ArticleBMC genetics2013
Genome-wide association study for backfat thickness in Canchim beef cattle using Random Forest approach.
Article in BMC genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Whole genome sequencing of simmental cattle for SNP and CNV discovery.BMC genomics · 2023Article
- A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning.Plant methods · 2022Article
- Article
- A Random Forest-Based Genome-Wide Scan Reveals Fertility-Related Candidate Genes and Potential Inter-Chromosomal Epistatic Regions Associated With Age at First Calving in Nellore Cattle.Frontiers in genetics · 2022Article
- Integrating Single-Step GWAS and Bipartite Networks Reconstruction Provides Novel Insights into Yearling Weight and Carcass Traits in Hanwoo Beef Cattle.Animals : an open access journal from MDPI · 2020Article
- Genome-wide prediction for complex traits under the presence of dominance effects in simulated populations using GBLUP and machine learning methods.Journal of animal science · 2020Article
- Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean.Frontiers in plant science · 2020Article
- Sliding window haplotype approaches overcome single SNP analysis limitations in identifying genes for meat tenderness in Nelore cattle.BMC genetics · 2019Article
- A copy number variant scan in the autochthonous Valdostana Red Pied cattle breed and comparison with specialized dairy populations.PloS one · 2018Article
- Candidate genes for male and female reproductive traits in Canchim beef cattle.Journal of animal science and biotechnology · 2017Article
- Study on the introgression of beef breeds in Canchim cattle using single nucleotide polymorphism markers.PloS one · 2017Article
- Dibutyryl-cAMP affecting fat deposition of finishing pigs by decreasing the inflammatory system related to insulin sensitive or lipolysis.Genes & nutrition · 2016Article
- Detection of selection signatures in Piemontese and Marchigiana cattle, two breeds with similar production aptitudes but different selection histories.Genetics, selection, evolution : GSE · 2015Article
- RAD-QTL Mapping Reveals Both Genome-Level Parallelism and Different Genetic Architecture Underlying the Evolution of Body Shape in Lake Whitefish (Coregonus clupeaformis) Species Pairs.G3 (Bethesda, Md.) · 2015Article
- Towards the identification of the loci of adaptive evolution.Methods in ecology and evolution · 2015Review
- Genome wide association analysis of the 16th QTL- MAS Workshop dataset using the Random Forest machine learning approach.BMC proceedings · 2014Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMeat quality involves many traits, such as marbling, tenderness, juiciness, and backfat thickness, all of which require attention from livestock producers. Backfat thickness improvement by means of traditional selection techniques in Canchim beef cattle has been challenging due to its low heritability, and it is measured late in an animal's life. Therefore, the implementation of new methodologies for identification of single nucleotide polymorphisms (SNPs) linked to backfat thickness are an important strategy for genetic improvement of carcass and meat quality.
resultsThe set of SNPs identified by the random forest approach explained as much as 50% of the deregressed estimated breeding value (dEBV) variance associated with backfat thickness, and a small set of 5 SNPs were able to explain 34% of the dEBV for backfat thickness. Several quantitative trait loci (QTL) for fat-related traits were found in the surrounding areas of the SNPs, as well as many genes with roles in lipid metabolism.
conclusionsThese results provided a better understanding of the backfat deposition and regulation pathways, and can be considered a starting point for future implementation of a genomic selection program for backfat thickness in Canchim beef cattle.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.