Donghai Fu

Dr. Donghai Fu

orcid

Research Focus & Metrics

Academic Career Length

8Years (2019-Present)

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

Representative Publications

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26 November 2019 in Genes
Verified
Journal Article
Open access
PDF file
XMxiaoming ma
xiaoming ma
CJCongjun Jia
Congjun Jia
MCMin Chu
Min Chu
DFDonghai Fu
Donghai Fu
QLqinhui lei
qinhui lei
CLChunnian Liang
Chunnian Liang
DNA methylation modifications are implicated in many biological processes. As the most common epigenetic mechanism DNA methylation also affects muscle growth and development. The majority of previous studies have focused on different varieties of yak, but little is known about the epigenetic regulation mechanisms in different age groups of animals. The development of muscles in the different stages of yak growth remains unclear. In this study, we selected the longissimus dorsi muscle tissue at three different growth stages of the yak, namely, 90-day-old fetuses (group E), six months old (group M), and three years old (group A). Using RNA-Seq transcriptome sequencing and methyl-RAD whole-genome methylation sequencing technology, changes in gene expression levels and DNA methylation status throughout the genome were investigated during the stages of yak development. Each group was represented by three biological replicates. The intersections of expression patterns of 7694 differentially expressed genes (DEGs) were identified (padj < 0.01, |log2FC| > 1.2) at each of the three developmental periods. Time-series expression profile clustering analysis indicated that the DEGs were significantly arranged into eight clusters which could be divided into two classes (padj < 0.05), class I profiles that were downregulated and class II profiles that were upregulated. Based on this cluster analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that DEGs from class I profiles were significantly (padj < 0.05) enriched in 21 pathways, the most enriched pathway being the Axon guidance signaling pathway. DEGs from the class II profile were significantly enriched in 58 pathways, the pathway most strongly enriched being Metabolic pathway. After establishing the methylation profiles of the whole genomes, and using two groups of comparisons, the three combinations of groups (M-vs.-E, M-vs.-A, A-vs.-E) were found to have 1344, 822, and 420 genes, respectively, that were differentially methylated at CCGG sites and 2282, 3056, and 537 genes, respectively, at CCWGG sites. The two sets of data were integrated and the negative correlations between DEGs and differentially methylated promoters (DMPs) analyzed, which confirmed that TMEM8C, IGF2, CACNA1S and MUSTN1 were methylated in the promoter region and that expression of the modified genes was negatively correlated. Interestingly, these four genes, from what was mentioned above, perform vital roles in yak muscle growth and represent a reference for future genomic and epigenomic studies in muscle development, in addition to enabling marker-assisted selection of growth traits.

17 June 2019 in Genes
Verified
Journal Article
Open access
PDF file
XMxiaoming ma
xiaoming ma
CJCongjun Jia
Congjun Jia
DFDonghai Fu
Donghai Fu
MCMin Chu
Min Chu
XDXuezhi Ding
Xuezhi Ding
PYPing Yan
Ping Yan
Yak (Bos grunniens) is an important domestic animal living in high-altitude plateaus. Due to inadequate disease prevention, each year, the yak industry suffers significant economic losses. The identification of causal genes that affect blood- and immunity-related cells could provide preliminary reference guidelines for the prevention of diseases in the population of yaks. The genome-wide association studies (GWASs) utilizing a single-marker or haplotype method were employed to analyze 15 hematological traits in the genome of 315 unrelated yaks. Single-marker GWASs identified a total of 43 significant SNPs, including 35 suggestive and eight genome-wide significant SNPs, associated with nine traits. Haplotype analysis detected nine significant haplotype blocks, including two genome-wide and seven suggestive blocks, associated with seven traits. The study provides data on the genetic variability of hematological traits in the yak. Five essential genes (GPLD1, EDNRA, APOB, HIST1H1E, and HIST1H2BI) were identified, which affect the HCT, HGB, RBC, PDW, PLT, and RDWSD traits and can serve as candidate genes for regulating hematological traits. The results provide a valuable reference to be used in the analysis of blood properties and immune diseases in the yak.

7 November 2019 in Animals
Verified
Journal Article
Open access
PDF file
DFDonghai Fu
Donghai Fu
XMxiaoming ma
xiaoming ma
CJCongjun Jia
Congjun Jia
MCMin Chu
Min Chu
QLqinhui lei
qinhui lei
CLChunnian Liang
Chunnian Liang
The aim of this study was to explore the possibility of applying GP to important economic traits in the domesticated yak, thus providing theoretical support for its molecular breeding. A reference population was constructed consisting of 354 polled yaks, measuring four growth traits and eight hematological traits related to resistance to disease (involved in immune response and phagocytosis). The Illumina bovine HD 770k chip was used to obtain SNP information of all the individuals. With these genotypes and phenotypes, GBLUP, Bayes B and Bayes Cπ methods were used to predict genomic estimated breeding values (GEBV) and assess prediction capability. The correlation coefficient of the association of GEBV with estimated breeding value (EBV) was used as PA for each trait. The prediction accuracy varied from 0.043 to 0.281 for different traits. Each trait displayed similar PAs when using the three methods. Lymphocyte counts (LYM) exhibited the highest predictive accuracy (0.319) during all GP, while chest girth (CG) provided the lowest predictive accuracy (0.043). Our results showed moderate PA in most traits such as body length (0.212) and hematocrit (0.23). Those traits with lower PA could be improved by using SNP chips designed specifically for yak, a better optimized reference group structure, and more efficient statistical algorithms and tools.