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

Upadhya, Suraj; Liu, Hongliang; Luo, Sheng; Lutz, Michael W.; Chiba‐Falek, Ornit

doi:10.3389/fnins.2022.827447

Cited by 6 publications

(8 citation statements)

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“…In this study, we found that depression polygenicity plays a role in comorbid depression in MS and did not change based on sex nor when compared with depression occurring as the primary disease. A similar increase in the hazard (approximately 30%) for hospital-based depression diagnosis was identified in a Danish study of 34,573 individuals from the general population, 27 when depression occurred as a comorbidity of Alzheimer disease, 28 and to that of approximately 7,000 moderate-to-severe cases with MDD. 23 Our study also provides evidence of a biological basis of depression for people with MS, whereas previous studies have identified conflicting associations between a family history of depression and the occurrence of depression in MS. 29,30 This study uses a direct measure of genetic variation of depression, as opposed to family history, which captures genetic and environmental influences.…”

Section: Discussionsupporting

confidence: 66%

Polygenicity of Comorbid Depression in Multiple Sclerosis

Kowalec¹,

Fitzgerald²,

Salter³

et al. 2023

Neurology

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Background:Depression is common in multiple sclerosis (MS); and is associated with faster disability progression. The etiology of comorbid depression in MS remains poorly understood. Identification of individuals with a high risk for depression, via polygenic scores (PGS), may facilitate earlier identification. Previous genetic studies of depression considered depression as a primary disorder, not a comorbidity, and thus findings may not generalize to MS. Body mass index (BMI) is a risk factor for both MS and depression and its association may highlight differences in depression in MS. To improve the understanding of comorbid depression in MS, we will investigate PGS in people with MS, with the hypothesis that higher depression PGS is associated with increased odds for comorbid depression in MS.Methods:Samples from three sources (Canada, UK Biobank, and the United States) were used. Individuals were grouped into cases (MS/comorbid depression) and compared to three control groups: MS/no depression, depression/no immune disease, and healthy persons. We employed three depression definitions: lifetime clinical diagnoses, self-reported diagnoses, and depressive symptoms. The PGS were tested in association with depression using regression.Results:106,682 individuals of European genetic ancestry were used: Canada (n=370; 213 with MS), UK Biobank (n=105,734; 1,390 MS), and USA (n=578 MS). Meta-analyses revealed individuals with MS and depression had a higher depression PGS compared to both MS without depression (odds ratio range per standard deviation [SD]: 1.29-1.38,P<0.05) and healthy controls (odds ratio range per SD: 1.49-1.53,P<0.025), regardless of the definition applied and when sex-stratified. The BMI PGS was associated with depressive symptoms (P≤.001). The depression PGS did not differ between depression occurring as a comorbid condition with MS or as the primary condition (odds ratio range per SD: 1.03-1.13, allP>0.05).Discussion:Higher depression genetic burden was associated with ∼30-40% increased odds of depression in European genetic ancestry participants with MS compared to those without depression and was no different compared to those with depression and no comorbid immune disease. This study paves the way for further investigations into the possible use of PGS for assessing psychiatric disorder risk in MS and its application to non-European genetic ancestries.

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Section: Discussionsupporting

confidence: 66%

Polygenicity of Comorbid Depression in Multiple Sclerosis

Kowalec¹,

Fitzgerald²,

Salter³

et al. 2023

Neurology

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“…This work applied differential gene expression and DNA-methylation analyses to study the endophenotype of depression in LOAD using a subset of the Religious Orders Study and Rush Memory and Aging Project (ROSMAP) dataset. This study extends prior work for the prediction of individuals with LOAD who are at greater risk of developing depression [ 9 , 10 ]. Collectively these studies aim to characterize the heterogeneity of depression in LOAD and further expand the field of heterogenous symptoms in LOAD by providing a guideline to study other endophenotypes of LOAD.…”

Section: Introductionsupporting

confidence: 72%

“…LOAD is characterized by multiple clinical symptoms [ 35 , 36 , 37 , 38 ], with NPS, including depression, showing high prevalence [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Prior work has explored the common genetics underlying MDD and LOAD, using genetics to predict those at greater risk of developing depression [ 9 , 10 ]. However, candidate genes and regulatory mechanisms underlying comorbid depression in LOAD have been understudied, and the transcriptomics and DNA-methylomics characteristics of depression heterogeneity in LOAD remain largely unknown.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, it was demonstrated that LOAD biomarkers, such as cerebrospinal fluid (CSF) Aβ 42 and CSF total and phosphorylated tau, are associated with depression trajectories in LOAD [ 8 ]. However, the genetic architecture underpinning the onset and heterogeneity of NPS in LOAD has been understudied, and there are only a few publications, including our previous work [ 9 , 10 ]. In prior work, we elucidated the shared genetic architecture between major depressive disorder (MDD) and LOAD.…”

Section: Introductionmentioning

confidence: 99%

“…We reported common genetic pathways, such as immune and inflammatory pathways, between MDD and LOAD [ 9 ]. Additionally, we constructed a polygenic risk score (PRS) that was shown to predict depression onset within LOAD patients, resulting in moderate predictability [ 10 ]. Collectively, these studies indicated that genetic factors contribute to comorbid depression in LOAD.…”

Section: Introductionmentioning

confidence: 99%

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Differential Gene Expression and DNA Methylation in the Risk of Depression in LOAD Patients

et al. 2022

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Depression is common among late-onset Alzheimer’s Disease (LOAD) patients. Only a few studies investigated the genetic variability underlying the comorbidity of depression in LOAD. Moreover, the epigenetic and transcriptomic factors that may contribute to comorbid depression in LOAD have yet to be studied. Using transcriptomic and DNA-methylomic datasets from the ROSMAP cohorts, we investigated differential gene expression and DNA-methylation in LOAD patients with and without comorbid depression. Differential expression analysis did not reveal significant association between differences in gene expression and the risk of depression in LOAD. Upon sex-stratification, we identified 25 differential expressed genes (DEG) in males, of which CHI3L2 showed the strongest upregulation, and only 3 DEGs in females. Additionally, testing differences in DNA-methylation found significant hypomethylation of CpG (cg20442550) on chromosome 17 (log2FC = −0.500, p = 0.004). Sex-stratified differential DNA-methylation analysis did not identify any significant CpG probes. Integrating the transcriptomic and DNA-methylomic datasets did not discover relationships underlying the comorbidity of depression and LOAD. Overall, our study is the first multi-omics genome-wide exploration of the role of gene expression and epigenome alterations in the risk of comorbid depression in LOAD patients. Furthermore, we discovered sex-specific differences in gene expression underlying the risk of depression symptoms in LOAD.

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Using polygenic scores in combination with symptom rating scales to identify attention-deficit/hyperactivity disorder

Høberg,

Solberg,

Hegvik

et al. 2024

BMC Psychiatry

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Background The inclusion of biomarkers could improve diagnostic accuracy of attention-deficit/hyperactivity disorder (ADHD). One potential biomarker is the ADHD polygenic score (PGS), a measure of genetic liability for ADHD. This study aimed to investigate if the ADHD PGS can provide additional information alongside ADHD rating scales and examination of family history of ADHD to distinguish between ADHD cases and controls. Methods Polygenic scores were calculated for 576 adults with ADHD and 530 ethnically matched controls. ADHD PGS was used alongside scores from the Wender-Utah Rating Scale (WURS) and the Adult ADHD Self-Report Scale (ASRS) as predictors of ADHD diagnosis in a set of nested logistic regression models. These models were compared by likelihood ratio (LR) tests, Akaike information criterion corrected for small samples (AICc), and Lee R². These analyses were repeated with family history of ADHD as a covariate in all models. Results The ADHD PGS increased the variance explained of the ASRS by 0.58% points (pp) (R2ASRS = 61.11%, R2ASRS + PGS=61.69%), the WURS by 0.61pp (R2WURS = 77.33%, R2WURS + PGS= 77.94%), of ASRS and WURS together by 0.57pp (R2ASRS + WURS=80.84%, R2ASRS + WURS+PGS=81.40%), and of self-reported family history by 1.40pp (R2family = 28.06%, R2family + PGS=29.46%). These increases were statistically significant, as measured by LR tests and AICc. Conclusion We found that the ADHD PGS contributed additional information to common diagnostic aids. However, the increase in variance explained was small, suggesting that the ADHD PGS is currently not a clinically useful diagnostic aid. Future studies should examine the utility of ADHD PGS in ADHD prediction alongside non-genetic risk factors, and the diagnostic utility of the ADHD PGS should be evaluated as more genetic data is accumulated and computational tools are further refined.

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

Cited by 6 publications

References 50 publications

Polygenicity of Comorbid Depression in Multiple Sclerosis

Polygenicity of Comorbid Depression in Multiple Sclerosis

Differential Gene Expression and DNA Methylation in the Risk of Depression in LOAD Patients

Using polygenic scores in combination with symptom rating scales to identify attention-deficit/hyperactivity disorder

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