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Improving precision and accuracy of genetic mapping with genotyping‐by‐sequencing data in outcrossing species.

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Title: Improving precision and accuracy of genetic mapping with genotyping‐by‐sequencing data in outcrossing species.
Authors: LaBonte, Nicholas R.1 (AUTHOR), Zerpa‐Catanho, Dessireé P.1 (AUTHOR), Liu, Siyao2 (AUTHOR), Xiao, Liang1 (AUTHOR), Dong, Hongxu2 (AUTHOR), Clark, Lindsay V.2 (AUTHOR), Sacks, Erik J.1,2 (AUTHOR) esacks@illinois.edu
Source: GCB Bioenergy. Jul2024, Vol. 16 Issue 7, p1-16. 16p.
Subject Terms: *Species, Gene mapping, Plant gene mapping, Locus (Genetics), False positive error, Genetic models, Pipeline failures
Abstract: Genotyping‐by‐sequencing (GBS) is a widely used strategy for obtaining large numbers of genetic markers in model and non‐model organisms. In crop plants, GBS‐derived marker datasets are frequently used to perform quantitative trait locus (QTL) mapping. In some plant species, however, high heterozygosity and complex genome structure mean that researchers must use care in handling GBS data to conduct QTL mapping most effectively. Such outbred crops include most of the perennial grass and tree species used for bioenergy. To identify strategies for increasing accuracy and precision of QTL mapping using GBS data in outbred crops, we conducted an empirical study of SNP‐calling and genetic map‐building pipeline parameters in a Miscanthus sinensis population, and a complementary simulation study to estimate the relationship between genome‐wide error rate, read depth, and marker number. The bioenergy grass Miscanthus is an obligate outcrossing species with a recent (diploidized) whole‐genome duplication. For the study of empirical M. sinensis data, we compared two SNP‐calling methods (one non‐reference‐based and one reference‐based), a series of depth filters (12×, 20×, 30×, and 40×) and two map‐construction methods (i.e., marker ordering: linkage‐only and order‐corrected based on a reference genome). We found that correcting the order of markers on a linkage map by using a high‐quality reference genome improved QTL precision (shorter confidence intervals). For typical GBS datasets of between 1000 and 5000 markers to build a genetic map for biparental populations, a depth filter set at 30× to 40× applied to outbred populations provided a genome‐wide genotype‐calling error rate of less than 1%, improved accuracy of QTL point estimates and minimized type I errors for identifying QTL. Based on these results, we recommend using a reference genome to correct the marker order of genetic maps and a robust genotype depth filter to improve QTL mapping for outbred crops. [ABSTRACT FROM AUTHOR]
Copyright of GCB Bioenergy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Improving precision and accuracy of genetic mapping with genotyping‐by‐sequencing data in outcrossing species.
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  Data: <searchLink fieldCode="AR" term="%22LaBonte%2C+Nicholas+R%2E%22">LaBonte, Nicholas R.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zerpa‐Catanho%2C+Dessireé+P%2E%22">Zerpa‐Catanho, Dessireé P.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Siyao%22">Liu, Siyao</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xiao%2C+Liang%22">Xiao, Liang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dong%2C+Hongxu%22">Dong, Hongxu</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Clark%2C+Lindsay+V%2E%22">Clark, Lindsay V.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sacks%2C+Erik+J%2E%22">Sacks, Erik J.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> esacks@illinois.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22GCB+Bioenergy%22">GCB Bioenergy</searchLink>. Jul2024, Vol. 16 Issue 7, p1-16. 16p.
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  Data: *<searchLink fieldCode="DE" term="%22Species%22">Species</searchLink><br /><searchLink fieldCode="DE" term="%22Gene+mapping%22">Gene mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+gene+mapping%22">Plant gene mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Locus+%28Genetics%29%22">Locus (Genetics)</searchLink><br /><searchLink fieldCode="DE" term="%22False+positive+error%22">False positive error</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+models%22">Genetic models</searchLink><br /><searchLink fieldCode="DE" term="%22Pipeline+failures%22">Pipeline failures</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Genotyping‐by‐sequencing (GBS) is a widely used strategy for obtaining large numbers of genetic markers in model and non‐model organisms. In crop plants, GBS‐derived marker datasets are frequently used to perform quantitative trait locus (QTL) mapping. In some plant species, however, high heterozygosity and complex genome structure mean that researchers must use care in handling GBS data to conduct QTL mapping most effectively. Such outbred crops include most of the perennial grass and tree species used for bioenergy. To identify strategies for increasing accuracy and precision of QTL mapping using GBS data in outbred crops, we conducted an empirical study of SNP‐calling and genetic map‐building pipeline parameters in a Miscanthus sinensis population, and a complementary simulation study to estimate the relationship between genome‐wide error rate, read depth, and marker number. The bioenergy grass Miscanthus is an obligate outcrossing species with a recent (diploidized) whole‐genome duplication. For the study of empirical M. sinensis data, we compared two SNP‐calling methods (one non‐reference‐based and one reference‐based), a series of depth filters (12×, 20×, 30×, and 40×) and two map‐construction methods (i.e., marker ordering: linkage‐only and order‐corrected based on a reference genome). We found that correcting the order of markers on a linkage map by using a high‐quality reference genome improved QTL precision (shorter confidence intervals). For typical GBS datasets of between 1000 and 5000 markers to build a genetic map for biparental populations, a depth filter set at 30× to 40× applied to outbred populations provided a genome‐wide genotype‐calling error rate of less than 1%, improved accuracy of QTL point estimates and minimized type I errors for identifying QTL. Based on these results, we recommend using a reference genome to correct the marker order of genetic maps and a robust genotype depth filter to improve QTL mapping for outbred crops. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of GCB Bioenergy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/gcbb.13167
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      – Code: eng
        Text: English
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        PageCount: 16
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    Subjects:
      – SubjectFull: Species
        Type: general
      – SubjectFull: Gene mapping
        Type: general
      – SubjectFull: Plant gene mapping
        Type: general
      – SubjectFull: Locus (Genetics)
        Type: general
      – SubjectFull: False positive error
        Type: general
      – SubjectFull: Genetic models
        Type: general
      – SubjectFull: Pipeline failures
        Type: general
    Titles:
      – TitleFull: Improving precision and accuracy of genetic mapping with genotyping‐by‐sequencing data in outcrossing species.
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              M: 07
              Text: Jul2024
              Type: published
              Y: 2024
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