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Attention Score in Context
| Title |
Exploring causal networks underlying fat deposition and muscularity in pigs through the integration of phenotypic, genotypic and transcriptomic data
|
|---|---|
| Published in |
BMC Systems Biology, September 2015
|
| DOI | 10.1186/s12918-015-0207-6 |
| Pubmed ID | |
| Authors |
Francisco Peñagaricano, Bruno D. Valente, Juan P. Steibel, Ronald O. Bates, Catherine W. Ernst, Hasan Khatib, Guilherme JM Rosa |
| Abstract |
Joint modeling and analysis of phenotypic, genotypic and transcriptomic data have the potential to uncover the genetic control of gene activity and phenotypic variation, as well as shed light on the manner and extent of connectedness among these variables. Current studies mainly report associations, i.e. undirected connections among variables without causal interpretation. Knowledge regarding causal relationships among genes and phenotypes can be used to predict the behavior of complex systems, as well as to optimize management practices and selection strategies. Here, we performed a multistep procedure for inferring causal networks underlying carcass fat deposition and muscularity in pigs using multi-omics data obtained from an F2 Duroc x Pietrain resource pig population. We initially explored marginal associations between genotypes and phenotypic and expression traits through whole-genome scans, and then, in genomic regions with multiple significant hits, we assessed gene-phenotype network reconstruction using causal structural learning algorithms. One genomic region on SSC6 showed significant associations with three relevant phenotypes, off-midline10th-rib backfat thickness, loin muscle weight, and average intramuscular fat percentage, and also with the expression of seven genes, including ZNF24, SSX2IP, and AKR7A2. The inferred network indicated that the genotype affects the three phenotypes mainly through the expression of several genes. Among the phenotypes, fat deposition traits negatively affected loin muscle weight. Our findings shed light on the antagonist relationship between carcass fat deposition and lean meat content in pigs. In addition, the procedure described in this study has the potential to unravel gene-phenotype networks underlying complex phenotypes. |
Mendeley readers
The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Israel | 1 | 2% |
| Brazil | 1 | 2% |
| Unknown | 49 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 20% |
| Researcher | 10 | 20% |
| Student > Doctoral Student | 4 | 8% |
| Professor > Associate Professor | 4 | 8% |
| Student > Master | 4 | 8% |
| Other | 4 | 8% |
| Unknown | 15 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 16 | 31% |
| Biochemistry, Genetics and Molecular Biology | 4 | 8% |
| Veterinary Science and Veterinary Medicine | 3 | 6% |
| Engineering | 3 | 6% |
| Computer Science | 2 | 4% |
| Other | 8 | 16% |
| Unknown | 15 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 November 2017.
All research outputs
#21,942,017
of 24,479,790 outputs
Outputs from BMC Systems Biology
#1,001
of 1,130 outputs
Outputs of similar age
#213,434
of 249,786 outputs
Outputs of similar age from BMC Systems Biology
#26
of 29 outputs
Altmetric has tracked 24,479,790 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,130 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 249,786 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.