eprintid: 5745
rev_number: 9
eprint_status: archive
userid: 30
dir: disk0/00/00/57/45
datestamp: 2012-03-16 08:52:00
lastmod: 2012-03-16 08:52:00
status_changed: 2012-03-16 08:52:00
type: article
metadata_visibility: show
contact_email: Library-ICRISAT@cgiar.org
creators_name: Crossa, J
creators_name: Vargas, M
creators_name: Eeuwijk, F A van
creators_name: Jiang, C
creators_name: Edmeades, G O
creators_name: Hoisington, D A
icrisatcreators_name: Hoisington, D A
affiliation: CIMMYT(Mexico)
affiliation: Universidad Autónoma Chapingo(Mexico)
affiliation: Wageningen Agricultural University(Wageningen)
affiliation: ICRISAT(Patancheru)
country: Mexico
country: Netherlands
country: India
title: Interpreting genotype × environment interaction in tropical maize using linked molecular markers and environmental covariables
ispublished: pub
subjects: s2.13
full_text_status: restricted
keywords: Biplot · Factorial regression · Genetic
marker · Genotype´environment interaction ·
Quantitative trait loci · Quantitative trait loci
´ environment interaction · Partial least squares
regression
note: D. A. Hoisington is presently with ICRISAT (Patancheru)
abstract: An understanding of the genetic and environmental
basis of genotype´environment interaction (GEI)
is of fundamental importance in plant breeding. In mapping
quantitative trait loci (QTLs), suitable genetic populations
are grown in different environments causing
QTLs´environment interaction (QEI). The main objective
of the present study is to show how Partial Least
Squares (PLS) regression and Factorial Regression (FR)
models using genetic markers and environmental covariables
can be used for studying QEI related to GEI. Biomass
data were analyzed from a multi-environment trial
consisting of 161 lines from a F3:4 maize segregating
population originally created with the purpose of mapping
QTLs loci and investigating adaptation differences
between highland and lowland tropical maize. PLS and
FR methods detected 30 genetic markers (out of 86) that
explained a sizeable proportion of the interaction of
maize lines over four contrasting environments involving
two low-altitude sites, one intermediate-altitude site, and
one high-altitude site for biomass production. Based on a
previous study, most of the 30 markers were associated
with QTLs for biomass and exhibited significant QEI. It
was found that marker loci in lines with positive GEI for
the highland environments contained more highland alleles,
whereas marker loci in lines with positive GEI for
intermediate and lowland environments contained more
lowland alleles. In addition, PLS and FR models identified maximum temperature as the most-important environmental
covariable for GEI. Using a stepwise variable
selection procedure, a FR model was constructed for
GEI and QEI that exclusively included cross products
between genetic markers and environmental covariables.
Higher maximum temperature in low- and intermediatealtitude
sites affected the expression of some QTLs,
while minimum temperature affected the expression of
other QTLs.
date: 1999
date_type: published
publication: TAG Theoretical and Applied Genetics
volume: 99
number: 3-4
publisher: Springer Verlag
pagerange: 611-625
refereed: TRUE
issn: 1432-2242
official_url: http://dx.doi.org/10.1007/s001220051276 
related_url_url: http://scholar.google.co.in/scholar?as_q=%22Interpreting+genotype+%C2%B4+environment+interaction+in+tropical+maize+using+linked+molecular+markers+and+environmental+covariables%22&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=title&as_sauthors=&
related_url_type: author
citation:   Crossa, J and Vargas, M and Eeuwijk, F A van and Jiang, C and Edmeades, G O and Hoisington, D A  (1999) Interpreting genotype × environment interaction in tropical maize using linked molecular markers and environmental covariables.  TAG Theoretical and Applied Genetics, 99 (3-4).  pp. 611-625.  ISSN 1432-2242     
document_url: http://oar.icrisat.org/5745/1/TheorApplGenet_99_611%E2%80%93625_1999.pdf