韓西坤

研究致力於解析人類疾病的遺傳學基礎：運用統計基因組學👼🏿、大型隊列🏵、遺傳流行病學、實驗生物學和深度學習等多學科方法，整合單細胞多組學及功能基因組學🧠，解析人類疾病（包括眼科疾病、神經精神類疾病等）的遺傳基礎和分子機製🌖⤵️，揭示疾病發生、發展、轉化的復雜過程，識別關鍵生物標誌物，發掘潛在藥物幹預靶點🍉，為疾病的早期篩查🕣、風險預測、治療及預後評估等提供精準化方案。

主要相關研究領域🥝💆🏿‍♂️：

1）結合遺傳流行病學🆕👨‍🍼、統計基因組學與進化基因組學等方法，解析疾病的遺傳學基礎。

2）采用單細胞多組學🐳、功能基因組學等方法與前沿技術🤞🏼，探究疾病從遺傳到表觀遺傳、從基因到蛋白表達👣、從細胞到組織器官的多層次信息調控機製。

3）深度學習在基因組學與健康醫療大數據中的方法及應用。

4）主要研究疾病領域👨‍👨‍👧‍👧：眼科疾病（青光眼👨‍⚕️，視黃斑變性等）、神經精神類疾病等👏🏻🤰🏼。

研究成果以第一/通訊作者發表於Nature Genetics（2篇,獨立通訊）, American Journal of Human Genetics, Cell Genomics, JAMA Ophthalmology （3篇）, Cell Reports Medicine, BMC Medicine, European Journal of Epidemiology, International Journal of Epidemiology, Human Molecular Genetics等期刊。

先後受邀為幾十個國際期刊審稿，包括Nature Genetics, Cell Genomics, American Journal of Human Genetics, Cell Reports, Nature Communications, Ophthalmology, JAMA Ophthalmology, BMC Medicine, European Journal of Epidemiology等。

代表性研究包括：

1）基於大腦組織單細胞多組學，探究神經精神類疾病的功能基因組學基礎（Nature🛡， under review, first author，2024）；

2）基於多組學、功能基因組學，鑒定潛在的青光眼基因藥物靶點(Nature Genetics 2023)🚁；

3）整合大型隊列遺傳學，鑒定青光眼易感位點🐷，系統評價遺傳風險預測模型及臨床應用價值(Nature Genetics 2020)；

4）采用深度學習🦒、大規模影像學🔨、基因組學💨，進行跨種族遺傳學研究(American Journal of Human Genetics 2021)。

上述研究方向常年招聘博士後🫸🏼、博士👊🏼，歡迎郵件溝通。

信息暫無

« † first author, *corresponding author »

1. Han X†*, Gharahkhani P†, Hamel AR, Ong JS, …, Hewitt AW, Craig JE, Pasquale LR, Mackey DA, Wiggs JL, Khawaja AP, Segrè AV, MacGregor S. Large scale multi-trait genome-wide association analysis identifies hundreds of glaucoma risk loci. Nature Genetics. 2023; 55(7):1116-1125.

2. Craig JE†, Han X†*, Qassim A†, Hassall M, …, Wiggs JL, Hewitt AW, MacGregor S. Multitrait analysis of glaucoma identifies new loci and enables effective polygenic risk score prediction of disease susceptibility, progression. Nature Genetics. 2020; 52(2):160-166.

3. Han X*, Steven K, Qassim A, Marshall HN, Bean C, Tremeer M, An J, Siggs OM, Gharahkhani P, Craig JE, Hewitt AW, Trzaskowski M, MacGregor S. Automated AI labelling of optic nerve head enables new insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA. American Journal of Human Genetics. 2021; 108(7):1204-1216.

4. Li C, Chen K, Fang Q, Shi S, Nan J, He J, Yin Y, Li X, Li J, Hou L, Hu X, Kellis M, Han X*, Xiong X*. Crosstalk between epitranscriptomic and epigenomic modifications and its implication in human diseases. Cell Genomics. 2024, doi: 10.1016/j.xgen.2024.100605.

5. Han X*, Lains I, Li J, Li J, Chen Y, Yu B, Qi Q, Boerwinkle E, Kaplan R, Thyagarajan B, Daviglus M, Joslin CE, …, Miller J, Hu F, Willett W, Lasky-Su J, Kraft P, Richards JB, MacGregor S, Husain D, Liang L. Integrating genetics and metabolomics from multi-ethnic and multi-fluid data reveals putative mechanisms for age-related macular degeneration. Cell Reports Medicine. 2023;4(7):101085.

6. Han X*, Hewitt AW, MacGregor S. Predicting the future of genetic risk profiling of glaucoma: a narrative review. JAMA ophthalmology. 2021;139(2):224-231.

7. Han X†, Souzeau E†, Ong JS, An J, Siggs OM, Burdon KP, …, Hewitt AW, Gharahkhani P, Craig JE, MacGregor S*. Myocilin Gene Gln368Ter Variant Penetrance and Association With Glaucoma in Population-Based and Registry-Based Studies. JAMA Ophthalmology. 2019; 137(1):28-35.

8. Han X*, Gharahkhani P, Mitchell P, Liew G, Hewitt AW, MacGregor S. Meta-analysis of genome-wide association studies identify novel genes for age-related macular degeneration. Journal of Human Genetics. 2020; 65(8):657-665.

9. Han X*, Ong JS, Hewitt AW, Gharahkhani P, MacGregor S. The effects of eight serum lipid biomarkers on age-related macular degeneration risk: a Mendelian randomization study. International journal of epidemiology. 2021; 50(1):325-336.

10. Han X*, Liang L*. metabolomicsR: a streamlined workflow to analyze metabolomic data in R. Bioinformatics Advances. 2022; 2(1):vbac067.