Dajiang Liu, PhD, MA - Penn State Cancer Institute
Researcher Profile
Dajiang Liu, PhD, MA
Professor and Vice Chair for Research, Department of Public Health Sciences
Division of Biostatistics and Bioinformatics
Professor, Department of Biochemistry and Molecular Biology
Associate Professor, Department of Public Health Sciences
Associate Professor, Department of Biochemistry and Molecular Biology
Assistant Professor, Department of Public Health Sciences
Division of Biostatistics and Bioinformatics
Professor, Department of Biochemistry and Molecular Biology
Associate Professor, Department of Public Health Sciences
Associate Professor, Department of Biochemistry and Molecular Biology
Assistant Professor, Department of Public Health Sciences
Scientific Program:Cancer Control
Research Interests
Dr. Dajiang Liu's lab is a small research group at Penn State College of Medicine that works on statistical genetics, complex trait genetics and functional genomics. The lab actively develops novel methods and applies them to interesting datasets. It also generates datasets using high-throughput genotyping and sequencing.
The lab's statistical genetics research focuses on method development for analyzing large scale sequence-based association studies. The Liu lab developed the widely-used software package RAREMETAL and RVTESTS for association analysis and meta-analysis and is actively conducting research in this area to develop more efficient and powerful methods for studying the genetic basis for complex traits.
Dr. Liu's lab develops computational methods to interpret the GWAS findings using integrative approaches and is also developing methods to study the functional genomics for X chromosome inactivation.
The lab studies a few complex traits in great detail, including lipids and cardiovascular disease, smoking drinking addiction as well as lupus. Many of the applied projects offered interesting problems and motivated the lab's methodology research.
- Genes
- Meta-Analysis
- Exome
- Multifactorial Inheritance
- Genome-Wide Association Study
- Datasets
- Lipids
- Genome
- Phenotype
- Proteins
- Genotype
- High-Throughput Nucleotide Sequencing
Recent Publications
2023
Khunsriraksakul, C, Li, Q, Markus, H, Patrick, MT, Sauteraud, R, McGuire, D, Wang, X, Wang, C, Wang, L, Chen, S, Shenoy, G, Li, B, Zhong, X, Olsen, NJ, Carrel, L, Tsoi, LC, Jiang, B & Liu, DJ 2023, 'Multi-ancestry and multi-trait genome-wide association meta-analyses inform clinical risk prediction for systemic lupus erythematosus', Nature communications, vol. 14, no. 1, 668. https://doi.org/10.1038/s41467-023-36306-5
Chen, F, Wang, X, Jang, SK, Quach, BC, Weissenkampen, JD, Khunsriraksakul, C, Yang, L, Sauteraud, R, Albert, CM, Allred, NDD, Arnett, DK, Ashley-Koch, AE, Barnes, KC, Barr, RG, Becker, DM, Bielak, LF, Bis, JC, Blangero, J, Boorgula, MP, Chasman, DI, Chavan, S, Chen, YDI, Chuang, LM, Correa, A, Curran, JE, David, SP, Fuentes, LDL, Deka, R, Duggirala, R, Faul, JD, Garrett, ME, Gharib, SA, Guo, X, Hall, ME, Hawley, NL, He, J, Hobbs, BD, Hokanson, JE, Hsiung, CA, Hwang, SJ, Hyde, TM, Irvin, MR, Jaffe, AE, Johnson, EO, Kaplan, R, Kardia, SLR, Kaufman, JD, Kelly, TN, Kleinman, JE, Kooperberg, C, Lee, IT, Levy, D, Lutz, SM, Manichaikul, AW, Martin, LW, Marx, O, McGarvey, ST, Minster, RL, Moll, M, Moussa, KA, Naseri, T, North, KE, Oelsner, EC, Peralta, JM, Peyser, PA, Psaty, BM, Rafaels, N, Raffield, LM, Reupena, MS, Rich, SS, Rotter, JI, Schwartz, DA, Shadyab, AH, Sheu, WHH, Sims, M, Smith, JA, Sun, X, Taylor, KD, Telen, MJ, Watson, H, Weeks, DE, Weir, DR, Yanek, LR, Young, KA, Young, KL, Zhao, W, Hancock, DB, Jiang, B, Vrieze, S & Liu, DJ 2023, 'Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing', Nature Genetics, vol. 55, no. 2, pp. 291-300. https://doi.org/10.1038/s41588-022-01282-x
Ang, K, Canfield, VA, Foster, TC, Harbaugh, TD, Early, KA, Harter, RL, Reid, KP, Leong, SL, Kawasawa, Y, Liu, D, Hawley, JW & Cheng, K 2023, 'Native American genetic ancestry and pigmentation allele contributions to skin color in a Caribbean population', eLife, vol. 12. https://doi.org/10.7554/eLife.77514
2022
Gao, GF, Liu, D, Zhan, X & Li, B 2022, 'Analysis of KIR gene variants in The Cancer Genome Atlas and UK Biobank using KIRCLE', BMC Biology, vol. 20, no. 1, 191. https://doi.org/10.1186/s12915-022-01392-2
Vu, TN, Khunsriraksakul, C, Vorobeychik, Y, Liu, A, Sauteraud, R, Shenoy, G, Liu, DJ & Cohen, SP 2022, 'Association of Spinal Cord Stimulator Implantation with Persistent Opioid Use in Patients with Postlaminectomy Syndrome', JAMA network open. https://doi.org/10.1001/jamanetworkopen.2021.45876
Khunsriraksakul, C, Markus, H, Olsen, NJ, Carrel, L, Jiang, B & Liu, DJ 2022, 'Construction and Application of Polygenic Risk Scores in Autoimmune Diseases', Frontiers in immunology, vol. 13, 889296. https://doi.org/10.3389/fimmu.2022.889296
23andMe Research Team & The Biobank Japan Project 2022, 'Genetic diversity fuels gene discovery for tobacco and alcohol use', Nature, vol. 612, no. 7941, pp. 720-724. https://doi.org/10.1038/s41586-022-05477-4
Depicolzuane, LC, Roberts, CM, Thomas, NJ, Anderson-Fears, K, Liu, D, Barbosa, JPP, Souza, FR, Pimentel, AS, Floros, J & Gandhi, CK 2022, 'Hydrophilic But Not Hydrophobic Surfactant Protein Genetic Variants Are Associated With Severe Acute Respiratory Syncytial Virus Infection in Children', Frontiers in immunology, vol. 13, 922956. https://doi.org/10.3389/fimmu.2022.922956
Khunsriraksakul, C, McGuire, D, Sauteraud, R, Chen, F, Yang, L, Wang, L, Hughey, J, Eckert, S, Dylan Weissenkampen, J, Shenoy, G, Marx, O, Carrel, L, Jiang, B & Liu, DJ 2022, 'Integrating 3D genomic and epigenomic data to enhance target gene discovery and drug repurposing in transcriptome-wide association studies', Nature communications, vol. 13, no. 1, 3258. https://doi.org/10.1038/s41467-022-30956-7
Yang, S, Wang, S, Wang, Y, Rong, R, Kim, J, Li, B, Koh, AY, Xiao, G, Li, Q, Liu, DJ & Zhan, X 2022, 'MB-SupCon: Microbiome-based Predictive Models via Supervised Contrastive Learning', Journal of Molecular Biology, vol. 434, no. 15, 167693. https://doi.org/10.1016/j.jmb.2022.167693
Weissenrieder, JS, Weissenkampen, JD, Reed, JL, Green, MV, Zheng, C, Neighbors, JD, Liu, DJ & Hohl, RJ 2022, 'RNAseq reveals extensive metabolic disruptions in the sensitive SF-295 cell line treated with schweinfurthins', Scientific reports, vol. 12, no. 1, 359. https://doi.org/10.1038/s41598-021-04117-7
AMP-T2D-GENES, Myocardial Infarction Genetics Consortium, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium & NHLBI TOPMed Lipids Working Group 2022, 'Rare coding variants in 35 genes associate with circulating lipid levels—A multi-ancestry analysis of 170,000 exomes', American Journal of Human Genetics, vol. 109, no. 1, pp. 81-96. https://doi.org/10.1016/j.ajhg.2021.11.021
Jang, SK, Evans, L, Fialkowski, A, Arnett, DK, Ashley-Koch, AE, Barnes, KC, Becker, DM, Bis, JC, Blangero, J, Bleecker, ER, Boorgula, MP, Bowden, DW, Brody, JA, Cade, BE, Jenkins, BWC, Carson, AP, Chavan, S, Cupples, LA, Custer, B, Damrauer, SM, David, SP, de Andrade, M, Dinardo, CL, Fingerlin, TE, Fornage, M, Freedman, BI, Garrett, ME, Gharib, SA, Glahn, DC, Haessler, J, Heckbert, SR, Hokanson, JE, Hou, L, Hwang, SJ, Hyman, MC, Judy, R, Justice, AE, Kaplan, RC, Kardia, SLR, Kelly, S, Kim, W, Kooperberg, C, Levy, D, Lloyd-Jones, DM, Loos, RJF, Manichaikul, AW, Gladwin, MT, Martin, LW, Nouraie, M, Melander, O, Meyers, DA, Montgomery, CG, North, KE, Oelsner, EC, Palmer, ND, Payton, M, Peljto, AL, Peyser, PA, Preuss, M, Psaty, BM, Qiao, D, Rader, DJ, Rafaels, N, Redline, S, Reed, RM, Reiner, AP, Rich, SS, Rotter, JI, Schwartz, DA, Shadyab, AH, Silverman, EK, Smith, NL, Smith, JG, Smith, AV, Smith, JA, Tang, W, Taylor, KD, Telen, MJ, Vasan, RS, Gordeuk, VR, Wang, Z, Wiggins, KL, Yanek, LR, Yang, IV, Young, KA, Young, KL, Zhang, Y, Liu, DJ, Keller, MC & Vrieze, S 2022, 'Rare genetic variants explain missing heritability in smoking', Nature Human Behaviour, vol. 6, no. 11, pp. 1577-1586. https://doi.org/10.1038/s41562-022-01408-5
Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC) 2022, 'Type 1 Diabetes in Acute Pancreatitis Consortium: From Concept to Reality', Pancreas, vol. 51, no. 6, pp. 563-567. https://doi.org/10.1097/MPA.0000000000002073
2021
Patrick, MT, Stuart, PE, Zhang, H, Zhao, Q, Yin, X, He, K, Zhou, XJ, Mehta, NN, Voorhees, JJ, Boehnke, M, Gudjonsson, JE, Nair, RP, Handelman, SK, Elder, JT, Liu, DJ & Tsoi, LC 2021, 'Causal Relationship and Shared Genetic Loci between Psoriasis and Type 2 Diabetes through Trans-Disease Meta-Analysis', Journal of Investigative Dermatology, vol. 141, no. 6, pp. 1493-1502. https://doi.org/10.1016/j.jid.2020.11.025
Sauteraud, R, Stahl, JM, James, J, Englebright, M, Chen, F, Zhan, X, Carrel, L & Liu, DJ 2021, 'Inferring genes that escape X-Chromosome inactivation reveals important contribution of variable escape genes to sex-biased diseases', Genome research, vol. 31, no. 9, pp. 1629-1637. https://doi.org/10.1101/gr.275677.121
Rong, R, Jiang, S, Xu, L, Xiao, G, Xie, Y, Liu, DJ, Li, Q & Zhan, X 2021, 'MB-GAN: Microbiome Simulation via Generative Adversarial Network', GigaScience, vol. 10, no. 2, giab005. https://doi.org/10.1093/gigascience/giab005
Zaorsky, NG, Khunsriraksakul, C, Acri, SL, Liu, DJ, Ba, DM, Lin, JL, Liu, G, Segel, JE, Drabick, JJ, Mackley, HB & Leslie, DL 2021, 'Medical Service Use and Charges for Cancer Care in 2018 for Privately Insured Patients Younger Than 65 Years in the US', JAMA network open, vol. 4, no. 10, 27784. https://doi.org/10.1001/jamanetworkopen.2021.27784
Kim, J, Jiang, S, Yiqing, W, Xiao, G, Xie, Y, Liu, DJ, Li, Q, Koh, A & Zhan, X 2021, 'MetaPrism: A versatile toolkit for joint taxa/gene analysis of metagenomic sequencing data', G3: Genes, Genomes, Genetics, vol. 11, no. 4, jkab046. https://doi.org/10.1093/g3journal/jkab046
GWAS and Sequencing Consortium of Alcohol and Nicotine Use (GSCAN) 2021, 'Model-based assessment of replicability for genome-wide association meta-analysis', Nature communications, vol. 12, no. 1, 1964. https://doi.org/10.1038/s41467-021-21226-z
COGA Collaborators 2021, 'Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction', Nature Neuroscience, vol. 24, no. 10, pp. 1367-1376. https://doi.org/10.1038/s41593-021-00908-3
Carrel, L, Arnold-Croop, S, Achtermann, T, Chen, F, Cheng, Y, Liu, D & Eyster, ME 2021, 'Prothrombotic variants as modifiers of clinical phenotype in four related individuals with haemophilia A', Haemophilia, vol. 27, no. 4, pp. e591-e595. https://doi.org/10.1111/hae.14348
2020
Jiang, Y, Chen, S, Wang, X, Liu, M, Iacono, WG, Hewitt, JK, Hokanson, JE, Krauter, K, Laakso, M, Li, KW, Lutz, SM, McGue, M, Pandit, A, Zajac, GJM, Boehnke, M, Abecasis, GR, Vrieze, SI, Jiang, B, Zhan, X & Liu, DJ 2020, 'Association analysis and meta-analysis of multiallelic variants for large-scale sequence data', Genes, vol. 11, no. 5, 585. https://doi.org/10.3390/genes11050585
LifeLines Cohort Study, EPIC-CVD, EPIC-InterAct, Understanding Society Scientific Group & Million Veteran Program 2020, 'Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals', Nature Genetics, vol. 52, no. 12, pp. 1314-1332. https://doi.org/10.1038/s41588-020-00713-x
Jang, SK, Saunders, G, Liu, MZ, Jiang, Y, Liu, DJ & Vrieze, S 2022, 'Genetic correlation, pleiotropy, and causal associations between substance use and psychiatric disorder', Psychological medicine, vol. 52, no. 5, pp. 968-978. https://doi.org/10.1017/S003329172000272X
Cygan, PH, Arnold-Croop, SE, Weidman, EA, Chen, F, Liu, DJ, Eyster, ME & Carrel, L 2020, 'Investigation of discordant phenotype in mild Hemophilia A using whole exome sequencing', Thrombosis Research, vol. 193, pp. 36-39. https://doi.org/10.1016/j.thromres.2020.05.044
Yang, S, Wen, J, Eckert, ST, Wang, Y, Liu, DJ, Wu, R, Li, R & Zhan, X 2020, 'Prioritizing genetic variants in GWAS with lasso using permutation-assisted tuning', Bioinformatics, vol. 36, no. 12, pp. 3811-3817. https://doi.org/10.1093/bioinformatics/btaa229
Yang, L, Jiang, S, Jiang, B, Liu, DJ & Zhan, X 2020, 'Seqminer2: An efficient tool to query and retrieve genotypes for statistical genetics analyses from biobank scale sequence dataset', Bioinformatics, vol. 36, no. 19, pp. 4951-4954. https://doi.org/10.1093/bioinformatics/btaa628
2019
23andMe Research Team, HUNT All-In Psychiatry, Liu, M, Jiang, Y, Wedow, R, Li, Y, Brazel, DM, Chen, F, Datta, G, Davila-Velderrain, J, McGuire, D, Tian, C, Zhan, X, Agee, M, Alipanahi, B, Auton, A, Bell, RK, Bryc, K, Elson, SL, Fontanillas, P, Furlotte, NA, Hinds, DA, Hromatka, BS, Huber, KE, Kleinman, A, Litterman, NK, McIntyre, MH, Mountain, JL, Northover, CAM, Sathirapongsasuti, JF, Sazonova, OV, Shelton, JF, Shringarpure, S, Tian, C, Tung, JY, Vacic, V, Wilson, CH, Pitts, SJ, Mitchell, A, Skogholt, AH, Winsvold, BS, Sivertsen, B, Stordal, E, Morken, G, Kallestad, H, Heuch, I, Zwart, JA, Fjukstad, KK, Pedersen, LM, Gabrielsen, ME, Johnsen, MB & Liu, D 2019, 'Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use', Nature Genetics, vol. 51, no. 2, pp. 237-244. https://doi.org/10.1038/s41588-018-0307-5
CHD Exome+ Consortium, Consortium for Genetics of Smoking Behaviour, Brazel, DM, Jiang, Y, Hughey, JM, Turcot, V, Zhan, X, Gong, J, Batini, C, Weissenkampen, JD, Liu, MZ, Surendran, P, Young, R, Barnes, DR, Nielsen, SF, Rasheed, A, Samuel, M, Zhao, W, Kontto, J, Perola, M, Caslake, M, de Craen, AJM, Trompet, S, Uria-Nickelsen, M, Malarstig, A, Reily, DF, Hoek, M, Vogt, T, Jukema, JW, Sattar, N, Ford, I, Packard, CJ, Alam, DS, Majumder, AAS, Di Angelantonio, E, Chowdhury, R, Amouyel, P, Arveiler, D, Blankenberg, S, Ferrières, J, Kee, F, Kuulasmaa, K, Müller-Nurasyid, M, Veronesi, G, Virtamo, J, EPIC-CVD Consortium, C, Frossard, P, Nordestgaard, BG, Saleheen, D, Danesh, J, Butterworth, AS & Liu, D 2019, 'Exome Chip Meta-analysis Fine Maps Causal Variants and Elucidates the Genetic Architecture of Rare Coding Variants in Smoking and Alcohol Use', Biological Psychiatry, vol. 85, no. 11, pp. 946-955. https://doi.org/10.1016/j.biopsych.2018.11.024
Understanding Society Scientific Group, EPIC-CVD, GSCAN, Consortium for Genetics of Smoking Behaviour, CHD Exome+ consortium, Erzurumluoglu, AM, Liu, M, Jackson, VE, Barnes, DR, Datta, G, Melbourne, CA, Young, R, Batini, C, Surendran, P, Jiang, T, Adnan, SD, Afaq, S, Agrawal, A, Altmaier, E, Antoniou, AC, Asselbergs, FW, Baumbach, C, Bierut, L, Bertelsen, S, Boehnke, M, Bots, ML, Brazel, DM, Chambers, JC, Chang-Claude, J, Chen, C, Corley, J, Chou, YL, David, SP, de Boer, RA, de Leeuw, CA, Dennis, JG, Dominiczak, AF, Dunning, AM, Easton, DF, Eaton, C, Elliott, P, Evangelou, E, Faul, JD, Foroud, T, Goate, A, Gong, J, Grabe, HJ, Haessler, J, Haiman, C, Hallmans, G, Hammerschlag, AR, Harris, SE, Hattersley, A, Heath, A & Liu, D 2020, 'Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci', Molecular Psychiatry, vol. 25, no. 10, pp. 2392-2409. https://doi.org/10.1038/s41380-018-0313-0
Weissenkampen, JD, Jiang, Y, Eckert, S, Jiang, B, Li, B & Liu, DJ 2019, 'Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits', Current protocols in human genetics, vol. 101, no. 1, e83. https://doi.org/10.1002/cphg.83
T2D-Genes Consortium, The MAGIC Investigators, CHD Exome+ Consortium, Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, EPIC-CVD Consortium, ExomeBP Consortium, Global Lipids Genetic Consortium, GoT2D Genes Consortium, InterAct & ReproGen Consortium 2019, 'Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution', Nature Genetics, vol. 51, no. 3, pp. 452-469. https://doi.org/10.1038/s41588-018-0334-2
CHD Exome+ Consortium, EPIC-CVD Consortium, ExomeBP Consortium, Global Lipids Genetic Consortium, GoT2D Genes Consortium, EPIC InterAct Consortium, INTERVAL Study, ReproGen Consortium, T2D-Genes Consortium, The MAGIC Investigators & Understanding Society Scientific Group 2019, 'Publisher Correction: Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity (Nature Genetics, (2018), 50, 1, (26-41), 10.1038/s41588-017-0011-x)', Nature Genetics, vol. 51, no. 7, pp. 1191-1192. https://doi.org/10.1038/s41588-019-0447-2