Heteroscedastic Personalized Regression Unveils Genetic Basis of Alzheimer’s Disease Stratified by Cognitive Level
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In contemporary medical research, patient heterogeneity plays a pivotal role in comprehending intricate diseases such as Alzheimer’s disease and various forms of cancer. Specifically, in the genomic analysis of Alzheimer’s disease, individual patients may exhibit unique causal mutations that significantly influence their therapeutic trajectory. Conventional models that share numerous parameters across all individuals struggle to discern this heterogeneity and identify the influential factors for individuals. To tackle this challenge, we propose an innovative approach called Heteroscedastic Personalized Regression (Het-PR) to estimate the heterogeneity across samples and obtain personalized models for each sample. We demonstrate the effectiveness and robustness of Het-PR through both simulation and real data experiments. In the simulation experiment, we show that Het-PR outperforms other state-of-the-art models in capturing inter-sample heterogeneity. In the real data experiment, we apply Het-PR to Alzheimer’s data and show that it can identify persuasive selected genetic factors for each individual patient. Interestingly, our results suggest that there might be different associative SNPs for AD patients stratified by different cognitive levels.
Author summary
In medical research, it has been observed that causes of a disease vary significantly among individuals, especially when looking at complex diseases like Alzheimer’s disease and cancer. For Alzheimer’s disease, obesity, age, gender, and depression may play different roles across different patients. When studying the genes of Alzheimer’s patients, we find that each person might have their own unique genetic changes that can affect their treatment. For example, Alzheimer’s patients with different genetic mutations may respond differently to the same treatment. Traditional research methods often miss these individual differences and can’t always pinpoint important personalized factors for each patient, because they usually use one model for all patients. To better understand these differences, we’ve introduced a new method, Heteroscedastic Personalized Regression (Het-PR), which generates a personalized model for each individual. Our experiments show that Het-PR is more effective than other leading methods in identifying these patient differences and recognizing Alzheimer’s genetic basis for each patient through both simulation and real data experiments. When we used Het-PR on real Alzheimer’s data, it helped us spot key genetic factors for each patient. Additionally, in our study, we excitedly find that different genetic markers in Alzheimer’s patients are possibly based on their cognitive abilities. Software for Heteroscedastic Personalized Regression is available in https://github.com/rong-hash/Het-PR .
