Biomedical subjects

Kevin S O'Connell

Publications and source records attributed to Kevin S O'Connell.

8 recordsLinked to original sources

PubMed2026

Distinguishing different psychiatric disorders using DDx-PRS.

Despite great progress on case-control polygenic prediction, an unmet need remains for a method that genetically distinguishes clinically related disorders (e.g., schizophrenia (SCZ) versus bipolar disorder (BIP) versus major depressive disorder (MDD) versus controls). We introduce differential diagnosis-polygenic risk score (DDx-PRS), which jointly estimates the posterior probabilities of each diagnostic category (e.g., SCZ = 50%, BIP = 25%, MDD = 15%, control = 10%) by modeling variance-covariance structure across disorders, leveraging case-control polygenic risk scores and prior clinical probabilities for each diagnostic category. We applied DDx-PRS to Psychiatric Genomics Consortium SCZ, BIP, MDD and control data, including summary-level training data from three case-control genome-wide association studies (n = 41,917-173,140 cases; total n = 1,048,683) and held-out test data from different cohorts with equal numbers for each diagnostic category (total n = 11,460). DDx-PRS was well calibrated and well powered (consistent with simulations) and produced comparable results to methods that require tuning data. True diagnosis probabilities in the top deciles of predicted diagnosis probabilities were considerably larger than prior baseline probabilities, implying appreciable potential for clinical utility in certain settings.

Humans↗

PubMed2026

Genomic relationship between polyendocrine metabolic ovarian syndrome and bipolar disorder.

Women with bipolar disorder (BIP) have a higher risk of developing polyendocrine metabolic ovarian syndrome (PMOS). Shared genetic architecture may underlie this comorbidity. Valproate, a mood-stabilizer commonly used to treat BIP, increases the risk of PMOS. Still, the mechanism underlying PMOS in BIP remains unknown. Here, we aimed to identify genetic variants shared between BIP and PMOS, as well as their interaction with valproate. We used the results of large-scale genome-wide association studies of BIP (41,510 cases and 354,340 controls), and PMOS (3609 cases and 229,788 controls). Using conditional false discovery rate, we discovered genetic variants jointly associated with BIP and PMOS. Gene mapping of identified variants was performed using the Open Targets platforms. We analyzed the tissue-specific expression, interaction with valproate, and involvement in biological pathways of the mapped genes. We identified two loci shared between BIP and PMOS. Among the 10 genes mapped to the locus on chromosome 8:11,444,837-11,463,015, GATA4, NEIL2, and FDFT1 showed expression profiles suggesting their role in the observed comorbidity. Mapped to the locus on chromosome 12:2499,849-2514,270, CACNA1C, FKBP4, DCP1B, and ITFG2 are expressed in both the ovaries and the brain. Valproate interacts with CACNA1C, and CACNA1C is part of biological pathways that also include other genes interacting with valproate. We identified shared genetic underpinnings of BIP and PMOS and highlighted genes that may potentially contribute to the biological mechanisms underlying their comorbidity and to a hypothesized role of valproate in these mechanisms.

Female↗

PubMed2026

Leveraging the genetics of psychiatric disorders to prioritize potential drug targets and compounds.

Genetics can inform biologically relevant drug development and repurposing, which may improve patient care. Here, we leverage the genetics of psychiatric disorders to prioritize potential drug targets and compounds. We used the genome-wide association studies of four psychiatric disorders [attention deficit hyperactivity disorder (ADHD), bipolar disorder, depression, and schizophrenia] and genes encoding drug targets. We conducted drug enrichment analyses incorporating the novel and biologically specific GSA-MiXeR tool. We conducted multiple molecular trait analyses using large-scale transcriptomic and proteomic datasets sampled from brain and blood tissue. This included the novel use of the UK Biobank proteomic data for a proteome-wide association study of psychiatric disorders. With the accumulated evidence, we prioritize potential drug targets and compounds for each disorder. We reveal candidate drug targets associated with a single or multiple disorders that implicate glutamate signaling. Drug prioritization indicated genetic support for psychotropic medications, including several top-ranked antipsychotics for schizophrenia. We also observed genetic support for commonly used psychotropics for psychiatric treatment (e.g., clozapine, duloxetine, and lithium). Revealed opportunities for drug repurposing included cholinergic drugs for ADHD, estrogen modulators for depression, and matrix metalloproteinases for ADHD and depression. Our findings indicate the genetic liability to schizophrenia is associated with reduced brain and blood expression of CYP2D6, a gene encoding a metabolizer of drugs and neurotransmitters, suggesting a genetic risk for poor drug response and altered neurotransmission. Our extensive analyses highlight the utility of genetics for informing drug development and repurposing for psychiatric disorders, providing novel opportunities for improving patient outcomes. Depicted is the series of analyses conducted to generate a list of prioritized drug targets and compounds. First pairings of genome-wide association study (GWAS) traits with drugs are generated using enrichment analyses. Next, a series of molecular trait analyses is conducted to generate and rank a list of potential drug targets for each GWAS trait. Finally, enrichment and molecular trait results are combined to generate a ranked list of prioritized drugs for each GWAS trait based on supporting genetic evidence. ADHD = Attention deficit hyperactivity disorder, BIP = Bipolar disorder, DEP = Depression, SCZ = Schizophrenia, DBP = Diastolic blood pressure, T2D = Type 2 diabetes, RNA = ribonucleic acid, XWAS = both transcriptome and proteome-wide association studies, MR = Mendelian randomization, coloc = colocalization.

Humans↗

PubMed2025

A genome-wide analysis of the shared genetic risk architecture of complex neurological and psychiatric disorders.

Although neurological and psychiatric disorders have historically been considered to reflect distinct pathogenic entities, recent findings suggest shared pathophysiological mechanisms. However, the extent to which these heritable disorders share genetic influences remains unclear. Here we performed a comprehensive analysis of genome-wide association study data, involving nearly 1 million cases across ten neurological diseases and ten psychiatric disorders, to compare their common genetic signal and biological associations. Using complementary statistical tools, we demonstrate that a large set of common genetic variants impacts the risk of multiple neurological and psychiatric disorders, even in the absence of genetic correlations. Furthermore, genome-wide association studies on psychiatric disorders consistently implicate neuronal biology, whereas neurological diseases are associated with diverse neurobiological processes. Together, this study elucidates the genetic relationship between complex neurological and psychiatric disorders, indicating a larger degree of genetic pleiotropy than previously recognized. The findings have implications for disease classification, precision medicine and clinical practice.

Humans↗

PubMed2025

Optimizing genetic ancestry adjustment in DNA methylation studies: a comparative analysis of approaches.

BACKGROUND: Genetic ancestry is an important factor to account for in DNA methylation studies because genetic variation influences DNA methylation patterns. One approach uses principal components (PCs) calculated from CpG sites that overlap with common SNPs to adjust for ancestry when genotyping data is not available. However, this method does not remove technical and biological variations, such as sex and age, prior to calculating the PCs. The first PC is therefore often associated with factors other than ancestry. METHODS: We developed and adapted the adapted EpiAnceR+ approach, which includes (1) residualizing the CpG data overlapping with common SNPs for control probe PCs, sex, age, and cell type proportions to remove the effects of technical and biological factors, and (2) integrating the residualized data with genotype calls from the SNP probes (commonly referred to as rs probes) present on the arrays, before calculating PCs and evaluated the clustering ability and relationship to genetic ancestry. RESULTS: The PCs generated by EpiAnceR+ led to improved clustering for repeated samples from the same individual and stronger associations with genetic ancestry groups predicted from genotype information compared to the original approach. EpiAnceR+ also outperformed the use of DNA methylation PCs or surrogate variables for ancestry adjustment. CONCLUSIONS: We show that the EpiAnceR+ approach improves the adjustment for genetic ancestry in DNA methylation studies. EpiAnceR+ can be integrated into existing R pipelines for commercial methylation arrays, such as 450 K, EPIC v1, and EPIC v2. The code is available on GitHub ( https://github.com/KiraHoeffler/EpiAnceR ).

DNA Methylation↗

PubMed2025

Genomic relationship between polycystic ovary syndrome and bipolar disorder.

Women with bipolar disorder (BIP) have a higher risk of developing polycystic ovary syndrome (PCOS). Shared genetic architecture may underlie this comorbidity. Valproate, a mood-stabilizer commonly used to treat BIP, increases the risk of PCOS. Still, the mechanism underlying PCOS in BIP remains unknown. Here, we aimed to identify genetic variants shared between BIP and PCOS, as well as their interaction with valproate. We used the results of large-scale genome-wide association studies of BIP (41,510 cases and 354,340 controls), and PCOS (3,609 cases and 229,788 controls). Using conditional false discovery rate, we discovered genetic variants jointly associated with BIP and PCOS. Gene mapping of identified variants was performed using the Open Targets platforms. We analyzed the tissue-specific expression, interaction with valproate, and involvement in biological pathways of the mapped genes. We identified two loci shared between BIP and PCOS. Among the 10 genes mapped to the locus on chromosome 8:11455262, GATA4, NEIL2, and FDFT1 showed expression profiles suggesting their role in the observed comorbidity. Mapped to the locus on chromosome 12:2499849, CACNA1C, FKBP4, DCP1B, and ITFG2 are expressed in both the ovaries and the brain. CACNA1C expression is affected by valproate, and CACNA1C plays a role in biological pathways involving other valproate-affected genes. We identified shared genetic underpinnings of BIP and PCOS, and implicated genes which may explain the biological mechanisms of the comorbidity between these disorders and a potential mechanism for the role of valproate.

bipolar disorder↗

PubMed2025

Comorbidity alters the genetic relationship between anxiety disorders and major depression.

BACKGROUND: Comorbid anxiety disorders (ANX) and major depression (MD) have worse clinical outcomes than either disorder alone. Analysis of genomic data based on comorbidity status may reveal more precise biological pathways and causal relationships with potential clinical implications. We investigated the genetic relationship between ANX and MD with and without mutual comorbidity. METHODS: We leveraged data from UK Biobank to perform disorder-specific genome-wide association studies (GWAS) of ANX-only (n=189,422) and MD-only (n=194,339) and generate polygenic risk scores (PRS). The Norwegian Mother, Father, and Child Cohort (MoBa, n = 130,992) served to test the associations of PRS with diagnoses. MD and ANX GWAS, including comorbidities (MD-comorbid and ANX-comorbid), were used for comparison. Genetic correlations were compared by comorbidity status, and Mendelian randomization was employed to assess causal relationships. RESULTS: The MD-only PRS showed a stronger association with MD-only compared to ANX-only cases (Z=3.74; Padjusted=0.002); however, MD-comorbid PRS did not show a significant difference (Z=2.71; Padjusted=0.08). The genetic correlation between ANX-only and MD-only was 0.53, lower than between ANX-comorbid and MD-comorbid (0.90). ANX-only showed a causal relationship with MD-only (Padjusted=0.015), but not vice versa, and contrasted the bidirectional causal relationship (Padjusted=2.9e-12, and Padjusted=9.3e-06) when comorbidity was included. Gene sets of MD-comorbid, ANX-comorbid, and MD-only, but not of ANX-only, were enriched for immune regulation pathways such as interleukin production. CONCLUSIONS: ANX and MD show more distinct genetics when comorbid cases are excluded, and ANX may be causal for MD. Disorder-specific genetic studies help uncover more relevant biological mechanisms and guide tailored clinical interventions.

Journal Article↗

PubMed2024

Distinguishing different psychiatric disorders using DDx-PRS.

Despite great progress on methods for case-control polygenic prediction (e.g. schizophrenia vs. control), there remains an unmet need for a method that genetically distinguishes clinically related disorders (e.g. schizophrenia (SCZ) vs. bipolar disorder (BIP) vs. depression (MDD) vs. control); such a method could have important clinical value, especially at disorder onset when differential diagnosis can be challenging. Here, we introduce a method, Differential Diagnosis-Polygenic Risk Score (DDx-PRS), that jointly estimates posterior probabilities of each possible diagnostic category (e.g. SCZ=50%, BIP=25%, MDD=15%, control=10%) by modeling variance/covariance structure across disorders, leveraging case-control polygenic risk scores (PRS) for each disorder (computed using existing methods) and prior clinical probabilities for each diagnostic category. DDx-PRS uses only summary-level training data and does not use tuning data, facilitating implementation in clinical settings. In simulations, DDx-PRS was well-calibrated (whereas a simpler approach that analyzes each disorder marginally was poorly calibrated), and effective in distinguishing each diagnostic category vs. the rest. We then applied DDx-PRS to Psychiatric Genomics Consortium SCZ/BIP/MDD/control data, including summary-level training data from 3 case-control GWAS ( N =41,917-173,140 cases; total N =1,048,683) and held-out test data from different cohorts with equal numbers of each diagnostic category (total N =11,460). DDx-PRS was well-calibrated and well-powered relative to these training sample sizes, attaining AUCs of 0.66 for SCZ vs. rest, 0.64 for BIP vs. rest, 0.59 for MDD vs. rest, and 0.68 for control vs. rest. DDx-PRS produced comparable results to methods that leverage tuning data, confirming that DDx-PRS is an effective method. True diagnosis probabilities in top deciles of predicted diagnosis probabilities were considerably larger than prior baseline probabilities, particularly in projections to larger training sample sizes, implying considerable potential for clinical utility under certain circumstances. In conclusion, DDx-PRS is an effective method for distinguishing clinically related disorders.

Journal Article↗