Table 2 The summary of the functional annotation sets in this study
From: Integrating multi-layered biological priors to improve genomic prediction accuracy in beef cattle
Omics | Partitions (Number of animals) | Targeted variant sets (no. of variants) |
|---|---|---|
Genomic | Conserved 100 species (NA) | Bovine genome sites lifted over from human sites with PhastCon score  > 0.9 calculated using genomes of 100 vertebrate species (192825) |
| Â | Annotation (NA) | SnpEff was used to annotate the variants which was annotated as UTR (43,207), intergenic (6,132,394), geneend (863,401), intron (3,096,627), regulatory.related (11,534) and coding.related (66,762) |
| Â | Selection signature (44) | Regions at the top 1% with the highest average |iHS| score and SNP numbers greater than 10 were regarded as candidate regions under positive selection (109,325) |
| Â | P-variants (NA) | Variants have been identified in cattle: those relating to body size and beef production |
| Â | LD (1577) | First quartile (2,553,483), second quartile (2,553,481), third quartile (2,553,486), and fourth quartile (2,553,475) |
| Â | Freq (1577) | First quartile (2,557,044), second quartile (2,555,826), third quartile (2,550,339), and fourth quartile (2,550,716) |
| Â | Variant density (1577) | First quartile (2,564,482), second quartile (2,564,733), third quartile (2,538,073), and fourth quartile (2,546,637) |
Metabolomics | mQTLs (117) | mQTLs with meta-analysis P < (0.05/10,213,925) from 66 types of metabolites (386,995) |
Transcriptomics | eQTLs(227 muscle, 120 liver and 117 adipose) | eQTLs with meta-analysis (0.05) from 3 types of tissues (240,683) |
Epigenomics | OCR (10) | Peak calling for muscle samples was performed using Genrich with -m 30, -j (ATAC-seq mode), -r (remove PCR duplicates), -e MT (to exclude mitochondrial chromosome), -q 0.05 (FDR-adjusted P-value) (495,903) |
| Â | HMRs (10) | chose a 10Â kb window size with the default parameters (762,835) |