The gray matter volume of the brain is used as one of the important indicators to evaluate cognitive function. That is, the smaller the gray matter volume in the region of memory, the lower the cognitive function. Although there are several factors in the reduction of gray matter volume, it has been found that genetic factors also play a role through numerous recent studies. Genetic factors can involve in biological activities not only independently, but also collectively with complex interactions. In this study, we propose a method for predicting brain volume and deriving significant genetic variants from single-nucleotide polymorphisms (SNP) network that reflects the interactions between SNPs. The proposed method constructs a linear regression model to predict brain volume using refined SNP features obtained through feature propagation on the SNP network. The prediction model was applied to biobank innovations for chronic cerebrovascular disease with Alzheimer's disease study (BICWALZS) participants in Ajou University Hospital, Korea.
This study was conducted with biospecimens and data from the consortium of the Biobank Innovations for Chronic Cerebrovascular Disease with ALZheimer's Disease Study (BICWALZS), which was funded by the Korea Disease Control and Prevention Agency for the Korea Biobank Project (#4845-303) and the Biobank of Ajou University Hospital, a member of Korea Biobank Network. The authors would also like to gratefully acknowledge supported from the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2021R1A2C2003474), BK21 FOUR program of the National Research Foundation of Korea funded by the Ministry of Education (NRF5199991014091) and the Ajou University research fund.
