1932

Abstract

While sequence-based genetic tests have long been available for specific loci, especially for Mendelian disease, the rapidly falling costs of genome-wide genotyping arrays, whole-exome sequencing, and whole-genome sequencing are moving us toward a future where full genomic information might inform the prognosis and treatment of a variety of diseases, including complex disease. Similarly, the availability of large populations with full genomic information has enabled new insights about the etiology and genetic architecture of complex disease. Insights from the latest generation of genomic studies suggest that our categorization of diseases as complex may conceal a wide spectrum of genetic architectures and causal mechanisms that ranges from Mendelian forms of complex disease to complex regulatory structures underlying Mendelian disease. Here, we review these insights, along with advances in the prediction of disease risk and outcomes from full genomic information.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-genom-083117-021136
2018-08-31
2024-03-28
Loading full text...

Full text loading...

/deliver/fulltext/genom/19/1/annurev-genom-083117-021136.html?itemId=/content/journals/10.1146/annurev-genom-083117-021136&mimeType=html&fmt=ahah

Literature Cited

  1. 1.  Abifadel M, Varret M, Rabès J-P, Allard D, Ouguerram K et al. 2003. Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nat. Genet. 34:154–56
    [Google Scholar]
  2. 2.  Agarwala V, Flannick J, Sunyaev S, GoT2D Consort., Altshuler D 2013. Evaluating empirical bounds on complex disease genetic architecture. Nat. Genet. 45:1418–27Describes a simulation-based methodology to directly measure the genetic architecture of type 2 diabetes.
    [Google Scholar]
  3. 3.  Arpaia E, Dumbrille-Ross A, Maler T, Neote K, Tropak M et al. 1988. Identification of an altered splice site in Ashkenazi Tay-Sachs disease. Nature 333:85–86
    [Google Scholar]
  4. 4.  Bamshad MJ, Ng SB, Bigham AW, Tabor HK, Emond MJ et al. 2011. Exome sequencing as a tool for Mendelian disease gene discovery. Nat. Rev. Genet. 12:745–55
    [Google Scholar]
  5. 5.  Berge KE, Tian H, Graf GA, Yu L, Grishin NV et al. 2000. Accumulation of dietary cholesterol in sitosterolemia caused by mutations in adjacent ABC transporters. Science 290:1771–75
    [Google Scholar]
  6. 6.  Brown BC, Asian Genet. Epidemiol. Netw. Type 2 Diabetes, Ye CJ, Price AL, Zaitlen N. 2016. Transethnic genetic correlation estimates from summary statistics. Am. J. Hum. Genet 99:76–88
    [Google Scholar]
  7. 7.  Bycroft C, Freeman C, Petkova D, Band G, Elliott LT et al. 2017. Genome-wide genetic data on ∼500,000 UK Biobank participants. bioRxiv 166298. https://doi.org/10.1101/166298
    [Crossref]
  8. 8. CARDIoGRAMplusC4D Consort., Deloukas P, Kanoni S, Willenborg C, Farrall M et al. 2013. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat. Genet. 45:25–33
    [Google Scholar]
  9. 9.  Castel SE, Cervera A, Mohammadi P, Aguet F, Reverter F et al. 2017. Modified penetrance of coding variants by cis-regulatory variation shapes human traits. bioRxiv 190397. . https://doi.org/10.1101/190397 Detects the presence of widespread regulatory variants modifying the penetrance of Mendelian disease alleles.
  10. 10.  Ceyhan-Birsoy O, Machini K, Lebo MS, Yu TW, Agrawal PB et al. 2017. A curated gene list for reporting results of newborn genomic sequencing. Genet. Med. 19:809–18
    [Google Scholar]
  11. 11.  Chatterjee N, Shi J, García-Closas M 2016. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat. Rev. Genet. 17:392–406
    [Google Scholar]
  12. 12.  Cho YS, Chen C-H, Hu C, Long J, Ong RTH et al. 2011. Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians. Nat. Genet. 44:67–72
    [Google Scholar]
  13. 13. Coron. Artery Dis. (C4D) Genet. Consort. 2011. A genome-wide association study in Europeans and South Asians identifies five new loci for coronary artery disease. Nat. Genet 43:339–44
    [Google Scholar]
  14. 14.  Cruchaga C, Haller G, Chakraverty S, Mayo K, Vallania FLM et al. 2012. Rare variants in APP, PSEN1 and PSEN2 increase risk for AD in late-onset Alzheimer's disease families. PLOS ONE 7:e31039
    [Google Scholar]
  15. 15.  Deo RC, Reich D, Tandon A, Akylbekova E, Patterson N et al. 2009. Genetic differences between the determinants of lipid profile phenotypes in African and European Americans: the Jackson Heart Study. PLOS Genet 5:e1000342
    [Google Scholar]
  16. 16. Diabetes Genet. Initiat. Broad Inst. Harv. MIT, Lund Univ., Novartis Inst. BioMed. Res. 2007. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316:1331–36
    [Google Scholar]
  17. 17.  Dudbridge F 2013. Power and predictive accuracy of polygenic risk scores. PLOS Genet 9:e1003348Analyzes the predictive power of PRSs to predict disease risk.
    [Google Scholar]
  18. 18.  Erdmann J, Grosshennig A, Braund PS, König IR, Hengstenberg C et al. 2009. New susceptibility locus for coronary artery disease on chromosome 3q22.3. Nat. Genet. 41:280–82
    [Google Scholar]
  19. 19.  Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R et al. 2017. Genetic analysis of over one million people identifies 535 novel loci for blood pressure. bioRxiv 198234. https://doi.org/10.1101/198234
    [Crossref]
  20. 20.  Evans DM, Visscher PM, Wray NR 2009. Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk. Hum. Mol. Genet. 18:3525–31
    [Google Scholar]
  21. 21.  Flannick J, Johansson S, Njølstad PR 2016. Common and rare forms of diabetes mellitus: towards a continuum of diabetes subtypes. Nat. Rev. Endocrinol. 12:394–406Reviews the concept of disease subtypes existing on a spectrum from Mendelian to complex.
    [Google Scholar]
  22. 22.  Franceschini N, Carty C, Bůzková P, Reiner AP, Garrett T et al. 2011. Association of genetic variants and incident coronary heart disease in multiethnic cohorts: the PAGE study. Circ. Cardiovasc. Genet. 4:661–72
    [Google Scholar]
  23. 23.  Franke A, McGovern DPB, Barrett JC, Wang K, Radford-Smith GL et al. 2010. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci. Nat. Genet. 42:1118–25
    [Google Scholar]
  24. 24.  Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V et al. 2016. The genetic architecture of type 2 diabetes. Nature 536:41–47Uses the methodology of Agarwala et al. (2) to measure the genetic architecture of type 2 diabetes.
    [Google Scholar]
  25. 25.  Garcia CK 2001. Autosomal recessive hypercholesterolemia caused by mutations in a putative LDL receptor adaptor protein. Science 292:1394–98
    [Google Scholar]
  26. 26.  Himes P, Kauffman TL, Muessig KR, Amendola LM, Berg JS et al. 2017. Genome sequencing and carrier testing: decisions on categorization and whether to disclose results of carrier testing. Genet. Med. 19:803–8
    [Google Scholar]
  27. 27. Int. Consort. Blood Press. Genome-Wide Assoc. Stud. 2011. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478:103–9
    [Google Scholar]
  28. 28. Int. Mult. Scler. Genet. Consort. (IMSGC). 2010. Evidence for polygenic susceptibility to multiple sclerosis—the shape of things to come. Am. J. Hum. Genet. 86:621–25
    [Google Scholar]
  29. 29. Int. Schizophr. Consort. 2009. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460:748–52
    [Google Scholar]
  30. 30.  Khera AV, Chaffin M, Aragam K, Emdin CA, Klarin D et al. 2017. Genome-wide polygenic score to identify a monogenic risk-equivalent for coronary disease. bioRxiv 218388 . https://doi.org/10.1101/218388 Demonstrates that PRS quantiles can have equal predictive power to monogenic variants.
    [Crossref]
  31. 31.  Khera AV, Emdin CA, Drake I, Natarajan P, Bick AG et al. 2016. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375:2349–58Describes a methodology of stratifying patients by PRS quantiles to predict genetic risk.
    [Google Scholar]
  32. 32.  Khera AV, Kathiresan S 2017. Genetics of coronary artery disease: discovery, biology and clinical translation. Nat. Rev. Genet. 18:331–44
    [Google Scholar]
  33. 33.  Khera AV, Kathiresan S 2017. Is coronary atherosclerosis one disease or many? Setting realistic expectations for precision medicine. Circulation 135:1005–7Outlines the importance of identifying complex disease risk as homogeneous or heterogeneous.
    [Google Scholar]
  34. 34.  Kooperberg C, LeBlanc M, Obenchain V 2010. Risk prediction using genome-wide association studies. Genet. Epidemiol. 34:643–52
    [Google Scholar]
  35. 35.  Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN et al. 2010. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467:832–38
    [Google Scholar]
  36. 36.  Lehrman M, Schneider W, Sudhof T, Brown M, Goldstein J, Russell D 1985. Mutation in LDL receptor: Alu-Alu recombination deletes exons encoding transmembrane and cytoplasmic domains. Science 227:140–46
    [Google Scholar]
  37. 37.  Lerman C, Narod S, Schulman K, Hughes C, Gomez-Caminero A et al. 1996. BRCA1 testing in families with hereditary breast-ovarian cancer: a prospective study of patient decision making and outcomes. JAMA 275:1885–92
    [Google Scholar]
  38. 38.  Li Z, Chen J, Yu H, He L, Xu Y et al. 2017. Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia. Nat. Genet. 49:1576–83
    [Google Scholar]
  39. 39.  Machiela MJ, Chen C-Y, Chen C, Chanock SJ, Hunter DJ, Kraft P 2011. Evaluation of polygenic risk scores for predicting breast and prostate cancer risk. Genet. Epidemiol. 35:506–14
    [Google Scholar]
  40. 40.  Márquez-Luna C, Loh P-R, South Asian Type 2 Diabetes (SAT2D) Consort., SIGMA Type 2 Diabetes Consort., Price AL. 2017. Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genet. Epidemiol 41:811–23
    [Google Scholar]
  41. 41.  Martin AR, Costa HA, Lappalainen T, Henn BM, Kidd JM et al. 2014. Transcriptome sequencing from diverse human populations reveals differentiated regulatory architecture. PLOS Genet 10:e1004549
    [Google Scholar]
  42. 42.  Martin AR, Gignoux CR, Walters RK, Wojcik GL, Neale BM et al. 2017. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100:635–49Demonstrates the impact of genetic ancestry on the accuracy of current PRS methods.
    [Google Scholar]
  43. 43.  Medina-Gomez C, Felix JF, Estrada K, Peters MJ, Herrera L et al. 2015. Challenges in conducting genome-wide association studies in highly admixed multi-ethnic populations: the Generation R Study. Eur. J. Epidemiol. 30:317–30
    [Google Scholar]
  44. 44.  Mega JL, Stitziel NO, Smith JG, Chasman DI, Caulfield M et al. 2015. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet 385:2264–71
    [Google Scholar]
  45. 45.  Miki Y, Swensen J, Shattuck-Eidens D, Futreal PA, Harshman K et al. 1994. A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. . Science 266:66–71
    [Google Scholar]
  46. 46.  Mitchell JJ, Capua A, Clow C, Scriver CR 1996. Twenty-year outcome analysis of genetic screening programs for Tay-Sachs and β-thalassemia disease carriers in high schools. Am. J. Hum. Genet. 59:793–98
    [Google Scholar]
  47. 47.  Müller C 2009. Xanthomata, hypercholesterolemia, angina pectoris. Acta Med. Scand. 95:75–84
    [Google Scholar]
  48. 48.  Natarajan P, Young R, Stitziel NO, Padmanabhan S, Baber U et al. 2017. Polygenic risk score identifies subgroup with higher burden of atherosclerosis and greater relative benefit from statin therapy in the primary prevention setting. Circulation 135:2091–2101
    [Google Scholar]
  49. 49.  Nikpay M, Goel A, Won H-H, Hall LM, Willenborg C et al. 2015. A comprehensive 1,000 Genomes–based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47:1121–30
    [Google Scholar]
  50. 50.  Pharoah PDP, Antoniou AC, Easton DF, Ponder BAJ 2008. Polygenes, risk prediction, and targeted prevention of breast cancer. N. Engl. J. Med. 358:2796–803
    [Google Scholar]
  51. 51.  Popejoy AB, Fullerton SM 2016. Genomics is failing on diversity. Nature 538:161–64
    [Google Scholar]
  52. 52.  Rietveld CA, Medland SE, Derringer J, Yang J, Esko T et al. 2013. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340:1467–71
    [Google Scholar]
  53. 53.  Riordan JR, Rommens JM, Kerem B, Alon N, Rozmahel R et al. 1989. Identification of the cystic fibrosis gene: cloning and characterization of complementary DNA. Science 245:1066–73
    [Google Scholar]
  54. 54.  Ripke S, O'Dushlaine C, Chambert K, Moran JL, Kähler AK et al. 2013. Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nat. Genet. 45:1150–59
    [Google Scholar]
  55. 55.  Rivas MA, Beaudoin M, Gardet A, Stevens C, Sharma Y et al. 2011. Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease. Nat. Genet. 43:1066–73
    [Google Scholar]
  56. 56.  Rommens J, Iannuzzi M, Kerem B, Drumm M, Melmer G et al. 1989. Identification of the cystic fibrosis gene: chromosome walking and jumping. Science 245:1059–65
    [Google Scholar]
  57. 57. Schizophr. Psychiatr. Genome-Wide Assoc. Study (GWAS) Consor. 2011. Genome-wide association study identifies five new schizophrenia loci. Nat. Genet 43:969–76
    [Google Scholar]
  58. 58. Schizophr. Work. Group Psychiatr. Genom. Consort. 2014. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511:421–27
    [Google Scholar]
  59. 59.  Schunkert H, König IR, Kathiresan S, Reilly MP, Assimes TL et al. 2011. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat. Genet. 43:333–38
    [Google Scholar]
  60. 60.  Scott RA, Scott LJ, Mägi R, Marullo L, Gaulton KJ, et al. [Diabetes Genet. Replication Meta-Anal. (DIAGRAM) Consort.]. 2017. An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes 66:2888–902
    [Google Scholar]
  61. 61.  Sim X, Ong RT-H, Suo C, Tay W-T, Liu J et al. 2011. Transferability of type 2 diabetes implicated loci in multi-ethnic cohorts from Southeast Asia. PLOS Genet 7:e1001363
    [Google Scholar]
  62. 62.  Soria LF, Ludwig EH, Clarke HR, Vega GL, Grundy SM, McCarthy BJ 1989. Association between a specific apolipoprotein B mutation and familial defective apolipoprotein B-100. PNAS 86:587–91
    [Google Scholar]
  63. 63.  Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G et al. 2010. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42:937–48
    [Google Scholar]
  64. 64.  Stahl EA, Wegmann D, Trynka G, Gutierrez-Achury J, Do R et al. 2012. Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat. Genet. 44:483–89
    [Google Scholar]
  65. 65.  van der Harst P, Verweij N 2018. Identification of 64 novel genetic loci provides an expanded view on the genetic architecture of coronary artery disease. Circ. Res. 122:433–43
    [Google Scholar]
  66. 66.  Wei Z, Wang K, Qu H-Q, Zhang H, Bradfield J et al. 2009. From disease association to risk assessment: an optimistic view from genome-wide association studies on type 1 diabetes. PLOS Genet 5:e1000678
    [Google Scholar]
  67. 67.  Willer CJ, Sanna S, Jackson AU, Scuteri A, Bonnycastle LL et al. 2008. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat. Genet. 40:161–69
    [Google Scholar]
  68. 68.  Wojcik G, Graff M, Nishimura KK, Tao R, Haessler J et al. 2017. Genetic diversity turns a new PAGE in our understanding of complex traits. bioRxiv 188094. https://doi.org/10.1101/188094
    [Crossref]
  69. 69.  Won H-H, Natarajan P, Dobbyn A, Jordan DM, Roussos P et al. 2015. Disproportionate contributions of select genomic compartments and cell types to genetic risk for coronary artery disease. PLOS Genet 11:e1005622
    [Google Scholar]
  70. 70.  Wooster R, Bignell G, Lancaster J, Swift S, Seal S et al. 1995. Identification of the breast cancer susceptibility gene BRCA2. . Nature 378:789–92
    [Google Scholar]
/content/journals/10.1146/annurev-genom-083117-021136
Loading
/content/journals/10.1146/annurev-genom-083117-021136
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error