  We develop a feature allocation model for inference on genetic tumor
variation using next-generation sequencing data. Specifically, we record single
nucleotide variants (SNVs) based on short reads mapped to human reference
genome and characterize tumor heterogeneity by latent haplotypes defined as a
scaffold of SNVs on the same homologous genome. For multiple samples from a
single tumor, assuming that each sample is composed of some sample-specific
proportions of these haplotypes, we then fit the observed variant allele
fractions of SNVs for each sample and estimate the proportions of haplotypes.
Varying proportions of haplotypes across samples is evidence of tumor
heterogeneity since it implies varying composition of cell subpopulations.
Taking a Bayesian perspective, we proceed with a prior probability model for
all relevant unknown quantities, including, in particular, a prior probability
model on the binary indicators that characterize the latent haplotypes. Such
prior models are known as feature allocation models. Specifically, we define a
simplified version of the Indian buffet process, one of the most traditional
feature allocation models. The proposed model allows overlapping clustering of
SNVs in defining latent haplotypes, which reflects the evolutionary process of
subclonal expansion in tumor samples.

Applications;Genomics;Populations and Evolution