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      Genome-wide association study for morphological, phenological, quality, and yield traits in einkorn ( Triticum monococcum L. subsp. monococcum)


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          Einkorn ( Triticum monococcum L. subsp. monococcum, 2 n = 2× = 14, A m A m ) is a diploid wheat whose cultivation was widespread in the Mediterranean and European area till the Bronze Age, before it was replaced by the more productive durum and bread wheats. Although scarcely cultivated nowadays, it has gained renewed interest due to its relevant nutritional properties and as source of genetic diversity for crop breeding. However, the molecular basis of many traits of interest in einkorn remain still unknown. A panel of 160 einkorn landraces, from different parts of the distribution area, was characterized for several phenotypic traits related to morphology, phenology, quality, and yield for 4 years in two locations. An approach based on co-linearity with the A genome of bread wheat, supported also by that with Triticum urartu genome, was exploited to perform association mapping, even without an einkorn anchored genome. The association mapping approach uncovered numerous marker-trait associations; for 37 of these, a physical position was inferred by homology with the bread wheat genome. Moreover, numerous associated regions were also assigned to the available T. monococcum contigs. Among the intervals detected in this work, three overlapped with regions previously described as involved in the same trait, while four other regions were localized in proximity of loci previously described and presumably refer to the same gene/QTL. The remaining associated regions identified in this work could represent a novel and useful starting point for breeding approaches to improve the investigated traits in this neglected species.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

            We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
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              PLINK: a tool set for whole-genome association and population-based linkage analyses.

              Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.

                Author and article information

                Role: Editor
                G3 (Bethesda)
                G3: Genes|Genomes|Genetics
                Oxford University Press
                November 2021
                23 August 2021
                23 August 2021
                : 11
                : 11
                : jkab281
                [1 ] CREA—Research Centre for Cereal and Industrial Crops , 13100 Vercelli, Italy
                [2 ] CREA—Research Centre for Genomics and Bioinformatics , 29017 Fiorenzuola d’Arda, Italy and
                [3 ] CREA—Research Centre for Animal Production and Aquaculture , 26900 Lodi, Italy
                Author notes
                Corresponding author: CREA—Research Centre for Vegetable and Ornamental Crops, Corso Inglesi 508, 18038 Sanremo, IM, Italy. Email: andrea.volante@ 123456crea.gov.it , Present address: CREA—Research Centre for Vegetable and Ornamental Crops, Corso Inglesi 508, 18038 Sanremo, IM, Italy.

                Andrea Volante and Delfina Barabaschi authors contributed equally to the paper.

                © The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 27 July 2021
                : 15 January 2021
                : 14 October 2021
                Page count
                Pages: 12
                Funded by: Risorse Genetiche Vegetali;
                Funded by: Food and Agriculture Organization;
                Award ID: DM3825

                einkorn,wheat,genome-wide association study (gwas),marker-trait associations (mtas)
                einkorn, wheat, genome-wide association study (gwas), marker-trait associations (mtas)


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