Statistical Genetics Lab
Statistical genetics involves the development of novel
statistical models and innovative methods for the analysis and interpretation
of genetic data. Intrigued by Mendel’s laws which were fundamentally
probabilistic, R. A.
Fisher is one of the greatest statisticians in the 20th century
who initiated the fundamental work using statistical approaches to solve
genetic problems. With advanced
molecular technologies and instruments, high throughput genetic/genomic data
can now be generated from any organisms almost with no limits, which bring new
opportunities as well as great challenges. The rapidly emerging field of
statistical genetics promises to lead to advances in the diagnosis, treatment,
and prevention of many genetic disorders from a purely lab-based science to an
information-based science. As
statisticians, we are playing pivotal roles in this unique field with
intriguing prospect, yet facing numerous challenges.
My training in both biology and statistics lays me a solid foundation to conduct cutting-edge interdisciplinary research covering a variety of fields (e.g. Statistics, Genetics and Genomics) across a spectrum of species including humans, animals and plants. I strongly believe that the unique interdisciplinary research environment can greatly foster the cross fertilization of new ideas which in turn can enhance our learning and understanding, and ultimately help us unravel the mystery of the biological world.
My overall research interest is to develop novel statistical methods and efficient computational algorithms with an optimal goal of solving real-world biological problems covering genetics, development, physiology, evolution and biomedicine. At present, my specific research is focused on, but is not limited to, the following areas
●
Genetic
association studies, nucleotide mapping complex traits/diseases, Genome-wide
association studies, SNP and haplotype inference
●
QTL
mapping, QTL mapping genomic imprintingGenetical
genomics including eQTL mapping and gene network
inference
●
Functional
genomics, Systems biology, Gene set enrichment analysis and Pathway analysis
●
Applied
functional and longitudinal data analysis
Publications in peer reviewed journals click here
Research
grants (we are very grateful for funding from the following resources)
NSF MSU-IRGP NIH
Current
Ph.D. students
Gengxin Li Ph.D.
candidate (Research supported by an MSU IRGP award, NSF award DMS 0707031,
and an MSU QBI Fellowship)
Shaoyu Li Ph.D. candidate (Research supported in part by NSF award DMS 0707031, an MSU Summer Support Fellowship and an MSU QBI Fellowship)
Wei-Wen Hsu PhD candidate (co-advisor, joint with D. Todem)
Group meeting
Our group meets every week on Tuesday
afternoon from 1:30 to 2:30 to discuss new development in statistical
genetics/bioinformatics and progress on individual projects. Click here for details.
Past and
current collaborators (alphabetical order)
•
Casella, George
(statistics, Department of Statistics,
•
Cheverud,
James (mouse genetics, Department of Anatomy and Neurobiology,
•
Fu, Wenjiang (statistical
epidemiology, Department of Epidemiology, MSU)
•
Kim, Dong-Yun (statistics, Department of Statistics, Virginia
Tech)
•
Larkins,
Brian (plant genetics, Department of Plant Science,
•
Lu, Qing (statistical genetics,
Department of Epidemiology, MSU)
•
Schutte,
Debra (Alzheimer genetics,
•
Shin-Han Shiu (plant genomics, Department of Plant Biology, MSU)
•
Uhal,
Bruce (lung genetics, Department of Physiology, MSU)
•
Wang, Dechun (soybean genetics, Department of Crop and Soil
Sci., MSU)
•
Wu, Rongling (statistical
genetics, Department of Statistics,
•
Zhang, Huanmin (chicken genetics, USDA-ARS)
•
Zhu, Jun
(quantitative genetics,
MSU
interdisciplinary research programs I’m involved in
•
Genetic
Program (member of the program executive committee)
•
Quantitative
Biology (QB) Program
•
Center for System
Biology
•
Ecology,
Evolutionary Biology, and Behavior (EEBB) Program