Insights into the genomics of affective disorders

2020 ◽  
Vol 32 (1) ◽  
pp. 9-18
Author(s):  
Andreas J. Forstner ◽  
Per Hoffmann ◽  
Markus M. Nöthen ◽  
Sven Cichon
Keyword(s):  
Bipolar Disorder ◽  
Major Depression ◽  
Affective Disorders ◽  
Association Studies ◽  
Phenotypic Variability ◽  
Genetic Research ◽  
Autism Spectrum ◽  
Risk Scores ◽  
Depression Symptoms ◽  
Genome Wide Association Studies

Abstract Affective disorders, or mood disorders, are a group of neuropsychiatric illnesses that are characterized by a disturbance of mood or affect. Most genetic research in this field to date has focused on bipolar disorder and major depression. Symptoms of major depression include a depressed mood, reduced energy, and a loss of interest and enjoyment. Bipolar disorder is characterized by the occurrence of (hypo)manic episodes, which generally alternate with periods of depression. Formal and molecular genetic studies have demonstrated that affective disorders are multifactorial diseases, in which both genetic and environmental factors contribute to disease development. Twin and family studies have generated heritability estimates of 58–85 % for bipolar disorder and 40 % for major depression. Large genome-wide association studies have provided important insights into the genetics of affective disorders via the identification of a number of common genetic risk factors. Based on these studies, the estimated overall contribution of common variants to the phenotypic variability (single-nucleotide polymorphism [SNP]-based heritability) is 17–23 % for bipolar disorder and 9 % for major depression. Bioinformatic analyses suggest that the associated loci and implicated genes converge into specific pathways, including calcium signaling. Research suggests that rare copy number variants make a lower contribution to the development of affective disorders than to other psychiatric diseases, such as schizophrenia or the autism spectrum disorders, which would be compatible with their less pronounced negative impact on reproduction. However, the identification of rare sequence variants remains in its infancy, as available next-generation sequencing studies have been conducted in limited samples. Future research strategies will include the enlargement of genomic data sets via innovative recruitment strategies; functional analyses of known associated loci; and the development of new, etiologically based disease models. Researchers hope that deeper insights into the biological causes of affective disorders will eventually lead to improved diagnostics and disease prediction, as well as to the development of new preventative, diagnostic, and therapeutic strategies. Pharmacogenetics and the application of polygenic risk scores represent promising initial approaches to the future translation of genomic findings into psychiatric clinical practice.

scholarly journals Prediction of response to drug therapy in psychiatric disorders

Open Biology ◽  
2018 ◽  
Vol 8 (5) ◽  
pp. 180031 ◽  
Author(s):  
Shani Stern ◽  
Sara Linker ◽  
Krishna C. Vadodaria ◽  
Maria C. Marchetto ◽  
Fred H. Gage
Keyword(s):  
Autism Spectrum Disorder ◽  
Bipolar Disorder ◽  
Major Depression ◽  
Personalized Medicine ◽  
Psychiatric Disorders ◽  
Association Studies ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Genome Wide Association Studies ◽  
Patient Reported

Personalized medicine has become increasingly relevant to many medical fields, promising more efficient drug therapies and earlier intervention. The development of personalized medicine is coupled with the identification of biomarkers and classification algorithms that help predict the responses of different patients to different drugs. In the last 10 years, the Food and Drug Administration (FDA) has approved several genetically pre-screened drugs labelled as pharmacogenomics in the fields of oncology, pulmonary medicine, gastroenterology, haematology, neurology, rheumatology and even psychiatry. Clinicians have long cautioned that what may appear to be similar patient-reported symptoms may actually arise from different biological causes. With growing populations being diagnosed with different psychiatric conditions, it is critical for scientists and clinicians to develop precision medication tailored to individual conditions. Genome-wide association studies have highlighted the complicated nature of psychiatric disorders such as schizophrenia, bipolar disorder, major depression and autism spectrum disorder. Following these studies, association studies are needed to look for genomic markers of responsiveness to available drugs of individual patients within the population of a specific disorder. In addition to GWAS, the advent of new technologies such as brain imaging, cell reprogramming, sequencing and gene editing has given us the opportunity to look for more biomarkers that characterize a therapeutic response to a drug and to use all these biomarkers for determining treatment options. In this review, we discuss studies that were performed to find biomarkers of responsiveness to different available drugs for four brain disorders: bipolar disorder, schizophrenia, major depression and autism spectrum disorder. We provide recommendations for using an integrated method that will use available techniques for a better prediction of the most suitable drug.

scholarly journals Increasing Sample Diversity in Psychiatric Genetics – Introducing a new Cohort of Patients with Schizophrenia and Controls from Vietnam – Results from a Pilot Study

2021 ◽  
Author(s):  
VT Nguyen ◽  
A Braun ◽  
J Kraft ◽  
TMT Ta ◽  
GM Panagiotaropoulou ◽  
...  
Keyword(s):  
Pilot Study ◽  
Data Collection ◽  
Predictive Power ◽  
Association Studies ◽  
East Asian ◽  
Genetic Research ◽  
European Ancestry ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractObjectivesGenome-Wide Association Studies (GWAS) of Schizophrenia (SCZ) have provided new biological insights; however, most cohorts are of European ancestry. As a result, derived polygenic risk scores (PRS) show decreased predictive power when applied to populations of different ancestries. We aimed to assess the feasibility of a large-scale data collection in Hanoi, Vietnam, contribute to international efforts to diversify ancestry in SCZ genetic research and examine the transferability of SCZ-PRS to individuals of Vietnamese Kinh ancestry.MethodsIn a pilot study, 368 individuals (including 190 SCZ cases) were recruited at the Hanoi Medical University’s associated psychiatric hospitals and outpatient facilities. Data collection included sociodemographic data, baseline clinical data, clinical interviews assessing symptom severity and genome-wide SNP genotyping. SCZ-PRS were generated using different training data sets: i) European, ii) East-Asian and iii) trans-ancestry GWAS summary statistics from the latest SCZ GWAS meta-analysis.ResultsSCZ-PRS significantly predicted case status in Vietnamese individuals using mixed-ancestry (R2 liability=4.9%, p=6.83*10−8), East-Asian (R2 liability=4.5%, p=2.73*10−7) and European (R2 liability=3.8%, p = 1.79*10−6) discovery samples.DiscussionOur results corroborate previous findings of reduced PRS predictive power across populations, highlighting the importance of ancestral diversity in GWA studies.

scholarly journals Using Genetics to Examine a General Liability to Childhood Psychopathology

2019 ◽  
Vol 50 (4) ◽  
pp. 213-220 ◽  
Author(s):  
Lucy Riglin ◽  
Ajay K. Thapar ◽  
Beate Leppert ◽  
Joanna Martin ◽  
Alexander Richards ◽  
...  
Keyword(s):  
Association Studies ◽  
Full Range ◽  
Population Based ◽  
Autism Spectrum ◽  
Developmental Changes ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Childhood Psychopathology ◽  
Specific Factors ◽  
P Factor

AbstractPsychiatric disorders show phenotypic as well as genetic overlaps. There are however also marked developmental changes throughout childhood. We investigated the extent to which, for a full range of early childhood psychopathology, a general “p” factor was explained by genetic liability, as indexed by multiple different psychiatric polygenic risk scores (PRS) and whether these relationships altered with age. The sample was a UK, prospective, population-based cohort with psychopathology data at age 7 (N = 8161) and age 13 (N = 7017). PRS were generated from large published genome-wide association studies. At both ages, we found evidence for a childhood “p” factor as well as for specific factors. Schizophrenia and attention-deficit/hyperactivity disorder (ADHD) PRS were associated with this general “p” factor at both ages but depression and autism spectrum disorder (ASD) PRS were not. We also found some evidence of associations between schizophrenia, ADHD and depression PRS with specific factors, but these were less robust and there was evidence for developmental changes.

scholarly journals Genome-Wide Association Studies of Schizophrenia and Bipolar Disorder in a Diverse Cohort of US Veterans

2020 ◽  
Author(s):  
Tim B Bigdeli ◽  
Ayman H Fanous ◽  
Yuli Li ◽  
Nallakkandi Rajeevan ◽  
Frederick Sayward ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Association Studies ◽  
Genome Wide Association ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Susceptibility Loci ◽  
New Associations ◽  
Genome Wide ◽  
Us Veterans

Abstract Background Schizophrenia (SCZ) and bipolar disorder (BIP) are debilitating neuropsychiatric disorders, collectively affecting 2% of the world’s population. Recognizing the major impact of these psychiatric disorders on the psychosocial function of more than 200 000 US Veterans, the Department of Veterans Affairs (VA) recently completed genotyping of more than 8000 veterans with SCZ and BIP in the Cooperative Studies Program (CSP) #572. Methods We performed genome-wide association studies (GWAS) in CSP #572 and benchmarked the predictive value of polygenic risk scores (PRS) constructed from published findings. We combined our results with available summary statistics from several recent GWAS, realizing the largest and most diverse studies of these disorders to date. Results Our primary GWAS uncovered new associations between CHD7 variants and SCZ, and novel BIP associations with variants in Sortilin Related VPS10 Domain Containing Receptor 3 (SORCS3) and downstream of PCDH11X. Combining our results with published summary statistics for SCZ yielded 39 novel susceptibility loci including CRHR1, and we identified 10 additional findings for BIP (28 326 cases and 90 570 controls). PRS trained on published GWAS were significantly associated with case-control status among European American (P < 10–30) and African American (P < .0005) participants in CSP #572. Conclusions We have demonstrated that published findings for SCZ and BIP are robustly generalizable to a diverse cohort of US veterans. Leveraging available summary statistics from GWAS of global populations, we report 52 new susceptibility loci and improved fine-mapping resolution for dozens of previously reported associations.

scholarly journals Characterisation of age and polarity at onset in bipolar disorder

2021 ◽  
pp. 1-11
Author(s):  
Janos L. Kalman ◽  
Loes M. Olde Loohuis ◽  
Annabel Vreeker ◽  
Andrew McQuillin ◽  
Eli A. Stahl ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Educational Attainment ◽  
Association Studies ◽  
Autism Spectrum ◽  
Screening Tools ◽  
Psychotic Symptoms ◽  
Significant Heterogeneity ◽  
Genome Wide Association Studies ◽  
Genetic Characteristics ◽  
Disease Characteristics

Background Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools. Aims To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics. Method Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts. Results Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO. Conclusions AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

scholarly journals Gamete simulation improves polygenic transmission disequilibrium analysis

2020 ◽  
Author(s):  
Jiawen Chen ◽  
Jing You ◽  
Zijie Zhao ◽  
Zheng Ni ◽  
Kunling Huang ◽  
...  
Keyword(s):  
Complex Traits ◽  
Statistical Power ◽  
Association Studies ◽  
Autism Spectrum ◽  
Genetic Maps ◽  
Risk Scores ◽  
Parental Genotype ◽  
Genome Wide Association Studies ◽  
Transmission Disequilibrium ◽  
Polygenic Risk

AbstractPolygenic risk scores (PRS) derived from summary statistics of genome-wide association studies (GWAS) have enjoyed great popularity in human genetics research. Applied to population cohorts, PRS can effectively stratify individuals by risk group and has promising applications in early diagnosis and clinical intervention. However, our understanding of within-family polygenic risk is incomplete, in part because the small samples per family significantly limits power. Here, to address this challenge, we introduce ORIGAMI, a computational framework that uses parental genotype data to simulate offspring genomes. ORIGAMI uses state-of-the-art genetic maps to simulate realistic recombination events on phased parental genomes and allows quantifying the prospective PRS variability within each family. We quantify and showcase the substantially reduced yet highly heterogeneous PRS variation within families for numerous complex traits. Further, we incorporate within-family PRS variability to improve polygenic transmission disequilibrium test (pTDT). Through simulations, we demonstrate that modeling within-family risk substantially improves the statistical power of pTDT. Applied to 7,805 trios of autism spectrum disorder (ASD) probands and healthy parents, we successfully replicated previously reported over-transmission of ASD, educational attainment, and schizophrenia risk, and identified multiple novel traits with significant transmission disequilibrium. These results provided novel etiologic insights into the shared genetic basis of various complex traits and ASD.

scholarly journals Exploring shared genetic bases and causal relationships of schizophrenia and bipolar disorder with 28 cardiovascular and metabolic traits

2018 ◽  
Vol 49 (08) ◽  
pp. 1286-1298 ◽  
Author(s):  
Hon-Cheong So ◽  
Kwan-Long Chau ◽  
Fu-Kiu Ao ◽  
Cheuk-Hei Mo ◽  
Pak-Chung Sham
Keyword(s):  
Bipolar Disorder ◽  
Large Scale ◽  
Association Studies ◽  
Risk Scores ◽  
Exact Nature ◽  
Genome Wide Association Studies ◽  
Inverse Association ◽  
Causal Relationships ◽  
Shared Genetic ◽  
Genetic Bases

AbstractBackgroundCardiovascular diseases represent a major health issue in patients with schizophrenia (SCZ) and bipolar disorder (BD), but the exact nature of cardiometabolic (CM) abnormalities involved and the underlying mechanisms remain unclear. Psychiatric medications are known risk factors, but it is unclear whether there is a connection between the disorders (SCZ/BD) themselves and CM abnormalities.MethodsUsing polygenic risk scores and linkage disequilibrium score regression, we investigated the shared genetic bases of SCZ and BD with 28 CM traits. We performed Mendelian randomization (MR) to elucidate causal relationships between the two groups of disorders. The analysis was based on large-scale meta-analyses of genome-wide association studies. We also identified the potential shared genetic variants and inferred the pathways involved.ResultsWe found tentative polygenic associations of SCZ with glucose metabolism abnormalities, adverse adipokine profiles, increased waist-to-hip ratio and visceral adiposity (false discovery rate or FDR<0.05). However, there was an inverse association with body mass index. For BD, we observed several polygenic associations with favorable CM profiles at FDR<0.05. MR analysis showed that SCZ may be causally linked to raised triglyceride and that lower fasting glucose may be linked to BD. We also identified numerous single nucleotide polymorphisms and pathways shared between SCZ/BD with CM traits, some of which are related to inflammation or the immune system.ConclusionsOur findings suggest that SCZ patients may be genetically predisposed to several CM abnormalities independent of medication side effects. On the other hand, CM abnormalities in BD may be more likely to be secondary. However, the findings require further validation.

scholarly journals Using genetics to examine a general liability to childhood psychopathology

2018 ◽  
Author(s):  
Lucy Riglin ◽  
Ajay K Thapar ◽  
Beate Leppert ◽  
Joanna Martin ◽  
Alexander Richards ◽  
...  
Keyword(s):  
Association Studies ◽  
Full Range ◽  
Population Based ◽  
Autism Spectrum ◽  
Developmental Changes ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Childhood Psychopathology ◽  
Specific Factors ◽  
P Factor

AbstractPsychiatric disorders show phenotypic as well as genetic overlaps. There are however also marked developmental changes throughout childhood. We investigated the extent to which, for a full range of early childhood psychopathology, a general “p” factor was explained by genetic liability, as indexed by multiple different psychiatric polygenic risk scores (PRS) and whether these relationships altered with age. The sample was a UK, prospective, population-based cohort with psychopathology data at age 7 (N=8161) and age 13 (N=7017). PRS were generated from large published genome-wide association studies. At both ages, we found evidence for a childhood “p” factor as well as for specific factors. Schizophrenia and attention-deficit/hyperactivity disorder (ADHD) PRS were associated with this general “p” factor at both ages but depression and autism spectrum disorder (ASD) PRS were not. Schizophrenia, ADHD and depression PRS were also associated with specific factors but there was evidence for developmental changes.FundingThis work was supported by the Wellcome Trust (204895/Z/16/Z).

scholarly journals Genotype-phenotype association mining in bipolar disorder: market research meets complex genetics

2017 ◽  
Author(s):  
René Breuer ◽  
Manuel Mattheisen ◽  
Josef Frank ◽  
Bertram Krumm ◽  
Jens Treutlein ◽  
...  
Keyword(s):  
Data Mining ◽  
Bipolar Disorder ◽  
Market Research ◽  
Association Studies ◽  
Genetic Research ◽  
Gene Discovery ◽  
Genome Wide Association Studies ◽  
Complex Disorders ◽  
Complex Genetics ◽  
Genome Wide

AbstractDisentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: applying a data mining procedure to identify yet undetected genotype-phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2,835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted. Two of these - one associated with eating disorder and the other with anxiety - remained significant in an independent dataset after robust correction for multiple testing, showing considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively). Our approach may help detect novel specific genotype-phenotype relationships in BD typically not explored by analyses like GWAS. While we adapted the data mining tool within the context of BD gene discovery, it may facilitate identifying highly specific genotype-phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.

scholarly journals S173. GENOME-WIDE ASSOCIATION STUDIES OF SCHIZOPHRENIA AND BIPOLAR DISORDER IN A DIVERSE COHORT OF US VETERANS

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S103-S103
Author(s):  
Tim Bigdeli ◽  
Ayman Fanous ◽  
Nallakkandi Rajeevan ◽  
Frederick Sayward ◽  
Yuli Li ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Association Studies ◽  
Meta Analysis ◽  
Genome Wide Association ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Polygenic Risk ◽  
Genome Wide ◽  
Us Veterans

Abstract Background Schizophrenia and bipolar disorder are debilitating neuropsychiatric illnesses collectively affecting 2% of the world’s population, and which cause tremendous human suffering that impacts patients, their families and their communities. Recognizing the major impact of these disorders on the psychosocial function of more than 200,000 US Veterans, the Department of Veterans Affairs (VA) recently genotyping of nearly 9,000 veterans with schizophrenia or bipolar I disorder in Cooperative Studies Program (CSP) #572: “Genetics of Functional Disability in Schizophrenia and Bipolar Illness”, all of whom were extensively assessed for neurocognitive function and disability, and genotyped using a custom Affymetrix Axiom Biobank array. Methods Primary genome-wide association studies (GWAS) of schizophrenia and bipolar disorder were performed across and within ancestry goups, with attempted replication in matched subjects from the PGC and Genomic Psychiatry Cohort (GPC). We combined results for CSP#572 with available summary statistics from the PGC, Indonesia Schizophrenia Consortium and Genetic REsearch on schizophreniA neTwork-China and Netherland (GREAT-CN) study, and multi-ethnic GPC cohorts, achieving among the largest and most diverse studies of these disorders to date. Results Polygenic risk scores based on published PGC summary statistics for schizophrenia or bipolar disorder were significantly associated with case status among EA (P<10–30) and AA (P<0.0005) participants in CSP#572. Our primary analyses of schizophrenia yielded a single genome-wide significant association with variants in CHD7 at 8q12.2 for European-American (EA) participants, which remained significant in a joint analysis of EA and African-American (AA) subjects (P=4.62e-08). While no genome-wide significant associations were detected by our within-ancestry analyses of bipolar disorder, a cross-ancestry meta-analysis of CSP#572 participants yielded a significant finding at 10q25 with variants in SORCS3 (P=2.62e-08). Among loci attaining P<0.0001 in our within-ancestry analyses, 4 and 8 subsequently achieved genome-wide significance, respectively, when jointly analyzed with matched subjects from the PGC and GPC. Combining our results with published summary statistics, we performed a cross-ancestry GWAS meta-analysis of 69,280 schizophrenia cases and 138,379 controls, identifying 200 genome-wide significant loci of which 76 are newly reported here. Cross-ancestry analysis of 28,326 bipolar cases and 90,570 controls identified 24 genome-wide significant loci, including novel associations with common variants in PAX5, DOCK2, MACROD2, BRE, KCNG1, and LINC01378. Discussion We newly describe genome-wide analyses in a diverse cohort of US Veterans with schizophrenia or bipolar disorder, benchmarking the predictive value of polygenic risk scores based on published GWAS findings. Leveraging available summary statistics from studies of global populations, we add to burgeoning lists of genomic loci implicated in the etiologies of these disorders.

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