Xin studied Human Molecular Genetics at Imperial College London before gaining her PhD in Statistical Genetics at the University of Cambridge. Her PhD study focused on bioinformatical and statistical analysis of next-generation sequencing data with applications to type 1 diabetes studies. Before re-joining the University of Cambridge, Xin worked as a Principal Statistician at GlaxoSmithKline and at Open Targets in Wellcome Genome Campus, Hinxton. In this role, she provided statistical support and consulting in multiple drug discovery studies to optimise the use of experimental resources and to increase the confidence and value of experimental results.
Xin’s research has focused on the development and validation of cancer risk prediction algorithms for multiple ethnicities. The risk prediction algorithms have the potential of improving individualised cancer risk assessment and population risk-stratification, which can be used for identifying those at high risk who are most likely to benefit from risk-reducing interventions.
Xin is also interested in characterisation of cancer risks for carriers of rare pathogenic variants using the collaborative studies of the PALB2 Interest Group, data from the RAD51C and RAD51D collaborative studies and data from the CIMBA consortium.
Xin Yang, Goska Leslie, Alicja Doroszuk, et al. (2020) Cancer Risks Associated With Germline PALB2 Pathogenic Variants: An International Study of 524 Families, Journal of Clinical Oncology 38:7, 674-685.
Xin Yang, Honglin Song, Goska Leslie, et al. (2020) Ovarian and breast cancer risks associated with pathogenic variants in RAD51C and RAD51D Journal of the National Cancer Institute, 14;112(12):1242-1250.
Andrew Lee*, Xin Yang*, Jonathan Tyrer, et al. (2021) Comprehensive epithelial tubo-ovarian cancer risk prediction model incorporating genetic and epidemiological risk factors, Journal of Medical Genetics, [Epub ahead of print], doi:10.1136/, jmedgenet-2021-107904 (*Joint first authors)
Xin Yang, Goska Leslie, Aleksandra Gentry-Maharaj, et al. (2018) Evaluation of polygenic risk scores for ovarian cancer risk prediction in a prospective cohort study Journal of Medical Genetics, 55(8):546-554