A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.)

Abstract

Despite QTL mapping being a routine procedure in plant breeding, approaches that fully exploit data from multi-trait multi-environment (MTME) trials are limited. Mixed models have been proposed both for multi-trait QTL analysis and multi-environment QTL analysis, but these approaches break down when the number of traits and environments increases. We present models for an efficient QTL analysis of MTME data with mixed models by reducing the dimensionality of the genetic variance-covariance matrix by structuring this matrix using direct products of relatively simple matrices representing variation in the trait and environmental dimension. In the context of MTME data, we address how to model QTL by environment interactions and the genetic basis of heterogeneity of variance and correlations between traits and environments. We illustrate our approach with an example including five traits across eight stress trials in CIMMYT maize. We detected 36 QTLs affecting yield, anthesis-silking interval, male flowering, ear number, and plant height in maize. Our approach does not require specialised software as it can be implemented in any statistical package with mixed model facilities.

Genetic analysis of agronomic traits associated with plant architecture by QTL mapping in maize

“…For traits PH and EH, many studies on QTL mapping have been reported, and a large number of QTLs have been identified on 10 chromosomes in maize (Kraja and Dudley, 2000;Sibov et al, 2003;Lima et al, 2006;Malosetti et al, 2008;Bai et al, 2010). No new QTLs were identified for PH and EH, and our experiments were similar to previous studies in many aspects such as the identity of the parents, population type, genetic map, and ecological conditions.…”

“…No new QTLs were identified for PH and EH, and our experiments were similar to previous studies in many aspects such as the identity of the parents, population type, genetic map, and ecological conditions. However, previous studies were focused on F 2 populations (Malosetti et al, 2008;Zhang et al, 2010). The F 2 population can only be used once because the plants cannot propagate.…”

“…In maize, numerous QTLs have been identified that control agronomic traits, and these QTLs have been mapped to 10 chromosomes in maize (Tsonev et al, 2009;Qiu et al, 2011). For the traits related to plant architecture, PH and EH have been frequently studied (Kraja and Dudley, 2000;Sibov et al, 2003;Lima et al, 2006;Malosetti et al, 2008;Bai et al, 2010), and a large number of QTLs were found to be located in different chromosomal regions. There are also many reports on QTL identification of the LN trait (Jiang et al, 1999;Lima et al, 2006;Tang et al, 2007;Wang et al, 2008), and several studies have been performed on the LA and LO traits (Mickelson et al, 2002;Lu et al, 2007;Ku et al, 2010).…”

Multi-environment QTL mixed models for drought stress adaptation in wheat

“…For example, taller lines may have stored more water-soluble carbohydrate available for re-translocation and so have a potential advantage in environments with post-anthesis drought (Loss and Siddique 1994). To deal with trait interactions, Malosetti et al (2008) have proposed an extension of the methodology presented here to accommodate multi-trait multi-environment correlations.…”

Understanding the genetic basis of potato development using a multi-trait QTL analysis

“…As a consequence, a multi-trait QTL analysis of a single population allows the detection of closely linked chromosomal regions affecting several traits simultaneously (Jiang and Zeng 1995). Although different methodologies have been proposed not only to map multiple trait simultaneously (Jiang and Zeng 1995;Knott and Haley 2000;Malosetti et al 2008) but also to differentiate between close linkage and pleiotropy of coincident QTL (Jiang and Zeng 1995;Knott and Haley 2000;Lebreton et al 1998;Liu et al 2007), the identification of pleiotropic genes requires additional genomic information such as high density linkage maps and genome sequence information.…”