Source: Cell Genomics
, , , , , , , , , , , , ,
- 1
- Faculty of Veterinary & Agricultural Science, the University of Melbourne, Parkville, VIC 3052, Australia
- 2
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
- 3
- Cambridge-Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
- 4
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, the University of Edinburgh, Edinburgh, UK
- 5
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
- 6
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
- 7
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
- 8
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, the University of Edinburgh, Midlothian EH25 9RG, UK
- 9
- Institute for Molecular Bioscience, the University of Queensland, Brisbane, QLD 4072, Australia
- 10
- Queensland Brain Institute, the University of Queensland, Brisbane, QLD 4072, Australia
- 11
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
Highlights
Map cis and trans eQTLs and RNA splicing sQTLs in 16 tissues of 4,725 cattle
Use cis and trans e/sQTLs to partition heritability (h2) of 37 traits of 120,000 cattle
cis and trans e/sQTLs explained an average of 69.2% of h2 across phenotypic traits
cis and trans e/sQTLs are essential for mammalian phenotypes
Summary
Many quantitative trait loci (QTLs) are in non-coding regions. Therefore, QTLs are assumed to affect gene regulation. Gene expression and RNA splicing are primary steps of transcription, so DNA variants changing gene expression (eVariants) or RNA splicing (sVariants) are expected to significantly affect phenotypes. We quantify the contribution of eVariants and sVariants detected from 16 tissues (n = 4,725) to 37 traits of ∼120,000 cattle (average magnitude of genetic correlation between traits = 0.13). Analyzed in Bayesian mixture models, averaged across 37 traits, cis and trans eVariants and sVariants detected from 16 tissues jointly explain 69.2% (SE = 0.5%) of heritability, 44% more than expected from the same number of random variants. This 69.2% includes an average of 24% from trans e-/sVariants (14% more than expected). Averaged across 56 lipidomic traits, multi-tissue cis and trans e-/sVariants also explain 71.5% (SE = 0.3%) of heritability, demonstrating the essential role of proximal and distal regulatory variants in shaping mammalian phenotypes.
Please click here to read the full article.
