Biomedical subjects

Piotr Jaholkowski

Publications and source records attributed to Piotr Jaholkowski.

6 recordsLinked to original sources

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

Genome-wide analysis of screen behaviors among adolescents identifies novel loci and overlap with educational attainment and mental disorders.

Technological devices play a central role in adolescents' life. Despite concerns about negative effects of excessive screen time, there is little knowledge of screen behaviors' genetic architecture. Using self-reports from adolescents in the Norwegian Mother, Father, and Child Cohort Study (n = 18,490), we performed genome-wide association analysis for four screen behaviors: time spent (1) watching television; (2) gaming; (3) sitting/lying down with a screen device; and (4) using social media. The resulting summary statistics were analysed using the conditional false discovery rate (condFDR) approach to increase genetic discovery. We also estimated SNP-heritabilities of the screen behaviors and genetic correlations with eight psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, anorexia nervosa, cannabis use disorder and alcohol use disorder), and educational attainment. Screen behaviors displayed significant SNP-heritabilities (0.048-0.12). We observed significant genetic correlations between screen behaviors and psychiatric disorders (rg range: 0.21-0.42). Educational attainment demonstrated negative genetic correlation with screen behaviors, most strongly with social media use (rg = - 0.69). CondFDR analysis identified three novel loci associated with social media use. Thus, we show that screen behaviors are heritable, polygenic traits that partly share genetic signal with mental disorders and educational attainment.

Humans↗

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↗