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Combining schizophrenia and depression polygenic risk scores improves the genetic prediction of lithium response in bipolar disorder patients.

ABSTRACT: Lithium is the gold standard therapy for Bipolar Disorder (BD) but its effectiveness differs widely between individuals. The molecular mechanisms underlying treatment response heterogeneity are not well understood, and personalized treatment in BD remains elusive. Genetic analyses of the lithium treatment response phenotype may generate novel molecular insights into lithium's therapeutic mechanisms and lead to testable hypotheses to improve BD management and outcomes. We used fixed effect meta-analysis techniques to develop meta-analytic polygenic risk scores (MET-PRS) from combinations of highly correlated psychiatric traits, namely schizophrenia (SCZ), major depression (MD) and bipolar disorder (BD). We compared the effects of cross-disorder MET-PRS and single genetic trait PRS on lithium response. For the PRS analyses, we included clinical data on lithium treatment response and genetic information for n = 2283 BD cases from the International Consortium on Lithium Genetics (ConLi⁺Gen; www.ConLiGen.org ). Higher SCZ and MD PRSs were associated with poorer lithium treatment response whereas BD-PRS had no association with treatment outcome. The combined MET2-PRS comprising of SCZ and MD variants (MET2-PRS) and a model using SCZ and MD-PRS sequentially improved response prediction, compared to single-disorder PRS or to a combined score using all three traits (MET3-PRS). Patients in the highest decile for MET2-PRS loading had 2.5 times higher odds of being classified as poor responders than patients with the lowest decile MET2-PRS scores. An exploratory functional pathway analysis of top MET2-PRS variants was conducted. Findings may inform the development of future testing strategies for personalized lithium prescribing in BD.

SUBMITTER: Schubert KO

PROVIDER: S-EPMC8630000 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Combining schizophrenia and depression polygenic risk scores improves the genetic prediction of lithium response in bipolar disorder patients.

Schubert Klaus Oliver KO Thalamuthu Anbupalam A Amare Azmeraw T AT Frank Joseph J Streit Fabian F Adl Mazda M Akula Nirmala N Akiyama Kazufumi K Ardau Raffaella R Arias Bárbara B Aubry Jean-Michel JM Backlund Lena L Bhattacharjee Abesh Kumar AK Bellivier Frank F Benabarre Antonio A Bengesser Susanne S Biernacka Joanna M JM Birner Armin A Marie-Claire Cynthia C Cearns Micah M Cervantes Pablo P Chen Hsi-Chung HC Chillotti Caterina C Cichon Sven S Clark Scott R SR Cruceanu Cristiana C Czerski Piotr M PM Dalkner Nina N Dayer Alexandre A Degenhardt Franziska F Del Zompo Maria M DePaulo J Raymond JR Étain Bruno B Falkai Peter P Forstner Andreas J AJ Frisen Louise L Frye Mark A MA Fullerton Janice M JM Gard Sébastien S Garnham Julie S JS Goes Fernando S FS Grigoroiu-Serbanescu Maria M Grof Paul P Hashimoto Ryota R Hauser Joanna J Heilbronner Urs U Herms Stefan S Hoffmann Per P Hou Liping L Hsu Yi-Hsiang YH Jamain Stephane S Jiménez Esther E Kahn Jean-Pierre JP Kassem Layla L Kuo Po-Hsiu PH Kato Tadafumi T Kelsoe John J Kittel-Schneider Sarah S Ferensztajn-Rochowiak Ewa E König Barbara B Kusumi Ichiro I Laje Gonzalo G Landén Mikael M Lavebratt Catharina C Leboyer Marion M Leckband Susan G SG Maj Mario M Manchia Mirko M Martinsson Lina L McCarthy Michael J MJ McElroy Susan S Colom Francesc F Mitjans Marina M Mondimore Francis M FM Monteleone Palmiero P Nievergelt Caroline M CM Nöthen Markus M MM Novák Tomas T O'Donovan Claire C Ozaki Norio N Ösby Urban U Papiol Sergi S Pfennig Andrea A Pisanu Claudia C Potash James B JB Reif Andreas A Reininghaus Eva E Rouleau Guy A GA Rybakowski Janusz K JK Schalling Martin M Schofield Peter R PR Schweizer Barbara W BW Severino Giovanni G Shekhtman Tatyana T Shilling Paul D PD Shimoda Katzutaka K Simhandl Christian C Slaney Claire M CM Squassina Alessio A Stamm Thomas T Stopkova Pavla P Tekola-Ayele Fasil F Tortorella Alfonso A Turecki Gustavo G Veeh Julia J Vieta Eduard E Witt Stephanie H SH Roberts Gloria G Zandi Peter P PP Alda Martin M Bauer Michael M McMahon Francis J FJ Mitchell Philip B PB Schulze Thomas G TG Rietschel Marcella M Baune Bernhard T BT

Translational psychiatry 20211129 1

Lithium is the gold standard therapy for Bipolar Disorder (BD) but its effectiveness differs widely between individuals. The molecular mechanisms underlying treatment response heterogeneity are not well understood, and personalized treatment in BD remains elusive. Genetic analyses of the lithium treatment response phenotype may generate novel molecular insights into lithium's therapeutic mechanisms and lead to testable hypotheses to improve BD management and outcomes. We used fixed effect meta-a ...[more]

PMID: 34845190

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Project description:ImportanceSerious mental illnesses, including schizophrenia, bipolar disorder, and depression, are heritable, highly multifactorial disorders and major causes of disability worldwide.ObjectiveTo benchmark the penetrance of current neuropsychiatric polygenic risk scores (PRSs) in the Veterans Health Administration health care system and to explore associations between PRS and broad categories of human disease via phenome-wide association studies.Design, setting, and participantsExtensive Veterans Health Administration's electronic health records were assessed from October 1999 to January 2021, and an embedded cohort of 9378 individuals with confirmed diagnoses of schizophrenia or bipolar 1 disorder were found. The performance of schizophrenia, bipolar disorder, and major depression PRSs were compared in participants of African or European ancestry in the Million Veteran Program (approximately 400 000 individuals), and associations between PRSs and 1650 disease categories based on ICD-9/10 billing codes were explored. Last, genomic structural equation modeling was applied to derive novel PRSs indexing common and disorder-specific genetic factors. Analysis took place from January 2021 to January 2022.Main outcomes and measuresDiagnoses based on in-person structured clinical interviews were compared with ICD-9/10 billing codes. PRSs were constructed using summary statistics from genome-wide association studies of schizophrenia, bipolar disorder, and major depression.ResultsOf 707 299 enrolled study participants, 459 667 were genotyped at the time of writing; 84 806 were of broadly African ancestry (mean [SD] age, 58 [12.1] years) and 314 909 were of broadly European ancestry (mean [SD] age, 66.4 [13.5] years). Among 9378 individuals with confirmed diagnoses of schizophrenia or bipolar 1 disorder, 8962 (95.6%) were correctly identified using ICD-9/10 codes (2 or more). Among those of European ancestry, PRSs were robustly associated with having received a diagnosis of schizophrenia (odds ratio [OR], 1.81 [95% CI, 1.76-1.87]; P < 10-257) or bipolar disorder (OR, 1.42 [95% CI, 1.39-1.44]; P < 10-295). Corresponding effect sizes in participants of African ancestry were considerably smaller for schizophrenia (OR, 1.35 [95% CI, 1.29-1.42]; P < 10-38) and bipolar disorder (OR, 1.16 [95% CI, 1.11-1.12]; P < 10-10). Neuropsychiatric PRSs were associated with increased risk for a range of psychiatric and physical health problems.Conclusions and relevanceUsing diagnoses confirmed by in-person structured clinical interviews and current neuropsychiatric PRSs, the validity of an electronic health records-based phenotyping approach in US veterans was demonstrated, highlighting the potential of PRSs for disentangling biological and mediated pleiotropy.

Project description:ImportanceAlthough the usefulness of polygenic risk scores as a measure of genetic liability for major depression (MD) has been established, their association with depression in the general population remains relatively unexplored.ObjectiveTo evaluate whether polygenic risk scores for MD, bipolar disorder (BD), and schizophrenia (SZ) are associated with depression in the general population and explore whether these polygenic liabilities are associated with heterogeneity in terms of age at onset and severity at the initial depression diagnosis.Design, setting, and participantsParticipants were drawn from the Danish iPSYCH2012 case-cohort study, a representative sample drawn from the population of Denmark born between May 1, 1981, and December 31, 2005. The hazard of depression was estimated using Cox regressions modified to accommodate the case-cohort design. Case-only analyses were conducted using linear and multinomial regressions. The data analysis was conducted from February 2017 to June 2018.ExposuresPolygenic risk scores for MD, BD, and SZ trained using the most recent genome-wide association study results from the Psychiatric Genomics Consortium.Main outcomes and measuresThe main outcome was first depressive episode (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10] code F32) treated in hospital-based psychiatric care. Severity at the initial diagnosis was measured using the ICD-10 code severity specifications (mild, moderate, severe without psychosis, and severe with psychosis) and treatment setting (inpatient, outpatient, and emergency).ResultsOf 34 573 participants aged 10 to 31 years at censoring, 68% of those with depression were female compared with 48.9% of participants without depression. Each SD increase in polygenic liability for MD, BD, and SZ was associated with 30% (hazard ratio [HR], 1.30; 95% CI, 1.27-1.33), 5% (HR, 1.05; 95% CI, 1.02-1.07), and 12% (HR, 1.12; 95% CI, 1.09-1.15) increases in the hazard of depression, respectively. Among cases, a higher polygenic liability for BD was associated with earlier depression onset (β = -.07; SE = .02; P = .002).Conclusions and relevancePolygenic liability for MD is associated with first depression in the general population, which supports the idea that these scores tap into an underlying liability for developing the disorder. The fact that polygenic risk for BD and polygenic risk for SZ also were associated with depression is consistent with prior evidence that these disorders share some common genetic overlap. Variations in polygenic liability may contribute slightly to heterogeneity in clinical presentation, but these associations appear minimal.

Project description:OBJECTIVE:Family and twin studies indicate substantial overlap of genetic influences on psychotic and mood disorders. Linkage and candidate gene studies have also suggested overlap across schizophrenia, bipolar disorder, and major depressive disorder. The purpose of this study was to apply genomewide association study (GWAS) analysis to address the specificity of genetic effects on these disorders. METHOD:The authors combined GWAS data from three large effectiveness studies of schizophrenia (CATIE, genotyped: N=741), bipolar disorder (STEP-BD, geno-typed: N=1,575), and major depressive disorder (STAR*D, genotyped: N=1,938) as well as from psychiatrically screened control subjects (NIMH-Genetics Repository: N=1,204). A two-stage analytic procedure involving an omnibus test of allele frequency differences among case and control groups was applied, followed by a model selection step to identify the best-fitting model of allelic effects across disorders. RESULTS:The strongest result was seen for a single nucleotide polymorphism near the adrenomedullin (ADM) gene (rs6484218), with the best-fitting model indicating that the effect was specific to bipolar II disorder. Findings also revealed evidence suggesting that several genes may have effects that transcend clinical diagnostic boundaries, including variants in NPAS3 that showed pleiotropic effects across schizophrenia, bipolar disorder, and major depressive disorder. CONCLUSIONS:This study provides the first genomewide significant evidence implicating variants near the ADM gene on chromosome 11p15 in psychopathology, with effects that appear to be specific to bipolar II disorder. Although genomewide significant evidence of cross-disorder effects was not detected, the results provide evidence that there are both pleiotropic and disorder-specific effects on major mental illness and illustrate an approach to dissecting the genetic basis of mood and psychotic disorders that can inform future large-scale cross-disorder GWAS analyses.

Dataset Information

Combining schizophrenia and depression polygenic risk scores improves the genetic prediction of lithium response in bipolar disorder patients.

Publications

Combining schizophrenia and depression polygenic risk scores improves the genetic prediction of lithium response in bipolar disorder patients.

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