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      Genome‐wide association study for 13 agronomic traits reveals distribution of superior alleles in bread wheat from the Yellow and Huai Valley of China

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          Summary

          Bread wheat is a leading cereal crop worldwide. Limited amount of superior allele loci restricted the progress of molecular improvement in wheat breeding. Here, we revealed new allelic variation distribution for 13 yield‐related traits in series of genome‐wide association studies ( GWAS) using the wheat 90K genotyping assay, characterized in 163 bread wheat cultivars. Agronomic traits were investigated in 14 environments at three locations over 3 years. After filtering SNP data sets, GWAS using 20 689 high‐quality SNPs associated 1769 significant loci that explained, on average, ~20% of the phenotypic variation, both detected already reported loci and new promising genomic regions. Of these, repetitive and pleiotropic SNPs on chromosomes 6 AS, 6 AL, 6 BS, 5 BL and 7 AS were significantly linked to thousand kernel weight, for example BS00021705_51 on 6 BS and wsnp_Ex_c32624_41252144 on 6 AS, with phenotypic variation explained ( PVE) of ~24%, consistently identified in 12 and 13 of the 14 environments, respectively. Kernel length‐related SNPs were mainly identified on chromosomes 7 BS, 6 AS, 5 AL and 5 BL. Plant height‐related SNPs on chromosomes 4 DS, 6 DL, 2 DS and 1 BL were, respectively, identified in more than 11 environments, with averaged PVE of ~55%. Four SNPs were confirmed to be important genetic loci in two RIL populations. Based on repetivity and PVE, a total of 41 SNP loci possibly played the key role in modulating yield‐related traits of the cultivars surveyed. Distribution of superior alleles at the 41 SNP loci indicated that superior alleles were getting popular with time and modern cultivars had integrated many superior alleles, especially for peduncle length‐ and plant height‐related superior alleles. However, there were still 19 SNP loci showing less than percentages of 50% in modern cultivars, suggesting they should be paid more attention to improve yield‐related traits of cultivars in the Yellow and Huai wheat region. This study could provide useful information for dissection of yield‐related traits and valuable genetic loci for marker‐assisted selection in Chinese wheat breeding programme.

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          A modified algorithm for the improvement of composite interval mapping.

          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.
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            Population stratification and spurious allelic association.

            Great efforts and expense have been expended in attempts to detect genetic polymorphisms contributing to susceptibility to complex human disease. Concomitantly, technology for detection and scoring of single nucleotide polymorphisms (SNPs) has undergone rapid development, extensive catalogues of SNPs across the genome have been constructed, and SNPs have been increasingly used as a means for investigation of the genetic causes of complex human diseases. For many diseases, population-based studies of unrelated individuals--in which case-control and cohort studies serve as standard designs for genetic association analysis--can be the most practical and powerful approach. However, extensive debate has arisen about optimum study design, and considerable concern has been expressed that these approaches are prone to population stratification, which can lead to biased or spurious results. Over the past decade, a great shift has been noted, away from case-control and cohort studies, towards family-based association designs. These designs have fewer problems with population stratification but have greater genotyping and sampling requirements, and data can be difficult or impossible to gather. We discuss past evidence for population stratification on genotype-phenotype association studies, review methods to detect and account for it, and present suggestions for future study design and analysis.
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              Genome-wide association study for grain yield and related traits in an elite spring wheat population grown in temperate irrigated environments.

              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.
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                Author and article information

                Contributors
                chf0088@163.com
                Journal
                Plant Biotechnol J
                Plant Biotechnol. J
                10.1111/(ISSN)1467-7652
                PBI
                Plant Biotechnology Journal
                John Wiley and Sons Inc. (Hoboken )
                1467-7644
                1467-7652
                02 March 2017
                August 2017
                : 15
                : 8 ( doiID: 10.1111/pbi.2017.15.issue-8 )
                : 953-969
                Affiliations
                [ 1 ] Agronomy College/National Key Laboratory of Wheat and Maize Crop Science/Collaborative Innovation Center of Henan Grain Crops Henan Agricultural University Zhengzhou China
                [ 2 ] Henan Key Laboratory of Nuclear Agricultural Sciences/Isotope Institute Co., Ltd Henan Academy of Sciences Zhengzhou China
                Author notes
                [*] [* ] Correspondence (Tel +86 371 63558537; fax +86 371 63558122; email chf0088@ 123456163.com )
                Article
                PBI12690
                10.1111/pbi.12690
                5506658
                28055148
                21462b16-c40e-4a9e-ad60-f613d370f4bd
                © 2017 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 24 October 2016
                : 23 December 2016
                : 29 December 2016
                Page count
                Figures: 6, Tables: 5, Pages: 17, Words: 14294
                Funding
                Funded by: National Natural Science Foundation
                Award ID: 31370031
                Funded by: National Key Research and Development Program
                Award ID: 2016YFD0101802
                Funded by: Henan Major Science and Technology Projects
                Award ID: 161100110500
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                pbi12690
                August 2017
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.1.4 mode:remove_FC converted:19.07.2017

                Biotechnology
                bread wheat (triticum aestivum l.),gwas,qtl mapping,wheat 90k snp assay,yield‐related traits,superior allele

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