A004 A markov chain-based bayesian population model for somatic instability in human white blood cells

<h3></h3> Using a database of HTT CAG-length distributions determined by amplicon sequencing in White Blood Cells (WBCs) collected from 1447 participant-visits from the Registry and Enroll-HD Studies, we build a Bayesian population model based on the assumption that CAG length distributions evolve over time according to a continuous time Markov chain in which changes in CAG length occur in single CAG length jumps. These Markov chains have jump rates that increase with CAG length, resulting in both expansions and contractions with a bias toward expansion. The effects of PCR-induced slippage were corrected using single molecule data, a novel multinomial logit model and a novel algorithm based on convex programming. Models were fit to the PCR slippage-corrected data using a fast BFGS algorithm in R. This algorithm minimizes both least squares and Kullback-Liebler loss functions and uses analytic gradients of the matrix exponential function. Participant-level models are shown to fit very well and predict CAG length distributions at the second visit 2 from distributions at visit 1. Model parameters for jump rate and expansion bias are correlated with inherited CAG length, CAP score, age-at-baseline, and time- between-visits. Finally, a Bayesian population-based model (implemented in the Stan Statistical Software Package) was used to model the precision of the participant-level model fits using a novel application of the Dirichlet distribution. Implications for studies of somatic instability in other cell types are discussed as are the uses of the statistical methodology described here in biomarker development.