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      Population- and genome-specific patterns of linkage disequilibrium and SNP variation in spring and winter wheat ( Triticum aestivum L.)

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          Single nucleotide polymorphisms (SNPs) are ideally suited for the construction of high-resolution genetic maps, studying population evolutionary history and performing genome-wide association mapping experiments. Here, we used a genome-wide set of 1536 SNPs to study linkage disequilibrium (LD) and population structure in a panel of 478 spring and winter wheat cultivars ( Triticum aestivum) from 17 populations across the United States and Mexico.


          Most of the wheat oligo pool assay (OPA) SNPs that were polymorphic within the complete set of 478 cultivars were also polymorphic in all subpopulations. Higher levels of genetic differentiation were observed among wheat lines within populations than among populations. A total of nine genetically distinct clusters were identified, suggesting that some of the pre-defined populations shared significant proportion of genetic ancestry. Estimates of population structure (F ST) at individual loci showed a high level of heterogeneity across the genome. In addition, seven genomic regions with elevated F ST were detected between the spring and winter wheat populations. Some of these regions overlapped with previously mapped flowering time QTL. Across all populations, the highest extent of significant LD was observed in the wheat D-genome, followed by lower LD in the A- and B-genomes. The differences in the extent of LD among populations and genomes were mostly driven by differences in long-range LD ( > 10 cM).


          Genome- and population-specific patterns of genetic differentiation and LD were discovered in the populations of wheat cultivars from different geographic regions. Our study demonstrated that the estimates of population structure between spring and winter wheat lines can identify genomic regions harboring candidate genes involved in the regulation of growth habit. Variation in LD suggests that breeding and selection had a different impact on each wheat genome both within and among populations. The higher extent of LD in the wheat D-genome versus the A- and B-genomes likely reflects the episodes of recent introgression and population bottleneck accompanying the origin of hexaploid wheat. The assessment of LD and population structure in this assembled panel of diverse lines provides critical information for the development of genetic resources for genome-wide association mapping of agronomically important traits in wheat.

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          Most cited references 40

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          Efficiency and power in genetic association studies.

          We investigated selection and analysis of tag SNPs for genome-wide association studies by specifically examining the relationship between investment in genotyping and statistical power. Do pairwise or multimarker methods maximize efficiency and power? To what extent is power compromised when tags are selected from an incomplete resource such as HapMap? We addressed these questions using genotype data from the HapMap ENCODE project, association studies simulated under a realistic disease model, and empirical correction for multiple hypothesis testing. We demonstrate a haplotype-based tagging method that uniformly outperforms single-marker tests and methods for prioritization that markedly increase tagging efficiency. Examining all observed haplotypes for association, rather than just those that are proxies for known SNPs, increases power to detect rare causal alleles, at the cost of reduced power to detect common causal alleles. Power is robust to the completeness of the reference panel from which tags are selected. These findings have implications for prioritizing tag SNPs and interpreting association studies.
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            Linkage disequilibrium in humans: models and data.

            In this review, we describe recent empirical and theoretical work on the extent of linkage disequilibrium (LD) in the human genome, comparing the predictions of simple population-genetic models to available data. Several studies report significant LD over distances longer than those predicted by standard models, whereas some data from short, intergenic regions show less LD than would be expected. The apparent discrepancies between theory and data present a challenge-both to modelers and to human geneticists-to identify which important features are missing from our understanding of the biological processes that give rise to LD. Salient features may include demographic complications such as recent admixture, as well as genetic factors such as local variation in recombination rates, gene conversion, and the potential segregation of inversions. We also outline some implications that the emerging patterns of LD have for association-mapping strategies. In particular, we discuss what marker densities might be necessary for genomewide association scans.
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              Genome plasticity a key factor in the success of polyploid wheat under domestication.

              Wheat was domesticated about 10,000 years ago and has since spread worldwide to become one of the major crops. Its adaptability to diverse environments and end uses is surprising given the diversity bottlenecks expected from recent domestication and polyploid speciation events. Wheat compensates for these bottlenecks by capturing part of the genetic diversity of its progenitors and by generating new diversity at a relatively fast pace. Frequent gene deletions and disruptions generated by a fast replacement rate of repetitive sequences are buffered by the polyploid nature of wheat, resulting in subtle dosage effects on which selection can operate.

                Author and article information

                BMC Genomics
                BMC Genomics
                BioMed Central
                29 December 2010
                : 11
                : 727
                [1 ]USDA ARS Genotyping Laboratory, Biosciences Research Laboratory, Fargo, ND, USA
                [2 ]Department of Plant Sciences, University of California, Davis, CA, USA
                [3 ]Plant Science Building, University of Nebraska, Lincoln, NE, USA
                [4 ]Department of Plant Pathology, Kansas State University, Manhattan, KS, USA
                [5 ]WestBred, LLC, Bozeman, MT, USA
                [6 ]Department of Plant Sciences, Montana State University, Bozeman, MT, USA
                [7 ]Dept. of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN, USA
                [8 ]Genetic Resources and Enhancement Unit, CIMMYT, Mexico, D.F., Mexico
                [9 ]Plant Science Department, South Dakota State University, Brookings, SD, USA
                [10 ]University of Idaho Aberdeen Research & Extension Center, Aberdeen ID, USA
                [11 ]USDA-ARS Wheat Genetics, Quality, Physiology & Disease Research Unit, Washington State University, Pullman WA, USA
                [12 ]Plant Sciences and Plant Pathology, Bozeman, MT, USA
                [13 ]Texas AgriLife Research and Extension Center, Amarillo, TX, USA
                [14 ]Soil and Crop Sciences Department, Colorado State University, Fort Collins, CO, USA
                [15 ]Oklahoma State University, Department of Plant and Soil Sciences, Stillwater, OK, USA
                [16 ]WestBred, LLC, Haven, KS, USA
                [17 ]Plant Breeding and Genetics, Cornell University, Ithaca, NY, USA
                [18 ]Institute of Biochemistry and Genetics, RAS, Ufa Russia
                Copyright ©2010 Chao et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<url></url>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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