Most significant genome regions involved in the control of earliness traits in bread wheat, as revealed by QTL meta-analysis

Abstract

Earliness is one of the most important adaptation traits in plant breeding. Our purpose was to identify the genome regions of bread wheat involved in the control of earliness and its three components: photoperiod sensitivity (PS), vernalization requirement (VR) and intrinsic earliness (IE). A QTL meta-analysis was carried out to examine the replicability of QTL across 13 independent studies and to propose meta-QTL (MQTL). Initial QTL were projected on a recent consensus map (2004). Quality criteria were proposed to assess the reliability of this projection. These criteria were based on the distances between markers in the QTL regions. Chromosomes of groups 2 and 5 had a greater incidence on earliness control as they carry the known, major genes Ppd and Vrn. Other chromosome regions played an intermediate role in earliness control: 4A [heading date (HD) Meta-QTL], 4B (HD MQTL), 2B (VR MQTL) and 5B (IE MQTL). Markers at this four MQTL should prove helpful in marker-assisted selection, to better control earliness.

Quantitative Trait Loci for Grain Yield and Adaptation of Durum Wheat (Triticum durum Desf.) Across a Wide Range of Water Availability

“…However, earliness per se genes have been mapped on chrs. 2BL and 2DL (Scarth and Law 1983;Worland 1996) and a vernalization QTL was positioned near the QTL region detected in our population (Hanocq et al 2006).…”

“…QTL for heading date: HD has long been considered as a major adaptive trait with a particularly relevant role also in domestication (Snape et al 2001;Peng et al 2003;Laurie et al 2004;Hanocq et al 2006). Adaptation of wheat to a range of environments with very different photoperiod conditions and winter temperatures is mainly accomplished by combinations of natural alleles at major genes for vernalization requirements and photoperiod sensitivity (Worland 1996).…”

Integration of Major QTLs of Important Agronomic Traits in Soybean

“…The meta-analysis approach, as developed by Goffinet and Gerber (2000), however does provide a means to alleviate the extent of this uncertainty, since it improves the capacity to identify the true number of QTL present, and the precision of their location by reducing their associated CI. QTL associated with early maturity in bread wheat have recently been identified using a meta-analysisbased approach (Hanocq, 2007), and similarly, resistance to gray leaf spot in maize has been genetically defined by the integration of >50 QTL (Shi, 2007). The proportion of the phenotypic variance explained by a given QTL (its R 2 v a l u e ) i s t h e m o s t i m p o r t a n t parameter in deciding whether marker assistance can be more efficient than conventional phenotypic selection alone (Bernardo 2001;Bernardo and Charcosset 2006).…”

Rice Root Genetic Architecture: Meta-analysis from a Drought QTL Database

“…Software packages based on this methodology are available such as BioMercator (Arcade et al 2004) and Meta-QTL (Veyrieras et al 2007), a new software that removes some of the limitations of BioMercator in the number of QTLs it can handle at once. Meta-QTL analyses for flowering time in maize ), resistance to a nematode in soybean (Guo et al 2006), earliness in bread wheat (Hanocq et al 2007), and N uptake in maize (Coque et al 2008) have already shown the usefulness of this approach in determining the number of "true" QTLs underlying the observed QTLs and in positioning consensus QTLs with more precision. In rice, Norton et al (2008) conducted a study on root QTLs, but it was limited to the Azucena × Bala population and only three QTL clusters.…”