Hongxia Hu

Dr. Hongxia Hu


Research Focus & Metrics

Academic Career Length

41Years (1986-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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9 June 2021 in Evolutionary Applications
Verified
Journal Article
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HSHailiang Song
Hailiang Song
HHHongxia Hu
Hongxia Hu
Genomic selection (GS) has great potential to increase genetic gain in aquaculture breeding; however, its implementation is hindered owing to high genotyping cost and the large number of individuals to genotype. This study investigated the efficiency of genomic prediction in four aquaculture species. In total, 749 to 1481 individuals with records for disease resistance and growth traits were genotyped using SNP arrays ranging from 12K to 40K. We compared the prediction accuracies and bias of breeding values obtained from BLUP, genomic BLUP (GBLUP), Bayesian mixture (BayesR), weighted GBLUP (WGBLUP), and genomic feature BLUP (GFBLUP). For GFBLUP, the genomic feature matrix was constructed based on prior information from genome‐wide association studies. Fivefold cross‐validation was performed with 20 replicates. Moreover, to reduce the cost of GS, we reduced the SNP density based on linkage disequilibrium as well as the reference population size. The results showed that the methods with marker information produced more accurate predictions than the pedigree‐based BLUP method. For the genomic model, BayesR performed prediction with a similar or higher accuracy compared to GBLUP. For the four traits, WGBLUP yielded an average of 1.5% higher accuracy than GBLUP. However, the accuracy of genomic prediction decreased by an average of 6.2% for GFBLUP compared to GBLUP. When the density of SNP panels was reduced to 3K, which was sufficient to obtain accuracies similar to those using the whole dataset in the four species, the cost of GS was estimated to be 50% lower than that of genotyping all animals with high‐density panels. In addition, when the reference population size was reduced by 10%, evenly from full‐sib family, the accuracy of genomic prediction was almost unchanged, and the cost reduction was 8% in the four populations. Our results have important implications for translating the benefits of GS to most aquaculture species.

29 September 2017 in Fish Physiology and Biochemistry
Verified
Journal Article
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WWWei Wang
Wei Wang
HZHua Zhu
Hua Zhu
YDYing Dong
Ying Dong
ZTZhaoHui Tian
ZhaoHui Tian
TDTian Dong
Tian Dong
Cuijuan NiuCuijuan Niu
Cuijuan Niu

6 April 2018 in Frontiers in Genetics
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JCJi Chen
Ji Chen
WWWei Wang
Wei Wang
ZTZhaohui Tian
Zhaohui Tian
YDYing Dong
Ying Dong
TDTian Dong
Tian Dong
HWHu Wei
Hu Wei
The sturgeon (Acipenseriformes) is an important farmed species because of its economical value. However, neither gene transfer nor gene editing techniques have been established in sturgeon for molecular breeding and gene functional study until now. In this study, we accomplished gene transfer and gene editing in sterlet (Acipenser ruthenus), which has the shortest sexual maturation period of sturgeons. The plasmid encoding enhanced green fluorescent protein (EGFP) was transferred into the embryos of sterlet at injection concentration of 100 ng/μL, under which condition high survival rate and gene transfer rate could be achieved. Subsequently, exogenous EGFP was efficiently disrupted by transcription activator-like effector nucleases (TALENs) or clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 nuclease/guide RNA (gRNA), with injection concentrations of 300 ng/μL TALENs, or 100 ng/μL Cas9 nuclease and 30 ng/μL gRNA, respectively, under which condition high survival rate and gene mutation rate could be achieved. Finally, the endogenous gene no tail in sterlet was successfully mutated by Cas9 nuclease/gRNA. We observed the CRISPR-induced no tail mutation, at a high efficiency with the mutant P0 embryos displaying the expected phenotype of bent spine and twisted tail.

25 March 2020 in Frontiers in Endocrinology
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WWWei Wang
Wei Wang
HZHua Zhu
Hua Zhu
ZTZhaoHui Tian
ZhaoHui Tian
ASAi Sun
Ai Sun
YDYing Dong
Ying Dong
HHHongxia Hu
Hongxia Hu
11-ketotestosterone (11-KT) is a non-aromatizable and the most potent androgen in a few teleost. It has been reported that 11-KT in serum had a high concentration and increased sharply before the period of yolk deposition in females of few fishes. The aim of this study was to analyze the role of 11-KT both in vivo and in vitro on ovarian development, related gene expression levels, Vitellogenin (Vtg) synthesis, and serum sex steroid concentrations in previtellogenic cultured sterlet (Acipenser ruthenus). Silastic strips embedded with 11-KT (5 or 25 mg/kg) were implanted in vivo for 30 days. Ovarian masculinization or sex reversal was not observed. Histological analysis showed that 11-KT promoted sterlet ovarian development in a dose-dependent manner. Vtg and testosterone (T) increased significantly, while 17β-estradiol (E2) decreased with no significant difference among groups. The expression of genes androgen receptor (ar), vtg and lipoprotein lipase (lpl) were significantly increased in liver. However, 11-KT had no effect on the expression of foxl2 and cyp19a1 in ovary. In vitro, after incubation with 11-KT (10 and 100 μM) for 5 days, both T and E2 concentrations increased in both hepatic explants and ovarian explants culture medium; the concentration of Vtg also increased in hepatic explants culture medium. The expression of ar, era, vtg, and lpl increased significantly in hepatic explants. However, only the expression of era significantly increased in cultured ovarian explants. Altogether, these results suggest that 11-KT induced ovarian development, as well as Vtg and lipid synthesis, and could be an important factor facilitating the initiation of Vtg synthesis in the liver of the previtellogenic sterlet.

1 August 2019 in Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology
Verified
Journal Article
Closed access
ASAi Sun
Ai Sun
HZHua Zhu
Hua Zhu
XWXiaowen Wang
Xiaowen Wang
QHQiaomu Hu
Qiaomu Hu
ZTZhaohui Tian
Zhaohui Tian
HHHongxia Hu
Hongxia Hu