Improving multi-population genomic prediction accuracy using multi-trait GBLUP models which incorporate global or local genetic correlation information.

In the application of genomic prediction, a situation often faced is that there are multiple populations in which genomic prediction (GP) need to be conducted. A common way to handle the multi-population GP is simply to combine the multiple populations into a single population. However, since these populations may be subject to different environments, there may exist genotype-environment interactions which may affect the accuracy of genomic prediction. In this study, we demonstrated that multi-trait genomic best linear unbiased prediction (MTGBLUP) can be used for multi-population genomic prediction, whereby the performances of a trait in different populations are regarded as different traits, and thus multi-population prediction is regarded as multi-trait prediction by employing the between-population genetic correlation. Using real datasets, we proved that MTGBLUP outperformed the conventional multi-population model that simply combines different populations together. We further proposed that MTGBLUP can be improved by partitioning the global between-population genetic correlation into local genetic correlations (LGC). We suggested two LGC models, LGC-model-1 and LGC-model-2, which partition the genome into regions with and without significant LGC (LGC-model-1) or regions with and without strong LGC (LGC-model-2). In analysis of real datasets, we demonstrated that the LGC models could increase universally the prediction accuracy and the relative improvement over MTGBLUP reached up to 163.86% (25.64% on average).

Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data.

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 integrate 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 generally small. In view of the cost of genotyping, using chip data is also a good option for breed identification.

Longitudinal genome-wide association studies of milk production traits in Holstein cattle using whole-genome sequence data imputed from medium-density chip data.

Longitudinal traits, such as milk production traits in dairy cattle, are featured by having phenotypic values at multiple time points, which change dynamically over time. In this study, we first imputed SNP chip (50-100K) data to whole-genome sequence (WGS) data in a Chinese Holstein population consisting of 6,470 cows. The imputation accuracies were 0.88 to 0.97 on average after quality control. We then performed longitudinal GWAS in this population based on a random regression test-day model using the imputed WGS data. The longitudinal GWAS revealed 16, 39, and 75 quantitative trait locus regions associated with milk yield, fat percentage, and protein percentage, respectively. We estimated the 95% confidence intervals (CI) for these quantitative trait locus regions using the logP drop method and identified 581 genes involved in these CI. Further, we focused on the CI that covered or overlapped with only 1 gene or the CI that contained an extremely significant top SNP. Twenty-eight candidate genes were identified in these CI. Most of them have been reported in the literature to be associated with milk production traits, such as DGAT1, HSF1, MGST1, GHR, ABCG2, ADCK5, and CSN1S1. Among the unreported novel genes, some also showed good potential as candidate genes, such as CCSER1, CUX2, SNTB1, RGS7, OSR2, and STK3, and are worth being further investigated. Our study provided not only new insights into the candidate genes for milk production traits, but also a general framework for longitudinal GWAS based on random regression test-day model using WGS data.

Why do Chinese enterprises make imitative innovation?-An empirical explanation based on government subsidies.

The previous literature analyzed the widespread imitative innovation of Chinese enterprises from various perspectives, including enterprises' rational choice of cost-gain, property rights system, human capital and policy environment. However, this paper provides a brand-new perspective on government subsidies for the reasons behind the imitative innovation of enterprises. According to the statistics from Chinese enterprise-labor matching, we found that government subsidies stimulated enterprises to make "imitative innovation" through patent purchase rather than independent R&D. Government subsidies were used for low-risk "imitative innovation" because of enterprises' rent-seeking behavior, low R&D ability and the review of government subsidy projects. Based on the above conclusions, this paper suggests that the government should reduce or withdraw its intervention in enterprise innovation and implement the post-subsidy and post-evaluation mechanism for government-subsidized programs.

Supply Chain Management Practices Influence Supply Chain Performance With Mediation Role of Innovation and Moderation Role of Top Management Support.

The study focuses on supply chain management practices, innovation, top management commitment, and supply chain performance at companies. The study's main objective is to investigate the association between supply chain management practices and supply chain performance and the intervening effect of innovation, the interaction effect of top management commitment. In this study, a simple random sampling technique and the sample size selected with G* power software (N = 208). The readymade questionnaire was used to collect data from National Logistic Corporation (NLC), Food and Beverage Companies Groups (FMCG) at China. The data analyzed through Smart-PLS (SEM → small and medium enterprises) and SPSS software. Meanwhile, innovational significant and positively mediated the relationship between supply chain management five practices and organizational performance. The findings of this study will help managers of SMEs enhance their performance. The results showed that SCMP directly and significantly affected supply chain performance, and customer relationship management was insignificant with supply chain performance. Supplier and customer relationship management both have a significant impact on innovation. In addition, innovation is considered a significant positive predictor for supply chain performance with the intervening approach. But top management commitment proved insignificant for customer relationship management and supply chain performance. The study further concluded that supply chain management practices would not be productive for supply chain performance if the top management does not apply innovative technologies in the organizations.

Assessment of the performance of different imputation methods for low-coverage sequencing in Holstein cattle.

Low-coverage sequencing (LCS) followed by imputation has been proposed as a cost-effective genotyping approach for obtaining genotypes of whole-genome variants. Imputation performance is essential for the effectiveness of this approach. Several imputation methods have been proposed and successfully applied in genomic studies in human and other species. However, there are few reports on the performance of these methods in livestock. Here, we evaluated a variety of imputation methods, including Beagle v4.1, GeneImp v1.3, GLIMPSE v1.1.0, QUILT v1.0.0, Reveel, and STITCH v1.6.5, with varying sequencing depth, sample size, and reference panel size using LCS data of Holstein cattle. We found that all of these methods, except Reveel, performed well in most cases with an imputation accuracy over 0.9; on the whole, GLIMPSE, QUILT, and STITCH performed better than the other methods. For species with no reference panel available, STITCH followed by Beagle would be an optimal strategy, whereas for species with reference panel available, QUILT would be the method of choice. Overall, this study illustrated the promising potential of LCS for genomic analysis in livestock.

Towards a Cost-Effective Implementation of Genomic Prediction Based on Low Coverage Whole Genome Sequencing in Dezhou Donkey.

Low-coverage whole genome sequencing is a low-cost genotyping technology. Combined with genotype imputation approaches, it is likely to become a critical component of cost-effective genomic selection programs in agricultural livestock. Here, we used the low-coverage sequence data of 617 Dezhou donkeys to investigate the performance of genotype imputation for low-coverage whole genome sequence data and genomic prediction based on the imputed genotype data. The specific aims were as follows: 1) to measure the accuracy of genotype imputation under different sequencing depths, sample sizes, minor allele frequency (MAF), and imputation pipelines and 2) to assess the accuracy of genomic prediction under different marker densities derived from the imputed sequence data, different strategies for constructing the genomic relationship matrixes, and single-vs. multi-trait models. We found that a high imputation accuracy (>0.95) can be achieved for sequence data with a sequencing depth as low as 1x and the number of sequenced individuals ≥400. For genomic prediction, the best performance was obtained by using a marker density of 410K and a G matrix constructed using expected marker dosages. Multi-trait genomic best linear unbiased prediction (GBLUP) performed better than single-trait GBLUP. Our study demonstrates that low-coverage whole genome sequencing would be a cost-effective approach for genomic prediction in Dezhou donkey.

Characterization of the chloroplast genome of the Osmanthus didymopetalus.

In this study, we assembled the chloroplast genome of Osmanthus didymopetalus (Oleaceae), a rare evergreen tree native to Hainan, China. The genome of O. didymopetalus was 155,155 bp in length and contained a pair of inverted repeats (IR, 25,697-25,704 bp) regions, which were separated by the small single copy (SSC, 17,591 bp) and the large single copy (LSC, 86,225 bp) regions. The cp genome encoded 133 genes including 88 protein-coding genes, 37 tRNA genes, and eight rRNA ribosomal genes. The overall GC content of O. didymopetalus chloroplast genome is 37.8%. Phylogenetic results showed that O. didymopetalus was more closely to O. yunnanensis, O. fragrans and O. insularis. This study will be beneficial for the evolutionary study and phylogenetic reconstruction of Osmanthus.