Pub Date : 2024-10-01DOI: 10.1016/j.euroneuro.2024.08.076
Sarah Guagliardo , Mischa Lundberg , Andrew Schork , Nancy Cox , Megan Shuey
<div><div>Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that impairs executive functioning, vigilance-attention, and motivation. Due to this, individuals with ADHD are at higher risk of addiction, poor academic and professional outcomes, and social deficits. Heterogeneity in presenting symptoms is well-established and may result in a delayed or missed diagnosis. The prevalence of ADHD is reported from two to seven times higher for males than females. The prevalence of ADHD appears consistent in childhood and adulthood. Only half of those diagnosed in childhood report persisting symptoms, implying many are first diagnoses as adults and those first diagnosed in adulthood tend towards a different symptom profile. Such sex and age trends may reflect protective effects of “female sex”, children “growing out of” ADHD, or adults experiencing a different clinical entity. However, others argue that the high heritability of ADHD (0.6-0.85) and similar genetic risk in females suggests that these trends may be due to a heterogenous expression of symptoms in response to the environment (e.g., modulated by the female or adult experience). We use Vanderbilt University Medical Center's (VUMC) biobank (n=3,285,882 electronic health records (EHR) and 119,750 genotyped samples) to analyze ADHD prevalence and genetic architecture. We observed the ADHD-associated ICD codes (n=38,419) were less frequent in EHR-recorded females relative to males (n=14,395 vs. 24,024) and the median age at first diagnosis was substantially older (21.72 years, IQR=20.96 vs.15.05 years, IQR=9.1). Among subset of European ancestry patient genotyped in VUMC (n=69,397), we observed an ADHD polygenic risk scores (PRS) was significant independent predictor of diagnosis, with stronger effects on females (males, beta= 13.47, p=0.03; females, beta=16.90, p=7.4e-7), and female cases having higher average PRS than male cases (p=0.04). In a sex-specific phenome-wide association study (PheWAS), the ADHD PRS was associated with similar phenotypes regardless of sex, including substance/tobacco use, other psychiatric disorders, obesity, diabetes mellitus, and respiratory problems. Our findings that female patients with ADHD appear to have higher genetic liability for the condition despite lower rates of diagnosis are consistent with previous studies. Additionally, ADHD PRS did not demonstrate differential comorbidity structures based on sex in VUMC. One explanation for this is that established genetic proxies of disease inadequately reflect the nuances of particular behaviors of ADHD subtypes, including but not limited to exhibition of externalizing hyperactive subtype (ADHD-H) opposed to internalizing inattentive subtype (ADHD-I), which is reported more frequently in females. Therefore, obtaining clinical diagnoses in females may require symptom manifestations that are largely overlapping with their male counterparts. Additional work in various EHR resources may shed
{"title":"EVALUATING THE IMPACT OF BIOLOGICAL SEX ON ADHD PRESENTATION, PREVALENCE, AND GENETIC RISK","authors":"Sarah Guagliardo , Mischa Lundberg , Andrew Schork , Nancy Cox , Megan Shuey","doi":"10.1016/j.euroneuro.2024.08.076","DOIUrl":"10.1016/j.euroneuro.2024.08.076","url":null,"abstract":"<div><div>Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that impairs executive functioning, vigilance-attention, and motivation. Due to this, individuals with ADHD are at higher risk of addiction, poor academic and professional outcomes, and social deficits. Heterogeneity in presenting symptoms is well-established and may result in a delayed or missed diagnosis. The prevalence of ADHD is reported from two to seven times higher for males than females. The prevalence of ADHD appears consistent in childhood and adulthood. Only half of those diagnosed in childhood report persisting symptoms, implying many are first diagnoses as adults and those first diagnosed in adulthood tend towards a different symptom profile. Such sex and age trends may reflect protective effects of “female sex”, children “growing out of” ADHD, or adults experiencing a different clinical entity. However, others argue that the high heritability of ADHD (0.6-0.85) and similar genetic risk in females suggests that these trends may be due to a heterogenous expression of symptoms in response to the environment (e.g., modulated by the female or adult experience). We use Vanderbilt University Medical Center's (VUMC) biobank (n=3,285,882 electronic health records (EHR) and 119,750 genotyped samples) to analyze ADHD prevalence and genetic architecture. We observed the ADHD-associated ICD codes (n=38,419) were less frequent in EHR-recorded females relative to males (n=14,395 vs. 24,024) and the median age at first diagnosis was substantially older (21.72 years, IQR=20.96 vs.15.05 years, IQR=9.1). Among subset of European ancestry patient genotyped in VUMC (n=69,397), we observed an ADHD polygenic risk scores (PRS) was significant independent predictor of diagnosis, with stronger effects on females (males, beta= 13.47, p=0.03; females, beta=16.90, p=7.4e-7), and female cases having higher average PRS than male cases (p=0.04). In a sex-specific phenome-wide association study (PheWAS), the ADHD PRS was associated with similar phenotypes regardless of sex, including substance/tobacco use, other psychiatric disorders, obesity, diabetes mellitus, and respiratory problems. Our findings that female patients with ADHD appear to have higher genetic liability for the condition despite lower rates of diagnosis are consistent with previous studies. Additionally, ADHD PRS did not demonstrate differential comorbidity structures based on sex in VUMC. One explanation for this is that established genetic proxies of disease inadequately reflect the nuances of particular behaviors of ADHD subtypes, including but not limited to exhibition of externalizing hyperactive subtype (ADHD-H) opposed to internalizing inattentive subtype (ADHD-I), which is reported more frequently in females. Therefore, obtaining clinical diagnoses in females may require symptom manifestations that are largely overlapping with their male counterparts. Additional work in various EHR resources may shed ","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Page 30"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.euroneuro.2024.08.064
Rujia Wang , Helena Davies , Sanghyuck Lee , Jonathan Coleman , Raquel Iniesta , Thalia Eley , Gerome Breen
Major depressive disorder (MDD) is a complex psychiatric disorder influenced by genetic, social, and environmental factors. Family history and polygenic risk scores of MDD and related psychiatric disorders are strong predictors for MDD, while childhood trauma (CT) also plays a crucial role. This study aimed to jointly model the predictive effect of multi-family history (mFH), multi-PRS (mPRS), and childhood trauma on the development of MDD and the number of MDD episodes experienced. Our aim was to identify predictive model useful for stratification to more or less intensive treatment plans and interventions.
Data were obtained from the NIHR BioResource Genetic Links to Anxiety and Depression (GLAD) study and UK Biobank (UKB). MDD diagnosis followed DSM-V criteria using the same online mental health questionnaire data in GLAD and UKB. Family history (Yes/No) was reported for up to 22 psychiatric disorders. MegaPRS was used to calculate PRSs based on large genome-wide association studies. Reported childhood trauma was identified via the5-item childhood trauma screener questionnaire. Elastic net regression with nested cross-validation was applied.
In GLAD (9,927 MDD cases, 4,452 controls), mFH explained 16.85% of MDD variance, followed by CT (10.62%), demographics (9.92%), and mPRS (7.73%). All predictors together explained 33.87% of MDD variance, with corresponding areas under the receiver operating characteristic curve (AUC) of 0.84 and a positive predictive value (PPV) of 0.81. In UKB (40,667 MDD cases, 70,755 controls), mFH explained 13.56% of MDD variance, followed by demographics (5.95%), CT (5.87%), and mPRS (3.69%). Together, all predictors explained 23.68% of variance (AUC=0.74, PPV=0.66). The strongest individual predictor in both cohorts is family history of depression, followed by CT, sex, family history of anxiety, and PRS for depression. The modal number of MDD episodes among MDD cases is ≥ 13 episodes in GLAD, compared to 1 episode in UKB. Additionally, the mean age of onset is 21 years in GLAD and 33 years in UKB. When the model was applied to other MDD phenotypes, all predictors accounted for 25.80% of the variances for the number of MDD episodes and 8.41% for age of onset in GLAD, and 11.92% and 6.01% in UKB, respectively.
Integrating multi-family history, multi-PRS, childhood trauma, and demographics enhances MDD prediction. The prediction model performs effectively in both severe MDD cohort (GLAD) and population-based cohort (UKB), suggesting its potential generalizability to broader populations. The strongest predictors are family history of depression and childhood trauma, both of which are easily measurable in clinical settings. Furthermore, the model trained for MDD prediction also proves to be a strong predictor for the number of MDD episodes and age of onset, indicating its effectiveness in predicting the severity of MDD.
{"title":"JOINT MULTI-FAMILY HISTORY AND MULTI-POLYGENIC SCORE PREDICTION OF MAJOR DEPRESSIVE DISORDER","authors":"Rujia Wang , Helena Davies , Sanghyuck Lee , Jonathan Coleman , Raquel Iniesta , Thalia Eley , Gerome Breen","doi":"10.1016/j.euroneuro.2024.08.064","DOIUrl":"10.1016/j.euroneuro.2024.08.064","url":null,"abstract":"<div><div>Major depressive disorder (MDD) is a complex psychiatric disorder influenced by genetic, social, and environmental factors. Family history and polygenic risk scores of MDD and related psychiatric disorders are strong predictors for MDD, while childhood trauma (CT) also plays a crucial role. This study aimed to jointly model the predictive effect of multi-family history (mFH), multi-PRS (mPRS), and childhood trauma on the development of MDD and the number of MDD episodes experienced. Our aim was to identify predictive model useful for stratification to more or less intensive treatment plans and interventions.</div><div>Data were obtained from the NIHR BioResource Genetic Links to Anxiety and Depression (GLAD) study and UK Biobank (UKB). MDD diagnosis followed DSM-V criteria using the same online mental health questionnaire data in GLAD and UKB. Family history (Yes/No) was reported for up to 22 psychiatric disorders. MegaPRS was used to calculate PRSs based on large genome-wide association studies. Reported childhood trauma was identified via the5-item childhood trauma screener questionnaire. Elastic net regression with nested cross-validation was applied.</div><div>In GLAD (9,927 MDD cases, 4,452 controls), mFH explained 16.85% of MDD variance, followed by CT (10.62%), demographics (9.92%), and mPRS (7.73%). All predictors together explained 33.87% of MDD variance, with corresponding areas under the receiver operating characteristic curve (AUC) of 0.84 and a positive predictive value (PPV) of 0.81. In UKB (40,667 MDD cases, 70,755 controls), mFH explained 13.56% of MDD variance, followed by demographics (5.95%), CT (5.87%), and mPRS (3.69%). Together, all predictors explained 23.68% of variance (AUC=0.74, PPV=0.66). The strongest individual predictor in both cohorts is family history of depression, followed by CT, sex, family history of anxiety, and PRS for depression. The modal number of MDD episodes among MDD cases is ≥ 13 episodes in GLAD, compared to 1 episode in UKB. Additionally, the mean age of onset is 21 years in GLAD and 33 years in UKB. When the model was applied to other MDD phenotypes, all predictors accounted for 25.80% of the variances for the number of MDD episodes and 8.41% for age of onset in GLAD, and 11.92% and 6.01% in UKB, respectively.</div><div>Integrating multi-family history, multi-PRS, childhood trauma, and demographics enhances MDD prediction. The prediction model performs effectively in both severe MDD cohort (GLAD) and population-based cohort (UKB), suggesting its potential generalizability to broader populations. The strongest predictors are family history of depression and childhood trauma, both of which are easily measurable in clinical settings. Furthermore, the model trained for MDD prediction also proves to be a strong predictor for the number of MDD episodes and age of onset, indicating its effectiveness in predicting the severity of MDD.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Pages 24-25"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.euroneuro.2024.08.072
Molly Sacks , Marieke Klein , Omar Shanta , Mohammad Ahangari , Oanh Hong , Jeff MacDonald , Bhooma Thiruvahindrapuram , Sebastien Jacquemont , Tim Bigdeli , Matthew Oetjens , Mart Kals , Stephen H. Scherer , Jonathan Sebat , The Bipolar Disorder, Schizophrenia, Post-Traumatic Stress Disorder, Attention-Deficit/Hyperactivity Disorder, Major Depressive Disorder, Autism Spectrum Disorder and Copy Number Variation working groups of the Psychiatric Genomics Consortium , Genes to Mental Health Network
Recurrent CNVs are known to be major risk factors in neuropsychiatric disorders, yet phenotypic variability between carriers of the same CNV remains unexplained. A possible explanation is that the effect of a CNV depends on genetic background, which can be quantified by a polygenic risk score (PRS). Using data from the PGC and UKBB (combined n=543,111), this project aims to characterize the individual and combined effects of recurrent CNVs and PRS on six major psychiatric disorders: ADHD, ASD, BD, PTSD, MDD, and SCZ.
We first quantified the effects of 42 recurrent CNV loci across all 6 disorders using logistic regression. After Bonferroni correction, 24 loci had a significant association with at least one disorder in either the deletion or duplication. We next documented evidence for the traditional liability threshold model of disease risk; cases carrying CNVs with weak main effects had higher PRS than cases carrying CNVs with strong main effects. This pattern was strongest for SCZ (p=2.13e-4) but was evident in BD as well. Additionally, we tested a composite CNV-PRS model, which demonstrates how PRS can be a useful tool for predicting outcomes in CNV carriers. For example, a carrier of 16p11.2 proximal duplication (a well-known SCZ association) is not at increased risk for SCZ if they have a low PRS-SCZ.
To increase power to detect statistical interactions between CNVs and PRS, we conducted a meta-analysis of CNVxPRS effects on BMI and height in four biobanks: UK Biobank, Estonian Biobank, Geisinger Health, and Million Veterans Program, (n=975,408). Of the 32 CNVs that were sufficiently powered for this analysis (n > 225), 3 had nominally significant (p < .05) interactions with PRS on BMI. In all three cases, the sign on the interaction was the same as the main effect of the CNV, suggesting that these interactions are synergistic. When we collapsed CNVs by their main effect direction, we saw a significant negative interaction between the BMI decreasing CNVs and PRS (p=9.98e-4). These interactions were robust to rescaling of the BMI response variable via inverse normalization or Box-Cox. We observed no significant interactions for Height.
Taken together, these analyses demonstrate that the effect of recurrent CNVs is moderated by PRS. In addition to emphasizing the importance of considering genetic background when studying the effects of rare variants, this study also demonstrates that genetic factors may have non-additive effects on complex traits.
{"title":"COLLABORATIVE STUDY OF THE COMBINED EFFECTS OF RARE CNVS AND POLYGENIC RISK ON PSYCHIATRIC TRAITS","authors":"Molly Sacks , Marieke Klein , Omar Shanta , Mohammad Ahangari , Oanh Hong , Jeff MacDonald , Bhooma Thiruvahindrapuram , Sebastien Jacquemont , Tim Bigdeli , Matthew Oetjens , Mart Kals , Stephen H. Scherer , Jonathan Sebat , The Bipolar Disorder, Schizophrenia, Post-Traumatic Stress Disorder, Attention-Deficit/Hyperactivity Disorder, Major Depressive Disorder, Autism Spectrum Disorder and Copy Number Variation working groups of the Psychiatric Genomics Consortium , Genes to Mental Health Network","doi":"10.1016/j.euroneuro.2024.08.072","DOIUrl":"10.1016/j.euroneuro.2024.08.072","url":null,"abstract":"<div><div>Recurrent CNVs are known to be major risk factors in neuropsychiatric disorders, yet phenotypic variability between carriers of the same CNV remains unexplained. A possible explanation is that the effect of a CNV depends on genetic background, which can be quantified by a polygenic risk score (PRS). Using data from the PGC and UKBB (combined n=543,111), this project aims to characterize the individual and combined effects of recurrent CNVs and PRS on six major psychiatric disorders: ADHD, ASD, BD, PTSD, MDD, and SCZ.</div><div>We first quantified the effects of 42 recurrent CNV loci across all 6 disorders using logistic regression. After Bonferroni correction, 24 loci had a significant association with at least one disorder in either the deletion or duplication. We next documented evidence for the traditional liability threshold model of disease risk; cases carrying CNVs with weak main effects had higher PRS than cases carrying CNVs with strong main effects. This pattern was strongest for SCZ (p=2.13e-4) but was evident in BD as well. Additionally, we tested a composite CNV-PRS model, which demonstrates how PRS can be a useful tool for predicting outcomes in CNV carriers. For example, a carrier of 16p11.2 proximal duplication (a well-known SCZ association) is not at increased risk for SCZ if they have a low PRS-SCZ.</div><div>To increase power to detect statistical interactions between CNVs and PRS, we conducted a meta-analysis of CNVxPRS effects on BMI and height in four biobanks: UK Biobank, Estonian Biobank, Geisinger Health, and Million Veterans Program, (n=975,408). Of the 32 CNVs that were sufficiently powered for this analysis (n > 225), 3 had nominally significant (p < .05) interactions with PRS on BMI. In all three cases, the sign on the interaction was the same as the main effect of the CNV, suggesting that these interactions are synergistic. When we collapsed CNVs by their main effect direction, we saw a significant negative interaction between the BMI decreasing CNVs and PRS (p=9.98e-4). These interactions were robust to rescaling of the BMI response variable via inverse normalization or Box-Cox. We observed no significant interactions for Height.</div><div>Taken together, these analyses demonstrate that the effect of recurrent CNVs is moderated by PRS. In addition to emphasizing the importance of considering genetic background when studying the effects of rare variants, this study also demonstrates that genetic factors may have non-additive effects on complex traits.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Pages 28-29"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.euroneuro.2024.08.106
{"title":"FAST AND EFFICIENT MIXED-EFFECTS ALGORITHM (FEMA) FOR LONGITUDINAL GWAS AND SNP × TIME INTERACTION: APPLICATIONS AND OPPORTUNITIES IN MOBA","authors":"","doi":"10.1016/j.euroneuro.2024.08.106","DOIUrl":"10.1016/j.euroneuro.2024.08.106","url":null,"abstract":"","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Page 43"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.euroneuro.2024.08.029
{"title":"THE BASICS OF MENDELIAN RANDOMISATION AND SPECIFIC CONSIDERATIONS FOR MENTAL HEALTH TRAITS","authors":"","doi":"10.1016/j.euroneuro.2024.08.029","DOIUrl":"10.1016/j.euroneuro.2024.08.029","url":null,"abstract":"","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Page 10"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.euroneuro.2024.08.041
{"title":"PRELIMINARY INVESTIGATIONS INTO THE GUT MICROBIOME'S ROLE IN SCHIZOPHRENIA","authors":"","doi":"10.1016/j.euroneuro.2024.08.041","DOIUrl":"10.1016/j.euroneuro.2024.08.041","url":null,"abstract":"","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Page 14"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.euroneuro.2024.08.036
Karl Heilbron , Julia Kraft , Alice Braun , Swapnil Awasthi , Georgia Panagiotaropoulou , Marijn Schipper , Nathaniel Bell , Danielle Posthuma , Antonio Pardiñas , Stephan Ripke
The latest schizophrenia GWAS meta-analysis found 287 loci that reached genome-wide statistical significance (67,390 cases and 94,015 controls). In these loci, 120 genes were prioritized using fine-mapping, summary-based Mendelian Randomization (SMR), and enhancer-promoter interaction (via Hi-C). However, these methods only use information within a given locus, ignoring information from the rest of the genome. Combining locus-based approaches with tools that incorporate genome-wide information such as the Polygenic Priority Score (PoPS) have been shown to improve gene prioritization precision. To more accurately characterize genes that play a role in schizophrenia etiology, we prioritized 62 genes based on their distance to GWAS signals, PoPS, fine-mapped coding variants, and ultra-rare coding variant burden tests. We prioritized DRD2, the target of most approved antipsychotics, which was not highlighted by previous efforts. In addition, we prioritized 9 genes that are targeted by approved or investigational drugs and may therefore present drug repurposing opportunities. These included drugs targeting calcium channels (CACNA1C and CACNB2), glutamatergic receptors (GRIN2A and GRM3), and GABAB receptor (GABBR2). We highlighted 3 additional genes (PDE4B, VRK2, and PLCL2) in loci that are shared with a recent addiction GWAS. While it is challenging to assess psychotic symptoms in rodents, high-quality rodent addiction models exist for a wide range of substances. Modulation of these genes could be tested in rodent addiction models and, if successful, may warrant further testing in human clinical trials of addiction and/or schizophrenia. Adding to previous gene prioritization efforts, we hope that our list of prioritized genes will ultimately facilitate the development of new medicines for people living with schizophrenia.
{"title":"IDENTIFYING DRUG TARGETS FOR SCHIZOPHRENIA THROUGH GENE PRIORITIZATION","authors":"Karl Heilbron , Julia Kraft , Alice Braun , Swapnil Awasthi , Georgia Panagiotaropoulou , Marijn Schipper , Nathaniel Bell , Danielle Posthuma , Antonio Pardiñas , Stephan Ripke","doi":"10.1016/j.euroneuro.2024.08.036","DOIUrl":"10.1016/j.euroneuro.2024.08.036","url":null,"abstract":"<div><div>The latest schizophrenia GWAS meta-analysis found 287 loci that reached genome-wide statistical significance (67,390 cases and 94,015 controls). In these loci, 120 genes were prioritized using fine-mapping, summary-based Mendelian Randomization (SMR), and enhancer-promoter interaction (via Hi-C). However, these methods only use information within a given locus, ignoring information from the rest of the genome. Combining locus-based approaches with tools that incorporate genome-wide information such as the Polygenic Priority Score (PoPS) have been shown to improve gene prioritization precision. To more accurately characterize genes that play a role in schizophrenia etiology, we prioritized 62 genes based on their distance to GWAS signals, PoPS, fine-mapped coding variants, and ultra-rare coding variant burden tests. We prioritized DRD2, the target of most approved antipsychotics, which was not highlighted by previous efforts. In addition, we prioritized 9 genes that are targeted by approved or investigational drugs and may therefore present drug repurposing opportunities. These included drugs targeting calcium channels (CACNA1C and CACNB2), glutamatergic receptors (GRIN2A and GRM3), and GABAB receptor (GABBR2). We highlighted 3 additional genes (PDE4B, VRK2, and PLCL2) in loci that are shared with a recent addiction GWAS. While it is challenging to assess psychotic symptoms in rodents, high-quality rodent addiction models exist for a wide range of substances. Modulation of these genes could be tested in rodent addiction models and, if successful, may warrant further testing in human clinical trials of addiction and/or schizophrenia. Adding to previous gene prioritization efforts, we hope that our list of prioritized genes will ultimately facilitate the development of new medicines for people living with schizophrenia.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Page 12"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.euroneuro.2024.08.049
Sarah Colbert , The Suicide Working Group of the Psychiatric Genomics Consortium , Douglas Ruderfer , Anna Docherty , Niamh Mullins
<div><div>Suicidal thoughts and behaviors, specifically suicidal ideation (SI), suicide attempt (SA) and suicide death (SD), are substantially heritable, with twin and family studies estimating heritabilities in the range of 30-55%. Recently, genome-wide association studies (GWAS) have reached sufficient sample sizes to conduct well-powered analyses, leading to the identification of 4, 12 and 2 loci associated with SI, SA, and SD, respectively. Importantly, these phenotypes show strong, yet incomplete, genetic correlations with each other, motivating genetic studies of each phenotype separately to understand their underlying biology and the progression from one to the next. Here, we present an update on the progress of the latest and most extensive GWAS of SI, SA, and SD, conducted by the Psychiatric Genomics Consortium Suicide Working Group (PGC SUI).</div><div><strong>Methods:</strong> Data comprise 30 cohorts contributing to the SI GWAS (N cases=256,257, N controls=1,298,106), 42 cohorts contributing to the SA GWAS (N cases=73,087, N controls=1,327,350), and 6 cohorts contributing to the SD GWAS (N cases=6,775, N controls=841,216). Notably, these cohorts comprise individuals from four diverse genetic ancestry groups: admixed European ancestries (EUR), admixed African ancestries (AA), East Asian ancestries (EA) and admixed Latino ancestries (LAT). New phenotyping and analytic protocols have been developed by PGC SUI to ensure exceptional rigor and comparability across cohorts. GWAS meta-analyses will be conducted via inverse variance-weighted fixed effects models to identify novel genetic risk loci. Post-GWAS analyses include pathway, tissue and drug target enrichment, and examination of the SNP-heritabilities (h2SNP), and genetic relationships between SI, SA, and SD.</div><div>Preliminary analysis using the currently available SA data (SA cases = 47,174, controls = 941,010 from 26 cohorts) yielded a h2SNP of 5.6% (se = 0.003, p = 1.2e-68) and ten replicated and three novel genome-wide significant (GWS) loci, containing FYN, AIG1, and DCC. Eight GWS loci were identified in the EUR meta-analysis (h2SNP = 7%, se = 0.004) which replicated previous findings. No GWS loci were identified in the AA (h2SNP = 9.8%, se = 0.02), EA (h2SNP 5.1%, se = 0.04) or LAT (h2SNP = 10%, se =0.07) GWAS meta-analyses. We also identified significant enrichment in genes expressed in several brain tissues from GTEx and summary data-based Mendelian Randomization revealed two novel genes (GMPPB, FURIN) significantly associated with SA. This SA GWAS showed significant genetic correlations with published GWAS of SI (rg = 0.80, se = 0.04), SD (rg = 0.77, se = 0.05), and several psychiatric disorders (rgs = 0.26-0.70).</div><div>Additional data intake is almost complete within PGC SUI, and this presentation will share the final GWAS results and novel biological insights. Increased sample sizes in combination with streamlined protocols for phenotyping and analyzing suicidal tho
{"title":"GENOME-WIDE ASSOCIATION STUDIES OF SUICIDAL THOUGHTS AND BEHAVIORS: AN UPDATE FROM THE PSYCHIATRIC GENOMICS CONSORTIUM SUICIDE WORKING GROUP","authors":"Sarah Colbert , The Suicide Working Group of the Psychiatric Genomics Consortium , Douglas Ruderfer , Anna Docherty , Niamh Mullins","doi":"10.1016/j.euroneuro.2024.08.049","DOIUrl":"10.1016/j.euroneuro.2024.08.049","url":null,"abstract":"<div><div>Suicidal thoughts and behaviors, specifically suicidal ideation (SI), suicide attempt (SA) and suicide death (SD), are substantially heritable, with twin and family studies estimating heritabilities in the range of 30-55%. Recently, genome-wide association studies (GWAS) have reached sufficient sample sizes to conduct well-powered analyses, leading to the identification of 4, 12 and 2 loci associated with SI, SA, and SD, respectively. Importantly, these phenotypes show strong, yet incomplete, genetic correlations with each other, motivating genetic studies of each phenotype separately to understand their underlying biology and the progression from one to the next. Here, we present an update on the progress of the latest and most extensive GWAS of SI, SA, and SD, conducted by the Psychiatric Genomics Consortium Suicide Working Group (PGC SUI).</div><div><strong>Methods:</strong> Data comprise 30 cohorts contributing to the SI GWAS (N cases=256,257, N controls=1,298,106), 42 cohorts contributing to the SA GWAS (N cases=73,087, N controls=1,327,350), and 6 cohorts contributing to the SD GWAS (N cases=6,775, N controls=841,216). Notably, these cohorts comprise individuals from four diverse genetic ancestry groups: admixed European ancestries (EUR), admixed African ancestries (AA), East Asian ancestries (EA) and admixed Latino ancestries (LAT). New phenotyping and analytic protocols have been developed by PGC SUI to ensure exceptional rigor and comparability across cohorts. GWAS meta-analyses will be conducted via inverse variance-weighted fixed effects models to identify novel genetic risk loci. Post-GWAS analyses include pathway, tissue and drug target enrichment, and examination of the SNP-heritabilities (h2SNP), and genetic relationships between SI, SA, and SD.</div><div>Preliminary analysis using the currently available SA data (SA cases = 47,174, controls = 941,010 from 26 cohorts) yielded a h2SNP of 5.6% (se = 0.003, p = 1.2e-68) and ten replicated and three novel genome-wide significant (GWS) loci, containing FYN, AIG1, and DCC. Eight GWS loci were identified in the EUR meta-analysis (h2SNP = 7%, se = 0.004) which replicated previous findings. No GWS loci were identified in the AA (h2SNP = 9.8%, se = 0.02), EA (h2SNP 5.1%, se = 0.04) or LAT (h2SNP = 10%, se =0.07) GWAS meta-analyses. We also identified significant enrichment in genes expressed in several brain tissues from GTEx and summary data-based Mendelian Randomization revealed two novel genes (GMPPB, FURIN) significantly associated with SA. This SA GWAS showed significant genetic correlations with published GWAS of SI (rg = 0.80, se = 0.04), SD (rg = 0.77, se = 0.05), and several psychiatric disorders (rgs = 0.26-0.70).</div><div>Additional data intake is almost complete within PGC SUI, and this presentation will share the final GWAS results and novel biological insights. Increased sample sizes in combination with streamlined protocols for phenotyping and analyzing suicidal tho","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Page 18"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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