Chun Chieh Fan

FEMA-Long enables efficient modeling of time-dependent effects in longitudinal data, discovering genetic variants with time-dependent effects on infant growth traits.

This study compares models to estimate how much variance in crystallized intelligence is explained by cortical surface area in adolescents using ABCD data.

The paper identifies neuroticism-related resting-state functional connectivity patterns using UK Biobank data, revealing genetically and psychometrically defined clusters.

This study uses polygenic scores to predict impulsivity and substance use in adolescents, finding age-related changes in genetic associations and highlighting caffeine as an early risk factor.

The paper introduces a variational Bayes algorithm to estimate variance explained by whole-brain imaging features for zero-inflated outcomes, showing higher variance explained for externalizing behaviors in the ABCD Study.

FEMA enables fast and efficient mixed-effects analyses of large-scale whole-brain imaging data, revealing distinct spatial patterns of brain maturation in early adolescence.

This study identifies genetic overlap between functional brain connectivity and six psychiatric disorders using multivariate genome-wide association analysis in 30,701 UK Biobank individuals.

This study examines the relationship between sex, gender diversity, and brain structure in children aged 9-11, finding that sex explains some variance while gender diversity does not independently predict neurostructural differences.

BRAINIAC estimates variance explained by features for a cognitive phenotype and assesses annotation impact on feature enrichment using resting state fMRI data.

The paper identifies abundant genetic pleiotropy across neuroimaging modalities using a multivariate genome-wide association study in two cohorts.

The paper evaluates the replication of genome-wide association loci discovered using MOSTest across different cohorts and finds higher replication yields and rates compared to min-P.

This paper evaluates how population stratification affects multimodal neuroimaging measures in a large, diverse sample and shows that structural imaging metrics are more confounded by population stratification than functional ones.

This study identifies genetic resilience factors for Alzheimer's disease by constructing polygenic resilience scores that reduce genetic risk penetrance and correlate with polygenic risk scores in unaffected individuals.

This study uses a multivariate genetic approach to analyze personality and cognitive traits, identifying pleiotropy and improving polygenic prediction.

The paper uses a multivariate GWAS approach to identify 780 genetic loci associated with cortical morphology, showing a 10-fold increase in loci compared to univariate methods.

The paper improves prostate cancer risk stratification across multiple ancestries using a new polygenic hazard score with additional SNPs, showing higher hazard ratios and positive predictive value for clinically significant prostate cancer.

This study uses multivariate GWAS on diffusion imaging data from two large datasets to identify 503 genetic loci affecting brain tissue composition and uncover molecular pathways related to axonal growth, synaptogenesis, and neuroinflammation.

The paper assesses how the performance of an African-ancestry-specific polygenic hazard score varies with local ancestry in 8q24 using genetic data from 6,253 men with African ancestry.

This study evaluates if adding polygenic hazard scores to clinical variables improves association with fatal prostate cancer in the Cohort of Swedish Men.

This study evaluates a polygenic hazard score in multi-ethnic populations and finds it is associated with age at prostate cancer diagnosis, aggressive diagnosis, and death across European, Asian, and African ancestries.

The paper extends a Gaussian mixture model to account for linkage disequilibrium and heterozygosity in GWAS summary statistics, improving estimates of genetic quantities like heritability.

The paper introduces MOSTest, a multivariate statistical method for GWAS, identifying 347 genetic loci associated with brain morphology in UK Biobank data.

This paper evaluates how sample size affects polygenic hazard score models for prostate cancer, finding that 20-30k samples are needed for discovery models, but 10k suffice if SNPs are pre-established.

The paper estimates polygenicity and discoverability of causal SNPs for human traits using a univariate Gaussian mixture model applied to GWAS summary statistics.

The paper uses MOSTest to analyze cortical morphology in 10,145 children, showing that regional cortical patterns predict cognitive performance after accounting for global brain measures.

The paper examines common genetic variants' contribution to bipolar disorder risk in multiplex families and finds higher polygenic risk scores in affected individuals compared to controls and unaffected family members.

This study identifies 73 genome-wide significant loci for mood disorders, revealing shared and distinct genetic factors between major depressive disorder and bipolar disorder.

RICOPILI is a Perl-based pipeline for efficient GWAS analysis, including quality control, imputation, and meta-analysis, with automated parallelization for rapid data processing.

MiXeR quantifies polygenic overlap between complex traits using GWAS summary statistics, revealing shared causal variants beyond genetic correlation.

This study identifies 30 genome-wide significant loci associated with bipolar disorder, including novel genes involved in ion channels, neurotransmitters, and synaptic components.