Title:	Flexible Genotyping for Polyploids
Version:	2.1.5
Description:	Implements empirical Bayes approaches to genotype polyploids from next generation sequencing data while accounting for allele bias, overdispersion, and sequencing error. The main functions are flexdog() and multidog(), which allow the specification of many different genotype distributions. Also provided are functions to simulate genotypes, rgeno(), and read-counts, rflexdog(), as well as functions to calculate oracle genotyping error rates, oracle_mis(), and correlation with the true genotypes, oracle_cor(). These latter two functions are useful for read depth calculations. Run browseVignettes(package = "updog") in R for example usage. See Gerard et al. (2018) <doi:10.1534/genetics.118.301468> and Gerard and Ferrao (2020) <doi:10.1093/bioinformatics/btz852> for details on the implemented methods.
Depends:	R (≥ 3.4.0)
License:	GPL-3
BugReports:	https://github.com/dcgerard/updog/issues
URL:	https://github.com/dcgerard/updog/
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.2.3
LinkingTo:	Rcpp, RcppArmadillo
Imports:	Rcpp (≥ 0.12.16), RcppArmadillo, assertthat, ggplot2, reshape2, foreach, future, doFuture, doRNG, methods, iterators
Suggests:	covr, testthat, SuppDists, knitr, rmarkdown, VariantAnnotation, GenomicRanges, S4Vectors, IRanges
VignetteBuilder:	knitr
NeedsCompilation:	yes
Packaged:	2023-11-28 17:50:18 UTC; dgerard
Author:	David Gerard [aut, cre]
Maintainer:	David Gerard <gerard.1787@gmail.com>
Repository:	CRAN
Date/Publication:	2023-11-29 15:50:02 UTC

updog Flexible Genotyping for Polyploids

Description

Implements empirical Bayes approaches to genotype polyploids from next generation sequencing data while accounting for allele bias, overdispersion, and sequencing error. The main functions are flexdog() and multidog(), which allow the specification of many different genotype distributions. Also provided are functions to simulate genotypes, rgeno(), and read-counts, rflexdog(), as well as functions to calculate oracle genotyping error rates, oracle_mis(), and correlation with the true genotypes, oracle_cor(). These latter two functions are useful for read depth calculations. Run browseVignettes(package = "updog") in R for example usage. See Gerard et al. (2018) <doi:10.1534/genetics.118.301468> and Gerard and Ferrao (2020) <doi:10.1093/bioinformatics/btz852> for details on the implemented methods.

Details

The package is named updog for "Using Parental Data for Offspring Genotyping" because we originally developed the method for full-sib populations, but it works now for more general populations.

Our best competitor is probably the fitPoly package, which you can check out at https://cran.r-project.org/package=fitPoly. Though, we think that updog returns better calibrated measures of uncertainty when you have next-generation sequencing data.

If you find a bug or want an enhancement, please submit an issue at https://github.com/dcgerard/updog/issues.

updog Functions

flexdog()

The main function that fits an empirical Bayes approach to genotype polyploids from next generation sequencing data.

multidog()

A convenience function for running flexdog() over many SNPs. This function provides support for parallel computing.

format_multidog()

Return arrayicized elements from the output of multidog().

filter_snp()

Filter SNPs based on the output of multidog()

rgeno()

simulate the genotypes of a sample from one of the models allowed in flexdog().

rflexdog()

Simulate read-counts from the flexdog() model.

plot.flexdog()

Plotting the output of flexdog().

plot.multidog()

Plotting the output of multidog().

oracle_joint()

The joint distribution of the true genotype and an oracle estimator.

oracle_plot()

Visualize the output of oracle_joint().

oracle_mis()

The oracle misclassification error rate (Bayes rate).

oracle_cor()

Correlation between the true genotype and the oracle estimated genotype.

updog Datasets

snpdat

A small example dataset for using flexdog.

uitdewilligen

A small example dataset

Author(s)

David Gerard

References

• Gerard, D., Ferrão, L. F. V., Garcia, A. A. F., & Stephens, M. (2018). Genotyping Polyploids from Messy Sequencing Data. Genetics, 210(3), 789-807. doi:10.1534/genetics.118.301468.

• Gerard, David, and Luís Felipe Ventorim Ferrão. "Priors for genotyping polyploids." Bioinformatics 36, no. 6 (2020): 1795-1800. doi:10.1093/bioinformatics/btz852.

The Beta-Binomial Distribution

Description

Density, distribution function, quantile function and random generation for the beta-binomial distribution when parameterized by the mean mu and the overdispersion parameter rho rather than the typical shape parameters.

Usage

dbetabinom(x, size, mu, rho, log)

pbetabinom(q, size, mu, rho, log_p)

qbetabinom(p, size, mu, rho)

rbetabinom(n, size, mu, rho)

Arguments

Details

Let \mu and \rho be the mean and overdispersion parameters. Let \alpha and \beta be the usual shape parameters of a beta distribution. Then we have the relation

\mu = \alpha/(\alpha + \beta),

and

\rho = 1/(1 + \alpha + \beta).

This necessarily means that

\alpha = \mu (1 - \rho)/\rho,

and

\beta = (1 - \mu) (1 - \rho)/\rho.

Value

Either a random sample (rbetabinom), the density (dbetabinom), the tail probability (pbetabinom), or the quantile (qbetabinom) of the beta-binomial distribution.

Functions

• dbetabinom(): Density function.

• pbetabinom(): Distribution function.

• qbetabinom(): Quantile function.

• rbetabinom(): Random generation.

Author(s)

David Gerard

Examples

x <- rbetabinom(n = 10, size = 10, mu = 0.1, rho = 0.01)
dbetabinom(x = 1, size = 10, mu = 0.1, rho = 0.01, log = FALSE)
pbetabinom(q = 1, size = 10, mu = 0.1, rho = 0.01, log_p = FALSE)
qbetabinom(p = 0.6, size = 10, mu = 0.1, rho = 0.01)

Filter SNPs based on the output of multidog().

Description

Filter based on provided logical predicates in terms of the variable names in x$snpdf. This function filters both x$snpdf and x$inddf.

Usage

filter_snp(x, expr)

Arguments

Author(s)

David Gerard

See Also

multidog():

For the variables in x$snpdf which you can filter by.

Examples

## Not run: 
data("uitdewilligen")
mout <- multidog(refmat = t(uitdewilligen$refmat),
                 sizemat = t(uitdewilligen$sizemat),
                 ploidy = uitdewilligen$ploidy,
                 nc = 2)

## The following filters are for educational purposes only and should
## not be taken as a default filter:
mout2 <- filter_snp(mout, bias < 0.8 & od < 0.003)

## End(Not run)

Flexible genotyping for polyploids from next-generation sequencing data.

Description

Genotype polyploid individuals from next generation sequencing (NGS) data while assuming the genotype distribution is one of several forms. flexdog does this while accounting for allele bias, overdispersion, sequencing error. The method is described in detail in Gerard et. al. (2018) and Gerard and Ferrão (2020). See multidog() for running flexdog on multiple SNPs in parallel.

Usage

flexdog(
  refvec,
  sizevec,
  ploidy,
  model = c("norm", "hw", "bb", "s1", "s1pp", "f1", "f1pp", "flex", "uniform", "custom"),
  p1ref = NULL,
  p1size = NULL,
  p2ref = NULL,
  p2size = NULL,
  snpname = NULL,
  bias_init = exp(c(-1, -0.5, 0, 0.5, 1)),
  verbose = TRUE,
  prior_vec = NULL,
  ...
)

Arguments

Details

Possible values of the genotype distribution (values of model) are:

"norm"

A distribution whose genotype frequencies are proportional to the density value of a normal with some mean and some standard deviation. Unlike the "bb" and "hw" options, this will allow for distributions both more and less dispersed than a binomial. This seems to be the most robust to violations in modeling assumptions, and so is the default. This prior class was developed in Gerard and Ferrão (2020).

"hw"

A binomial distribution that results from assuming that the population is in Hardy-Weinberg equilibrium (HWE). This actually does pretty well even when there are minor to moderate deviations from HWE. Though it does not perform as well as the '"norm"' option when there are severe deviations from HWE.

"bb"

A beta-binomial distribution. This is an overdispersed version of "hw" and can be derived from a special case of the Balding-Nichols model.

"s1"

This prior assumes the individuals are all full-siblings resulting from one generation of selfing. I.e. there is only one parent. This model assumes a particular type of meiotic behavior: polysomic inheritance with bivalent, non-preferential pairing.

"f1"

This prior assumes the individuals are all full-siblings resulting from one generation of a bi-parental cross. This model assumes a particular type of meiotic behavior: polysomic inheritance with bivalent, non-preferential pairing.

"f1pp"

This prior allows for double reduction and preferential pairing in an F1 population of tretraploids.

"s1pp"

This prior allows for double reduction and preferential pairing in an S1 population of tretraploids.

"flex"

Generically any categorical distribution. Theoretically, this works well if you have a lot of individuals. In practice, it seems to be much less robust to violations in modeling assumptions.

"uniform"

A discrete uniform distribution. This should never be used in practice.

"custom"

A pre-specified prior distribution. You specify it using the prior_vec argument. You should almost never use this option in practice.

You might think a good default is model = "uniform" because it is somehow an "uninformative prior." But it is very informative and tends to work horribly in practice. The intuition is that it will estimate the allele bias and sequencing error rates so that the estimated genotypes are approximately uniform (since we are assuming that they are approximately uniform). This will usually result in unintuitive genotyping since most populations don't have a uniform genotype distribution. I include it as an option only for completeness. Please don't use it.

The value of prop_mis is a very intuitive measure for the quality of the SNP. prop_mis is the posterior proportion of individuals mis-genotyped. So if you want only SNPS that accurately genotype, say, 95% of the individuals, you could discard all SNPs with a prop_mis over 0.05.

The value of maxpostprob is a very intuitive measure for the quality of the genotype estimate of an individual. This is the posterior probability of correctly genotyping the individual when using geno (the posterior mode) as the genotype estimate. So if you want to correctly genotype, say, 95% of individuals, you could discard all individuals with a maxpostprob of under 0.95. However, if you are just going to impute missing genotypes later, you might consider not discarding any individuals as flexdog's genotype estimates will probably be more accurate than other more naive approaches, such as imputing using the grand mean.

In most datasets I've examined, allelic bias is a major issue. However, you may fit the model assuming no allelic bias by setting update_bias = FALSE and bias_init = 1.

Prior to using flexdog, during the read-mapping step, you could try to get rid of allelic bias by using WASP (doi:10.1101/011221). If you are successful in removing the allelic bias (because its only source was the read-mapping step), then setting update_bias = FALSE and bias_init = 1 would be reasonable. You can visually inspect SNPs for bias by using plot_geno().

flexdog(), like most methods, is invariant to which allele you label as the "reference" and which you label as the "alternative". That is, if you set refvec with the number of alternative read-counts, then the resulting genotype estimates will be the estimated allele dosage of the alternative allele.

Value

An object of class flexdog, which consists of a list with some or all of the following elements:

bias

The estimated bias parameter.

seq

The estimated sequencing error rate.

od

The estimated overdispersion parameter.

num_iter

The number of EM iterations ran. You should be wary if this equals itermax.

llike

The maximum marginal log-likelihood.

postmat

A matrix of posterior probabilities of each genotype for each individual. The rows index the individuals and the columns index the allele dosage.

genologlike

A matrix of genotype log-likelihoods of each genotype for each individual. The rows index the individuals and the columns index the allele dosage.

gene_dist

The estimated genotype distribution. The ith element is the proportion of individuals with genotype i-1.

par

A list of the final estimates of the parameters of the genotype distribution. The elements included in par depends on the value of model:

model = "norm":

mu:

The normal mean.

sigma:

The normal standard deviation (not variance).

model = "hw":

alpha:

The major allele frequency.

model = "bb":

alpha:

The major allele frequency.

tau:

The overdispersion parameter. See the description of rho in the Details of betabinom().

model = "s1":

pgeno:

The allele dosage of the parent.

alpha:

The mixture proportion of the discrete uniform (included and fixed at a small value mostly for numerical stability reasons). See the description of fs1_alpha in flexdog_full().

model = "f1":

p1geno:

The allele dosage of the first parent.

p2geno:

The allele dosage of the second parent.

alpha:

The mixture proportion of the discrete uniform (included and fixed at a small value mostly for numerical stability reasons). See the description of fs1_alpha in flexdog_full().

model = "s1pp":

ell1:

The estimated dosage of the parent.

tau1:

The estimated double reduction parameter of the parent. Available if ell1 is 1, 2, or 3. Identified if ell1 is 1 or 3.

gamma1:

The estimated preferential pairing parameter. Available if ell1 is 2. However, it is not returned in an identified form.

alpha:

The mixture proportion of the discrete uniform (included and fixed at a small value mostly for numerical stability reasons). See the description of fs1_alpha in flexdog_full().

model = "f1pp":

ell1:

The estimated dosage of parent 1.

ell2:

The estimated dosage of parent 2.

tau1:

The estimated double reduction parameter of parent 1. Available if ell1 is 1, 2, or 3. Identified if ell1 is 1 or 3.

tau2:

The estimated double reduction parameter of parent 2. Available if ell2 is 1, 2, or 3. Identified if ell2 is 1 or 3.

gamma1:

The estimated preferential pairing parameter of parent 1. Available if ell1 is 2. However, it is not returned in an identified form.

gamma2:

The estimated preferential pairing parameter of parent 2. Available if ell2 is 2. However, it is not returned in an identified form.

alpha:

The mixture proportion of the discrete uniform (included and fixed at a small value mostly for numerical stability reasons). See the description of fs1_alpha in flexdog_full().

model = "flex":

par is an empty list.

model = "uniform":

par is an empty list.

model = "custom":

par is an empty list.

geno

The posterior mode genotype. These are your genotype estimates.

maxpostprob

The maximum posterior probability. This is equivalent to the posterior probability of correctly genotyping each individual.

postmean

The posterior mean genotype. In downstream association studies, you might want to consider using these estimates.

input$refvec

The value of refvec provided by the user.

input$sizevec

The value of sizevec provided by the user.

input$ploidy

The value of ploidy provided by the user.

input$model

The value of model provided by the user.

input$p1ref

The value of p1ref provided by the user.

input$p1size

The value of p1size provided by the user.

input$p2ref

The value of p2ref provided by the user.

input$p2size

The value of p2size provided by the user.

input$snpname

The value of snpname provided by the user.

prop_mis

The posterior proportion of individuals genotyped incorrectly.

Author(s)

David Gerard

References

• Gerard, D., Ferrão, L. F. V., Garcia, A. A. F., & Stephens, M. (2018). Genotyping Polyploids from Messy Sequencing Data. Genetics, 210(3), 789-807. doi:10.1534/genetics.118.301468.

• Gerard, David, and Luís Felipe Ventorim Ferrão. "Priors for genotyping polyploids." Bioinformatics 36, no. 6 (2020): 1795-1800. doi:10.1093/bioinformatics/btz852.

See Also

Run browseVignettes(package = "updog") in R for example usage. Other useful functions include:

multidog()

For running flexdog() on multiple SNPs in parallel.

flexdog_full()

For additional parameter options when running flexdog().

rgeno()

For simulating genotypes under the allowable prior models in flexdog().

rflexdog()

For simulating read-counts under the assumed likelihood model in flexdog().

plot.flexdog()

For plotting the output of flexdog().

oracle_mis()

For calculating the oracle genotyping error rates. This is useful for read-depth calculations before collecting data. After you have data, using the value of prop_mis is better.

oracle_cor()

For calculating the correlation between the true genotypes and an oracle estimator (useful for read-depth calculations before collecting data).

Examples

## An S1 population where the first individual
## is the parent.
data("snpdat")
ploidy  <- 6
refvec  <- snpdat$counts[snpdat$snp == "SNP2"]
sizevec <- snpdat$size[snpdat$snp == "SNP2"]
fout    <- flexdog(refvec   = refvec[-1],
                   sizevec  = sizevec[-1],
                   ploidy   = ploidy,
                   model    = "s1",
                   p1ref    = refvec[1],
                   p1size   = sizevec[1])
plot(fout)



## A natural population. We will assume a
## normal prior since there are so few
## individuals.
data("uitdewilligen")
ploidy  <- 4
refvec  <- uitdewilligen$refmat[, 1]
sizevec <- uitdewilligen$sizemat[, 1]
fout    <- flexdog(refvec  = refvec,
                   sizevec = sizevec,
                   ploidy  = ploidy,
                   model   = "norm")
plot(fout)

Flexible genotyping for polyploids from next-generation sequencing data.

Description

Genotype polyploid individuals from next generation sequencing (NGS) data while assuming the genotype distribution is one of several forms. flexdog_full() does this while accounting for allele bias, overdispersion, and sequencing error. This function has more options than flexdog and is only meant for expert users. The method is described in detail in Gerard et. al. (2018) and Gerard and Ferrão (2020).

Usage

flexdog_full(
  refvec,
  sizevec,
  ploidy,
  model = c("norm", "hw", "bb", "s1", "s1pp", "f1", "f1pp", "flex", "uniform", "custom"),
  verbose = TRUE,
  mean_bias = 0,
  var_bias = 0.7^2,
  mean_seq = -4.7,
  var_seq = 1,
  mean_od = -5.5,
  var_od = 0.5^2,
  seq = 0.005,
  bias = 1,
  od = 0.001,
  update_bias = TRUE,
  update_seq = TRUE,
  update_od = TRUE,
  itermax = 200,
  tol = 10^-4,
  fs1_alpha = 10^-3,
  p1ref = NULL,
  p1size = NULL,
  p2ref = NULL,
  p2size = NULL,
  snpname = NULL,
  prior_vec = NULL,
  seq_upper = 0.05
)

Arguments

Details

Possible values of the genotype distribution (values of model) are:

"norm"

A distribution whose genotype frequencies are proportional to the density value of a normal with some mean and some standard deviation. Unlike the "bb" and "hw" options, this will allow for distributions both more and less dispersed than a binomial. This seems to be the most robust to violations in modeling assumptions, and so is the default. This prior class was developed in Gerard and Ferrão (2020).

"hw"

A binomial distribution that results from assuming that the population is in Hardy-Weinberg equilibrium (HWE). This actually does pretty well even when there are minor to moderate deviations from HWE. Though it does not perform as well as the '"norm"' option when there are severe deviations from HWE.

"bb"

A beta-binomial distribution. This is an overdispersed version of "hw" and can be derived from a special case of the Balding-Nichols model.

"s1"

This prior assumes the individuals are all full-siblings resulting from one generation of selfing. I.e. there is only one parent. This model assumes a particular type of meiotic behavior: polysomic inheritance with bivalent, non-preferential pairing.

"f1"

This prior assumes the individuals are all full-siblings resulting from one generation of a bi-parental cross. This model assumes a particular type of meiotic behavior: polysomic inheritance with bivalent, non-preferential pairing.

"f1pp"

This prior allows for double reduction and preferential pairing in an F1 population of tretraploids.

"s1pp"

This prior allows for double reduction and preferential pairing in an S1 population of tretraploids.

"flex"

Generically any categorical distribution. Theoretically, this works well if you have a lot of individuals. In practice, it seems to be much less robust to violations in modeling assumptions.

"uniform"

A discrete uniform distribution. This should never be used in practice.

"custom"

A pre-specified prior distribution. You specify it using the prior_vec argument. You should almost never use this option in practice.

You might think a good default is model = "uniform" because it is somehow an "uninformative prior." But it is very informative and tends to work horribly in practice. The intuition is that it will estimate the allele bias and sequencing error rates so that the estimated genotypes are approximately uniform (since we are assuming that they are approximately uniform). This will usually result in unintuitive genotyping since most populations don't have a uniform genotype distribution. I include it as an option only for completeness. Please don't use it.

The value of prop_mis is a very intuitive measure for the quality of the SNP. prop_mis is the posterior proportion of individuals mis-genotyped. So if you want only SNPS that accurately genotype, say, 95% of the individuals, you could discard all SNPs with a prop_mis over 0.05.

The value of maxpostprob is a very intuitive measure for the quality of the genotype estimate of an individual. This is the posterior probability of correctly genotyping the individual when using geno (the posterior mode) as the genotype estimate. So if you want to correctly genotype, say, 95% of individuals, you could discard all individuals with a maxpostprob of under 0.95. However, if you are just going to impute missing genotypes later, you might consider not discarding any individuals as flexdog's genotype estimates will probably be more accurate than other more naive approaches, such as imputing using the grand mean.

In most datasets I've examined, allelic bias is a major issue. However, you may fit the model assuming no allelic bias by setting update_bias = FALSE and bias_init = 1.

Prior to using flexdog, during the read-mapping step, you could try to get rid of allelic bias by using WASP (doi:10.1101/011221). If you are successful in removing the allelic bias (because its only source was the read-mapping step), then setting update_bias = FALSE and bias_init = 1 would be reasonable. You can visually inspect SNPs for bias by using plot_geno().

flexdog(), like most methods, is invariant to which allele you label as the "reference" and which you label as the "alternative". That is, if you set refvec with the number of alternative read-counts, then the resulting genotype estimates will be the estimated allele dosage of the alternative allele.

Value

An object of class flexdog, which consists of a list with some or all of the following elements:

bias

The estimated bias parameter.

seq

The estimated sequencing error rate.

od

The estimated overdispersion parameter.

num_iter

The number of EM iterations ran. You should be wary if this equals itermax.

llike

The maximum marginal log-likelihood.

postmat

A matrix of posterior probabilities of each genotype for each individual. The rows index the individuals and the columns index the allele dosage.

genologlike

A matrix of genotype log-likelihoods of each genotype for each individual. The rows index the individuals and the columns index the allele dosage.

gene_dist

The estimated genotype distribution. The ith element is the proportion of individuals with genotype i-1.

par

A list of the final estimates of the parameters of the genotype distribution. The elements included in par depends on the value of model:

model = "norm":

mu:

The normal mean.

sigma:

The normal standard deviation (not variance).

model = "hw":

alpha:

The major allele frequency.

model = "bb":

alpha:

The major allele frequency.

tau:

The overdispersion parameter. See the description of rho in the Details of betabinom().

model = "s1":

pgeno:

The allele dosage of the parent.

alpha:

The mixture proportion of the discrete uniform (included and fixed at a small value mostly for numerical stability reasons). See the description of fs1_alpha in flexdog_full().

model = "f1":

p1geno:

The allele dosage of the first parent.

p2geno:

The allele dosage of the second parent.

alpha:

The mixture proportion of the discrete uniform (included and fixed at a small value mostly for numerical stability reasons). See the description of fs1_alpha in flexdog_full().

model = "s1pp":

ell1:

The estimated dosage of the parent.

tau1:

The estimated double reduction parameter of the parent. Available if ell1 is 1, 2, or 3. Identified if ell1 is 1 or 3.

gamma1:

The estimated preferential pairing parameter. Available if ell1 is 2. However, it is not returned in an identified form.

alpha:

The mixture proportion of the discrete uniform (included and fixed at a small value mostly for numerical stability reasons). See the description of fs1_alpha in flexdog_full().

model = "f1pp":

ell1:

The estimated dosage of parent 1.

ell2:

The estimated dosage of parent 2.

tau1:

The estimated double reduction parameter of parent 1. Available if ell1 is 1, 2, or 3. Identified if ell1 is 1 or 3.

tau2:

The estimated double reduction parameter of parent 2. Available if ell2 is 1, 2, or 3. Identified if ell2 is 1 or 3.

gamma1:

The estimated preferential pairing parameter of parent 1. Available if ell1 is 2. However, it is not returned in an identified form.

gamma2:

The estimated preferential pairing parameter of parent 2. Available if ell2 is 2. However, it is not returned in an identified form.

alpha:

The mixture proportion of the discrete uniform (included and fixed at a small value mostly for numerical stability reasons). See the description of fs1_alpha in flexdog_full().

model = "flex":

par is an empty list.

model = "uniform":

par is an empty list.

model = "custom":

par is an empty list.

geno

The posterior mode genotype. These are your genotype estimates.

maxpostprob

The maximum posterior probability. This is equivalent to the posterior probability of correctly genotyping each individual.

postmean

The posterior mean genotype. In downstream association studies, you might want to consider using these estimates.

input$refvec

The value of refvec provided by the user.

input$sizevec

The value of sizevec provided by the user.

input$ploidy

The value of ploidy provided by the user.

input$model

The value of model provided by the user.

input$p1ref

The value of p1ref provided by the user.

input$p1size

The value of p1size provided by the user.

input$p2ref

The value of p2ref provided by the user.

input$p2size

The value of p2size provided by the user.

input$snpname

The value of snpname provided by the user.

prop_mis

The posterior proportion of individuals genotyped incorrectly.

Author(s)

David Gerard

References

• Gerard, D., Ferrão, L. F. V., Garcia, A. A. F., & Stephens, M. (2018). Genotyping Polyploids from Messy Sequencing Data. Genetics, 210(3), 789-807. doi:10.1534/genetics.118.301468.

• Gerard, David, and Luís Felipe Ventorim Ferrão. "Priors for genotyping polyploids." Bioinformatics 36, no. 6 (2020): 1795-1800. doi:10.1093/bioinformatics/btz852.

See Also

Run browseVignettes(package = "updog") in R for example usage. Other useful functions include:

multidog

For running flexdog on multiple SNPs in parallel.

flexdog

For a more user-friendly version of flexdog_full.

rgeno

For simulating genotypes under the allowable prior models in flexdog.

rflexdog

For simulating read-counts under the assumed likelihood model in flexdog.

plot.flexdog

For plotting the output of flexdog.

oracle_mis

For calculating the oracle genotyping error rates. This is useful for read-depth calculations before collecting data. After you have data, using the value of prop_mis is better.

oracle_cor

For calculating the correlation between the true genotypes and an oracle estimator (useful for read-depth calculations before collecting data).

Examples

## A natural population. We will assume a
## normal prior since there are so few
## individuals.
data("uitdewilligen")
ploidy  <- 4
refvec  <- uitdewilligen$refmat[, 1]
sizevec <- uitdewilligen$sizemat[, 1]
fout    <- flexdog_full(refvec  = refvec,
                        sizevec = sizevec,
                        ploidy  = ploidy,
                        model   = "norm")
plot(fout)

Return arrayicized elements from the output of multidog.

Description

This function will allow you to have genotype estimates, maximum posterior probability, and other values in the form of a matrix/array. If multiple variable names are provided, the data are formatted as a 3-dimensional array with the dimensions corresponding to (individuals, SNPs, variables).

Usage

format_multidog(x, varname = "geno")

Arguments

Details

Note that the order of the individuals will be reshuffled. The order of the SNPs should be the same as in x$snpdf.

Author(s)

David Gerard

Return the probabilities of an offspring's genotype given its parental genotypes for all possible combinations of parental and offspring genotypes. This is for species with polysomal inheritance and bivalent, non-preferential pairing.

Description

Return the probabilities of an offspring's genotype given its parental genotypes for all possible combinations of parental and offspring genotypes. This is for species with polysomal inheritance and bivalent, non-preferential pairing.

Usage

get_q_array(ploidy)

Arguments

Value

An three-way array of proportions. The (i, j, k)th element is the probability of an offspring having k - 1 reference alleles given that parent 1 has i - 1 reference alleles and parent 2 has j - 1 reference alleles. Each dimension of the array is ploidy + 1. In the dimension names, "A" stands for the reference allele and "a" stands for the alternative allele.

Author(s)

David Gerard

Examples

qarray <- get_q_array(6)
apply(qarray, c(1, 2), sum) ## should all be 1's.

Tests if an argument is a flexdog object.

Description

Tests if an argument is a flexdog object.

Usage

is.flexdog(x)

Arguments

Value

A logical. TRUE if x is a flexdog object, and FALSE otherwise.

Author(s)

David Gerard

Examples

is.flexdog("anything")
# FALSE

Tests if an argument is a multidog object.

Description

Tests if an argument is a multidog object.

Usage

is.multidog(x)

Arguments

Value

A logical. TRUE if x is a multidog object, and FALSE otherwise.

Author(s)

David Gerard

Examples

is.multidog("anything")
# FALSE

Log-sum-exponential trick.

Description

Log-sum-exponential trick.

Usage

log_sum_exp(x)

Arguments

Value

The log of the sum of the exponential of the elements in x.

Author(s)

David Gerard

Log-sum-exponential trick using just two doubles.

Description

Log-sum-exponential trick using just two doubles.

Usage

log_sum_exp_2(x, y)

Arguments

Value

The log of the sum of the exponential of x and y.

Author(s)

David Gerard

Fit flexdog to multiple SNPs.

Description

This is a convenience function that will run flexdog over many SNPs. Support is provided for parallel computing through the doParallel package. This function has not been extensively tested. Please report any bugs to https://github.com/dcgerard/updog/issues.

Usage

multidog(
  refmat,
  sizemat,
  ploidy,
  model = c("norm", "hw", "bb", "s1", "s1pp", "f1", "f1pp", "flex", "uniform", "custom"),
  nc = 1,
  p1_id = NULL,
  p2_id = NULL,
  bias_init = exp(c(-1, -0.5, 0, 0.5, 1)),
  prior_vec = NULL,
  ...
)

Arguments

Details

You should format your reference counts and total read counts in two separate matrices. The rows should index the markers (SNPs) and the columns should index the individuals. Row names are how we ID the SNPs and column names are how we ID the individuals, and so they are required attributes.

If your data are in VCF files, I would recommend importing them using the VariantAnnotation package from Bioconductor https://bioconductor.org/packages/VariantAnnotation/. It's a great VCF parser.

See the details of flexdog for the possible values of model.

If model = "f1", model = "s1", model = "f1pp" or model = "s1pp" then the user may provide the individual ID for parent(s) via the p1_id and p2_id arguments.

The output is a list containing two data frames. The first data frame, called snpdf, contains information on each SNP, such as the allele bias and the sequencing error rate. The second data frame, called inddf, contains information on each individual at each SNP, such as the estimated genotype and the posterior probability of being classified correctly.

SNPs that contain 0 reads (or all missing data) are entirely removed.

Value

A list-like object of two data frames.

snpdf

A data frame containing properties of the SNPs (markers). The rows index the SNPs. The variables include:

snp

The name of the SNP (marker).

bias

The estimated allele bias of the SNP.

seq

The estimated sequencing error rate of the SNP.

od

The estimated overdispersion parameter of the SNP.

prop_mis

The estimated proportion of individuals misclassified in the SNP.

num_iter

The number of iterations performed during the EM algorithm for that SNP.

llike

The maximum marginal likelihood of the SNP.

ploidy

The provided ploidy of the species.

model

The provided model for the prior genotype distribution.

p1ref

The user-provided reference read counts of parent 1.

p1size

The user-provided total read counts of parent 1.

p2ref

The user-provided reference read counts of parent 2.

p2size

The user-provided total read counts of parent 2.

Pr_k

The estimated frequency of individuals with genotype k, where k can be any integer between 0 and the ploidy level.

Model specific parameter estimates

See the return value of par in the help page of flexdog.

inddf

A data frame containing the properties of the individuals at each SNP. The variables include:

snp

The name of the SNP (marker).

ind

The name of the individual.

ref

The provided reference counts for that individual at that SNP.

size

The provided total counts for that individual at that SNP.

geno

The posterior mode genotype for that individual at that SNP. This is the estimated reference allele dosage for a given individual at a given SNP.

postmean

The posterior mean genotype for that individual at that SNP. This is a continuous genotype estimate of the reference allele dosage for a given individual at a given SNP.

maxpostprob

The maximum posterior probability. This is the posterior probability that the individual was genotyped correctly.

Pr_k

The posterior probability that a given individual at a given SNP has genotype k, where k can vary from 0 to the ploidy level of the species.

logL_k

The genotype log-likelihoods for dosage k for a given individual at a given SNP, where k can vary f rom 0 to the ploidy level of the species.

Parallel Computation

The multidog() function supports parallel computing. It does so through the future package.

If you are just running multidog() on a local machine, then you can use the nc argument to specify the parallelization. Any value of nc greater than 1 will result in multiple background R sessions to genotype all of the SNPs. The maximum value of nc you should try can be found by running future::availableCores(). Running multidog() using nc is equivalent to setting the future plan with future::plan(future::multisession, workers = nc).

Using the future package means that different evaluation strategies are possible. In particular, if you are using a high performance machine, you can explore using the future.batchtools package to evaluate multidog() using schedulers like Slurm or TORQUE/PBS.

To use a different strategy, set nc = NA and then run future::plan() prior to running multidog(). For example, to set up forked R processes on your current machine (instead of using background R sessions), you would run (will not work on Windows): future::plan(future::multicore), followed by running multidog() with nc = NA. See the examples below.

Author(s)

David Gerard

See Also

flexdog():

For the underlying genotyping function.

format_multidog():

For converting the output of multidog() to a matrix.

filter_snp():

For filtering SNPs using the output of multidog().

Examples

## Not run: 
data("uitdewilligen")

## Run multiple R sessions using the `nc` variable.
mout <- multidog(refmat = t(uitdewilligen$refmat),
                 sizemat = t(uitdewilligen$sizemat),
                 ploidy = uitdewilligen$ploidy,
                 nc = 2)
mout$inddf
mout$snpdf

## Run multiple external R sessions on the local machine.
## Note that we set `nc = NA`.
cl <- parallel::makeCluster(2, timeout = 60)
future::plan(future::cluster, workers = cl)
mout <- multidog(refmat = t(uitdewilligen$refmat),
                 sizemat = t(uitdewilligen$sizemat),
                 ploidy = uitdewilligen$ploidy,
                 nc = NA)
mout$inddf
mout$snpdf

## Close cluster and reset future to current R process
parallel::stopCluster(cl)
future::plan(future::sequential)

## End(Not run)

Calculates the correlation between the true genotype and an oracle estimator.

Description

Calculates the correlation between the oracle MAP estimator (where we have perfect knowledge about the data generation process) and the true genotype. This is a useful approximation when you have a lot of individuals.

Usage

oracle_cor(n, ploidy, seq, bias, od, dist)

Arguments

Details

To come up with dist, you need some additional assumptions. For example, if the population is in Hardy-Weinberg equilibrium and the allele frequency is alpha then you could calculate dist using the R code: dbinom(x = 0:ploidy, size = ploidy, prob = alpha). Alternatively, if you know the genotypes of the individual's two parents are, say, ref_count1 and ref_count2, then you could use the get_q_array function from the updog package: get_q_array(ploidy)[ref_count1 + 1, ref_count2 + 1, ].

Value

The Pearson correlation between the true genotype and the oracle estimator.

Author(s)

David Gerard

References

• Gerard, D., Ferrão, L. F. V., Garcia, A. A. F., & Stephens, M. (2018). Genotyping Polyploids from Messy Sequencing Data. Genetics, 210(3), 789-807. doi:10.1534/genetics.118.301468.

Examples

## Hardy-Weinberg population with allele-frequency of 0.75.
## Moderate bias and moderate overdispersion.
## See how correlation decreases as we
## increase the ploidy.
ploidy <- 2
dist <- stats::dbinom(0:ploidy, ploidy, 0.75)
oracle_cor(n = 100, ploidy = ploidy, seq = 0.001,
           bias = 0.7, od = 0.01, dist = dist)

ploidy <- 4
dist <- stats::dbinom(0:ploidy, ploidy, 0.75)
oracle_cor(n = 100, ploidy = ploidy, seq = 0.001,
           bias = 0.7, od = 0.01, dist = dist)

ploidy <- 6
dist <- stats::dbinom(0:ploidy, ploidy, 0.75)
oracle_cor(n = 100, ploidy = ploidy, seq = 0.001,
           bias = 0.7, od = 0.01, dist = dist)

Calculate the correlation of the oracle estimator with the true genotype from the joint distribution matrix.

Description

Calculates the correlation between the oracle MAP estimator (where we have perfect knowledge about the data generation process) and the true genotype. This is a useful approximation when you have a lot of individuals.

Usage

oracle_cor_from_joint(jd)

Arguments

Value

The Pearson correlation between the true genotype and the oracle estimator.

Author(s)

David Gerard

References

• Gerard, D., Ferrão, L. F. V., Garcia, A. A. F., & Stephens, M. (2018). Genotyping Polyploids from Messy Sequencing Data. Genetics, 210(3), 789-807. doi:10.1534/genetics.118.301468.

See Also

oracle_joint for getting jd. oracle_cor for not having to first calculate jd.

Examples

## Hardy-Weinberg population with allele-frequency of 0.75.
## Moderate bias and moderate overdispersion.
ploidy <- 6
dist <- stats::dbinom(0:ploidy, ploidy, 0.75)
jd <- oracle_joint(n = 100, ploidy = ploidy, seq = 0.001,
                   bias = 0.7, od = 0.01, dist = dist)
oracle_cor_from_joint(jd = jd)

## Compare to oracle_cor
oracle_cor(n = 100, ploidy = ploidy, seq = 0.001,
           bias = 0.7, od = 0.01, dist = dist)

The joint probability of the genotype and the genotype estimate of an oracle estimator.

Description

This returns the joint distribution of the true genotypes and an oracle estimator given perfect knowledge of the data generating process. This is a useful approximation when you have a lot of individuals.

Usage

oracle_joint(n, ploidy, seq, bias, od, dist)

Arguments

Details

To come up with dist, you need some additional assumptions. For example, if the population is in Hardy-Weinberg equilibrium and the allele frequency is alpha then you could calculate dist using the R code: dbinom(x = 0:ploidy, size = ploidy, prob = alpha). Alternatively, if you know the genotypes of the individual's two parents are, say, ref_count1 and ref_count2, then you could use the get_q_array function from the updog package: get_q_array(ploidy)[ref_count1 + 1, ref_count2 + 1, ].

See the Examples to see how to reconcile the output of oracle_joint with oracle_mis and oracle_mis_vec.

Value

A matrix. Element (i, j) is the joint probability of estimating the genotype to be i+1 when the true genotype is j+1. That is, the estimated genotype indexes the rows and the true genotype indexes the columns. This is when using an oracle estimator.

Author(s)

David Gerard

References

• Gerard, D., Ferrão, L. F. V., Garcia, A. A. F., & Stephens, M. (2018). Genotyping Polyploids from Messy Sequencing Data. Genetics, 210(3), 789-807. doi:10.1534/genetics.118.301468.

See Also

oracle_plot

For visualizing the joint distribution output from oracle_joint.

oracle_mis_from_joint

For obtaining the same results as oracle_mis directly from the output of oracle_joint.

oracle_mis_vec_from_joint

For obtaining the same results as oracle_mis_vec directly from the output of oracle_joint.

oracle_cor_from_joint

For obtaining the same results as oracle_cor directly from the output of oracle_joint.

Examples

## Hardy-Weinberg population with allele-frequency of 0.75.
## Moderate bias and moderate overdispersion.
ploidy <- 4
dist <- stats::dbinom(0:ploidy, ploidy, 0.75)
jd <- oracle_joint(n = 100, ploidy = ploidy, seq = 0.001,
                   bias = 0.7, od = 0.01, dist = dist)
jd

## Get same output as oracle_mis this way:
1 - sum(diag(jd))
oracle_mis(n = 100, ploidy = ploidy, seq = 0.001,
           bias = 0.7, od = 0.01, dist = dist)

## Get same output as oracle_mis_vec this way:
1 - diag(sweep(x = jd, MARGIN = 2, STATS = colSums(jd), FUN = "/"))
oracle_mis_vec(n = 100, ploidy = ploidy, seq = 0.001,
               bias = 0.7, od = 0.01, dist = dist)

Calculate oracle misclassification error rate.

Description

Given perfect knowledge of the data generating parameters, oracle_mis calculates the misclassification error rate, where the error rate is taken over both the data generation process and the allele-distribution. This is an ideal level of the misclassification error rate and any real method will have a larger rate than this. This is a useful approximation when you have a lot of individuals.

Usage

oracle_mis(n, ploidy, seq, bias, od, dist)

Arguments

Details

To come up with dist, you need some additional assumptions. For example, if the population is in Hardy-Weinberg equilibrium and the allele frequency is alpha then you could calculate dist using the R code: dbinom(x = 0:ploidy, size = ploidy, prob = alpha). Alternatively, if you know the genotypes of the individual's two parents are, say, ref_count1 and ref_count2, then you could use the get_q_array function from the updog package: get_q_array(ploidy)[ref_count1 + 1, ref_count2 + 1, ].

Value

A double. The oracle misclassification error rate.

Author(s)

David Gerard

References

• Gerard, D., Ferrão, L. F. V., Garcia, A. A. F., & Stephens, M. (2018). Genotyping Polyploids from Messy Sequencing Data. Genetics, 210(3), 789-807. doi:10.1534/genetics.118.301468.

Examples

## Hardy-Weinberg population with allele-frequency of 0.75.
## Moderate bias and moderate overdispersion.
## See how oracle misclassification error rates change as we
## increase the ploidy.
ploidy <- 2
dist <- stats::dbinom(0:ploidy, ploidy, 0.75)
oracle_mis(n = 100, ploidy = ploidy, seq = 0.001,
           bias = 0.7, od = 0.01, dist = dist)

ploidy <- 4
dist <- stats::dbinom(0:ploidy, ploidy, 0.75)
oracle_mis(n = 100, ploidy = ploidy, seq = 0.001,
           bias = 0.7, od = 0.01, dist = dist)

ploidy <- 6
dist <- stats::dbinom(0:ploidy, ploidy, 0.75)
oracle_mis(n = 100, ploidy = ploidy, seq = 0.001,
           bias = 0.7, od = 0.01, dist = dist)

Get the oracle misclassification error rate directly from the joint distribution of the genotype and the oracle estimator.

Description

Get the oracle misclassification error rate directly from the joint distribution of the genotype and the oracle estimator.

Usage

oracle_mis_from_joint(jd)

Arguments

Value

A double. The oracle misclassification error rate.

Author(s)

David Gerard

References

• Gerard, D., Ferrão, L. F. V., Garcia, A. A. F., & Stephens, M. (2018). Genotyping Polyploids from Messy Sequencing Data. Genetics, 210(3), 789-807. doi:10.1534/genetics.118.301468.

See Also

oracle_joint for getting jd. oracle_mis for not having to first calculate jd.

Examples

## Hardy-Weinberg population with allele-frequency of 0.75.
## Moderate bias and moderate overdispersion.
ploidy <- 6
dist <- stats::dbinom(0:ploidy, ploidy, 0.75)
jd <- oracle_joint(n = 100, ploidy = ploidy, seq = 0.001,
                   bias = 0.7, od = 0.01, dist = dist)
oracle_mis_from_joint(jd = jd)

## Compare to oracle_cor
oracle_mis(n = 100, ploidy = ploidy, seq = 0.001,
           bias = 0.7, od = 0.01, dist = dist)

Returns the oracle misclassification rates for each genotype.

Description

Given perfect knowledge of the data generating parameters, oracle_mis_vec calculates the misclassification error rate at each genotype. This differs from oracle_mis in that this will not average over the genotype distribution to get an overall misclassification error rate. That is, oracle_mis_vec returns a vector of misclassification error rates conditional on each genotype.

Usage

oracle_mis_vec(n, ploidy, seq, bias, od, dist)

Arguments

Details

This is an ideal level of the misclassification error rate and any real method will have a larger rate than this. This is a useful approximation when you have a lot of individuals.

To come up with dist, you need some additional assumptions. For example, if the population is in Hardy-Weinberg equilibrium and the allele frequency is alpha then you could calculate dist using the R code: dbinom(x = 0:ploidy, size = ploidy, prob = alpha). Alternatively, if you know the genotypes of the individual's two parents are, say, ref_count1 and ref_count2, then you could use the get_q_array function from the updog package: get_q_array(ploidy)[ref_count1 + 1, ref_count2 + 1, ].

Value

A vector of numerics. Element i is the oracle misclassification error rate when genotyping an individual with actual genotype i + 1.

Author(s)

David Gerard

References

• Gerard, D., Ferrão, L. F. V., Garcia, A. A. F., & Stephens, M. (2018). Genotyping Polyploids from Messy Sequencing Data. Genetics, 210(3), 789-807. doi:10.1534/genetics.118.301468.

Examples

## Hardy-Weinberg population with allele-frequency of 0.75.
## Moderate bias and moderate overdispersion.
ploidy <- 4
dist <- stats::dbinom(0:ploidy, ploidy, 0.75)
om <- oracle_mis_vec(n = 100, ploidy = ploidy, seq = 0.001,
                     bias = 0.7, od = 0.01, dist = dist)
om

## Get same output as oracle_mis this way:
sum(dist * om)
oracle_mis(n = 100, ploidy = ploidy, seq = 0.001,
           bias = 0.7, od = 0.01, dist = dist)

Get the oracle misclassification error rates (conditional on true genotype) directly from the joint distribution of the genotype and the oracle estimator.

Description

Get the oracle misclassification error rates (conditional on true genotype) directly from the joint distribution of the genotype and the oracle estimator.

Usage

oracle_mis_vec_from_joint(jd)

Arguments

Value

A vector of numerics. Element i is the oracle misclassification error rate when genotyping an individual with actual genotype i + 1.

Author(s)

David Gerard

References

• Gerard, D., Ferrão, L. F. V., Garcia, A. A. F., & Stephens, M. (2018). Genotyping Polyploids from Messy Sequencing Data. Genetics, 210(3), 789-807. doi:10.1534/genetics.118.301468.

See Also

oracle_joint for getting jd. oracle_mis_vec for not having to first calculate jd.

Examples

## Hardy-Weinberg population with allele-frequency of 0.75.
## Moderate bias and moderate overdispersion.
ploidy <- 6
dist <- stats::dbinom(0:ploidy, ploidy, 0.75)
jd <- oracle_joint(n = 100, ploidy = ploidy, seq = 0.001,
                   bias = 0.7, od = 0.01, dist = dist)
oracle_mis_vec_from_joint(jd = jd)

## Compare to oracle_cor
oracle_mis_vec(n = 100, ploidy = ploidy, seq = 0.001,
               bias = 0.7, od = 0.01, dist = dist)

Construct an oracle plot from the output of oracle_joint.

Description

After obtaining the joint distribution of the true genotype with the estimated genotype from the oracle estimator using oracle_joint, you can use oracle_plot to visualize this joint distribution.

Usage

oracle_plot(jd)

Arguments

Value

A ggplot object containing the oracle plot. The x-axis indexes the possible values of the estimated genotype. The y-axis indexes the possible values of the true genotype. The number in cell (i, j) is the probability that an individual will have true genotype i but is estimated to have genotype j. This is when using an oracle estimator. The cells are also color-coded by the size of the probability in each cell. At the top are listed the oracle misclassification error rate and the correlation of the true genotype with the estimated genotype. Both of these quantities may be derived from the joint distribution.

Author(s)

David Gerard

References

• Gerard, D., Ferrão, L. F. V., Garcia, A. A. F., & Stephens, M. (2018). Genotyping Polyploids from Messy Sequencing Data. Genetics, 210(3), 789-807. doi:10.1534/genetics.118.301468.

See Also

oracle_joint for obtaining jd.

Examples

ploidy <- 6
dist <- stats::dbinom(0:ploidy, ploidy, 0.75)
jd <- oracle_joint(n = 100, ploidy = ploidy, seq = 0.001,
                   bias = 0.7, od = 0.01, dist = dist)
pl <- oracle_plot(jd = jd)
print(pl)

Draw a genotype plot from the output of flexdog.

Description

A wrapper for plot_geno. This will create a genotype plot for a single SNP.

Usage

## S3 method for class 'flexdog'
plot(x, use_colorblind = TRUE, ...)

Arguments

Details

On a genotype plot, the x-axis contains the counts of the non-reference allele and the y-axis contains the counts of the reference allele. The dashed lines are the expected counts (both reference and alternative) given the sequencing error rate and the allele-bias. The plots are color-coded by the maximum-a-posterior genotypes. Transparency is proportional to the maximum posterior probability for an individual's genotype. Thus, we are less certain of the genotype of more transparent individuals. These types of plots are used in Gerard et. al. (2018) and Gerard and Ferrão (2020).

Value

A ggplot object for the genotype plot.

Author(s)

David Gerard

References

• Gerard, D., Ferrão, L. F. V., Garcia, A. A. F., & Stephens, M. (2018). Genotyping Polyploids from Messy Sequencing Data. Genetics, 210(3), 789-807. doi:10.1534/genetics.118.301468.

• Gerard, David, and Luís Felipe Ventorim Ferrão. "Priors for genotyping polyploids." Bioinformatics 36, no. 6 (2020): 1795-1800. doi:10.1093/bioinformatics/btz852.

See Also

plot_geno

The underlying plotting function.

flexdog

Creates a flexdog object.

Plot the output of multidog.

Description

Produce genotype plots from the output of multidog. You may select which SNPs to plot.

Usage

## S3 method for class 'multidog'
plot(x, indices = seq(1, min(5, nrow(x$snpdf))), ...)

Arguments

Details

On a genotype plot, the x-axis contains the counts of the non-reference allele and the y-axis contains the counts of the reference allele. The dashed lines are the expected counts (both reference and alternative) given the sequencing error rate and the allele-bias. The plots are color-coded by the maximum-a-posterior genotypes. Transparency is proportional to the maximum posterior probability for an individual's genotype. Thus, we are less certain of the genotype of more transparent individuals. These types of plots are used in Gerard et. al. (2018) and Gerard and Ferrão (2020).

Author(s)

David Gerard

References

• Gerard, D., Ferrão, L. F. V., Garcia, A. A. F., & Stephens, M. (2018). Genotyping Polyploids from Messy Sequencing Data. Genetics, 210(3), 789-807. doi:10.1534/genetics.118.301468.

• Gerard, David, and Luís Felipe Ventorim Ferrão. "Priors for genotyping polyploids." Bioinformatics 36, no. 6 (2020): 1795-1800. doi:10.1093/bioinformatics/btz852.

See Also

plot_geno.

Make a genotype plot.

Description

The x-axis is the counts of the non-reference allele, and the y-axis is the counts of the reference allele. Transparency is controlled by the maxpostprob vector. These types of plots are used in Gerard et. al. (2018) and Gerard and Ferrão (2020).

Usage

plot_geno(
  refvec,
  sizevec,
  ploidy,
  p1ref = NULL,
  p1size = NULL,
  p2ref = NULL,
  p2size = NULL,
  geno = NULL,
  seq = 0,
  bias = 1,
  maxpostprob = NULL,
  p1geno = NULL,
  p2geno = NULL,
  use_colorblind = TRUE
)

Arguments

Details

If parental genotypes are provided (p1geno and p2geno) then they will be colored the same as the offspring. Since they are often hard to see, a small black dot will also indicate their position.

Value

A ggplot object for the genotype plot.

Author(s)

David Gerard

References

• Gerard, D., Ferrão, L. F. V., Garcia, A. A. F., & Stephens, M. (2018). Genotyping Polyploids from Messy Sequencing Data. Genetics, 210(3), 789-807. doi:10.1534/genetics.118.301468.

• Gerard, David, and Luís Felipe Ventorim Ferrão. "Priors for genotyping polyploids." Bioinformatics 36, no. 6 (2020): 1795-1800. doi:10.1093/bioinformatics/btz852.

Examples

data("snpdat")
refvec  <- snpdat$counts[snpdat$snp == "SNP1"]
sizevec <- snpdat$size[snpdat$snp == "SNP1"]
ploidy  <- 6
plot_geno(refvec = refvec, sizevec = sizevec, ploidy = ploidy)

Simulate GBS data from the flexdog likelihood.

Description

This will take a vector of genotypes and a vector of total read-counts, then generate a vector of reference counts. To get the genotypes, you could use rgeno. The likelihood used to generate read-counts is described in detail in Gerard et. al. (2018).

Usage

rflexdog(sizevec, geno, ploidy, seq = 0.005, bias = 1, od = 0.001)

Arguments

Value

A vector the same length as sizevec. The ith element is the number of reference counts for individual i.

Author(s)

David Gerard

References

• Gerard, D., Ferrão, L. F. V., Garcia, A. A. F., & Stephens, M. (2018). Genotyping Polyploids from Messy Sequencing Data. Genetics, 210(3), 789-807. doi:10.1534/genetics.118.301468.

• Gerard, David, and Luís Felipe Ventorim Ferrão. "Priors for genotyping polyploids." Bioinformatics 36, no. 6 (2020): 1795-1800. doi:10.1093/bioinformatics/btz852.

See Also

rgeno for a way to generate genotypes of individuals. rbetabinom for how we generate the read-counts.

Examples

set.seed(1)
n       <- 100
ploidy  <- 6

## Generate the genotypes of individuals from an F1 population,
## where the first parent has 1 copy of the reference allele
## and the second parent has two copies of the reference
## allele.
genovec <- rgeno(n = n, ploidy = ploidy, model = "f1",
                 p1geno = 1, p2geno = 2)

## Get the total number of read-counts for each individual.
## Ideally, you would take this from real data as the total
## read-counts are definitely not Poisson.
sizevec <- stats::rpois(n = n, lambda = 200)

## Generate the counts of reads with the reference allele
## when there is a strong bias for the reference allele
## and there is no overdispersion.
refvec  <- rflexdog(sizevec = sizevec, geno = genovec,
                    ploidy = ploidy, seq = 0.001,
                    bias = 0.5, od = 0)

## Plot the simulated data using plot_geno.
plot_geno(refvec = refvec, sizevec = sizevec,
          ploidy = ploidy, seq = 0.001, bias = 0.5)

Simulate individual genotypes from one of the supported flexdog models.

Description

This will simulate genotypes of a sample of individuals drawn from one of the populations supported by flexdog. See the details of flexdog for the models allowed. These genotype distributions are described in detail in Gerard and Ferrão (2020).

Usage

rgeno(
  n,
  ploidy,
  model = c("hw", "bb", "norm", "f1", "s1", "flex", "uniform"),
  allele_freq = NULL,
  od = NULL,
  p1geno = NULL,
  p2geno = NULL,
  pivec = NULL,
  mu = NULL,
  sigma = NULL
)

Arguments

Details

List of non-NULL arguments:

model = "flex":

pivec

model = "hw":

allele_freq

model = "f1":

p1geno and p2geno

model = "s1":

p1geno

model = "uniform":

no non-NULL arguments

model = "bb":

allele_freq and od

model == "norm":

mu and sigma

Value

A vector of length n with the genotypes of the sampled individuals.

Author(s)

David Gerard

References

• Gerard, D., Ferrão, L. F. V., Garcia, A. A. F., & Stephens, M. (2018). Genotyping Polyploids from Messy Sequencing Data. Genetics, 210(3), 789-807. doi:10.1534/genetics.118.301468.

• Gerard, David, and Luís Felipe Ventorim Ferrão. "Priors for genotyping polyploids." Bioinformatics 36, no. 6 (2020): 1795-1800. doi:10.1093/bioinformatics/btz852.

Examples

## F1 Population where parent 1 has 1 copy of the referenc allele
## and parent 2 has 4 copies of the reference allele.
ploidy <- 6
rgeno(n = 10, ploidy = ploidy, model = "f1", p1geno = 1, p2geno = 4)

## A population in Hardy-Weinberge equilibrium with an
## allele frequency of 0.75
rgeno(n = 10, ploidy = ploidy, model = "hw", allele_freq = 0.75)

GBS data from Shirasawa et al (2017)

Description

Contains counts of reference alleles and total read counts from the GBS data of Shirasawa et al (2017) for the three SNPs used as examples in Gerard et. al. (2018).

Usage

snpdat

Format

A tibble with 419 rows and 4 columns:

id

The identification label of the individuals.

snp

The SNP label.

counts

The number of read-counts that support the reference allele.

size

The total number of read-counts at a given SNP.

Value

A tibble. See the Format Section.

Source

doi:10.1038/srep44207

References

• Shirasawa, Kenta, Masaru Tanaka, Yasuhiro Takahata, Daifu Ma, Qinghe Cao, Qingchang Liu, Hong Zhai, Sang-Soo Kwak, Jae Cheol Jeong, Ung-Han Yoon, Hyeong-Un Lee, Hideki Hirakawa, and Sahiko Isobe "A high-density SNP genetic map consisting of a complete set of homologous groups in autohexaploid sweetpotato (Ipomoea batatas)." Scientific Reports 7 (2017). doi:10.1038/srep44207

• Gerard, D., Ferrão, L. F. V., Garcia, A. A. F., & Stephens, M. (2018). Genotyping Polyploids from Messy Sequencing Data. Genetics, 210(3), 789-807. doi:10.1534/genetics.118.301468.

Subset of individuals and SNPs from Uitdewilligen et al (2013).

Description

A list containing a matrix of reference counts, a matrix of total counts, and the ploidy level (4) of the species. This is a subset of the data from Uitdewilligen et al (2013).

Usage

uitdewilligen

Format

A list containing three objects. Two matrices and a numeric scalar:

refmat

A matrix of read counts containing the reference allele. The rows index the individuals and the columns index the SNPs.

sizemat

A matrix of the total number of read counts. The rows index the individuals and the columns index the SNPs.

ploidy

The ploidy level of the species (just 4).

Value

A list. See the Format Section.

Source

doi:10.1371/journal.pone.0062355

References

• Uitdewilligen, J. G., Wolters, A. M. A., Bjorn, B., Borm, T. J., Visser, R. G., & van Eck, H. J. (2013). A next-generation sequencing method for genotyping-by-sequencing of highly heterozygous autotetraploid potato. PLoS One, 8(5), e62355. doi:10.1371/journal.pone.0062355

EM algorithm to fit weighted ash objective.

Description

Solves the following optimization problem

\max_{\pi} \sum_k w_k \log(\sum_j \pi_j \ell_jk).

It does this using a weighted EM algorithm.

Usage

wem(weight_vec, lmat, pi_init, lambda, itermax, obj_tol)

Arguments

Value

A vector of numerics.

Author(s)

David Gerard

Examples

set.seed(2)
n <- 3
p <- 5
lmat <- matrix(stats::runif(n * p), nrow = n)
weight_vec <- seq_len(p)
pi_init <- stats::runif(n)
pi_init <- pi_init / sum(pi_init)
wem(weight_vec = weight_vec,
    lmat       = lmat,
    pi_init    = pi_init,
    lambda     = 0,
    itermax    = 100,
    obj_tol    = 10^-6)