Qiongshi Lu

Statistical Geneticist
PhD Candidate in Biostatistics

See my work

About Me

Qiongshi is a fifth-year doctoral student in the biostatistics department at Yale School of Public Health. His thesis advisor is Dr. Hongyu Zhao. Qiongshi is currently leading several research projects in Yale Center for Statistical Genomics and Proteomics. He is also actively involved in multiple collaborative studies of human complex diseases.
 

Education

2012 - 2017      Ph.D. in Biostatistics, Yale University
2008 - 2012      B.S. in Mathematics, Tsinghua University
 

Selected Honors and Awards

2017      Liberty Mutual Insurance Student Poster Award, the 31st NESS
2016      ACGA Trainee Award - Predoctoral Basic Sciences
2016      Best Student Poster Award, the 30th NESS
2015      ASHG/Charles J. Epstein Trainee Award for Excellence in Human Genetics Research - Finalist
2010      Tsinghua Xuetang Mathematics Program
2010      National Scholarship, Ministry of Education of the Peoples Republic of China
 

Contact

Address:   60 College Street, New Haven, CT, 06520
Email:   qiongshi DOT lu AT yale DOT edu   
 

Links

Yale Biostatistics
Dr. Hongyu Zhao's Lab | Center for Statistical Genomics and Proteomics
 

Research

Qiongshi's research focuses on genomic functional annotations and their applications in human genetics. He is interested in developing statistical methods to leverage functional annotations in GWAS signal prioritization, functional variant fine mapping, and genetic risk prediction.
 

Publications

*   junior author with equal contribution
† senior author with equal contribution

2017

[14] 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. A powerful approach to estimating annotation-stratified genetic covariance using GWAS summary statistics. (submitted)
Winner of Liberty Mutual Insurance Student Poster Award at the 31st New England Statistics Symposium

[13] Lu Q.*, Powles R.*, Abdallah S., Ou D., Wang Q., Hu Y., Lu Y., Liu W., Li B., Mukherjee S., Crane P., Zhao H. Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimers disease. (submitted)
Winner of 2016 ACGA Trainee Award - Predoctoral Basic Sciences

[12] Jin S.*, Homsy J.*, Zaidi S.*, Lu Q., Morton S., DePalma S., Zeng X., Qi H., Chang W., Hung W., Sierant M., Haider S., Zhang J., Knight J., Bjornson R., Castaldi C., Tikhonoa I., Bilguvar K., Mane S., Sanders S., Mital S., Russell M., Gaynor W., Deanfield J., Giardini A., Porter G., Srivastava D., Lo C., Shen Y., Watkins S., Yandell M., Yost J., Tristani-Firouzi M., Newburger J., Roberts A., Kim R., Zhao H., Kaltman J., Goldmuntz E., Chung W., Seidman J., Gelb B., Seidman C., Lifton R., Brueckner M. Contribution of rare transmitted and de novo variants among 2,871 congenital heart disease probands. (submitted)

[11] Hu Y., Lu Q., Liu W., Zhang Y., Li M., Zhao H. (2017). Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction. PLOS Genetics, 13(6): e1006836.

[10] Hu Y.*, Lu Q.*, Powles R., Yao X., Yang C., Fang F., Xu X., Zhao H. (2017). Leveraging functional annotations in genetic risk prediction for human complex diseases. PLOS Computational Biology, 13(6): e1005589.

[9] Lu Q., Jin C., Sun J., Bowler R., Kechris K., Kaminski N., Zhao H. (2017). Post-GWAS prioritization through data integration provides novel insights on chronic obstructive pulmonary disease. Statistics in Biosciences, in press.

[8] Zhao B., Lu Q., Cheng Y., Belcher J., Siew E., Leaf D., Body S., Fox A., Waikar S., Collard C., Thiessen-Philbrook H., Ikizler T., Ware L., Edelstein C., Garg A., Choi M., Schaub J., Zhao H., Lifton R., Parikh C. for the TRIBE-AKI Consortium. (2017). A genome-wide association study to identify single nucleotide polymorphisms for acute kidney injury. American Journal of Respiratory and Critical Care Medicine, 195(4), 482-490.

[7] Li M., Foli Y., Liu Z., Wang G., Hu Y., Lu Q., Selvaraj S., Lam W., Paintsil E. (2017). High frequency of mitochondrial DNA mutations in HIV-infected treatment-experienced individuals. HIV Medicine, 18(1), 45-55.

2016

[6] Timberlake A., Choi J., Zaidi S., Lu Q., Nelson-Williams C., Brooks E., Bilguvar K., Tikhonova I., Mane S., Yang J., Sawh-Martinez R., Persing S., Zellner E., Loring E., Chuang C., Galm A., Hashim P., Steinbacher D., DiLuna M., Duncan C., Pelphrey K., Zhao H., Persing J., Lifton R. (2016). Two locus inheritance of non-syndromic midline craniosynostosis via rare SMAD6 and common BMP2 alleles. eLife, 5: e20125.

[5] Lu Q.*, Powles R.*, Wang Q., He B., Zhao H. (2016). Integrative tissue-specific functional annotations in the human genome provide novel insights on many complex traits and improve signal prioritization in genome wide association studies. PLOS Genetics, 12(4): e1005947.
Predoctoral Finalist of 2015 ASHG/Charles J. Epstein Trainee Award for Excellence in Human Genetics Research
Winner of Best Student Poster Award at the 30th New England Statistics Symposium

[4] Lu Q., Yao X., Hu Y., Zhao H. (2016). GenoWAP: GWAS signal prioritization through integrated analysis of genomic functional annotation. Bioinformatics, 32(4), 542-548.

2015 and Earlier

[3] Lu Q., Hu Y., Sun J., Cheng Y., Cheung K., Zhao H. (2015). A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data. Scientific Reports, 5, 10576.

[2] Wang Q.*, Lu Q.*, Zhao H. (2015). A review of study designs and statistical methods for genomic epidemiology studies using next generation sequencing. Frontiers in Genetics, 6:149.

[1] Lu Q., Ren S., Lu M., Zhang Y., Zhu D., Zhang X., Li T. (2013). Computational prediction of associations between long non-coding RNAs and proteins. BMC Genomics, 14(1), 651.
 

Presentations

Talks and Seminars

09/2017      Advanced Psychometrics Methods in Cognitive Aging Research 2017, Friday Harbour, WA
08/2017      JSM 2017, Baltimore, MD
07/2017      AAIC 2017, London, UK
06/2017      The 14th Graybill Conference on Statistical Genomics and Genetics, CSU, Fort Collins, CO
03/2017      Department of Biostatistics, Yale University, New Haven, CT
03/2017      Cushing/Whitney Medical Library, Yale University, New Haven, CT
02/2017      Department of Biostatistics and Department of Genetics, UNC, Chapel Hill, NC
01/2017      Department of Biostatistics & Medical Informatics, University of Wisconsin, Madison, WI
12/2016      The 10th ICSA International Conference, SJTU, Shanghai, China
12/2016      SJTU-Yale Joint Center for Biostatistics, Shanghai, China
08/2016      JSM 2016, Chicago, IL
07/2016      AAIC 2016, Toronto, Canada
10/2015      ASHG 2015, Baltimore, MD
07/2015      Workshop on Data Science in Biomedicine, HKBU, Hong Kong, China
05/2015      Bioinformatics Transition Workshop, SAMSI, Research Triangle Park, NC
02/2015      Omics Data Integration Workshop, SAMSI, Research Triangle Park, NC

Poster Presentations

04/2017      The 31st NESS, University of Connecticut, Storrs, CT  
10/2016      ASHG 2016, Vancouver, Canada  
07/2016      AAIC 2016, Toronto, Canada  
04/2016      The 30th NESS, Yale University, New Haven, CT    
06/2015      ENCODE 2015 Research Applications and Users Meeting, Potomac, MD  
04/2015      The 29th NESS, University of Connecticut, Storrs, CT  
 

Software

GNOVA

Estimate annotation-stratified genetic covariance using GWAS summary statistics.

AnnoPred

Genetic risk prediction using large-scale GWAS summary data and integrative functional annotations.

GenoSkyline

GenoSkyline is a collection of tissue-specific functional annotations. The tissue-specific functionality is inferred through integrative analysis of Roadmap epigenomic data. Currently available tissue/cell types include brain, GI, lung, heart, blood, muscle, epithelium, breast, ESC, and fetal cells. The GenoSkyline framework is general so that it can be extended to cell types of your own interest.

GenoWAP

GenoWAP (Genome Wide Association Prioritizer) is a GWAS signal prioritization method. It uses empirical Bayes techniques to evaluate the functional potential of each SNP in genome-wide association studies. The software as well as the source code can be accessed on its website.

GenoCanyon

GenoCanyon is a statistical framework to predict functional non-coding regions in the human genome through integrated analysis of computational and experimental annotation data. Pre-calculated functional scores for hg19 can be downloaded from the GenoCanyon website. We also provide a web application to visualize GenoCanyon scores.