A genome-wide association study coupled with machine learning approaches to identify influential demographic and genomic factors underlying Parkinson’s disease - Rahman, Md Asad; Liu, Jinling (Frontiers in Genetics, 2023)
Background: Despite the recent success of genome-wide association studies in identifying 90 independent risk loci for Parkinson's disease (PD), the genomic underpinning of PD is still largely unknown. At the same time, accurate and reliable predictive models utilizing genomic or demographic features are desired in the clinic for predicting the risk of Parkinson's disease.To identify influential demographic and genomic factors associated with PD and further develop predictive models, we utilized demographic data, incorporating 200 variables across 33,473 participants, along with genomic data involving 447,089 SNPs across 8,840 samples, both derived from the Fox Insight online study. We first applied correlation and genome-wide association study (GWAS) analysis respectively to find the top demographic and genomic factors associated with PD. We further developed and compared a variety of machine learning (ML) models for predicting PD. From the developed ML models, we did feature importance analysis to reveal the predictability of each demographic or genomic input feature for PD. Finally, we performed gene set enrichment analysis over our GWAS results to identify PD-associated pathways.In our study, we identified both novel and well-known demographic and genetic factors (along with the enriched pathways) related to PD. In addition, we developed predictive models that performed robustly, with AUC = 0.89 for demographic data and AUC = 0.74 for genomic data. Our GWAS analysis identified several novel and significant variants and gene loci, including three intron variants in LMNA (p-values smaller than 4.0e-21) and one missense variant in SEMA4A (p-value = 1.11e-26). Our feature importance analysis from the PD-predictive ML models highlighted some significant and novel variants from our GWAS analysis (e.g., the intron variant rs1749409 in the RIT1 gene) as well as helped identify potentially causative variants that were missed by GWAS, such as rs11264300, a missense variant in the gene of DCST1, and rs11584630, an intron variant in the gene of KCNN3.In summary, through combining a genome-wide association study with advanced machine learning models, we identified both known and novel demographic and genomic factors, as well as built well-performing ML models for predicting Parkinson's disease.