4 results on '"Su, Guosheng"'
Search Results
2. Genomic prediction of service sire effect on female reproductive performance in Holstein cattle: A comparison between different methods, validation population and marker densities.
- Author
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Shi, Rui, Chen, Ziwei, Su, Guosheng, Luo, Hanpeng, Liu, Lin, Guo, Gang, and Wang, Yachun
- Subjects
HOLSTEIN-Friesian cattle ,POPULATION density ,DAIRY cattle ,CATTLE reproduction ,FORECASTING ,SIMMENTAL cattle ,STILLBIRTH - Abstract
Reproductive traits of dairy cattle are bound to the actual efficiency of farm operation, which therefore show great economic importance. Among them, some traits were deemed to be simultaneously affected by service sire and mating cow. Service sires are proved to play an important role in reproduction process of cows. However, limited study explored the genetic effect of service sire (GESS), let alone the genomic prediction of this effect. In the present study, 2244 genotyped bulls together with phenotypic records were used to predict the GESS on conception rate, 56‐day non‐return rate, calving ease, stillbirth and gestation length. The feasibilities of multi‐step genomic best linear unbiased predictor (msGBLUP) and single‐step genomic best linear unbiased predictor (ssGBLUP) were investigated under different scenarios, that is, different marker densities and validation population. The predictive accuracies and unbiasedness for GESS ranged from 0.159 to 0.647 and from 0.202 to 2.018, respectively, when validated on young bulls, while the accuracies and unbiasedness ranged from 0.409 to 0.802 and 0.333 to 1.146 when validated on random split data sets. It is feasible to predict GESS on reproductive traits by using a linear mixed model and genomic data, and high‐density marker panel had limited contribution to the prediction. This research investigated the potential factors that influence the genomic prediction of GESS on reproductive traits and indicated the possibility of genomic selection on GESS, both in ideal and practical circumstances. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
3. Improving Genomic Prediction Accuracy in the Chinese Holstein Population by Combining with the Nordic Holstein Reference Population.
- Author
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Zhang, Zipeng, Shi, Shaolei, Zhang, Qin, Aamand, Gert P., Lund, Mogens S., Su, Guosheng, and Ding, Xiangdong
- Subjects
FOOT ,MILKFAT ,GENETIC correlations ,MILK yield ,SOMATIC cells ,FORECASTING ,GENOTYPE-environment interaction - Abstract
Simple Summary: The size of the reference population is critical to the accuracy of genomic prediction. In addition, joining the reference populations from different breeding organizations is a convenient and effective method by which to enlarge reference populations. By adding the Nordic Holstein reference population to the Chinese Holstein reference population, we found that the accuracy of genomic prediction in the Chinese Holstein population was improved substantially for the traits with high or moderate genetic correlation between the two populations; however, the low-genetic-correlation traits did not improve. These findings are important for the purposes of multi-country joint genomic evaluation. The size of the reference population is critical in order to improve the accuracy of genomic prediction. Indeed, improving genomic prediction accuracy by combining multinational reference populations has proven to be effective. In this study, we investigated the improvement of genomic prediction accuracy in seven complex traits (i.e., milk yield; fat yield; protein yield; somatic cell count; body conformation; feet and legs; and mammary system conformation) by combining the Chinese and Nordic Holstein reference populations. The estimated genetic correlations between the Chinese and Nordic Holstein populations are high with respect to protein yield, fat yield, and milk yield—whereby these correlations range from 0.621 to 0.720—and are moderate with respect to somatic cell count (0.449), but low for the three conformation traits (which range from 0.144 to 0.236). When utilizing the joint reference data and a two-trait GBLUP model, the genomic prediction accuracy in the Chinese Holsteins improves considerably with respect to the traits with moderate-to-high genetic correlations, whereas the improvement in Nordic Holsteins is small. When compared with the single population analysis, using the joint reference population for genomic prediction in younger animals, results in a 2.3 to 8.1 percent improvement in accuracy. Meanwhile, 10 replications of five-fold cross-validation were also implemented in order to evaluate the performance of joint genomic prediction, thereby resulting in a 1.6 to 5.2 percent increase in accuracy. With respect to joint genomic prediction, the bias was found to be quite low. However, for traits with low genetic correlations, the joint reference data do not improve the prediction accuracy substantially for either population. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
4. Genome‐wide association study and genomic prediction for intramuscular fat content in Suhuai pigs using imputed whole‐genome sequencing data.
- Author
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Wang, Binbin, Li, Pinghua, Hou, Liming, Zhou, Wuduo, Tao, Wei, Liu, Chenxi, Liu, Kaiyue, Niu, Peipei, Zhang, Zongping, Li, Qiang, Su, Guosheng, and Huang, Ruihua
- Subjects
GENOME-wide association studies ,PROTEIN kinase C ,LINKAGE disequilibrium ,SWINE ,SINGLE nucleotide polymorphisms - Abstract
Integrating the single‐nucleotide polymorphisms (SNPs) significantly affecting target traits from imputed whole‐genome sequencing (iWGS) data into the genomic prediction (GP) model is an economic, efficient, and feasible strategy to improve prediction accuracy. The objective was to dissect the genetic architecture of intramuscular fat content (IFC) by genome wide association studies (GWAS) and to investigate the accuracy of GP based on pedigree‐based BLUP (PBLUP) model, genomic best linear unbiased prediction (GBLUP) models and Bayesian mixture (BayesMix) models under different strategies. A total of 482 Suhuai pigs were genotyped using an 80 K SNP chip. Furthermore, 30 key samples were selected for resequencing and were used as a reference panel to impute the 80 K chip data to the WGS dataset. The 80 K data and iWGS data were used to perform GWAS and test GP accuracies under different scenarios. GWAS results revealed that there were four major regions affecting IFC. Two important functional candidate genes were found in the two most significant regions, including protein kinase C epsilon (PRKCE) and myosin light chain 2 (MYL2). The results of the predictions showed that the PBLUP model had the lowest reliability (0.096 ± 0.032). The reliability (0.229 ± 0.035) was improved by replacing pedigree information with 80 K chip data. Compared with using 80 K SNPs alone, pruning iWGS SNPs with the R‐squared cutoff of linkage disequilibrium (0.55) led to a slight improvement (0.006), adding significant iWGS SNPs led to an improvement of reliability by 0.050 when using a one‐component GBLUP, a further increase of 0.033 when using a two‐component GBLUP model. For BayesMix models, compared with using 80 K SNPs alone, adding additional significant iWGS SNPs into one‐ or two‐component BayesMix models led to improvements of reliabilities for IFC by 0.040 and 0.089, respectively. Our results may facilitate further identification of causal genes for IFC and may be beneficial for the improvement of IFC in pig breeding programs. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
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