Conservation programs require rigorous evaluation to ensure the preservation of genetic diversity and viability of conservation populations. In this study, we conducted a comparative analysis of two indigenous Chinese...Conservation programs require rigorous evaluation to ensure the preservation of genetic diversity and viability of conservation populations. In this study, we conducted a comparative analysis of two indigenous Chinese chicken breeds, Gushi and Xichuan black-bone, using whole-genome SNPs to understand their genetic diversity, track changes over time and population structure. The breeds were divided into five conservation populations(GS1, 2010, ex-situ;GS2, 2019, ex-situ;GS3, 2019, in-situ;XB1, 2010, in-situ;and XB2, 2019, in-situ) based on conservation methods and generations. The genetic diversity indices of three conservation populations of Gushi chicken showed consistent trends, with the GS3 population under in-situ strategy having the highest diversity and GS2 under ex-situ strategy having the lowest. The degree of inbreeding of GS2 was higher than that of GS1 and GS3. Conserved populations of Xichuan black-bone chicken showed no obvious changes in genetic diversity between XB1 and XB2. In terms of population structure, the GS3 population were stratified relative to GS1 and GS2. According to the conservation priority, GS3 had the highest contribution to the total gene and allelic diversity in GS breed, whereas the contribution of XB1 and XB2 were similar. We also observed that the genetic diversity of GS2 was lower than GS3, which were from the same generation but under different conservation programs(in-situ and ex-situ). While XB1 and XB2 had similar levels of genetic diversity. Overall, our findings suggested that the conservation programs performed in ex-situ could slow down the occurrence of inbreeding events, but could not entirely prevent the loss of genetic diversity when the conserved population size was small, while in-situ conservation populations with large population size could maintain a relative high level of genetic diversity.展开更多
Background Breed identification is useful in a variety of biological contexts.Breed identification usually involves two stages,i.e.,detection of breed-informative SNPs and breed assignment.For both stages,there are se...Background Breed identification is useful in a variety of biological contexts.Breed identification usually involves two stages,i.e.,detection of breed-informative SNPs and breed assignment.For both stages,there are several methods proposed.However,what is the optimal combination of these methods remain unclear.In this study,using the whole genome sequence data available for 13 cattle breeds from Run 8 of the 1,000 Bull Genomes Project,we compared the combinations of three methods(Delta,FST,and In)for breed-informative SNP detection and five machine learning methods(KNN,SVM,RF,NB,and ANN)for breed assignment with respect to different reference population sizes and difference numbers of most breed-informative SNPs.In addition,we evaluated the accuracy of breed identification using SNP chip data of different densities.Results We found that all combinations performed quite well with identification accuracies over 95%in all scenarios.However,there was no combination which performed the best and robust across all scenarios.We proposed to inte-grate the three breed-informative detection methods,named DFI,and integrate the three machine learning methods,KNN,SVM,and RF,named KSR.We found that the combination of these two integrated methods outperformed the other combinations with accuracies over 99%in most cases and was very robust in all scenarios.The accuracies from using SNP chip data were only slightly lower than that from using sequence data in most cases.Conclusions The current study showed that the combination of DFI and KSR was the optimal strategy.Using sequence data resulted in higher accuracies than using chip data in most cases.However,the differences were gener-ally small.In view of the cost of genotyping,using chip data is also a good option for breed identification.展开更多
To extend the contemporary understanding into the grain yield heterosis of wheat, the current study investigated the contribution of deleterious alleles in shaping mid-parent heterosis(MPH). These alleles occur at low...To extend the contemporary understanding into the grain yield heterosis of wheat, the current study investigated the contribution of deleterious alleles in shaping mid-parent heterosis(MPH). These alleles occur at low frequency in the genome and are often missed by automated genotyping platforms like SNP arrays. The deleterious alleles herein were detected using a quantitative measurement of evolutionary conservation based on the phylogeny of wheat and investigations were made to:(1) assess the benefit of including deleterious alleles into MPH prediction models and(2) understand the genetic underpinnings of deleterious SNPs for grain yield MPH using contrasting crosses viz. elite × elite(Exp. 1) and elite × plant genetic resources(PGR;Exp. 2). In our study, we found a lower allele frequency of moderately deleterious alleles in elites compared to PGRs. This highlights the role of purifying selection for the development of elite wheat cultivars. It was shown that deleterious alleles are informative for MPH prediction models: modelling their additive-by-additive effects in Exp. 1 and dominance as well as associated digenic epistatic effects in Exp. 2 significantly boosts prediction accuracies of MPH. Furthermore,heterotic-quantitative trait loci's underlying MPH was investigated and their properties were contrasted in the two crosses. Conclusively, it was proposed that incomplete dominance of deleterious alleles contributes to grain yield heterosis in elite crosses(Exp. 1).展开更多
基金supported by the Key Research Project of the Shennong Laboratory,Henan Province,China(SN012022-05)the National Natural Science Foundation of China(32272866)+1 种基金the Young Elite Scientists Sponsorship Program by CAST(2021QNRC001)the Starting Foundation for Outstanding Young Scientists of Henan Agricultural University,China(30500664&30501280)。
文摘Conservation programs require rigorous evaluation to ensure the preservation of genetic diversity and viability of conservation populations. In this study, we conducted a comparative analysis of two indigenous Chinese chicken breeds, Gushi and Xichuan black-bone, using whole-genome SNPs to understand their genetic diversity, track changes over time and population structure. The breeds were divided into five conservation populations(GS1, 2010, ex-situ;GS2, 2019, ex-situ;GS3, 2019, in-situ;XB1, 2010, in-situ;and XB2, 2019, in-situ) based on conservation methods and generations. The genetic diversity indices of three conservation populations of Gushi chicken showed consistent trends, with the GS3 population under in-situ strategy having the highest diversity and GS2 under ex-situ strategy having the lowest. The degree of inbreeding of GS2 was higher than that of GS1 and GS3. Conserved populations of Xichuan black-bone chicken showed no obvious changes in genetic diversity between XB1 and XB2. In terms of population structure, the GS3 population were stratified relative to GS1 and GS2. According to the conservation priority, GS3 had the highest contribution to the total gene and allelic diversity in GS breed, whereas the contribution of XB1 and XB2 were similar. We also observed that the genetic diversity of GS2 was lower than GS3, which were from the same generation but under different conservation programs(in-situ and ex-situ). While XB1 and XB2 had similar levels of genetic diversity. Overall, our findings suggested that the conservation programs performed in ex-situ could slow down the occurrence of inbreeding events, but could not entirely prevent the loss of genetic diversity when the conserved population size was small, while in-situ conservation populations with large population size could maintain a relative high level of genetic diversity.
基金funded by National Key Research and Development Program of China(2021YFD1200404)the Yangzhou University Interdisciplinary Research Foundation for Animal Science Discipline of Targeted Support(yzuxk202016)the Project of Genetic Improvement for Agricultural Species(Dairy Cattle)of Shandong Province(2019LZGC011).
文摘Background Breed identification is useful in a variety of biological contexts.Breed identification usually involves two stages,i.e.,detection of breed-informative SNPs and breed assignment.For both stages,there are several methods proposed.However,what is the optimal combination of these methods remain unclear.In this study,using the whole genome sequence data available for 13 cattle breeds from Run 8 of the 1,000 Bull Genomes Project,we compared the combinations of three methods(Delta,FST,and In)for breed-informative SNP detection and five machine learning methods(KNN,SVM,RF,NB,and ANN)for breed assignment with respect to different reference population sizes and difference numbers of most breed-informative SNPs.In addition,we evaluated the accuracy of breed identification using SNP chip data of different densities.Results We found that all combinations performed quite well with identification accuracies over 95%in all scenarios.However,there was no combination which performed the best and robust across all scenarios.We proposed to inte-grate the three breed-informative detection methods,named DFI,and integrate the three machine learning methods,KNN,SVM,and RF,named KSR.We found that the combination of these two integrated methods outperformed the other combinations with accuracies over 99%in most cases and was very robust in all scenarios.The accuracies from using SNP chip data were only slightly lower than that from using sequence data in most cases.Conclusions The current study showed that the combination of DFI and KSR was the optimal strategy.Using sequence data resulted in higher accuracies than using chip data in most cases.However,the differences were gener-ally small.In view of the cost of genotyping,using chip data is also a good option for breed identification.
基金supported by the German Federal Ministry of Food and Agriculture (FKZ2818408B18)the Federal Ministry of Education and Research of Germany (FKZ031B0184A, B)the China Scholarship Council (201906350045)。
文摘To extend the contemporary understanding into the grain yield heterosis of wheat, the current study investigated the contribution of deleterious alleles in shaping mid-parent heterosis(MPH). These alleles occur at low frequency in the genome and are often missed by automated genotyping platforms like SNP arrays. The deleterious alleles herein were detected using a quantitative measurement of evolutionary conservation based on the phylogeny of wheat and investigations were made to:(1) assess the benefit of including deleterious alleles into MPH prediction models and(2) understand the genetic underpinnings of deleterious SNPs for grain yield MPH using contrasting crosses viz. elite × elite(Exp. 1) and elite × plant genetic resources(PGR;Exp. 2). In our study, we found a lower allele frequency of moderately deleterious alleles in elites compared to PGRs. This highlights the role of purifying selection for the development of elite wheat cultivars. It was shown that deleterious alleles are informative for MPH prediction models: modelling their additive-by-additive effects in Exp. 1 and dominance as well as associated digenic epistatic effects in Exp. 2 significantly boosts prediction accuracies of MPH. Furthermore,heterotic-quantitative trait loci's underlying MPH was investigated and their properties were contrasted in the two crosses. Conclusively, it was proposed that incomplete dominance of deleterious alleles contributes to grain yield heterosis in elite crosses(Exp. 1).
