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Jie Kong

Publications and source records attributed to Jie Kong.

2 recordsLinked to original sources
PubMed2026

Genome-Wide Characterization of the ZIP Transporter Family in Sea Island Cotton (Gossypium barbadense L.) and Expression Profiling Under Heavy Metal and Pathogen Stresses.

G. barbadense represents an indispensable germplasm resource for high-quality textile fiber and disease resistance; nevertheless, systematic information regarding its ZRT/IRT-like protein (ZIP) gene family remains limited. Here, a total of 46 GbZIP genes were identified across the G. barbadense genome. Comprehensive bioinformatic investigations revealed uneven chromosomal distribution and confirmed that segmental/whole-genome duplications, supplemented by localized tandem duplications, drove family expansion. Members clustered within the same phylogenetic clades shared conserved motif organization and gene architecture, while promoter regions harbored abundant cis-acting elements associated with phytohormone and stress signaling. Transcriptome profiling indicated distinct expression patterns across vegetative/reproductive tissues, fiber and ovule developmental stages, and diverse abiotic stress conditions (cold, hot, drought, and salt). Quantitative Real-Time PCR (qRT-PCR) further validated that several GbZIP candidates exhibited temporal expression variations upon exposure to cadmium toxicity, V. dahliae infection, and combined Cd-V. dahliae stress. Specifically, GbZIP13, GbZIP18, GbZIP27, and GbZIP36 displayed prominent broad-spectrum responses to all three stress conditions, whereas GbZIP16, GbZIP29, and GbZIP30 showed stress-specific regulatory divergence. Overall, this study aims to systematically analyze the evolutionary characteristics and expression patterns of the GbZIP family, and to specifically evaluate the response differences under Cd stress, V. dahliae stress, and combined stress, in order to identify potential key candidate genes.

Gossypium barbadense↗
PubMed2026

Pedigree-assisted genotype imputation enables cost-effective genomic prediction in Penaeus vannamei.

Genomic selection in Penaeus vannamei has long been constrained by the high cost of dense genotyping. To address this limitation, we evaluated genotype imputation from a low-density 1 K panel to a medium-density 55 K panel of the "Yellow Sea Array No. 1" and examined its impact on genomic prediction for harvest body weight in P. vannamei. A four-generation pedigree including 30 great-grandparents, 39 grandparents, 100 parents, and 608 offspring was genotyped using the 55 K panel. A two-step experimental design was implemented to (i) assess the performance of different imputation algorithms under reference population scenarios with varying proportions of siblings, and (ii) compare six alternative reference population structures incorporating parents, ancestors, and siblings. Genotype imputation using the pedigree-based method FImpute v3.0 consistently achieved higher accuracy than the population-based method Beagle v5.5. Using this pedigree-assisted approach, imputation accuracy increased from 0.73 when only parental genotypes were used to 0.84 with the inclusion of 10% siblings, and subsequently plateaued at 0.87-0.90 when sibling representation reached 20%. Across the six reference population structures, imputation accuracy was primarily driven by the availability of parental genotypes, ranging from 0.50 to 0.56 in the absence of parents to 0.88-0.89 when both parents and ancestral generations were included. Accuracy remained high when both parents were available (0.84-0.87 with siblings; 0.73 without siblings) but declined substantially when only one parent was genotyped (0.65-0.68). Imputation accuracy was positively associated with both minor allele frequency (MAF) and linkage disequilibrium (max r2LD), with LD exerting the stronger influence. Heritability estimates derived from imputed 55 K genotypes were highly consistent with those obtained from the original 55 K data (0.39 ± 0.14 vs. 0.41 ± 0.14), indicating that genotype imputation did not compromise variance component estimation. In predictive ability analyses, pedigree-based BLUP (PBLUP) achieved higher predictive ability than genomic BLUP (GBLUP) based on the 1 K panel, with predictive abilities of 0.42-0.44 for PBLUP compared with 0.34-0.35 for GBLUP. Using imputed genotypes for genomic prediction further improved predictive ability relative to the true 1 K panel, yielding values ranging from 0.35 to 0.47. Notably, when parental genotypes were included in the reference population, GBLUP based on imputed genotypes surpassed the predictive ability of PBLUP and approached that achieved with the original 55 K genotypes (0.45-0.47). Collectively, these results provide the first empirical evidence that low- to medium-density genotype imputation, combined with pedigree information, can effectively support genomic prediction in P. vannamei. This study establishes a cost-efficient and scalable framework for implementing genomic selection in P. vannamei and provides a practical reference for the application of genomic selection in other aquaculture species with constrained breeding budgets.

Animals↗