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Jinlong He

Researcher at South China Agricultural University

Publications -  7
Citations -  321

Jinlong He 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 5, co-authored 7 publications receiving 253 citations.

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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

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.
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Profiling the genome-wide DNA methylation pattern of porcine ovaries using reduced representation bisulfite sequencing

TL;DR: The genome-wide profile of DNA methylation in porcine ovaries is investigated at single-base resolution using reduced representation bisulfite sequencing and found hypermethylation frequently occurred in regions with low gene abundance, while hypomethylation in areas with high gene abundance.
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Improving accuracy of genomic prediction by genetic architecture based priors in a Bayesian model

TL;DR: Knowledge of traits’ genetic architectures can be integrated into practices of genomic prediction by assigning locus-specific priors to markers, which will help Bayesian approaches perform better in variable selection and marker effects shrinkage.
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Whole genomic prediction of growth and carcass traits in a Chinese quality chicken population.

TL;DR: The results showed the potential of implementing genomic selection in small breeding herds by increasing the selection accuracy by 20% or more depending on the prediction model used, the trait under consideration, and the genetic connectedness between the training and validation individuals.