B0398
Title: Improved detection of allelic imbalance using biologically informed priors
Authors: Sally Paganin - Harvard T.H. Chan School of Public Health (United States) [presenting]
Jeff Miller - Harvard University (United States)
Abstract: The focus is on the analysis of DNA sequencing data derived from non-invasive procedures such as blood samples. At early cancer stages, such samples contain DNA from a majority of normal cells and a low fraction of tumour cells. Cancer presence can be assessed by measuring allelic imbalance: since a person inherits one allele from each parent, the allele proportion at heterozygous loci is close to 0.5 in normal cells, whereas significant deviations from 0.5 are indicative of the presence of cancer. To efficiently and sensitively detect such deviations, the allele proportions are modelled over the genome via a novel Bayesian hierarchical Hidden Markov Model. Prior knowledge is leveraged from population genome databases while borrowing information across multiple samples from the same subject. Hypothesis testing for cancer presence is embedded in the model via a spike and slab prior.
