Principal Investigator: Professor Stephen Hsu
Michigan State University, 249 Administration Building, 426 Auditorium Road, East Lansing MI 48824-1046, United StatesTags: 15326, Complex Traits, diabetes, Machine Learning, prevention
1.Dr Carson Chow
2.Professor James Lee
Collaborating Institutions and Addresses:
1.NIDDK, Laboratory of Biological Modeling, NIH/NIDDK/LBM, 12 South Dr, Rm 4007, MSC 5621, Bethesda 20892, United States.
2.University of Minnesota Twin Cities, Department of Psychology, N409 Elliott Hall, 75 East River Parkway, Minneapolis MN 55455. United States.
Summary of research
Diabetes, Machine Learning, Complex Traits, Prevention
Application Lay Summary:
1a: Our goal is to test new computational methods for determining the genetic architecture of complex traits, including highly heritable conditions such as Type 1 Diabetes, Alzheimer’s, and others. The techniques we plan to use have been the subject of intense recent activity in fields such as optimization, signal processing and machine learning, but so far have just begun to be applied in genomics.
1b: The research will produce improved predictive models which, based on individual genomics, identify individuals at high risk for certain diseases. It will also identify the many alleles associated with this risk. Early intervention with high risk individuals may decrease rates of incidence and reduce health care costs. Elaboration of underlying genetic architecture is important basic science and may lead to improved treatments (e.g., drug development).
1c: We wish to obtain access to genomic data and phenotype data relevant to highly heritable disease conditions (e.g., Type 1 Diabetes) as well as complex traits such as height, BMI, cognitive ability. Advanced computational algorithms will be used to study the genetic architecture of these traits. The techniques we plan to use have been the subject of intense recent activity in fields such as optimization, signal processing and machine learning, but so far have just begun to be applied in genomics. Analysis will be performed on high-performance computing clusters.
1d: We would like access to the full cohort (SNP genotypes), and several relevant phenotypes.