11 results on '"Su, Guosheng"'
Search Results
2. Comparisons of improved genomic predictions generated by different imputation methods for genotyping by sequencing data in livestock populations
- Author
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Wang, Xiao, Su, Guosheng, Hao, Dan, Lund, Mogens Sandø, and Kadarmideen, Haja N.
- Published
- 2020
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3. Improving genomic predictions by correction of genotypes from genotyping by sequencing in livestock populations
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Wang, Xiao, Lund, Mogens Sandø, Ma, Peipei, Janss, Luc, Kadarmideen, Haja N., and Su, Guosheng
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- 2019
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4. 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
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5. Genome‐wide association study and genomic prediction for intramuscular fat content in Suhuai pigs using imputed whole‐genome sequencing data.
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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
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6. 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.
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Gebreyesus, Grum, Lund, Mogens Sandø, Sahana, Goutam, and Su, Guosheng
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SINGLE nucleotide polymorphisms ,NUCLEOTIDE sequencing ,GENOME-wide association studies ,HAPLOTYPES ,DAIRY cattle - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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7. High-Throughput Sequencing With the Preselection of Markers Is a Good Alternative to SNP Chips for Genomic Prediction in Broilers.
- Author
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Liu, Tianfei, Luo, Chenglong, Ma, Jie, Wang, Yan, Shu, Dingming, Su, Guosheng, and Qu, Hao
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SINGLE nucleotide polymorphisms ,GENETIC markers ,BODY weight - Abstract
The choice of a genetic marker genotyping platform is important for genomic prediction in livestock and poultry. High-throughput sequencing can produce more genetic markers, but the genotype quality is lower than that obtained with single nucleotide polymorphism (SNP) chips. The aim of this study was to compare the accuracy of genomic prediction between high-throughput sequencing and SNP chips in broilers. In this study, we developed a new SNP marker screening method, the pre-marker-selection (PMS) method, to determine whether an SNP marker can be used for genomic prediction. We also compared a method which preselection marker based results from genome-wide association studies (GWAS). With the two methods, we analysed body weight at the12
th week (BW) and feed conversion ratio (FCR) in a local broiler population. A total of 395 birds were selected from the F2 generation of the population, and 10X specific-locus amplified fragment sequencing (SLAF-seq) and the Illumina Chicken 60K SNP Beadchip were used for genotyping. The genomic best linear unbiased prediction method (GBLUP) was used to predict the genomic breeding values. The accuracy of genomic prediction was validated by the leave-one-out cross-validation method. Without SNP marker screening, the accuracies of the genomic estimated breeding value (GEBV) of BW and FCR were 0.509 and 0.249, respectively, when using SLAF-seq, and the accuracies were 0.516 and 0.232, respectively, when using the SNP chip. With SNP marker screening by the PMS method, the accuracies of GEBV of the two traits were 0.671 and 0.499, respectively, when using SLAF-seq, and 0.605 and 0.422, respectively, when using the SNP chip. Our SNP marker screening method led to an increase of prediction accuracy by 0.089–0.250. With SNP marker screening by the GWAS method, the accuracies of genomic prediction for the two traits were also improved, but the gains of accuracy were less than the gains with PMS method for all traits. The results from this study indicate that our PMS method can improve the accuracy of GEBV, and that more accurate genomic prediction can be obtained from an increased number of genomic markers when using high-throughput sequencing in local broiler populations. Due to its lower genotyping cost, high-throughput sequencing could be a good alternative to SNP chips for genomic prediction in breeding programmes of local broiler populations. [ABSTRACT FROM AUTHOR]- Published
- 2020
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8. Impact of Relationships between Test and Reference Animals and between Reference Animals on Reliability of Genomic Prediction
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Wu, Xiaoping, Lund, Mogens Sandø, Sun, Dongxiao, Zhang, Qin, and Su, Guosheng
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reliability ,genomic relationship ,genomic prediction - Abstract
This study investigated reliability of genomic prediction in various scenarios with regard to relationship between test and reference animals and between animals within the reference population. Different reference populations were generated from EuroGenomics data and 1288 Nordic Holstein bulls as a common test population. A GBLUP model and a Bayesian mixture model were applied to predict Genomic breeding values for bulls in the test data. Result showed that a closer relationship between test and reference animals led to a higher reliability, while a closer relationship between reference animal resulted in a lower reliability. Therefore, the design of reference population is important for improving the reliability of genomic prediction. With regard to model, the Bayesian mixture model in general led to slightly a higher reliability of genomic prediction than the GBLUP model.
- Published
- 2014
9. Should the markers on X chromosome be used for genomic prediction?
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Su, Guosheng, Guldbrandtsen, Bernt, Aamand, Gert Pedersen, Strandén, I, and Lund, Mogens Sandø
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the X chromosome ,genomic prediction ,genotype imputation - Abstract
This study investigated theaccuracy of imputation from LD (7K) to 54K panel and compared accuracy ofgenomic prediction with or without the X chromosome information, based on data ofNordic Holstein bulls. Beagle and Findhap were used for imputation. Averagedover two imputation datasets, the allele correct rates of imputation usingFindhap were 98.2% for autosomal markers, 89.7% for markers on the pseudoautosomal region of the X chromosome, and 96.4% for X-specific markers. Theallele correct rates were 98.9%, 91.2% and 96.8%, respectively, when usingBeagle. Genomic predictions were carried out for 15 traits based on 54K markerdata, imputed 54K for test animals, and imputed 54K for half of referenceanimals. GBLUP models with or without residual polygenic effect were used forgenomic prediction. For all three data sets, genomic prediction using allmarkers gave slightly higher reliability than prediction excluding the X chromosome.Averaged over 15 traits, the gains in reliability from the X chromosome rangedfrom 0.3% to 0.5% points among the three data sets and models. Using a model with a G-matrix accounting for sex-linkedrelationship appropriately or a model which divided genomic breeding value intoan autosomal component and an X chromosomal component did not led to betterprediction based on the present data where all animals were bulls. A modelincluding polygenic effect did not recover the loss of prediction accuracy dueto exclusion of the X chromosome. It is recommended using markers on the X chromosomefor routine genomic evaluation
- Published
- 2013
10. Use of biological priors enhances understanding of genetic architecture and genomic prediction of complex traits within and between dairy cattle breeds.
- Author
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Lingzhao Fang, Sahana, Goutam, Ma, Peipei, Su, Guosheng, Ying Yu, Shengli Zhang, Lund, Mogens Sandø, and Sørensen, Peter
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GENE ontology ,BOVINE mastitis ,DAIRY cattle breeding ,BIOINFORMATICS ,MILK yield - Abstract
Background: A better understanding of the genetic architecture underlying complex traits (e.g., the distribution of causal variants and their effects) may aid in the genomic prediction. Here, we hypothesized that the genomic variants of complex traits might be enriched in a subset of genomic regions defined by genes grouped on the basis of "Gene Ontology" (GO), and that incorporating this independent biological information into genomic prediction models might improve their predictive ability. Results: Four complex traits (i.e., milk, fat and protein yields, and mastitis) together with imputed sequence variants in Holstein (HOL) and Jersey (JER) cattle were analysed. We first carried out a post-GWAS analysis in a HOL training population to assess the degree of enrichment of the association signals in the gene regions defined by each GO term. We then extended the genomic best linear unbiased prediction model (GBLUP) to a genomic feature BLUP (GFBLUP) model, including an additional genomic effect quantifying the joint effect of a group of variants located in a genomic feature. The GBLUP model using a single random effect assumes that all genomic variants contribute to the genomic relationship equally, whereas GFBLUP attributes different weights to the individual genomic relationships in the prediction equation based on the estimated genomic parameters. Our results demonstrate that the immune-relevant GO terms were more associated with mastitis than milk production, and several biologically meaningful GO terms improved the prediction accuracy with GFBLUP for the four traits, as compared with GBLUP. The improvement of the genomic prediction between breeds (the average increase across the four traits was 0.161) was more apparent than that it was within the HOL (the average increase across the four traits was 0.020). Conclusions: Our genomic feature modelling approaches provide a framework to simultaneously explore the genetic architecture and genomic prediction of complex traits by taking advantage of independent biological knowledge. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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11. Use of a Bayesian model including QTL markers increases prediction reliability when test animals are distant from the reference population.
- Author
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Ma, Peipei, Lund, Mogens S., Aamand, Gert P., and Su, Guosheng
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Relatedness between reference and test animals has an important effect on the reliability of genomic prediction for test animals. Because genomic prediction has been widely applied in practical cattle breeding and bulls have been selected according to genomic breeding value without progeny testing, the sires or grandsires of candidates might not have phenotypic information and might not be in the reference population when the candidates are selected. The objective of this study was to investigate the decreasing trend of the reliability of genomic prediction given distant reference populations, using genomic best linear unbiased prediction (GBLUP) and Bayesian variable selection models with or without including the quantitative trait locus (QTL) markers detected from sequencing data. The data used in this study consisted of 22,242 bulls genotyped using the 54K SNP array from EuroGenomics. Among them, 1,444 Danish bulls born from 2006 to 2010 were selected as test animals. Different reference populations with varying relationships to test animals were created according to pedigree-based relationships. The reference individuals having a relationship with one or more test animals higher than 0.4 (scenario ρ < 0.4), 0.2 (ρ < 0.2), or 0.1 (ρ < 0.1, where ρ = relationship coefficient) were removed from reference sets; these represented the distance between reference and test animals being 2 generations, 3 generations, and 4 generations, respectively. Imputed whole-genome sequencing data of bulls from Denmark were used to conduct a genome-wide association study (GWAS). A small number of significant variants (QTL markers) from the GWAS were added to the array data. To compare the effects of different models, the basic GBLUP model, a Bayesian selection variable model, a GBLUP model with 2 components of genetic effects, and a Bayesian model with pooled array data and QTL markers were used for estimating genomic estimated breeding values (GEBV) of test animals. The reliability of genomic prediction decreased when the test animals were more generations away from the reference population. The reliability of genomic prediction was 0.461 for 1 generation away and 0.396 for 3 generations away, with the same number of individuals in the reference set, using a GBLUP model with chip markers only. The results showed that using the Bayesian method and QTL markers improved the reliability of genomic prediction in all scenarios of relationship between test and reference animals, in a range of 1.3% and 65.1% (4 generations away with only 841 individuals in the reference set). However, most gains were for predictions of milk yield and fat yield. There was little improvement for predictions of protein yield and mastitis, and no improvement for prediction of fertility, except for scenario ρ < 0.1, in which there was a large improvement for predictions of all traits. On the other hand, models including more than 10% polygenic effect decreased prediction reliability when the relationship between test and reference animals was distant. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
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