Understanding how DNA mutations change phenotypes is a central research topic in biology. This can also be said for genome-wide DNA markers, which can be used to predict phenotype and allows for better treatment of patients and genomic selection in agriculture.

Genome-wide association studies (GWAS) prioritize genetic loci associated with complex traits. However, due to linkage disequilibrium (LD), it has been difficult for GWAS to identify causal mutations for complex traits. Also, while genomic prediction has revolutionized the breeding industry, its accuracy is far from being perfect. Recent efforts in functional genomics present opportunities to prioritize potentially causal mutations independent of LD.

In this talk, Ruidong will present the results and methodologies regarding the incorporation of functional and evolutionary information into the genome-wide analysis of cattle complex traits. The results show that using functional and evolutionary information not only improves the mapping of causal variants, but mapped variants can also be integrated into a customized SNP chip to improve genomic prediction of cattle traits worldwide.

Dr. Ruidong Xiang Research Fellow, Computational Biology; Faculty of Veterinary and Agricultural Sciences; The University of Melbourne, Australia.

Dr. Ruidong Xiang obtained his PhD in animal genetics at the University of Adelaide, Australia. He is currently a Research Fellow at The University of Melbourne and Agribio, Agriculture Victoria, Australia. Ruidong investigates how to use multi-trait and multi-omics data to improve the mapping and genomic selection of cattle complex traits, and how to integrate biological and functional information into breeding programs to select productive and healthy animals more accurately. He has published more than 30 papers in peer-reviewed journals, including PNAS and Nature Communications.