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PubMed2024

Obesity: exploring its connection to brain function through genetic and genomic perspectives.

Obesity represents an escalating global health burden with profound medical and economic impacts. The conventional perspective on obesity revolves around its classification as a "pure" metabolic disorder, marked by an imbalance between calorie consumption and energy expenditure. Present knowledge, however, recognizes the intricate interaction of rare or frequent genetic factors that favor the development of obesity, together with the emergence of neurodevelopmental and mental abnormalities, phenotypes that are modulated by environmental factors such as lifestyle. Thirty years of human genetic research has unveiled >20 genes, causing severe early-onset monogenic obesity and ~1000 loci associated with common polygenic obesity, most of those expressed in the brain, depicting obesity as a neurological and mental condition. Therefore, obesity's association with brain function should be better recognized. In this context, this review seeks to broaden the current perspective by elucidating the genetic determinants that contribute to both obesity and neurodevelopmental and mental dysfunctions. We conduct a detailed examination of recent genetic findings, correlating them with clinical and behavioral phenotypes associated with obesity. This includes how polygenic obesity, influenced by a myriad of genetic variants, impacts brain regions associated with addiction and reward, differentiating it from monogenic forms. The continuum between non-syndromic and syndromic monogenic obesity, with evidence from neurodevelopmental and cognitive assessments, is also addressed. Current therapeutic approaches that target these genetic mechanisms, yielding improved clinical outcomes and cognitive advantages, are discussed. To sum up, this review corroborates the genetic underpinnings of obesity, affirming its classification as a neurological disorder that may have broader implications for neurodevelopmental and mental conditions. It highlights the promising intersection of genetics, genomics, and neurobiology as a foundation for developing tailored medical approaches to treat obesity and its related neurological aspects.

Humans↗

PubMed2024

The role of co-occurring conditions and genetics in the associations of eating disorders with attention-deficit/hyperactivity disorder and autism spectrum disorder.

Eating disorders (EDs) commonly co-occur with other psychiatric and neurodevelopmental disorders including attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD); however, the pattern of family history and genetic overlap among them requires clarification. This study investigated the diagnostic, familial, and genetic associations of EDs with ADHD and ASD. The nationwide population-based cohort study included all individuals born in Denmark, 1981-2008, linked to their siblings and cousins. Cox regression was used to estimate associations between EDs and ADHD or ASD, and mediation analysis was used to assess the effects of intermediate mood or anxiety disorders. Polygenic scores (PGSs) were used to investigate the genetic association between anorexia nervosa (AN) and ADHD or ASD. Significantly increased risk for any ED was observed following an ADHD or ASD diagnosis. Mediation analysis suggested that intermediate mood or anxiety disorders could account for 44%-100% of the association between ADHD or ASD and ED. Individuals with a full sibling or maternal half sibling with ASD had increased risk of AN compared to those with siblings without ASD. A positive association was found between ASD-PGS and AN risk whereas a negative association was found between AN-PGS and ADHD. In this study, positive phenotypic associations between EDs and ADHD or ASD, mediation by mood or anxiety disorder, and genetic associations between ASD-PGS and AN and between AN-PGS and ADHD were observed. These findings could guide future research in the development of new treatments that can mitigate the development of EDs among individuals with ADHD or ASD.

Humans↗

PubMed2026

Genetics of major depressive disorder in a homogeneous population with uniform phenotyping.

Harmonized phenotyping and diverse population-specific studies are crucial for advancing gene discovery in psychiatric genetics. We conducted a genome-wide association (GWAS) mega-analysis of DSM-defined lifetime major depressive disorder (MDD) in 64 941 participants (25.7% cases) from the Dutch BIObanks Netherlands Internet Collaboration (BIONIC) consortium. Liability-scale SNP-based heritability was 12.0% (SE = 1.4%) as estimated by LDSC (assuming a lifetime prevalence of 15%) and 26.6% (SE = 1.1%) when estimated by LDAK-REML on individual-level genotype data, indicating substantial common-variant signal in this clinically harmonized sample. The genetic correlation with the latest major depression GWAS from the Psychiatric Genomics Consortium (PGC-MD) was high (rG = 0.89, SE = 0.048). Polygenic scores (PGSs) based on BIONIC predicted depression in UK Biobank, and PGSs derived from PGC-MD predicted MDD in BIONIC, supporting transferability of depression polygenic signal across cohorts and phenotype definitions. Within-family PGS analyses in twins suggested that the observed prediction was not primarily driven by detectable family-level confounding, and twin concordance for MDD increased with polygenic burden. We identified one genome-wide significant locus, indexed by rs3818852 in PALMD, but this finding currently lacks independent replication and should be interpreted cautiously. Finally, genetic correlation and latent causal variable analyses identified multiple traits showing shared or directionally consistent genetic associations with MDD. Together, these findings underscore the value of clinically harmonized phenotyping in regional biobank collaborations for studying the genetic architecture of MDD.

Humans↗

PubMed2026

Distinct contributions of schizophrenia and neurotransmitter pathway genetic liability to neurocognition and antipsychotic efficacy in drug-naïve first-episode schizophrenia.

The genetic mechanisms underlying heterogeneity in symptom presentation and antipsychotic response in schizophrenia remain unclear, limiting the development of personalized treatment. We integrated genome-wide schizophrenia polygenic risk scores (SZ-PRS) and pathway-specific PRSs (pPRSs) for four major neurotransmitter systems to examine their associations with clinical phenotypes across the course of illness. Primary analyses were conducted in 394 drug-naïve, first-episode patients from the Chinese First-Episode Schizophrenia Trial (CNFEST) to investigate associations with baseline symptom severity, neurocognitive impairment, and longitudinal treatment response. The CNFEST cohort included 52-week longitudinal assessments of symptoms and neurocognition using the Positive and Negative Syndrome Scale and a modified version of the MATRICS Consensus Cognitive Battery. An independent case-control cohort evaluated associations with schizophrenia diagnosis, while a cohort of 514 healthy adults assessed whether PRS-cognition associations are specific to schizophrenia. Higher SZ-PRS predicted schizophrenia diagnosis (OR = 2.28, Pfdr = 0.003) and poorer baseline executive function (β = -0.44, Pfdr = 0.006) and working memory (β = -0.49, Pfdr = 0.018), but these associations were absent in healthy adults. In contrast, pPRSs showed weaker associations with diagnosis and baseline cognition but were more informative for treatment outcomes: higher serotonin-pPRS predicted greater improvement in depressive symptoms (Pfdr = 0.023-0.032), and higher GABA-pPRS predicted greater improvement in overall symptoms (Pfdr = 0.038-0.043) during weeks 4-24. Exploratory drug-specific analyses further suggested that treatment response varied across antipsychotics and was differentially associated with pPRSs. These findings demonstrate that genome-wide and pathway-specific PRSs contribute distinctly to schizophrenia phenotypes, supporting their integration for personalized stratification and treatment.

Humans↗

PubMed2022

Polygenic heterogeneity in antidepressant treatment and placebo response.

The genetic architecture of antidepressant response is poorly understood. Polygenic risk scores (PRS), exploration of placebo response and the use of sub-scales might provide insights. Here, we investigate the association between PRSs for relevant complex traits and response to vortioxetine treatment and placebo using clinical scales, including sub-scales and self-reported assessments. We collected a clinical test sample of Major Depressive Disorder (MDD) patients treated with vortioxetine (N = 907) or placebo (N = 455) from seven randomized, double-blind, clinical trials. In parallel, we obtained data from an observational web-based study of vortioxetine-treated patients (N = 642) with self-reported response. PRSs for antidepressant response, psychiatric disorders, and symptom traits were derived using summary statistics from well-powered genome-wide association studies (GWAS). Association tests were performed between the PRSs and treatment response in each of the two test samples and empirical p-values were evaluated. In the clinical test sample, no PRSs were significantly associated with response to vortioxetine treatment or placebo following Bonferroni correction. However, clinically assessed treatment response PRS was nominally associated with vortioxetine treatment and placebo response given by several secondary outcome scales (improvement on HAM-A, HAM-A Psychic Anxiety sub-scale, CPFQ & PDQ), (P ≤ 0.026). Further, higher subjective well-being PRS (P ≤ 0.033) and lower depression PRS (P = 0.01) were nominally associated with higher placebo response. In the self-reported test sample, higher schizophrenia PRS was significantly associated with poorer self-reported response (P = 0.0001). The identified PRSs explain a low proportion of the variance (1.2-5.3%) in placebo and treatment response. Although the results were limited, we believe that PRS associations bear unredeemed potential as a predictor for treatment response, as more well-powered and phenotypically similar GWAS bases become available.

Humans↗

PubMed2026

Integrating biological pathway polygenic scores and trauma in psychosis: findings from the EU-GEI study.

Psychotic disorders are complex, multifactorial conditions influenced by both genetic liability and early environmental adversity. Polygenic risk scores (PRSs) derived from genome-wide association studies have shown utility in capturing genetic predisposition, but their biological interpretability remains limited. In this study, we evaluated whether biologically informed pathway-specific polygenic scores (pPGSs) for psychosis, restricted to neurotransmitter-related pathways, could help clarify gene-environment interplay. Using data from 1 192 individuals in the EU-GEI multi-site case-control study, we constructed pPGSs for dopamine, glutamate, GABA, and serotonin systems. We investigated associations between pPGSs and childhood trauma (rGE), their interactions on psychosis risk (GxE), and the influence of the genome-wide psychosis PRS on these relationships. Serotonin, dopamine, and glutamate pPGSs were positively associated with a composite trauma exposure (i.e., abuse and neglect), suggesting shared genetic factors contributing to both psychosis liability and early adversity. Significant negative GxE effects were observed for both dopamine and serotonin pPGSs, indicating that higher trauma exposure diminished the relative influence of genetic liability on psychosis risk. Adjustment for the genome-wide psychosis PRS attenuated most effects, but serotonergic and dopaminergic associations remained robust, supporting pathway-specific contributions beyond general polygenic risk. These findings provide proof-of-concept for the utility of pPGSs in psychiatric research, suggesting both genetic contributions to trauma exposure and GxE effects on psychosis risk. Further research incorporating epigenetic data and longitudinal designs may enhance mechanistic insight and translational potential.

Adult↗

PubMed2025

Identification of plasma proteomic markers underlying polygenic risk of type 2 diabetes and related comorbidities.

Genomics can provide insight into the etiology of type 2 diabetes and its comorbidities, but assigning functionality to non-coding variants remains challenging. Polygenic scores, which aggregate variant effects, can uncover mechanisms when paired with molecular data. Here, we test polygenic scores for type 2 diabetes and cardiometabolic comorbidities for associations with 2,922 circulating proteins in the UK Biobank. The genome-wide type 2 diabetes polygenic score associates with 617 proteins, of which 75% also associate with another cardiometabolic score. Partitioned type 2 diabetes scores, which capture distinct disease biology, associate with 342 proteins (20% unique). In this work, we identify key pathways (e.g., complement cascade), potential therapeutic targets (e.g., FAM3D in type 2 diabetes), and biomarkers of diabetic comorbidities (e.g., EFEMP1 and IGFBP2) through causal inference, pathway enrichment, and Cox regression of clinical trial outcomes. Our results are available via an interactive portal ( https://public.cgr.astrazeneca.com/t2d-pgs/v1/ ).

Humans↗

PubMed2025

Diabetes mellitus polygenic risk scores: heterogeneity and clinical translation.

Diabetes mellitus encompasses several disorders, each with differing clinical presentation, prognoses and pathophysiology. Distinct polygenic architectures underlie type 1 diabetes mellitus and type 2 diabetes mellitus, and govern numerous pathophysiological pathways that converge on dysglycaemia. Over the previous decade, polygenic risk scores (PRS) derived from large genome-wide association studies have become broadly recognized for their potential in precision medicine. PRS, and now partitioned polygenic scores generated by clustering of risk variants, can quantify individual genetic predisposition to diabetes mellitus and reveal molecular heterogeneity responsible for variation in clinical presentation and prognoses. In this Review, we examine and contrast progress in the development of type 1 diabetes mellitus PRS and type 2 diabetes mellitus PRS, and discuss paths to further methodological advances. We examine how studies in the past 10 years have harnessed PRS and novel partitioned polygenic scores to reveal insights into diabetes mellitus aetiology and characterize changes in cellular and tissue-specific disease-modifying molecular pathways. Additionally, we discuss advances and opportunities in areas of clinical translation, including improved classification of diabetes mellitus type, screening of those at risk and personalized interventions informed by PRS. Finally, we emphasize the urgent need to overcome ancestry-related challenges and highlight current progress and gaps in ensuring the equitable translation of PRS for diabetes mellitus precision medicine.

Humans↗

PubMed2025

Polygenic and developmental profiles of autism differ by age at diagnosis.

Although autism has historically been conceptualized as a condition that emerges in early childhood1,2, many autistic people are diagnosed later in life3-5. It is unknown whether earlier- and later-diagnosed autism have different developmental trajectories and genetic profiles. Using longitudinal data from four independent birth cohorts, we demonstrate that two different socioemotional and behavioural trajectories are associated with age at diagnosis. In independent cohorts of autistic individuals, common genetic variants account for approximately 11% of the variance in age at autism diagnosis, similar to the contribution of individual sociodemographic and clinical factors, which typically explain less than 15% of this variance. We further demonstrate that the polygenic architecture of autism can be broken down into two modestly genetically correlated (rg = 0.38, s.e. = 0.07) autism polygenic factors. One of these factors is associated with earlier autism diagnosis and lower social and communication abilities in early childhood, but is only moderately genetically correlated with attention deficit-hyperactivity disorder (ADHD) and mental-health conditions. Conversely, the second factor is associated with later autism diagnosis and increased socioemotional and behavioural difficulties in adolescence, and has moderate to high positive genetic correlations with ADHD and mental-health conditions. These findings indicate that earlier- and later-diagnosed autism have different developmental trajectories and genetic profiles. Our findings have important implications for how we conceptualize autism and provide a model to explain some of the diversity found in autism.

Humans↗

PubMed2026

Consensus meta-analysis of genome-wide association studies for Alzheimer's disease and related dementias.

To better characterize the genetic architecture underlying Alzheimer's disease (AD) and related dementias (ADRD), we performed a meta-analysis of European-ancestry genome-wide association studies in 128,681 cases or proxy cases of ADRD and 849,833 (proxy) controls. We identified 91 genetic loci associated with ADRD risk, of which 16 are new and 56 are specifically detected in clinically diagnosed AD cases. We also provide a list of 18 loci (15 new) requiring further external validation. A polygenic score combining the effects of ADRD loci other than APOE was primarily associated with AD rather than non-AD pathology. Individuals in the tenth decile of the score exhibited a twofold increased risk of presenting with Braak neurofibrillary tangles stage of >4 and moderate-to-severe neuritic amyloid plaque pathology at death compared to individuals in the median score group. In conclusion, our study validated a large number of loci associated with the risk of clinically diagnosed AD, while further investigations are required to confirm the impact of the other loci on AD clinical diagnosis and of each locus on AD pathology.

Humans↗

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

Rare variant effect estimation and polygenic risk prediction.

Due to their low frequency, estimating the effects of rare variants is challenging. Here we propose RareEffect, a method that first estimates gene-based or region-based heritability and then each variant effect size using an empirical Bayes approach. Our method uses a variance component model, which is popular in rare variant tests, and is designed to provide two levels of effect sizes-gene/region level and variant level-that can provide better interpretation. To adjust for the case-control imbalance in phenotypes, our approach uses a fast implementation of the Firth bias correction. We demonstrate the accuracy and computational efficiency of our method through extensive simulations and analysis of UK Biobank whole-exome sequencing data for 100 traits. Additionally, we show that the effect sizes obtained from our model can be leveraged to improve polygenic score performance, thereby outperforming recently developed methods for rare variant polygenic scoring.

Humans↗

PubMed2026

Genome-wide association analyses highlight the neuronal contribution to multiple sclerosis susceptibility.

Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease. Previous genetic studies have identified susceptibility loci that primarily impact immune cells and microglia. Here we performed a multi-ancestry genome-wide association study of 20,831 MS cases and 729,220 controls and identified 236 susceptibility variants outside of the major histocompatibility complex, including four novel genomic loci. We also derived a polygenic score for MS; while optimized for European ancestry, it is informative for African American and Latino individuals. Integrating single-cell data from blood and brain tissue, we identified 76 candidate causal genes. Inhibitory neurons emerged as a key target cell type for MS-associated variants, with seven loci, including STAT3, displaying altered expression only in these cells. The STAT3 variant is also associated with cognition and white matter integrity in individuals with no MS and greater sNfL levels in individuals with MS, suggesting that MS susceptibility may reflect reduced central nervous system resilience to inflammatory challenges.

Humans↗

PubMed2024

Multi-ancestry polygenic mechanisms of type 2 diabetes.

Type 2 diabetes (T2D) is a multifactorial disease with substantial genetic risk, for which the underlying biological mechanisms are not fully understood. In this study, we identified multi-ancestry T2D genetic clusters by analyzing genetic data from diverse populations in 37 published T2D genome-wide association studies representing more than 1.4 million individuals. We implemented soft clustering with 650 T2D-associated genetic variants and 110 T2D-related traits, capturing known and novel T2D clusters with distinct cardiometabolic trait associations across two independent biobanks representing diverse genetic ancestral populations (African, n = 21,906; Admixed American, n = 14,410; East Asian, n =2,422; European, n = 90,093; and South Asian, n = 1,262). The 12 genetic clusters were enriched for specific single-cell regulatory regions. Several of the polygenic scores derived from the clusters differed in distribution among ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a body mass index (BMI) of 30 kg m-2 in the European subpopulation and 24.2 (22.9-25.5) kg m-2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg m-2 in the East Asian group. Thus, these multi-ancestry T2D genetic clusters encompass a broader range of biological mechanisms and provide preliminary insights to explain ancestry-associated differences in T2D risk profiles.

Humans↗

PubMed2025

Polygenic prediction of body mass index and obesity through the life course and across ancestries.

Polygenic scores (PGSs) for body mass index (BMI) may guide early prevention and targeted treatment of obesity. Using genetic data from up to 5.1 million people (4.6% African ancestry, 14.4% American ancestry, 8.4% East Asian ancestry, 71.1% European ancestry and 1.5% South Asian ancestry) from the GIANT consortium and 23andMe, Inc., we developed ancestry-specific and multi-ancestry PGSs. The multi-ancestry score explained 17.6% of BMI variation among UK Biobank participants of European ancestry. For other populations, this ranged from 16% in East Asian-Americans to 2.2% in rural Ugandans. In the ALSPAC study, children with higher PGSs showed accelerated BMI gain from age 2.5 years to adolescence, with earlier adiposity rebound. Adding the PGS to predictors available at birth nearly doubled explained variance for BMI from age 5 onward (for example, from 11% to 21% at age 8). Up to age 5, adding the PGS to early-life BMI improved prediction of BMI at age 18 (for example, from 22% to 35% at age 5). Higher PGSs were associated with greater adult weight gain. In intensive lifestyle intervention trials, individuals with higher PGSs lost modestly more weight in the first year (0.55 kg per s.d.) but were more likely to regain it. Overall, these data show that PGSs have the potential to improve obesity prediction, particularly when implemented early in life.

Adolescent↗

PubMed2024

Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores.

The All of Us Research Program (AoU) is an initiative designed to gather a comprehensive and diverse dataset from at least one million individuals across the USA. This longitudinal cohort study aims to advance research by providing a rich resource of genetic and phenotypic information, enabling powerful studies on the epidemiology and genetics of human diseases. One critical challenge to maximizing its use is the development of accurate algorithms that can efficiently and accurately identify well-defined disease and disease-free participants for case-control studies. This study aimed to develop and validate type 1 (T1D) and type 2 diabetes (T2D) algorithms in the AoU cohort, using electronic health record (EHR) and survey data. Building on existing algorithms and using diagnosis codes, medications, laboratory results, and survey data, we developed and implemented algorithms for identifying prevalent cases of type 1 and type 2 diabetes. The first set of algorithms used only EHR data (EHR-only), and the second set used a combination of EHR and survey data (EHR+). A universal algorithm was also developed to identify individuals without diabetes. The performance of each algorithm was evaluated by testing its association with polygenic scores (PSs) for type 1 and type 2 diabetes. We demonstrated the feasibility and utility of using AoU EHR and survey data to employ diabetes algorithms. For T1D, the EHR-only algorithm showed a stronger association with T1D-PS compared to the EHR + algorithm (DeLong p-value = 3 × 10-5). For T2D, the EHR + algorithm outperformed both the EHR-only and the existing T2D definition provided in the AoU Phenotyping Library (DeLong p-values = 0.03 and 1 × 10-4, respectively), identifying 25.79% and 22.57% more cases, respectively, and providing an improved association with T2D PS. We provide a new validated type 1 diabetes definition and an improved type 2 diabetes definition in AoU, which are freely available for diabetes research in the AoU. These algorithms ensure consistency of diabetes definitions in the cohort, facilitating high-quality diabetes research.

Humans↗

PubMed2026

[Sex-specific depression risk].

Major depression displays significant heritability, and its genetic background has been extensively explored by genome-wide association studies. On the whole, these results indicate a highly polygenic landscape with significant sex-linked differences revealing a higher and more polygenic genetic influence in females than in males.

Humans↗

PubMed2025

Psychiatric Polygenic Risk Scores and Week-by-Week Symptomatic Status in Youth with Bipolar Disorder: An Exploratory Study.

Introduction: Prior studies have demonstrated that, in both adults and youth, bipolar disorder (BD) is a polygenic illness. However, no studies have examined polygenic risk scores (PRSs) in relation to the longitudinal course of mood symptoms in youth with BD. Methods: This study included 246 youth of European ancestry with BD (7-20 years old at intake) from the Course and Outcome of Bipolar Youth study and Centre for Youth Bipolar Disorder. Mood symptom severity was assessed at intake and, for 168 participants, prospectively for a median of 8.7 years. PRSs for BD, schizophrenia (SCZ), major depressive disorder (MDD), and attention-deficit/hyperactivity disorder (ADHD) were constructed using genome-wide summary statistics from independent adult cohorts. Results: Higher BD-PRS was significantly associated with lower most severe lifetime depression score at intake (β = -0.14, p = 0.03). Higher SCZ-PRS and MDD-PRS were associated with significantly less time spent in euthymia (SCZ-PRS: β = -0.21, p = 0.02; MDD-PRS: β = -0.22, p = 0.01) and more time with any subsyndromal mood symptoms (i.e., any mania, mixed, or depression symptoms; SCZ-PRS: β = 0.15, p = 0.04; MDD-PRS: β = 0.17, p = 0.01) during follow-up. PRSs for BD and ADHD were not significantly associated with any longitudinal mood variable. Conclusions: This exploratory analysis was the first to examine psychiatric PRSs in relation to the prospective course of mood symptoms among youth with BD. Results from the current study can serve to guide future youth BD studies with larger sample sizes on this topic.

Humans↗