Publications with primary or secondary contribution

* denotes equal contribution

  1. Flannick J*, Fuchsberger C*, Mahajan A*, Teslovich TM, Agarwala V, Gaulton KJ, et al. Sequence data and association statistics from 12,940 type 2 diabetes cases and controls. Sci Data. 2018;5.
  2. Fuchsberger C*, Flannick J*, Teslovich TM*, Mahajan A*, Agarwala V*, Gaulton KJ*, et al. The genetic architecture of type 2 diabetes. Nature. 2016;536(7614):41-7.
  3. Flannick J, Florez JC. Type 2 diabetes: genetic data sharing to advance complex disease research. Nature Reviews Genetics. 2016;17(9):535-49.
  4. Flannick J, Johansson S, Njolstad PR. Common and rare forms of diabetes mellitus: towards a continuum of diabetes subtypes. Nature Reviews Endocrinology. 2016;12(7):394-406.
  5. Majithia AR, Flannick J, Shahinian P, Guo M, Bray MA et al. Rare variants in PPARG with decreased activity in adipocyte differentiation are associated with increased risk of type 2 diabetes. Proceedings of the National Academy of Sciences of the United States of America. 2014;111(36):13127-32.
  6. Flannick J, Thorleifsson G, Beer NL, Jacobs SB, Grarup N et al. Loss-of-function mutations in SLC30A8 protect against type 2 diabetes. Nature Genetics. 2014;46(4):357-63.
  7. Flannick J*, Beer NL*, Bick AG, Agarwala V, Molnes J et al. Assessing the phenotypic effects in the general population of rare variants in genes for a dominant Mendelian form of diabetes. Nature Genetics. 2013;45(11):1380-5.
  8. Agarwala V*, Flannick J*, Sunyaev S, GoTD Consortium, Altshuler D. Evaluating empirical bounds on complex disease genetic architecture. Nature Genetics. 2013;45(12):1418-27.
  9. Flannick J, Korn JM, Fontanillas P, Grant GB, Banks E, Depristo MA, Altshuler D. Efficiency and power as a function of sequence coverage, SNP array density, and imputation. PLoS Computational Biology. 2012;8(7):e1002604.
  10. Bick AG, Flannick J, Ito K, Cheng S, Vasan RS et al. Burden of rare sarcomere gene variants in the Framingham and Jackson Heart Study cohorts. American Journal of Human Genetics. 2012;91(3):513-9.
  11. Jimenez NL, Flannick J, Yahyavi M, Li J, Bardakjian T, Tonkin L, Schneider A, Sherr EH, Slavotinek AM. Targeted ‘next-generation’ sequencing in anophthalmia and microphthalmia patients confirms SOX2, OTX2 and FOXE3 mutations. BMC Medical Genetics. 2011;12:172.
  12. Flannick J, Novak A, Do CB, Srinivasan BS, Batzoglou S. Automatic parameter learning for multiple local network alignment. Journal of Computational Biology. 2009;16(8):1001-22.
  13. Flannick J, Novak A, Do CB, Srinivasan BS, Batzoglou S. Automatic parameter learning for multiple network alignment. Twelfth Annual International Conference on Research in Computational Molecular Biology; 2008; Singapore.
  14. Flannick J*, Novak A*, Srinivasan BS, McAdams HH, Batzoglou S. Graemlin: general and robust alignment of multiple large interaction networks. Genome Research. 2006;16(9):1169-81.
  15. Flannick J, Batzoglou S. Using multiple alignments to improve seeded local alignment algorithms. Nucleic acids research. 2005;33(14):4563-77.

Additional publications

  1. Bonàs-Guarch S, Guindo-Martínez M, Miguel-Escalada I, Grarup N, Sebastian D et al. Re-analysis of public genetic data reveals a rare X-chromosomal variant associated with type 2 diabetes. Nat Commun. 2018;9(1):321.
  2. Crawford KM, Gallego-Fabrega C, Kourkoulis C, Miyares L, Marini S, et al. Cerebrovascular Disease Knowledge Portal: An Open-Access Data Resource to Accelerate Genomic Discoveries in Stroke. Stroke. 2018 Feb;49(2):470-475.
  3. Noh HJ, Tang R, Flannick J, O’Dushlaine C, Swofford R et al. Integrating evolutionary and regulatory information with a multispecies approach implicates genes and pathways in obsessive-compulsive disorder. Nat Commun. 2017;8(1):774.
  4. Mercader JM, Liao RG, Bell AD, Dymek Z, Estrada K et al. A Loss-of-Function Splice Acceptor Variant in IGF2 Is Protective for Type 2 Diabetes. Diabetes. 2017;66(11):2903-2914.
  5. Rusu V, Hoch E, Mercader JM, Tenen DE, Gymrek M et al. Type 2 Diabetes Variants Disrupt Function of SLC16A11 through Two Distinct Mechanisms. Cell. 2017;170(1):199-212.
  6. Manning A, Highland HM, Gasser J, Sim X, Tukiainen T et al. A Low-Frequency Inactivating AKT2 Variant Enriched in the Finnish Population Is Associated With Fasting Insulin Levels and Type 2 Diabetes Risk. Diabetes. 2017;66(7):2019-2032.
  7. Najmi LA, Aukrust I, Flannick J, Molnes J, Burtt N et al. Functional Investigations of HNF1A Identify Rare Variants as Risk Factors for Type 2 Diabetes in the General Population. Diabetes. 2017;66(2):335-346.
  8. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536(7616):285-91.
  9. Clapham KR, Chu AY, Wessel J, Natarajan P, Flannick J et al. A null mutation in ANGPTL8 does not associate with either plasma glucose or type 2 diabetes in humans. BMC Endocrine Disorders. 2016;16:7.
  10. Imamura M, Takahashi A, Yamauchi T, Hara K, Yasuda K et al. Genome-wide association studies in the Japanese population identify seven novel loci for type 2 diabetes. Nature Communications. 2016;7:10531.
  11. Gaulton KJ, Ferreira T, Lee Y, Raimondo A, Magi R et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nature Genetics. 2015;47(12):1415-25.
  12. Moutsianas L, Agarwala V, Fuchsberger C, Flannick J, Rivas MA et al. The power of gene-based rare variant methods to detect disease-associated variation and test hypotheses about complex disease. PLoS Genetics. 2015;11(4):e1005165.
  13. Mahajan A, Sim X, Ng HJ, Manning A, Rivas MA et al. Identification and functional characterization of G6PC2 coding variants influencing glycemic traits define an effector transcript at the G6PC2-ABCB11 locus. PLoS Genetics. 2015;11(1):e1004876.
  14. Roberts AM, Ware JS, Herman DS, Schafer S, Baksi J et al. Integrated allelic, transcriptional, and phenomic dissection of the cardiac effects of titin truncations in health and disease. Science Translational Medicine. 2015;7(270):270ra6.
  15. Lim ET, Liu YP, Chan Y, Tiinamaija T, Karajamaki A et al. A novel test for recessive contributions to complex diseases implicates Bardet-Biedl syndrome gene BBS10 in idiopathic type 2 diabetes and obesity. American Journal of Human Genetics. 2014;95(5):509-20.
  16. Jaiswal S, Fontanillas P, Flannick J, Manning A, Grauman PV et al. Age-related clonal hematopoiesis associated with adverse outcomes. The New England Journal of Medicine. 2014;371(26):2488-98.
  17. Lim ET, Wurtz P, Havulinna AS, Palta P, Tukiainen T et al. Distribution and medical impact of loss-of-function variants in the Finnish founder population. PLoS Genetics. 2014;10(7):e1004494.
  18. SIGMA Type 2 Diabetes Consortium, Estrada K, Aukrust I, Bjorkhaug L, Burtt NP et al. Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. JAMA. 2014;311(22):2305-14.
  19. Wang SR, Agarwala V, Flannick J, Chiang CW, Altshuler D et al. Simulation of Finnish population history, guided by empirical genetic data, to assess power of rare-variant tests in Finland. American Journal of Human Genetics. 2014;94(5):710-20.
  20. Lange LA, Hu Y, Zhang H, Xue C, Schmidt EM et al. Whole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol. American Journal of Human Genetics. 2014;94(2):233-45.
  21. Ito K, Bick AG, Flannick J, Friedman DJ, Genovese G et al. Increased burden of cardiovascular disease in carriers of APOL1 genetic variants. Circulation Research. 2014;114(5):845-50.
  22. Liu L, Sabo A, Neale BM, Nagaswamy U, Stevens C et al. Analysis of rare, exonic variation amongst subjects with autism spectrum disorders and population controls. PLoS Genetics. 2013;9(4):e1003443.
  23. Lim ET, Raychaudhuri S, Sanders SJ, Stevens C, Sabo A et al. Rare complete knockouts in humans: population distribution and significant role in autism spectrum disorders. Neuron. 2013;77(2):235-42.
  24. Diogo D, Kurreeman F, Stahl EA, Liao KP, Gupta N et al. Rare, low-frequency, and common variants in the protein-coding sequence of biological candidate genes from GWASs contribute to risk of rheumatoid arthritis. American Journal of Human Genetics. 2013;92(1):15-27.
  25. Neale BM, Kou Y, Liu L, Ma’ayan A, Samocha KE et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature. 2012;485(7397):242-5.
  26. Srinivasan BS, Evans EA, Flannick J, Patterson AS, Chang CC et al. A universal carrier test for the long tail of Mendelian disease. Reproductive Biomedicine Online. 2010;21(4):537-51.
  27. Boutte CC, Srinivasan BS, Flannick J, Novak AF, Martens AT et al. Genetic and computational identification of a conserved bacterial metabolic module. PLoS Genetics. 2008;4(12):e1000310.
  28. Srinivasan BS, Shah NH, Flannick J, Abeliuk E, Novak AF et al. Current progress in network research: toward reference networks for key model organisms. Briefings in Bioinformatics. 2007;8(5):318-32.
  29. Burdick D, Calimlin M, Flannick J, Gehrke J, Yiu T. MAFIA: A performance study of mining maximal frequent itemsets. Workshop on Frequent Itemset Mining Implementations (FIMI); Melbourne, FL2003.
  30. Ayres J, Flannick J, Gehrke J, Yiu T, editors. Sequential pattern mining using a bitmap representation. Eighth ACM SIGKDD international conference on knowledge discovery and data mining (KDD); 2002; Edmonton, AB, Canada.

Book chapters

  1. Flannick J, Lowe WL. SLC30A8: A complex road from association to function. In: Florez JC, editor. The Genetics of Type 2 Diabetes and Related Traits: Springer; 2016. p. 379-401.
  2. Gaulton KJ, Flannick J, Fuchsberger C. Whole genome and exome sequencing of type 2 diabetes. In: Gloyn AL, McCarthy MI, editors. Genetics in Diabetes. Basel: Karger; 2014. p. 29-41.


  1. Flannick JAlgorithms for biological network alignment. Stanford, CA: Stanford University; 2009.