Publications with primary or secondary contribution

* denotes equal contribution

  1. Kwak SH, Srinivasan S, Chen L, Todd J, Mercader J, Jensen E, Divers J, Mottl A, Pihoker C, Gandica R, Laffel L, Isganaitis E, Haymond M, Levitsky L, Pollin T, Florez J, Flannick J. Genetic architecture and biology of youth-onset type 2 diabetes. Nature Metabolism. 2024 Feb.
  2. Costanzo MC, Roselli C, Brandes M, Duby M, Hoang Q, Jang D, Koesterer R, Kudtarkar P, Moriondo A, Nguyen T, Ruebenacker O, Smadbeck P, Sun Y, Butterworth AS, Aragam KG, Lumbers RT, Khera AV, Lubitz SA, Ellinor PT, Gaulton KJ, Flannick J*, Burtt NP*. Cardiovascular Disease Knowledge Portal: A Community Resource for Cardiovascular Disease Research. Circ Genom Precis Med. 2023 Dec;16(6):e004181.
  3. Kudtarkar P, Costanzo MC, Sun Y, Jang D, Koesterer R, Mychaleckyj JC, Nayak U, Onengut-Gumuscu S, Rich SS, Flannick J*, Gaulton KJ*, Burtt NP*. Leveraging type 1 diabetes human genetic and genomic data in the T1D knowledge portal. PLoS Biol. 2023 Aug 10;21(8):e3002233.
  4. Costanzo MC, von Grotthuss M, Massung J, Jang D, Caulkins L, Koesterer R, Gilbert C, Welch RP, Kudtarkar P, Hoang Q, Boughton AP, Singh P, Sun Y, Duby M, Moriondo A, Nguyen T, Smadbeck P, Alexander BR, Brandes M, Carmichael M, Dornbos P, Green T, Huellas-Bruskiewicz KC, Ji Y, Kluge A, McMahon AC, Mercader JM, Ruebenacker O, Sengupta S, Spalding D, Taliun D; AMP-T2D Consortium; Smith P, Thomas MK, Akolkar B, Brosnan MJ, Cherkas A, Chu AY, Fauman EB, Fox CS, Kamphaus TN, Miller MR, Nguyen L, Parsa A, Reilly DF, Ruetten H, Wholley D, Zaghloul NA, Abecasis GR, Altshuler D, Keane TM, McCarthy MI, Gaulton KJ, Florez JC, Boehnke M, Burtt NP*, Flannick J*. The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits. Cell Metab. 2023 Apr 4;35(4):695-710.e6.
  5. Dornbos P, Koesterer R, Ruttenburg A, Nguyen T, Cole JB; AMP-T2D-GENES Consortium; Leong A, Meigs JB, Florez JC, Rotter JI, Udler MS, Flannick J. A combined polygenic score of 21,293 rare and 22 common variants improves diabetes diagnosis based on hemoglobin A1C levels. Nat Genet. 2022 Nov;54(11):1609-1614.
  6. Dornbos P, Singh P, Jang DK, Mahajan A, Biddinger SB, Rotter JI, McCarthy MI, Flannick J. Evaluating human genetic support for hypothesized metabolic disease genes. Cell Metab. 2022 May 3;34(5):661-666.
  7. Flannick J. Data-driven type 2 diabetes patient clusters predict metabolic surgery outcomes. Lancet Diabetes Endocrinol. 2022 Mar;10(3):150-151.
  8. Mejhert N, Gabriel KR, Frendo-Cumbo S, Krahmer N, Song J, Kuruvilla L, Chitraju C, Boland S, Jang DK, von Grotthuss M, Costanzo MC, Rydén M, Olzmann JA, Flannick J, Burtt NP, Farese RV Jr, Walther TC. The Lipid Droplet Knowledge Portal: A resource for systematic analyses of lipid droplet biology. Dev Cell. 2022 Feb 7;57(3):387-397.e4.
  9. Hindy G, Dornbos P, Chaffin MD, Liu DJ, Wang M, Selvaraj MS, Zhang D, et al. Rare coding variants in 35 genes associate with circulating lipid levels-A multi-ancestry analysis of 170,000 exomes. Am J Hum Genet. 2022 Jan 6;109(1):81-96.
  10. Kiel DP, Kemp JP, Rivadeneira F, Westendorf JJ, Karasik D, Duncan EL, Imai Y, Müller R, Flannick J, Bonewald L, Burtt N. The Musculoskeletal Knowledge Portal: Making Omics Data Useful to the Broader Scientific Community. J Bone Miner Res. 2020 Sep;35(9):1626-1633.
  11. Flannick J, Mercader JM, Fuchsberger C, Udler MS, Mahajan A, et al. Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls. Nature. 2019 Jun;570(7759):71-76.
  12. Flannick J. The Contribution of Low-Frequency and Rare Coding Variation to Susceptibility to Type 2 Diabetes. Curr Diab Rep. 2019 Apr 8;19(5):25.
  13. Udler MS, Kim J, von Grotthuss M, Bonàs-Guarch S, Cole JB et al. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis. PLoS Med. 2018 Sep 21;15(9):e1002654.
  14. 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.
  15. 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.
  16. Flannick J, Florez JC. Type 2 diabetes: genetic data sharing to advance complex disease research. Nature Reviews Genetics. 2016;17(9):535-49.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. Flannick J, Batzoglou S. Using multiple alignments to improve seeded local alignment algorithms. Nucleic acids research. 2005;33(14):4563-77.

Additional publications

  1. DeForest N, Kavitha B, Hu S, Isaac R, Krohn L et al. Human gain-of-function variants in HNF1A confer protection from diabetes but independently increase hepatic secretion of atherogenic lipoproteins. Cell Genom. 2023 May 30;3(7):100339.
  2. Huerta-Chagoya A, Schroeder P, Mandla R, Deutsch AJ et al. The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes. Diabetologia. 2023 Jul;66(7):1273-1288.
  3. RADIANT Study Group. The Rare and Atypical Diabetes Network (RADIANT) Study: Design and Early Results. Diabetes Care. 2023 Jun 1;46(6):1265-1270.
  4. Han SK, McNulty MT, Benway CJ, Wen P, Greenberg A et al. Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs. Nat Commun. 2023 Apr 19;14(1):2229.
  5. Wieder N, Fried JC, Kim C, Sidhom EH, Brown MR, Marshall JL et al. FALCON systematically interrogates free fatty acid biology and identifies a novel mediator of lipotoxicity. Cell Metab. 2023 May 2;35(5):887-905.e11.
  6. Aragam KG, Jiang T, Goel A, Kanoni S, Wolford BN, Atri DS et al Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nat Genet. 2022 Dec;54(12):1803-1815.
  7. Zhang J, Chen W, Chen G, Flannick J, Fikse E, Smerin G, Degner K, Yang Y, Xu C; AMP-T2D-GENES Consortium; Li Y, Hanover JA, Simonds WF. Ancestry-specific high-risk gene variant profiling unmasks diabetes-associated genes. Hum Mol Genet. 2022 Oct 18:ddac255.
  8. Unni DR, Moxon SAT, Bada M, Brush M, Bruskiewich R et al. Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science. Clin Transl Sci. 2022 Aug;15(8):1848-1855.
  9. Zhong S, Chèvre R, Castaño Mayan D, Corlianò M et al. Haploinsufficiency of CYP8B1 associates with increased insulin sensitivity in humans. J Clin Invest. 2022 Nov 1;132(21):e152961.
  10. Deaton AM, Dubey A, Ward LD, Dornbos P, Flannick J et al. Rare loss of function variants in the hepatokine gene INHBE protect from abdominal obesity. Nat Commun. 2022 Jul 27;13(1):4319.
  11. Fecho K, Thessen AE, Baranzini SE, Bizon C, Hadlock JJ, Huang S et al. Progress toward a universal biomedical data translator. Clin Transl Sci. 2022 May 25;15(8):1838–47.
  12. Forgetta V, Jiang L, Vulpescu NA, Hogan MS, Chen S, Morris JA et al. An effector index to predict target genes at GWAS loci. Hum Genet. 2022 Aug;141(8):1431-1447.
  13. McNulty MT, Fermin D, Eichinger F, Jang D, Kretzler M, Burtt NP, Pollak MR, Flannick J, Weins A, Friedman DJ; Nephrotic Syndrome Study Network (NEPTUNE); Sampson MG. A glomerular transcriptomic landscape of apolipoprotein L1 in Black patients with focal segmental glomerulosclerosis. Kidney Int. 2022 Jul;102(1):136-148.
  14. Santoro N, Chen L, Todd J, Divers J, Shah AS, Gidding SS, Burke B, Haymond M, Lange L, Marcovina S, Flannick J, Caprio S, Florez JC, Srinivasan S. Genome-wide Association Study of Lipid Traits in Youth With Type 2 Diabetes. J Endocr Soc. 2021 Aug 18;5(11):bvab139.
  15. Todd JN, Kleinberger JW, Zhang H, Srinivasan S, Tollefsen SE, Levitsky LL et al. Monogenic Diabetes in Youth With Presumed Type 2 Diabetes: Results From the Progress in Diabetes Genetics in Youth (ProDiGY) Collaboration. Diabetes Care. 2021 Aug 6;44(10):2312–9.
  16. Goodrich JK, Singer-Berk M, Son R, Sveden A, Wood J et al. Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes. Nat Commun. 2021 Jun 9;12(1):3505.
  17. Srinivasan S, Chen L, Todd J, Divers J, Gidding S, Chernausek S, Gubitosi-Klug RA, Kelsey MM, Shah R, Black MH, Wagenknecht LE, Manning A, Flannick J, Imperatore G, Mercader JM, Dabelea D, Florez JC; ProDiGY Consortium. The First Genome-Wide Association Study for Type 2 Diabetes in Youth: The Progress in Diabetes Genetics in Youth (ProDiGY) Consortium. Diabetes. 2021 Apr;70(4):996-1005.
  18. Kessler MD, Loesch DP, Perry JA, Heard-Costa NL, Taliun D et al. De novo mutations across 1,465 diverse genomes reveal mutational insights and reductions in the Amish founder population. Proc Natl Acad Sci U S A. 2020 Feb 4;117(5):2560-2569.
  19. Dwivedi OP, Lehtovirta M, Hastoy B, Chandra V, Krentz NAJ, Kleiner S et al. Loss of ZnT8 function protects against diabetes by enhanced insulin secretion. Nat Genet. 2019 Nov;51(11):1596-1606.
  20. Jiao Y, Ahmed U, Sim MFM, Bejar A, Zhang X, Talukder MMU et al. Discovering metabolic disease gene interactions by correlated effects on cellular morphology. Mol Metab. 2019 Jun;24:108-119.
  21. Gusarova V, O'Dushlaine C, Teslovich TM, Benotti PN, Mirshahi T et al. Genetic inactivation of ANGPTL4 improves glucose homeostasis and is associated with reduced risk of diabetes. Nat Commun. 2018 Jun 13;9(1):2252.
  22. Ganna A, Satterstrom FK, Zekavat SM, Das I, Kurki MI, Churchhouse C, Alfoldi J et al. Quantifying the Impact of Rare and Ultra-rare Coding Variation across the Phenotypic Spectrum. Am J Hum Genet. 2018 Jun 7;102(6):1204-1211.
  23. Martagón AJ, Bello-Chavolla OY, Arellano-Campos O, Almeda-Valdés P et al. Mexican Carriers of the HNF1A p.E508K Variant Do Not Experience an Enhanced Response to Sulfonylureas. Diabetes Care. 2018 Aug;41(8):1726-1731.
  24. Mahajan A, Wessel J, Willems SM, Zhao W, Robertson NR et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat Genet. 2018 Apr;50(4):559-571.
  25. Merino J, Leong A, Liu CT, Porneala B, Walford GA, von Grotthuss M et al. Metabolomics insights into early type 2 diabetes pathogenesis and detection in individuals with normal fasting glucose. Diabetologia. 2018 Jun;61(6):1315-1324.
  26. Kayatekin C, Amasino A, Gaglia G, Flannick J, Bonner JM et al. Translocon Declogger Ste24 Protects against IAPP Oligomer-Induced Proteotoxicity. Cell. 2018 Mar 22;173(1):62-73.e9.
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. 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.
  32. 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.
  33. 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.
  34. 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.
  35. 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.
  36. 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.
  37. 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.
  38. 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.
  39. 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.
  40. 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.
  41. 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.
  42. 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.
  43. 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.
  44. 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.
  45. 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.
  46. 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.
  47. 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.
  48. 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.
  49. 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.
  50. 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.
  51. 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.
  52. 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.
  53. 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.
  54. 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.
  55. 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.
  56. 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 J. Algorithms for biological network alignment. Stanford, CA: Stanford University; 2009.