Genome-wide linkage mapping of QTL for physiological traits in a Chinese wheat population using the 90K SNP array

Composite interval mapping (CIM) is the most commonly used method for mapping quantitative trait loci (QTL) with populations derived from biparental crosses. However, the algorithm implemented in the popular QTL Cartographer software may not completely ensure all its advantageous properties. In addition, different background marker selection methods may give very different mapping results, and the nature of the preferred method is not clear. A modified algorithm called inclusive composite interval mapping (ICIM) is proposed in this article. In ICIM, marker selection is conducted only once through stepwise regression by considering all marker information simultaneously, and the phenotypic values are then adjusted by all markers retained in the regression equation except the two markers flanking the current mapping interval. The adjusted phenotypic values are finally used in interval mapping (IM). The modified algorithm has a simpler form than that used in CIM, but a faster convergence speed. ICIM retains all advantages of CIM over IM and avoids the possible increase of sampling variance and the complicated background marker selection process in CIM. Extensive simulations using two genomes and various genetic models indicated that ICIM has increased detection power, a reduced false detection rate, and less biased estimates of QTL effects.

A restricted range in height and phenology of the elite Seri/Babax recombinant inbred line (RIL) population makes it ideal for physiological and genetic studies. Previous research has shown differential expression for yield under water deficit associated with canopy temperature (CT). In the current study, 167 RILs plus parents were phenotyped under drought (DRT), hot irrigated (HOT), and temperate irrigated (IRR) environments to identify the genomic regions associated with stress-adaptive traits. In total, 104 QTL were identified across a combination of 115 traits × 3 environments × 2 years, of which 14, 16, and 10 QTL were associated exclusively with DRT, HOT, and IRR, respectively. Six genomic regions were related to a large number of traits, namely 1B-a, 2B-a, 3B-b, 4A-a, 4A-b, and 5A-a. A yield QTL located on 4A-a explained 27 and 17% of variation under drought and heat stress, respectively. At the same location, a QTL explained 28% of the variation in CT under heat, while 14% of CT variation under drought was explained by a QTL on 3B-b. The T1BL.1RS (rye) translocation donated by the Seri parent was associated with decreased yield in this population. There was no co-location of consistent yield and phenology or height-related QTL, highlighting the utility of using a population with a restricted range in anthesis to facilitate QTL studies. Common QTL for drought and heat stress traits were identified on 1B-a, 2B-a, 3B-b, 4A-a, 4B-b, and 7A-a confirming their generic value across stresses. Yield QTL were shown to be associated with components of other traits, supporting the prospects for dissecting crop performance into its physiological and genetic components in order to facilitate a more strategic approach to breeding. Electronic supplementary material The online version of this article (doi:10.1007/s00122-010-1351-4) contains supplementary material, which is available to authorized users.

Through genome-wide association study, loci for grain yield and yield components were identified in chromosomes 5A and 6A in spring wheat (Triticum aestivum). Genome-wide association study (GWAS) was conducted for grain yield (YLD) and yield components on a wheat association mapping initiative (WAMI) population of 287 elite, spring wheat lines grown under temperate irrigated high-yield potential condition in Ciudad Obregón, Mexico, during four crop cycles (from 2009-2010 to 2012-2013). The population was genotyped with high-density Illumina iSelect 90K single nucleotide polymorphisms (SNPs) assay. An analysis of traits across subpopulations indicated that lines with 1B/1R translocation had higher YLD, grain weight, and taller plants than lines without the translocation. GWAS using 18,704 SNPs identified 31 loci that explained 5-14 % of the variation in individual traits. We identified SNPs in chromosome 5A and 6A that were significantly associated with yield and yield components. Four loci were detected for YLD in chromosomes 3B, 5A, 5B, and 6A and the locus in 5A explained 5 % of the variation for grain number/m(2). The locus for YLD in chromosome 6A also explained 6 % of the variation in grain weight. Loci significantly associated with maturity were identified in chromosomes 2B, 3B, 4B, 4D, and 6A and for plant height in 1A and 6A. Loci were also detected for canopy temperature at grain filling (2D, 4D, 6A), chlorophyll index at grain filling (3B and 6A), biomass (3D and 6A) and harvest index (1D, 1B, and 3B) that explained 5-10 % variation. These markers will be further validated.