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Related Research Units

The Manton Center for Orphan Disease Research
Genetics and Genomics Research

Research Overview

The Flannick Lab develops computational approaches to use human genetic and broader genomic data to understand or better treat human diseases, with a current focus on diabetes. Our research lies at the intersection of computer science, statistical genetics, and computational biology, and it includes the development of fundamental statistical methods and computational algorithms, large-scale exome sequence association analysis, and computational disease modeling. We are interested specifically in developing methods to quantify the phenotypic and molecular effects of coding mutations, so that they might be used to identify or prioritize novel therapeutic targets. We also have a commitment to make genetic data and tools more useful to communities who traditionally lack the means to access or interpret it.

Laboratory Projects:

  1. Interpretation of coding mutations from large-scale exome sequence data:We are involved in international consortia to aggregate and analyze exome sequence data for associations with metabolic traits, with sample sizes currently exceeding 50,000 and expected to exceed hundreds of thousands in the next few years. Our goals are to develop methods to use these data to quantify the likelihood that molecular perturbations of hypothesized disease genes impact disease-relevant phenotypes.
  2. Using data from common diseases to understand rare diseases:Because of their higher prevalence, more genetic data is often available for common diseases than for rare diseases. We are interested to explore whether data collected and analyzed for a common disease can be used to better understand rare diseases with overlapping clinical symptoms.
  3. Knowledge portals for human genetic disease:Our group develops and maintains the Knowledge Portal Network, a series of portals that aggregate and disseminate genetic association for a range of common diseases. Our goals, over time, are to extend these portals to rare diseases as well.
  4. Disease modeling and data integration:We are interested in computational models of the human disease process and using a variety of ‘omic datasets to calibrate these models, both to understand the molecular and cellular mechanisms of disease associations and to classify diseases based on shared pathophysiology. Our work on the NCATS Biomedical Translator is a first step toward this goal.

Research Background

Jason Flannick is an Assistant Professor of Pediatrics at Harvard Medical School and the Division of Genetics and Genomics at Boston Children’s Hospital, and an Associate Member of the Broad Institute of Harvard and MIT. Dr. Flannick obtained his PhD in Computer Science at Stanford University before training as a Postdoctoral Fellow in Human Genetics at Massachusetts Hospital and the Broad Institute. Dr. Flannick plays a leadership role in several national and international genetics and bioinformatics consortia, including the Accelerating Medicines Partnership for Type 2 Diabetes, a public/private partnership that funds his group to develop a public knowledge portal to make human genetic data broadly accessible to the global research community. Dr. Flannick’s research has spanned pure computer science, comparative genomics, and finally human genetics, with a current focus on using computational and statistical methods to impact human health.

Selected Publications

  1. 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.
  2. Flannick J, Florez JC. Type 2 diabetes: genetic data sharing to advance complex disease research. Nature Reviews Genetics. 2016;17(9):535-49.
  3. 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.
  4. 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.
  5. 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.
  6. 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.

Publications

  1. An alternatively translated isoform of PPARG proposes AF-1 domain inhibition as an insulin sensitization target. Diabetes. 2025 Jan 24. View Abstract
  2. Integrative proteogenomic analysis identifies COL6A3-derived endotrophin as a mediator of the effect of obesity on coronary artery disease. Nat Genet. 2025 Jan 24. View Abstract
  3. The Neurodegenerative Disease Knowledge Portal: Propelling Discovery Through the Sharing of Neurodegenerative Disease Genomic Resources. medRxiv. 2024 Dec 12. View Abstract
  4. Genome-wide discovery and integrative genomic characterization of insulin resistance loci using serum triglycerides to HDL-cholesterol ratio as a proxy. Nat Commun. 2024 Sep 14; 15(1):8068. View Abstract
  5. Rare coding variant analysis for human diseases across biobanks and ancestries. Nat Genet. 2024 Sep; 56(9):1811-1820. View Abstract
  6. Ancestry-specific high-risk gene variant profiling unmasks diabetes-associated genes. Hum Mol Genet. 2024 04 08; 33(8):655-666. View Abstract
  7. Assessing the genetic contribution of cumulative behavioral factors associated with longitudinal type 2 diabetes risk highlights adiposity and the brain-metabolic axis. medRxiv. 2024 Jan 31. View Abstract
  8. Genetic architecture and biology of youth-onset type 2 diabetes. Nat Metab. 2024 Feb; 6(2):226-237. View Abstract
  9. Cardiovascular Disease Knowledge Portal: A Community Resource for Cardiovascular Disease Research. Circ Genom Precis Med. 2023 12; 16(6):e004181. View Abstract
  10. Leveraging type 1 diabetes human genetic and genomic data in the T1D knowledge portal. PLoS Biol. 2023 08; 21(8):e3002233. View Abstract
  11. Human gain-of-function variants in HNF1A confer protection from diabetes but independently increase hepatic secretion of atherogenic lipoproteins. Cell Genom. 2023 Jul 12; 3(7):100339. View Abstract
  12. Insights from rare variants into the genetic architecture and biology of youth-onset type 2 diabetes. Res Sq. 2023 May 18. View Abstract
  13. The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes. Diabetologia. 2023 07; 66(7):1273-1288. View Abstract
  14. Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs. Nat Commun. 2023 04 19; 14(1):2229. View Abstract
  15. FALCON systematically interrogates free fatty acid biology and identifies a novel mediator of lipotoxicity. Cell Metab. 2023 05 02; 35(5):887-905.e11. View Abstract
  16. The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits. Cell Metab. 2023 04 04; 35(4):695-710.e6. View Abstract
  17. FALCON systematically interrogates free fatty acid biology and identifies a novel mediator of lipotoxicity. bioRxiv. 2023 Feb 20. View Abstract
  18. Leveraging type 1 diabetes human genetic and genomic data in the T1D Knowledge Portal. bioRxiv. 2023 Feb 05. View Abstract
  19. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nat Genet. 2022 12; 54(12):1803-1815. View Abstract
  20. Haploinsufficiency of CYP8B1 associates with increased insulin sensitivity in humans. J Clin Invest. 2022 11 01; 132(21). View Abstract
  21. Erratum. The First Genome-Wide Association Study for Type 2 Diabetes in Youth: The Progress in Diabetes Genetics in Youth (ProDiGY) Consortium. Diabetes 2021;70:996-1005. Diabetes. 2022 Oct 29. View Abstract
  22. A combined polygenic score of 21,293 rare and 22 common variants improves diabetes diagnosis based on hemoglobin A1C levels. Nat Genet. 2022 11; 54(11):1609-1614. View Abstract
  23. Rare loss of function variants in the hepatokine gene INHBE protect from abdominal obesity. Nat Commun. 2022 07 27; 13(1):4319. View Abstract
  24. Evaluating human genetic support for hypothesized metabolic disease genes. Cell Metab. 2022 05 03; 34(5):661-666. View Abstract
  25. An effector index to predict target genes at GWAS loci. Hum Genet. 2022 Aug; 141(8):1431-1447. View Abstract
  26. Data-driven type 2 diabetes patient clusters predict metabolic surgery outcomes. Lancet Diabetes Endocrinol. 2022 03; 10(3):150-151. View Abstract
  27. The Lipid Droplet Knowledge Portal: A resource for systematic analyses of lipid droplet biology. Dev Cell. 2022 02 07; 57(3):387-397.e4. View Abstract
  28. Rare coding variants in 35 genes associate with circulating lipid levels-A multi-ancestry analysis of 170,000 exomes. Am J Hum Genet. 2022 01 06; 109(1):81-96. View Abstract
  29. A glomerular transcriptomic landscape of apolipoprotein L1 in Black patients with focal segmental glomerulosclerosis. Kidney Int. 2022 07; 102(1):136-148. View Abstract
  30. Erratum. The First Genome-Wide Association Study for Type 2 Diabetes in Youth: The Progress in Diabetes Genetics in Youth (ProDiGY) Consortium. Diabetes 2021;70:996-1005. Diabetes. 2021 Oct 29. View Abstract
  31. Genome-wide Association Study of Lipid Traits in Youth With Type 2 Diabetes. J Endocr Soc. 2021 Nov 01; 5(11):bvab139. View Abstract
  32. Monogenic Diabetes in Youth With Presumed Type 2 Diabetes: Results From the Progress in Diabetes Genetics in Youth (ProDiGY) Collaboration. Diabetes Care. 2021 Aug 06. View Abstract
  33. Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes. Nat Commun. 2021 06 09; 12(1):3505. View Abstract
  34. The First Genome-Wide Association Study for Type 2 Diabetes in Youth: The Progress in Diabetes Genetics in Youth (ProDiGY) Consortium. Diabetes. 2021 04; 70(4):996-1005. View Abstract
  35. The Musculoskeletal Knowledge Portal: Making Omics Data Useful to the Broader Scientific Community. J Bone Miner Res. 2020 09; 35(9):1626-1633. View Abstract
  36. Loss of ZnT8 function protects against diabetes by enhanced insulin secretion. Nat Genet. 2019 11; 51(11):1596-1606. View Abstract
  37. Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls. Nature. 2019 06; 570(7759):71-76. View Abstract
  38. The Contribution of Low-Frequency and Rare Coding Variation to Susceptibility to Type 2 Diabetes. Curr Diab Rep. 2019 04 08; 19(5):25. View Abstract
  39. Discovering metabolic disease gene interactions by correlated effects on cellular morphology. Mol Metab. 2019 06; 24:108-119. View Abstract
  40. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis. PLoS Med. 2018 09; 15(9):e1002654. View Abstract
  41. Genetic inactivation of ANGPTL4 improves glucose homeostasis and is associated with reduced risk of diabetes. Nat Commun. 2018 06 13; 9(1):2252. View Abstract
  42. Quantifying the Impact of Rare and Ultra-rare Coding Variation across the Phenotypic Spectrum. Am J Hum Genet. 2018 06 07; 102(6):1204-1211. View Abstract
  43. Publisher Correction: Re-analysis of public genetic data reveals a rare X-chromosomal variant associated with type 2 diabetes. Nat Commun. 2018 05 30; 9(1):2162. View Abstract
  44. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat Genet. 2018 04; 50(4):559-571. View Abstract
  45. Metabolomics insights into early type 2 diabetes pathogenesis and detection in individuals with normal fasting glucose. Diabetologia. 2018 06; 61(6):1315-1324. View Abstract
  46. Translocon Declogger Ste24 Protects against IAPP Oligomer-Induced Proteotoxicity. Cell. 2018 03 22; 173(1):62-73.e9. View Abstract
  47. Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls. Sci Data. 2018 01 23; 5:180002. View Abstract
  48. Re-analysis of public genetic data reveals a rare X-chromosomal variant associated with type 2 diabetes. Nat Commun. 2018 01 22; 9(1):321. View Abstract
  49. Cerebrovascular Disease Knowledge Portal: An Open-Access Data Resource to Accelerate Genomic Discoveries in Stroke. Stroke. 2018 02; 49(2):470-475. View Abstract
  50. Sequence data and association statistics from 12,940 type 2 diabetes cases and controls. Sci Data. 2017 12 19; 4:170179. View Abstract
  51. Integrating evolutionary and regulatory information with a multispecies approach implicates genes and pathways in obsessive-compulsive disorder. Nat Commun. 2017 10 17; 8(1):774. View Abstract
  52. A Loss-of-Function Splice Acceptor Variant in IGF2 Is Protective for Type 2 Diabetes. Diabetes. 2017 11; 66(11):2903-2914. View Abstract
  53. A Low-Frequency Inactivating AKT2 Variant Enriched in the Finnish Population Is Associated With Fasting Insulin Levels and Type 2 Diabetes Risk. Diabetes. 2017 07; 66(7):2019-2032. View Abstract
  54. Functional Investigations of HNF1A Identify Rare Variants as Risk Factors for Type 2 Diabetes in the General Population. Diabetes. 2017 02; 66(2):335-346. View Abstract
  55. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016 08 18; 536(7616):285-91. View Abstract
  56. The genetic architecture of type 2 diabetes. Nature. 2016 08 04; 536(7614):41-47. View Abstract
  57. Type 2 diabetes: genetic data sharing to advance complex disease research. Nat Rev Genet. 2016 09; 17(9):535-49. View Abstract
  58. Common and rare forms of diabetes mellitus: towards a continuum of diabetes subtypes. Nat Rev Endocrinol. 2016 07; 12(7):394-406. View Abstract
  59. Genome-wide association studies in the Japanese population identify seven novel loci for type 2 diabetes. Nat Commun. 2016 Jan 28; 7:10531. View Abstract
  60. A null mutation in ANGPTL8 does not associate with either plasma glucose or type 2 diabetes in humans. BMC Endocr Disord. 2016 Jan 28; 16:7. View Abstract
  61. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat Genet. 2015 Dec; 47(12):1415-25. View Abstract
  62. The power of gene-based rare variant methods to detect disease-associated variation and test hypotheses about complex disease. PLoS Genet. 2015 Apr; 11(4):e1005165. View Abstract
  63. Identification and functional characterization of G6PC2 coding variants influencing glycemic traits define an effector transcript at the G6PC2-ABCB11 locus. PLoS Genet. 2015 Jan; 11(1):e1004876. View Abstract
  64. Integrated allelic, transcriptional, and phenomic dissection of the cardiac effects of titin truncations in health and disease. Sci Transl Med. 2015 Jan 14; 7(270):270ra6. View Abstract
  65. Age-related clonal hematopoiesis associated with adverse outcomes. N Engl J Med. 2014 Dec 25; 371(26):2488-98. View Abstract
  66. A novel test for recessive contributions to complex diseases implicates Bardet-Biedl syndrome gene BBS10 in idiopathic type 2 diabetes and obesity. Am J Hum Genet. 2014 Nov 06; 95(5):509-20. View Abstract
  67. Rare variants in PPARG with decreased activity in adipocyte differentiation are associated with increased risk of type 2 diabetes. Proc Natl Acad Sci U S A. 2014 Sep 09; 111(36):13127-32. View Abstract
  68. Distribution and medical impact of loss-of-function variants in the Finnish founder population. PLoS Genet. 2014 Jul; 10(7):e1004494. View Abstract
  69. Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. JAMA. 2014 Jun 11; 311(22):2305-14. View Abstract
  70. Simulation of Finnish population history, guided by empirical genetic data, to assess power of rare-variant tests in Finland. Am J Hum Genet. 2014 May 01; 94(5):710-20. View Abstract
  71. Loss-of-function mutations in SLC30A8 protect against type 2 diabetes. Nat Genet. 2014 Apr; 46(4):357-63. View Abstract
  72. Whole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol. Am J Hum Genet. 2014 Feb 06; 94(2):233-45. View Abstract
  73. Increased burden of cardiovascular disease in carriers of APOL1 genetic variants. Circ Res. 2014 Feb 28; 114(5):845-50. View Abstract
  74. Evaluating empirical bounds on complex disease genetic architecture. Nat Genet. 2013 Dec; 45(12):1418-27. View Abstract
  75. Assessing the phenotypic effects in the general population of rare variants in genes for a dominant Mendelian form of diabetes. Nat Genet. 2013 Nov; 45(11):1380-5. View Abstract
  76. Analysis of rare, exonic variation amongst subjects with autism spectrum disorders and population controls. PLoS Genet. 2013 Apr; 9(4):e1003443. View Abstract
  77. Rare complete knockouts in humans: population distribution and significant role in autism spectrum disorders. Neuron. 2013 Jan 23; 77(2):235-42. View Abstract
  78. Rare, low-frequency, and common variants in the protein-coding sequence of biological candidate genes from GWASs contribute to risk of rheumatoid arthritis. Am J Hum Genet. 2013 Jan 10; 92(1):15-27. View Abstract
  79. Burden of rare sarcomere gene variants in the Framingham and Jackson Heart Study cohorts. Am J Hum Genet. 2012 Sep 07; 91(3):513-9. View Abstract
  80. Efficiency and power as a function of sequence coverage, SNP array density, and imputation. PLoS Comput Biol. 2012; 8(7):e1002604. View Abstract
  81. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature. 2012 Apr 04; 485(7397):242-5. View Abstract
  82. Targeted 'next-generation' sequencing in anophthalmia and microphthalmia patients confirms SOX2, OTX2 and FOXE3 mutations. BMC Med Genet. 2011 Dec 28; 12:172. View Abstract
  83. A universal carrier test for the long tail of Mendelian disease. Reprod Biomed Online. 2010 Oct; 21(4):537-51. View Abstract
  84. Automatic parameter learning for multiple local network alignment. J Comput Biol. 2009 Aug; 16(8):1001-22. View Abstract
  85. Genetic and computational identification of a conserved bacterial metabolic module. PLoS Genet. 2008 Dec; 4(12):e1000310. View Abstract
  86. Current progress in network research: toward reference networks for key model organisms. Brief Bioinform. 2007 Sep; 8(5):318-32. View Abstract
  87. Graemlin: general and robust alignment of multiple large interaction networks. Genome Res. 2006 Sep; 16(9):1169-81. View Abstract
  88. Using multiple alignments to improve seeded local alignment algorithms. Nucleic Acids Res. 2005; 33(14):4563-77. View Abstract

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