The segtest
package contains a suite of functions to
test and evaluate segregation distortion in F1 populations of
tetraploids. We allow for various types of polyploids (auto, allo, and
segmental) without having the user specify the type of polyploid they
are studying. We also account for genotype uncertainty through the use
of genotype likelihoods, which can be obtained through many genotyping
programs (like updog
, fitpoly
, and
polyRAD
). Details of these methods may be found in Gerard
et al. (2024). The main functions are:
multi_lrt()
: Run any of the likelihood ratio tests for
segregation distortion in parallel across many SNPs.multidog_to_g
: Format the genotyping output from
updog::multidog()
to be compatible withe input of
multi_lrt()
.lrt_men_g4()
: Likelihood ratio test for segregation
distortion using known genotypes.lrt_men_gl4()
: Likelihood ratio test for segregation
distortion using genotype likelihoods.offspring_gf_2()
: Offspring genotype frequencies under
the two parameter model of meiosis.offspring_gf_3()
: Offspring genotype frequencies under
the three parameter model of meiosis.simf1g()
: Simulate genotype counts from an F1
population of tetraploids.simf1gl()
: Simulate genotype likelihoods from an F1
population of tetraploids.Here, we will demonstrate some of our functions.
We can obtain offspring genotype frequencies via
offspring_gf_2()
and offspring_gf_3()
. These
are two different parameterizations of the same model for meiosis. For
offspring_gf_3()
, you insert the following parameters:
tau
: Probability of quadrivalent formation.beta
: Probability of double reduction given
quadrivalent formationgamma1
: Probability of AA_aa
pairing in
parent 1 given bivalent formation. Only applicable when
p1 = 2
.gamma2
: Probability of AA_aa
pairing in
parent 2 given bivalent formation. Only applicable when
p2 = 2
.p1
: The first parent’s genotype.p2
: The second parent’s genotype.Let’s generate some example genotype frequencies. You can play around with the parameter values yourself.
gf <- offspring_gf_3(
tau = 1,
beta = 1/6,
gamma1 = 1/3,
gamma2 = 1/3,
p1 = 1,
p2 = 2)
plot(
x = 0:4,
y = gf,
type = "h",
xlab = "Genotype",
ylab = "Frequency",
ylim = c(0, 1))
The offspring_gf_3()
function is safer to use because
there is a dependence between the preferential pairing parameter and the
double reduction rate that bounds these values in
offspring_gf_2()
, and so in the two-parameter model you
might accidentally choose values that are impossible. I did not set up
checks for these values because the bounds depend on the maximum rate of
double reduction, which can vary significantly. Please see Gerard et
al. (2024) for details.
We’ll first simulate some data where the null of no segregation distortion is true.
set.seed(1)
g1 <- 1
g2 <- 2
alpha <- 1/6
xi1 <- 1/3
xi2 <- 1/3
n <- 20
rd <- 10
x <- simf1g(
n = n,
g1 = g1,
g2 = g2,
alpha = alpha,
xi1 = xi1,
xi2 = xi2)
gl <- simf1gl(
n = n,
rd = rd,
g1 = g1,
g2 = g2,
alpha = alpha,
xi1 = xi1,
xi2 = xi2)
The LRT has a large \(p\)-value, which is appropriate since there is no segregation distortion.
When we simulate data where the alternative is true, we get a very small \(p\)-value.
Gerard D, Thakkar M, & Ferrão LFV (2024). “Tests for segregation distortion in tetraploid F1 populations.” bioRxiv. doi:10.1101/2024.02.07.579361.