Use of the additive main effects and multiplicative interaction model in QTL mapping for adaptation in barley
- PMID: 24162195
- DOI: 10.1007/BF00225723
Use of the additive main effects and multiplicative interaction model in QTL mapping for adaptation in barley
Abstract
The additive main effects and multiplicative interaction (AMMI) model has emerged as a powerful analytical tool for genotype x environment studies. The objective of the present study was to assess its value in quantitative trait locus (QTL) mapping. This was done through the analysis of a large two-way table of genotype-by-environment data of barley (Hordeum vulgare L.) grain yields, where the genotypes constituted a genetic population suitable for mapping studies. Grain yield data of 150 doubled haploid lines derived from the 'Steptoe' x 'Morex' cross, and the two parental lines, were taken by the North American Barley Genome Mapping Project (NABGMP) at 16 environments throughout the barley production areas of the USA and Canada. Four regions of the genome were responsible for most of the differential genotypic expression across environments. They accounted for approximately 50% of the genotypic main effect and 30% of the genotype x environment interaction (GE) sums of squares. The magnitude and sign of AMMI scores for genotypes and sites facilitate inferences about specific interactions. The parallel use of classification (cluster analysis of environments) and ordination (principal component analysis of GE matrix) techniques allowed most of the variation present in the genotype x environment matrix to be summarized in just a few dimensions, specifically four QTLs showing differential adaptation to four clusters of environments. Thus, AMMI genotypic scores, when the genotypes constituted a population suitable for QTL mapping, could provide an adequate way of resolving the magnitude and nature of QTL x environment interactions.
Similar articles
-
Isolate-specific QTLs of resistance to leaf stripe (Pyrenophora graminea) in the 'Steptoe' x 'Morex' spring barley cross.Theor Appl Genet. 2003 Feb;106(4):668-75. doi: 10.1007/s00122-002-1115-x. Epub 2002 Oct 19. Theor Appl Genet. 2003. PMID: 12595996
-
Fine mapping of a malting-quality QTL complex near the chromosome 4H S telomere in barley.Theor Appl Genet. 2004 Aug;109(4):750-60. doi: 10.1007/s00122-004-1688-7. Theor Appl Genet. 2004. PMID: 15164174
-
Genotype by environment interaction using AMMI model and estimation of additive and epistasis gene effects for 1000-kernel weight in spring barley (Hordeum vulgare L.).J Appl Genet. 2019 May;60(2):127-135. doi: 10.1007/s13353-019-00490-2. Epub 2019 Mar 15. J Appl Genet. 2019. PMID: 30877656 Free PMC article.
-
Molecular dissection of a dormancy QTL region near the chromosome 7 (5H) L telomere in barley.Theor Appl Genet. 2003 Aug;107(3):552-9. doi: 10.1007/s00122-003-1281-5. Epub 2003 May 8. Theor Appl Genet. 2003. PMID: 12736778
-
Hordoindolines are associated with a major endosperm-texture QTL in barley (Hordeum vulgare).Genome. 2002 Jun;45(3):584-91. doi: 10.1139/g02-008. Genome. 2002. PMID: 12033628
Cited by
-
Using probe genotypes to dissect QTL × environment interactions for grain yield components in winter wheat.Theor Appl Genet. 2010 Nov;121(8):1501-17. doi: 10.1007/s00122-010-1406-6. Epub 2010 Aug 10. Theor Appl Genet. 2010. PMID: 20697687
-
Multi-environment QTL mapping in blackcurrant (Ribes nigrum L.) using mixed models.Theor Appl Genet. 2010 Nov;121(8):1483-8. doi: 10.1007/s00122-010-1404-8. Epub 2010 Jul 23. Theor Appl Genet. 2010. PMID: 20652803
-
Use of trial clustering to study QTL x environment effects for grain yield and related traits in maize.Theor Appl Genet. 2004 Dec;110(1):92-105. doi: 10.1007/s00122-004-1781-y. Epub 2004 Nov 12. Theor Appl Genet. 2004. PMID: 15551040
-
Genome-wide association mapping for kernel and malting quality traits using historical European barley records.PLoS One. 2014 Nov 5;9(11):e110046. doi: 10.1371/journal.pone.0110046. eCollection 2014. PLoS One. 2014. PMID: 25372869 Free PMC article.
-
An expectation and maximization algorithm for estimating Q X E interaction effects.Theor Appl Genet. 2012 May;124(8):1375-87. doi: 10.1007/s00122-012-1794-x. Theor Appl Genet. 2012. PMID: 22297562
References
LinkOut - more resources
Full Text Sources