Biomedical subjects

Varun Warrier

Publications and source records attributed to Varun Warrier.

5 recordsLinked to original sources

PubMed2026

Common genetic variants are associated with increased likelihood of latent co-occurring neurodevelopmental and mental health factors among autistic individuals.

Autistic individuals show elevated rates of co-occurring neurodevelopmental and mental health conditions, yet the genetic architecture of those comorbidities remains unclear. Using phenotypic (N = 74,204) and genetic (N = 17,582) data from the SPARK study, we investigated the factor structure, heritability, genetic correlation with autism (pleiotropy) and corresponding conditions in the general population (additivity). First, confirmatory factor analysis identified three correlated factors mirroring general population patterns: behavioural (ADHD, disruptive behaviour disorders), cothymic (depression, anxiety), and thought disorder (schizophrenia, bipolar). Second, all three factors had significant SNP heritabilities whilst rare variants were not associated with the tested factors in our sample. Third, polygenic scores and genetic correlations revealed positive shared genetics between the three factors and corresponding conditions in the general population but not with autism, supporting the additivity hypothesis. Fourth, within-family analyses (N = 5236 trios) demonstrated direct but not indirect genetic effects for the behavioural and cothymic factors. In sum, we find evidence for additive effects of other genetic factors in contributing to some latent co-occurring neurodevelopmental and mental health conditions in autism.

Journal Article↗

PubMed2026

The contribution of common and rare genetic variation to emotional and behavioural symptoms in childhood and adolescence.

Genetic factors influence vulnerability to common mental health conditions, but their role in early-life mental health remains understudied. We analysed genotype array (n = 4709-6687) and exome sequence data (n = 4500-5424) from the Millennium Cohort Study (MCS) and Avon Longitudinal Study of Parents and Children (ALSPAC) to assess the contribution of common variants and rare deleterious coding variants to internalising and externalising symptoms across development. In longitudinal analysis spanning ages 5-17 years, we identified several associations between common genetic variation, indexed by polygenic indices (PGIs), and both symptom domains that generally remained stable across development. Effect sizes were modest, with the largest estimates observed for PGIs for attention deficit hyperactivity disorder (ADHD) and externalising behaviour with externalising symptoms (β = 0.13-0.18; p-adj<3.5×10⁻29). Evidence for direct genetic effects was strongest for externalising symptoms, including for associations with the ADHD and externalising behaviour PGIs. Concordant results were observed in the Born in Bradford cohort. A higher exome-wide burden of deleterious rare variants was associated with increased externalising and internalising symptoms (β = 0.04-0.06, p-adj<0.03); within-family models indicated direct genetic effects on externalising in MCS (β = 0.07; p < 0.05, p-adj>0.05) and on internalising symptoms in ALSPAC (β = 0.12, p-adj<0.02). Common and rare genetic variants contributed independently, jointly explaining 2% of the variance in internalising and 5-7% in externalising symptoms. This study shows that early-life mental health is influenced by both common and rare genetic variation, with several associations explained by direct genetic effects.

Journal Article↗

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↗

PubMed2025

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

Although autism has been historically conceptualised as a condition that emerges in early childhood, many autistic people are diagnosed later in life. 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, comparable 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 decomposed into two modestly genetically correlated (rg = 0.38, SE = 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 modestly genetically correlated with ADHD and mental health conditions. Conversely, the second factor is associated with later autism diagnosis, increased socioemotional and behavioural difficulties in adolescence, and has moderate to high positive genetic correlations with Attention-Deficit/Hyperactivity Disorder 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 conceptualise autism and provide one model to explain some of the diversity within autism.

Journal Article↗

PubMed2025

Genomic and Developmental Models to Predict Cognitive and Adaptive Outcomes in Autistic Children.

IMPORTANCE: Although early signs of autism are often observed between 18 and 36 months of age, there is considerable uncertainty regarding future development. Clinicians lack predictive tools to identify those who will later be diagnosed with co-occurring intellectual disability (ID). OBJECTIVE: To predict ID in children diagnosed with autism. DESIGN, SETTING, AND PARTICIPANTS: This prognostic study involved the development and validation of models integrating genetic variants and developmental milestones to predict ID. Models were trained, cross-validated, and tested for generalizability across 3 autism cohorts: Simons Foundation Powering Autism Research (SPARK), Simons Simplex Collection, and MSSNG. Autistic participants were assessed older than 6 years of age for ID. Study data were analyzed from January 2023 to July 2024. EXPOSURES: Ages at attaining early developmental milestones, occurrence of language regression, polygenic scores for cognitive ability and autism, rare copy number variants, de novo loss-of-function and missense variants impacting constrained genes. MAIN OUTCOMES AND MEASURES: The out-of-sample performance of predictive models was assessed using the area under the receiver operating characteristic curve (AUROC), positive predictive values (PPVs), and negative predictive values (NPVs). RESULTS: A total of 5633 autistic participants (4574 male [81.2%]) were included in this analysis. On average, participants were diagnosed with autism at 4 (IQR, 3-7) years of age and assessed for ID at 11 (8-14) years of age, with 1159 participants (20.6%) being diagnosed with ID. The model integrating all predictors yielded an AUROC of 0.653 (95% CI, 0.625-0.681), and this predictive performance was cross-validated and generalized across cohorts. This modest performance reflected that only a subset of individuals carried large-effect variants, high polygenic scores, or presented delayed milestones. However, combinations of genetic variants that are typically not considered clinically relevant by diagnostic laboratories achieved PPVs of 55% and correctly identified 10% of individuals developing ID. The addition of polygenic scores to developmental milestones specifically improved NPVs rather than PPVs. Notably, the ability to stratify ID probabilities using genetic variants was up to 2-fold higher in individuals with delayed milestones compared with those with typical development. CONCLUSIONS AND RELEVANCE: Results of this prognostic study suggest that the growing number of neurodevelopmental condition-associated variants cannot, in most cases, be used alone for predicting ID. However, models combining different classes of variants with developmental milestones provide clinically relevant individual-level predictions that could be useful for targeting early interventions.

Humans↗