Silvia Paracchini

Biologically-informed Interpretable Deep Learning Framework for Phenotype Prediction and Gene Interaction Detection

This paper introduces GENEPAIR, a DNN interpretation framework for detecting gene-gene interactions in genomic data, demonstrating its ability to uncover both known and novel gene interactions related to BMI.

A GWAS for grip strength in cohorts of children, advantages of analysing young participants for this trait.

This study conducts the first GWAS on grip strength in children, identifying a novel association and replicating adult findings, suggesting children may be better for genetic studies of this trait.

A genome-wide association study of Chinese and English language abilities in Hong Kong Chinese children

This study conducts a genome-wide association study on Chinese and English language abilities in bilingual Hong Kong children, identifying 9 significant genetic loci and showing polygenic overlap with educational attainment and ADHD.

Quantitative multidimensional phenotypes improve genetic analysis of laterality traits

This study shows that quantitative multidimensional laterality phenotypes capture more genetic variation than binary traits in handedness, footedness, and eyedness.

Light-induced asymmetries in the embryonic retina are mediated by the vascular system and extracellular matrix

The paper investigates light-induced left-right asymmetries in the chick retina, finding transient gene expression changes and links to vascular and extracellular matrix pathways.

Genome-wide association study and polygenic risk score analysis for hearing measures in children

This study analyzes hearing measures in children using GWAS and PRS, finding associations with cognitive abilities and schizophrenia risk.

The dyslexia susceptibility KIAA0319 gene shows a highly specific expression pattern during zebrafish development supporting a role beyond neuronal migration.

This study examines the expression pattern of the zebrafish kiaa0319 orthologue during development, revealing specific expression in the notochord and suggesting roles beyond neuronal migration.

Genome Wide Association Scan identifies new variants associated with a cognitive predictor of dyslexia.

The paper conducts a GWAS to identify genetic variants associated with a cognitive predictor of dyslexia, finding significant associations with variants near MIR924HG and NKAIN3.