Single-step genomic best linear unbiased prediction(ss GBLUP) is now intensively investigated and widely used in livestock breeding due to its beneficial feature of combining information from both genotyped and ungeno...Single-step genomic best linear unbiased prediction(ss GBLUP) is now intensively investigated and widely used in livestock breeding due to its beneficial feature of combining information from both genotyped and ungenotyped individuals in the single model. With the increasing accessibility of whole-genome sequence(WGS) data at the population level, more attention is being paid to the usage of WGS data in ss GBLUP. The predictive ability of ss GBLUP using WGS data might be improved by incorporating biological knowledge from public databases. Thus, we extended ss GBLUP, incorporated genomic annotation information into the model, and evaluated them using a yellow-feathered chicken population as the examples. The chicken population consisted of 1 338 birds with 23 traits, where imputed WGS data including 5 127 612 single nucleotide polymorphisms(SNPs) are available for 895 birds. Considering different combinations of annotation information and models, original ss GBLUP, haplotype-based ss GHBLUP, and four extended ss GBLUP incorporating genomic annotation models were evaluated. Based on the genomic annotation(GRCg6a) of chickens, 3 155 524 and 94 837 SNPs were mapped to genic and exonic regions, respectively. Extended ss GBLUP using genic/exonic SNPs outperformed other models with respect to predictive ability in 15 out of 23 traits, and their advantages ranged from 2.5 to 6.1% compared with original ss GBLUP. In addition, to further enhance the performance of genomic prediction with imputed WGS data, we investigated the genotyping strategies of reference population on ss GBLUP in the chicken population. Comparing two strategies of individual selection for genotyping in the reference population, the strategy of evenly selection by family(SBF) performed slightly better than random selection in most situations. Overall, we extended genomic prediction models that can comprehensively utilize WGS data and genomic annotation information in the framework of ss GBLUP, and validated the idea that properly handling the genomic annotation information and WGS data increased the predictive ability of ss GBLUP. Moreover, while using WGS data, the genotyping strategy of maximizing the expected genetic relationship between the reference and candidate population could further improve the predictive ability of ss GBLUP. The results from this study shed light on the comprehensive usage of genomic annotation information in WGS-based single-step genomic prediction.展开更多
To explore the genetic features and signatures of selection in indigenous pigs from South China and Duroc pigs,259 pigs from six populations were genotyped using single-nucleotide polymorphism(SNP)BeadChips.Principal ...To explore the genetic features and signatures of selection in indigenous pigs from South China and Duroc pigs,259 pigs from six populations were genotyped using single-nucleotide polymorphism(SNP)BeadChips.Principal component analysis(PCA),effective population size(Ne),linkage disequilibrium(LD),and signatures of selection were explored and investigated among the six pig populations.The results showed the Ne of five South China indigenous pig populations has been decreasing rapidly since 100 generations ago.The LD between pairwise SNP distance at 100 kb ranged from 0.16 to 0.20 for the five indigenous pig populations,while it was 0.32 for the Duroc population.However,the LD of all six pig populations showed the opposite order at long distances(>5 Mb).Furthermore,15 potential signatures of selection associated with meat quality and age at puberty were exclusively detected in South China indigenous pigs,while eight potential signatures of selection associated with growth traits were detected in Duroc pigs.Our work provides valuable insights for the utilization and conservation of South China indigenous pigs.展开更多
基金supported by the National Natural Science Foundation of China(32022078)the Local Innovative and Research Teams Project of Guangdong Province,China(2019BT02N630)the support from the National Supercomputer Center in Guangzhou,China。
文摘Single-step genomic best linear unbiased prediction(ss GBLUP) is now intensively investigated and widely used in livestock breeding due to its beneficial feature of combining information from both genotyped and ungenotyped individuals in the single model. With the increasing accessibility of whole-genome sequence(WGS) data at the population level, more attention is being paid to the usage of WGS data in ss GBLUP. The predictive ability of ss GBLUP using WGS data might be improved by incorporating biological knowledge from public databases. Thus, we extended ss GBLUP, incorporated genomic annotation information into the model, and evaluated them using a yellow-feathered chicken population as the examples. The chicken population consisted of 1 338 birds with 23 traits, where imputed WGS data including 5 127 612 single nucleotide polymorphisms(SNPs) are available for 895 birds. Considering different combinations of annotation information and models, original ss GBLUP, haplotype-based ss GHBLUP, and four extended ss GBLUP incorporating genomic annotation models were evaluated. Based on the genomic annotation(GRCg6a) of chickens, 3 155 524 and 94 837 SNPs were mapped to genic and exonic regions, respectively. Extended ss GBLUP using genic/exonic SNPs outperformed other models with respect to predictive ability in 15 out of 23 traits, and their advantages ranged from 2.5 to 6.1% compared with original ss GBLUP. In addition, to further enhance the performance of genomic prediction with imputed WGS data, we investigated the genotyping strategies of reference population on ss GBLUP in the chicken population. Comparing two strategies of individual selection for genotyping in the reference population, the strategy of evenly selection by family(SBF) performed slightly better than random selection in most situations. Overall, we extended genomic prediction models that can comprehensively utilize WGS data and genomic annotation information in the framework of ss GBLUP, and validated the idea that properly handling the genomic annotation information and WGS data increased the predictive ability of ss GBLUP. Moreover, while using WGS data, the genotyping strategy of maximizing the expected genetic relationship between the reference and candidate population could further improve the predictive ability of ss GBLUP. The results from this study shed light on the comprehensive usage of genomic annotation information in WGS-based single-step genomic prediction.
基金the National Key R&D Program of China(2018YFD0501200)the earmarked fund for China Agriculture Research System(CARS-35)the Key R&D Program of Guangdong Province,China(2018B020203003)。
文摘To explore the genetic features and signatures of selection in indigenous pigs from South China and Duroc pigs,259 pigs from six populations were genotyped using single-nucleotide polymorphism(SNP)BeadChips.Principal component analysis(PCA),effective population size(Ne),linkage disequilibrium(LD),and signatures of selection were explored and investigated among the six pig populations.The results showed the Ne of five South China indigenous pig populations has been decreasing rapidly since 100 generations ago.The LD between pairwise SNP distance at 100 kb ranged from 0.16 to 0.20 for the five indigenous pig populations,while it was 0.32 for the Duroc population.However,the LD of all six pig populations showed the opposite order at long distances(>5 Mb).Furthermore,15 potential signatures of selection associated with meat quality and age at puberty were exclusively detected in South China indigenous pigs,while eight potential signatures of selection associated with growth traits were detected in Duroc pigs.Our work provides valuable insights for the utilization and conservation of South China indigenous pigs.
