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Simulate Next-Generation Sequencing Data

David Gerard

Abstract

We demonstrate how to simulate NGS data under various genotype distributions, then fit these data using flexdog. The genotyping methods are described in Gerard et al. (2018).

Analysis

Let’s suppose that we have 100 hexaploid individuals, with varying levels of read-depth.

set.seed(1)
library(updog)
nind    <- 100
ploidy  <- 6
sizevec <- round(stats::runif(n   = nind, 
                              min = 50, 
                              max = 200))

We can simulate their read-counts under various genotype distributions, allele biases, overdispersions, and sequencing error rates using the rgeno and rflexdog functions.

F1 Population

Suppose these individuals are all siblings where the first parent has 4 copies of the reference allele and the second parent has 5 copies of the reference allele. Then the following code, using rgeno, will simulate the individuals’ genotypes.

true_geno <- rgeno(n      = nind, 
                   ploidy = ploidy,
                   model  = "f1", 
                   p1geno = 4,
                   p2geno = 5)

Once we have their genotypes, we can simulate their read-counts using rflexdog. Let’s suppose that there is a moderate level of allelic bias (0.7) and a small level of overdispersion (0.005). Generally, in the real data that I’ve seen, the bias will range between 0.5 and 2 and the overdispersion will range between 0 and 0.02, with only a few extremely overdispersed SNPs above 0.02.

refvec <- rflexdog(sizevec = sizevec, 
                   geno    = true_geno, 
                   ploidy  = ploidy, 
                   seq     = 0.001, 
                   bias    = 0.7, 
                   od      = 0.005)

When we plot the data, it looks realistic

plot_geno(refvec  = refvec, 
          sizevec = sizevec, 
          ploidy  = ploidy, 
          bias    = 0.7, 
          seq     = 0.001,
          geno    = true_geno)

We can test flexdog on these data

fout <- flexdog(refvec  = refvec, 
                sizevec = sizevec, 
                ploidy  = ploidy, 
                model   = "f1")
#>          Fit: 1 of 5 
#> Initial Bias: 0.3678794 
#> Log-Likelihood: -363.9369 
#> Keeping new fit.
#> 
#>          Fit: 2 of 5 
#> Initial Bias: 0.6065307 
#> Log-Likelihood: -363.937 
#> Keeping old fit.
#> 
#>          Fit: 3 of 5 
#> Initial Bias: 1 
#> Log-Likelihood: -363.9369 
#> Keeping new fit.
#> 
#>          Fit: 4 of 5 
#> Initial Bias: 1.648721 
#> Log-Likelihood: -381.6123 
#> Keeping old fit.
#> 
#>          Fit: 5 of 5 
#> Initial Bias: 2.718282 
#> Log-Likelihood: -412.8604 
#> Keeping old fit.
#> 
#> Done!

flexdog gives us reasonable genotyping, and it accurately estimates the proportion of individuals mis-genotyped.

plot(fout)


## Estimated proportion misgenotyped
fout$prop_mis
#> [1] 0.07011089

## Actual proportion misgenotyped
mean(fout$geno != true_geno)
#> [1] 0.05

HWE Population

Now run the same simulations assuming the individuals are in Hardy-Weinberg population with an allele frequency of 0.75.

true_geno <- rgeno(n      = nind,
                   ploidy = ploidy,
                   model  = "hw", 
                   allele_freq = 0.75)
refvec <- rflexdog(sizevec = sizevec, 
                   geno    = true_geno, 
                   ploidy  = ploidy, 
                   seq     = 0.001, 
                   bias    = 0.7, 
                   od      = 0.005)
fout <- flexdog(refvec  = refvec, 
                sizevec = sizevec, 
                ploidy  = ploidy, 
                model   = "hw")
#>          Fit: 1 of 5 
#> Initial Bias: 0.3678794 
#> Log-Likelihood: -377.9226 
#> Keeping new fit.
#> 
#>          Fit: 2 of 5 
#> Initial Bias: 0.6065307 
#> Log-Likelihood: -377.9226 
#> Keeping old fit.
#> 
#>          Fit: 3 of 5 
#> Initial Bias: 1 
#> Log-Likelihood: -377.9226 
#> Keeping old fit.
#> 
#>          Fit: 4 of 5 
#> Initial Bias: 1.648721 
#> Log-Likelihood: -377.9226 
#> Keeping new fit.
#> 
#>          Fit: 5 of 5 
#> Initial Bias: 2.718282 
#> Log-Likelihood: -377.9226 
#> Keeping old fit.
#> 
#> Done!
plot(fout)


## Estimated proportion misgenotyped
fout$prop_mis
#> [1] 0.07625987

## Actual proportion misgenotyped
mean(fout$geno != true_geno)
#> [1] 0.07

## Estimated allele frequency close to true allele frequency
fout$par$alpha
#> [1] 0.7473264

References

Gerard, David, Luís Felipe Ventorim Ferrão, Antonio Augusto Franco Garcia, and Matthew Stephens. 2018. “Genotyping Polyploids from Messy Sequencing Data.” Genetics 210 (3). Genetics: 789–807. https://doi.org/10.1534/genetics.118.301468.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.