1
|
Cui H, Wang Y, Zhu Y, Liu X, Liu L, Wang J, Tan X, Wang Y, Xing S, Luo N, Liu L, Liu R, Zheng M, Zhao G, Wen J. Genomic insights into the contribution of de novo lipogenesis to intramuscular fat deposition in chicken. J Adv Res 2024; 65:19-31. [PMID: 38065407 DOI: 10.1016/j.jare.2023.12.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/29/2023] [Accepted: 12/01/2023] [Indexed: 10/21/2024] Open
Abstract
INTRODUCTION The proportion of animal based foods in daily diet of consumers is constantly increasing, with chicken being highly favored due to its high protein and low fat characteristics. The consumption of chicken around the world is steadily increasing. Intramuscular fat (IMF) is a key indicator affecting meat quality. OBJECT High IMF content can contribute to improve the quality of chicken meat. The regulatory mechanism of IMF deposition in chicken is poorly understood, so its complete elucidation is essential to improve chicken meat quality. METHOD Here, we performed whole genome resequencing on 516 yellow feather chickens and single-cell RNA sequencing on 3 63-day-old female JXY chickens. In addition, transcriptome sequencing techniques were also performed on breast muscle tissue of JXY chickens at different developmental stages. And 13C isotope tracing technique was applied. RESULTS In this study, a large-scale genetic analysis of an IMF-selected population and a control population identified fatty acid synthase (FASN) as a key gene for improving IMF content. Also, contrary to conventional view, de novo lipogenesis (DNL) was deemed to be an important contributor to IMF deposition. As expected, further analyses by isotope tracing and other techniques, confirmed that DNL mainly occurs in myocytes, contributing about 40% of the total fatty acids through the regulation of FASN, using the available FAs as substrates. Additionally, we also identified a relevant causal mutation in the FASN gene with effects on FA composition. CONCLUSION These findings contribute to the understanding of fat metabolism in muscle tissue of poultry, and provide the feasible strategy for the production of high-quality chicken meat.
Collapse
Affiliation(s)
- Huanxian Cui
- State Key Laboratory of Animal Biotech Breeding, State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Yongli Wang
- State Key Laboratory of Animal Biotech Breeding, State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Yuting Zhu
- State Key Laboratory of Animal Biotech Breeding, State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Xiaojing Liu
- State Key Laboratory of Animal Biotech Breeding, State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Lu Liu
- State Key Laboratory of Animal Biotech Breeding, State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Jie Wang
- State Key Laboratory of Animal Biotech Breeding, State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Xiaodong Tan
- State Key Laboratory of Animal Biotech Breeding, State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Yidong Wang
- State Key Laboratory of Animal Biotech Breeding, State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Siyuan Xing
- State Key Laboratory of Animal Biotech Breeding, State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Na Luo
- State Key Laboratory of Animal Biotech Breeding, State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Li Liu
- State Key Laboratory of Animal Biotech Breeding, State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Ranran Liu
- State Key Laboratory of Animal Biotech Breeding, State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Maiqing Zheng
- State Key Laboratory of Animal Biotech Breeding, State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Guiping Zhao
- State Key Laboratory of Animal Biotech Breeding, State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China.
| | - Jie Wen
- State Key Laboratory of Animal Biotech Breeding, State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China.
| |
Collapse
|
2
|
Liu Y, Zhang Y, Zhou F, Yao Z, Zhan Y, Fan Z, Meng X, Zhang Z, Liu L, Yang J, Wu Z, Cai G, Zheng E. Increased Accuracy of Genomic Prediction Using Preselected SNPs from GWAS with Imputed Whole-Genome Sequence Data in Pigs. Animals (Basel) 2023; 13:3871. [PMID: 38136908 PMCID: PMC10740755 DOI: 10.3390/ani13243871] [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: 11/02/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023] Open
Abstract
Enhancing the accuracy of genomic prediction is a key goal in genomic selection (GS) research. Integrating prior biological information into GS methods using appropriate models can improve prediction accuracy for complex traits. Genome-wide association study (GWAS) is widely utilized to identify potential candidate loci associated with complex traits in livestock and poultry, offering essential genomic insights. In this study, a GWAS was conducted on 685 Duroc × Landrace × Yorkshire (DLY) pigs to extract significant single-nucleotide polymorphisms (SNPs) as genomic features. We compared two GS models, genomic best linear unbiased prediction (GBLUP) and genomic feature BLUP (GFBLUP), by using imputed whole-genome sequencing (WGS) data on 651 Yorkshire pigs. The results revealed that the GBLUP model achieved prediction accuracies of 0.499 for backfat thickness (BFT) and 0.423 for loin muscle area (LMA). By applying the GFBLUP model with GWAS-based SNP preselection, the average prediction accuracies for BFT and LMA traits reached 0.491 and 0.440, respectively. Specifically, the GFBLUP model displayed a 4.8% enhancement in predicting LMA compared to the GBLUP model. These findings suggest that, in certain scenarios, the GFBLUP model may offer superior genomic prediction accuracy when compared to the GBLUP model, underscoring the potential value of incorporating genomic features to refine GS models.
Collapse
Affiliation(s)
- Yiyi Liu
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Yuling Zhang
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Fuchen Zhou
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Zekai Yao
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Yuexin Zhan
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Zhenfei Fan
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Xianglun Meng
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Zebin Zhang
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Langqing Liu
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Jie Yang
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Zhenfang Wu
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
- Guangdong Zhongxin Breeding Technology Co., Ltd., Guangzhou 510642, China
| | - Gengyuan Cai
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
- Guangdong Zhongxin Breeding Technology Co., Ltd., Guangzhou 510642, China
| | - Enqin Zheng
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| |
Collapse
|
3
|
Zhuang Z, Wu J, Qiu Y, Ruan D, Ding R, Xu C, Zhou S, Zhang Y, Liu Y, Ma F, Yang J, Sun Y, Zheng E, Yang M, Cai G, Yang J, Wu Z. Improving the accuracy of genomic prediction for meat quality traits using whole genome sequence data in pigs. J Anim Sci Biotechnol 2023; 14:67. [PMID: 37161604 PMCID: PMC10170792 DOI: 10.1186/s40104-023-00863-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 03/05/2023] [Indexed: 05/11/2023] Open
Abstract
BACKGROUND Pork quality can directly affect customer purchase tendency and meat quality traits have become valuable in modern pork production. However, genetic improvement has been slow due to high phenotyping costs. In this study, whole genome sequence (WGS) data was used to evaluate the prediction accuracy of genomic best linear unbiased prediction (GBLUP) for meat quality in large-scale crossbred commercial pigs. RESULTS We produced WGS data (18,695,907 SNPs and 2,106,902 INDELs exceed quality control) from 1,469 sequenced Duroc × (Landrace × Yorkshire) pigs and developed a reference panel for meat quality including meat color score, marbling score, L* (lightness), a* (redness), and b* (yellowness) of genomic prediction. The prediction accuracy was defined as the Pearson correlation coefficient between adjusted phenotypes and genomic estimated breeding values in the validation population. Using different marker density panels derived from WGS data, accuracy differed substantially among meat quality traits, varied from 0.08 to 0.47. Results showed that MultiBLUP outperform GBLUP and yielded accuracy increases ranging from 17.39% to 75%. We optimized the marker density and found medium- and high-density marker panels are beneficial for the estimation of heritability for meat quality. Moreover, we conducted genotype imputation from 50K chip to WGS level in the same population and found average concordance rate to exceed 95% and r2 = 0.81. CONCLUSIONS Overall, estimation of heritability for meat quality traits can benefit from the use of WGS data. This study showed the superiority of using WGS data to genetically improve pork quality in genomic prediction.
Collapse
Affiliation(s)
- Zhanwei Zhuang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Jie Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Yibin Qiu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Donglin Ruan
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Rongrong Ding
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Cineng Xu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Shenping Zhou
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Yuling Zhang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Yiyi Liu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Fucai Ma
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Jifei Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Ying Sun
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Enqin Zheng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Ming Yang
- College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China
| | - Gengyuan Cai
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Jie Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China.
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China.
| | - Zhenfang Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China.
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China.
- Yunfu Subcenter of Guangdong Laboratory for Lingnan Modern Agriculture, Yunfu, 527400, China.
| |
Collapse
|
4
|
Zhao W, Zhang Z, Ma P, Wang Z, Wang Q, Zhang Z, Pan Y. The effect of high-density genotypic data and different methods on joint genomic prediction: A case study in large white pigs. Anim Genet 2023; 54:45-54. [PMID: 36414135 DOI: 10.1111/age.13275] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 11/07/2022] [Accepted: 11/07/2022] [Indexed: 11/24/2022]
Abstract
Joint genomic prediction (GP) is an attractive method to improve the accuracy of GP by combining information from multiple populations. However, many factors can negatively influence the accuracy of joint GP, such as differences in linkage disequilibrium phasing between single nucleotide polymorphisms (SNPs) and causal variants, minor allele frequencies and causal variants' effect sizes across different populations. The objective of this study was to investigate whether the imputed high-density genotype data can improve the accuracy of joint GP using genomic best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP), multi-trait GBLUP (MT-GBLUP) and GBLUP based on genomic relationship matrix considering heterogenous minor allele frequencies across different populations (wGBLUP). Three traits, including days taken to reach slaughter weight, backfat thickness and loin muscle area, were measured on 67 276 Large White pigs from two different populations, for which 3334 were genotyped by SNP array. The results showed that a combined population could substantially improve the accuracy of GP compared with a single-population GP, especially for the population with a smaller size. The imputed SNP data had no effect for single population GP but helped to yield higher accuracy than the medium-density array data for joint GP. Of the four methods, ssGLBUP performed the best, but the advantage of ssGBLUP decreased as more individuals were genotyped. In some cases, MT-GBLUP and wGBLUP performed better than GBLUP. In conclusion, our results confirmed that joint GP could be beneficial from imputed high-density genotype data, and the wGBLUP and MT-GBLUP methods are promising for joint GP in pig breeding.
Collapse
Affiliation(s)
- Wei Zhao
- Department of Animal Science, College of Animal Science, Zhejiang University, Hangzhou, China
| | - Zhenyang Zhang
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Peipei Ma
- Department of Animal Science, College of Animal Science, Zhejiang University, Hangzhou, China
| | - Zhen Wang
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Qishan Wang
- Department of Animal Science, College of Animal Science, Zhejiang University, Hangzhou, China
| | - Zhe Zhang
- Department of Animal Science, College of Animal Science, Zhejiang University, Hangzhou, China
| | - Yuchun Pan
- Department of Animal Science, College of Animal Science, Zhejiang University, Hangzhou, China.,Hainan Research Institute, Zhejiang University, Sanya, China
| |
Collapse
|
5
|
Paiva JT, Mota RR, Lopes PS, Hammami H, Vanderick S, Oliveira HR, Veroneze R, Silva FFE, Gengler N. Genomic prediction and genetic correlations estimated for milk production and fatty acid traits in Walloon Holstein cattle using random regression models. J DAIRY RES 2022; 89:1-9. [PMID: 36062502 DOI: 10.1017/s0022029922000474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The aims of this study were to: (1) estimate genetic correlation for milk production traits (milk, fat and protein yields and fat and protein contents) and fatty acids (FA: C16:0, C18:1 cis-9, LCFA, SFA, and UFA) over days in milk, (2) investigate the performance of genomic predictions using single-step GBLUP (ssGBLUP) based on random regression models (RRM), and (3) identify the optimal scaling and weighting factors to be used in the construction of the H matrix. A total of 302 684 test-day records of 63.875 first lactation Walloon Holstein cows were used. Positive genetic correlations were found between milk yield and fat and protein yield (rg from 0.46 to 0.85) and between fat yield and milk FA (rg from 0.17 to 0.47). On the other hand, negative correlations were estimated between fat and protein contents (rg from -0.22 to -0.59), between milk yield and milk FA (rg from -0.22 to -0.62), and between protein yield and milk FA (rg from -0.11 to -0.19). The selection for high fat content increases milk FA throughout lactation (rg from 0.61 to 0.98). The test-day ssGBLUP approach showed considerably higher prediction reliability than the parent average for all milk production and FA traits, even when no scaling and weighting factors were used in the H matrix. The highest validation reliabilities (r2 from 0.09 to 0.38) and less biased predictions (b1 from 0.76 to 0.92) were obtained using the optimal parameters (i.e., ω = 0.7 and α = 0.6) for the genomic evaluation of milk production traits. For milk FA, the optimal parameters were ω = 0.6 and α = 0.6. However, biased predictions were still observed (b1 from 0.32 to 0.81). The findings suggest that using ssGBLUP based on RRM is feasible for the genomic prediction of daily milk production and FA traits in Walloon Holstein dairy cattle.
Collapse
Affiliation(s)
- José Teodoro Paiva
- Department of Animal Sciences, Universidade Federal de Viçosa, Viçosa, MG, Brazil
| | - Rodrigo Reis Mota
- Gembloux Agro-Bio Tech, University of Liège, TERRA Teaching and Research Centre, B-5030 Gembloux, Belgium
| | - Paulo Sávio Lopes
- Department of Animal Sciences, Universidade Federal de Viçosa, Viçosa, MG, Brazil
| | - Hedi Hammami
- Gembloux Agro-Bio Tech, University of Liège, TERRA Teaching and Research Centre, B-5030 Gembloux, Belgium
| | - Sylvie Vanderick
- Gembloux Agro-Bio Tech, University of Liège, TERRA Teaching and Research Centre, B-5030 Gembloux, Belgium
| | - Hinayah Rojas Oliveira
- Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, Canada
| | - Renata Veroneze
- Department of Animal Sciences, Universidade Federal de Viçosa, Viçosa, MG, Brazil
| | | | - Nicolas Gengler
- Gembloux Agro-Bio Tech, University of Liège, TERRA Teaching and Research Centre, B-5030 Gembloux, Belgium
| |
Collapse
|
6
|
Blay C, Haffray P, D'Ambrosio J, Prado E, Dechamp N, Nazabal V, Bugeon J, Enez F, Causeur D, Eklouh-Molinier C, Petit V, Phocas F, Corraze G, Dupont-Nivet M. Genetic architecture and genomic selection of fatty acid composition predicted by Raman spectroscopy in rainbow trout. BMC Genomics 2021; 22:788. [PMID: 34732127 PMCID: PMC8564959 DOI: 10.1186/s12864-021-08062-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 09/29/2021] [Indexed: 01/22/2023] Open
Abstract
Background In response to major challenges regarding the supply and sustainability of marine ingredients in aquafeeds, the aquaculture industry has made a large-scale shift toward plant-based substitutions for fish oil and fish meal. But, this also led to lower levels of healthful n−3 long-chain polyunsaturated fatty acids (PUFAs)—especially eicosapentaenoic (EPA) and docosahexaenoic (DHA) acids—in flesh. One potential solution is to select fish with better abilities to retain or synthesise PUFAs, to increase the efficiency of aquaculture and promote the production of healthier fish products. To this end, we aimed i) to estimate the genetic variability in fatty acid (FA) composition in visceral fat quantified by Raman spectroscopy, with respect to both individual FAs and groups under a feeding regime with limited n-3 PUFAs; ii) to study the genetic and phenotypic correlations between FAs and processing yields- and fat-related traits; iii) to detect QTLs associated with FA composition and identify candidate genes; and iv) to assess the efficiency of genomic selection compared to pedigree-based BLUP selection. Results Proportions of the various FAs in fish were indirectly estimated using Raman scattering spectroscopy. Fish were genotyped using the 57 K SNP Axiom™ Trout Genotyping Array. Following quality control, the final analysis contained 29,652 SNPs from 1382 fish. Heritability estimates for traits ranged from 0.03 ± 0.03 (n-3 PUFAs) to 0.24 ± 0.05 (n-6 PUFAs), confirming the potential for genomic selection. n-3 PUFAs are positively correlated to a decrease in fat deposition in the fillet and in the viscera but negatively correlated to body weight. This highlights the potential interest to combine selection on FA and against fat deposition to improve nutritional merit of aquaculture products. Several QTLs were identified for FA composition, containing multiple candidate genes with indirect links to FA metabolism. In particular, one region on Omy1 was associated with n-6 PUFAs, monounsaturated FAs, linoleic acid, and EPA, while a region on Omy7 had effects on n-6 PUFAs, EPA, and linoleic acid. When we compared the effectiveness of breeding programmes based on genomic selection (using a reference population of 1000 individuals related to selection candidates) or on pedigree-based selection, we found that the former yielded increases in selection accuracy of 12 to 120% depending on the FA trait. Conclusion This study reveals the polygenic genetic architecture for FA composition in rainbow trout and confirms that genomic selection has potential to improve EPA and DHA proportions in aquaculture species. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-08062-7.
Collapse
Affiliation(s)
- Carole Blay
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
| | | | - Jonathan D'Ambrosio
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France.,SYSAAF, Station LPGP-INRAE, Rennes, France
| | - Enora Prado
- University of Rennes, CNRS, ISCR - UMR 6226, ScanMAT - UMS 2001, Rennes, France
| | - Nicolas Dechamp
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
| | - Virginie Nazabal
- University of Rennes, CNRS, ISCR - UMR 6226, ScanMAT - UMS 2001, Rennes, France
| | | | | | - David Causeur
- Laboratoire de Mathématiques Appliquées, IRMAR, Agrocampus Ouest, Rennes, France
| | | | | | - Florence Phocas
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
| | - Geneviève Corraze
- INRAE, University of Pau & Pays Adour, E2S UPPA, UMR1419 NuMéA, St Pée sur, Nivelle, France
| | | |
Collapse
|
7
|
McGaugh SE, Lorenz AJ, Flagel LE. The utility of genomic prediction models in evolutionary genetics. Proc Biol Sci 2021; 288:20210693. [PMID: 34344180 PMCID: PMC8334854 DOI: 10.1098/rspb.2021.0693] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 07/15/2021] [Indexed: 12/25/2022] Open
Abstract
Variation in complex traits is the result of contributions from many loci of small effect. Based on this principle, genomic prediction methods are used to make predictions of breeding value for an individual using genome-wide molecular markers. In breeding, genomic prediction models have been used in plant and animal breeding for almost two decades to increase rates of genetic improvement and reduce the length of artificial selection experiments. However, evolutionary genomics studies have been slow to incorporate this technique to select individuals for breeding in a conservation context or to learn more about the genetic architecture of traits, the genetic value of missing individuals or microevolution of breeding values. Here, we outline the utility of genomic prediction and provide an overview of the methodology. We highlight opportunities to apply genomic prediction in evolutionary genetics of wild populations and the best practices when using these methods on field-collected phenotypes.
Collapse
Affiliation(s)
- Suzanne E. McGaugh
- Ecology, Evolution, and Behavior, University of Minnesota, 140 Gortner Lab, 1479 Gortner Avenue, Saint Paul, MN 55108, USA
| | - Aaron J. Lorenz
- Agronomy and Plant Genetics, University of Minnesota, 411 Borlaug Hall, 1991 Upper Buford Circle, Saint Paul, MN 55108, USA
| | - Lex E. Flagel
- Plant and Microbial Biology, University of Minnesota, 140 Gortner Lab, 1479 Gortner Avenue, Saint Paul, MN 55108, USA
- Bayer Crop Science, 700 W Chesterfield Parkway, Chesterfield, MO 63017, USA
| |
Collapse
|
8
|
Klein SL, Yin T, Swalve HH, König S. Single-step genomic best linear unbiased predictor genetic parameter estimations and genome-wide associations for milk fatty acid profiles, interval from calving to first insemination, and ketosis in Holstein cattle. J Dairy Sci 2021; 104:10921-10933. [PMID: 34334206 DOI: 10.3168/jds.2021-20416] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 06/15/2021] [Indexed: 11/19/2022]
Abstract
Milk fatty acids (FA) have been suggested as biomarkers for early-lactation metabolic diseases and for female fertility status. The aim of the present study was to infer associations between FA, the metabolic disorder ketosis (KET), and the interval from calving to first insemination (ICF) genetically and genomically. In this regard, we focused on a single-step genomic BLUP approach, allowing consideration of genotyped and ungenotyped cows simultaneously. The phenotypic data set considered 38,375 first-lactation Holstein cows, kept in 45 large-scale co-operator herds from 2 federal states in Germany. The calving years for these cows were from 2014 to 2017. Concentrations in milk from the first official milk recording test-day for saturated, unsaturated (UFA), monounsaturated (MUFA), polyunsaturated, palmitic, and stearic (C18:0) FA were determined via Fourier-transform infrared spectroscopy. Ketosis was defined as a binary trait according to a veterinarian diagnosis key, considering diagnoses within a 6-wk interval after calving. A subset of 9,786 cows was genotyped for 40,989 SNP markers. Variance components and heritabilities for all Gaussian distributed FA and for ICF, and for binary KET were estimated by applying single-step genomic BLUP single-trait linear and threshold models, respectively. Genetic correlations were estimated in series of bivariate runs. Genomic breeding values for the single-step genomic BLUP estimations were dependent traits in single-step GWAS. Heritabilities for FA were moderate in the range from 0.09 to 0.20 (standard error = 0.02-0.03), but quite small for ICF (0.08, standard error = 0.01) and for KET (0.05 on the underlying liability scale, posterior standard deviation = 0.02). Genetic correlations between KET and UFA, MUFA, and C18:0 were large (0.74 to 0.85, posterior standard deviation = 0.14-0.19), and low positive between KET and ICF (0.17, posterior standard deviation = 0.22). Genetic correlations between UFA, MUFA, and C18:0 with ICF ranged from 0.34 to 0.46 (standard error = 0.12). In single-step GWAS, we identified a large proportion of overlapping genomic regions for the different FA, especially for UFA and MUFA, and for saturated and palmitic FA. One identical significantly associated SNP was identified for C18:0 and KET on BTA 15. However, there was no genomic segment simultaneously significantly affecting all trait categories ICF, FA, and KET. Nevertheless, some of the annotated potential candidate genes DGKA, IGFBP4, and CXCL8 play a role in lipid metabolism and fertility mechanisms, and influence production diseases in early lactation. Genetic and genomic associations indicate that Fourier-transform infrared spectroscopy FA concentrations in milk from the first official test-day are valuable predictors for KET and for ICF.
Collapse
Affiliation(s)
- S-L Klein
- Institute of Animal Breeding and Genetics, Justus-Liebig-University Gießen, 35390 Gießen, Germany
| | - T Yin
- Institute of Animal Breeding and Genetics, Justus-Liebig-University Gießen, 35390 Gießen, Germany
| | - H H Swalve
- Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, 06120 Halle (Saale), Germany
| | - S König
- Institute of Animal Breeding and Genetics, Justus-Liebig-University Gießen, 35390 Gießen, Germany.
| |
Collapse
|
9
|
Gebreyesus G, Lund MS, Sahana G, Su G. Reliabilities of Genomic Prediction for Young Stock Survival Traits Using 54K SNP Chip Augmented With Additional Single-Nucleotide Polymorphisms Selected From Imputed Whole-Genome Sequencing Data. Front Genet 2021; 12:667300. [PMID: 34349779 PMCID: PMC8326759 DOI: 10.3389/fgene.2021.667300] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 06/23/2021] [Indexed: 11/16/2022] Open
Abstract
This study investigated effects of integrating single-nucleotide polymorphisms (SNPs) selected based on previous genome-wide association studies (GWASs), from imputed whole-genome sequencing (WGS) data, in the conventional 54K chip on genomic prediction reliability of young stock survival (YSS) traits in dairy cattle. The WGS SNPs included two groups of SNP sets that were selected based on GWAS in the Danish Holstein for YSS index (YSS_SNPs, n = 98) and SNPs chosen as peaks of quantitative trait loci for the traits of Nordic total merit index in Denmark–Finland–Sweden dairy cattle populations (DFS_SNPs, n = 1,541). Additionally, the study also investigated the possibility of improving genomic prediction reliability for survival traits by modeling the SNPs within recessive lethal haplotypes (LET_SNP, n = 130) detected from the 54K chip in the Nordic Holstein. De-regressed proofs (DRPs) were obtained from 6,558 Danish Holstein bulls genotyped with either 54K chip or customized LD chip that includes SNPs in the standard LD chip and some of the selected WGS SNPs. The chip data were subsequently imputed to 54K SNP together with the selected WGS SNPs. Genomic best linear unbiased prediction (GBLUP) models were implemented to predict breeding values through either pooling the 54K and selected WGS SNPs together as one genetic component (a one-component model) or considering 54K SNPs and selected WGS SNPs as two separate genetic components (a two-component model). Across all the traits, inclusion of each of the selected WGS SNP sets led to negligible improvements in prediction accuracies (0.17 percentage points on average) compared to prediction using only 54K. Similarly, marginal improvement in prediction reliability was obtained when all the selected WGS SNPs were included (0.22 percentage points). No further improvement in prediction reliability was observed when considering random regression on genotype code of recessive lethal alleles in the model including both groups of the WGS SNPs. Additionally, there was no difference in prediction reliability from integrating the selected WGS SNP sets through the two-component model compared to the one-component GBLUP.
Collapse
Affiliation(s)
- Grum Gebreyesus
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| |
Collapse
|
10
|
Mora-Poblete F, Ballesta P, Lobos GA, Molina-Montenegro M, Gleadow R, Ahmar S, Jiménez-Aspee F. Genome-wide association study of cyanogenic glycosides, proline, sugars, and pigments in Eucalyptus cladocalyx after 18 consecutive dry summers. PHYSIOLOGIA PLANTARUM 2021; 172:1550-1569. [PMID: 33511661 DOI: 10.1111/ppl.13349] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 01/07/2021] [Accepted: 01/20/2021] [Indexed: 06/12/2023]
Abstract
Natural variation of cyanogenic glycosides, soluble sugars, proline, and nondestructive optical sensing of pigments (chlorophyll, flavonols, and anthocyanins) was examined in ex situ natural populations of Eucalyptus cladocalyx F. Muell. grown under dry environmental conditions in the southern Atacama Desert, Chile. After 18 consecutive dry seasons, considerable plant-to-plant phenotypic variation for all the traits was observed in the field. For example, leaf hydrogen cyanide (HCN) concentrations varied from 0 (two acyanogenic individuals) to 1.54 mg cyanide g-1 DW. Subsequent genome-wide association study revealed associations with several genes with a known function in plants. HCN content was associated robustly with genes encoding Cytochrome P450 proteins, and with genes involved in the detoxification mechanism of HCN in cells (β-cyanoalanine synthase and cyanoalanine nitrilase). Another important finding was that sugars, proline, and pigment content were linked to genes involved in transport, biosynthesis, and/or catabolism. Estimates of genomic heritability (based on haplotypes) ranged between 0.46 and 0.84 (HCN and proline content, respectively). Proline and soluble sugars had the highest predictive ability of genomic prediction models (PA = 0.65 and PA = 0.71, respectively). PA values for HCN content and flavonols were relatively moderate, with estimates ranging from 0.44 to 0.50. These findings provide new understanding on the genetic architecture of cyanogenic capacity, and other key complex traits in cyanogenic E. cladocalyx.
Collapse
Affiliation(s)
| | - Paulina Ballesta
- Institute of Biological Sciences, Universidad de Talca, Talca, Chile
| | - Gustavo A Lobos
- Plant Breeding and Phenomic Center, Faculty of Agricultural Sciences, Universidad de Talca, Talca, Chile
| | - Marco Molina-Montenegro
- Institute of Biological Sciences, Universidad de Talca, Talca, Chile
- Centro de Estudios Avanzados en Zonas Áridas (CEAZA), Facultad de Ciencias del Mar, Universidad Católica del Norte, Coquimbo, Chile
| | - Roslyn Gleadow
- School of Biological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Sunny Ahmar
- Institute of Biological Sciences, Universidad de Talca, Talca, Chile
- College of Plant Sciences and Technology, Huazhong Agricultural University, Wuhan, China
| | - Felipe Jiménez-Aspee
- Department of Food Biofunctionality, Institute of Nutritional Sciences, University of Hohenheim, Stuttgart, Germany
- Departamento de Ciencias Básicas Biomédicas, Facultad de Ciencias de la Salud, Universidad de Talca, Talca, Chile
| |
Collapse
|
11
|
Correddu F, Cesarani A, Dimauro C, Gaspa G, Macciotta NPP. Principal component and multivariate factor analysis of detailed sheep milk fatty acid profile. J Dairy Sci 2021; 104:5079-5094. [PMID: 33516547 DOI: 10.3168/jds.2020-19087] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 11/05/2020] [Indexed: 11/19/2022]
Abstract
Fatty acid (FA) profile is one of the most important aspects of the nutritional properties of milk. The FA content in milk is affected by several factors such as diet, physiology, environment, and genetics. Recently, principal component analysis (PCA) and multivariate factor analysis (MFA) have been used to summarize the complex correlation pattern of the milk FA profile by extracting a reduced number of new variables. In this work, the milk FA profile of a sample of 993 Sarda breed ewes was analyzed with PCA and MFA to compare the ability of these 2 multivariate statistical techniques in investigating the possible existence of latent substructures, and in studying the influence of physiological and environmental effects on the new extracted variables. Individual scores of PCA and MFA were analyzed with a mixed model that included the fixed effects of parity, days in milking, lambing month, number of lambs born, altitude of flock location, and the random effect of flock nested within altitude. Both techniques detected the same number of latent variables (9) explaining 80% of the total variance. In general, PCA structures were difficult to interpret, with only 4 principal components being associated with a clear meaning. Principal component 1 in particular was the easiest to interpret and agreed with the interpretation of the first factor, with both being associated with the FA of mammary origin. On the other hand, MFA was able to identify a clear structure for all the extracted latent variables, confirming the ability of this technique to group FA according to their function or metabolic origin. Key pathways of the milk FA metabolism were identified as mammary gland de novo synthesis, ruminal biohydrogenation, desaturation performed by stearoyl-coenzyme A desaturase enzyme, and rumen microbial activity, confirming previous findings in sheep and in other species. In general, the new extracted variables were mainly affected by physiological factors as days in milk, parity, and lambing month; the number of lambs born had no effect on the new variables, and altitude influenced only one principal component and factor. Both techniques were able to summarize a larger amount of the original variance into a reduced number of variables. Moreover, factor analysis confirmed its ability to identify latent common factors clearly related to FA metabolic pathways.
Collapse
Affiliation(s)
- F Correddu
- Department of Agricultural Sciences, University of Sassari, 07100 Sassari, Italy.
| | - A Cesarani
- Department of Agricultural Sciences, University of Sassari, 07100 Sassari, Italy; Department of Animal and Dairy Science, University of Georgia, Athens 30602
| | - C Dimauro
- Department of Agricultural Sciences, University of Sassari, 07100 Sassari, Italy
| | - G Gaspa
- Department of Agricultural, Forestry and Alimentary Sciences, University of Torino, 10095 Grugliasco, Italy
| | - N P P Macciotta
- Department of Agricultural Sciences, University of Sassari, 07100 Sassari, Italy
| |
Collapse
|
12
|
Farooq M, van Dijk ADJ, Nijveen H, Aarts MGM, Kruijer W, Nguyen TP, Mansoor S, de Ridder D. Prior Biological Knowledge Improves Genomic Prediction of Growth-Related Traits in Arabidopsis thaliana. Front Genet 2021; 11:609117. [PMID: 33552126 PMCID: PMC7855462 DOI: 10.3389/fgene.2020.609117] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 12/21/2020] [Indexed: 01/11/2023] Open
Abstract
Prediction of growth-related complex traits is highly important for crop breeding. Photosynthesis efficiency and biomass are direct indicators of overall plant performance and therefore even minor improvements in these traits can result in significant breeding gains. Crop breeding for complex traits has been revolutionized by technological developments in genomics and phenomics. Capitalizing on the growing availability of genomics data, genome-wide marker-based prediction models allow for efficient selection of the best parents for the next generation without the need for phenotypic information. Until now such models mostly predict the phenotype directly from the genotype and fail to make use of relevant biological knowledge. It is an open question to what extent the use of such biological knowledge is beneficial for improving genomic prediction accuracy and reliability. In this study, we explored the use of publicly available biological information for genomic prediction of photosynthetic light use efficiency (Φ PSII ) and projected leaf area (PLA) in Arabidopsis thaliana. To explore the use of various types of knowledge, we mapped genomic polymorphisms to Gene Ontology (GO) terms and transcriptomics-based gene clusters, and applied these in a Genomic Feature Best Linear Unbiased Predictor (GFBLUP) model, which is an extension to the traditional Genomic BLUP (GBLUP) benchmark. Our results suggest that incorporation of prior biological knowledge can improve genomic prediction accuracy for both Φ PSII and PLA. The improvement achieved depends on the trait, type of knowledge and trait heritability. Moreover, transcriptomics offers complementary evidence to the Gene Ontology for improvement when used to define functional groups of genes. In conclusion, prior knowledge about trait-specific groups of genes can be directly translated into improved genomic prediction.
Collapse
Affiliation(s)
- Muhammad Farooq
- Bioinformatics Group, Wageningen University, Wageningen, Netherlands
- Molecular Virology and Gene Silencing Lab, Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Punjab, Pakistan
| | - Aalt D. J. van Dijk
- Bioinformatics Group, Wageningen University, Wageningen, Netherlands
- Biometris, Wageningen University, Wageningen, Netherlands
| | - Harm Nijveen
- Bioinformatics Group, Wageningen University, Wageningen, Netherlands
| | - Mark G. M. Aarts
- Laboratory of Genetics, Wageningen University, Wageningen, Netherlands
| | - Willem Kruijer
- Biometris, Wageningen University, Wageningen, Netherlands
| | - Thu-Phuong Nguyen
- Laboratory of Genetics, Wageningen University, Wageningen, Netherlands
| | - Shahid Mansoor
- Molecular Virology and Gene Silencing Lab, Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Punjab, Pakistan
| | - Dick de Ridder
- Bioinformatics Group, Wageningen University, Wageningen, Netherlands
| |
Collapse
|
13
|
Palombo V, Pegolo S, Conte G, Cesarani A, Macciotta NPP, Stefanon B, Ajmone Marsan P, Mele M, Cecchinato A, D'Andrea M. Genomic prediction for latent variables related to milk fatty acid composition in Holstein, Simmental and Brown Swiss dairy cattle breeds. J Anim Breed Genet 2020; 138:389-402. [PMID: 33331079 DOI: 10.1111/jbg.12532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 11/27/2020] [Accepted: 12/02/2020] [Indexed: 12/19/2022]
Abstract
Genomic selection (GS) reports on milk fatty acid (FA) profiles have been published quite recently and are still few despite this trait represents the most important aspect of milk nutritional and sensory quality. Reasons for this can be found in the high costs of phenotype recording but also in issues related to its nature of complex trait constituted by multiple genetically correlated variables with low heritabilities. One possible strategy to deal with such constraint is represented by the use of dimension reduction methods. We analysed 40 individual FAs from Italian Brown Swiss, Holstein and Simmental milk through multivariate factor analysis (MFA) to study the genetics of milk FA-related latent variables (factors) and assess their potential use in breeding. A total of nine factors were obtained, and their genetic parameters were inferred under a Bayesian framework using two statistical approaches: the classical pedigree best linear unbiased prediction (ABLUP) and the single-step genomic BLUP (ssGBLUP). The resulting factorial solutions were able to represent groups of FAs with common origin and function and can be considered concise pathway-level phenotypes. The heritability (h2 ) values showed relevant variations across different factors in each breed (0.03 ≤ h2 ≤ 0.38). The accuracies of breeding values predicted were low to high, ranging from 0.13 to 0.72 and from 0.18 to 0.74 considering the pedigree and the genomic model, respectively. The gain in accuracy in genetic prediction due to the addition of genomic information was ~30% and ~5% in validation and training groups respectively, confirming the contribution of genomic information in yielding more accurate predictions compared to the traditional ABLUP methodology. Our results suggest that MFA in combination with GS can be a valuable tool in dairy cattle breeding and deserves to be further investigated for use in future breeding programs to improve cow's milk FA-related traits.
Collapse
Affiliation(s)
- Valentino Palombo
- Dipartimento Agricoltura, Ambiente e Alimenti, Università degli Studi del Molise, Campobasso, Italy
| | - Sara Pegolo
- Dipartimento di Agronomia, Animali, Alimenti, Risorse naturali e Ambiente (DAFNAE), Università di Padova, Padova, Italy
| | - Giuseppe Conte
- Dipartimento di Scienze Agrarie, Alimentari e Agro-ambientali, Università di Pisa, Pisa, Italy
| | - Alberto Cesarani
- Dipartimento di Agraria, Sezione Scienze Zootecniche, Università degli Studi di Sassari, Sassari, Italy.,Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
| | | | - Bruno Stefanon
- Dipartimento di Scienze Agroambientali, Alimentari e Animali, Università di Udine, Udine, Italy
| | - Paolo Ajmone Marsan
- Dipartimento di Scienze Animali, degli Alimenti e della Nutrizione - DIANA e Centro di Ricerca Nutrigenomica e Proteomica - PRONUTRIGEN, Università Cattolica del Sacro Cuore, Piacenza, Italy
| | - Marcello Mele
- Dipartimento di Scienze Agrarie, Alimentari e Agro-ambientali, Università di Pisa, Pisa, Italy
| | - Alessio Cecchinato
- Dipartimento di Agronomia, Animali, Alimenti, Risorse naturali e Ambiente (DAFNAE), Università di Padova, Padova, Italy
| | - Mariasilvia D'Andrea
- Dipartimento Agricoltura, Ambiente e Alimenti, Università degli Studi del Molise, Campobasso, Italy
| |
Collapse
|
14
|
Bobbo T, Penasa M, Cassandro M. Genetic Parameters of Bovine Milk Fatty Acid Profile, Yield, Composition, Total and Differential Somatic Cell Count. Animals (Basel) 2020; 10:E2406. [PMID: 33339148 PMCID: PMC7765606 DOI: 10.3390/ani10122406] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 12/09/2020] [Accepted: 12/13/2020] [Indexed: 11/16/2022] Open
Abstract
The growing interest of consumers for milk and dairy products of high nutritional value has pushed researchers to evaluate the feasibility of including fatty acids (FA) in selection programs to modify milk fat profile and improve its nutritional quality. Therefore, the aim of this study was to estimate genetic parameters of FA profile predicted by mid-infrared spectroscopy, milk yield, composition, and total and differential somatic cell count. Edited data included 35,331 test-day records of 25,407 Italian Holstein cows from 652 herds. Variance components and heritability were estimated using single-trait repeatability animal models, whereas bivariate repeatability animal models were used to estimate genetic and phenotypic correlations between traits, including the fixed effects of stage of lactation, parity, and herd-test-date, and the random effects of additive genetic animal, cow permanent environment and the residual. Heritabilities and genetic correlations obtained in the present study reflected both the origins of FA (extracted from the blood or synthesized de novo by the mammary gland) and their grouping according to saturation or chain length. In addition, correlations among FA groups were in line with correlation among individual FA. Moderate negative genetic correlations between FA and milk yield and moderate to strong positive correlations with fat, protein, and casein percentages suggest that actual selection programs are currently affecting all FA groups, not only the desired ones (e.g., polyunsaturated FA). The absence of association with differential somatic cell count and the weak association with somatic cell score indicate that selection on FA profile would not affect selection on resistance to mastitis and vice versa. In conclusion, our findings suggest that genetic selection on FA content is feasible, as FA are variable and moderately heritable. Nevertheless, in the light of correlations with other milk traits estimated in this study, a clear breeding goal should first be established.
Collapse
Affiliation(s)
- Tania Bobbo
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy; (M.P.); (M.C.)
| | | | | |
Collapse
|
15
|
Rezende FM, Haile-Mariam M, Pryce JE, Peñagaricano F. Across-country genomic prediction of bull fertility in Jersey dairy cattle. J Dairy Sci 2020; 103:11618-11627. [PMID: 32981736 DOI: 10.3168/jds.2020-18910] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 07/15/2020] [Indexed: 12/11/2022]
Abstract
The use of information across populations is an attractive approach to increase the accuracy of genomic predictions for numerically small breeds and traits that are time-consuming and difficult to measure, such as male fertility in cattle. This study was conducted to evaluate genomic prediction of Jersey bull fertility using an across-country reference population combining records from the United States and Australia. The data set consisted of 1,570 US Jersey bulls with sire conception rate (SCR) records, 603 Australian Jersey bulls with semen fertility value (SFV) records and SNP genotypes for roughly 90,000 loci. Both SCR and SFV are evaluations of service sire fertility based on cow field data, and both are intended as phenotypic evaluations because the estimates include genetic and nongenetic effects. Within- and across-country genomic predictions were evaluated using univariate and bivariate genomic best linear unbiased prediction models. Predictive ability was assessed in 5-fold cross-validation using the correlation between observed and predicted fertility values and mean squared error of prediction. Within-country genomic predictions exhibited predictive correlations of around 0.28 and 0.02 for the United States and Australia, respectively. The Australian Jersey population is genetically diverse and small in size, so careful selection of the reference population by including only closely related animals (e.g., excluding New Zealand bulls, which is a less-related population) increased the predictive correlations up to 0.20. Notably, the use of bivariate models fitting all US Jersey records and the optimized Australian population resulted in predictive correlations around of 0.24 for SFV values, which is a relative increase in predictive ability of 20%. Conversely, for predicting SCR values, the use of an across-country reference population did not outperform the standard approach using pure US Jersey reference data set. Our findings indicate that genomic prediction of male fertility in dairy cattle is feasible, and the use of an across-country reference population would be beneficial when local populations are small and genetically diverse.
Collapse
Affiliation(s)
- Fernanda M Rezende
- Department of Animal Sciences, University of Florida, Gainesville 32611; Faculdade de Medicina Veterinária, Universidade Federal de Uberlândia, Uberlândia MG 38410-337, Brazil
| | - Mekonnen Haile-Mariam
- Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia
| | - Jennie E Pryce
- Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia; School of Applied Systems Biology, La Trobe University, Bundoora, Victoria 3083, Australia
| | - Francisco Peñagaricano
- Department of Animal Sciences, University of Florida, Gainesville 32611; Department of Animal and Dairy Sciences, University of Wisconsin-Madison, 53706.
| |
Collapse
|
16
|
Xu Y, Liu X, Fu J, Wang H, Wang J, Huang C, Prasanna BM, Olsen MS, Wang G, Zhang A. Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants. PLANT COMMUNICATIONS 2020; 1:100005. [PMID: 33404534 PMCID: PMC7747995 DOI: 10.1016/j.xplc.2019.100005] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Although long-term genetic gain has been achieved through increasing use of modern breeding methods and technologies, the rate of genetic gain needs to be accelerated to meet humanity's demand for agricultural products. In this regard, genomic selection (GS) has been considered most promising for genetic improvement of the complex traits controlled by many genes each with minor effects. Livestock scientists pioneered GS application largely due to livestock's significantly higher individual values and the greater reduction in generation interval that can be achieved in GS. Large-scale application of GS in plants can be achieved by refining field management to improve heritability estimation and prediction accuracy and developing optimum GS models with the consideration of genotype-by-environment interaction and non-additive effects, along with significant cost reduction. Moreover, it would be more effective to integrate GS with other breeding tools and platforms for accelerating the breeding process and thereby further enhancing genetic gain. In addition, establishing an open-source breeding network and developing transdisciplinary approaches would be essential in enhancing breeding efficiency for small- and medium-sized enterprises and agricultural research systems in developing countries. New strategies centered on GS for enhancing genetic gain need to be developed.
Collapse
Affiliation(s)
- Yunbi Xu
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- CIMMYT-China Tropical Maize Research Center, Foshan University, Foshan 528231, China
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 201400, China
| | - Xiaogang Liu
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Junjie Fu
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Hongwu Wang
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Jiankang Wang
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Changling Huang
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Boddupalli M. Prasanna
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Michael S. Olsen
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Guoying Wang
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Aimin Zhang
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| |
Collapse
|
17
|
Wang Q, Bovenhuis H. Combined use of milk infrared spectra and genotypes can improve prediction of milk fat composition. J Dairy Sci 2019; 103:2514-2522. [PMID: 31882213 DOI: 10.3168/jds.2019-16784] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 11/05/2019] [Indexed: 12/26/2022]
Abstract
It has been shown that milk infrared (IR) spectroscopy can be used to predict detailed milk fat composition. In addition, polymorphisms with substantial effects on milk fat composition have been identified. In this study, we investigated the combined use of milk IR spectroscopy and genotypes of dairy cows on the accuracy of predicting milk fat composition. Milk fat composition data based on gas chromatography and milk IR spectra were available for 1,456 Dutch Holstein Friesian cows. In addition, genotypes for the diacylglycerol acyltransferase 1 (DGAT1) K232A and stearoyl-CoA desaturase 1 (SCD1) A293V polymorphisms and a SNP located in an intron of the fatty acid synthase (FASN) gene were available. Adding SCD1 genotypes to the milk IR spectra resulted in a considerable improvement of the prediction accuracy for the unsaturated fatty acids C10:1, C12:1, C14:1 cis-9, and C16:1 cis-9 and their corresponding unsaturation indices. Adding DGAT1 genotypes to the milk IR spectra resulted in an improvement of the prediction accuracy for C16:1 cis-9 and C16 index. Adding genotypes of the FASN SNP to the IR spectra did not improve prediction of milk fat composition. This study demonstrated the potential of combining milk IR spectra with genotypic information from 3 polymorphisms to predict milk fat composition. We hypothesize that prediction accuracy of milk fat composition can be further improved by combining milk IR spectra with genomic breeding values.
Collapse
Affiliation(s)
- Qiuyu Wang
- Animal Breeding and Genomics, Wageningen University, PO Box 338, 6700 AH, Wageningen, the Netherlands
| | - Henk Bovenhuis
- Animal Breeding and Genomics, Wageningen University, PO Box 338, 6700 AH, Wageningen, the Netherlands.
| |
Collapse
|