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      Genomic consequences of apple improvement

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          Abstract

          The apple ( Malus domestica) is one of the world’s most commercially important perennial crops and its improvement has been the focus of human effort for thousands of years. Here, we genetically characterise over 1000 apple accessions from the United States Department of Agriculture (USDA) germplasm collection using over 30,000 single-nucleotide polymorphisms (SNPs). We confirm the close genetic relationship between modern apple cultivars and their primary progenitor species, Malus sieversii from Central Asia, and find that cider apples derive more of their ancestry from the European crabapple, Malus sylvestris, than do dessert apples. We determine that most of the USDA collection is a large complex pedigree: over half of the collection is interconnected by a series of first-degree relationships. In addition, 15% of the accessions have a first-degree relationship with one of the top 8 cultivars produced in the USA. With the exception of ‘Honeycrisp’, the top 8 cultivars are interconnected to each other via pedigree relationships. The cultivars ‘Golden Delicious’ and ‘Red Delicious’ were found to have over 60 first-degree relatives, consistent with their repeated use by apple breeders. We detected a signature of intense selection for red skin and provide evidence that breeders also selected for increased firmness. Our results suggest that Americans are eating apples largely from a single family tree and that the apple’s future improvement will benefit from increased exploitation of its tremendous natural genetic diversity.

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          Most cited references63

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          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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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.
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              The variant call format and VCFtools

              Summary: The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API. Availability: http://vcftools.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Contributors
                GanYuan.Zhong@ars.usda.gov
                sean.myles@dal.ca
                Journal
                Hortic Res
                Hortic Res
                Horticulture Research
                Nature Publishing Group UK (London )
                2662-6810
                2052-7276
                1 January 2021
                1 January 2021
                2021
                : 8
                : 9
                Affiliations
                [1 ]GRID grid.55602.34, ISNI 0000 0004 1936 8200, Department of Plant, Food, and Environmental Sciences, Faculty of Agriculture, , Dalhousie University, ; Truro, NS Canada
                [2 ]GRID grid.463419.d, ISNI 0000 0001 0946 3608, USDA-ARS, , Plant Germplasm Preservation Research Unit, ; Fort Collins, CO USA
                [3 ]GRID grid.507316.6, USDA-ARS, , Grape Genetics Research Unit, ; Geneva, NY USA
                [4 ]GRID grid.55614.33, ISNI 0000 0001 1302 4958, Present Address: Agriculture and Agri-Food Canada, , Fredericton Research and Development Centre, ; Fredericton, NB Canada
                Author information
                http://orcid.org/0000-0002-3931-1258
                http://orcid.org/0000-0002-9978-6079
                http://orcid.org/0000-0001-6499-3337
                Article
                441
                10.1038/s41438-020-00441-7
                7775473
                33384408
                62d8ec34-e869-4d55-b314-320bdcde9281
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 October 2020
                : 9 November 2020
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                © The Author(s) 2021

                agricultural genetics,population genetics
                agricultural genetics, population genetics

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