ArticlePloS one2013
From QTL to QTN: candidate gene set approach and a case study in porcine IGF1-FoxO pathway.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 17 citations in OpenAlex.
- The Effect of rs80860411 Polymorphism on Fattening, Slaughter, and Pork Quality Traits in Polish Large White and Pulawska Breeds.Animals : an open access journal from MDPI · 2025Article
- Impact of genotype-calling methodologies on genome-wide association and genomic prediction in polyploids.The plant genome · 2023Article
- Hunting for Genes Underlying Emotionality in the Laboratory Rat: Maps, Tools and Traps.Current neuropharmacology · 2023Review
- Transcriptome assembly and identification of genes and SNPs associated with growth traits in largemouth bass (Micropterus salmoides).Genetica · 2017Article
- Gene set based association analyses for the WSSV resistance of Pacific white shrimp Litopenaeus vannamei.Scientific reports · 2017Article
- A Genome-Wide mRNA Screen and Functional Analysis Reveal FOXO3 as a Candidate Gene for Chicken Growth.PloS one · 2015Article
- Association study between gene polymorphisms in PPAR signaling pathway and porcine meat quality traits.Mammalian genome : official journal of the International Mammalian Genome Society · 2013Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unraveling the genetic background of economic traits is a major goal in modern animal genetics and breeding. Both candidate gene analysis and QTL mapping have previously been used for identifying genes and chromosome regions related to studied traits. However, most of these studies may be limited in their ability to fully consider how multiple genetic factors may influence a particular phenotype of interest. If possible, taking advantage of the combined effect of multiple genetic factors is expected to be more powerful than analyzing single sites, as the joint action of multiple loci within a gene or across multiple genes acting in the same gene set will likely have a greater influence on phenotypic variation. Thus, we proposed a pipeline of gene set analysis that utilized information from multiple loci to improve statistical power. We assessed the performance of this approach by both simulated and a real IGF1-FoxO pathway data set. The results showed that our new method can identify the association between genetic variation and phenotypic variation with higher statistical power and unravel the mechanisms of complex traits in a point of gene set. Additionally, the proposed pipeline is flexible to be extended to model complex genetic structures that include the interactions between different gene sets and between gene sets and environments.
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