N
Ning Gao
Researcher at South China Agricultural University
Publications - 27
Citations - 662
Ning Gao is an academic researcher from South China Agricultural University. The author has contributed to research in topics: Population & Genetic architecture. The author has an hindex of 10, co-authored 25 publications receiving 442 citations. Previous affiliations of Ning Gao include Guangzhou Higher Education Mega Center & University of Göttingen.
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Journal ArticleDOI
Improving the Accuracy of Whole Genome Prediction for Complex Traits Using the Results of Genome Wide Association Studies
TL;DR: This is the first study incorporating public GWAS results formally into the standard GBLUP model and it is thought that the BLUP|GA approach deserves further investigations in animal breeding, plant breeding as well as human genetics.
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Accuracy of Whole-Genome Prediction Using a Genetic Architecture-Enhanced Variance-Covariance Matrix
Zhe Zhang,Zhe Zhang,Malena Erbe,Jinlong He,Ulrike Ober,Ning Gao,Hao Zhang,Henner Simianer,Jiaqi Li +8 more
TL;DR: Predictive ability and difference of accuracies for BLUP|GA and GBLUP significantly correlate with the distance between the T and G matrices, which is mainly due to the increased similarity between the trait-specific relationship matrix (T matrix) and the genetic relationship matrix at unobserved causal loci.
Journal ArticleDOI
Genomic prediction with epistasis models: on the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE)
Johannes W. R. Martini,Ning Gao,Ning Gao,Diercles F. Cardoso,Diercles F. Cardoso,Valentin Wimmer,Malena Erbe,Rodolfo Juan Carlos Cantet,Henner Simianer +8 more
TL;DR: It is demonstrated that the coding-dependent performance of EGBLUP offers the possibility to incorporate prior experimental information into the prediction method by adapting the coding to already available phenotypic records on other traits, and CE can be a valuable alternative since it does not possess the undesired theoretical properties of E GBLUP.
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Integrating Gene Expression Data Into Genomic Prediction.
TL;DR: The utility of transcriptome data for phenotype prediction was tested with 185 inbred lines of Drosophila melanogaster for nine traits in two sexes and the results suggest that accounting for transcriptomeData has the potential to improve genomic predictions if transcriptome Data can be included on a larger scale.
Journal ArticleDOI
Imputation from SNP chip to sequence: a case study in a Chinese indigenous chicken population
Shaopan Ye,Xiaolong Yuan,Xiran Lin,Ning Gao,Yuanyu Luo,Zanmou Chen,Jiaqi Li,Xiquan Zhang,Zhe Zhang +8 more
TL;DR: This work comprehensively investigated the impacts of several key factors on genotype imputation and found that with a fixed sequencing cost, the optimal imputation enhance the performance of WGP and GWAS.