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      High-quality, genome-wide SNP genotypic data for pedigreed germplasm of the diploid outbreeding species apple, peach, and sweet cherry through a common workflow

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          Abstract

          High-quality genotypic data is a requirement for many genetic analyses. For any crop, errors in genotype calls, phasing of markers, linkage maps, pedigree records, and unnoticed variation in ploidy levels can lead to spurious marker-locus-trait associations and incorrect origin assignment of alleles to individuals. High-throughput genotyping requires automated scoring, as manual inspection of thousands of scored loci is too time-consuming. However, automated SNP scoring can result in errors that should be corrected to ensure recorded genotypic data are accurate and thereby ensure confidence in downstream genetic analyses. To enable quick identification of errors in a large genotypic data set, we have developed a comprehensive workflow. This multiple-step workflow is based on inheritance principles and on removal of markers and individuals that do not follow these principles, as demonstrated here for apple, peach, and sweet cherry. Genotypic data was obtained on pedigreed germplasm using 6-9K SNP arrays for each crop and a subset of well-performing SNPs was created using ASSIsT. Use of correct (and corrected) pedigree records readily identified violations of simple inheritance principles in the genotypic data, streamlined with FlexQTL software. Retained SNPs were grouped into haploblocks to increase the information content of single alleles and reduce computational power needed in downstream genetic analyses. Haploblock borders were defined by recombination locations detected in ancestral generations of cultivars and selections. Another round of inheritance-checking was conducted, for haploblock alleles (i.e., haplotypes). High-quality genotypic data sets were created using this workflow for pedigreed collections representing the U.S. breeding germplasm of apple, peach, and sweet cherry evaluated within the RosBREED project. These data sets contain 3855, 4005, and 1617 SNPs spread over 932, 103, and 196 haploblocks in apple, peach, and sweet cherry, respectively. The highly curated phased SNP and haplotype data sets, as well as the raw iScan data, of germplasm in the apple, peach, and sweet cherry Crop Reference Sets is available through the Genome Database for Rosaceae.

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          Genotyping errors: causes, consequences and solutions.

          Although genotyping errors affect most data and can markedly influence the biological conclusions of a study, they are too often neglected. Errors have various causes, but their occurrence and effect can be limited by considering these causes in the production and analysis of the data. Procedures that have been developed for dealing with errors in linkage studies, forensic analyses and non-invasive genotyping should be applied more broadly to any genetic study. We propose a protocol for estimating error rates and recommend that these measures be systemically reported to attest the reliability of published genotyping studies.
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            Microsatellite genotyping errors: detection approaches, common sources and consequences for paternal exclusion.

            Microsatellite genotyping errors will be present in all but the smallest data sets and have the potential to undermine the conclusions of most downstream analyses. Despite this, little rigorous effort has been made to quantify the size of the problem and to identify the commonest sources of error. Here, we use a large data set comprising almost 2000 Antarctic fur seals Arctocephalus gazella genotyped at nine hypervariable microsatellite loci to explore error detection methods, common sources of error and the consequences of errors on paternal exclusion. We found good concordance among a range of contrasting approaches to error-rate estimation, our range being 0.0013 to 0.0074 per single locus PCR (polymerase chain reaction). The best approach probably involves blind repeat-genotyping, but this is also the most labour-intensive. We show that several other approaches are also effective at detecting errors, although the most convenient alternative, namely mother-offspring comparisons, yielded the lowest estimate of the error rate. In total, we found 75 errors, emphasizing their ubiquitous presence. The most common errors involved the misinterpretation of allele banding patterns (n = 60, 80%) and of these, over a third (n = 22, 36.7%) were due to confusion between homozygote and adjacent allele heterozygote genotypes. A specific test for whether a data set contains the expected number of adjacent allele heterozygotes could provide a useful tool with which workers can assess the likely size of the problem. Error rates are also positively correlated with both locus polymorphism and product size, again indicating aspects where extra effort at error reduction should be directed. Finally, we conducted simulations to explore the potential impact of genotyping errors on paternity exclusion. Error rates as low as 0.01 per allele resulted in a rate of false paternity exclusion exceeding 20%. Errors also led to reduced estimates of male reproductive skew and increases in the numbers of pups that matched more than one candidate male. Because even modest error rates can be strongly influential, we recommend that error rates should be routinely published and that researchers make an attempt to calculate how robust their analyses are to errors.
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              Genome-Wide SNP Detection, Validation, and Development of an 8K SNP Array for Apple

              As high-throughput genetic marker screening systems are essential for a range of genetics studies and plant breeding applications, the International RosBREED SNP Consortium (IRSC) has utilized the Illumina Infinium® II system to develop a medium- to high-throughput SNP screening tool for genome-wide evaluation of allelic variation in apple (Malus×domestica) breeding germplasm. For genome-wide SNP discovery, 27 apple cultivars were chosen to represent worldwide breeding germplasm and re-sequenced at low coverage with the Illumina Genome Analyzer II. Following alignment of these sequences to the whole genome sequence of ‘Golden Delicious’, SNPs were identified using SoapSNP. A total of 2,113,120 SNPs were detected, corresponding to one SNP to every 288 bp of the genome. The Illumina GoldenGate® assay was then used to validate a subset of 144 SNPs with a range of characteristics, using a set of 160 apple accessions. This validation assay enabled fine-tuning of the final subset of SNPs for the Illumina Infinium® II system. The set of stringent filtering criteria developed allowed choice of a set of SNPs that not only exhibited an even distribution across the apple genome and a range of minor allele frequencies to ensure utility across germplasm, but also were located in putative exonic regions to maximize genotyping success rate. A total of 7867 apple SNPs was established for the IRSC apple 8K SNP array v1, of which 5554 were polymorphic after evaluation in segregating families and a germplasm collection. This publicly available genomics resource will provide an unprecedented resolution of SNP haplotypes, which will enable marker-locus-trait association discovery, description of the genetic architecture of quantitative traits, investigation of genetic variation (neutral and functional), and genomic selection in apple.
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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: MethodologyRole: SoftwareRole: Writing – original draft
                Role: Data curationRole: Formal analysisRole: MethodologyRole: SoftwareRole: Writing – original draft
                Role: Data curationRole: Formal analysisRole: MethodologyRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: MethodologyRole: Writing – review & editing
                Role: Data curationRole: Formal analysis
                Role: MethodologyRole: SoftwareRole: Writing – review & editing
                Role: MethodologyRole: SoftwareRole: Writing – review & editing
                Role: Funding acquisitionRole: Resources
                Role: Funding acquisitionRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Funding acquisitionRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: ResourcesRole: SoftwareRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                27 June 2019
                2019
                : 14
                : 6
                : e0210928
                Affiliations
                [1 ] Department of Horticulture, Washington State University, Pullman, WA, United States of America
                [2 ] Department of Horticultural Science, University of Minnesota, St Paul, MN, United States of America
                [3 ] Institute of Biology and Environmental Sciences, Carl von Ossietzky Universität, Oldenburg, Germany
                [4 ] Department of Horticulture, Michigan State University, East Lansing, MI, United States of America
                [5 ] Department of Plant and Environmental Sciences, Clemson University, Clemson, South Carolina, United States of America
                [6 ] Biometris, Wageningen UR, Wageningen, The Netherlands
                [7 ] Research & Technology Center, Hendrix Genetics, Boxmeer, The Netherlands
                [8 ] USDA-ARS, National Clonal Germplasm Repository, Corvallis, OR, United States of America
                [9 ] Plant Breeding, Wageningen UR, Wageningen, The Netherlands
                College of Agricultural Sciences, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-7206-7421
                http://orcid.org/0000-0003-0706-1108
                Article
                PONE-D-18-37133
                10.1371/journal.pone.0210928
                6597046
                31246947
                27c4092c-b37f-4e27-b0ad-24c4256a1df1

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 4 January 2019
                : 19 April 2019
                Page count
                Figures: 2, Tables: 4, Pages: 33
                Funding
                This project was co-funded by the USDA-NIFA-Specialty Crop Research Initiative projects, “RosBREED: Enabling marker-assisted breeding in Rosaceae” (2009-51181-05808), “RosBREED: Combining disease resistance with horticultural quality in new rosaceous cultivars” (2014-51181-22378), USDA NIFA Hatch projects 0211277 and 1014919, and the FruitBreedomics project No 265582: “Integrated approach for increasing breeding efficiency in fruit tree crops” that was co-funded by the EU seventh Framework Programme. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Genetics
                Molecular Genetics
                Biology and Life Sciences
                Molecular Biology
                Molecular Genetics
                Biology and Life Sciences
                Genetics
                Heredity
                Genetic Mapping
                Haplotypes
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Gene Mapping
                Research and Analysis Methods
                Molecular Biology Techniques
                Gene Mapping
                Biology and Life Sciences
                Organisms
                Eukaryota
                Plants
                Fruits
                Peaches
                Biology and Life Sciences
                Organisms
                Eukaryota
                Plants
                Fruits
                Cherries
                Biology and Life Sciences
                Organisms
                Eukaryota
                Plants
                Fruits
                Apples
                Biology and Life Sciences
                Agriculture
                Agronomy
                Plant Breeding
                Biology and Life Sciences
                Agriculture
                Crop Science
                Crops
                Fruit Crops
                Custom metadata
                The private data might be obtained with the permission of the germplasm owners by contacting the crop’s respective RosBREED team leader. Please contact Jim Luby for apple and Ksenija Gasic for peach. Jim Luby can be reached at lubyx001@ 123456umn.edu and Ksenija Gasic can be reached at kgasic@ 123456clemson.edu .

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