Research Summary

Leveraging on about 600,000 coronary artery disease-related genome sequencing, electronic medical records, imaging data, etc., in his lab, Dr.Wang is developing key algorithms, service facilities, and open computing platforms necessitating for the analysis of big health data, such as population cohorts and biobanks data. Focusing on the clinical needs of genomic medicine, he is committed to analyzing the basic genetic mechanism of cardiovascular and cerebrovascular diseases, discovering new pathogenic causes, new drug targets and, developing new artificial intelligence models for disease risk prediction, and establishing new models and applicable solutions for precise prevention and treatment of common complex diseases, based on large-scale genomics and phenomics data. Dr.Wang has published 36 SCI papers in N. Engl. J. Med, J. Am. Coll. Cardiol., Mol Biol Evol., Nat Commun.,PNAS., J Am Soc Nephrol., and other internationally renowned journals, of those 14 papers were published as the first or corresponding author (including co-author). A series of achievements have realized the transformation from genetic mechanism research to clinical services, and some results have been deployed in hospital clinics. Some of the research work has been rated by the National Human Genome Research Institute(US) as one of the ten important breakthroughs in the field of genetic information for clinical practice, and has been reported by scientific magazines such as in Science and media highlights such as on The Times. 


Research Areas

  • Leveraging on the coronary artery disease-related data of about 560,000 people (50,000 patients and 510,000 health controls), each individual has whole-genome/whole-exome sequencing data, medical imaging data, and electronic health record data, Wang lab aims to dissect the genetic mechanisms of coronary artery disease by integral analyzing the common and rare functional variants;

  • Integrating large-scale population cohort genetics data, multidimensional omics data, and clinical imaging data for uncovering the genetic basis of cardiovascular and cerebrovascular diseases in order to discover new disease risk genes, new biomarkers, and new therapeutic targets; 

  • Based on the big health data from Chinese populations, Wang lab aims to develop new disease risk prediction models optimized and tailored for the Chinese population, and broadly applicable to people of East Asian ancestry; 

  • Utilize modern cloud facilities to develop safe, efficient, and shareable key facilities and platforms for biomedical big data analyses; 

  • Develop new statistical genetics, machine learning, and artificial intelligence algorithms to analyze biobank-level medical genetics data.


Education and Work Experience

2018-01 ~ 2021-08, The Broad Institute of MIT and Harvard, Computational Biologist

2015-03 ~ 2018-01, Harvard Medical School/Beth Israel Deaconess Medical Center, Postdoc

2009-09 ~ 2015-01, CAS-MPG Partner Institute for Computational Biology, Shanghai, China


Publications

   
Papers

  2. [1]  Saaket Agrawal*, Wang, Minxian*, Marcus D.R. Klarqvist, Kirk Smith, et al. “Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots.” Nature Communications 13.1 (2022), p. 2021.08.24.21262564. DOI: 10.1038/s41467-022-30931-2. (Equal contributions).

  4. [2]  Amit V Khera*, Wang, Minxian*, Mark Chaffin Ms, Connor A Emdin, et al. “Gene Sequencing Identifies Perturbation in Nitric Oxide Signaling as a Nonlipid Molecular Subtype of Coronary Artery Disease.”Circulation. Genomic and precision medicine 003598.December (2022), pp. 1–9. DOI: 10.1161/CIRCGEN.121. 003598. (Equal contributions).

  6. [3]  Aniruddh P Patel, Jacqueline S Dron, Wang , Minxian, James P Pirruccello, et al. “Association of Pathogenic DNA Variants Predisposing to Cardiomyopathy With Cardiovascular Disease Outcomes and All-Cause Mortality.” JAMA Cardiol 7.7 (July 2022), pp. 723–732.

  8. [4]  Julian Milosavljevic, Lempicki, [...], Wang, Minxian, et al. “Nephrotic syndrome gene TBC1D8B is re- quired for endosomal maturation and nephrin endocytosis in Drosophila.” J. Am. Soc. Nephrol. (Sept. 2022), ASN.2022030275.

  10. [5]  Lihong Peng, Jialiang Yang, Wang, Minxian, and Liqian Zhou. “Editorial: Machine learning-based meth- ods for RNA data analysis.” Front. Genet. 13 (May 2022), p. 828575.

  12. [6]  Akl C. Fahed, Wang, Minxian, Aniruddh P. Patel, Ezimamaka Ajufo, et al. “Association of the Interaction Between Familial Hypercholesterolemia Variants and Adherence to a Healthy Lifestyle With Risk of Coro- nary Artery Disease.” JAMA Network Open 5.3 (2022), e222687. DOI: 10.1001/jamanetworkopen.2022.2687.

  14. [7]  Kiran J Biddinger, Connor A Emdin, Mary E Haas, Wang, Minxian, George Hindy, Patrick T Ellinor, and Sekar Kathiresan. “Association of Habitual Alcohol Intake With Risk of Cardiovascular Disease.” JAMA Network Open 5.3 (2022), pp. 1–12. DOI: 10.1001/jamanetworkopen.2022.3849.

  16. [8]  Justin Chun, Cristian Riella, [...], Wang, Minxian, et al. “DGAT2 Inhibition Potentiates Lipid Droplet Formation to Reduce Cytotoxicity in APOL1 Kidney Risk Variants.” Journal of the American Society of Nephrology (Mar. 2022). DOI: 10.1681/ASN.2021050723.

    [9]  Wang, Minxian, Vivian S. Lee-Kim, Deepak S. Atri, Nadine H. Elowe, et al. “Rare, Damaging DNA Variants in CORIN and Risk of Coronary Artery Disease: Insights From Functional Genomics and Large- Scale Sequencing Analyses.” Circulation: Genomic and Precision Medicine (Oct. 2021). DOI: 10.1161/CIRCGEN. 121.003399. (First author).

  2. [10]  George Hindy, Peter Dornbos, [...], Wang, Minxian, et al. “Rare coding variants in 35 genes associate with circulating lipid levels—A multi-ancestry analysis of 170,000 exomes.” The American Journal of Human Genetics (2021), pp. 1–16. DOI: 10.1016/j.ajhg.2021.11.021.

  4. [11]  Mary E. Haas, James P. Pirruccello, Samuel N. Friedman, Wang, Minxian, et al. “Machine learning en- ables new insights into genetic contributions to liver fat accumulation.” Cell Genomics 1.3 (2021), p. 100066. DOI: 10.1016/j.xgen.2021.100066. URL: https://doi.org/10.1016/j.xgen.2021.100066.

  6. [12]  Veryan Codd, Qingning Wang, [...], Wang, Minxian, et al. “Polygenic basis and biomedical consequences of telomere length variation.” Nature Genetics 2021 53:10 53.10 (Oct. 2021), pp. 1425–1433. DOI: 10.1038/ s41588-021-00944-6.

  8. [13]  Aniruddh P. Patel, Wang, Minxian, Uri Kartoun, Kenney Ng, and Amit V. Khera. “Quantifying and Un- derstanding the Higher Risk of Atherosclerotic Cardiovascular Disease Among South Asian Individuals.”Circulation (Aug. 2021), pp. 410–422. DOI: 10.1161/CIRCULATIONAHA.120.052430.

  10. [14]  María José Pérez-Sáez, Audrey Uffing, [...], Wang, Minxian, et al. “Immunological Impact of a Gluten- Free Dairy-Free Diet in Children With Kidney Disease: A Feasibility Study.” Frontiers in Immunology12.June (2021), pp. 1–11. DOI: 10.3389/fimmu.2021.624821.

  12. [15]  Yudong Cai, Jialiang Yang, Tao Huang, and Wang, Minxian. “Editorial: Computational Methods in Pre- dicting Complex Disease Associated Genes and Environmental Factors.” Frontiers in Genetics (2021). DOI:10.3389/fgene.2021.679651.

  14. [16]  Jacqueline S Dron*, Wang, Minxian*, Aniruddh P Patel, Uri Kartoun, Kenney Ng, Robert A Hegele, and Amit V Khera. “Genetic Predictor to Identify Individuals With High Lipoprotein(a) Concentrations.”Circulation: Genomic and Precision Medicine (Feb. 2021), CIRCGEN120003182. DOI: 10.1161/CIRCGEN.120. 003182. (Equal contributions).

  16. [17]  Patricia L Weng, Amar J Majmundar, [...], Wang, Minxian, et al. “De novo TRIM8 variants impair its protein localization to nuclear bodies and cause developmental delay, epilepsy, and focal segmental glomerulosclerosis.” American journal of human genetics 108.2 (Feb. 2021), pp. 357–367. DOI: 10.1016/j. ajhg.2021.01.008.

  18. [18]  Wang, Minxian, Ramesh Menon, Sanghamitra Mishra, Aniruddh P. Patel, et al. “Validation of a Genome- Wide Polygenic Score for Coronary Artery Disease in South Asians.” Journal of the American College of Cardiology 76.6 (2020), pp. 703–714. DOI: 10.1016/j.jacc.2020.06.024. (First author).

  20. [19]  Akl C Fahed*, Wang, Minxian*, Julian R Homburger*, Aniruddh P Patel, et al. “Polygenic background modifies penetrance of monogenic variants for tier 1 genomic conditions.” Nature communications 11.1 (2020), p. 3635. DOI: 10.1038/s41467-020-17374-3. (Equal contributions).

  22. [20]  Aniruddh P. Patel*, Wang, Minxian*, Akl C. Fahed, Heather Mason-Suares, et al. “Association of Rare Pathogenic DNA Variants for Familial Hypercholesterolemia, Hereditary Breast and Ovarian Cancer Syn- drome, and Lynch Syndrome With Disease Risk in Adults According to Family History.” JAMA network open 3.4 (2020), e203959. DOI: 10.1001/jamanetworkopen.2020.3959. (Equal contributions).

  24. [21]  Connor A. Emdin*, Pallav Bhatnagar*, Wang, Minxian*, Sreekumar G. Pillai, et al. “Genome-wide poly- genic score and cardiovascular outcomes with evacetrapib in patients with high-risk vascular disease: A nested case-control study.” Circulation: Genomic and Precision Medicine February (2020), pp. 30–32. DOI:10.1161/CIRCGEN.119.002767. (Equal contributions).

  26. [22]  Justin Chun*, Wang, Minxian*, Maris S. Wilkins, Andrea U. Knob, Ava Benjamin, Lihong Bu, and Martin R. Pollak. “Autosomal Dominant Tubulointerstitial Kidney Disease—Uromodulin Misclassified as Focal Segmental Glomerulosclerosis or Hereditary Glomerular Disease.” Kidney International Reports 5.4 (2020), pp. 519–529. DOI: 10.1016/j.ekir.2019.12.016. (Equal contributions).

  2. [23]  Aniruddh P. Patel, Wang, Minxian, James P. Pirruccello, Patrick T. Ellinor, Kenney Ng, Sekar Kathire- san, and Amit V. Khera. “Lp(a) (Lipoprotein[a]) Concentrations and Incident Atherosclerotic Cardiovas- cular Disease.” Arteriosclerosis, Thrombosis, and Vascular Biology December (2020), pp. 1–10. DOI: 10.1161/ atvbaha.120.315291.

  4. [24]  James P Pirruccello, Alexander Bick, Wang, Minxian, Mark Chaffin, et al. “Analysis of cardiac magnetic resonance imaging in 36,000 individuals yields genetic insights into dilated cardiomyopathy.” Nature com- munications 11.1 (2020), p. 2254. DOI: 10.1038/s41467-020-15823-7.

  6. [25]  Di Feng, Mukesh Kumar, [...], Wang, Minxian, et al. “Phosphorylation of ACTN4 leads to podocyte vulnerability and proteinuric glomerulosclerosis.” Journal of the American Society of Nephrology 31.7 (2020), pp. 1479–1495. DOI: 10.1681/ASN.2019101032.

  8. [26]  Sumeet A. Khetarpal*, Wang, Minxian*, and Amit V. Khera. “Volanesorsen, familial chylomicronemia syndrome, and thrombocytopenia.” New England Journal of Medicine 381.26 (2019), pp. 2582–2584. DOI:10.1056/NEJMc1912350. (Equal contributions).

  10. [27]  Wang, Minxian, Justin Chun, Giulio Genovese, Andrea U Knob, et al. “Contributions of rare gene vari- ants to familial and sporadic FSGS.” Journal of the American Society of Nephrology 30.9 (2019), pp. 1625– 1640. DOI: 10.1681/ASN.2019020152. (First author).

  12. [28]  Amit V. Khera, Heather Mason-Suares, Deanna Brockman, Wang, Minxian, et al. “Rare Genetic Vari- ants Associated With Sudden Cardiac Death in Adults.” Journal of the American College of Cardiology 74.21 (2019), pp. 2623–2634. DOI: 10.1016/j.jacc.2019.08.1060.

  14. [29]  Cristian Riella, Tobias A. Siemens, Wang, Minxian, Rodrigo P. Campos, et al. “APOL1-Associated Kid- ney Disease in Brazil.” Kidney International Reports 4.7 (2019), pp. 923–929. DOI: 10.1016/j.ekir.2019. 03.006.

  16. [30]  Jia-Yue Zhang*, Wang, Minxian*, Lei Tian, Giulio Genovese, et al. “UBD modifies APOL1 -induced kidney disease risk.” Proceedings of the National Academy of Sciences (2018), p. 201716113. DOI: 10.1073/ pnas.1716113115. (Equal contributions, joint corresponding author).

  18. [31]  Di Feng, Jacob Notbohm, Ava Benjamin, Shijie He, Wang, Minxian, Lay-hong Ang, and Minaspi Bantawa. “Disease-causing mutation in α -actinin-4 promotes podocyte detachment through maladaptation to pe- riodic stretch.” Proc Natl Acad Sci U S A 115.7 (2018), pp. 1517–1522. DOI: 10.1073/pnas.1717870115.

  20. [32]  Yungang He, Wang, Minxian, Xin Huang, Ran Li, Hongyang Xu, Shuhua Xu, and Li Jin. “A probabilistic method for testing and estimating selection differences between populations.” Genome Research 25.12 (Dec. 2015), pp. 1903–1909. DOI: 10.1101/gr.192336.115.

  22. [33]  Bin Zhou, Hui Dong, [...], Wang, Minxian, et al. “Composition and Interactions of Hepatitis B Virus Quasispecies Defined the Virological Response during Telbivudine Therapy.” Scientific Reports 5.July (2015), pp. 1–10. DOI: 10.1038/srep17123.

  24. [34]  Wang, Minxian, Xin Huang, Ran Li, Hongyang Xu, Li Jin, and Yungang He. “Detecting Recent Posi- tive Selection with High Accuracy and Reliability by Conditional Coalescent Tree.” Molecular biology and evolution 31.11 (Aug. 2014), pp. 3068–3080. DOI: 10.1093/molbev/msu244. (First author).

  26. [35]  Yueming Jiang*, Wang, Minxian*, Hongxiang Zheng, Wei R Wang, Li Jin, and Yungang He. “Resolving ambiguity in the phylogenetic relationship of genotypes A, B, and C of hepatitis B virus.” BMC evolutionary biology 13.1 (Jan. 2013), p. 120. DOI: 10.1186/1471-2148-13-120. (Equal contributions).

  28. [36]  Ran Li, Wang, Minxian, Li Jin, and Yungang He. “A Monte Carlo permutation test for random mating using genome sequences.” PloS one 8.8 (Jan. 2013), e71496. DOI: 10.1371/journal.pone.0071496. 


Students

现指导学生

李佳  硕士研究生  0710Z1-基因组学  

陈秋丽  硕士研究生  0710Z1-基因组学  

阿云嘎  博士研究生  0710J3-生物信息学  

陈星宇  博士研究生  0710J3-生物信息学  

胡圣禹  硕士研究生  0710J3-生物信息学  

王劭祺  硕士研究生  0710J3-生物信息学  

于康  硕士研究生  0710J3-生物信息学  

于宇  硕士研究生  0710J3-生物信息学  

周欣雨  博士研究生  0710J3-生物信息学