Construction of a SNP and SSR linkage map in autotetraploid blueberry using genotyping by sequencing

McCallum, Susan; Graham, Julie; Jorgensen, Linzi; Rowland, Lisa J.; Bassil, Nahla V.; Hancock, James F.; Wheeler, E. J.; Vining, Kelly J.; Poland, Jesse; Olmstead, James W.; Buck, Emily; Wiedow, Claudia; Jackson, Eric W.; Brown, Allan; Hackett, Christine A.

doi:10.1007/s11032-016-0443-5

Cited by 62 publications

(52 citation statements)

References 47 publications

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“…Our second contribution is to consider information from Mendelian segregation in NGS genotyping methods. Many experimental designs in plant breeding are derived from progeny test data [Li et al, 2014, Tennessen et al, 2014, McCallum et al, 2016, Shirasawa et al, 2017. Such progenies often result from a bi-parental cross, including half and full-sib families, or from selfing.…”

Section: Introductionmentioning

confidence: 99%

Genotyping Polyploids from Messy Sequencing Data

Gerard¹,

Ferrão

Garcia

et al. 2018

Preprint

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Detecting and quantifying the differences in individual genomes (i.e. genotyping), plays a fundamental role in most modern bioinformatics pipelines. Many scientists now use reduced representation nextgeneration sequencing (NGS) approaches for genotyping. Genotyping diploid individuals using NGS is a well-studied field and similar methods for polyploid individuals are just emerging. However, there are many aspects of NGS data, particularly in polyploids, that remain unexplored by most methods. We provide two main contributions in this paper: (i) We draw attention to, and then model, common aspects of NGS data: sequencing error, allelic bias, overdispersion, and outlying observations. (ii) Many datasets feature related individuals, and so we use the structure of Mendelian segregation to build an empirical Bayes approach for genotyping polyploid individuals. We assess the accuracy of our method in simulations and apply it to a dataset of hexaploid sweet potatoes (Ipomoea batatas). An R package implementing our method is available at https://github.com/dcgerard/updog.

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Section: Introductionmentioning

confidence: 99%

Genotyping Polyploids from Messy Sequencing Data

Gerard¹,

Ferrão

Garcia

et al. 2018

Preprint

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“…Examples of allopolyploid SNP arrays include cotton (Hulse-Kemp et al, 2015), oat (Tinker et al, 2014), oilseed rape (Dalton- Morgan et al, 2014;Clarke et al, 2016), peanut (Pandey et al, 2017), strawberry (Bassil et al, 2015) and wheat (Akhunov et al, 2009;Cavanagh et al, 2013;Wang et al, 2014b;Winfield et al, 2016). Untargeted approaches such as genotyping-by-sequencing have also been applied, for example in autopolyploids such as alfalfa Yu et al, 2017), blueberry (McCallum et al, 2016), bluestem prairie grass (Andropogon gerardii) (McAllister and Miller, 2016), cocksfoot (Dactylis glomerata) (Bushman et al, 2016), potato (Uitdewilligen et al, 2013;Sverrisdóttir et al, 2017), sugarcane (Balsalobre et al, 2017;Yang et al, 2017b) and sweet potato (Shirasawa et al, 2017), and in allopolyploids such as coffee (Moncada et al, 2016), cotton (Islam et al, 2015;Reddy et al, 2017), intermediate wheatgrass (Thinopyrum intermedium) (Kantarski et al, 2017), oat (Chaffin et al, 2016), prairie cordgrass (Spartina pectinata) (Crawford et al, 2016), shepherd's purse (Capsella bursa-pastoris) (Cornille et al, 2016), wheat (Poland et al, 2012;Edae et al, 2015) and zoysiagrass (Zoysia japonica) (McCamy et al, 2018) (noting that the precise classification of some of these species as auto-or allopolyploids has yet to be conclusively determined). Whatever the technology used, it is clear that we are currently witnessing an explosion of interest in polyploid genomics.…”

Section: Genotyping Technologiesmentioning

confidence: 99%

“…Probably the most well-known autopolyploid mapping software is TetraploidMap (Hackett and Luo, 2003;Hackett et al, 2007), which has been used in studies of various autotetraploid species such as potato, alfalfa, rose and blueberry (e.g. Robins et al, 2008;Gar et al, 2011;McCallum et al, 2016)). Recently, its successor TetraploidSNPMap (TSNPM) has been released to accommodate high-density marker data from SNP arrays (Hackett et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Those multi-dose markers can connect homologous chromosomes within parents and between parents and can therefore be used to integrate them. For tetraploids, methods to estimate linkage between higher dose dominant markers have been developed , and applied to construct integrated linkage maps Luo et al, 2001;McCallum et al, 2016). Later, these methods have been extended and applied to bi-allelic SNP markers (Hackett et al, 2013;.…”

Section: Introductionmentioning

confidence: 99%

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Genetic mapping in polyploids

Bourke¹

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“…Probably the most well-known autopolyploid mapping software is TetraploidMap (Hackett and Luo, 2003;Hackett et al, 2007), which has been used in studies of various autotetraploid species such as potato, alfalfa, rose and blueberry (e.g. (Bradshaw et al, 2008;Robins et al, 2008;Gar et al, 2011;McCallum et al, 2016)). Recently, its successor TetraploidSNPMap (TSNPM) has been released to accommodate high-density marker data from SNP arrays (Hackett et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

polymapR – linkage analysis and genetic map construction from F1 populations of outcrossing polyploids

Bourke

Geest

Voorrips

et al. 2017

Preprint

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MotivationPolyploid species carry more than two copies of each chromosome, a condition found in many of the world's most important crops. Genetic mapping in polyploids is more complex than in diploid species, resulting in a lack of available software tools. These are needed if we are to realise all the opportunities offered by modern genotyping platforms for genetic research and breeding in polyploid crops. ResultspolymapR is an R package for genetic linkage analysis and integrated genetic map construction from bi-parental populations of outcrossing autopolyploids. It can currently analyse triploid, tetraploid and hexaploid marker datasets and is applicable to various crops including potato, leek, alfalfa, blueberry, chrysanthemum, sweet potato or kiwifruit. It can detect, estimate and correct for preferential chromosome pairing, and has been tested on high-density marker datasets from potato, rose and chrysanthemum, generating high-density integrated linkage maps in all of these crops. Availability and ImplementationpolymapR is freely available under the general public license from the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/packages=polymapR.

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Construction of a SNP and SSR linkage map in autotetraploid blueberry using genotyping by sequencing

Cited by 62 publications

References 47 publications

Genotyping Polyploids from Messy Sequencing Data

Genotyping Polyploids from Messy Sequencing Data

Genetic mapping in polyploids

polymapR – linkage analysis and genetic map construction from F1 populations of outcrossing polyploids

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