Marr, M. | Search bioRxiv

bioRxiv2026

A high-resolution atlas of cattle regulatory variants and their cross-species activity in matched human cells

Identifying causal noncoding variants underlying complex traits in cattle remains challenging because high-resolution functional maps of regulatory variation are lacking. Here we combine massively parallel reporter assays with graph genomics to measure autonomous transcriptional activity from >1.5 billion DNA fragments spanning both cattle subspecies. In primary bovine cells, we assay >15 million variants and identify >150,000 expression-modulating variants enriched at cattle eQTL and GWAS loci. This enables the refinement of broad association signals to small sets of candidate functional regulatory variants. Our haplotype-aware framework captures rare, multi-allelic and tightly linked variants poorly resolved by conventional eQTL studies, and quantifies the disproportionate impact of larger variants on transcription. Furthermore, we use these data to train a deep-learning model that successfully predicts bovine promoter activity directly from sequence. Profiling the same cattle DNA in matched primary human cells reveals widespread conservation of promoter and enhancer activity, allelic effects and regulatory grammar, supporting the transfer of annotations and models across species. However, species-dependent effects are enriched in evolutionarily young sequences and p53-family motifs, highlighting the limits to simple cross-species extrapolation. Together, these data provide a high-resolution atlas of cattle regulatory variation and a framework for prioritising causal noncoding variants for cattle trait improvement.

genetics↗

bioRxiv2024

The potential of regulatory variant prediction AI models to improve cattle traits

Considerable progress has been made in developing machine learning models for predicting human functional variants, but progress in livestock species has been more limited. This is despite the disproportionate potential benefits such models could have to livestock research, from improving breeding values to prioritising functional variants at trait-associated loci. A key open question is what datasets and modelling approaches are most important to close this performance gap between species. In this work we have developed a new framework for predicting regulatory variants, that includes deriving key conservation metrics in cattle for the first time, variant annotation and model training. When trained on expression quantitative trait loci (eQTL) and massively parallel reporter assay (MPRA) data for human and cattle we show that this framework has a high performance at predicting regulatory variants, with a maximum area under the receiver operating characteristic curve (AUROC) score of 0.86 in human and 0.81 in cattle. We explore various approaches to further close this performance gap, including integrating advanced DNA sequence models and generating extra chromatin data, but illustrate that the best approach would be generating improved gold-standard sets of known cattle regulatory variants. Importantly we demonstrate both the human and cattle models substantially enrich for variants linked to important traits, with up to 18-fold enrichments for functional variants observed. Consequently, with our framework developed to be applicable across species these results not only demonstrate its potential utility for fine-mapping functional variants and improving breeding values in cattle, but also its potential for wider use across animal species.

genomics↗