1
|
Yin C, Shi H, Zhou P, Wang Y, Tao X, Yin Z, Zhang X, Liu Y. Genomic Prediction of Growth Traits in Yorkshire Pigs of Different Reference Group Sizes Using Different Estimated Breeding Value Models. Animals (Basel) 2024; 14:1098. [PMID: 38612337 PMCID: PMC11010886 DOI: 10.3390/ani14071098] [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: 03/12/2024] [Revised: 03/31/2024] [Accepted: 04/02/2024] [Indexed: 04/14/2024] Open
Abstract
The need for sufficient reference population data poses a significant challenge in breeding programs aimed at improving pig farming on a small to medium scale. To overcome this hurdle, investigating the advantages of combing reference populations of varying sizes is crucial for enhancing the accuracy of the genomic estimated breeding value (GEBV). Genomic selection (GS) in populations with limited reference data can be optimized by combining populations of the same breed or related breeds. This study focused on understanding the effect of combing different reference group sizes on the accuracy of GS for determining the growth effectiveness and percentage of lean meat in Yorkshire pigs. Specifically, our study investigated two important traits: the age at 100 kg live weight (AGE100) and the backfat thickness at 100 kg live weight (BF100). This research assessed the efficiency of genomic prediction (GP) using different GEBV models across three Yorkshire populations with varying genetic backgrounds. The GeneSeek 50K GGP porcine high-density array was used for genotyping. A total of 2295 Yorkshire pigs were included, representing three Yorkshire pig populations with different genetic backgrounds-295 from Danish (small) lines from Huaibei City, Anhui Province, 500 from Canadian (medium) lines from Lixin County, Anhui Province, and 1500 from American (large) lines from Shanghai. To evaluate the impact of different population combination scenarios on the GS accuracy, three approaches were explored: (1) combining all three populations for prediction, (2) combining two populations to predict the third, and (3) predicting each population independently. Five GEBV models, including three Bayesian models (BayesA, BayesB, and BayesC), the genomic best linear unbiased prediction (GBLUP) model, and single-step GBLUP (ssGBLUP) were implemented through 20 repetitions of five-fold cross-validation (CV). The results indicate that predicting one target population using the other two populations yielded the highest accuracy, providing a novel approach for improving the genomic selection accuracy in Yorkshire pigs. In this study, it was found that using different populations of the same breed to predict small- and medium-sized herds might be effective in improving the GEBV. This investigation highlights the significance of incorporating population combinations in genetic models for predicting the breeding value, particularly for pig farmers confronted with resource limitations.
Collapse
Affiliation(s)
- Chang Yin
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China; (C.Y.); (H.S.); (P.Z.); (Y.W.); (X.T.)
| | - Haoran Shi
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China; (C.Y.); (H.S.); (P.Z.); (Y.W.); (X.T.)
| | - Peng Zhou
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China; (C.Y.); (H.S.); (P.Z.); (Y.W.); (X.T.)
| | - Yuwei Wang
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China; (C.Y.); (H.S.); (P.Z.); (Y.W.); (X.T.)
| | - Xuzhe Tao
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China; (C.Y.); (H.S.); (P.Z.); (Y.W.); (X.T.)
| | - Zongjun Yin
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China; (Z.Y.); (X.Z.)
| | - Xiaodong Zhang
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China; (Z.Y.); (X.Z.)
| | - Yang Liu
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China; (C.Y.); (H.S.); (P.Z.); (Y.W.); (X.T.)
| |
Collapse
|
2
|
Haque MA, Lee YM, Ha JJ, Jin S, Park B, Kim NY, Won JI, Kim JJ. Genomic Predictions in Korean Hanwoo Cows: A Comparative Analysis of Genomic BLUP and Bayesian Methods for Reproductive Traits. Animals (Basel) 2023; 14:27. [PMID: 38200758 PMCID: PMC10778388 DOI: 10.3390/ani14010027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/07/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024] Open
Abstract
This study aimed to predict the accuracy of genomic estimated breeding values (GEBVs) for reproductive traits in Hanwoo cows using the GBLUP, BayesB, BayesLASSO, and BayesR methods. Accuracy estimates of GEBVs for reproductive traits were derived through fivefold cross-validation, analyzing a dataset comprising 11,348 animals and employing an Illumina Bovine 50K SNP chip. GBLUP showed an accuracy of 0.26 for AFC, while BayesB, BayesLASSO, and BayesR demonstrated values of 0.28, 0.29, and 0.29, respectively. For CI, GBLUP attained an accuracy of 0.19, whereas BayesB, BayesLASSO, and BayesR scored 0.21, 0.24, and 0.25, respectively. The accuracy for GL was uniform across GBLUP, BayesB, and BayesR at 0.31, whereas BayesLASSO showed a slightly higher accuracy of 0.33. For NAIPC, GBLUP showed an accuracy of 0.24, while BayesB, BayesLASSO, and BayesR recorded 0.22, 0.27, and 0.30, respectively. The variation in genomic prediction accuracy among methods indicated Bayesian approaches slightly outperformed GBLUP. The findings suggest that Bayesian methods, notably BayesLASSO and BayesR, offer improved predictive capabilities for reproductive traits. Future research may explore more advanced genomic approaches to enhance predictive accuracy and genetic gains in Hanwoo cattle breeding programs.
Collapse
Affiliation(s)
- Md Azizul Haque
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea; (M.A.H.); (Y.-M.L.)
| | - Yun-Mi Lee
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea; (M.A.H.); (Y.-M.L.)
| | - Jae-Jung Ha
- Gyeongbuk Livestock Research Institute, Yeongju 36052, Republic of Korea;
| | - Shil Jin
- Hanwoo Research Institute, National Institute of Animal Science, Pyeongchang 25340, Republic of Korea; (S.J.); (B.P.); (N.-Y.K.)
| | - Byoungho Park
- Hanwoo Research Institute, National Institute of Animal Science, Pyeongchang 25340, Republic of Korea; (S.J.); (B.P.); (N.-Y.K.)
| | - Nam-Young Kim
- Hanwoo Research Institute, National Institute of Animal Science, Pyeongchang 25340, Republic of Korea; (S.J.); (B.P.); (N.-Y.K.)
| | - Jeong-Il Won
- Hanwoo Research Institute, National Institute of Animal Science, Pyeongchang 25340, Republic of Korea; (S.J.); (B.P.); (N.-Y.K.)
| | - Jong-Joo Kim
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea; (M.A.H.); (Y.-M.L.)
| |
Collapse
|
3
|
Haque MA, Iqbal A, Bae H, Lee SE, Park S, Lee YM, Kim JJ. Assessment of genomic breeding values and their accuracies for carcass traits in Jeju Black cattle using whole-genome SNP chip panels. J Anim Breed Genet 2023; 140:519-531. [PMID: 37102238 DOI: 10.1111/jbg.12776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 04/28/2023]
Abstract
The objective of the present study was to evaluate the breeding value and accuracy of genomic estimated breeding values (GEBVs) of carcass traits in Jeju Black cattle (JBC) using Hanwoo steers and JBC as a reference population using the single-trait animal model. Our research included genotype and phenotype information on 19,154 Hanwoo steers with 1097 JBC acting as the reference population. Likewise, the test population consisted of 418 genotyped JBC individuals with no phenotypic records for those carcass traits. For estimating the accuracy of GEBV, we divided the entire population into three groups. Hanwoo and JBC make up the first group; Hanwoo and JBC, who has both the genotype and phenotypic records, are referred to as the reference (training) population, and JBC, who lacks phenotypic information is referred to as the test (validation) population. The second group consists of the JBC (without phenotype) as the test population and Hanwoo as a reference population with phenotype and genotypic data. The only JBCs in the third group are those who have genotypic and phenotypic data on them as a reference population but no phenotypic data on them as a test population. The single-trait animal model was used in all three groups for statistical purposes. The reference populations estimated heritabilities for carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS) as 0.30, 0.26, 0.26, and 0.34 for the Hanwoo steer and 0.42, 0.27, 0.26, and 0.48 for JBC. The average accuracy for carcass traits in Group 1 was 0.80 for the Hanwoo and JBC reference population compared with 0.73 for the JBC test population. Although the average accuracy for carcass traits in Group 2 was 0.80, it was 0.80 for the Hanwoo reference population and only 0.56 for the JBC test population. The average accuracy for the JBC reference and test populations was 0.68 and 0.50, respectively, when they were included in the accuracy comparison without the Hanwoo reference population. Groups 1 and 2 used Hanwoo as reference population, which led to a better average accuracy; however, Group 3 only used the JBC reference and test population, which led to a lower average accuracy. This might be due to the fact that Group 3 used a smaller reference size than the group that came before it and that the genetic makeup of the Hanwoo and JBC breeds differed. The GEBV accuracy for MS was higher than that of other traits across all three analysis groups, followed by CWT, EMA, and BF, which may be partially explained by the MS traits' higher heritability. This study suggests that in order to achieve more accuracy, a large reference population particular to a breed should be established. Therefore, to increase the accuracy of GEBV prediction and the genetic benefit from genomic selection in JBC, individual reference breeds, and large populations are required.
Collapse
Affiliation(s)
- Md Azizul Haque
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk, Korea
| | - Asif Iqbal
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk, Korea
| | - Haechang Bae
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk, Korea
| | - Seung Eun Lee
- Department of Biomedical Informatics, Jeju National University, Jeju, Korea
| | - Sepil Park
- Department of Biomedical Informatics, Jeju National University, Jeju, Korea
| | - Yun Mi Lee
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk, Korea
| | - Jong Joo Kim
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk, Korea
| |
Collapse
|
4
|
Park SJ, Kim H, Piao M, Kang HJ, Fassah DM, Jung DJS, Kim SY, Na SW, Beak SH, Jeong IH, Yoo SP, Hong SJ, Lee DH, Lee SH, Haque MN, Shin DJ, Kwon JA, Jo C, Baik M. Effects of genomic estimated breeding value and dietary energy to protein ratio on growth performance, carcass trait, and lipogenic gene expression in Hanwoo steer. Animal 2023; 17:100728. [PMID: 36870258 DOI: 10.1016/j.animal.2023.100728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 02/12/2023] Open
Abstract
"Genome-based precision feeding" is a concept that involves the application of customised diets to different genetic groups of cattle. We investigated the effects of the genomic estimated breeding value (gEBV) and dietary energy to protein ratio (DEP) on growth performance, carcass traits, and lipogenic gene expression in Hanwoo (Korean cattle) steers. Forty-four Hanwoo steers (BW = 636 kg, age = 26.9 months) were genotyped using the Illumina Bovine 50 K BeadChip. The gEBV was calculated using genomic best linear unbiased prediction. Animals were separated into high gEBV of marbling score or low-gMS groups based on the upper and lower 50% groupings of the reference population, respectively. Animals were assigned to one of four groups in a 2 × 2 factorial arrangement: high gMS/high DEP (0.084 MJ/g), high gMS/low DEP (0.079 MJ/g), low gMS/high DEP, and low gMS/low DEP. Steers were fed concentrate with a high or low DEP for 31 weeks. The BW tended to be higher (0.05 < P < 0.1) in the high-gMS groups compared to the low-gMS groups at 0, 4, 8, 12, and 20 weeks. The average daily gain (ADG) tended to be lower (P = 0.08) in the high-gMS group than in the low-gMS group. Final BW and measured carcass weight (CW) were positively correlated with the gEBV of carcass weight (gCW). The DEP did not affect ADG. Neither the gMS nor the DEP affected the MS and beef quality grade. The intramuscular fat (IMF) content in the longissimus thoracis (LT) tended to be higher (P = 0.08) in the high-gMS groups than in the low-gMS groups. The mRNA levels of lipogenic acetyl-CoA carboxylase and fatty acid binding protein 4 genes in the LT were higher (P < 0.05) in the high-gMS group than in the low-gMS group. Overall, the IMF content tended to be affected by the gMS, and the genetic potential (i.e., gMS) was associated with the functional activity of lipogenic gene expression. The gCW was associated with the measured BW and CW. The results demonstrated that the gMS and the gCW may be used as early prediction indexes for meat quality and growth potential of beef cattle.
Collapse
Affiliation(s)
- S J Park
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Science, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - H Kim
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Science, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - M Piao
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Science, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - H J Kang
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Science, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - D M Fassah
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Science, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - D J S Jung
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Science, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - S Y Kim
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Science, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - S W Na
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Science, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - S-H Beak
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Science, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - I H Jeong
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Science, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - S P Yoo
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Science, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - S J Hong
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Science, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - D H Lee
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Republic of Korea
| | - S H Lee
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Republic of Korea
| | - M N Haque
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Science, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - D-J Shin
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Science, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - J A Kwon
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Science, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - C Jo
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Science, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea; Institutes of Green Bio Science Technology, Pyeongchang-daero, Daehwa-myeon, Pyeongchang-gun, Gangwon 25354, Republic of Korea
| | - M Baik
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Science, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea; Institutes of Green Bio Science Technology, Pyeongchang-daero, Daehwa-myeon, Pyeongchang-gun, Gangwon 25354, Republic of Korea.
| |
Collapse
|
5
|
Exploring and Identifying Candidate Genes and Genomic Regions Related to Economically Important Traits in Hanwoo Cattle. Curr Issues Mol Biol 2022; 44:6075-6092. [PMID: 36547075 PMCID: PMC9777506 DOI: 10.3390/cimb44120414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/28/2022] [Accepted: 11/29/2022] [Indexed: 12/12/2022] Open
Abstract
The purpose of the current review was to explore and summarize different studies concerning the detection and characterization of candidate genes and genomic regions associated with economically important traits in Hanwoo beef cattle. Hanwoo cattle, the indigenous premium beef cattle of Korea, were introduced for their marbled fat, tenderness, characteristic flavor, and juiciness. To date, there has been a strong emphasis on the genetic improvement of meat quality and yields, such as backfat thickness (BFT), marbling score (MS), carcass weight (CW), eye muscle area (EMA), and yearling weight (YW), as major selection criteria in Hanwoo breeding programs. Hence, an understanding of the genetics controlling these traits along with precise knowledge of the biological mechanisms underlying the traits would increase the ability of the industry to improve cattle to better meet consumer demands. With the development of high-throughput genotyping, genomewide association studies (GWAS) have allowed the detection of chromosomal regions and candidate genes linked to phenotypes of interest. This is an effective and useful tool for accelerating the efficiency of animal breeding and selection. The GWAS results obtained from the literature review showed that most positional genes associated with carcass and growth traits in Hanwoo are located on chromosomes 6 and 14, among which LCORL, NCAPG, PPARGC1A, ABCG2, FAM110B, FABP4, DGAT1, PLAG1, and TOX are well known. In conclusion, this review study attempted to provide comprehensive information on the identified candidate genes associated with the studied traits and genes enriched in the functional terms and pathways that could serve as a valuable resource for future research in Hanwoo breeding programs.
Collapse
|
6
|
Naserkheil M, Mehrban H, Lee D, Park MN. Evaluation of Genome-Enabled Prediction for Carcass Primal Cut Yields Using Single-Step Genomic Best Linear Unbiased Prediction in Hanwoo Cattle. Genes (Basel) 2021; 12:genes12121886. [PMID: 34946834 PMCID: PMC8701981 DOI: 10.3390/genes12121886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 11/16/2021] [Accepted: 11/24/2021] [Indexed: 11/16/2022] Open
Abstract
There is a growing interest worldwide in genetically selecting high-value cut carcass weights, which allows for increased profitability in the beef cattle industry. Primal cut yields have been proposed as a potential indicator of cutability and overall carcass merit, and it is worthwhile to assess the prediction accuracies of genomic selection for these traits. This study was performed to compare the prediction accuracy obtained from a conventional pedigree-based BLUP (PBLUP) and a single-step genomic BLUP (ssGBLUP) method for 10 primal cut traits-bottom round, brisket, chuck, flank, rib, shank, sirloin, striploin, tenderloin, and top round-in Hanwoo cattle with the estimators of the linear regression method. The dataset comprised 3467 phenotypic observations for the studied traits and 3745 genotyped individuals with 43,987 single-nucleotide polymorphisms. In the partial dataset, the accuracies ranged from 0.22 to 0.30 and from 0.37 to 0.54 as evaluated using the PBLUP and ssGBLUP models, respectively. The accuracies of PBLUP and ssGBLUP with the whole dataset varied from 0.45 to 0.75 (average 0.62) and from 0.52 to 0.83 (average 0.71), respectively. The results demonstrate that ssGBLUP performed better than PBLUP averaged over the 10 traits, in terms of prediction accuracy, regardless of considering a partial or whole dataset. Moreover, ssGBLUP generally showed less biased prediction and a value of dispersion closer to 1 than PBLUP across the studied traits. Thus, the ssGBLUP seems to be more suitable for improving the accuracy of predictions for primal cut yields, which can be considered a starting point in future genomic evaluation for these traits in Hanwoo breeding practice.
Collapse
Affiliation(s)
- Masoumeh Naserkheil
- Animal Breeding and Genetics Division, National Institute of Animal Science, Cheonan-si 31000, Chungcheongnam-do, Korea;
| | - Hossein Mehrban
- Department of Animal Science, Shahrekord University, Shahrekord 88186-34141, Iran;
| | - Deukmin Lee
- Department of Animal Life and Environment Sciences, Hankyong National University, Jungang-ro 327, Anseong-si 17579, Gyeonggi-do, Korea
- Correspondence: (D.L.); (M.N.P.); Tel.: +82-31-670-5091 (D.L.); +82-41-580-3355 (M.N.P.)
| | - Mi Na Park
- Animal Breeding and Genetics Division, National Institute of Animal Science, Cheonan-si 31000, Chungcheongnam-do, Korea;
- Correspondence: (D.L.); (M.N.P.); Tel.: +82-31-670-5091 (D.L.); +82-41-580-3355 (M.N.P.)
| |
Collapse
|
7
|
Mehrban H, Naserkheil M, Lee D, Ibáñez-Escriche N. Multi-Trait Single-Step GBLUP Improves Accuracy of Genomic Prediction for Carcass Traits Using Yearling Weight and Ultrasound Traits in Hanwoo. Front Genet 2021; 12:692356. [PMID: 34394186 PMCID: PMC8363309 DOI: 10.3389/fgene.2021.692356] [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: 04/08/2021] [Accepted: 07/02/2021] [Indexed: 11/25/2022] Open
Abstract
There has been a growing interest in the genetic improvement of carcass traits as an important and primary breeding goal in the beef cattle industry over the last few decades. The use of correlated traits and molecular information can aid in obtaining more accurate estimates of breeding values. This study aimed to assess the improvement in the accuracy of genetic predictions for carcass traits by using ultrasound measurements and yearling weight along with genomic information in Hanwoo beef cattle by comparing four evaluation models using the estimators of the recently developed linear regression method. We compared the performance of single-trait pedigree best linear unbiased prediction [ST-BLUP and single-step genomic (ST-ssGBLUP)], as well as multi-trait (MT-BLUP and MT-ssGBLUP) models for the studied traits at birth and yearling date of steers. The data comprised of 15,796 phenotypic records for yearling weight and ultrasound traits as well as 5,622 records for carcass traits (backfat thickness, carcass weight, eye muscle area, and marbling score), resulting in 43,949 single-nucleotide polymorphisms from 4,284 steers and 2,332 bulls. Our results indicated that averaged across all traits, the accuracy of ssGBLUP models (0.52) was higher than that of pedigree-based BLUP (0.34), regardless of the use of single- or multi-trait models. On average, the accuracy of prediction can be further improved by implementing yearling weight and ultrasound data in the MT-ssGBLUP model (0.56) for the corresponding carcass traits compared to the ST-ssGBLUP model (0.49). Moreover, this study has shown the impact of genomic information and correlated traits on predictions at the yearling date (0.61) using MT-ssGBLUP models, which was advantageous compared to predictions at birth date (0.51) in terms of accuracy. Thus, using genomic information and high genetically correlated traits in the multi-trait model is a promising approach for practical genomic selection in Hanwoo cattle, especially for traits that are difficult to measure.
Collapse
Affiliation(s)
- Hossein Mehrban
- Department of Animal Science, Shahrekord University, Shahrekord, Iran
| | - Masoumeh Naserkheil
- Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.,Department of Animal Life and Environment Sciences, Hankyong National University, Gyeonggi-do, South Korea
| | - Deukhwan Lee
- Department of Animal Life and Environment Sciences, Hankyong National University, Gyeonggi-do, South Korea
| | - Noelia Ibáñez-Escriche
- Institute for Animal Science and Technology, Universitat Politècnica de València, València, Spain
| |
Collapse
|
8
|
Genomic Prediction in Local Breeds: The Rendena Cattle as a Case Study. Animals (Basel) 2021; 11:ani11061815. [PMID: 34207091 PMCID: PMC8234894 DOI: 10.3390/ani11061815] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 06/16/2021] [Accepted: 06/16/2021] [Indexed: 01/26/2023] Open
Abstract
Simple Summary Although genomic selection is being used in many livestock species, it has not yet been considered in local breeds due to the lower population size and the potential less effective impact on the genetic evaluation of these breeds. The current research aims to investigate how genomic data can impact the accuracy of genetic predictions for beef traits in Rendena, a small local cattle breed of the North-East of Italy selected for a dual purpose. Classical animal models using only phenotypic information were compared with two models that integrated genomic data with pedigree information. The genomic models presented better accuracy in estimated breeding values of the animals than the ‘classical’ animal model, especially the ‘simpler’ one assuming homogeneous variances of single nucleotide polymorphisms. Our results show that the inclusion of genomic information can be successfully applied to breeding selection scenarios even in small local cattle breeds such as Rendena. Abstract The maintenance of local cattle breeds is key to selecting for efficient food production, landscape protection, and conservation of biodiversity and local cultural heritage. Rendena is an indigenous cattle breed from the alpine North-East of Italy, selected for dual purpose, but with lesser emphasis given to beef traits. In this situation, increasing accuracy for beef traits could prevent detrimental effects due to the antagonism with milk production. Our study assessed the impact of genomic information on estimated breeding values (EBVs) in Rendena performance-tested bulls. Traits considered were average daily gain, in vivo EUROP score, and in vivo estimate of dressing percentage. The final dataset contained 1691 individuals with phenotypes and 8372 animals in pedigree, 1743 of which were genotyped. Using the cross-validation method, three models were compared: (i) Pedigree-BLUP (PBLUP); (ii) single-step GBLUP (ssGBLUP), and (iii) weighted single-step GBLUP (WssGBLUP). Models including genomic information presented higher accuracy, especially WssGBLUP. However, the model with the best overall properties was the ssGBLUP, showing higher accuracy than PBLUP and optimal values of bias and dispersion parameters. Our study demonstrated that integrating phenotypes for beef traits with genomic data can be helpful to estimate EBVs, even in a small local breed.
Collapse
|
9
|
Mancin E, Sosa-Madrid BS, Blasco A, Ibáñez-Escriche N. Genotype Imputation to Improve the Cost-Efficiency of Genomic Selection in Rabbits. Animals (Basel) 2021; 11:ani11030803. [PMID: 33805619 PMCID: PMC8000098 DOI: 10.3390/ani11030803] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/04/2021] [Accepted: 03/05/2021] [Indexed: 01/19/2023] Open
Abstract
Simple Summary Genotyping costs are still the major limitation for the uptake of genomic selection by the rabbit meat industry, as a large number of genetic markers are needed for improving the prediction of breeding values by genomic data. In this study, several genotyping strategies were examined through simulation scenarios to disentangle the best feasible options of implementing genomic selection in rabbit breeding programs. Most scenarios emphasized the genotyping of candidate animals with a low Single Nucleotide Polymorphism (SNP) density platform. Imputation accuracies were high for the scenarios with ancestors genotyped at high or medium SNP-densities. However, the scenario with male ancestors genotyped at high SNP-density and only dams genotyped at medium SNP-density showed the best economically feasible strategy, taking into account the trade-off among genotyping costs, the accuracy of breeding values and response to selection. The results confirmed that by combining the imputation technique with a mindful selection of the animals to be genotyped, it is possible to achieve better performance than Best Linear Unbiased Prediction (BLUP), reducing genotyping cost at the same time. Abstract Genomic selection uses genetic marker information to predict genomic breeding values (gEBVs), and can be a suitable tool for selecting low-hereditability traits such as litter size in rabbits. However, genotyping costs in rabbits are still too high to enable genomic prediction in selective breeding programs. One method for decreasing genotyping costs is the genotype imputation, where parents are genotyped at high SNP-density (HD) and the progeny are genotyped at lower SNP-density, followed by imputation to HD. The aim of this study was to disentangle the best imputation strategies with a trade-off between genotyping costs and the accuracy of breeding values for litter size. A selection process, mimicking a commercial breeding rabbit selection program for litter size, was simulated. Two different Quantitative Trait Nucleotide (QTN) models (QTN_5 and QTN_44) were generated 36 times each. From these simulations, seven different scenarios (S1–S7) and a further replicate of the third scenario (S3_A) were created. Scenarios consist of a different combination of genotyping strategies. In these scenarios, ancestors and progeny were genotyped with a mix of three different platforms, containing 200,000, 60,000, and 600 SNPs under a cost of EUR 100, 50 and 11 per animal, respectively. Imputation accuracy (IA) was measured as a Pearson’s correlation between true genotype and imputed genotype, whilst the accuracy of gEBVs was the correlation between true breeding value and the estimated one. The relationships between IA, the accuracy of gEBVs, genotyping costs, and response to selection were examined under each QTN model. QTN_44 presented better performance, according to the results of genomic prediction, but the same ranks between scenarios remained in both QTN models. The highest IA (0.99) and the accuracy of gEBVs (0.26; QTN_44, and 0.228; QTN_5) were observed in S1 where all ancestors were genotyped at HD and progeny at medium SNP-density (MD). Nevertheless, this was the most expensive scenario compared to the others in which the progenies were genotyped at low SNP-density (LD). Scenarios with low average costs presented low IA, particularly when female ancestors were genotyped at LD (S5) or non-genotyped (S7). The S3_A, imputing whole-genomes, had the lowest accuracy of gEBVs (0.09), even worse than Best Linear Unbiased Prediction (BLUP). The best trade-off between genotyping costs and the accuracy of gEBVs (0.234; QTN_44 and 0.199) was in S6, in which dams were genotyped with MD whilst grand-dams were non-genotyped. However, this relationship would depend mainly on the distribution of QTN and SNP across the genome, suggesting further studies on the characterization of the rabbit genome in the Spanish lines. In summary, genomic selection with genotype imputation is feasible in the rabbit industry, considering only genotyping strategies with suitable IA, accuracy of gEBVs, genotyping costs, and response to selection.
Collapse
Affiliation(s)
- Enrico Mancin
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, viale dell’Università 16, 35020 Legnaro, PD, Italy;
| | - Bolívar Samuel Sosa-Madrid
- Institute for Animal Science and Technology, Universitat Politècnica de València, 46022 Valencia, Spain;
- Correspondence: (B.S.S.-M.); (N.I.-E.); Tel.: +34-963877438 (N.I.-E.)
| | - Agustín Blasco
- Institute for Animal Science and Technology, Universitat Politècnica de València, 46022 Valencia, Spain;
| | - Noelia Ibáñez-Escriche
- Institute for Animal Science and Technology, Universitat Politècnica de València, 46022 Valencia, Spain;
- Correspondence: (B.S.S.-M.); (N.I.-E.); Tel.: +34-963877438 (N.I.-E.)
| |
Collapse
|
10
|
Mehrban H, Naserkheil M, Lee DH, Cho C, Choi T, Park M, Ibáñez-Escriche N. Genomic Prediction Using Alternative Strategies of Weighted Single-Step Genomic BLUP for Yearling Weight and Carcass Traits in Hanwoo Beef Cattle. Genes (Basel) 2021; 12:genes12020266. [PMID: 33673102 PMCID: PMC7917987 DOI: 10.3390/genes12020266] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 02/08/2021] [Accepted: 02/11/2021] [Indexed: 01/20/2023] Open
Abstract
The weighted single-step genomic best linear unbiased prediction (GBLUP) method has been proposed to exploit information from genotyped and non-genotyped relatives, allowing the use of weights for single-nucleotide polymorphism in the construction of the genomic relationship matrix. The purpose of this study was to investigate the accuracy of genetic prediction using the following single-trait best linear unbiased prediction methods in Hanwoo beef cattle: pedigree-based (PBLUP), un-weighted (ssGBLUP), and weighted (WssGBLUP) single-step genomic methods. We also assessed the impact of alternative single and window weighting methods according to their effects on the traits of interest. The data was comprised of 15,796 phenotypic records for yearling weight (YW) and 5622 records for carcass traits (backfat thickness: BFT, carcass weight: CW, eye muscle area: EMA, and marbling score: MS). Also, the genotypic data included 6616 animals for YW and 5134 for carcass traits on the 43,950 single-nucleotide polymorphisms. The ssGBLUP showed significant improvement in genomic prediction accuracy for carcass traits (71%) and yearling weight (99%) compared to the pedigree-based method. The window weighting procedures performed better than single SNP weighting for CW (11%), EMA (11%), MS (3%), and YW (6%), whereas no gain in accuracy was observed for BFT. Besides, the improvement in accuracy between window WssGBLUP and the un-weighted method was low for BFT and MS, while for CW, EMA, and YW resulted in a gain of 22%, 15%, and 20%, respectively, which indicates the presence of relevant quantitative trait loci for these traits. These findings indicate that WssGBLUP is an appropriate method for traits with a large quantitative trait loci effect.
Collapse
Affiliation(s)
- Hossein Mehrban
- Department of Animal Science, Shahrekord University, Shahrekord 88186-34141, Iran;
| | - Masoumeh Naserkheil
- Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj 77871-31587, Iran;
- Department of Animal Life and Environment Sciences, Hankyong National University, Jungang-ro 327, Anseong-si 17579, Gyeonggi-do, Korea
| | - Deuk Hwan Lee
- Department of Animal Life and Environment Sciences, Hankyong National University, Jungang-ro 327, Anseong-si 17579, Gyeonggi-do, Korea
- Correspondence: ; Tel.: +82-316-705-091
| | - Chungil Cho
- Hanwoo Genetic Improvement Center, NongHyup Agribusiness Group Inc., Seosan 31948, Korea;
| | - Taejeong Choi
- Animal Breeding and Genetics Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000, Korea; (T.C.); (M.P.)
| | - Mina Park
- Animal Breeding and Genetics Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000, Korea; (T.C.); (M.P.)
| | - Noelia Ibáñez-Escriche
- Institute for Animal Science and Technology, Universitat Politècnica de València, 46022 València, Spain;
| |
Collapse
|