Disentangling Mature Cow Weight and Body Condition Score: A Comparative Single-Step GWAS of Phenotypic Pre-adjustment vs. Recursive Modeling
Clicks: 12
ID: 322767
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
3.3
/100
12 views
3 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #108 of 223 articles by views in italian journal of animal science
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 223 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Mature cow weight (MWT) is a trait genetically correlated with body condition score (BCS). Previous research has shown that sire rankings can shift depending on how BCS is accounted for, indicating that different modeling strategies can influence selection outcomes. The recursive modeling approach has been established as a method for obtaining MWT that is genetically independent of BCS, providing an alternative to phenotypic pre-adjustment. The objective of this study was to determine whether different modeling approaches capture different genetic architectures or merely produce statistical artifacts. Genome-wide association studies (GWAS) and functional genomic analyses were performed to compare the genomic architecture of phenotypically pre-adjusted MWT (MWTadj) with MWT that is genetically independent of BCS, obtained using the recursive approach (MWTRM). A total of 42 significant SNP across 8 chromosomes were identified for MWTadj and 44 SNP across 9 chromosomes for MWTRM, with 28 SNP shared between models. These variants corresponded to 107 annotated genes in MWTadj and 137 in MWTRM, including 62 shared genes. Major association signals were concentrated on BTA20, BTA7, and BTA14 for both models, with all significant SNP jointly explaining 3.93% of the total additive genetic variance for MWTadj and 4.29% for MWTRM. The Pearson correlation coefficient of estimated SNP effects between the models was 0.76, while the correlation of genomic estimated breeding values was 0.87. Compared to MWTadj, which shared 19 genes with unadjusted MWT, the MWTRM shared 33 genes. In addition, 28 of the 31 pathways identified for MWTRM were also identified for unadjusted MWT, whereas no Gene Ontology pathways were shared between MWTadj and unadjusted MWT. The MWTRM was associated with genes annotated to growth, skeletal development, feed efficiency, and carcass-related traits, whereas MWTadj identified a distinct set of genes and pathways. Despite these differences, MWTadj and MWTRM converged on similar core biological signals, highlighting that they effectively capture the primary genetic drivers of MWT independent of BCS.
| Reference Key |
openalex_W7171520732
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Ayooluwa O Ojo, Henrique A Mulim-McCarthy, André Garcia, Kelli Retallick-Riley, Hinayah R Oliveira |
| Journal | italian journal of animal science |
| Year | 2026 |
| DOI |
10.1093/jas/skag231
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.