University of Oxford, Department of Statistics.
We are a research group based at Oxford University’s Department of Statistics and at the Wellcome Centre for Human Genetics, working at the intersection of computer science, statistics, and genetics. We develop statistical and machine learning algorithms to enable new types of analyses in large human genomic data sets. Specific areas of research in human genomics include:
- Developing algorithms for simulation, reconstruction, and analysis of large-scale genealogical data (gene genealogies, haplotype sharing, phasing, imputation).
- Studying demographic events and evolutionary parameters (migration, expansion/contraction of populations, natural selection, mutation/recombination rates).
- Studying the genetic architecture of complex traits and detecting disease-causing variation in the human genome (heritability, polygenic prediction, association).
May 1, 2023
New paper, “Biobank-scale inference of ancestral recombination graphs enables genealogical analysis of complex traits”, out in Nature Genetics! The ARG-Needle and ASMC-clust programs are available here.
Jan 15, 2023
Miriam, Yiorgos, and Sinan defended their thesis!
Oct 29, 2022
Arni, Hrushi, Miriam, Yiorgos, and Zoi presented their work at ASHG 2022. Zoi’s talk received an Epstein Trainee Finalist Award for Excellence in Human Genetics Research (top 2.6% scored abstracts). Congrats!
Oct 1, 2022
Jaromir Sant (Postdoctoral Research Assistant) and Jiazheng Zhu (DPhil student) join the lab!
Aug 10, 2022
Brian and Juba defended their thesis!
Aug 4, 2022
New preprint, “Haplotype-based inference of recent effective population size in modern and ancient DNA samples” by Fournier et al., available on bioRxiv.
Jul 15, 2022
Pier gave a talk at UCLA’s Computational Genomics Summer Institute.
Mar 28, 2022
Miriam, Pier, Romain, Yiorgos, and Zoi presented their work at ProbGen 2022.
Nov 4, 2021
New preprint, “Biobank-scale inference of ancestral recombination graphs enables genealogy-based mixed model association of complex traits” by Zhang et al., available on bioRxiv.
Oct 13, 2021