个人简介

杨剑，西湖大学教授，2003年本科毕业于浙江大学，2008年于浙大取得博士学位，同年赴澳大利亚昆士兰医学研究所从事博士后研究工作。2012年加入澳大利亚昆士兰大学，历任研究员、高级研究员、副教授、教授。2020年加入西湖大学生命科学学院。主要致力于统计遗传学、基因组学研究，以及人类复杂性状和疾病（如：身高、肥胖、精神分裂和癌症）的大数据分析。

曾获得澳大利亚Centenary Institute劳伦斯创新奖（2012），澳大利亚科学院Ruth Stephens Gani人类遗传学奖章（2015），澳大利亚总理科学奖（2017年度生命科学家）。2018至2021连续四年被列入Clarivate Highly Cited Researchers。

学术成果

复杂性状和疾病的遗传结构一直是人类遗传学研究领域的一个未解之谜，其典型代表是2008年提出的“遗传率丢失”（missing heritability）问题。杨剑等人提出的GREML等一系列方法，利用全基因组单核苷酸多态数据在自然群体中估计复杂性状的遗传率，解释了“遗传率丢失”的主要原因，并提出可以用经典的微效多基因模型解释常见性状和疾病的遗传复杂性。他和团体发现与性状关联的遗传位点普遍受到负向自然选择的影响，从进化生物学的角度阐明了人类常见性状和疾病微效多基因遗传结构的形成机制。

全基因组关联研究（Genome-Wide Association Study，GWAS）已成功地检测出数以万计与复杂性状和疾病关联的DNA变异位点，但是GWAS技术本身无法鉴别这些位点对应的易感基因。杨剑团队所开发的整合GWAS和多组学数据（如转录组和表观基因组等）的分析方法不仅能够筛选GWAS信号对应的易感基因（潜在的药物新靶点），而且能够用于推测相应的遗传调控机制，为GWAS结果的临床转化提供了重要的理论和方法。

杨剑团队开发的GCTA、SMR、OSCA等计算生物学分析软件已在人类遗传学和基因组学研究领域广泛应用。这些软件不仅包含了上述复杂性状遗传率分析和基因定位方法，还整合了该团队开发的一系列GWAS方法。例如，该团队开发的fastGWA和fastGWA-GLMM，使得GWAS能够高效地在几十万甚至百万级人群样本中应用。

杨剑实验室目前主要致力于研究人类基因组在群体内和群体间的变异，并研究这些变异与健康的关联。目前主要的研究方向包括（但不限于）如下几个方面：

1. 基因组变异和群体健康

2. 癌症、心脑血管疾病、糖尿病、精神类疾病等复杂疾病新治疗靶点的发掘

3. 单细胞基因组学和表观基因组学

4. 大数据建模和深度学习

5. 癌症基因组学和进化

6. 多组学和精准医疗

7. 高性能统计遗传学分析方法和生物信息学软件开发

代表论文

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.

联系方式

电子邮箱：jian.yang@westlake.edu.cn

实验室网站：https://yanglab.westlake.edu.cn