View source for Heterozygosity

The heterozygosity is the proportion of heterozygous genotypes. This is in some sense encapsulated in the theta estimates. And this page will just serve as a quick short example which is also written elsewhere on the wiki.

An important aspect with this approach is that we '''DO NOT''' require to fix the major and minor, or make any other apriori assumptions other than supplying the ancestral state.

The heterozygosity can either be a global estimate or a local estimate.

For diploid single samples the hetereo zygosity is simply second value in the SFS/AFS.  By fixing the ancestral we loop over the 3 possible derived alleles, or we can use the reference as the ancestral and fold the spectrum.

=Global estimate=
This is simply the [[SFS Estimation]] for single samples.
A short example is:
<pre>
./angsd -i my.bam -anc ancestral.fa -dosaf 1 -gl 1
#OR
./angsd -i my.bam -anc ref.fa -dosaf 1 -fold 1
#followed by the actual estimation
./realSFS angsdput.saf.idx >est.ml
</pre>
The heterozygosity is then:
<pre>
#in R
a<-scan("est.ml")
a[2]/sum(a)
</pre>
Things to consider is:

1. Add -C 50 -ref ref.fa -minQ 20 -minmapq 30 to the angsd parameters to weed out the worst reads and bases.

2. The output file could be very big. One might argue that we just need a reasonable large subset of the genome to estimate the one samples SFS (is is only 2 free parameters). So you could limit the analysis to a single chromosome by adding -r chr1. to the angsd part. Or you could add -nSites to the ''realSFS'' function.

3. if you work with ancient data. You can discard transition by adding -noTrans 1, to the angsd part of the code.

=Local estimate=
This is the single sample version of the theta estimation. 

1. We need to have a prior of the global heterozygosity as estimated from the example above 'est.ml'
Then we generate persite thetas.