Estimating the genetic architecture of quantitative traits
- PMID: 10689805
- DOI: 10.1017/s0016672399004255
Estimating the genetic architecture of quantitative traits
Abstract
Understanding and estimating the structure and parameters associated with the genetic architecture of quantitative traits is a major research focus in quantitative genetics. With the availability of a well-saturated genetic map of molecular markers, it is possible to identify a major part of the structure of the genetic architecture of quantitative traits and to estimate the associated parameters. Multiple interval mapping, which was recently proposed for simultaneously mapping multiple quantitative trait loci (QTL), is well suited to the identification and estimation of the genetic architecture parameters, including the number, genomic positions, effects and interactions of significant QTL and their contribution to the genetic variance. With multiple traits and multiple environments involved in a QTL mapping experiment, pleiotropic effects and QTL by environment interactions can also be estimated. We review the method and discuss issues associated with multiple interval mapping, such as likelihood analysis, model selection, stopping rules and parameter estimation. The potential power and advantages of the method for mapping multiple QTL and estimating the genetic architecture are discussed. We also point out potential problems and difficulties in resolving the details of the genetic architecture as well as other areas that require further investigation. One application of the analysis is to improve genome-wide marker-assisted selection, particularly when the information about epistasis is used for selection with mating.
Similar articles
-
Mapping the genetic architecture of complex traits in experimental populations.Bioinformatics. 2007 Jun 15;23(12):1527-36. doi: 10.1093/bioinformatics/btm143. Epub 2007 Apr 25. Bioinformatics. 2007. PMID: 17459962
-
Multiple interval mapping for quantitative trait loci.Genetics. 1999 Jul;152(3):1203-16. doi: 10.1093/genetics/152.3.1203. Genetics. 1999. PMID: 10388834 Free PMC article.
-
Ensemble Learning of QTL Models Improves Prediction of Complex Traits.G3 (Bethesda). 2015 Aug 13;5(10):2073-84. doi: 10.1534/g3.115.021121. G3 (Bethesda). 2015. PMID: 26276383 Free PMC article.
-
Modeling the genetic architecture of complex traits with molecular markers.Recent Pat Nanotechnol. 2007;1(1):41-9. doi: 10.2174/187221007779814835. Recent Pat Nanotechnol. 2007. PMID: 19076019 Review.
-
The genetic architecture of quantitative traits.Annu Rev Genet. 2001;35:303-39. doi: 10.1146/annurev.genet.35.102401.090633. Annu Rev Genet. 2001. PMID: 11700286 Review.
Cited by
-
Genetic Determinants of the Network of Primary Metabolism and Their Relationships to Plant Performance in a Maize Recombinant Inbred Line Population.Plant Cell. 2015 Jul;27(7):1839-56. doi: 10.1105/tpc.15.00208. Epub 2015 Jul 17. Plant Cell. 2015. PMID: 26187921 Free PMC article.
-
Two-stage two-locus models in genome-wide association.PLoS Genet. 2006 Sep 22;2(9):e157. doi: 10.1371/journal.pgen.0020157. PLoS Genet. 2006. PMID: 17002500 Free PMC article.
-
From Classical to Modern Computational Approaches to Identify Key Genetic Regulatory Components in Plant Biology.Int J Mol Sci. 2023 Jan 28;24(3):2526. doi: 10.3390/ijms24032526. Int J Mol Sci. 2023. PMID: 36768850 Free PMC article. Review.
-
The genetic architecture of reproductive isolation in Louisiana irises: flowering phenology.Genetics. 2007 Apr;175(4):1803-12. doi: 10.1534/genetics.106.068338. Epub 2007 Jan 21. Genetics. 2007. PMID: 17237511 Free PMC article.
-
Cytoplasmic genetic variation and extensive cytonuclear interactions influence natural variation in the metabolome.Elife. 2013 Oct 8;2:e00776. doi: 10.7554/eLife.00776. Elife. 2013. PMID: 24150750 Free PMC article.
Publication types
MeSH terms
Substances
Grants and funding
LinkOut - more resources
Molecular Biology Databases