private Map<String, Object> calculateIC(final VariantContext vc) {
final GenotypesContext genotypes =
(founderIds == null || founderIds.isEmpty())
? vc.getGenotypes()
: vc.getGenotypes(founderIds);
if (genotypes == null || genotypes.size() < MIN_SAMPLES) return null;
int idxAA = 0, idxAB = 1, idxBB = 2;
if (!vc.isBiallelic()) {
// for non-bliallelic case, do test with most common alt allele.
// Get then corresponding indeces in GL vectors to retrieve GL of AA,AB and BB.
int[] idxVector = vc.getGLIndecesOfAlternateAllele(vc.getAltAlleleWithHighestAlleleCount());
idxAA = idxVector[0];
idxAB = idxVector[1];
idxBB = idxVector[2];
}
double refCount = 0.0;
double hetCount = 0.0;
double homCount = 0.0;
int N = 0; // number of samples that have likelihoods
for (final Genotype g : genotypes) {
if (g.isNoCall() || !g.hasLikelihoods()) continue;
if (g.getPloidy() != 2) // only work for diploid samples
continue;
N++;
final double[] normalizedLikelihoods =
MathUtils.normalizeFromLog10(g.getLikelihoods().getAsVector());
refCount += normalizedLikelihoods[idxAA];
hetCount += normalizedLikelihoods[idxAB];
homCount += normalizedLikelihoods[idxBB];
}
if (N < MIN_SAMPLES) {
return null;
}
final double p =
(2.0 * refCount + hetCount)
/ (2.0 * (refCount + hetCount + homCount)); // expected reference allele frequency
final double q = 1.0 - p; // expected alternative allele frequency
final double F = 1.0 - (hetCount / (2.0 * p * q * (double) N)); // inbreeding coefficient
Map<String, Object> map = new HashMap<String, Object>();
map.put(getKeyNames().get(0), String.format("%.4f", F));
return map;
}
public void writeBeagleOutput(
VariantContext preferredVC, VariantContext otherVC, boolean isValidationSite, double prior) {
GenomeLoc currentLoc =
VariantContextUtils.getLocation(getToolkit().getGenomeLocParser(), preferredVC);
StringBuffer beagleOut = new StringBuffer();
String marker = String.format("%s:%d ", currentLoc.getContig(), currentLoc.getStart());
beagleOut.append(marker);
if (markers != null)
markers.append(marker).append("\t").append(Integer.toString(markerCounter++)).append("\t");
for (Allele allele : preferredVC.getAlleles()) {
String bglPrintString;
if (allele.isNoCall() || allele.isNull()) bglPrintString = "-";
else bglPrintString = allele.getBaseString(); // get rid of * in case of reference allele
beagleOut.append(String.format("%s ", bglPrintString));
if (markers != null) markers.append(bglPrintString).append("\t");
}
if (markers != null) markers.append("\n");
GenotypesContext preferredGenotypes = preferredVC.getGenotypes();
GenotypesContext otherGenotypes = goodSite(otherVC) ? otherVC.getGenotypes() : null;
for (String sample : samples) {
boolean isMaleOnChrX = CHECK_IS_MALE_ON_CHR_X && getSample(sample).getGender() == Gender.MALE;
Genotype genotype;
boolean isValidation;
// use sample as key into genotypes structure
if (preferredGenotypes.containsSample(sample)) {
genotype = preferredGenotypes.get(sample);
isValidation = isValidationSite;
} else if (otherGenotypes != null && otherGenotypes.containsSample(sample)) {
genotype = otherGenotypes.get(sample);
isValidation = !isValidationSite;
} else {
// there is magically no genotype for this sample.
throw new StingException(
"Sample "
+ sample
+ " arose with no genotype in variant or validation VCF. This should never happen.");
}
/*
* Use likelihoods if: is validation, prior is negative; or: is not validation, has genotype key
*/
double[] log10Likelihoods = null;
if ((isValidation && prior < 0.0) || genotype.hasLikelihoods()) {
log10Likelihoods = genotype.getLikelihoods().getAsVector();
// see if we need to randomly mask out genotype in this position.
if (GenomeAnalysisEngine.getRandomGenerator().nextDouble() <= insertedNoCallRate) {
// we are masking out this genotype
log10Likelihoods =
isMaleOnChrX ? HAPLOID_FLAT_LOG10_LIKELIHOODS : DIPLOID_FLAT_LOG10_LIKELIHOODS;
}
if (isMaleOnChrX) {
log10Likelihoods[1] = -255; // todo -- warning this is dangerous for multi-allele case
}
}
/** otherwise, use the prior uniformly */
else if (!isValidation && genotype.isCalled() && !genotype.hasLikelihoods()) {
// hack to deal with input VCFs with no genotype likelihoods. Just assume the called
// genotype
// is confident. This is useful for Hapmap and 1KG release VCFs.
double AA = (1.0 - prior) / 2.0;
double AB = (1.0 - prior) / 2.0;
double BB = (1.0 - prior) / 2.0;
if (genotype.isHomRef()) {
AA = prior;
} else if (genotype.isHet()) {
AB = prior;
} else if (genotype.isHomVar()) {
BB = prior;
}
log10Likelihoods = MathUtils.toLog10(new double[] {AA, isMaleOnChrX ? 0.0 : AB, BB});
} else {
log10Likelihoods =
isMaleOnChrX ? HAPLOID_FLAT_LOG10_LIKELIHOODS : DIPLOID_FLAT_LOG10_LIKELIHOODS;
}
writeSampleLikelihoods(beagleOut, preferredVC, log10Likelihoods);
}
beagleWriter.println(beagleOut.toString());
}
