Ruoyi Cai

BACKGROUND: Variants in the ion channel gene KCNQ1 are the leading cause of congenital long QT syndrome (LQTS), a disorder characterized by QT prolongation and risk of fatal arrhythmias. The expanding use of genetic testing has led to the identification of numerous variants of uncertain significance, complicating clinical interpretation and risk stratification. Functional characterization of KCNQ1 variants is essential to define their role in long QT syndrome and advance precision medicine in cardiovascular care. METHODS: We generated a nearly comprehensive variant library of 13 403 protein-coding variants in KCNQ1 . Using 4 pooled assays followed by high-throughput sequencing, we characterized the variants’ effects on cell surface abundance and channel function in both the variant-only and heterozygous states. Case–control analyses and data from large-scale biobanks were used to assess the clinical impact of loss-of-function variants on QT prolongation and long QT syndrome. Heterozygous assay results were used to determine dominant-negative effects, which were also evaluated for association with arrhythmia risk. Protein structural clustering of distinct classes of deleterious variants was analyzed. RESULTS: Across the 4 assays, we generated 49 305 functional measurements of KCNQ1 variants. Of the 13 403 KCNQ1 studied variants, 1757 demonstrated strong pathogenic evidence, and 6660 showed moderate benign evidence. Loss-of-function variants were significantly associated with QT prolongation and long QT syndrome in case–control and biobank analyses. Deleterious variants clustered within distinct structural domains based on their functional classification. A majority of pathogenic variants exhibited dominant-negative effects; these variants were linked to a higher risk of arrhythmia diagnoses than were haploinsufficient variants. CONCLUSIONS: This comprehensive KCNQ1 functional atlas provides novel functional evidence for most KCNQ1 variants of uncertain significance. It also reveals mechanistic principles linking genetic variation to ion channel biology and arrhythmia susceptibility, advancing cardiovascular precision medicine.

Background: Clinical trials are essential for therapeutic development but increasingly face challenges due to imprecise inclusion criteria, leading to low event rates and the need for large sample sizes. This inefficiency makes modern trials costly and time-consuming. Despite the availability of extensive clinical, genomic, and biological data, current trial enrollment strategies do not fully leverage this information. Incorporating genomic information into trial design could enable risk-based participant enrichment by preferentially enrolling individuals with higher disease risk, thereby increasing event rates and improving trial efficiency. Methods: In this study, we developed an in silico framework for evaluating prognostic enrichment guided by polygenic risk scores (PRS) in clinical trial design using genomic and electronic health record data from large-scale biobanks. Naturally occurring protective genetic variants were used as analogs of therapeutic interventions, with variant carriers treated as ’treatment’ arms and non-carriers as ’control’ arms. We compared unenriched designs, in which carriers and non-carriers were drawn from the full population, against PRS-enriched designs in which both arms were restricted to participants in the upper 75%, 50%, or 25% of the PRS distribution, respectively. Across these four designs, we quantified disease prevalence, statistical power, sample size requirements, and time-to-event accrual. Results: We applied this approach to the UK Biobank using three model gene–disease pairs: the protective variant p.Arg46Leu in PCSK9 for coronary artery disease (CAD), p.Gln175His in ANGPTL7 for glaucoma, and p.Arg381Gln in IL23R for inflammatory bowel disease (IBD). Across all three disease contexts, PRS-enriched designs increased disease prevalence, improved empirical power, and accelerated event accrual relative to unenriched cohorts. At 80% power, restricting enrollment to the upper 25% of the PRS distribution reduced required per-arm sample sizes by approximately 60% for CAD-PCSK9 and 78% for IBD-IL23R. Consistent reductions in time-to-event were also observed across enriched strata, suggesting that PRS-enriched trials could achieve target event counts with both smaller sample sizes and shorter follow-up. However, for glaucoma-ANGPTL7, the most restrictive threshold did not yield additional gains over moderate enrichment, as reduced sample size attenuated the detectable difference between arms. These results highlight the need to balance enrichment for higher-risk participants against retaining a sufficient eligible population, and underscore that optimal PRS thresholds are disease-context dependent. Conclusions: These findings establish a generalizable, data-driven framework for prospectively evaluating PRS-guided prognostic enrichment prior to trial initiation. In general, PRS-guided study designs lead to improved empirical power, lower required sample sizes, and faster trials. As population-scale genomic data become increasingly available within healthcare systems and biobanks, this framework provides a scalable foundation for integrating genetic risk information into clinical trial design.

We present an identity‐by‐descent mapping approach to test the association between genome‐wide loci and complex traits. Our method evaluates whether levels of genetic similarities at specific genomic locations, captured by local relatedness matrices derived from multi‐individual IBD sharing, are associated with phenotypic variation in complex traits. In addition, we propose an approach to adjust for multiple testing in genome‐wide IBD mapping scans based on the correlation structure between test statistics across the genome. Through simulation studies, we demonstrate that our test has a well‐controlled genome‐wide type I error rate and superior power to detect rare and untyped variants compared to standard single‐variant tests. We applied our method to systolic blood pressure data from White British individuals in the UK Biobank.

We present an identity-by-descent mapping approach to test the association between genome-wide loci and complex traits. Our method evaluates whether levels of genetic similarities at specific genomic locations, captured by local relatedness matrices derived from multi-individual IBD sharing, are associated with phenotypic variation in complex traits. In addition, we propose an approach to adjust for multiple testing in genome-wide IBD mapping scans based on the correlation structure between test statistics across the genome. Through simulation studies, we demonstrate that our test has a well-controlled genome-wide type-I error rate and superior power to detect rare and untyped variants compared to standard single-variant tests. We applied our method to systolic blood pressure data from White British individuals in the UK Biobank.

The effective size of a population ( N _e ) in the recent past can be estimated through analysis of identity-by-descent (IBD) segments. Several methods have been developed for estimating N _e from autosomal IBD segments, but no such effort has been made with X chromosome IBD segments. In this work, we propose a method to estimate the X chromosome effective population size from X chromosome IBD segments. We show how to use the estimated autosome N _e and X chromosome N _e to estimate female and male effective population sizes. We demonstrate the accuracy of our autosome and X chromosome N _e estimation with simulated data. We find that estimated female and male effective population sizes generally reflect the simulated sex-specific effective population sizes across the past 100 generations, but that short-term differences between the estimated sex-specific N _e across tens of generations may not reliably indicate true sex-specific differences. We analyzed the effective size of populations represented by samples of sequenced UK White British and UK Indian individuals from the UK Biobank.