Yield-related salinity tolerance traits identified in a nested association mapping (NAM) population of wild barley

Abstract

Producing sufficient food for nine billion people by 2050 will be constrained by soil salinity, especially in irrigated systems. To improve crop yield, greater understanding of the genetic control of traits contributing to salinity tolerance in the field is needed. Here, we exploit natural variation in exotic germplasm by taking a genome-wide association approach to a new nested association mapping population of barley called HEB-25. The large population (1,336 genotypes) allowed cross-validation of loci, which, along with two years of phenotypic data collected from plants irrigated with fresh and saline water, improved statistical power. We dissect the genetic architecture of flowering time under high salinity and we present genes putatively affecting this trait and salinity tolerance. In addition, we identify a locus on chromosome 2H where, under saline conditions, lines homozygous for the wild allele yielded 30% more than did lines homozygous for the Barke allele. Introgressing this wild allele into elite cultivars could markedly improve yield under saline conditions.

Barley yield formation under abiotic stress depends on the interplay between flowering time genes and environmental cues

“…The usefulness of wild germplasm for future breeding has often been emphasized [14][15][16] , mostly as a source to improve biotic resistance and abiotic stress tolerance rather than to directly increase grain yield 17 . Recent studies in wild barley indicate the existence of vast phenological variation for important agronomic traits [18][19][20][21][22][23][24][25] .…”

The usefulness of multi-parent multi-environment QTL analysis: an illustration in different NAM populations

“…The potential weakness of QTLxE effects with respect to the main QTL effects in the US-NAM and EU-NAM populations, could explain why, in some cases, method M1 obtained better results than the GxE analyses (M2-M4). The existence of GxE effects could be more important in MPP-ME QTL experiments where the environmental conditions are more contrasted, for example, in experiments testing the same population in control versus heat or salt stress conditions (Saade et al, 2016). Table 2, method M3 detected on average more QTLs than M4.…”

“…Therefore, researchers have developed statistical procedures to detect QTLs taking the genotype by environment (GxE) interactions into consideration Korte et al, 2012). Several MPPs have been tested in multiple environments (MPP-ME) (Buckler et al, 2009;Giraud et al, 2014;Saade et al, 2016) but only few studies have proposed a proper MPP GxE QTL detection methodology (Piepho and Pillen, 2004;Verbyla et al, 2014). Most of the researches average the phenotypic values by calculating adjusted means or predictions across the environments that represent an average phenotypic value (Giraud et al, 2014;Poland et al, 2011;Buckler et al, 2009).…”

“…In other articles, people performed separate analyses in each environment (e.g. Saade et al (2016)).…”

Editorial: Recent Advances in Flowering Time Control

“…Furthermore, several flowering time genes like Ppd-H1 ( PRR37 ) and HvSDW1 ( GA20ox2 ) were linked to both, developmental and yield-related traits. In a salinity tolerance study with HEB-25, the wild barley allele at the HvCEN locus (Antirrhinum CENTRORADIALIS, TFL1 -like) promoted flowering and maturity, resulting in a higher harvest index and a higher yield under salt stress in the field (Saade et al, 2016). These findings indicate that searching for allelic variants of known flowering time genes, also taken from related wild species, may substantially support future plant breeding efforts to increase plant performance under optimal cultivation conditions as well as under stress.…”