Hailiang Huang

Studies on schizophrenia-associated rare copy number variants (CNVs) have predominantly focused on people of European (EUR) ancestry. Here we present a rare CNV study of schizophrenia in East Asian (EAS) populations, comprising 20,903 cases and 23,258 controls. We observed a significantly elevated genome-wide rare CNV burden in EAS cases compared with controls. Cross-population comparisons showed largely consistent rare CNV effects on schizophrenia risk. In the EAS sample, we identified nine genome-wide-significant schizophrenia-associated rare CNV loci. Meta-analysis with EUR data yielded 14 significant loci, including 8 that reached genome-wide significance for the first time. Genes within these 14 loci were significantly less tolerant to loss-of-function variants than genes in other CNV loci. The new rare CNVs associated with schizophrenia in EAS populations showed higher carrier frequencies in EAS than in EUR populations (0.38% versus 0.0017%). Overall, this study underscores the importance of increasing population diversity to fully capture the genetic underpinnings of schizophrenia.

Biobanks increasingly include individuals with admixed genomes, yet conventional genome-wide association study frameworks either exclude participants who cannot be confidently assigned to a discrete ancestry group or ignore ancestry-specific effects. We present FELIX, a scalable framework for local-ancestry-aware genetic analysis that retains all participants without requiring discrete ancestry assignment. FELIX combines a compact ancestry-resolved genotype representation (FELIXla) with an adaptive association test that jointly evaluates shared-effect and ancestry-specific models at each variant (FELIXassoc). Simulations demonstrated well-calibrated inference under case-control imbalance and power that adapted to the locus-optimal model. Across 24 phenotypes in 240,038 All of Us participants, FELIX analyzed the 12.1% of individuals excluded by global-ancestry clustering and identified 15.4% more genome-wide significant loci than global-ancestry meta-analysis. Additional discoveries arose from recovering ancestry-specific haplotypes carried by admixed participants and from detecting ancestry-dependent marginal effects. Full-cohort effect estimates also improved polygenic score prediction across ancestries and traits.

Here we developed and deployed the blended genome exome (BGE) method, a DNA library approach that generates low-pass whole-genome (1–4× mean depth) and deep whole-exome (30–40× mean depth) data in a single sequencing run. BGE is cost-effective, empowers most genomic discoveries possible with deep whole-genome sequencing and captures global common single-nucleotide polymorphism diversity. We applied BGE to sequence >53,000 samples from the PUMAS Project (Populations Underrepresented in Mental Illness Associations Studies), including African, African American and Latin American populations. Imputed genotypes showed high concordance with Illumina Global Screening Array calls ( R ² ≥ 95% for minor allele frequency ≥1%; ≥90% for minor allele frequency <1%), with consistent performance across local ancestries in admixed cohorts. For protein-coding copy number variants, deletions and duplications spanning at least three exons had a positive predicted value of ~90% relative to deep whole-genome data. At ~28% of the cost of deep whole-genome sequencing, BGE provides a scalable, reliable platform to expand genomic discovery and equitable access to sequencing in underrepresented populations.

Despite high heritability, much of the genetic architecture of schizophrenia (SZ) remains unexplained, and early-onset SZ may represent a more genetically homogeneous subtype. We examined genetically regulated expression risk scores (GeRS) to identify gene networks associated with early-onset SZ and characterize their biological functions. Using genotype data from patients in multiplex families (223 early-onset and 372 late-onset) and simplex families (matched for sex and onset age), SZ-GeRS were estimated based on single-nucleotide polymorphism (SNP)-expression prediction models derived from two transcriptomic reference panels (dorsolateral prefrontal cortex tissue from individuals with psychiatric disorders and brain cortex tissue from the general population). Panel-specific GeRS were constructed using transcriptome-wide association studies (TWAS)-derived effect sizes and integrated using a fixed-effects meta-analytic framework. Module-based SZ-GeRS were constructed by aggregating genes within empirically derived co-expression clusters, followed by network analysis to identify hub genes and functional mapping to infer biological context. Among 13 co-expression modules, meta-analysis identified a significant association between early-onset SZ and Module 10 GeRS (M10_sub-GeRS: adjusted odds ratio [aOR] = 1.22, 95%CI = 1.07-1.40). Hub genes prioritized by complementary network approaches also showed significant associations. Functional enrichment analyses indicated predominant involvement of excitatory neuronal processes and immune-related pathways. Sensitivity analyses using a variance-calibrated TWAS approach to account for polygenicity-driven inflation attenuated gene-level signals but did not materially alter module-level associations, with M10_sub-GeRS remaining robustly associated with early-onset SZ. These findings highlight gene regulatory networks linking genetic susceptibility to molecular mechanisms in early-onset SZ and may inform risk stratification and targeted interventions.

Inflammatory bowel diseases (IBD), principally Crohn’s disease (CD) and ulcerative colitis (UC), are common chronic disorders involving inflammation and often progressive tissue damage. Genome-wide association studies have mapped many risk signals, but the causal variants, effector genes and relevant cellular contexts remain difficult to resolve, limiting mechanistic interpretation and therapeutic translation. Here we performed a multi-ancestry GWAS meta-analysis of 125,992 individuals with IBD and more than 1.2 million controls, identifying 619 independent association signals (374 novel) at 420 IBD regions that account for 77–80% of SNP-based heritability. Fine-mapping resolved 81 high-confidence variants, 41 not previously reported. Although most signals were shared between CD and UC, 39% showed IBD subtype specificity, with UC signals showing stronger enrichment in functional annotations from intestinal epithelial, secretory and enteroendocrine cells, and CD showing stronger genetic correlations with circulating inflammatory biomarkers, including C-reactive protein and glycoprotein acetylation. Latent causal modelling supported a causal effect of decreased high-density lipoprotein on CD risk. By integrating bulk and single-cell eQTL and pQTL resources using colocalisation and Mendelian randomisation, together with coding-variant evidence from exome sequencing, we prioritised 664 candidate effector genes across 341 signals, including 390 newly implicated IBD genes, revealing new biological mechanisms and candidate therapeutic targets supported by human genetics.

Inflammatory bowel disease (IBD) is a chronic immune-mediated disorder of the gastrointestinal tract whose genetic basis is only partly resolved because most risk variants identified by genome-wide association studies (GWAS) lie in non-coding regions, limiting direct gene assignment and biological interpretation ^1,2 . Here we analysed whole-exome and whole-genome sequencing data from 86,213 IBD cases and 478,363 controls of European ancestry. We identified 68 IBD genes directly implicated by conditionally independent protein-coding associations across the allele frequency spectrum. Many newly implicated IBD genes are supported by orthogonal genomic or pleiotropic evidence, pointing to disease-related pathways and nominating targets with therapeutic relevance. We further identified allelic series and non-additive effects at key loci such as NOD2 and TYK2. These results show that large-scale sequencing can resolve disease genes and pathways that remain ambiguous from non-coding association alone, providing a more direct route from human genetics to biological insight and therapeutic hypotheses.

Methylation quantitative trait loci (mQTL) studies have predominantly focused on European populations (EUR), limiting understanding of the genetic regulation of DNA methylation in other populations. We conduct an East Asian (EAS) mQTL analysis, integrating data from three independent samples comprising 7619 Han Chinese individuals. We identified 331,048 mCpGs, including 28,978 novel mCpGs in EAS. While observing substantial sharing of mQTL between EUR and EAS, we also identify EAS-specific mQTLs, often driven by variants with low minor allele frequencies in EUR. We found that mQTLs enriched for disease and trait heritability, especially for matched-ancestry mQTLs, underscoring their utility for interpreting GWAS results and highlighting the role of DNA methylation in diseases. Our EAS mQTL resource provides valuable insights into the genetic architecture of DNA methylation and its contribution to complex traits.

While respiratory diseases such as chronic obstructive pulmonary disease (COPD) and asthma share many risk factors, most studies investigate them in isolation and in predominantly European-ancestry populations. Here, we conducted the most powerful multi-trait and multi-ancestry genetic analysis of respiratory diseases and auxiliary traits to date, identifying 25 new loci associated with lung function in individuals of East Asian ancestry. Using these results, we developed PRSxtra (cross-trait and cross-ancestry), a multi-trait and multi-ancestry polygenic risk score (PRS) approach that leverages shared components of heritable risk via pleiotropic effects. PRSxtra significantly improved the prediction of asthma, COPD and lung cancer compared to trait- and ancestry-matched PRSs in a multi-ancestry cohort from the All of Us Research Program, especially in diverse populations. Our results present a new framework for multi-trait and multi-ancestry studies of respiratory diseases to improve genetic discovery and polygenic prediction.

Genetic interactions play a crucial role in elucidating the susceptibility and etiology of complex multifactorial diseases. Despite significant efforts to identify disease-associated nonlinear effects in genome-wide association studies, efficient methods for detecting the epistatic impact of rare variants remain lacking. In this study, we propose iRUNNER, a novel and powerful mutation burden test focused on analyzing the interaction effects of rare variants on a binary trait. In contrast to conventional association tests that compare cases with controls, iRUNNER evaluates the relative enrichment of rare variant interaction burden of pairwise genes in patients against its baseline, estimated by a recursive truncated negative-binomial regression model that leverages multiple genomic features from public databases. Extensive simulations demonstrate that iRUNNER outperforms existing epistasis tests in statistical power and maintains reasonable type I error rates even when population stratification exists in control samples. When applied to real datasets from five complex diseases, iRUNNER yielded substantial gains in gene–gene interaction detection. Notably, the majority of these signals were missed by alternative methods, especially in small- to medium-sized samples. Furthermore, we found that these identified gene pairs of each trait can form interconnected networks, which may provide valuable insights into the underlying molecular mechanisms. We have implemented iRUNNER as a module in our integrative platform KGGSeq (http://pmglab.top/kggseq/) that enables rapid testing of pairwise interactions among all possible non-synonymous rare coding variants within hours.