A comparison of ten polygenic score methods for psychiatric disorders applied across multiple cohorts

Ni, Guiyan; Zeng, Jian; Revez, Joana R; Wang, Ying; Ge, Tian; Restaudi, Restaudi; Kiewa, Jackie; Nyholt, Dale R.; Coleman, Jonathan R. I.; Smoller, Jordan W.; Yang, Jian; Visscher, Peter M.; Wray, Naomi R.

doi:10.1101/2020.09.10.20192310

Cited by 60 publications

(85 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We conducted power analyses for classical, p-value threshold-based PRS using AVENGEME 44 . Because PRS calculated by PRS-CS have a higher power 45 , we assume that these estimates constitute lower boundaries of our real statistical power. According to these analyses, we had a power Table S7).…”

Section: Power Analysismentioning

confidence: 99%

Clinical and genetic differences between bipolar disorder type 1 and 2 in multiplex families

et al. 2021

View full text Add to dashboard Cite

The two major subtypes of bipolar disorder (BD), BD-I and BD-II, are distinguished based on the presence of manic or hypomanic episodes. Historically, BD-II was perceived as a less severe form of BD-I. Recent research has challenged this concept of a severity continuum. Studies in large samples of unrelated patients have described clinical and genetic differences between the subtypes. Besides an increased schizophrenia polygenic risk load in BD-I, these studies also observed an increased depression risk load in BD-II patients. The present study assessed whether such clinical and genetic differences are also found in BD patients from multiplex families, which exhibit reduced genetic and environmental heterogeneity. Comparing 252 BD-I and 75 BD-II patients from the Andalusian Bipolar Family (ABiF) study, the clinical course, symptoms during depressive and manic episodes, and psychiatric comorbidities were analyzed. Furthermore, polygenic risk scores (PRS) for BD, schizophrenia, and depression were assessed. BD-I patients not only suffered from more severe symptoms during manic episodes but also more frequently showed incapacity during depressive episodes. A higher BD PRS was significantly associated with suicidal ideation. Moreover, BD-I cases exhibited lower depression PRS. In line with a severity continuum from BD-II to BD-I, our results link BD-I to a more pronounced clinical presentation in both mania and depression and indicate that the polygenic risk load of BD predisposes to more severe disorder characteristics. Nevertheless, our results suggest that the genetic risk burden for depression also shapes disorder presentation and increases the likelihood of BD-II subtype development.

show abstract

Section: Power Analysismentioning

confidence: 99%

Clinical and genetic differences between bipolar disorder type 1 and 2 in multiplex families

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Classically, this summation is done using an approach termed "clumping and thresholding" (C+T), which first reduces summary statistics to independent SNPs and then applies one or multiple thresholds (usually based on P-values) to restrict summation to SNPs with high evidence for associations with phenotypes (Choi et al, 2020). As the optimal threshold for the C+T approach is unknown and should ideally be estimated in a separate dataset with available phenotype data, we computed PRSs using the Bayesian regression and continuous shrinkage priors (PRS-CS) approach, which has been shown to perform similar to or outperform other PRS computation approaches such as C+T (Ge et al, 2019;Ni et al, 2020).…”

Section: Prs Computationmentioning

confidence: 99%

Polygenic risk for immuno-metabolic markers and specific depressive symptoms: A multi-sample network analysis study

Kappelmann

Rost

Moser

et al. 2021

Preprint

View full text Add to dashboard Cite

BackgroundAbout every fourth patient with major depressive disorder (MDD) shows evidence of systemic inflammation. Previous studies have shown inflammation-depression associations of multiple serum inflammatory markers and multiple specific depressive symptoms. It remains unclear, however, if these associations extend to genetic/lifetime predisposition to higher inflammatory marker levels and what role metabolic factors such as Body Mass Index (BMI) play. It is also unclear whether inflammation-symptom associations reflect direct or indirect associations, which can be disentangled using network analysis.MethodsThis study examined associations of polygenic risk scores (PRSs) for immuno-metabolic markers (C-reactive protein [CRP], interleukin [IL]-6, IL-10, tumour necrosis factor [TNF]-α, BMI) with seven depressive symptoms in one general population sample, the UK Biobank study (n=110,010), and two patient samples, the Munich Antidepressant Response Signature (MARS, n=1,058) and Sequenced Treatment Alternatives to Relieve Depression (STAR*D, n=1,143) studies. Network analysis was applied jointly for these samples using fused graphical least absolute shrinkage and selection operator (FGL) estimation as primary analysis and, individually, using unregularized model search estimation. Stability of results was assessed using bootstrapping and three quality criteria were defined to appraise consistency of results across estimation methods, network bootstrapping, and samples.ResultsNetwork analysis results displayed to-be-expected PRS-PRS and symptom-symptom associations (termed edges), respectively, that were mostly positive. Using FGL estimation, results further suggested 28, 29, and six PRS-symptom edges in MARS, STAR*D, and UK Biobank samples, respectively. Unregularized model search estimation suggested three PRS-symptom edges in the UK Biobank sample. Applying our quality criteria to these associations indicated that only the association of higher CRP PRS with greater changes in appetite fulfilled all three criteria. Four additional associations fulfilled at least two quality criteria; specifically, higher CRP PRS was associated with greater fatigue and reduced anhedonia, higher TNF-α PRS was associated with greater fatigue, and higher BMI PRS with greater changes in appetite and anhedonia. Associations of the BMI PRS with anhedonia, however, showed an inconsistent valence across estimation methods.ConclusionsOur findings align with previous studies suggesting that systemic inflammatory markers are primarily associated with somatic/neurovegetative symptoms of depression such as changes in appetite and fatigue. We extend these findings by providing evidence that associations are direct (using network analysis) and extend to genetic predisposition to immuno-metabolic markers (using PRSs). Our findings can inform selection of patients with inflammation-related symptoms into clinical trials of immune-modulating drugs for MDD.

show abstract

“…a more accurate method may lead to more accurate Meta-GWAS scores. Nevertheless, given that LDpred generally performs well for polygenic traits in independent comparisons 55,56 , we believe it acts as a good proxy for other similar methods, such as lasso regression 9 , SBayesR 11 , and PRS-CS 10 . In the case of individual-level data and low polygenicity, L1-penalized regression may also provide more accurate PRSs than BOLT-LMM 20 .…”

Section: Discussionmentioning

confidence: 96%

Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction

Albiñana

Grove

McGrath

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

The accuracy of polygenic risk scores (PRSs) to predict complex diseases increases with the training sample size. PRSs are generally derived based on summary statistics from large meta-analyses of multiple genome-wide association studies (GWAS). However, it is now common for researchers to have access to large individual-level data as well, such as the UK biobank data. To the best of our knowledge, it has not yet been explored how to best combine both types of data (summary statistics and individual-level data) to optimize polygenic prediction. The most widely used approach to combine data is the meta-analysis of GWAS summary statistics (Meta-GWAS), but we show that it does not always provide the most accurate PRS. Through simulations and using twelve real case-control and quantitative traits from both iPSYCH and UK Biobank along with external GWAS summary statistics, we compare Meta-GWAS with two alternative data-combining approaches, stacked clumping and thresholding (SCT) and Meta-PRS. We find that, when large individual-level data is available, the linear combination of PRSs (Meta-PRS) is both a simple alternative to Meta-GWAS and often more accurate.

show abstract

A comparison of ten polygenic score methods for psychiatric disorders applied across multiple cohorts

Cited by 60 publications

References 74 publications

Clinical and genetic differences between bipolar disorder type 1 and 2 in multiplex families

Clinical and genetic differences between bipolar disorder type 1 and 2 in multiplex families

Polygenic risk for immuno-metabolic markers and specific depressive symptoms: A multi-sample network analysis study

Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction

Contact Info

Product

Resources

About