Benchmarks with Synthetic Data Sets:
ROHMM ’s performance was compared against PLINK andbcftools roh with our synthetic datasets. Under various
homozygosity and erroneous site levels ROHMM ’s allele
distribution model showed comparable performance against its competitors
under both genome and exome simulated data scenarios. Additionally
allele frequency model implemented in ROHMM performed similarly
if not better under all conditions compared to bcftools roh(Figure 2).
To test the stability and performance of ROHMM with various
levels of data density we used randomly down-sampled synthetic exome
samples from our simulated datasets. ROHMM ’s false positive rate
did not increase more than 0.06 % and false negative rate did not
increase more than 3.3 %. Additionally ROHMM ’s alternative
allele frequency model showed even lesser changes in both error types
which was comparable to what was published for bcftools roh(Narasimhan et al., 2016) (Figure 3).