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Defense: Probabilistic Graphical Inference of Pedigrees

Speaker Name: 
Thomas Ng
Speaker Title: 
Ph.D. Candidate
Speaker Organization: 
UCSC Baskin Engineering Biomolecular Engineering
Start Time: 
Friday, March 12, 2021 - 12:30pm
End Time: 
Friday, March 12, 2021 - 1:30pm
Zoom - - Passcode: 309456


Inference of pedigrees from genetic data is a fundamental problem in the field of population genetics. In this dissertation, I develop a novel representation of pedigrees as a type of factor graph that allows for the inference of multigenerational pedigrees within a proper, probabilistic framework. Using the Sum-Product algorithm, the factor-graph representation allows the rapid calculation of the pedigree likelihood under a variety of rearrangements, which provides an efficient mechanism for Metropolis-Hastings simulation of a Markov chain through the space of possible pedigrees. My software implementing this, pedFac, produces a sample of pedigrees from their posterior distribution, which allows for fully Bayesian, multigenerational pedigree inference. 

I show that pedFac performs as well as other state-of-the-art software for inferring two-generation pedigrees, but it also provides a far superior estimate of uncertainty. PedFac is also successful in inferring multigenerational pedigrees when sampling is incomplete. This means that pedFac can reconstruct multiple, true links in pedigrees through unobserved/unsampled individuals, a task not performed well by any other software available today. 

Pedigrees with loops in them present a difficult challenge for the factor-graph-based approach. I develop a method that permits the likelihood calculation of cyclic pedigrees by conditioning on sampled genotype values over a set of loop breakers. I show that this conditioning approach allows pedFac to successfully sample the pedigree space, whether it be cyclic or acyclic.

Event Type: 
Eric Anderson
Graduate Program: 
Biomolecular Engineering & Bioinformatics PhD