Jicai Jiang

Teng, J., Gao, Y., Yin, H., Bai, Z., Liu, S., Zeng, H., … Fang, L. (2024, January 4). A compendium of genetic regulatory effects across pig tissues. NATURE GENETICS, Vol. 1. https://doi.org/10.1038/s41588-023-01585-7

Zhuo, Y., Du, H., Diao, C., Li, W., Zhou, L., Jiang, L., … Liu, J. (2024). MAGE: metafounders-assisted genomic estimation of breeding value, a novel additive-dominance single-step model in crossbreeding systems. BIOINFORMATICS, 40(2). https://doi.org/10.1093/bioinformatics/btae044

Wang, J., Maltecca, C., Tiezzi, F., Huang, Y., & Jiang, J. (2023). 123 Benchmarking of Artificial Neural Network Models for Genomic Prediction of Quantitative Traits in Pigs. Journal of Animal Science, 101(Supplement_3), 17–18. https://doi.org/10.1093/jas/skad281.022

Liang, Z., Prakapenka, D., VanRaden, P. M., Jiang, J., Ma, L., & Da, Y. (2023). A Million-Cow Genome-Wide Association Study of Three Fertility Traits in US Holstein Cows. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 24(13). https://doi.org/10.3390/ijms241310496

Gao, Y., Marceau, A., Iqbal, V., Torres-Vazquez, J. A., Neupane, M., Jiang, J., … Ma, L. (2023). Genome-wide association analysis of heifer livability and early first calving in Holstein cattle. BMC GENOMICS, 24(1). https://doi.org/10.1186/s12864-023-09736-0

Lozada-Soto, E. A., Gaddis, K. L. P., Tiezzi, F., Jiang, J., Ma, L., Toghiani, S., … Maltecca, C. (2023). Inbreeding depression for producer-recorded udder, metabolic, and reproductive diseases in US dairy cattle. Journal of Dairy Science, 12. https://doi.org/10.3168/jds.2023-23909

Marceau, A., Wang, J., Iqbal, V., Jiang, J., Liu, G. E., & Ma, L. (2023). Investigation of lncRNA in <i>Bos taurus</i> Mammary Tissue during Dry and Lactation Periods. GENES, 14(9). https://doi.org/10.3390/genes14091789

Cheng, J., Maltecca, C., VanRaden, P. M., Jeffrey R. O'Connell, Ma, L., & Jiang, J. (2023). SLEMM: million-scale genomic predictions with window-based SNP weighting. BIOINFORMATICS, 39(3). https://doi.org/10.1093/bioinformatics/btad127

Jiang, J. (2023). Simulated Sequence Genotypes. https://doi.org/10.6084/m9.figshare.24432948

Jiang, J. (2023). sequence-genotypes.zip. https://doi.org/10.6084/m9.figshare.24431263

Duan, J. E., Jiang, J., & He, Y. (2022, July 5). Editorial: Bridging (Epi-) Genomics and Environmental Changes: The Livestock Research. FRONTIERS IN GENETICS, Vol. 13. https://doi.org/10.3389/fgene.2022.961232

He, Y., Tiezzi, F., Jiang, J., Howard, J., Huang, Y., Gray, K., … Maltecca, C. (2022). Exploring methods to summarize gut microbiota composition for microbiability estimation and phenotypic prediction in swine. JOURNAL OF ANIMAL SCIENCE, 100(9). https://doi.org/10.1093/jas/skac231

Lozada-Soto, E. A., Tiezzi, F., Jiang, J., Cole, J. B., VanRaden, P. M., & Maltecca, C. (2022). Genomic characterization of autozygosity and recent inbreeding trends in all major breeds of US dairy cattle. JOURNAL OF DAIRY SCIENCE, 105(11), 8956–8971. https://doi.org/10.3168/jds.2022-22116

Marceau, A., Gao, Y., Baldwin, R. L., Li, C.-jun, Jiang, J., Liu, G. E., & Ma, L. (2022). Investigation of rumen long noncoding RNA before and after weaning in cattle. BMC GENOMICS, 23(1). https://doi.org/10.1186/s12864-022-08758-4

Cheng, J., Tiezzi, F., Howard, J., Maltecca, C., & Jiang, J. (2022, January 6). The Addition of Epistatic Genetic Effects Increases Genomic Prediction Accuracy for Reproduction and Production Traits in Duroc Pigs Using Genomic Models. https://doi.org/10.21203/rs.3.rs-1182452/v1

Wang, J., Hicks, J., Jiang, J., & Liu, H.-C. (2022, October). Transcriptome Analysis of Chicken Reveals the Impact of Herpesvirus of Turkeys on Bursa RNA Expression in Marek's Disease Virus Infections. JOURNAL OF ANIMAL SCIENCE, Vol. 100, pp. 215–215. https://doi.org/10.1093/jas/skac247.392

He, Y., Tiezzi, F., Jiang, J., Howard, J. T., Huang, Y., Gray, K., … Maltecca, C. (2022). Use of Host Feeding Behavior and Gut Microbiome Data in Estimating Variance Components and Predicting Growth and Body Composition Traits in Swine. GENES, 13(5). https://doi.org/10.3390/genes13050767

Prakapenka, D., Liang, Z., Jiang, J., Ma, L., & Da, Y. (2021). A Large-Scale Genome-Wide Association Study of Epistasis Effects of Production Traits and Daughter Pregnancy Rate in US Holstein Cattle. GENES, 12(7). https://doi.org/10.3390/genes12071089

Shen, B., Freebern, E., Jiang, J., Maltecca, C., Cole, J. B., Liu, G. E., & Ma, L. (2021). Effect of Temperature and Maternal Age on Recombination Rate in Cattle. FRONTIERS IN GENETICS, 12. https://doi.org/10.3389/fgene.2021.682718

Jiang, J. (2021, October 13). On the Use of Z-Scores for Fine-Mapping with Related Individuals (Vol. 10). Vol. 10. https://doi.org/10.1101/2021.10.10.463846

Fang, L., Cai, W., Liu, S., Canela-Xandri, O., Gao, Y., Jiang, J., … Liu, G. E. (2020). Comprehensive analyses of 723 transcriptomes enhance genetic and biological interpretations for complex traits in cattle. Genome Research, 30(5), 790–801. https://doi.org/10.1101/gr.250704.119

Lozada‐Soto, E. A., Maltecca, C., Wackel, H., Flowers, W., Gray, K., He, Y., … Tiezzi, F. (2020). Evidence for recombination variability in purebred swine populations. Journal of Animal Breeding and Genetics, 138(2), 259–273. https://doi.org/10.1111/jbg.12510

Freebern, E., Santos, D. J. A., Fang, L., Jiang, J., Parker Gaddis, K. L., Liu, G. E., … Ma, L. (2020). GWAS and fine-mapping of livability and six disease traits in Holstein cattle. BMC GENOMICS, 21(1). https://doi.org/10.1186/s12864-020-6461-z

Jiang, J., Ma, L., Prakapenka, D., VanRaden, P. M., Cole, J. B., & Da, Y. (2019). A large-scale genome-wide association study in U.S. Holstein cattle. Frontiers in Genetics, 10(MAY). https://doi.org/10.3389/fgene.2019.00412

Fang, L., Zhou, Y., Liu, S., Jiang, J., Bickhart, D. M., Null, D. J., … Liu, G. E. (2019). Comparative analyses of sperm DNA methylomes among human, mouse and cattle provide insights into epigenomic evolution and complex traits. Epigenetics, 14(3), 260–276. https://doi.org/10.1080/15592294.2019.1582217

Jiang, J., Cole, J. B., Freebern, E., Da, Y., VanRaden, P. M., & Ma, L. (2019). Functional annotation and Bayesian fine-mapping reveals candidate genes for important agronomic traits in Holstein bulls. Communications Biology, 2(1). https://doi.org/10.1038/s42003-019-0454-y

Fang, L., Jiang, J., Li, B., Zhou, Y., Freebern, E., Vanraden, P. M., … Ma, L. (2019). Genetic and epigenetic architecture of paternal origin contribute to gestation length in cattle. Communications Biology, 2(1). https://doi.org/10.1038/s42003-019-0341-6

Fang, L., Zhou, Y., Liu, S., Jiang, J., Bickhart, D. M., Null, D. J., … Liu, G. E. (2019). Integrating Signals from Sperm Methylome Analysis and Genome-Wide Association Study for a Better Understanding of Male Fertility in Cattle. Epigenomes, 3(2), 10. https://doi.org/10.3390/epigenomes3020010

Shen, B., Jiang, J., Seroussi, E., Liu, G. E., & Ma, L. (2018). Characterization of recombination features and the genetic basis in multiple cattle breeds. BMC Genomics, 19(1). https://doi.org/10.1186/s12864-018-4705-y

Zhou, Y., Shen, B., Jiang, J., Padhi, A., Park, K.-E., Oswalt, A., … Ma, L. (2018). Construction of PRDM9 allele-specific recombination maps in cattle using large-scale pedigree analysis and genome-wide single sperm genomics. DNA Research, 25(2), 183–194. https://doi.org/10.1093/dnares/dsx048

Determination of quantitative trait nucleotides by concordance analysis between quantitative trait loci and marker genotypes of US Holsteins. (2018). Journal of Dairy Science, 101(10), 9089–9107. https://doi.org/10.3168/jds.2018-14816

Jiang, J., Cole, J. B., Da, Y., VanRaden, P. M., & Ma, L. (2018, September 26). Fast Bayesian fine-mapping of 35 production, reproduction and body conformation traits in dairy cattle (Vol. 9). Vol. 9. https://doi.org/10.1101/428227

Feng, X., Jiang, J., Padhi, A., Ning, C., Fu, J., Wang, A., … Liu, J.-F. (2017). Characterization of genome-wide segmental duplications reveals a common genomic feature of association with immunity among domestic animals. BMC Genomics, 18(1). https://doi.org/10.1186/s12864-017-3690-x

Jiang, J., Shen, B., O’Connell, J. R., VanRaden, P. M., Cole, J. B., & Ma, L. (2017). Dissection of additive, dominance, and imprinting effects for production and reproduction traits in Holstein cattle. BMC Genomics, 18(1). https://doi.org/10.1186/s12864-017-3821-4

Padhi, A., Shen, B., Jiang, J., Zhou, Y., Liu, G. E., & Ma, L. (2017). Ruminant-specific multiple duplication events of PRDM9 before speciation. BMC Evolutionary Biology, 17(1). https://doi.org/10.1186/s12862-017-0892-4

Yang, J., Jiang, J., Liu, X., Wang, H., Guo, G., Zhang, Q., & Jiang, L. (2016). Differential expression of genes in milk of dairy cattle during lactation. Animal Genetics, 47(2), 174–180. https://doi.org/10.1111/age.12394

Wang, Z., Shen, B., Jiang, J., Li, J., & Ma, L. (2016). Effect of sex, age and genetics on crossover interference in cattle. Scientific Reports, 6. https://doi.org/10.1038/srep37698

Xie, J., Li, R., Li, S., Ran, X., Wang, J., Jiang, J., & Zhao, P. (2016). Identification of copy number variations in Xiang and Kele pigs. PLoS ONE, 11(2). https://doi.org/10.1371/journal.pone.0148565

Zhu, B., Zhu, M., Jiang, J., Niu, H., Wang, Y., Wu, Y., … Li, J. (2016). The impact of variable degrees of freedom and scale parameters in Bayesian methods for genomic prediction in Chinese Simmental beef cattle. PLoS ONE, 11(5). https://doi.org/10.1371/journal.pone.0154118

Wang, J., Jiang, J., Wang, H., Kang, H., Zhang, Q., & Liu, J.-F. (2015). Improved detection and characterization of copy number variations among diverse pig breeds by array CGH. G3: Genes, Genomes, Genetics, 5(6), 1253–1261. https://doi.org/10.1534/g3.115.018473

Jiang, J., Zhang, Q., Ma, L., Li, J., Wang, Z., & Liu, J.-F. (2015). Joint prediction of multiple quantitative traits using a Bayesian multivariate antedependence model. Heredity, 115(1), 29–36. https://doi.org/10.1038/hdy.2015.9

Wang, J., Jiang, J., Wang, H., Kang, H., Zhang, Q., & Liu, J.-F. (2014). Enhancing genome-wide copy number variation identification by high density array CGH using diverse resources of pig breeds. PLoS ONE, 9(1). https://doi.org/10.1371/journal.pone.0087571

Jiang, J., Wang, J., Wang, H., Zhang, Y., Kang, H., Feng, X., … Liu, J.-F. (2014). Global copy number analyses by next generation sequencing provide insight into pig genome variation. BMC Genomics, 15(1). https://doi.org/10.1186/1471-2164-15-593

Jiang, L., Liu, X., Yang, J., Wang, H., Jiang, J., Liu, L., … Zhang, Q. (2014). Targeted resequencing of GWAS loci reveals novel genetic variants for milk production traits. BMC Genomics, 15(1). https://doi.org/10.1186/1471-2164-15-1105

Pan, D., Zhang, S., Jiang, J., Jiang, L., Zhang, Q., & Liu, J. F. (2013). Genome-Wide Detection of Selective Signature in Chinese Holstein. PLoS ONE, 8(3). https://doi.org/10.1371/journal.pone.0060440

Jiang, L., Jiang, J., Yang, J., Liu, X., Wang, J., Wang, H., … Zhang, Q. (2013). Genome-wide detection of copy number variations using high-density SNP genotyping platforms in Holsteins. BMC Genomics, 14(1). https://doi.org/10.1186/1471-2164-14-131

Wang, J., Wang, H., Jiang, J., Kang, H., Feng, X., Zhang, Q., & Liu, J.-F. (2013). Identification of Genome-Wide Copy Number Variations among Diverse Pig Breeds Using SNP Genotyping Arrays. PLoS ONE, 8(7). https://doi.org/10.1371/journal.pone.0068683

Wang, J., Jiang, J., Fu, W., Jiang, L., Ding, X., Liu, J.-F., & Zhang, Q. (2012). A genome-wide detection of copy number variations using SNP genotyping arrays in swine. BMC Genomics, 13(1). https://doi.org/10.1186/1471-2164-13-273

Zhou, C., Liu, Z., Jiang, J., Yu, Y., & Zhang, Q. (2012). Differential gene expression profiling of porcine epithelial cells infected with three enterotoxigenic Escherichia coli strains. BMC Genomics, 13(1). https://doi.org/10.1186/1471-2164-13-330

Jiang, L., Jiang, J., Wang, J., Ding, X., Liu, J., & Zhang, Q. (2012). Genome-Wide Identification of Copy Number Variations in Chinese Holstein. PLoS ONE, 7(11). https://doi.org/10.1371/journal.pone.0048732

Jiang, J., Jiang, L., Zhou, B., Fu, W., Liu, J.-F., & Zhang, Q. (2011). Snat: A SNP annotation tool for bovine by integrating various sources of genomic information. BMC Genetics, 12. https://doi.org/10.1186/1471-2156-12-85