Polygenic Risk Score Effectively Predicts Depression Onset in Alzheimer's Disease Based on Major Depressive Disorder Risk Variants

Suraj Upadhya; Hongliang Liu; Sheng Luo; Michael W Lutz; Ornit Chiba-Falek

doi:10.3389/fnins.2022.827447

Polygenic Risk Score Effectively Predicts Depression Onset in Alzheimer's Disease Based on Major Depressive Disorder Risk Variants

Front Neurosci. 2022 Mar 8:16:827447. doi: 10.3389/fnins.2022.827447. eCollection 2022.

Authors

Suraj Upadhya¹, Hongliang Liu^{2

3}, Sheng Luo², Michael W Lutz¹, Ornit Chiba-Falek^{1

4}

Affiliations

¹ Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, United States.
² Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, United States.
³ Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, United States.
⁴ Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, United States.

Abstract

Introduction: Depression is a common, though heterogenous, comorbidity in late-onset Alzheimer's Disease (LOAD) patients. In addition, individuals with depression are at greater risk to develop LOAD. In previous work, we demonstrated shared genetic etiology between depression and LOAD. Collectively, these previous studies suggested interactions between depression and LOAD. However, the underpinning genetic heterogeneity of depression co-occurrence with LOAD, and the various genetic etiologies predisposing depression in LOAD, are largely unknown.

Methods: Major Depressive Disorder (MDD) genome-wide association study (GWAS) summary statistics were used to create polygenic risk scores (PRS). The Religious Orders Society and Rush Memory and Aging Project (ROSMAP, n = 1,708) and National Alzheimer's Coordinating Center (NACC, n = 10,256) datasets served as discovery and validation cohorts, respectively, to assess the PRS performance in predicting depression onset in LOAD patients.

Results: The PRS showed marginal results in standalone models for predicting depression onset in both ROSMAP (AUC = 0.540) and NACC (AUC = 0.527). Full models, with baseline age, sex, education, and APOEε4 allele count, showed improved prediction of depression onset (ROSMAP AUC: 0.606, NACC AUC: 0.581). In time-to-event analysis, standalone PRS models showed significant effects in ROSMAP (P = 0.0051), but not in NACC cohort. Full models showed significant performance in predicting depression in LOAD for both datasets (P < 0.001 for all).

Conclusion: This study provided new insights into the genetic factors contributing to depression onset in LOAD and advanced our knowledge of the genetics underlying the heterogeneity of depression in LOAD. The developed PRS accurately predicted LOAD patients with depressive symptoms, thus, has clinical implications including, diagnosis of LOAD patients at high-risk to develop depression for early anti-depressant treatment.

Keywords: depression; heterogeneity; late-onset Alzheimer’s disease; major depressive disorder; neuropsychiatric symptoms; polygenic risk scores.