Amanda Jane Chamberlain

This paper evaluates milk mid-infrared spectroscopy combined with on-farm variables to predict serum biomarkers in dairy cows, finding that blood urea nitrogen (BUN) is most accurately predicted, while other biomarkers show limited external validation performance.

The paper presents a multi-tissue sheep atlas identifying regulatory variants and their effects on molecular phenotypes, revealing allelic heterogeneity and evolutionary patterns.

GSOR and MTAO detect widespread cis-regulatory effects on bovine complex traits by linking gene expression with genetic scores and multi-trait associations.

The paper tests three methods to identify causal genes for milk lipid traits in cattle using GWAS, transcriptomics, and gene ontology analysis, finding significant overlaps but limited power.

The paper quantifies how gene expression and splicing QTL explain heritability in cattle traits using Bayesian methods across 16 tissues.

The paper constructs a cattle Genotype-Tissue Expression atlas and identifies genetic regulatory variants linked to gene expression and traits in cattle.

The paper investigates the effects of classical mutant alleles on cattle complex traits and fitness, finding differential impacts on fertility, stature, milk production, and mastitis risk.

The paper uses Bayesian models to analyze cattle traits by incorporating functionally significant variants, showing improved genomic prediction accuracy when combining these variants with HD SNPs.

The paper identifies sequence variants with regulatory and evolutionary significance in cattle and ranks them using a Functional-And-Evolutionary Trait Heritability score to improve genomic prediction.

This study identifies microRNA expression and target sites in 17 bovine tissues, revealing tissue-specific regulation and genomic features of microRNA-target interactions.

The paper identifies bovine sQTLs associated with RNA splicing variation and shows they are shared across tissues and linked to complex traits.

The paper maps bovine TADs and CTCF binding motifs using genomic synteny and shows they reduce the search space for regulatory variants associated with complex traits.