Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity

A Publisher Correction to this article was published on 03 June 2019

A Publisher Correction to this article was published on 16 March 2018

An update to this article was published on 16 March 2018

This article has been updated

Abstract

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity.

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Fig. 1: Effect sizes of the 14 BMI-associated rare and low frequency coding variants by variant minor allele frequency.
Fig. 2: Heat map showing DEPICT gene set enrichment results for rare and low-frequency coding SNVs with suggestive and significant evidence of association with BMI.

Change history

  • 03 June 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

  • 16 March 2018

    In the version of this article originally published, one of the two authors with the name Wei Zhao was omitted from the author list and the affiliations for both authors were assigned to the single Wei Zhao in the author list. In addition, the ORCID for Wei Zhao (Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA) was incorrectly assigned to author Wei Zhou. The errors have been corrected in the HTML and PDF versions of the article.

  • 16 March 2018

    In the published version of this paper, the name of author Emanuele Di Angelantonio was misspelled. This error has now been corrected in the HTML and PDF versions of the article.

References

  1. 1.

    Bray, G. A. & Ryan, D. H. Update on obesity pharmacotherapy. Ann. NY Acad. Sci. 1311, 1–13 (2014).

    CAS  PubMed  Google Scholar 

  2. 2.

    Bray, G. A., Frühbeck, G., Ryan, D. H. & Wilding, J. P. Management of obesity. Lancet 387, 1947–1956 (2016).

    PubMed  Google Scholar 

  3. 3.

    Monda, K. L. et al. A meta-analysis identifies new loci associated with body mass index in individuals of African ancestry. Nat. Genet. 45, 690–696 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Wen, W. et al. Meta-analysis of genome-wide association studies in East Asian-ancestry populations identifies four new loci for body mass index. Hum. Mol. Genet. 23, 5492–5504 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Winkler, T. W. et al. The influence of age and sex on genetic associations with adult body size and shape: a large-scale genome-wide interaction study. PLoS Genet. 11, e1005378 (2015).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Akiyama, M. et al. Genome-wide association study identifies 112 new loci for body mass index in the Japanese population. Nat. Genet. 49, 1458–1467 (2017).

    CAS  PubMed  Google Scholar 

  8. 8.

    van der Klaauw, A. A. & Farooqi, I. S. The hunger genes: pathways to obesity. Cell 161, 119–132 (2015).

    PubMed  Google Scholar 

  9. 9.

    Edwards, S. L., Beesley, J., French, J. D. & Dunning, A. M. Beyond GWASs: illuminating the dark road from association to function. Am. J. Hum. Genet. 93, 779–797 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Stratigopoulos, G. et al. Hypomorphism of Fto and Rpgrip1l causes obesity in mice. J. Clin. Invest. 126, 1897–1910 (2016).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Stratigopoulos, G., LeDuc, C. A., Cremona, M. L., Chung, W. K. & Leibel, R. L. Cut-like homeobox 1 (CUX1) regulates expression of the fat mass and obesity-associated and retinitis pigmentosa GTPase regulator–interacting protein-1-like (RPGRIP1L) genes and coordinates leptin receptor signaling. J. Biol. Chem. 286, 2155–2170 (2011).

    CAS  PubMed  Google Scholar 

  12. 12.

    Stratigopoulos, G. et al. Hypomorphism for RPGRIP1L, a ciliary gene vicinal to the FTO locus, causes increased adiposity in mice. Cell Metab. 19, 767–779 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Smemo, S. et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507, 371–375 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Marouli, E. et al. Rare and low-frequency coding variants alter human adult height. Nature 542, 186–190 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Fuchsberger, C. et al. The genetic architecture of type 2 diabetes. Nature 536, 41–47 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Sina, M. et al. Phenotypes in three pedigrees with autosomal dominant obesity caused by haploinsufficiency mutations in the melanocortin-4 receptor gene. Am. J. Hum. Genet. 65, 1501–1507 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Pearce, L. R. et al. KSR2 mutations are associated with obesity, insulin resistance, and impaired cellular fuel oxidation. Cell 155, 765–777 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Hinney, A. et al. Several mutations in the melanocortin-4 receptor gene including a nonsense and a frameshift mutation associated with dominantly inherited obesity in humans. J. Clin. Endocrinol. Metab. 84, 1483–1486 (1999).

    CAS  PubMed  Google Scholar 

  20. 20.

    Purcell, S. M. et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185–190 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    van den Berg, L. et al. Melanocortin-4 receptor gene mutations in a Dutch cohort of obese children. Obesity 19, 604–611 (2011).

    Google Scholar 

  22. 22.

    Surendran, P. et al. Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension. Nat. Genet. 48, 1151–1161 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Lunetta, K. L. et al. Rare coding variants and X-linked loci associated with age at menarche. Nat. Commun. 6, 7756 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Zhou, Z. et al. Cidea-deficient mice have lean phenotype and are resistant to obesity. Nat. Genet. 35, 49–56 (2003).

    PubMed  Google Scholar 

  25. 25.

    Tews, D. et al. Comparative gene array analysis of progenitor cells from human paired deep neck and subcutaneous adipose tissue. Mol. Cell. Endocrinol. 395, 41–50 (2014).

    CAS  PubMed  Google Scholar 

  26. 26.

    Stutzmann, F. et al. Non-synonymous polymorphisms in melanocortin-4 receptor protect against obesity: the two facets of a Janus obesity gene. Hum. Mol. Genet. 16, 1837–1844 (2007).

    CAS  PubMed  Google Scholar 

  27. 27.

    Lin, H. Q., Wang, Y., Chan, K. L., Ip, T. M. & Wan, C. C. Differential regulation of lipid metabolism genes in the brain of acetylcholinesterase knockout mice. J. Mol. Neurosci. 53, 397–408 (2014).

    CAS  PubMed  Google Scholar 

  28. 28.

    Vignaud, A. et al. Genetic ablation of acetylcholinesterase alters muscle function in mice. Chem. Biol. Interact. 175, 129–130 (2008).

    CAS  PubMed  Google Scholar 

  29. 29.

    Ji, Z., Mei, F. C. & Cheng, X. Epac, not PKA catalytic subunit, is required for 3T3-L1 preadipocyte differentiation. Front. Biosci. 2, 392–398 (2010).

    Google Scholar 

  30. 30.

    Yan, J. et al. Enhanced leptin sensitivity, reduced adiposity, and improved glucose homeostasis in mice lacking exchange protein directly activated by cyclic AMP isoform 1. Mol. Cell. Biol. 33, 918–926 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Almahariq, M., Mei, F. C. & Cheng, X. Cyclic AMP sensor EPAC proteins and energy homeostasis. Trends Endocrinol. Metab. 25, 60–71 (2014).

    CAS  PubMed  Google Scholar 

  32. 32.

    Kai, A. K. et al. Exchange protein activated by cAMP 1 (Epac1)-deficient mice develop β-cell dysfunction and metabolic syndrome. FASEB J. 27, 4122–4135 (2013).

    CAS  PubMed  Google Scholar 

  33. 33.

    Hardie, D. G., Ross, F. A. & Hawley, S. A. AMPK: a nutrient and energy sensor that maintains energy homeostasis. Nat. Rev. Mol. Cell Biol. 13, 251–262 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Hardie, D. G. & Ashford, M. L. AMPK: regulating energy balance at the cellular and whole body levels. Physiology 29, 99–107 (2014).

    CAS  Google Scholar 

  35. 35.

    López, M., Nogueiras, R., Tena-Sempere, M. & Diéguez, C. Hypothalamic AMPK: a canonical regulator of whole-body energy balance. Nat. Rev. Endocrinol. 12, 421–432 (2016).

    PubMed  Google Scholar 

  36. 36.

    Minokoshi, Y. et al. AMP-kinase regulates food intake by responding to hormonal and nutrient signals in the hypothalamus. Nature 428, 569–574 (2004).

    CAS  PubMed  Google Scholar 

  37. 37.

    Viollet, B. et al. The AMP-activated protein kinase α2 catalytic subunit controls whole-body insulin sensitivity. J. Clin. Invest. 111, 91–98 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Xue, B. et al. Neuronal protein tyrosine phosphatase 1B deficiency results in inhibition of hypothalamic AMPK and isoform-specific activation of AMPK in peripheral tissues. Mol. Cell. Biol. 29, 4563–4573 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Warren, H. R. et al. Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk. Nat. Genet. 49, 403–415 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Chami, N. et al. Exome genotyping identifies pleiotropic variants associated with red blood cell traits. Am. J. Hum. Genet. 99, 8–21 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Li, M. et al. SOS2 and ACP1 loci identified through large-scale exome chip analysis regulate kidney development and function. J. Am. Soc. Nephrol. 28, 981–994 (2017).

    CAS  PubMed  Google Scholar 

  42. 42.

    Liu, C. et al. Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci. Nat. Genet. 48, 1162–1170 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Schwartz, M. W., Woods, S. C., Porte, D. Jr, Seeley, R. J. & Baskin, D. G. Central nervous system control of food intake. Nature 404, 661–671 (2000).

    CAS  PubMed  Google Scholar 

  44. 44.

    Garfield, A. S. et al. A neural basis for melanocortin-4 receptor–regulated appetite. Nat. Neurosci. 18, 863–871 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Huszar, D. et al. Targeted disruption of the melanocortin-4 receptor results in obesity in mice. Cell 88, 131–141 (1997).

    CAS  PubMed  Google Scholar 

  46. 46.

    Fan, W., Boston, B. A., Kesterson, R. A., Hruby, V. J. & Cone, R. D. Role of melanocortinergic neurons in feeding and the agouti obesity syndrome. Nature 385, 165–168 (1997).

    CAS  PubMed  Google Scholar 

  47. 47.

    Yeo, G. S. H. et al. A frameshift mutation in MC4R associated with dominantly inherited human obesity. Nat. Genet. 20, 111–112 (1998).

    CAS  PubMed  Google Scholar 

  48. 48.

    Vaisse, C., Clement, K., Guy-Grand, B. & Froguel, P. A frameshift mutation in human MC4R is associated with a dominant form of obesity. Nat. Genet. 20, 113–114 (1998).

    CAS  PubMed  Google Scholar 

  49. 49.

    Lubrano-Berthelier, C. et al. Melanocortin 4 receptor mutations in a large cohort of severely obese adults: prevalence, functional classification, genotype–phenotype relationship, and lack of association with binge eating. J. Clin. Endocrinol. Metab. 91, 1811–1818 (2006).

    CAS  PubMed  Google Scholar 

  50. 50.

    Farooqi, I. S. et al. Clinical spectrum of obesity and mutations in the melanocortin 4 receptor gene. N. Engl. J. Med. 348, 1085–1095 (2003).

    CAS  PubMed  Google Scholar 

  51. 51.

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Hinney, A. et al. Melanocortin-4 receptor gene: case–control study and transmission disequilibrium test confirm that functionally relevant mutations are compatible with a major gene effect for extreme obesity. J. Clin. Endocrinol. Metab. 88, 4258–4267 (2003).

    CAS  PubMed  Google Scholar 

  53. 53.

    Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Saxena, R. et al. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat. Genet. 42, 142–148 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Miyawaki, K. et al. Inhibition of gastric inhibitory polypeptide signaling prevents obesity. Nat. Med. 8, 738–742 (2002).

    CAS  PubMed  Google Scholar 

  56. 56.

    Hansotia, T. et al. Extrapancreatic incretin receptors modulate glucose homeostasis, body weight, and energy expenditure. J. Clin. Invest. 117, 143–152 (2007).

    CAS  PubMed  Google Scholar 

  57. 57.

    Fulurija, A. et al. Vaccination against GIP for the treatment of obesity. PLoS One 3, e3163 (2008).

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Finan, B. et al. Reappraisal of GIP pharmacology for metabolic diseases. Trends Mol. Med. 22, 359–376 (2016).

    CAS  PubMed  Google Scholar 

  59. 59.

    Irwin, N. & Flatt, P. R. Evidence for beneficial effects of compromised gastric inhibitory polypeptide action in obesity-related diabetes and possible therapeutic implications. Diabetologia 52, 1724–1731 (2009).

    CAS  PubMed  Google Scholar 

  60. 60.

    Revelli, J. P. et al. Profound obesity secondary to hyperphagia in mice lacking kinase suppressor of ras 2. Obesity 19, 1010–1018 (2011).

    CAS  Google Scholar 

  61. 61.

    Costanzo-Garvey, D. L. et al. KSR2 is an essential regulator of AMP kinase, energy expenditure, and insulin sensitivity. Cell Metab. 10, 366–378 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Brommage, R. et al. High-throughput screening of mouse knockout lines identifies true lean and obese phenotypes. Obesity 16, 2362–2367 (2008).

    CAS  Google Scholar 

  63. 63.

    Liu, L. et al. Proteomic characterization of the dynamic KSR-2 interactome, a signaling scaffold complex in MAPK pathway. Biochim. Biophys. Acta 1794, 1485–1495 (2009).

    CAS  PubMed  Google Scholar 

  64. 64.

    Kühnen, P. et al. Proopiomelanocortin deficiency treated with a melanocortin-4 receptor agonist. N. Engl. J. Med. 375, 240–246 (2016).

    PubMed  Google Scholar 

  65. 65.

    Xiang, Y. Y., Dong, H., Yang, B. B., Macdonald, J. F. & Lu, W. Y. Interaction of acetylcholinesterase with neurexin-1β regulates glutamatergic synaptic stability in hippocampal neurons. Mol. Brain 7, 15 (2014).

    PubMed  PubMed Central  Google Scholar 

  66. 66.

    Bartels, C. F., Zelinski, T. & Lockridge, O. Mutation at codon 322 in the human acetylcholinesterase (ACHE) gene accounts for YT blood group polymorphism. Am. J. Hum. Genet. 52, 928–936 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Farlow, M. R. et al. Effectiveness and tolerability of high-dose (23 mg/d) versus standard-dose (10 mg/d) donepezil in moderate to severe Alzheimer’s disease: a 24-week, randomized, double-blind study. Clin. Ther. 32, 1234–1251 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Farlow, M. et al. Safety and tolerability of donepezil 23 mg in moderate to severe Alzheimer’s disease. BMC Neurol. 11, 57 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Tariot, P., Salloway, S., Yardley, J., Mackell, J. & Moline, M. Long-term safety and tolerability of donepezil 23 mg in patients with moderate to severe Alzheimer’s disease. BMC Res. Notes 5, 283 (2012).

    PubMed  PubMed Central  Google Scholar 

  70. 70.

    Hu, Y. et al. Role of exchange protein directly activated by cyclic AMP isoform 1 in energy homeostasis: regulation of leptin expression and secretion in white adipose tissue. Mol. Cell. Biol. 36, 2440–2450 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Altarejos, J. Y. et al. The Creb1 coactivator Crtc1 is required for energy balance and fertility. Nat. Med. 14, 1112–1117 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Winnier, D. A. et al. Transcriptomic identification of ADH1B as a novel candidate gene for obesity and insulin resistance in human adipose tissue in Mexican Americans from the Veterans Administration Genetic Epidemiology Study (VAGES). PLoS One 10, e0119941 (2015).

    PubMed  PubMed Central  Google Scholar 

  73. 73.

    Molotkov, A., Deltour, L., Foglio, M. H., Cuenca, A. E. & Duester, G. Distinct retinoid metabolic functions for alcohol dehydrogenase genes Adh1 and Adh4 in protection against vitamin A toxicity or deficiency revealed in double null mutant mice. J. Biol. Chem. 277, 13804–13811 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    PubMed Central  Google Scholar 

  75. 75.

    Volpicelli-Daley, L. A., Duysen, E. G., Lockridge, O. & Levey, A. I. Altered hippocampal muscarinic receptors in acetylcholinesterase-deficient mice. Ann. Neurol. 53, 788–796 (2003).

    CAS  PubMed  Google Scholar 

  76. 76.

    Ivanenkov, V. V., Murphy-Piedmonte, D. M. & Kirley, T. L. Bacterial expression, characterization, and disulfide bond determination of soluble human NTPDase6 (CD39L2) nucleotidase: implications for structure and function. Biochemistry 42, 11726–11735 (2003).

    CAS  PubMed  Google Scholar 

  77. 77.

    Jain, R. N. et al. Hip1r is expressed in gastric parietal cells and is required for tubulovesicle formation and cell survival in mice. J. Clin. Invest. 118, 2459–2470 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Engqvist-Goldstein, A. E. et al. RNAi-mediated Hip1R silencing results in stable association between the endocytic machinery and the actin assembly machinery. Mol. Biol. Cell 15, 1666–1679 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Tao, Y. X. The melanocortin-4 receptor: physiology, pharmacology, and pathophysiology. Endocr. Rev. 31, 506–543 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Stutzmann, F. et al. Prevalence of melanocortin-4 receptor deficiency in Europeans and their age-dependent penetrance in multigenerational pedigrees. Diabetes 57, 2511–2518 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. 81.

    Vaisse, C. et al. Melanocortin-4 receptor mutations are a frequent and heterogeneous cause of morbid obesity. J. Clin. Invest. 106, 253–262 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Schönke, M., Myers, M. G. Jr., Zierath, J. R. & Björnholm, M. Skeletal muscle AMP-activated protein kinase γ1H151R overexpression enhances whole body energy homeostasis and insulin sensitivity. Am. J. Physiol. Endocrinol. Metab. 309, E679–E690 (2015).

    PubMed  PubMed Central  Google Scholar 

  83. 83.

    Pellinen, T. et al. Small GTPase Rab21 regulates cell adhesion and controls endosomal traffic of β1-integrins. J. Cell Biol. 173, 767–780 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Banerjee, U. & Cheng, X. Exchange protein directly activated by cAMP encoded by the mammalian rapgef3 gene: structure, function and therapeutics. Gene 570, 157–167 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Rippey, C. et al. Formation of chimeric genes by copy-number variation as a mutational mechanism in schizophrenia. Am. J. Hum. Genet. 93, 697–710 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. 86.

    Schmitz, C., Kinge, P. & Hutter, H. Axon guidance genes identified in a large-scale RNAi screen using the RNAi-hypersensitive Caenorhabditis elegans strain nre-1(hd20) lin-15b(hd126). Proc. Natl. Acad. Sci. USA 104, 834–839 (2007).

    CAS  PubMed  Google Scholar 

  87. 87.

    Setoguchi, R. et al. Repression of the transcription factor Th-POK by Runx complexes in cytotoxic T cell development. Science 319, 822–825 (2008).

    CAS  PubMed  Google Scholar 

  88. 88.

    Widom, R. L., Culic, I., Lee, J. Y. & Korn, J. H. Cloning and characterization of hcKrox, a transcriptional regulator of extracellular matrix gene expression. Gene 198, 407–420 (1997).

    CAS  PubMed  Google Scholar 

  89. 89.

    Sun, X. et al. Deletion of Atbf1/Zfhx3 in mouse prostate causes neoplastic lesions, likely by attenuation of membrane and secretory proteins and multiple signaling pathways. Neoplasia 16, 377–389 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. 90.

    Parsons, M. J. et al. The regulatory factor ZFHX3 modifies circadian function in SCN via an AT motif–driven axis. Cell 162, 607–621 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Balzani, E. et al. The Zfhx3-mediated axis regulates sleep and interval timing in mice. Cell Rep. 16, 615–621 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. 92.

    Kao, Y. H. et al. ZFHX3 knockdown increases arrhythmogenesis and dysregulates calcium homeostasis in HL-1 atrial myocytes. Int. J. Cardiol. 210, 85–92 (2016).

    PubMed  Google Scholar 

  93. 93.

    Auer, P. L., Reiner, A. P. & Leal, S. M. The effect of phenotypic outliers and non-normality on rare-variant association testing. Eur. J. Hum. Genet. 24, 1188–1194 (2016).

    PubMed  PubMed Central  Google Scholar 

  94. 94.

    Goldstein, J. I. et al. zCall: a rare variant caller for array-based genotyping: genetics and population analysis. Bioinformatics 28, 2543–2545 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Grove, M. L. et al. Best practices and joint calling of the HumanExome BeadChip: the CHARGE Consortium. PLoS One 8, e68095 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Zhan, X., Hu, Y., Li, B., Abecasis, G. R. & Liu, D. J. RVTESTS: an efficient and comprehensive tool for rare variant association analysis using sequence data. Bioinformatics 32, 1423–1426 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. 97.

    Liu, D. J. et al. Meta-analysis of gene-level tests for rare variant association. Nat. Genet. 46, 200–204 (2014).

    CAS  PubMed  Google Scholar 

  98. 98.

    Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014).

    PubMed  PubMed Central  Google Scholar 

  99. 99.

    Winkler, T. W. et al. EasyStrata: evaluation and visualization of stratified genome-wide association meta-analysis data. Bioinformatics 31, 259–261 (2015).

    CAS  PubMed  Google Scholar 

  100. 100.

    Wu, M. C. et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82–93 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. 101.

    Price, A. L. et al. Pooled association tests for rare variants in exon-resequencing studies. Am. J. Hum. Genet. 86, 832–838 (2010).

    PubMed  PubMed Central  Google Scholar 

  102. 102.

    Kiezun, A. et al. Exome sequencing and the genetic basis of complex traits. Nat. Genet. 44, 623–630 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. 103.

    Feng, S., Liu, D., Zhan, X., Wing, M. K. & Abecasis, G. R. RAREMETAL: fast and powerful meta-analysis for rare variants. Bioinformatics 30, 2828–2829 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. 104.

    Yang, J. et al. Genomic inflation factors under polygenic inheritance. Eur. J. Hum. Genet. 19, 807–812 (2011).

    PubMed  PubMed Central  Google Scholar 

  105. 105.

    Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. 106.

    Thorleifsson, G. et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat. Genet. 41, 18–24 (2009).

    CAS  PubMed  Google Scholar 

  107. 107.

    Denny, J. C. et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations. Bioinformatics 26, 1205–1210 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. 108.

    Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

A.P.R. was supported by R01DK089256. A.W.H. is supported by an NHMRC Practitioner Fellowship (APP1103329). A.K.M. received funding from NIH/NIDDK K01DK107836. A.T.H. is a Wellcome Trust Senior Investigator (WT098395) and an NIH Research Senior Investigator. A.P.M. is a Wellcome Trust Senior Fellow in Basic Biomedical Science (WT098017). A.R.W. is supported by the European Research Council (SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC). A.U.J. is supported by the American Heart Association (13POST16500011) and the NIH (R01DK089256, R01DK101855, K99HL130580). B.K. and E.K.S. were supported by the Doris Duke Medical Foundation, the NIH (R01DK106621), the University of Michigan Internal Medicine Department, Division of Gastroenterology, the University of Michigan Biological Sciences Scholars Program and the Central Society for Clinical Research. C.J.W. is supported by the NIH (HL094535, HL109946). D.J.L. is supported by R01HG008983 and R21DA040177. D.R.W. is supported by the Danish Diabetes Academy, which is funded by the Novo Nordisk Foundation. V. Salomaa has been supported by the Finnish Foundation for Cardiovascular Research. F.W.A. is supported by Dekker scholarship–Junior Staff Member 2014T001 Netherlands Heart Foundation and the UCL Hospitals NIHR Biomedical Research Centre. F.D. is supported by the UK MRC (MC_UU_12013/1-9). G.C.-P. received scholarship support from the University of Queensland and QIMR Berghofer. G.L. is funded by the Montreal Heart Institute Foundation and the Canada Research Chair program. H.Y. and T.M.F. are supported by the European Research Council (323195; SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC). I.M.H. is supported by BMBF (01ER1206) and BMBF (01ER1507m), the NIH and the Max Planck Society. J. Haessler was supported by NHLBI R21HL121422. J.N.H. is supported by NIH R01DK075787. K.E.N. was supported by the NIH (R01DK089256, R01HD057194, U01HG007416, R01DK101855) and the American Heart Association (13GRNT16490017). M.A.R. is supported by the Nuffield Department of Clinical Medicine Award, Clarendon Scholarship. M.I.M. is a Wellcome Trust Senior Investigator (WT098381) and an NIH Research Senior Investigator. M.D. is supported by the NCI (R25CA94880, P30CA008748). P.R.N. is supported by the European Research Council (AdG; 293574), the Research Council of Norway, the University of Bergen, the KG Jebsen Foundation and the Helse Vest, Norwegian Diabetes Association. P.T.E. is supported by the NIH (1R01HL092577, R01HL128914, K24HL105780), by an Established Investigator Award from the American Heart Association (13EIA14220013) and by the Foundation Leducq (14CVD01). P.L.A. was supported by NHLBI R21HL121422 and R01DK089256. P.L.H. is supported by the NIH (NS33335, HL57818). R.S.F. is supported by the NIH (T32GM096911). R.J.F.L. is supported by the NIH (R01DK110113, U01HG007417, R01DK101855, R01DK107786). S.A.L. is supported by the NIH (K23HL114724) and a Doris Duke Charitable Foundation Clinical Scientist Development Award. T.D.S. holds an ERC Advanced Principal Investigator award. T.A.M. is supported by an NHMRC Fellowship (APP1042255). T.H.P. received Lundbeck Foundation and Benzon Foundation support. V.T. is supported by a postdoctoral fellowship from the Canadian Institutes of Health Research (CIHR). Z.K. is supported by the Leenaards Foundation, the Swiss National Science Foundation (31003A-143914) and SystemsX.ch (51RTP0_151019). Part of this work was conducted using the UK Biobank resource (project numbers 1251 and 9072). A full list of acknowledgments appears in the Supplementary Note.

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