Martien A. M. Groenen

The paper develops BovCADD, a cattle-specific deleteriousness score for substitutions, and shows it can distinguish pathogenic variants from background variation across the genome.

This study profiles allele-specific expression across 42 tissues and 34 pig breeds using an open-source pipeline, revealing widespread and tissue-specific allelic imbalance.

The paper uses genomic and morphometric analyses to show that pigs in Wallacea and Oceania have diverse ancestries linked to Austronesian dispersal 4,000-3,000 years ago.

This paper applies genomic prediction to wing morphology traits in Nasonia vitripennis using GBLUP and GBS data, finding higher accuracy in 5-fold cross-validation than across-generation validations.

The paper introduces a pipeline for generating CADD scores across species, presenting updated chickenCADD and newly generated turkeyCADD scores.

The paper assembles a gap-free telomere-to-telomere pig genome, revealing new genes, structural variations, and insights into centromere evolution and breed-specific selection.

The paper analyzes whole-genome data from wild boars to trace their demographic history and identify genetic signatures of local adaptation in Central Asia.

The paper presents PigGTEx, a resource linking genetic variants to transcriptomic phenotypes in pigs, showing pigs' similarity to humans in gene regulation and complex traits.

This study identifies selection signatures and loci associated with complex traits in pigs by analyzing multi-omics data from Eastern and Western breeds.

The paper presents a high-quality chromosome-level turkey genome assembly using long-read technologies and Hi-C data, revealing structural variations and a large inversion on the Z chromosome.

The paper identifies a 12 kb multi-allelic CNV associated with mastitis resistance in dairy cattle by linking it to increased GC expression and a cis-regulatory enhancer.

The paper identifies functionally important variants in conserved non-protein-coding regions of the chicken genome using a variant effect prediction tool called chCADD.

The paper explores unmapped DNA and RNA reads from a songbird genome, generating de novo assemblies to identify sequences absent in the reference genome and potential pathogens.