Iuliana Ionita‐Laza

This paper introduces a framework for identifying disease-associated features tailored to individual profiles using localized test statistics, applied to single-cell RNA sequencing data for Alzheimer's disease.

The paper identifies context-dependent genetic regulation of gene expression in pigs using quantile regression, revealing eQTLs that are conserved with humans and influence disease-relevant genes.

The paper develops a framework to quantify predictive uncertainty in biological age estimates, revealing hidden dimensions of aging and improving risk interpretation.

The paper presents CattleGTEx, a resource characterizing regulatory effects in cattle, resolving GWAS signals and providing insights into adaptive evolution and selection.

The paper identifies sex-specific genetic regulatory effects in chickens using whole-genome sequencing and transcriptomes, revealing sex-dependent gene regulation and structural variants.

This paper introduces a framework for quantifying uncertainty in polygenic risk scores, showing that prediction intervals vary with genetic and environmental factors.

The paper demonstrates how quantile regression can correct for subtle population stratification in genome-wide association studies using human height as an example.

Regenie.QRS is a computationally efficient whole-genome quantile regression method for biobank-scale GWAS data, showing improved power to detect heterogeneous genetic effects.

This paper introduces a method to identify causal variants from genome-wide summary statistics, outperforming existing methods and discovering additional loci for complex traits.

This paper applies quantile regression to GWAS in the UK Biobank to identify significant loci for quantitative traits, finding 259 loci only detected by quantile regression.

This study identifies 20 genome-wide significant loci associated with serum IgA levels and finds genetic correlations with various immune and metabolic traits.

This paper develops a genome-wide polygenic score for chronic kidney disease that incorporates APOL1 risk genotypes and shows consistent performance across four major continental ancestries.

This study shows that adjusting for ancestry improves genome-wide estimates of regional intolerance by incorporating demographic history, leading to better variant classification.

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

This paper introduces GhostKnockoff, a method to improve causal variant identification in GWAS by increasing statistical power and reducing proxy variants.

This paper introduces a gene-based testing framework that improves power and reduces false positives by integrating chromatin interactions and knockoff genotypes, showing better performance in GWAS and whole-genome sequencing data.

This paper introduces a statistical method using knockoff statistics to identify causal loci in whole-genome sequencing data, showing increased discovery potential in Alzheimer's and COPD studies.

The paper decomposes multitrait genetic associations to identify pleiotropy profiles linking SNPs to biological pathways and drug targets.

This study identifies 99 genes associated with autism spectrum disorder through large-scale exome sequencing, showing differences in mutation frequencies between ASD and severe neurodevelopmental delay groups.

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

FUN-LDA predicts tissue-specific functional effects of noncoding variants by integrating epigenetic data, showing higher accuracy than existing methods in eQTL and GWAS analyses.