Integrating genomics and transcriptomics to empower dairy breeding for feed efficient animals

Improving feed efficiency of dairy cattle has been a major interest for animal scientists and dairy farmers over decades. The feed accounts for the largest part (i.e., 50%) of operating costs in dairy production. Improving FE increases the profit of dairy farmers without sacrificing milk production or animal health. Meanwhile, improving FE is expected to mitigate methane emission in cattle, contributing to reduced environmental footprint of the dairy population. So far, no QTL has been confirmed for FE from previous studies, with very few candidate QTL reported but lacking in validation. The previous studies on QTL mapping for FE were mostly carried out using medium-density (50–60K) single nucleotide polymorphism (SNP) panels. The applicant’s previous work showed benefits of increased marker density in QTL mapping for FE. Compared to using genotypes from SNP arrays, genotypic information from whole-genome sequencing offers potential to improve QTL mapping, since it potentially includes causal variants for the phenotype that may not be available in SNP arrays. Utilizing sequence-level genotypes also facilitate the calibration of genomic data with RNA-seq data, making it possible to integrate animals’ genomic and transcriptomic information into association analyses. Compared to sequencing every individual in a large population, imputation from lower-density SNP-array to sequence is a practical, efficient, and established approach to generate sequence-level genotypes for many individuals. Previous studies on cattle genomics have demonstrated the benefit of using imputed sequence genotypes in detecting and fine-mapping causal variants. Genomic selection has been used as an important tool for difficult-to-measure traits like FE. The application of genomic selection to improve dairy FE has been recently developed worldwide. However, the accuracy of genomic prediction is relatively low based on current prediction methods and data sizes. A well-recorded reference population and improved statistical methods could allow larger achievement in improving prediction accuracy for FE. Recent studies using large-scale dairy cattle data has proven the contribution of biological priors (e.g., variants with functional significance underlying traits) to improved accuracy of genomic prediction for dairy complex traits. In this project we will develop statistical methods to integrate the newly acquired functional variants associated with FE into genomic prediction for FE and assess the accuracy of prediction. The overall aim of the proposal is to develop and apply the established methods into dissecting the complex genetic basis of dairy FE through integrating population-level phenotypic, genomic, and transcriptomic information, which can be exploited to empower sustainable dairy breeding for feed efficient and environmentally-friendly animals. Three work packages will be conducted to identify genomic and regulatory variants associated with FE phenotypes and to develop and assess methods of genomic prediction for FE using functional variants.