Kangcheng Hou

The paper introduces KASM to integrate functional annotations and estimate heritability of rare protein coding variants across 786,871 exomes, finding higher heritability than previous estimates.

This paper introduces scDRS-FM, a method to distinguish causal from tagging disease associations in scRNA-seq data, showing it improves power and accuracy in identifying disease-relevant cell populations.

The paper uses admixture mapping in the All of Us Research Program to identify genetic ancestry associations with 22 traits, finding 71 novel loci where African ancestry explains phenotypic variation.

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

This study identifies genetic and transcriptomic factors associated with severe coccidioidomycosis, finding a founder variant in NLRX1 linked to African ancestry and increased disease risk.

The paper maps gene expression and chromatin accessibility QTLs in fibroblasts from European and Latin American ancestries, finding high concordance and shared regulatory mechanisms.

The paper develops PolyPWAS, a framework combining cis- and trans-pQTLs to prioritize disease-critical genes by improving protein prediction with functional annotations and showing better disease heritability explanation.

CalPred provides well-calibrated prediction intervals for polygenic risk scores, maintaining calibration across different contextual factors unlike other methods.

This study uses ancient DNA and genomic data to show natural selection has strengthened the immune system over 10,000 years, with both protective and harmful effects on immune-related diseases.

The paper shows that per-allele effect sizes of genetic variants in European populations are better predicted by African MAF than European MAF.

This study examines common and rare variant genetic contributions to autism in African Americans, highlighting differences in genetic effects and polygenic score transferability between European and African ancestries.

The paper compares GWAS methods in admixed populations, showing standard tests are more powerful due to allele frequency heterogeneity and identifying distinct LD patterns in admixed vs. single-continental segments of the same ancestry.

This paper introduces PHBC to estimate pleiotropic heritability across diseases, finding that about half of common disease heritability is shared with other diseases.

The paper integrates polygenic risk scores with single-cell methylation data to identify methylation-driven disease relevance in depression and other traits.

The paper improves polygenic prediction accuracy by using diverse datasets, showing greater benefits for underrepresented populations.

The paper introduces context-aware prediction intervals for polygenic scores to improve calibration across diverse populations and traits.

The paper identifies extensive isoform diversity in the developing human neocortex and links it to neuropsychiatric disorder risk mechanisms.

Admix-kit provides tools for genetic analysis of admixed populations, enabling simulation, association testing, and polygenic scoring.

This study uses bulk RNA-Seq from blood samples to estimate cell type proportions and eQTLs, revealing cell-type specific genetic associations with psychiatric disorders and medication effects.

This paper quantifies how differences in allelic effect sizes across ancestry backgrounds impact GWAS power in admixed populations and finds modeling these differences does not significantly improve results.

The paper investigates epigenomic and 3D chromatin conformation changes in human frontal cortex and hippocampus development using sn-m3C-seq, finding distinct chromatin conformations in neurons and glial cells and linking risk variants to regulatory regions.

The paper evaluates polygenic score accuracy across genetic ancestry continua in diverse populations, finding significant variation in performance linked to genetic distance from training data.

This paper introduces scDRS, a method to identify disease-associated cells in scRNA-seq data using polygenic risk scores, finding novel subpopulations linked to diseases like IBD and schizophrenia.

The paper quantifies similarity in causal effects across local ancestries in admixed individuals and finds high genetic correlation (radmix=0.95) within individuals.

This study shows that reducing RNA-seq coverage while increasing sample size improves eQTL discovery power.

The paper partitions gene-level heritability by allele frequency to identify disease-relevant genes with heritability from rare variants, finding 370 gene-trait pairs not detected by existing methods.

The paper introduces MACIE, a framework for predicting functional variants by integrating diverse annotations, and applies it to fine-mapping using GWAS data.

Genome-wide association study and integrative analyses identify 27 genes in inflammation and coagulation pathways linked to COVID-19 hospitalization risk and symptomology.

The paper shows that individual PRS estimates have large uncertainty, impacting risk stratification, and introduces a method to quantify this uncertainty using Bayesian approaches.

This study shows that low-coverage single-cell RNA sequencing of more samples increases statistical power for cell-type-specific eQTL analysis compared to high-coverage sequencing of fewer samples.