  Structural variation occurs in the genomes of individuals because of the
different positions occupied by repetitive genome elements like endogenous
retroviruses, or ERVs. The presence or absence of ERVs can be determined by
identifying the junction with the host genome using high-throughput sequence
technology and a clustering algorithm. The resulting data give the number of
sequence reads assigned to each ERV-host junction sequence for each sampled
individual. Variability in the number of reads from an individual integration
site makes it difficult to determine whether a site is present for low read
counts. We present a novel two-component mixture of negative binomial
distributions to model these counts and assign a probability that a given ERV
is present in a given individual. We explain how our approach is superior to
existing alternatives, including another form of two-component mixture model
and the much more common approach of selecting a threshold count for declaring
the presence of an ERV. We apply our method to a data set of ERV integrations
in mule deer [Odocoileus hemionus], a species for which no genomic resources
are available, and demonstrate that the discovered patterns of shared
integration sites contain information about animal relatedness.
