Hailiang Huang

FELIX improves genetic association analysis by accounting for local ancestry in admixed populations, increasing significant loci and improving polygenic score prediction.

This study identifies 619 IBD risk signals, resolves 81 high-confidence variants, and prioritizes 664 candidate effector genes using GWAS, eQTL, and exome sequencing.

This study uses exome and genome sequencing to identify 68 genes directly associated with inflammatory bowel disease, providing clearer genetic insights and therapeutic targets.

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

This paper improves genetic discovery and polygenic risk prediction for respiratory diseases by analyzing multiple traits and ancestries, identifying novel loci and developing a new polygenic risk score method.

The paper identifies CFTR loss-of-function variants as protective against IBD using large-scale exome sequencing data and evaluates variant prioritization methods.

This paper introduces rtPRS-CS, a real-time polygenic risk score method that dynamically updates predictions as new data streams in, improving accuracy for schizophrenia risk across diverse populations.

The paper identifies enriched coding variants in a French-Canadian founder population and explores their association with inflammatory bowel diseases.

GGI-RUNNER identifies rare variant gene interactions linked to complex diseases and reveals interconnected gene networks.

This study identifies population-specific eQTLs in brain tissues from European, African American, and Asian ancestry individuals, revealing new schizophrenia risk genes and variants missed in European-only datasets.

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 introduces BGE, a sequencing method that combines low-pass whole genome and deep whole exome data, showing high accuracy and cost-effectiveness for genetic variant discovery.

This paper introduces SuSiEx, a cross-population fine-mapping method that improves resolution and power by integrating data across ancestries, leading to more accurate identification of causal variants in complex traits and diseases.

This study evaluates the predictive accuracy of polygenic scores for psychiatric disorders in an Indian population using European GWAS data, finding moderate-to-high accuracy for bipolar disorder but lower for OCD and externalizing traits.

The paper introduces a cost-efficient sequencing method called Blended Genome Exome (BGE) that combines low-coverage whole genome and high-coverage exome data, achieving >99% concordance with 30x WGS for variant imputation and rare coding variant calling.

The paper identifies 979 novel genetic loci for 36 quantitative traits by analyzing data from the Taiwan Biobank, Biobank Japan, and UK Biobank, highlighting the value of diverse populations in GWAS.

This study shows that polygenic risk scores from multi-ancestry GWAS perform better in understudied populations than single-ancestry scores, except when the understudied group is small.

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

This study identifies somatic copy number variants (sCNVs) in schizophrenia cases, revealing their contribution to risk and specific NRXN1 disruptions.

This study identifies eight genes associated with adult cognitive function through rare coding variants and shows how KDM5B dosage affects cognitive traits in mice and humans.

The paper uses brain cell-type-specific protein interactomes to interpret genetic data in schizophrenia and identifies enriched networks and genes associated with the disease.

This study identifies genetic overlaps between psychiatric disorders, language ability, and creativity through a GWAS of school grades.

This study evaluates polygenic risk score methods across diverse biobanks and ancestries, finding that European reference panels often yield better performance, while PRS-CS outperforms P+T for high-heritability traits.

The paper introduces the Taiwan Biobank, a large-scale biobank with genetic and phenotypic data from 150,000 individuals, highlighting its population structure and potential for genetic research.

Meta-analysis of harmonized GWAS from up to 18 biobanks (six ancestry groups) across 14 endpoints replicated 320 known loci and identified 188 apparently novel loci.

The paper identifies MAPK3 as a driver gene in 16p11.2 deletion-associated neurodevelopmental disorders, showing haplotype-specific expression and cis-regulatory enhancer SNPs contribute to variable phenotypes.

This paper develops a trans-ancestry polygenic risk score for type 2 diabetes and validates it across diverse populations to improve equitable risk prediction.

This paper introduces PRS-CSx, a method that improves polygenic prediction across ancestrally diverse populations by integrating GWAS summary statistics and sharing genetic effects.

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