Christina Brady Del Azodi

This paper shows that differences in power and linkage disequilibrium affect eQTL portability across ancestries and introduces a method to correct for these factors.

The paper introduces two Bioconductor packages for multi-state QTL analysis, providing tools for data storage, analysis, and simulation.

This study evaluates factors affecting genomic prediction accuracy in switchgrass, finding that exome capture and multi-allelic variants improve predictions for complex traits.

The paper identifies cell-type-specific and disease-associated eQTL in the human lung using single-cell RNA-sequencing data from PF and unaffected donors.

splatPop simulates population-scale scRNA-seq data with eQTL effects and batch effects for method testing.

This study evaluates normalization, aggregation, and statistical methods for sc-eQTL mapping to improve eQTL discovery in single-cell RNA-seq data.

The paper models transcriptional responses to heat and drought stress in Arabidopsis thaliana using machine learning and deep learning, identifying cis-regulatory elements that predict gene response patterns.

This study predicts complex traits in maize using transcriptome and genetic marker data, finding similar performance between both approaches and highlighting transcriptome's role in capturing trait variation not captured by sequence data.

The paper evaluates intergenic transcribed regions (ITRs) in Poaceae species to determine if they represent functional genes or noisy transcription, finding most ITRs lack conservation and are likely nonfunctional.

This study benchmarks linear and non-linear algorithms for genomic prediction of complex traits across multiple plant species, finding that ensemble methods and feature selection improve performance.