Identifying individual risk rare variants using protein structure guided local tests (POINT)

摘要

Author summary While it is known that rare variants play an important role in understanding associations between genotype and complex diseases, pinpointing individual rare variants likely to be responsible for association is still a daunting task. Due to their low frequency in the population and reduced signal, localizing causal rare variants often requires additional information, such as type of DNA change or location of variant along the sequence, to be incorporated in a biologically meaningful fashion that does not overpower the genotype data. In this paper, we use the observation that important variants tend to cluster together on functional domains to propose a new approach for prioritizing rare variants: the protein structure guided local test (POINT). POINT uses a gene's 3-dimensional protein folding structure to guide aggregation of information from neighboring variants in the protein in a robust manner. We show how POINT improves selection performance over existing methods. We further illustrate how it can be used to prioritize individual rare variants using the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial data, finding promising variants within genes in association with lipoprotein-related outcomes.