Genotyping Polyploids from Messy Sequencing Data

Gerard, David; Ferrão, Luís Felipe Ventorim; Garcia, Antônio Augusto Franco; Stephens, Matthew

doi:10.1101/281550

Cited by 8 publications

(12 citation statements)

References 66 publications

(91 reference statements)

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“…In our simulations, we found that sequence depth had the largest effect on accurately estimating the genotype and admixture proportion of an individual for a given ploidal level, similar to findings in Gerard et al (2018). The degree of differentiation among demes (driven by F divergence from the ancestral population) had the second largest effect on the accuracy of genotype and ancestry estimates (as seen in Figures S2, S4 and S6, and Table S2).…”

Section: Discussionsupporting

confidence: 75%

Model‐based genotype and ancestry estimation for potential hybrids with mixed‐ploidy

Shastry

Adams

Lindtke

et al. 2021

Molecular Ecology Resources

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Species are distributed across geographic ranges and potentially heterogeneous environments and experience barriers to dispersal, leading to clinal genetic differentiation, genetic subdivisions into local populations or 'demes', or some combination of both (Bradburd et al., 2013;Endler, 1977;Gompert & Buerkle, 2016). Even species with high rates of dispersal can have geographic ranges that are large relative to dispersal distances (e.g., Novembre et al., 2008; Phifer-Rixey et al., 2018), such that the distribution of traits and alleles is commonly heterogeneous and stratified among geographic locations. Quantifying this population heterogeneity and stratification is a fundamental component of empirical population genetics, both as a context for the study of evolutionary dynamics and as a component of learning about trait genetics in natural populations. Information about population structure and mixtures can reveal aspects of the underlying evolutionary processes and has played a significant role in shaping our understanding of the nature of hybridization, speciation and adaptation. This includes knowledge of the prevalence of gene flow

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Section: Discussionsupporting

confidence: 75%

Model‐based genotype and ancestry estimation for potential hybrids with mixed‐ploidy

Shastry

Adams

Lindtke

et al. 2021

Molecular Ecology Resources

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“…Both, missing values and heterozygous undercalling, hinder linkage analysis, with a substantial impact in marker ordering and phasing, increasing the total map length. To overcome this limitation, new approaches have been developed to improve genotype calling in outcrossing species as well as to impute missing genotypes (Swarts et al, 2014;Covarrubias-Pazaran et al, 2016;Kim et al, 2016;Gerard et al, 2018). Here, we used a genotype calling method that takes advantages of the relative abundance of each allele (read counts) and the Mendelian properties of the mapping populations (Serang et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Construction of a High-Density Genetic Map of Acca sellowiana (Berg.) Burret, an Outcrossing Species, Based on Two Connected Mapping Populations

et al. 2021

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Acca sellowiana, known as feijoa or pineapple guava, is a diploid, (2n = 2x = 22) outcrossing fruit tree species native to Uruguay and Brazil. The species stands out for its highly aromatic fruits, with nutraceutical and therapeutic value. Despite its promising agronomical value, genetic studies on this species are limited. Linkage genetic maps are valuable tools for genetic and genomic studies, and constitute essential tools in breeding programs to support the development of molecular breeding strategies. A high-density composite genetic linkage map of A. sellowiana was constructed using two genetically connected populations: H5 (TCO × BR, N = 160) and H6 (TCO × DP, N = 184). Genotyping by sequencing (GBS) approach was successfully applied for developing single nucleotide polymorphism (SNP) markers. A total of 4,921 SNP markers were identified using the reference genome of the closely related species Eucalyptus grandis, whereas other 4,656 SNPs were discovered using a de novo pipeline. The individual H5 and H6 maps comprised 1,236 and 1,302 markers distributed over the expected 11 linkage groups, respectively. These two maps spanned a map length of 1,593 and 1,572 cM, with an average inter-marker distance of 1.29 and 1.21 cM, respectively. A large proportion of markers were common to both maps and showed a high degree of collinearity. The composite map consisted of 1,897 SNPs markers with a total map length of 1,314 cM and an average inter-marker distance of 0.69. A novel approach for the construction of composite maps where the meiosis information of individuals of two connected populations is captured in a single estimator is described. A high-density, accurate composite map based on a consensus ordering of markers provides a valuable contribution for future genetic research and breeding efforts in A. sellowiana. A novel mapping approach based on an estimation of multipopulation recombination fraction described here may be applied in the construction of dense composite genetic maps for any other outcrossing diploid species.

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“…reflecting genotype uncertainty (Gerard et al 2018), but requires excessive amounts of 48 computational time to run. SuperMASSA (Serang et al 2012) and fitPoly (Voorrips et al 2011) 49 were originally designed for calling polyploid genotypes from fluorescence-based SNP assays 50 and have been adapted for sequencing data, but fail to call genotypes when low read depth 51 results in high variance of read depth ratios.…”

Section: Introductionmentioning

confidence: 99%

“…Overview 79 polyRAD implements Bayesian genotype estimation, similar to that proposed and 80 implemented by several other groups (Li 2011;Nielsen et al 2011;Garrison and Marth 2012;81 Korneliussen et al 2014;Maruki and Lynch 2017;Gerard et al 2018;Blischak et al 2018). In 82 all polyRAD pipelines, genotype prior probabilities (P(Gi)) represent, for a given allele and 83 individual, the probability that i is the true allele copy number, before taking allelic read depth 84 into account.…”

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confidence: 99%

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polyRAD: Genotype calling with uncertainty from sequencing data in polyploids and diploids

Clark

Lipka

Sacks

2018

Preprint

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17Low or uneven read depth is a common limitation of genotyping-by-sequencing (GBS) 18 and restriction site-associated DNA sequencing (RAD-seq), resulting in high missing data rates, 19 heterozygotes miscalled as homozygotes, and uncertainty of allele copy number in heterozygous 20 polyploids. Bayesian genotype calling can mitigate these issues, but previously has only been 21 implemented in software that requires a reference genome or uses priors that may be 22 inappropriate for the population. Here we present several novel Bayesian algorithms that 23 estimate genotype posterior probabilities, all of which are implemented in a new R package, 24 polyRAD. Appropriate priors can be specified for mapping populations, populations in Hardy-25Weinberg equilibrium, or structured populations, and in each case can be informed by genotypes 26 at linked markers. The polyRAD software imports read depth from several existing pipelines, 27 and outputs continuous or discrete numerical genotypes suitable for analyses such as genome-28 wide association and genomic prediction. 29 30 Approximately 70% of vascular plant species are recent polyploids, yet genomic 31 resources and bioinformatics tools for polyploids typically lag behind those for diploids (Moghe 32 and Shiu 2014; Renny-Byfield and Wendel 2014; Bourke et al. 2018b). Reduced representation 33 DNA sequencing methods, such as genotyping-by-sequencing (GBS) and restriction site-34 associated DNA sequencing (RAD-seq), have made high-density genotyping considerably more 35 accessible and affordable (Poland and Rife 2012; Davey et al. 2013). However, the two most 36 popular pipelines for processing GBS and RAD-seq data, Stacks (Catchen et al. 2013) and 37 TASSEL (Glaubitz et al. 2014), do not output polyploid genotypes. Though pipelines for 38polyploids are available, each have limitations that prevent their general application. For 39 example, the UNEAK pipeline is designed for diploidized polyploids only (Lu et al. 2013). 40 HaploTag is specialized for self-fertilizing polyploids (Tinker et al. 2016). FreeBayes and 41 GATK can output polyploid genotypes, but require a reference genome (McKenna et al. 2010; 42 Garrison and Marth 2012). The software EBG imports read depth from other pipelines to 43 estimate auto-or allopolyploid genotypes (Blischak et al. 2018) but requires allele frequency 44 estimations from the parent species for allopolyploids. The R package updog estimates 45 polyploid genotypes from read depth, modeling preferential pairing and accounting for multiple 46 technical issues that can arise with sequencing data, and can output posterior mean genotypes 47

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Genotyping Polyploids from Messy Sequencing Data

Cited by 8 publications

References 66 publications

Model‐based genotype and ancestry estimation for potential hybrids with mixed‐ploidy

Model‐based genotype and ancestry estimation for potential hybrids with mixed‐ploidy

Construction of a High-Density Genetic Map of Acca sellowiana (Berg.) Burret, an Outcrossing Species, Based on Two Connected Mapping Populations

polyRAD: Genotype calling with uncertainty from sequencing data in polyploids and diploids

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