More than five million Americans are living with Alzheimer’s disease, a disease without a cure. Epidemiological studies that directly test associations between risk factors and Alzheimer’s disease are difficult to conduct because identified associations are in many cases confounded. The time period in which people are affected by risk factors may have ended years before clinical symptoms are observed.
This project will implement Mendelian randomization — using genetic instrumental variables to make inferences about causal effects based on observational data — to identify risk factors for Alzheimer’s. The project relies on integration with the Wisconsin Registry for Alzheimer’s Prevention.
The research is expected to produce an atlas of causal risk factors, including complex human traits, genes and their tissue-dependent transcriptional activities, serum and metabolites, for Alzheimer’s disease. These results can be used to guide future studies and therapeutics development. Methods developed in this project will be released as publicly accessible software packages.
Hyunseung Kang, Assistant Professor of Statistics
Qiongshi Lu, Assistant Professor of Biostatistics and Medical Informatics
Corinne Engleman, Associate Professor of Population Health Sciences
Recent evidence suggests that microbial communities in human bodies (i.e. human microbiome), particularly those found in the intestine, play an essential role in inflammation and age-related conditions. However, most previous microbiome studies on aging have been characterized by small sample sizes and limited measurements of aging biomarkers/phenotypes. Moreover, there has been limited progress in the ability to analyze longitudinal microbiome data.
This project draws on the Wisconsin Longitudinal Study (WLS), a study of older adults who have been tracked since birth, and who, now in their late 70s, are beginning to experience rapid changes in aging and inflammatory related chronic disease burden.
The research team will develop and apply novel methods to characterize the variations of gut microbial composition with advancing age and age-related chronic inflammation and associated diseases. Deliverables from this project include a valuable dataset for microbiome research in aging, new methods and new knowledge of microbiome in aging process that will be described through peer-reviewed publications in statistics and medical science, and freely available software packages and tools for analyzing microbiome data.
ZhengZheng Tang, Assistant Professor of Biostatistics and Medical Informatics
Pamela Herd, Professor of Sociology
Federico Rey, Assistant Professor of Bacteriology
Jun Zhu, Professor of Statistics