The majority of human diseases are influenced by genomic variations. Therefore, elucidating the relationship between genomic sequences and biological traits as well as diseases, along with the interplay between genes and the environment, constitutes a powerful approach to understanding disease mechanisms and discovering new strategies for disease prevention and treatment. The advancements in domestic and international biobanks and sequencing technologies have provided unprecedented opportunities for identifying disease-causing genes and reconstructing biological networks. However, achieving the aforementioned objectives, whether through bioinformatics methods or mechanistic research approaches, still requires new breakthroughs.
My group focuses on addressing the critical scientific question of how genomic sequences impact the occurrence and development of diseases and conducted research in the three major areas:
Integrating real-world medical big data, including electronic health records, genetic data, molecular testing, medical imaging, and multi-omics experimental data, we employ and develop bioinformatics, statistical, and machine learning algorithms to systematically analyze the genetic architecture and biological networks underlying complex diseases and traits. We also apply genetic-based causal inference algorithms to infer genetic-phenotypic interplay and predict new therapeutics. Our disease models include adult cerebrovascular disease, children developmental disorders, and COVID-19 infection.
Utilizing large-scale modern and ancient human population genomic datasets from international and national collaborative institutions, we employ computational algorithms such as genome selection, ancient DNA infiltration, polygenic adaptation, and phenotype-genome association analysis to explore the evolutionary spatiotemporal patterns of disease-related genes and their interplay with historical environments.
Our focus lies in addressing intricate structural variations within human genomes and devising an optimal research framework to alleviate the substantial financial constraints associated with large-scale genome sequencing investigations. This is achieved through the development of algorithms aimed at detecting and genotyping variants from genome sequencing data of medium to shallow depth.
Siyang, Yanhong, Xinxin, and Yuandan attended the ESHG 2024 conference in Berlin. Siyang delivered a talk, while Yanhong was recognized as a Best Poster Candidate. Together with Xinxin and Yuandan, they presented three posters.
We extend our gratitude to Professors Xiu Qiu from the Born in Guangzhou Birth Cohort, Xia Shen from Fudan University, Guobo Chen from Zhejiang People’s Hospital, and Qinle Zhang from Guangxi Women and Children’s Health Hospital for their participation in the evaluation of the defense. We especially appreciate Prof. Ruoqing Chen from our school for hosting the defense.
This afternoon, we had an enjoyable and enlightening scientific session with Dr. Guobo Chen at our school. Thank you for coming.
Profs. Anders Albrechtsen, Siyang Liu, Yonglun Luo, Malthe Rasmussen, Huanhuan Zhu, and Rasmus Heller served as the lecturers. The course covered statistical and bioinformatics methods for analyzing high-throughput sequencing data, population genetic analysis, genome-wide association studies, Mendelian randomization analysis, transcriptomics, and single-cell and spatial transcriptomics. Thank you for an excellent course.
Nov 1 – Nov 5, 2023 Lab Members Join the ASHG Conference 2023 in Washington
Zijing attended the ASHG 2023 conference in Washington DC. Together with Shengzhe and Yuqin, they presented three posters.
This afternoon, we had an enjoyable and enlightening scientific session with the speakers. Thank you for the lectures.