Quan Long

Biography

Quan Long, PhD, was trained in both mathematics and computer science. He is an Assistant Professor in the Communing School of Medicine with adjunct appointment in the Department of Mathematics and Statistics. His current research focuses on phenotype predictions and gene mapping via integration of multiscale –omics using statistics and informatics. He is also interested in within-host evolutionary analysis using sequences data that may lead better prediction of disease status such as cancer progression and pathogen antimicrobial resistance. He was a staff R & D engineer analyzing memory leak at IBM Research; then a staff scientist serving for the 1,000 Genomes Project and other evolution-focused projects at the Wellcome Trust Sanger Institute. Afterwards, he assumed the position of a postdoc fellow at the Gregor Mendel Institute, working on statistical methods development as well as real data analysis for DNA sequencing-based variants calling, association mapping, and population genetics. Before joining University of Calgary, he was an assistant professor (research track) in Icahn School of Medicine at Mount Sinai, working on phenotype predictions and gene expression networks. 

Publications

Real data analysis

• Mak L*, Perera D*, Lang R, Kossinna P, He J, Gill M J, Long Q#, van Marle G# (2020). “Evaluation of A Phylogenetic Pipeline to Examine Transmission Networks in A Canadian HIV Cohort”. Microorganisms. 8(2). pii: E196. doi: 10.3390/microorganisms8020196. PMID: 32023939
• Long Q, Argmann C, Houten SM, Huang T, Peng S, Zhao Y, Tu Z, The GTEx Consortium, Zhu J (2016). "Inter-tissue coexpression network analysis reveals DPP4 as an important gene in heart to blood communication". Genome Medicine, 2016 Feb 9;8(1):15. doi: 10.1186/s13073-016-0268-1, PMID: 26856537
• Long Q as a listed participant of the GTEx Consortium. “The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans”. Science 2015 May 8;348(6235):648-60. doi: 10.1126/science.1262110, PMID: 25954001
• Long Q, Rabanal FA, Meng D, Huber CD, Farlow A, Platzer A, Zhang Q, Vilhjálmsson BJ, Korte A, Nizhynska V, Voronin V, Korte P, Sedman L, Mandáková T, Lysak MA, Seren U, Hellmann I, Nordborg M (2013). “Massive genomic variation and strong selection in Arabidopsis thaliana lines from Sweden”. Nature Genetics 2013 Jun 23. doi: 10.1038/ng.2678, PMID: 23793030
• Long Q as a listed participant of the 1,000 Genomes Project, “A map of human genome variation from population-scale sequencing”. Nature, 2010. PMID: 20981092

Methods development for statistical genetics

• Cao C*, Ding B*, Li Q, Kwok D, Wu J#, Long Q#. (2021) “Power analysis of transcriptome-wide association studies: implications to practical protocol choice”. Accepted by PLoS Genet
• Cao C, Kwok D, Edie S, Li Q, Ding B, Kossinna P, Campbell S, Wu J, Greenberg M, Long Q. (2020) “kTWAS: integrating kernel-machine with transcriptome-wide association studies improves statistical power and reveals novel genes”. Briefings in Bioinformatics. 2020 Nov 17:bbaa270. doi: 10.1093/bib/bbaa270. Online ahead of print. PMID: 33200776.
• Xiong Z, Zhang Q, Platt A, Liao W, Shi X, de los Campos G, and Long Q. (2019) “OCMA: Fast, memory-efficient factorization of prohibitively large relationship matrices”. G3: Genes, Genomes, Genetics. 9(1):13-19. doi: 10.1534/g3.118.200908.
• Mak L*, Li M*, Cao C, Gordon P, Tarailo-Graovac M, Bousman C, Wang P, Long Q. (2018) “SimPEL: Simulation-based Power Estimation for sequencing studies of Low-prevalence conditions”. Genetic Epidemiology. 42(5):480-487. doi: 10.1002/gepi.22129.
• Zhang Q, Tyler-Smith C, Long Q. “An extended Tajima’s D neutrality test incorporating SNP calling and imputation uncertainties”. Statistics and Its Interface. 2015, vol.8(4), 447-456.
• Zhang Q, Long Q, Ott J. “AprioriGWAS, a new pattern mining strategy for detecting genetic variants associated with disease through interaction effects”. PLoS Computational Biology. 2014 Jun 5; 10(6), PMID: 24901472
• Long Q, Zhang Q, Vilhjalmsson BJ, Forai P, Seren Ü, Nordborg M. “JAWAMix5: an out-of-core HDF5-based java implementation of whole-genome association studies using mixed models”. Bioinformatics 2013 March, PMID: 23479353, doi: 10.1093/bioinformatics/btt122. (Corresponding author)
• Korte A, Vilhjálmsson BJ, Segura V, Platt A, Long Q, Nordborg M. “A mixed-model approach for genome-wide association studies of correlated traits in structured populations”. Nature Genetics2012 Aug 19. doi: 10.1038/ng.2376. PMID: 22902788
• Segura V, BJ Vilhjálmsson, Platt A, Korte A, Seren Ü, Long Q, Nordborg M.  "An efficient multi-locus mixed model approach for genome-wide association mapping". Nature Genetics 2012 Jun 17;44(7):825-30. doi: 10.1038/ng.2314. PMID: 22706313
• Long Q, Zhang Q, and Ott J, “Detecting Disease-Associated Genotype Patterns”, BMC Bioinformatics 2009 Jan, 10(Suppl 1): S75. PMID: 19208180

Methods development for genomics

• Cao C*, He J*, Mak L*, Perera D, Wang J, Kwok D, Mourier T, Li M, Gavriliuc S, Greenberg M, Morrissy S, Sycuro L, Yang G, Jeffares D, Long Q. (2021) “De novo reconstruction of microbial haplotypes by integrating statistical and physical linkage”. Molecular Biology and Evolution. 2021 Feb 6:msab037. doi: 10.1093/molbev/msab037. Online ahead of print. PMID: 33547786
• Cao C, Greenberg M, Long Q. (2021) “WgLink: reconstructing whole-genome viral strains using L0+L1-regularization”. Bioinformatics. 2021 Feb 3:btab076. doi: 10.1093/bioinformatics/btab076. Online ahead of print. PMID: 33532820.
• Cao C, Mak L, Jin G, Gordon P, Ye K, and Long Q#. (2019) “PRESM: Personalized Reference Editor for Somatic Mutation discovery in cancer genomics”. Bioinformatics. 35(9):1445-1452. doi: 10.1093/bioinformatics/bty812.
• Platzer A, Nizhynska V, and Long Q. “TE-Locate: A tool to locate and group transposable element occurrences using paired-end next-generation sequencing data.” Biology (Basel) 2012 1(2), 395-410. doi: 10.3390/biology1020395. PMID: 24832231
• Long Q, MacArthur D, Ning Z, and Tyler-Smith C, “HI: Haplotype Improver using paired-end short reads”. Bioinformatics, 2009. PMID: 19570807, DOI: 10.1093/bioinformatics/btp412.
• Ye K, Schulz M, Long Q, Apweiler R, and Ning Z, “Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads”. Bioinformatics, 2009. PMID: 19561018, DOI: 10.1093/bioinformatics/btp394 (Best paper awards in ISMB 2009 Short SIG meeting)

Computer science

• Li Y, Long Q, Lui T, and Qiu J, “Partition-Based Method and Apparatus for Diagnosing and Repairing Memory Leak in Java Systems”. USA Patent, 2014, Pub. No.: US8775769 B2 (Inventors ordered alphabetically)
• Li Y, Liu T, Long Q, Luo J, Wang C, “Method and Apparatus for Locating Memory Leak in a Program”. USA Patent, 2013, Pub. No.: US8479162 B2 (Inventors ordered alphabetically)
• Long Q, Qiu Z and Liu Z, “Formal use of design patterns and refactoring”. In International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, Oct. 13 - 15, 2008, Porto Sani, Greece. Communications in Computer and Information Science (vol 17), Springer-Verlag, 2008.
• Long Q, Qiu Z, Liu Z, Shao L, and He J， “POST: A Case Study for an Incremental Development in rCOS”. In Proceedings of 2nd International Colloquium on Theoretical Aspects of Computing, ICTAC 2005, October 17-21, 2005, Hanoi, Vietnam. Lecture Notes in Computer Science 3722, Springer-Verlag. DOI: 10.1007/11560647_32.
• Long Q, Liu Z, Li X and He J, “Consistent Code Generation from UML Models”. In Proceedings of 16th Australian Software Engineering Conference ASWEC 2005, Mar. 29 – Apr. 1, 2005, Brisbane, Australia. IEEE Computer Society. DOI: 10.1109/ASWEC.2005.17.
• Long Q, Qiu Z and Qin S, “The Equivalence of Statecharts”. In Proceedings of 5th International Conference on Formal Engineering Methods, ICFEM 2003, Nov. 5-7, 2003, Singapore. Lecture Notes in Computer Science 2885, Springer-Verlag. DOI: 10.1007/978-3-540-39893-6_9.

Journal Articles - Peer Reviewed

• Cao, Chen, Kwok, Devin, Edie, Shannon, Li, Qing, Ding, Bowei, Kossinna, Pathum, Campbell, Simone, Wu, Jingjing, Greenberg, Matthew and Long, Quan. "kTWAS: integrating kernel-machine with transcriptome-wide association studies improves statistical power and reveals novel genes". Briefings in Bioinformatics 22.4 (2021): 1-16. Print.
• Cao, Chen, Ding, Bowei, Li, Qing, Kwok, Devin, Wu, Jingjing and Long, Quan. "Power analysis of transcriptome-wide association study: implications for practical protocol choice". PLOS Genetics 17.2 (2021): 1-20. Print.