A family history of Alzheimer’s disease was one of the earliest known risk factors for developing the disease, and it is now clear that there is a strong genetic component to both early and late-onset forms. Our knowledge of genetic risk factors has increased tremendously over the past decade—thanks to better DNA sequencing and analysis methods and international biobanks with many thousands of human samples. These resources set the stage for Genome-Wide Association Studies (GWAS) to identify specific genetic variants associated with Alzheimer’s disease. The hope—and promise—of these studies is to better understand the biology of this complex disease and to find specific genes or molecules with therapeutic potential for drug development.
Analyzing vast numbers of human genomes to find the few variations in DNA sequences most likely contributing to Alzheimer’s disease risk is a difficult task requiring sophisticated knowledge of statistics. This kind of work often goes largely unseen by the audience—like the backstage crew for a stage production—but is absolutely critical. Dr. Lange’s expertise is in the statistical analyses of complex genetic information, and his work has been instrumental in the successful outcomes of the Alzheimer’s Genome Project (AGP) led by Dr. Rudy Tanzi. Broadly, Dr. Lange and team develop computational data analysis methods that help researchers confidently identify genetic variants most likely to contribute to the disease and not be misled by red herrings. They also have a track record of designing their tools to be shared and usable by other geneticists and computational scientists.
One of the most widely used genetic analysis tools for family studies is called family-based association testing (FBAT). As the name implies, these methods were developed to analyze genomic data from related individuals, which is how the AGP is designed. During the previous funding period, Dr. Lange and colleagues successfully extended the ability of FBAT software to narrow down the predicted set of genomic regions associated with Alzheimer’s disease in AGP datasets. However, there is still significant room to improve. He wants to keep pace with—if not surpass—new methods available to several AGP peer studies that use a population (instead of family) based design. Both types have their advantages. Population studies can detect common gene variants, while family-based studies may identify more rare gene variants. Dr. Lange points out that there have been incredible improvements to approaches for analyzing population-based genetic studies in the past few years, but that efforts to adapt these tools for family-based studies (like the AGP) are lagging—which could lead to delays in or missing insights from these valuable data.
In this follow-on, Dr. Lange proposes three aims to improve FBAT analysis methods. In the first aim, they will incorporate relevant and available data from external (non-AGP) sources about genetic variants of interest. These data can be used to help researchers fine-map the genomic regions of interest. Fine-mapping refers to the process of determining which specific variant(s) in a given region of DNA—usually out of many possible candidates—are most likely to contribute to disease, as opposed to others that might just be along for the ride. In the second aim, they will implement an approach called Mendelian Randomization (MR) into their analysis algorithms. MR is also used to predict which gene variants are most likely influencing the risk of disease. In the third aim, they will incorporate the latest statistical fine mapping approach called SUSIE (SUm of Single Effects). SUSIE is designed to find the smallest possible credible set of gene variants that might contribute to disease risk. Together, applying these analysis methods to AGP and other family-based studies should reduce the number of gene variants for potential follow-up studies to those with the highest likelihood of having a functional impact—ideally saving researchers time and resources that might be wasted chasing the wrong gene.
In the first year of funding, Dr. Lange secured additional resources that enhanced the project’s potential outputs. His team initiated a collaboration with Dr. Richard Mayeux (Columbia University) and gained access to more family patient data, increasing the study’s sample size by almost 150%. The team also secured additional NIH funding and began developing a refined FBAT approach that allows the incorporation of multiple variables connected to ancestry, environmental differences, and familial dynamics. Dr. Lange estimates this new approach will increase the statistical power of this study by almost 50%. With these additional resources, Dr. Lange reports Aims 1 and 3 as completed, with publications in progress for each. As they complete the refined FBAT, the team intends to apply the new approach to the expanded dataset incorporating the AGP and Columbia datasets. In addition to this, Dr. Lange reports progress on Aim 2 is underway and on track to be completed in 2025.
