Validation of the Prediction Accuracy for 13 Traits in Chinese Simmental Beef Cattle Using a Preselected Low-Density SNP Panel
Chinese Simmental beef cattle play a key role in the Chinese beef industry due to their great adaptability and marketability. To achieve efficient genetic gain at a low breeding cost, it is crucial to develop a customized cost-effective low-density SNP panel for this cattle population. Thirteen grow...
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-06-01
|
| Series: | Animals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-2615/11/7/1890 |
| _version_ | 1797528769112571904 |
|---|---|
| author | Ling Xu Qunhao Niu Yan Chen Zezhao Wang Lei Xu Hongwei Li Lingyang Xu Xue Gao Lupei Zhang Huijiang Gao Wentao Cai Bo Zhu Junya Li |
| author_facet | Ling Xu Qunhao Niu Yan Chen Zezhao Wang Lei Xu Hongwei Li Lingyang Xu Xue Gao Lupei Zhang Huijiang Gao Wentao Cai Bo Zhu Junya Li |
| author_sort | Ling Xu |
| collection | DOAJ |
| description | Chinese Simmental beef cattle play a key role in the Chinese beef industry due to their great adaptability and marketability. To achieve efficient genetic gain at a low breeding cost, it is crucial to develop a customized cost-effective low-density SNP panel for this cattle population. Thirteen growth, carcass, and meat quality traits and a BovineHD Beadchip genotyping of 1346 individuals were used to select trait-associated variants and variants contributing to great genetic variance. In addition, highly informative SNPs with high MAF in each 500 kb sliding window and in each genic region were also included separately. A low-density SNP panel consisting of 30,684 SNPs was developed, with an imputation accuracy of 97.4% when imputed to the 770 K level. Among 13 traits, the average prediction accuracy levels evaluated by genomic best linear unbiased prediction (GBLUP) and BayesA/B/Cπ were 0.22–0.47 and 0.18–0.60 for the ~30 K array and BovineHD Beadchip, respectively. Generally, the predictive performance of the ~30 K array was trait-dependent, with reduced prediction accuracies for seven traits. While differences in terms of prediction accuracy were observed among the 13 traits, the low-density SNP panel achieved moderate to high accuracies for most of the traits and even improved the accuracies for some traits. |
| first_indexed | 2024-03-10T10:03:40Z |
| format | Article |
| id | doaj.art-86bc85dbee42402281eb22a380bb54a3 |
| institution | Directory Open Access Journal |
| issn | 2076-2615 |
| language | English |
| last_indexed | 2024-03-10T10:03:40Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj.art-86bc85dbee42402281eb22a380bb54a32023-11-22T01:43:10ZengMDPI AGAnimals2076-26152021-06-01117189010.3390/ani11071890Validation of the Prediction Accuracy for 13 Traits in Chinese Simmental Beef Cattle Using a Preselected Low-Density SNP PanelLing Xu0Qunhao Niu1Yan Chen2Zezhao Wang3Lei Xu4Hongwei Li5Lingyang Xu6Xue Gao7Lupei Zhang8Huijiang Gao9Wentao Cai10Bo Zhu11Junya Li12Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, ChinaLaboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, ChinaLaboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, ChinaLaboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, ChinaLaboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, ChinaLaboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, ChinaLaboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, ChinaLaboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, ChinaLaboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, ChinaLaboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, ChinaLaboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, ChinaLaboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, ChinaLaboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, ChinaChinese Simmental beef cattle play a key role in the Chinese beef industry due to their great adaptability and marketability. To achieve efficient genetic gain at a low breeding cost, it is crucial to develop a customized cost-effective low-density SNP panel for this cattle population. Thirteen growth, carcass, and meat quality traits and a BovineHD Beadchip genotyping of 1346 individuals were used to select trait-associated variants and variants contributing to great genetic variance. In addition, highly informative SNPs with high MAF in each 500 kb sliding window and in each genic region were also included separately. A low-density SNP panel consisting of 30,684 SNPs was developed, with an imputation accuracy of 97.4% when imputed to the 770 K level. Among 13 traits, the average prediction accuracy levels evaluated by genomic best linear unbiased prediction (GBLUP) and BayesA/B/Cπ were 0.22–0.47 and 0.18–0.60 for the ~30 K array and BovineHD Beadchip, respectively. Generally, the predictive performance of the ~30 K array was trait-dependent, with reduced prediction accuracies for seven traits. While differences in terms of prediction accuracy were observed among the 13 traits, the low-density SNP panel achieved moderate to high accuracies for most of the traits and even improved the accuracies for some traits.https://www.mdpi.com/2076-2615/11/7/1890genomic predictionprediction accuracylow-density SNP panelChinese Simmental beef cattle |
| spellingShingle | Ling Xu Qunhao Niu Yan Chen Zezhao Wang Lei Xu Hongwei Li Lingyang Xu Xue Gao Lupei Zhang Huijiang Gao Wentao Cai Bo Zhu Junya Li Validation of the Prediction Accuracy for 13 Traits in Chinese Simmental Beef Cattle Using a Preselected Low-Density SNP Panel Animals genomic prediction prediction accuracy low-density SNP panel Chinese Simmental beef cattle |
| title | Validation of the Prediction Accuracy for 13 Traits in Chinese Simmental Beef Cattle Using a Preselected Low-Density SNP Panel |
| title_full | Validation of the Prediction Accuracy for 13 Traits in Chinese Simmental Beef Cattle Using a Preselected Low-Density SNP Panel |
| title_fullStr | Validation of the Prediction Accuracy for 13 Traits in Chinese Simmental Beef Cattle Using a Preselected Low-Density SNP Panel |
| title_full_unstemmed | Validation of the Prediction Accuracy for 13 Traits in Chinese Simmental Beef Cattle Using a Preselected Low-Density SNP Panel |
| title_short | Validation of the Prediction Accuracy for 13 Traits in Chinese Simmental Beef Cattle Using a Preselected Low-Density SNP Panel |
| title_sort | validation of the prediction accuracy for 13 traits in chinese simmental beef cattle using a preselected low density snp panel |
| topic | genomic prediction prediction accuracy low-density SNP panel Chinese Simmental beef cattle |
| url | https://www.mdpi.com/2076-2615/11/7/1890 |
| work_keys_str_mv | AT lingxu validationofthepredictionaccuracyfor13traitsinchinesesimmentalbeefcattleusingapreselectedlowdensitysnppanel AT qunhaoniu validationofthepredictionaccuracyfor13traitsinchinesesimmentalbeefcattleusingapreselectedlowdensitysnppanel AT yanchen validationofthepredictionaccuracyfor13traitsinchinesesimmentalbeefcattleusingapreselectedlowdensitysnppanel AT zezhaowang validationofthepredictionaccuracyfor13traitsinchinesesimmentalbeefcattleusingapreselectedlowdensitysnppanel AT leixu validationofthepredictionaccuracyfor13traitsinchinesesimmentalbeefcattleusingapreselectedlowdensitysnppanel AT hongweili validationofthepredictionaccuracyfor13traitsinchinesesimmentalbeefcattleusingapreselectedlowdensitysnppanel AT lingyangxu validationofthepredictionaccuracyfor13traitsinchinesesimmentalbeefcattleusingapreselectedlowdensitysnppanel AT xuegao validationofthepredictionaccuracyfor13traitsinchinesesimmentalbeefcattleusingapreselectedlowdensitysnppanel AT lupeizhang validationofthepredictionaccuracyfor13traitsinchinesesimmentalbeefcattleusingapreselectedlowdensitysnppanel AT huijianggao validationofthepredictionaccuracyfor13traitsinchinesesimmentalbeefcattleusingapreselectedlowdensitysnppanel AT wentaocai validationofthepredictionaccuracyfor13traitsinchinesesimmentalbeefcattleusingapreselectedlowdensitysnppanel AT bozhu validationofthepredictionaccuracyfor13traitsinchinesesimmentalbeefcattleusingapreselectedlowdensitysnppanel AT junyali validationofthepredictionaccuracyfor13traitsinchinesesimmentalbeefcattleusingapreselectedlowdensitysnppanel |