"Westlake University, the place to pursue your scientific dreams."
Biography
Jian Yang is a Professor of Statistical Genetics at the School of Life Sciences, Westlake University, China. He received his PhD in 2008 from Zhejiang University, China, before undertaking postdoctoral research at the QIMR Berghofer Medical Research Institute in Australia (2008-2011). He moved to The University of Queensland (UQ), Australia, as a Research Fellow in 2012 and was reappointed as a Senior Research Fellow and Group Leader in January 2014. He was promoted to be an Associate Professor in December 2014, and then a Professor in 2017 at UQ. He joined Westlake University in 2020.
His primary research interests are focused on understanding the genomic variations among individuals within and between populations and the links of genomic variations with health outcomes. The research interests of his lab include (but are not limited to):
· Population genomics and complex traits
· Discovery of new therapeutic targets for cancers, heart diseases, diabetes, and mental illness
· Single-cell genomics and epigenomics
· Big data modeling and deep learning
· Cancer genomics and evolution
· Multi-omics and precision medicine
· High-performance statistical genetics methods and bioinformatics tools
History
2018
Clarivate Highly Cited Researchers from 2018 to 2021
2017
Prime Minister's Prize for Sciences
2015
Australian Academy of Science Ruth Stephens Gani Medal
2012
The Centenary Institute Lawrence Creative Prize
Research
Representative Publications
1. Qi T, Wu Y, Fang H, Zhang F, Liu S, Zeng J, Yang J (2022) Genetic control of RNA splicing and its distinct role in complex trait variation. Nature Genetics, 54:1355-1363.
2. Jiang L, Zheng Z, Fang H, Yang J (2021) A generalized linear mixed model association tool for biobank-scale data. Nature Genetics, 53:1616-1621.
3. Wu Y, Qi T, Wang H, Zhang F, Zheng Z, Phillips-Cremins JE, Deary IJ, McRae AF, Wray NR, Zeng J, Yang J (2020) Promoter-anchored chromatin interactions predicted from genetic analysis of epigenomic data. Nature Communications, 11:2061.
4. Jiang L, Zheng Z, Qi T, Kemper KE, Wray NR, Visscher PM, Yang J (2019) A resource-efficient tool for mixed model association analysis of large-scale data. Nature Genetics, 51:1749-1755.
5. Wang H, Zhang F, Zeng J, Wu Y, Kemper KE, Xue A, Zhang M, Powell JE, Goddard ME, Wray NR, Visscher PM, McRae AF, Yang J (2019) Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank. Science Advances, Vol. 5, no. 8, eaaw3538.
6. Zhang F, Chen W, Zhu Z, Zhang Q, Nabais MF, Qi T, Deary IJ, Wray NR, Visscher PM, McRae AF, Yang J (2019) OSCA: a tool for omic-data-based complex trait analysis. Genome Biology, 20:107.
7. Zeng J, de Vlaming R, Wu Y, Robinson M, Lloyd-Jones LR, Yengo L, Yap CX, Xue A, Sidorenko J, McRae AF, Powell JE, Montgomery GW, Metspalu A, Esko T, Gibson G, Wray NR, Visscher PM, Yang J (2018) Signatures of negative selection in the genetic architecture of human complex traits. Nature Genetics, 50: 746-753.
8. Xue A, Wu Y, Zhu Z, Zhang F, Kemper KE, Zheng Z, Yengo L, Lloyd-Jones LR, Sidorenko J, Wu Y, eQTLGen Consortium, McRae AF, Visscher PM, Zeng J, Yang J (2018) Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nature Communications, 9:2941.
9. Qi T, Wu Y, Zeng J, Zhang F, Xue A, Jiang L, Zhu Z, Kemper K, Yengo L, Zheng Z, eQTLGen Consortium, Marioni RE, Montgomery GW, Deary IJ, Wray NR, Visscher PM, McRae AF, Yang J (2018) Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nature Communications, 9: 2282.
10. Wu Y, Zeng J, Zhang F, Zhu F, Qi T, Zheng Z, Lloyd-Jones LR, Marioni RE, Martin NG, Montgomery GW, Deary IJ, Wray NR, Visscher PM, McRae AF, Yang J (2018) Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nature Communications, 9: 918.
11. Zhu Z, Zheng Z, Zhang F, Wu Y, Trzaskowski M, Maier R, Robinson MR, McGrath JJ, Visscher PM, Wray NR, Yang J (2018) Causal associations between risk factors and common diseases inferred from GWAS summary data. Nature Communications, 9: 224.
12. Wu Y, Zheng Z, Visscher PM, Yang J (2017) Quantifying the mapping precision of genome-wide association studies using whole-genome sequencing data. Genome Biology, 18: 86.
13. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, Montgomery GW, Goddard ME, Wray NR, Visscher PM, Yang J (2016) Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nature Genetics, 48: 481-487.
14. Yang J, Bakshi A, Zhu Z, Hemani G, Vinkhuyzen AAE, …, Keller MC, Wray NR, Goddard ME, Visscher PM (2015) Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nature Genetics, 47: 1114-1120.
15. Yang J, Zaitlen NA, Goddard ME, Visscher PM, Price AL (2014) Advantages and pitfalls in the application of mixed model association methods. Nature Genetics, 46: 100–106.
16. Yang J, Loos RJF, Powell JE, Medland SE, et al. (2012) FTO genotype is associated with phenotypic variability of body mass index. Nature, 490: 267-272.
17. Yang J, Ferreira T, Morris AP, Medland SE, GIANT Consortium, DIAGRAM Consortium, Madden PAF, Heath AC, Martin NG, Montgomery GW, Weedon MN, Loos RJ, Frayling TM, McCarthy MI, Hirschhorn JN, Goddard ME, Visscher PM (2012) Conditional and joint multiple SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nature Genetics, 44: 369-375.
18. Yang J, Manolio TA, Pasquale LR, Boerwinkle E, Caporaso N, Cunningham JM, de Andrade M, Feenstra B, Feingold E, Hayes MG, Hill WG, Landi MT, Alonso A, Lettre G, Lin P, Ling H, Lowe W, Mathias RA, Melbye M, Pugh E, Cornelis MC, Weir BS, Goddard ME, Visscher PM (2011) Genome partitioning of genetic variation for complex traits using common SNPs. Nature Genetics, 43: 519-525.
19. Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet, 88: 76-82.
20. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW, Goddard ME, Visscher PM (2010) Common SNPs explain a large proportion of the heritability for human height. Nature Genetics, 42: 565-569.
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