Shaun Purcell

This study uses a computational model to show that age-related loss of recurrent excitation disrupts slow oscillations in sleep, impairing memory consolidation.

Loss of CACNA1G disrupts sleep rhythms and gene regulation, creating a mismatch between brain state and behavior linked to schizophrenia.

The paper examines sex differences in sleep EEG patterns among children with and without neurodevelopmental disorders, finding significant sex-based variations in sleep features but not in brain age predictions.

The paper analyzes sleep data from 1527 pediatric PSGs across five sites to study neurodevelopmental disorders, identifying sleep architecture disruptions in Down Syndrome and developing a sleep stager and brain age prediction model.

This study performs genome-wide association analysis on composite sleep health scores, identifying 28 novel loci and confirming 341 previously reported loci, with insights into genetic mechanisms and pathways.

This study identifies gene-environment interactions between excessive daytime sleepiness and genetic variants linked to obstructive sleep apnea severity, revealing novel therapeutic targets.

This study replicates group-level sleep EEG differences in schizophrenia and shows increased patient-to-patient variability in NREM metrics, with medication and age-related effects contributing to this heterogeneity.

The paper investigates spindle chirp and other sleep oscillatory features in young children with and without autism, finding significant differences in chirp negativity and coupling angles.

The paper predicts cognitive performance from sleep EEG features in older men, finding that certain EEG patterns are associated with cognitive ability, though this association weakens after adjusting for demographic and health factors.

This study identifies biases in group-level EEG microstate analysis and proposes a paired analysis approach to reduce errors from subgroup topography differences.

The paper develops a sleep-based metric to quantify brain maturation using EEG data from children, showing high accuracy in predicting chronological age and detecting neurodevelopmental differences.

The paper replicates and extends findings on EEG spectral slope variation across sleep states in a large sample, identifying factors influencing slope estimates and their relationship to sleep parameters.

This paper presents a transformer model to predict cognitive impairment from sleep EEG data, achieving 70.22% accuracy, outperforming traditional methods.

This study identifies NREM sleep and wake EEG biomarkers associated with schizophrenia symptom severity and disease status.

The paper reports whole-genome association analyses of sleep-disordered breathing traits in the NHLBI TOPMed program, identifying rare and common variant associations.

The paper introduces a group refactorization method for sleep EEG data to enable inter-individual analysis by aligning frequency factors across subjects.

The paper investigates the role of single-gene copy number variants in schizophrenia risk using exome-sequencing data from a Swedish cohort, identifying significant deletions in voltage-gated calcium channels.

This study identifies 42 genetic loci associated with excessive daytime sleepiness and links them to phenotypic subgroups and related traits.

The paper identifies DNA methylation sites associated with daytime sleepiness in African Americans using epigenome-wide association analysis in the Multi-Ethnic Study of Atherosclerosis.

The paper identifies 57 genomic regions associated with insomnia symptoms, linking them to phototransduction, ubiquitination, and potential causal relationships with coronary heart disease and depression.

The study finds that rare copy number variants (CNVs) contribute to bipolar disorder risk primarily in schizoaffective disorder bipolar type cases, with differences in CNV burden and schizophrenia polygenic risk scores between subtypes.

The paper introduces a Bayesian framework to prioritize risk genes for complex diseases by integrating gene-set information with variant counts, identifying novel epilepsy risk genes and linking them to immune pathways and drug targets.

mTADA jointly analyzes de novo mutation data from multiple neuropsychiatric disorders to identify shared and specific risk genes.

The paper uses Bayesian modeling to analyze rare exonic variants in schizophrenia and neurodevelopmental disorders, identifying 1,551 risk genes for schizophrenia and 98 new risk genes for developmental disorders.

The paper uses transcriptomic imputation to identify schizophrenia risk genes across multiple brain regions, finding 71 associated genes and 36 enriched pathways.

This study identifies new genetic loci associated with sleep disturbance traits and finds shared genetic links with neuropsychiatric and metabolic traits.

This paper introduces LDpred, a method that improves polygenic risk score accuracy by modeling linkage disequilibrium, showing increased R2 values in schizophrenia and multiple sclerosis datasets.

The paper shows that haplotypes of common SNPs explain more heritability of complex diseases than individual SNPs alone.

This paper partitions heritability across functional genomic elements using GWAS summary statistics to identify trait-specific enrichments.