Sébastien Fritz

The paper integrates long-read, short-read, and SNP data using a pangenome and imputation framework to identify structural variants associated with stature and milk traits in dairy cattle.

This study maps cis and trans meQTLs in cattle blood DNA methylation using EpiChip data, identifying regulatory genes and hotspots associated with methylation patterns.

The paper constructs a cattle pangenome to identify novel non-reference unique insertions (NRUIs) and links them to economically important traits.

The paper introduces the RUMIGEN EpiChip, a DNA methylation array for cattle, demonstrating its utility in studying phenotypic variation and age prediction through methylation analysis.

The paper detects structural variants in French cattle using long and short-read data, identifying 25,191 deletions and 30,118 insertions across 14 breeds.

This paper evaluates deep learning's ability to predict genetic values in crossbred populations by capturing heterosis, finding it improves performance when heterosis is present.

The paper constructs a bovine pangenome using long-read sequencing and identifies structural variants associated with key phenotypes in French cattle breeds.

The paper identifies a deep intronic splicing mutation in the GNPAT gene in cattle causing rhizomelic chondrodysplasia punctata, and demonstrates its phenotypic effects through genetic and functional analyses.

The paper detects recessive genetic defects in cattle using genomic data and life history analysis, identifying 33 new loci linked to juvenile mortality.