1 research outputs found
Visualizing shifts in Founder Assignment for an Admixed Population of Mice caused by a New Genomic Build
Populations of inbred laboratory mice are highly desirable as model organisms due to their
easily reproducible (isogenic) genomes. A combination of SNP genotyping arrays and a hidden Markov
model (HMM) algorithm have been used successfully to accurately determine the genetic architecture
of inbred mice. This HMM algorithm, known as the Founder Assignment algorithm, performs
statistical modeling on genotyping data to calculate the probability that a given segment of a mouse’s
genome was inherited from a particular founding strain of the population. However, the predictions
generated by this algorithm are sensitive to changes in the genomic positions of the SNP markers used
for genotyping. Although many groups have worked to improve the accuracy and resolution of HMM
algorithms, few have considered how new assemblies of the genome, in which entire sets of markers
may be reordered or removed, affect HMM predictions. In this study, the Founder Assignment
algorithm was adapted to work with a new assembly (Build 38) of the mouse genome. The algorithm
was then run on a collection of genotyping data from approximately 400 mice. A new visualization
tool, which more clearly displays the HMM probabilities, was developed in order to identify regions
where the algorithm assigned different founders between Build 38 and the previous build. In the future,
these insights will help us design versions of the algorithm to work with newer versions of the
genotyping array and with high-throughput sequencing data.Bachelor of Scienc