College of Agriculture & Natural Resources

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The collections in this community comprise faculty research works, as well as graduate theses and dissertations.

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Author: search.filters.author.Jiang, Jicai

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    Investigation of rumen long noncoding RNA before and after weaning in cattle
    (Springer Nature, 2022-07-20) Marceau, Alexis; Gao, Yahui; Baldwin VI, Ransom L.; Li, Cong-jun; Jiang, Jicai; Liu, George E.; Ma, Li
    This study aimed to identify long non-coding RNA (lncRNA) from the rumen tissue in dairy cattle, explore their features including expression and conservation levels, and reveal potential links between lncRNA and complex traits that may indicate important functional impacts of rumen lncRNA during the transition to the weaning period. A total of six cattle rumen samples were taken with three replicates from before and after weaning periods, respectively. Total RNAs were extracted and sequenced with lncRNA discovered based on size, coding potential, sequence homology, and known protein domains. As a result, 404 and 234 rumen lncRNAs were identified before and after weaning, respectively. However, only nine of them were shared under two conditions, with 395 lncRNAs found only in pre-weaning tissues and 225 only in post-weaning samples. Interestingly, none of the nine common lncRNAs were differentially expressed between the two weaning conditions. LncRNA averaged shorter length, lower expression, and lower conservation scores than the genome overall, which is consistent with general lncRNA characteristics. By integrating rumen lncRNA before and after weaning with large-scale GWAS results in cattle, we reported significant enrichment of both pre- and after-weaning lncRNA with traits of economic importance including production, reproduction, health, and body conformation phenotypes. The majority of rumen lncRNAs are uniquely expressed in one of the two weaning conditions, indicating a functional role of lncRNA in rumen development and transition of weaning. Notably, both pre- and post-weaning lncRNA showed significant enrichment with a variety of complex traits in dairy cattle, suggesting the importance of rumen lncRNA for cattle performance in the adult stage. These relationships should be further investigated to better understand the specific roles lncRNAs are playing in rumen development and cow performance.
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    Ruminant-specific multiple duplication events of PRDM9 before speciation
    (Springer Nature, 2017-03-14) Padhi, Abinash; Shen, Botong; Jiang, Jicai; Zhou, Yang; Liu, George E.; Ma, Li
    Understanding the genetic and evolutionary mechanisms of speciation genes in sexually reproducing organisms would provide important insights into mammalian reproduction and fitness. PRDM9, a widely known speciation gene, has recently gained attention for its important role in meiotic recombination and hybrid incompatibility. Despite the fact that PRDM9 is a key regulator of recombination and plays a dominant role in hybrid incompatibility, little is known about the underlying genetic and evolutionary mechanisms that generated multiple copies of PRDM9 in many metazoan lineages. The present study reports (1) evidence of ruminant-specific multiple gene duplication events, which likely have had occurred after the ancestral ruminant population diverged from its most recent common ancestor and before the ruminant speciation events, (2) presence of three copies of PRDM9, one copy (lineages I) in chromosome 1 (chr1) and two copies (lineages II & III) in chromosome X (chrX), thus indicating the possibility of ancient inter- and intra-chromosomal unequal crossing over and gene conversion events, (3) while lineages I and II are characterized by the presence of variable tandemly repeated C2H2 zinc finger (ZF) arrays, lineage III lost these arrays, and (4) C2H2 ZFs of lineages I and II, particularly the amino acid residues located at positions −1, 3, and 6 have evolved under strong positive selection. Our results demonstrated two gene duplication events of PRDM9 in ruminants: an inter-chromosomal duplication that occurred between chr1 and chrX, and an intra-chromosomal X-linked duplication, which resulted in two additional copies of PRDM9 in ruminants. The observation of such duplication between chrX and chr1 is rare and may possibly have happened due to unequal crossing-over millions of years ago when sex chromosomes were independently derived from a pair of ancestral autosomes. Two copies (lineages I & II) are characterized by the presence of variable sized tandem-repeated C2H2 ZFs and evolved under strong positive selection and concerted evolution, supporting the notion of well-established Red Queen hypothesis. Collectively, gene duplication, concerted evolution, and positive selection are the likely driving forces for the expansion of ruminant PRDM9 sub-family.
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    Dissection of additive, dominance, and imprinting effects for production and reproduction traits in Holstein cattle
    (Springer Nature, 2017-05-30) Jiang, Jicai; Shen, Botong; O’Connell, Jeffrey R.; VanRaden, Paul M.; Cole, John B.; Ma, Li
    Although genome-wide association and genomic selection studies have primarily focused on additive effects, dominance and imprinting effects play an important role in mammalian biology and development. The degree to which these non-additive genetic effects contribute to phenotypic variation and whether QTL acting in a non-additive manner can be detected in genetic association studies remain controversial. To empirically answer these questions, we analyzed a large cattle dataset that consisted of 42,701 genotyped Holstein cows with genotyped parents and phenotypic records for eight production and reproduction traits. SNP genotypes were phased in pedigree to determine the parent-of-origin of alleles, and a three-component GREML was applied to obtain variance decomposition for additive, dominance, and imprinting effects. The results showed a significant non-zero contribution from dominance to production traits but not to reproduction traits. Imprinting effects significantly contributed to both production and reproduction traits. Interestingly, imprinting effects contributed more to reproduction traits than to production traits. Using GWAS and imputation-based fine-mapping analyses, we identified and validated a dominance association signal with milk yield near RUNX2, a candidate gene that has been associated with milk production in mice. When adding non-additive effects into the prediction models, however, we observed little or no increase in prediction accuracy for the eight traits analyzed. Collectively, our results suggested that non-additive effects contributed a non-negligible amount (more for reproduction traits) to the total genetic variance of complex traits in cattle, and detection of QTLs with non-additive effect is possible in GWAS using a large dataset.
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    Characterization of recombination features and the genetic basis in multiple cattle breeds
    (Springer Nature, 2018-04-27) Shen, Botong; Jiang, Jicai; Seroussi, Eyal; Liu, George E.; Ma, Li
    Crossover generated by meiotic recombination is a fundamental event that facilitates meiosis and sexual reproduction. Comparative studies have shown wide variation in recombination rate among species, but the characterization of recombination features between cattle breeds has not yet been performed. Cattle populations in North America count millions, and the dairy industry has genotyped millions of individuals with pedigree information that provide a unique opportunity to study breed-level variations in recombination. Based on large pedigrees of Jersey, Ayrshire and Brown Swiss cattle with genotype data, we identified over 3.4 million maternal and paternal crossover events from 161,309 three-generation families. We constructed six breed- and sex-specific genome-wide recombination maps using 58,982 autosomal SNPs for two sexes in the three dairy cattle breeds. A comparative analysis of the six recombination maps revealed similar global recombination patterns between cattle breeds but with significant differences between sexes. We confirmed that male recombination map is 10% longer than the female map in all three cattle breeds, consistent with previously reported results in Holstein cattle. When comparing recombination hotspot regions between cattle breeds, we found that 30% and 10% of the hotspots were shared between breeds in males and females, respectively, with each breed exhibiting some breed-specific hotspots. Finally, our multiple-breed GWAS found that SNPs in eight loci affected recombination rate and that the PRDM9 gene associated with hotspot usage in multiple cattle breeds, indicating a shared genetic basis for recombination across dairy cattle breeds. Collectively, our results generated breed- and sex-specific recombination maps for multiple cattle breeds, provided a comprehensive characterization and comparison of recombination patterns between breeds, and expanded our understanding of the breed-level variations in recombination features within an important livestock species.
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    GWAS and fine-mapping of livability and six disease traits in Holstein cattle
    (Springer Nature, 2020-01-13) Freebern, Ellen; Santos, Daniel J. A.; Fang, Lingzhao; Jiang, Jicai; Parker Gaddis, Kristen L.; Liu, George E.; VanRaden, Paul M.; Maltecca, Christian; Cole, John B.; Ma, Li
    Health traits are of significant economic importance to the dairy industry due to their effects on milk production and associated treatment costs. Genome-wide association studies (GWAS) provide a means to identify associated genomic variants and thus reveal insights into the genetic architecture of complex traits and diseases. The objective of this study is to investigate the genetic basis of seven health traits in dairy cattle and to identify potential candidate genes associated with cattle health using GWAS, fine mapping, and analyses of multi-tissue transcriptome data.
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    Genetic Architecture of Complex Traits and Accuracy of Genomic Selection in Dairy Cattle
    (2018) Jiang, Jicai; Ma, Li; Animal Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Genomic selection has emerged as an effective approach in dairy cattle breeding, in which the key is prediction of genetic merit using dense SNP genotypes, i.e., genomic prediction. To improve the accuracy of genomic prediction, we need better understanding of the genetic architecture of complex traits and more sophisticated statistical modeling. In this dissertation, I developed several computing tools and performed a series of studies to investigate the genetic architecture of complex traits in dairy cattle and to improve genomic prediction models. First, we dissected additive, dominance, and imprinting effects for production, reproduction and health traits in dairy cattle. We found that non-additive effects contributed a non-negligible amount (more for reproduction traits) to the total genetic variance of complex traits in cattle. We also identified a dominant quantitative trait locus (QTL) for milk yield, revealing that detection of QTLs with non-additive effect is possible in genome-wide association studies (GWAS) using a large dataset. Second, we developed a powerful Bayesian method and a fast software tool (BFMAP) for SNP-set association and fine-mapping. We demonstrated that BFMAP achieves a power similar to or higher than existing software tools but is at least a few times faster for association tests. We also showed that BFMAP performs well for fine-mapping and can efficiently integrate fine-mapping with functional enrichment analysis. Third, we performed large-scale GWAS and fine-mapped 35 production, reproduction, and body conformation traits to single-gene resolution. We identified many novel association signals and many promising candidate genes. We also characterized causal effect enrichment patterns for a few functional annotations in dairy cattle genome and showed that our fine-mapping result can be readily used for future functional studies. Fourth, we developed an efficient Bayesian method and a fast computing tool (SSGP) for using functional annotations in genomic prediction. We demonstrated that the method and software have great potential to increase accuracy in genomic prediction and the capability to handle very large data. Collectively, these studies advance our understanding of the genetic architecture of complex traits in dairy cattle and provide fast computing tools for analyzing complex traits and improving genomic prediction.