Statistical Genetics Lab
The lab headed by Dr. Yongtao Guan is a computational lab with primary research interests in developing statistical and computational methods to address problems arising from genetics and genomics. Some of the current projects include:
- Detecting the strcuture of haplotypes and local ancestry inference.
Motivated by approximating coalescence and recombination, we developed a two-layer hidden Markov model to detect structure of (ancestry) haplotypes. This allows to model two scales of LD and thereby infer local ancestry of admixed individuals. Our method has the following advantage compare to competing methods: 1) it can directly work with dipoid genotypes in source population; 2) it cleanly handles missing data; and 3) it has a high resolution -- can detect ancestry track length of a few tenths of a centimorgan. See software page for manual and software ELAI.
- Haplotype association with multi-phenotypes.
From the aforementioned two-layer model, we are able to infer local haplotype sharing (LHS) between individuals. Derived from local haplotype enrichment, LHS is a measure of local genetic relatedness; thus allows us to use LHS to perform association testing with phenotypes. In many studies, phenotypes are observed over time, and jointly analyzing these time-course data will increase power to detect association. A visiting student, Hanli Xu, is working on this project.
- De novo assembly of short sequence reads and variants calling.
Identifying difficulties associated with the de Bruijn graph based approach for de novo assembly, we are currently developing algorithms and software packages for a Monte Carlo approach for de novo assembly. A postdoc, Liang Zhao, who is trained in compute science, is deligently working on this project.
- Detect epistatic interactions using directed acyclic graphs.
A directed acylic graph specifies a joint distribution on all nodes. When there is no epistatic interaction between parental nodes of a node of interest, the direction of edges can point either way with equil probabilies. If we penalize the edge that goes into the node of interest, the posterior edges that jointly point to the node must contain interactions that can overcome the penalty. This is the rationale behind using DAG to detect interaction. The main difficutlies are compuations. Quan Zhou, a graduate student from SCBMB program at Baylor, is working on this project.
Students who are interested in statistical genetics are encouraged to send Yongtao Guan, Ph.D an e-mail to discuss potential rotation projects.