Package org.broadinstitute.hellbender.tools.copynumber.models

Class AlleleFractionModeller

java.lang.Object
org.broadinstitute.hellbender.tools.copynumber.models.AlleleFractionModeller
public final class AlleleFractionModeller extends Object
Given segments and counts of alt and ref reads over a list of het sites, infers the minor-allele fraction of each segment. For example, a segment with (alt,ref) counts (10,90), (11,93), (88,12), (90,10) probably has a minor-allele fraction somewhere around 0.1. The model takes into account allelic reference bias due to mapping etc. by learning a global gamma distribution on allelic bias ratios.

We define the bias ratio of each het locus to be the expected ratio of mapped ref reads to mapped alt reads given equal amounts of DNA (that is, given a germline het). The model learns a common gamma distribution: bias ratio ~ Gamma(alpha = mu^2 / sigma^2, beta = mu / sigma^2) where mu and sigma^2 are the global mean and variance of bias ratios, and alpha, beta are the natural parameters of the gamma distribution.

Each segment has a minor-allele fraction f, and for each het within the locus the number of alt reads is drawn from a binomial distribution with total count n = #alt reads + #ref reads and alt probability f / (f + (1 - f) * bias ratio) if the locus is alt minor and (1 - f) / (1 - f + f * bias ratio) if the locus is ref minor. We also allow a prior on minor-allele fraction to be specified by the alpha parameter of the 4-parameter beta distribution Beta(alpha, 1, 0, 1/2).

Conceptually, the model contains latent variables corresponding to the bias ratio and indicators for alt minor/ref minor at each het locus. However, we integrate them out and the MCMC model below only contains the minor-allele fractions and the three hyperparameters of the model: the two parameters of the gamma distribution along with the global outlier probability.

See docs/CNV/archived/archived-CNV-methods.pdf for a thorough description of the model.