Yanyu Liang

The paper estimates heritability for behavioural and disease traits using 500,000 sibling pairs across diverse ancestries, finding substantial genetic contributions for smoking, alcohol use, personality, and diseases like T2D and asthma.

The paper performs a GWAS on PD progression metrics and identifies TRIM2 as a genetic modifier, showing its role in regulating neurofilament light and alpha-synuclein aggregation.

The paper analyzes the impact of CYP2C19 genotype on SSRI side effects and discontinuation rates in a large sample of individuals.

MetaboXcan predicts plasma metabolites from genetic data and associates them with complex traits, identifying disease risk genes and metabolites in chronic kidney disease.

This study finds that higher polygenic risk scores for breast cancer are associated with longer survival in patients, suggesting a link between genetic predisposition and less aggressive tumors.

This study uses a multivariate GWAS approach to identify genetic loci associated with reading ability and dyslexia, finding 35 new genome-wide significant loci and estimating SNP heritability at 24%.

RatXcan extends transcriptome-wide association studies to rats, enabling cross-species integration of GWAS and gene expression data to identify shared genetic mechanisms of complex traits.

BrainXcan uses genetic and imaging data to identify brain features linked to psychiatric traits, finding reduced axonal density and hippocampal features associated with schizophrenia risk.

This study compares eQTL discovery in African and European ancestry cohorts, finding more eQTLs in African samples and improved GWAS variant-to-gene mapping using African eQTLs.

This study identifies genetic correlations between hyposmia and Parkinson's disease, finding a strong causal link from PD to hyposmia but weaker evidence for the reverse.

This study investigates the shared genetic architecture between rhythm and dyslexia using large-scale genome-wide association studies.

This study shows that credible sets in GWAS are affected by imputation quality and missing variants, and proposes a reweighting method to improve accuracy.

The paper shows that polygenicity inflates false positives in transcriptome-wide association studies and proposes a variance control method to correct for this.

This paper develops software to compute polygenic transcriptome risk scores (PTRS) from GWAS summary statistics and compares three methods, finding clump-PTRS performs best for most traits.

The paper imputes parental haplotype origin from parental phenotypes to enhance GWAS power in late-onset disease studies.

The paper introduces a scalable framework integrating total and allele-specific gene expression to improve detection of genetic effects on gene expression, showing a 29% power gain equivalent to increased sample size.

The paper uses GTEx data to analyze GWAS loci, showing dose-dependent RNA effects and proposing target genes for 47% of loci using combined methods.

This study develops polygenic transcriptome risk scores (PTRS) to improve the portability of polygenic risk scores across ancestries, showing better performance in African populations compared to traditional PRS.

The paper presents a large ASE resource from GTEx v8 with 431 million SNP-level and 153 million haplotype-level measurements, and extends phASER to estimate cis regulatory variant effect sizes.

The paper uses bulk RNA-seq data to estimate cell type abundances and identifies cell type interaction QTLs that provide finer resolution of tissue specificity than bulk cis-eQTLs.

PhenomeXcan integrates GWAS and transcriptomic data to identify gene-trait associations, providing a resource for translational research with biological context.

The paper examines telomere length variation across 6,391 tissue samples from 952 individuals, showing tissue-specific differences, positive correlations between tissues, and associations with age and ancestry.

Integrating expression, allelic expression and splicing across tissues identifies rare variants with relevance to traits.

The paper quantifies protein levels across 32 human tissues and compares them to transcriptome data, revealing insights into post-transcriptional regulation and disease mechanisms.

The paper improves causal gene identification and transcriptome prediction by integrating fine-mapping and QTL tissue-sharing information into transcriptomic imputation models.

GTEx v8 maps expression and splicing QTLs in 49 tissues from 838 donors: cis-eQTLs cover 94.7% of protein-coding genes, and cross-tissue QTL sharing mirrors cell-type composition.

This paper introduces a statistical framework to identify risk genes from de novo mutations in whole-genome sequencing studies, finding new autism risk genes.