The Hendricks Team is committed to increasing opportunities for all people to learn about statistics, machine learning, and science. We are motivated to ask novel research questions and ensure the research is robust and accurate. The Team works at the intersection of biomedical research and statistical/machine learning method development with current projects including the development of methods to increase the utility of publicly available genetic resources, identifying the biological mechanisms of healthy diets, elucidating the genomic underpinnings of conditions and traits, and most recently, understanding the molecular determinants influencing health and disease over the menopause transition. We follow best practices of reproducibility and robust science by creating open source, well documented software and releasing all data and code used for our studies. Our team is highly collaborative working with people from a variety of backgrounds and education levels. We are always learning, improving, and pushing ourselves and others to be our best. In doing so, we produce first-class research for the broader community and train the next generation of biomedical scientists.
Estimating Case and Control Allele Frequencies from GWAS Summary Statistics
Evaluating genetic risk across sex and menopause
Characterizing substructure via mixture modeling in large-scale genetic summary statistics
PATH-GREU is a two-year program to introduce and train undergraduate students in genomics research to meet the increasing demands of genomics workforce.
PATH-GDS is a two-year program to introduce and train MS Statistics and MS Applied Mathematics students in genomics research to meet the increasing demands of a computationally and data driven genomics workforce.
Identifying foods and food compounds in healthy diets leading to improved health outcomes
Genome Sequencing Program
Maternal and Infant Nutrition Trial (MINT)
Rare variant association tests using external control data from genetic databases
Estimating ancestry proportions from genetic summary data