|FANG, LINGZHAO - University Of Maryland|
|CAI, WENTAO - China Agricultural University|
|LIU, SHULI - China Agricultural University|
|CANELA-XANDRI, ORIOL - University Of Edinburgh|
|GAO, YAHUI - University Of Maryland|
|JIANG, JICAI - University Of Maryland|
|RAWLIK, KONRAD - University Of Edinburgh|
|LI, BINGJIE - Oak Ridge Institute For Science And Education (ORISE)|
|Schroeder, Steven - Steve|
|SONSTEGARD, TAD - Recombinetics, Inc|
|ALEXANDER, LEESON - Retired ARS Employee|
|Van Tassell, Curtis - Curt|
|YU, YING - China Agricultural University|
|ZHANG, SHENGLI - China Agricultural University|
|TENESA, ALBERT - University Of Edinburgh|
|MA, LI - University Of Maryland|
|Liu, Ge - George|
Submitted to: Genome Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/1/2020
Publication Date: 11/21/2019
Citation: Fang, L., Cai, W., Liu, S., Canela-Xandri, O., Gao, Y., Jiang, J., Rawlik, K., Li, B., Schroeder, S.G., Rosen, B.D., Li, C., Sonstegard, T.S., Alexander, L.J., Van Tassell, C.P., Van Raden, P.M., Cole, J.B., Yu, Y., Zhang, S., Tenesa, A., Ma, L., Liu, G. 2019. Comprehensive analyses of 723 transcriptomes enhance biological interpretation and genomic prediction for complex traits in cattle. Genome Research. 30(5):790-801. https://doi.org/10.1101/gr.250704.119.
Interpretive Summary: RNA sequencing generates a major source of genomic data. By performing integrative analyses of RNA sequencing data with large-scale genome-wide association studies in cattle, we detected tissue-specific genes, trait-relevant tissues and cell types. More importantly, through incorporation of tissue-specific genes into genomic prediction models, we improved the prediction accuracy by an average of 0.03 for milk production traits in cattle. These results fill our knowledge gaps and provide the foundation for incorporating RNA sequencing data into the future goat breeding program. Farmers, scientist, and policy planners who need improve animal health and production based on genome-enable animal selection will benefit from this study.
Technical Abstract: By combining 156 newly generated RNA-seq data with previously published data, we uniformly analyzed a total of 723 transcriptomes from 91 tissues and cell types in cattle to identify tissue-specific genes. Our results demonstrated that tissue-specific genes are significantly associated with the tissue-relevant biology, showing lower tissue-specific promoter methylation, higher alternative splicing and RNA editing events, and tissue-driven evolution patterns (e.g., brain-specific genes evolve slowest, while testes-specific genes evolve fastest). Through integrative analyses of those tissue-specific genes with large-scale genome-wide association studies of 45 complex traits in cattle, we detected trait-relevant tissues and cell types, including brain for milk production, blood/immune system for male fertility, and multiple growth-related tissues for body size. We further validated these findings by using 27 whole-genome DNA methylation data across 14 major somatic tissues and sperm. More importantly, through incorporation of tissue-specific genes into genomic prediction models, we improved the prediction accuracy by an average of 0.03 for milk production traits in cattle. Collectively, our findings provided novel insights into the genetic and biological mechanisms underlying complex traits in cattle, and our transcriptome atlas can serve as a primary source for biological interpretation, functional validation, studies of adaptive evolution, and genomic improvement in livestock.