Profile
Dr. Xiaoyang Dai obtained his MSc and DPhil at the University of Bristol under the supervision of Professor Mark Beaumont, focus on developing Bayesian methods for inferring selection and demographics from historical and contemporary DNA sequences. Now he is Postdoctoral Research Assistant in Bochukova Lab, Genomics and Child Health, Blizard Institute. Our Lab main focus is on RNA-mediated and chromatin-mediated (epigenetic) mechanisms in metabolic disease, and his main work is using genome-wide association studies (GWAS) and next-generation sequencing to investigate the functional characterization of human obesity-associated genetic variation affecting non-protein-coding DNA and RNA (post-transcriptional RNA gene regulation and the role of non-coding RNAs).
Research
Research Interests:
Dr. Xiaoyang Dai’s research interests investigating the functional characterization of human obesity-associated genetic variation affecting non-protein-coding DNA and RNA( post-transcriptional RNA gene regulation and the role of non-coding RNAs). He also has interests in population genetics problems, which using time-series data to uncover the evolutionary process considering selection, migration, and population structure. Besides that, he is always interested in developing approximation Bayesian computation (ABC) methods and applying them to investigate population genetics problems using contemporary DNA sequences.
Publications
Key Publications
Gu, Z., Pan, S., Lin, Z., Dai, X., et al. Climate-driven flyway changes and memory-based long-distance migration. Nature 591, 259–264 (2021)
Przewieslik-Allen, A. M., Wilkinson, P. A., Burridge, A. J., Winfield, M. O., Dai, X., Beaumont, M., ... & Edwards, K. J. (2021). The role of gene flow and chromosomal instability in shaping the bread wheat genome. Nature Plants, 1-12.
He Z, Dai X, Beaumont M, et al. Estimation of natural selection and allele age from time series allele frequency data using a novel likelihood-based approach[J]. Genetics, 2020, 216(2): 463-480.
He Z, Dai X, Beaumont M, et al. Detecting and quantifying natural selection at two linked loci from time series data of allele frequencies with forward-in-time simulations[J]. Genetics, 2020, 216(2): 521-541.
