.49). Lastly, we showed that some resilience traits, such as the natural logarithm of variances of observed versus predicted body weights (lnvarweight), are more robust to lower observation frequencies and are repeatable over three different time periods of the finishing phase. CONCLUSIONS Our results will help future studies investigating resilience traits and resilience-related traits. Moreover, our study provides first results on standardization of quality control and efficient data sampling from automated feeding station data. Our findings will be valuable for breeding organizations as they offer evidence that pigs' general resilience can be selected on with good accuracy. Moreover, this methodology might be extended to other species to quantify resilience based on longitudinal data.
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Affiliation(s)
- Wim Gorssen
- Center for Animal Breeding and Genetics, Department of Biosystems, KU Leuven, Kasteelpark Arenberg 30 - Box 2472, 3001, Leuven, Belgium
| | - Carmen Winters
- Laboratory for Biological Psychology, KU Leuven, Tiensestraat 102 - Box 3714, 3000, Leuven, Belgium
| | - Roel Meyermans
- Center for Animal Breeding and Genetics, Department of Biosystems, KU Leuven, Kasteelpark Arenberg 30 - Box 2472, 3001, Leuven, Belgium
| | - Léa Chapard
- Center for Animal Breeding and Genetics, Department of Biosystems, KU Leuven, Kasteelpark Arenberg 30 - Box 2472, 3001, Leuven, Belgium
| | - Katrijn Hooyberghs
- Center for Animal Breeding and Genetics, Department of Biosystems, KU Leuven, Kasteelpark Arenberg 30 - Box 2472, 3001, Leuven, Belgium
| | - Steven Janssens
- Center for Animal Breeding and Genetics, Department of Biosystems, KU Leuven, Kasteelpark Arenberg 30 - Box 2472, 3001, Leuven, Belgium
| | - Abe Huisman
- Hendrix Genetics, P.O. Box 114, 5830 AC, Boxmeer, The Netherlands
| | - Katrijn Peeters
- Hendrix Genetics, P.O. Box 114, 5830 AC, Boxmeer, The Netherlands
| | - Han Mulder
- Research Animal Breeding and Genomics, Wageningen University, P.O. Box 338, 6700 AH, Wageningen, the Netherlands
| | - Nadine Buys
- Center for Animal Breeding and Genetics, Department of Biosystems, KU Leuven, Kasteelpark Arenberg 30 - Box 2472, 3001, Leuven, Belgium.
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Wang J, Bai Y, Zou X, Li C, Yang J, Ke Q, Zhao J, Zhou T, Xu P. First Genomic Prediction of Single-Step Models in Large Yellow Croaker. MARINE BIOTECHNOLOGY (NEW YORK, N.Y.) 2023; 25:603-611. [PMID: 37410311 DOI: 10.1007/s10126-023-10229-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 06/28/2023] [Indexed: 07/07/2023]
Abstract
Genome selection is mainly used in disease-resistant traits of aquatic species; however, its implementation is hindered by a high cost of genotype and phenotype data collection. Single-step genomic best linear unbiased prediction (SSGBLUP) can integrate phenotypes, genetic markers, and pedigree records into simultaneous prediction without increasing genotyping costs. The objective of this study is to investigate the performance of SSGBLUP in large yellow croaker and to evaluate the effects of the number of phenotypic records and genotyping per family on the predictive ability of SSGBLUP. A large yellow croaker population consists of 6898 individuals from 14 families with survival time resistant against Cryptocaryon irritans (C. irritans), body weight (BW), and body length (BL) traits were collected, of which 669 individuals were genotyped. Results showed that the mean predictive ability of all traits in the individuals randomly sampling for SSGBLUP, GBLUP, and BLUP was 0.738, 0.738, and 0.736, respectively. Moreover, the predictive ability of SSGBLUP and BLUP models did not increase with the extra phenotypic records per family, in which the predictive ability of SSGBLUP and BLUP in survival time was 0.853 and 0.851 for only genotyped data (N = 0) used, and 0.852, 0.845 for all phenotypic records (N = 600) used, respectively. However, with the increase in the genotype number of training set, the prediction ability of SSGBLUP and GBLUP model was increased and the highest predictive ability was gained when the genotype number per family was 40 or 45. In addition, the prediction ability of SSGBLUP model was higher than that of GBLUP. Our study showed that the SSGBLUP model still has great potential and advantages in genomic breeding of large yellow croakers. It is recommended that each family provide 100 phenotypic individuals, of which 40 individuals with genotyping data for SSGBLUP model prediction and family resistance evaluation.
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Affiliation(s)
- Jiaying Wang
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen, China
| | - Yulin Bai
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen, China
| | - Xiaoqing Zou
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen, China
| | - Chengyu Li
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen, China
| | - Junyi Yang
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen, China
| | - QiaoZhen Ke
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen, China
| | - Ji Zhao
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen, China
| | - Tao Zhou
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen, China
| | - Peng Xu
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen, China.
- Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen, China.
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Raffo MA, Cuyabano BCD, Rincent R, Sarup P, Moreau L, Mary-Huard T, Jensen J. Genomic prediction for grain yield and micro-environmental sensitivity in winter wheat. FRONTIERS IN PLANT SCIENCE 2023; 13:1075077. [PMID: 36816478 PMCID: PMC9929036 DOI: 10.3389/fpls.2022.1075077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 12/23/2022] [Indexed: 06/18/2023]
Abstract
Individuals within a common environment experience variations due to unique and non-identifiable micro-environmental factors. Genetic sensitivity to micro-environmental variation (i.e. micro-environmental sensitivity) can be identified in residuals, and genotypes with lower micro-environmental sensitivity can show greater resilience towards environmental perturbations. Micro-environmental sensitivity has been studied in animals; however, research on this topic is limited in plants and lacking in wheat. In this article, we aimed to (i) quantify the influence of genetic variation on residual dispersion and the genetic correlation between genetic effects on (expressed) phenotypes and residual dispersion for wheat grain yield using a double hierarchical generalized linear model (DHGLM); and (ii) evaluate the predictive performance of the proposed DHGLM for prediction of additive genetic effects on (expressed) phenotypes and its residual dispersion. Analyses were based on 2,456 advanced breeding lines tested in replicated trials within and across different environments in Denmark and genotyped with a 15K SNP-Illumina-BeadChip. We found that micro-environmental sensitivity for grain yield is heritable, and there is potential for its reduction. The genetic correlation between additive effects on (expressed) phenotypes and dispersion was investigated, and we observed an intermediate correlation. From these results, we concluded that breeding for reduced micro-environmental sensitivity is possible and can be included within breeding objectives without compromising selection for increased yield. The predictive ability and variance inflation for predictions of the DHGLM and a linear mixed model allowing heteroscedasticity of residual variance in different environments (LMM-HET) were evaluated using leave-one-line-out cross-validation. The LMM-HET and DHGLM showed good and similar performance for predicting additive effects on (expressed) phenotypes. In addition, the accuracy of predicting genetic effects on residual dispersion was sufficient to allow genetic selection for resilience. Such findings suggests that DHGLM may be a good choice to increase grain yield and reduce its micro-environmental sensitivity.
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Affiliation(s)
- Miguel A. Raffo
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Beatriz C. D. Cuyabano
- Université Paris Saclay, INRAE, AgroParisTech, GABI, Domaine de Vilvert, Jouy-en-Josas, France
| | - Renaud Rincent
- Génétique Quantitative et Evolution − Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, Gif−sur−Yvette, France
| | | | - Laurence Moreau
- Génétique Quantitative et Evolution − Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, Gif−sur−Yvette, France
| | - Tristan Mary-Huard
- Génétique Quantitative et Evolution − Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, Gif−sur−Yvette, France
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA-Paris Saclay, Palaiseau, France
| | - Just Jensen
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
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Yáñez JM, Yoshida G, Barria A, Palma-Véjares R, Travisany D, Díaz D, Cáceres G, Cádiz MI, López ME, Lhorente JP, Jedlicki A, Soto J, Salas D, Maass A. High-Throughput Single Nucleotide Polymorphism (SNP) Discovery and Validation Through Whole-Genome Resequencing in Nile Tilapia (Oreochromis niloticus). MARINE BIOTECHNOLOGY (NEW YORK, N.Y.) 2020; 22:109-117. [PMID: 31938972 DOI: 10.1007/s10126-019-09935-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 11/19/2019] [Indexed: 06/10/2023]
Abstract
Nile tilapia (Oreochromis niloticus) is the second most important farmed fish in the world and a sustainable source of protein for human consumption. Several genetic improvement programs have been established for this species in the world. Currently, the estimation of genetic merit of breeders is typically based on genealogical and phenotypic information. Genome-wide information can be exploited to efficiently incorporate traits that are difficult to measure into the breeding goal. Thus, single nucleotide polymorphisms (SNPs) are required to investigate phenotype-genotype associations and determine the genomic basis of economically important traits. We performed de novo SNP discovery in three different populations of farmed Nile tilapia. A total of 29.9 million non-redundant SNPs were identified through Illumina (HiSeq 2500) whole-genome resequencing of 326 individual samples. After applying several filtering steps, including removing SNP based on genotype and site quality, presence of Mendelian errors, and non-unique position in the genome, a total of 50,000 high-quality SNPs were selected for the development of a custom Illumina BeadChip SNP panel. These SNPs were highly informative in the three populations analyzed showing between 43,869 (94%) and 46,139 (99%) SNPs in Hardy-Weinberg Equilibrium; 37,843 (76%) and 45,171(90%) SNPs with a minor allele frequency (MAF) higher than 0.05; and 43,450 (87%) and 46,570 (93%) SNPs with a MAF higher than 0.01. The 50K SNP panel developed in the current work will be useful for the dissection of economically relevant traits, enhancing breeding programs through genomic selection, as well as supporting genetic studies in farmed populations of Nile tilapia using dense genome-wide information.
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Affiliation(s)
- José M Yáñez
- Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago, Chile.
- Núcleo Milenio INVASAL, Concepción, Chile.
| | - Grazyella Yoshida
- Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago, Chile
- Benchmark Genetics Chile, Puerto Montt, Chile
| | - Agustín Barria
- Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago, Chile
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK
| | - Ricardo Palma-Véjares
- Centro para la Regulación del Genoma, Universidad de Chile, Santiago, Chile
- Centro de Modelamiento Matemático UMI CNRS 2807, Universidad de Chile, Santiago, Chile
| | - Dante Travisany
- Centro para la Regulación del Genoma, Universidad de Chile, Santiago, Chile
- Centro de Modelamiento Matemático UMI CNRS 2807, Universidad de Chile, Santiago, Chile
| | - Diego Díaz
- Centro para la Regulación del Genoma, Universidad de Chile, Santiago, Chile
- Centro de Modelamiento Matemático UMI CNRS 2807, Universidad de Chile, Santiago, Chile
| | - Giovanna Cáceres
- Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago, Chile
| | - María I Cádiz
- Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago, Chile
| | - María E López
- Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago, Chile
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | | | - Ana Jedlicki
- Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago, Chile
| | - José Soto
- Grupo Acuacorporacion, Internacional (GACI), Cañas, Costa Rica
| | - Diego Salas
- Grupo Acuacorporacion, Internacional (GACI), Cañas, Costa Rica
| | - Alejandro Maass
- Centro para la Regulación del Genoma, Universidad de Chile, Santiago, Chile
- Centro de Modelamiento Matemático UMI CNRS 2807, Universidad de Chile, Santiago, Chile
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5
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Bruijning M, Metcalf CJE, Jongejans E, Ayroles JF. The Evolution of Variance Control. Trends Ecol Evol 2020; 35:22-33. [PMID: 31519463 PMCID: PMC7482585 DOI: 10.1016/j.tree.2019.08.005] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 08/08/2019] [Accepted: 08/12/2019] [Indexed: 12/12/2022]
Abstract
Genetically identical individuals can be phenotypically variable, even in constant environmental conditions. The ubiquity of this phenomenon, known as 'intra-genotypic variability', is increasingly evident and the relevant mechanistic underpinnings are beginning to be understood. In parallel, theory has delineated a number of formal expectations for contexts in which such a feature would be adaptive. Here, we review empirical evidence across biological systems and theoretical expectations, including nonlinear averaging and bet hedging. We synthesize existing results to illustrate the dependence of selection outcomes both on trait characteristics, features of environmental variability, and species' demographic context. We conclude by discussing ways to bridge the gap between empirical evidence of intra-genotypic variability, studies demonstrating its genetic component, and evidence that it is adaptive.
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Affiliation(s)
- Marjolein Bruijning
- Department of Animal Ecology and Physiology, Radboud University, 6500, GL, Nijmegen, The Netherlands; Department of Ecology and Evolutionary Biology, Princeton University, 08540 Princeton, NJ, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, 08540 Princeton, NJ, USA
| | - Eelke Jongejans
- Department of Animal Ecology and Physiology, Radboud University, 6500, GL, Nijmegen, The Netherlands
| | - Julien F Ayroles
- Department of Ecology and Evolutionary Biology, Princeton University, 08540 Princeton, NJ, USA.
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6
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Berghof TVL, Bovenhuis H, Mulder HA. Body Weight Deviations as Indicator for Resilience in Layer Chickens. Front Genet 2019; 10:1216. [PMID: 31921285 PMCID: PMC6923720 DOI: 10.3389/fgene.2019.01216] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 11/04/2019] [Indexed: 02/04/2023] Open
Abstract
Resilience is the capacity of an animal to be minimally affected by disturbances or to rapidly return to the state pertained before exposure to a disturbance. Less resilient animals are expected to be more susceptible to environmental perturbations, such as diseases, and will consequently show more and/or greater fluctuations in production than more resilient animals. Natural antibodies (NAb) are antibodies recognizing antigens without previous exposure to these, and are hypothesized to be an indication of general disease resistance. The objective of this research was to investigate genetic parameters of resilience indicators based on standardized body weight (BW) deviations and to investigate its relation with immunity (i.e. NAb) and disease resistance. Keyhole limpet hemocyanin-binding NAb were measured in layer chickens, which were selectively bred for high and low keyhole limpet hemocyanin-binding NAb levels during six generations. In addition, BW data of these layers were collected on a four-weekly interval from 4 weeks of age until 32 weeks of age. Standardized deviations of BW from an individual were compared to lines’ average BW (i.e. across individuals), and these were used to calculate resilience indicators: natural logarithm-transformed variance [ln(variance)], skewness, and lag-one autocorrelation of deviations (i.e. all within an individual). Heritabilities of resilience indicators were between 0.09 and 0.11. Genetic correlations between the three resilience indicators were between -0.20 and 0.40 (with high SE), which might suggest that the resilience indicators capture different aspects of resilience. Genetic correlations between resilience indicators and NAb were close to zero, which suggests that the resilience indicators and NAb capture different aspects of immunity. This might indicate that, in this dataset, environmental perturbations are only to a small extent affected by disease incidence, possibly due to a lack of disease occurrence. However, a lower estimated breeding value for ln(variance) was predictive for lower lesion scores after an avian pathogenic Escherichia coli inoculation and vice versa. In conclusion, this study shows that there is genetic variation in resilience indicators based on BW deviations in layer chickens, which opens up possibilities to improve resilience by means of selective breeding.
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Affiliation(s)
- Tom V L Berghof
- Wageningen University & Research Animal Breeding and Genomics, Wageningen, Netherlands
| | - Henk Bovenhuis
- Wageningen University & Research Animal Breeding and Genomics, Wageningen, Netherlands
| | - Han A Mulder
- Wageningen University & Research Animal Breeding and Genomics, Wageningen, Netherlands
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Iung LHDS, Carvalheiro R, Neves HHDR, Mulder HA. Genetics and genomics of uniformity and resilience in livestock and aquaculture species: A review. J Anim Breed Genet 2019; 137:263-280. [PMID: 31709657 DOI: 10.1111/jbg.12454] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 10/15/2019] [Accepted: 10/16/2019] [Indexed: 01/29/2023]
Abstract
Genetic control of residual variance offers opportunities to increase uniformity and resilience of livestock and aquaculture species. Improving uniformity and resilience of animals will improve health and welfare of animals and lead to more homogenous products. Our aims in this review were to summarize the current models and methods to study genetic control of residual variance, genetic parameters and genomic results for residual variance and discuss future research directions. Typically, the genetic coefficient of variation is high (median = 0.27; range 0-0.86) and the heritability of residual variance is low (median = 0.01; range 0-0.10). Higher heritabilities can be achieved when increasing the number of records per animal. Divergent selection experiments have supported the feasibility of selecting for high or low residual variance. Genomic studies have revealed associations in regions related to stress, including those from the heat shock protein family. Although the number of studies is growing, genetic control of residual variance is still poorly understood, but big data and genomics offer great opportunities.
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Affiliation(s)
- Laiza Helena de Souza Iung
- School of Agricultural and Veterinarian Sciences, São Paulo State University (Unesp), Jaboticabal, Brazil.,CRV Lagoa, Sertãozinho, Brazil
| | - Roberto Carvalheiro
- School of Agricultural and Veterinarian Sciences, São Paulo State University (Unesp), Jaboticabal, Brazil.,National Council for Scientific and Technological Development (CNPq), Brasília, Brazil
| | | | - Herman Arend Mulder
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
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Yoshida GM, Barria A, Correa K, Cáceres G, Jedlicki A, Cadiz MI, Lhorente JP, Yáñez JM. Genome-Wide Patterns of Population Structure and Linkage Disequilibrium in Farmed Nile Tilapia ( Oreochromis niloticus). Front Genet 2019; 10:745. [PMID: 31552083 PMCID: PMC6737105 DOI: 10.3389/fgene.2019.00745] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Accepted: 07/16/2019] [Indexed: 01/29/2023] Open
Abstract
Nile tilapia (Oreochromis niloticus) is one of the most produced farmed fish in the world and represents an important source of protein for human consumption. Farmed Nile tilapia populations are increasingly based on genetically improved stocks, which have been established from admixed populations. To date, there is scarce information about the population genomics of farmed Nile tilapia, assessed by dense single nucleotide polymorphism (SNP) panels. The patterns of linkage disequilibrium (LD) may affect the success of genome-wide association studies (GWAS) and genomic selection (GS), and also provide key information about demographic history of farmed Nile tilapia populations. The objectives of this study were to provide further knowledge about the population structure and LD patterns, as well as, estimate the effective population size (N e ) for three farmed Nile tilapia populations, one from Brazil (POP A) and two from Costa Rica (POP B and POP C). A total of 55 individuals from each population, were genotyped using a 50K SNP panel selected from a whole-genome sequencing (WGS) experiment. The first two principal components explained about 20% of the total variation and clearly differentiated between the three populations. Population genetic structure analysis showed evidence of admixture, especially for POP C. The contemporary N e estimated, based on LD values, ranged from 78 to 159. No differences were observed in the LD decay among populations, with a rapid decrease of r 2 with increasing inter-marker distance. Average r 2 between adjacent SNP pairs ranged from 0.19 to 0.03 for both POP A and C, and 0.20 to 0.03 f or POP B. Based on the number of independent chromosome segments in the Nile tilapia genome, at least 9.4, 7.6, and 4.6K SNPs for POP A, POP B, and POP C respectively, are required for the implementation of GS in the present farmed Nile tilapia populations.
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Affiliation(s)
- Grazyella M. Yoshida
- Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago, Chile
- Benchmark Genetics Chile, Puerto Montt, Chile
| | - Agustín Barria
- Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago, Chile
| | | | - Giovanna Cáceres
- Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago, Chile
| | - Ana Jedlicki
- Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago, Chile
| | - María I. Cadiz
- Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago, Chile
| | | | - José M. Yáñez
- Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago, Chile
- Benchmark Genetics Chile, Puerto Montt, Chile
- Nucleo Milenio INVASAL, Concepción, Chile
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Berghof TVL, Poppe M, Mulder HA. Opportunities to Improve Resilience in Animal Breeding Programs. Front Genet 2019; 9:692. [PMID: 30693014 PMCID: PMC6339870 DOI: 10.3389/fgene.2018.00692] [Citation(s) in RCA: 121] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 12/11/2018] [Indexed: 01/30/2023] Open
Abstract
Resilience is the capacity of an animal to be minimally affected by disturbances or to rapidly return to the state pertained before exposure to a disturbance. However, indicators for general resilience to environmental disturbances have not yet been defined, and perhaps therefore resilience is not yet included in breeding goals. The current developments on big data collection give opportunities to determine new resilience indicators based on longitudinal data, which can aid to incorporate resilience in animal breeding goals. The objectives of this paper were: (1) to define resilience indicator traits based on big data, (2) to define economic values for resilience, and (3) to show the potential to improve resilience of livestock through inclusion of resilience in breeding goals. Resilience might be measured based on deviations from expected production levels over a period of time. Suitable resilience indicators could be the variance of deviations, the autocorrelation of deviations, the skewness of deviations, and the slope of a reaction norm. These (new) resilience indicators provide opportunity to include resilience in breeding programs. Economic values of resilience indicators in the selection index can be calculated based on reduced costs due to labor and treatments. For example, when labor time is restricted, the economic value of resilience increases with an increasing number of animals per farm, and can become as large as the economic value of production. This shows the importance of including resilience in breeding goals. Two scenarios were described to show the additional benefit of including resilience in breeding programs. These examples showed that it is hard to improve resilience with only production traits in the selection index, but that it is possible to greatly improve resilience by including resilience indicators in the selection index. However, when health-related traits are already present in the selection index, the effect is smaller. Nevertheless, inclusion of resilience indicators in the selection index increases the response in the breeding goal and resilience, which results in less labor-demanding, and thus easier-to-manage livestock. Current developments on massive collection of data, and new phenotypes based on these data, offer exciting opportunities to breed for improved resilience of livestock.
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Affiliation(s)
- Tom V. L. Berghof
- Wageningen University & Research Animal Breeding and Genomics, Wageningen, Netherlands
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Genomic Prediction Accuracy for Resistance Against Piscirickettsia salmonis in Farmed Rainbow Trout. G3-GENES GENOMES GENETICS 2018; 8:719-726. [PMID: 29255117 PMCID: PMC5919750 DOI: 10.1534/g3.117.300499] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Salmonid rickettsial syndrome (SRS), caused by the intracellular bacterium Piscirickettsia salmonis, is one of the main diseases affecting rainbow trout (Oncorhynchus mykiss) farming. To accelerate genetic progress, genomic selection methods can be used as an effective approach to control the disease. The aims of this study were: (i) to compare the accuracy of estimated breeding values using pedigree-based best linear unbiased prediction (PBLUP) with genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP), Bayes C, and Bayesian Lasso (LASSO); and (ii) to test the accuracy of genomic prediction and PBLUP using different marker densities (0.5, 3, 10, 20, and 27 K) for resistance against P. salmonis in rainbow trout. Phenotypes were recorded as number of days to death (DD) and binary survival (BS) from 2416 fish challenged with P. salmonis. A total of 1934 fish were genotyped using a 57 K single-nucleotide polymorphism (SNP) array. All genomic prediction methods achieved higher accuracies than PBLUP. The relative increase in accuracy for different genomic models ranged from 28 to 41% for both DD and BS at 27 K SNP. Between different genomic models, the highest relative increase in accuracy was obtained with Bayes C (∼40%), where 3 K SNP was enough to achieve a similar accuracy to that of the 27 K SNP for both traits. For resistance against P. salmonis in rainbow trout, we showed that genomic predictions using GBLUP, ssGBLUP, Bayes C, and LASSO can increase accuracy compared with PBLUP. Moreover, it is possible to use relatively low-density SNP panels for genomic prediction without compromising accuracy predictions for resistance against P. salmonis in rainbow trout.
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Yoshida GM, Carvalheiro R, Rodríguez FH, Lhorente JP, Yáñez JM. Single-step genomic evaluation improves accuracy of breeding value predictions for resistance to infectious pancreatic necrosis virus in rainbow trout. Genomics 2018; 111:127-132. [PMID: 29357303 DOI: 10.1016/j.ygeno.2018.01.008] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 12/30/2017] [Accepted: 01/14/2018] [Indexed: 11/26/2022]
Abstract
The aim of this study was to compare the accuracy of breeding values (EBVs) predicted using the traditional pedigree based Best Linear Unbiased Prediction (PBLUP) and the single-step genomic Best Linear Unbiased Prediction (ssGBLUP) for resistance against infectious pancreatic necrosis virus (IPNV) in rainbow trout. A total of 2278 animals were challenged against IPNV and 768 individuals were genotyped using a 57 K single nucleotide polymorphism array for rainbow trout. Accuracies for both methods were assessed using five-fold cross-validation. The heritabilities were higher for PBLUP compared to ssGBLUP. The ssGBLUP accuracies outperformed PBLUP in 7 and 11% for days to death and binary survival, respectively. The ssGBLUP could be an alternative approach to improve the accuracy of breeding values for resistance against infectious pancreatic necrosis virus in rainbow trout, using information from genotyped and non-genotyped animals.
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Affiliation(s)
- Grazyella M Yoshida
- Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Av Santa Rosa 11735, La Pintana, Santiago 8820808, Chile; Animal Science Department, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), Faculdade de Ciências Agrárias e Veterinárias (FCAV), Campus Jaboticabal, Via de Acesso Prof. Paulo Donato Castellane, 14884-900 Jaboticabal, Brazil
| | - Roberto Carvalheiro
- Animal Science Department, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), Faculdade de Ciências Agrárias e Veterinárias (FCAV), Campus Jaboticabal, Via de Acesso Prof. Paulo Donato Castellane, 14884-900 Jaboticabal, Brazil
| | - Francisco H Rodríguez
- Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Av Santa Rosa 11735, La Pintana, Santiago 8820808, Chile; Facultad de Medicina Veterinaria y Zootecnia, Universidad Nacional del Altiplano, Av. Floral 1153, Puno, Perú
| | | | - José M Yáñez
- Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Av Santa Rosa 11735, La Pintana, Santiago 8820808, Chile; Aquainnovo, Cardonal S/N, Puerto Montt, Chile; Núcleo Milenio INVASAL, Concepción, Chile.
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