# Simulate Next-Generation Sequencing Data" In updog: Flexible Genotyping for Polyploids

```knitr::opts_chunk\$set(
collapse = TRUE,
comment = "#>",
fig.width=4.5,
fig.height=3.5
)
```

# 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")
```

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

```plot(fout)

## Estimated proportion misgenotyped
fout\$prop_mis

## Actual proportion misgenotyped
mean(fout\$geno != true_geno)
```

## 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")
plot(fout)

## Estimated proportion misgenotyped
fout\$prop_mis

## Actual proportion misgenotyped
mean(fout\$geno != true_geno)

## Estimated allele frequency close to true allele frequency
fout\$par\$alpha
```

# 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.

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updog documentation built on Oct. 18, 2022, 9:07 a.m.