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Theor Appl Genet. 2017 May;130(5):1065-1079. doi: 10.1007/s00122-017-2871-y. Epub 2017 Mar 25.

Development of a QTL-environment-based predictive model for node addition rate in common bean.

Author information

1
Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, 32611, USA.
2
School of Forest Resources and Conservation, University of Florida, Gainesville, FL, 32611, USA.
3
Horticultural Sciences Department, University of Florida, Gainesville, FL, 32611, USA.
4
Agronomy Department, University of Florida, Gainesville, FL, 32611, USA.
5
Department of Plant Sciences, North Dakota State University, Fargo, ND, 58108, USA.
6
CIAT, Cali, Colombia.
7
University of Puerto Rico, Mayagüez, 00682, Puerto Rico.
8
Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, 32611, USA. correllm@ufl.edu.

Abstract

This work reports the effects of the genetic makeup, the environment and the genotype by environment interactions for node addition rate in an RIL population of common bean. This information was used to build a predictive model for node addition rate. To select a plant genotype that will thrive in targeted environments it is critical to understand the genotype by environment interaction (GEI). In this study, multi-environment QTL analysis was used to characterize node addition rate (NAR, node day- 1) on the main stem of the common bean (Phaseolus vulgaris L). This analysis was carried out with field data of 171 recombinant inbred lines that were grown at five sites (Florida, Puerto Rico, 2 sites in Colombia, and North Dakota). Four QTLs (Nar1, Nar2, Nar3 and Nar4) were identified, one of which had significant QTL by environment interactions (QEI), that is, Nar2 with temperature. Temperature was identified as the main environmental factor affecting NAR while day length and solar radiation played a minor role. Integration of sites as covariates into a QTL mixed site-effect model, and further replacing the site component with explanatory environmental covariates (i.e., temperature, day length and solar radiation) yielded a model that explained 73% of the phenotypic variation for NAR with root mean square error of 16.25% of the mean. The QTL consistency and stability was examined through a tenfold cross validation with different sets of genotypes and these four QTLs were always detected with 50-90% probability. The final model was evaluated using leave-one-site-out method to assess the influence of site on node addition rate. These analyses provided a quantitative measure of the effects on NAR of common beans exerted by the genetic makeup, the environment and their interactions.

PMID:
28343247
DOI:
10.1007/s00122-017-2871-y
[Indexed for MEDLINE]

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