Qiongshi Lu, PhD
Associate Professor
Department of Biostatistics & Medical Informatics
       

Biography

Qiongshi "Q" Lu received his B.S. in mathematics from Tsinghua University in 2012 and Ph.D. in biostatistics from Yale University in 2017. In 2017, he was appointed in the Department of Biostatistics and Medical Informatics at University of Wisconsin-Madison. Dr. Lu's research focuses on developing statistical and computational methods to study complex trait genetics. In particular, he is interested in noncoding genome annotation, genetic risk prediction, genetic correlation estimation, and gene-environment interaction.

Selected Publications

* junior authors with equal contribution;    † senior authors with equal contribution

Miao J.*, Guo H.*, Song G., Zhao Z., Hou L.^†, Lu Q.^† (2023). Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics. Nature Communications, 14, 832.   

Miao J., Lin Y., Wu Y., Zheng B., Schmitz L., Fletcher J., Lu Q. (2022). A quantile integral linear model to quantify genetic effects on phenotypic variability. Proceedings of the National Academy of Sciences, 119(39): e2212959119.   

Guo H., Hou L., Shi Y., Jin S., Zeng X., Li B., Lifton R., Brueckner M., Zhao H.^†, Lu Q.^† (2022). Quantifying concordant genetic effects of de novo mutations on multiple disorders. eLife, 11: e75551.   

Wu Y., Furuya S., Wang Z., Nobles J., Fletcher J.^†, Lu Q.^† (2022). GWAS on birth year infant mortality rates provides evidence of recent natural selection. Proceedings of the National Academy of Sciences, 119(12): e2117312119.   

Zhao Z.*, Yi Y.*, Song J., Wu Y., Zhong X., Lin Y., Hohman T., Fletcher J., Lu Q. (2021). PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics. Genome Biology, 22, 257.   

Zhang Y.*, Lu Q.*, Ye Y., Huang K., Liu W., Wu Y., Zhong X., Li B., Yu Z., Travers B., Werling D., Li J., Zhao H. (2021). SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits. Genome Biology, 22, 262.   

Wu Y., Zhong X., Lin Y., Zhao Z., Chen J., Zheng B., Li J., Fletcher J., Lu Q. (2021). Estimating genetic nurture with summary statistics of multi-generational genome-wide association studies. Proceedings of the National Academy of Sciences, 118(25): e2023184118.   

Guo H., Li J., Lu Q.^†, Hou L.^† (2021). Detecting local genetic correlations with scan statistics. Nature Communications, 12: 2033.   

Huang K., Wu Y., Shin J., Zheng Y., Siahpirani A., Lin Y., Ni Z., Chen J., You J., Keles S., Wang D., Roy S., Lu Q. (2021). Transcriptome-wide transmission disequilibrium analysis identifies novel risk genes for autism spectrum disorder. PLOS Genetics, 17(2): e1009309.   

Hu B., Shen N., Li J., Kang H., Hong J., Fletcher J., Greenberg J., Mailick M., Lu Q. (2019). Genome-wide association study reveals sex-specific genetic architecture of facial attractiveness. PLOS Genetics, 15(4): e1007973.   

Hu Y.*, Li M.*, Lu Q.*, Weng H., Wang J., Zekavat M., Yu Z., Li B., Gu J., Muchnik S., Shi Y., Kunkle B., Mukherjee S., Natarajan P., Naj A., Kuzma A., Zhao Y., Crane P., Alzheimer's Disease Genetics Consortium (ADGC), Lu H., Zhao H. (2019). A statistical framework for cross-tissue transcriptome-wide association analysis. Nature Genetics, 51(3), 568-576.   

Lu Q., Li B., Ou D., Erlendsdottir M., Powles R., Jiang T., Hu Y., Chang D., Jin C., Dai W., He Q., Liu Z., Mukherjee S., Crane P., Zhao H. (2017). A powerful approach to estimating annotation-stratified genetic covariance via GWAS summary statistics. The American Journal of Human Genetics, 101(6), 939-964.   

Lu Q.*, Powles R.*, Abdallah S., Ou D., Wang Q., Hu Y., Lu Y., Liu W., Li B., Mukherjee S., Crane P., Zhao H. (2017). Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease. PLOS Genetics, 13(7): e1006933.