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] 0.05HWE 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