1932

Abstract

Disease classification, or nosology, was historically driven by careful examination of clinical features of patients. As technologies to measure and understand human phenotypes advanced, so too did classifications of disease, and the advent of genetic data has led to a surge in genetic subtyping in the past decades. Although the fundamental process of refining disease definitions and subtypes is shared across diverse fields, each field is driven by its own goals and technological expertise, leading to inconsistent and conflicting definitions of disease subtypes. Here, we review several classical and recent subtypes and subtyping approaches and provide concrete definitions to delineate subtypes. In particular, we focus on subtypes with distinct causal disease biology, which are of primary interest to scientists, and subtypes with pragmatic medical benefits, which are of primary interest to physicians. We propose genetic heterogeneity as a gold standard for establishing biologically distinct subtypes of complex polygenic disease. We focus especially on methods to find and validate genetic subtypes, emphasizing common pitfalls and how to avoid them.

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2020-08-31
2024-04-14
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Literature Cited

  1. 1. 
    Adeponle AB, Thombs BD, Groleau D, Jarvis E, Kirmayer LJ 2012. Using the cultural formulation to resolve uncertainty in diagnoses of psychosis among ethnoculturally diverse patients. Psychiatr. Serv. 63:147–53
    [Google Scholar]
  2. 2. 
    Ahlqvist E, Storm P, Käräjämäki A, Martinell M, Dorkhan M et al. 2018. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 6:361–69
    [Google Scholar]
  3. 3. 
    Albert PS, Ratnasinghe D, Tangrea J, Wacholder S 2001. Limitations of the case-only design for identifying gene-environment interactions. Am. J. Epidemiol. 154:687–93
    [Google Scholar]
  4. 4. 
    Arnedo J, Svrakic DM, del Val C, Romero-Zaliz R, Hernández-Cuervo H et al. 2015. Uncovering the hidden risk architecture of the schizophrenias: confirmation in three independent genome-wide association studies. Am. J. Psychiatry 172:139–53
    [Google Scholar]
  5. 5. 
    Bansal V, Mitjans M, Burik CAP, Linnér RK, Okbay A et al. 2018. Genome-wide association study results for educational attainment aid in identifying genetic heterogeneity of schizophrenia. Nat. Commun. 9:3078
    [Google Scholar]
  6. 6. 
    Barreiro LB, Tailleux L, Pai AA, Gicquel B, Marioni JC, Gilad Y 2012. Deciphering the genetic architecture of variation in the immune response to Mycobacterium tuberculosis infection. PNAS 109:1204–9
    [Google Scholar]
  7. 7. 
    Bentley A, Sung YJ, Brown MR, Winkler TW, Kraja AT et al. 2019. Multi-ancestry genome-wide gene–smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids. Nat. Genet. 51:636–48
    [Google Scholar]
  8. 8. 
    Berg JJ, Harpak A, Sinnott-Armstrong N, Joergensen AM, Mostafavi H et al. 2019. Reduced signal for polygenic adaptation of height in UK Biobank. eLife 8:e39725
    [Google Scholar]
  9. 9. 
    Bernier R, Golzio C, Xiong B, Stessman HAF, Coe BP et al. 2014. Disruptive CHD8 mutations define a subtype of autism early in development. Cell 158:263–76
    [Google Scholar]
  10. 10. 
    Bønnelykke K, Ober C 2016. Leveraging gene-environment interactions and endotypes for asthma gene discovery. J. Allergy Clin. Immunol. 137:667–79
    [Google Scholar]
  11. 11. 
    Boyle EA, Li YI, Pritchard JK 2017. An expanded view of complex traits: from polygenic to omnigenic. Cell 169:1177–86
    [Google Scholar]
  12. 12. 
    Brainstorm Consort., Anttila V, Bulik-Sullivan B, Finucane HK, Walters RK et al. 2018. Analysis of shared heritability in common disorders of the brain. Science 360:eaap8757
    [Google Scholar]
  13. 13. 
    Broeks A, Schmidt MK, Sherman ME, Couch FJ, Hopper JL et al. 2011. Low penetrance breast cancer susceptibility loci are associated with specific breast tumor subtypes: findings from the Breast Cancer Association Consortium. Hum. Mol. Genet. 20:3289–303
    [Google Scholar]
  14. 14. 
    Bromet EJ, Kotov R, Fochtmann LJ, Carlson GA, Tanenberg-Karant M et al. 2011. Diagnostic shifts during the decade following first admission for psychosis. Am. J. Psychiatry 168:1186–94
    [Google Scholar]
  15. 15. 
    Brown AA, Buil A, Vinuela A, Lappalainen T, Zheng HF et al. 2014. Genetic interactions affecting human gene expression identified by variance association mapping. eLife 3:e01381
    [Google Scholar]
  16. 16. 
    Bruining H, Eijkemans MJC, Kas MJH, Curran SR, Vorstman JAS, Bolton PF 2014. Behavioral signatures related to genetic disorders in autism. Mol. Autism 5:11
    [Google Scholar]
  17. 17. 
    Buu MC, Sanders LM, Mayo JA, Milla CE, Wise PH 2016. Assessing differences in mortality rates and risk factors between Hispanic and non-Hispanic patients with cystic fibrosis in California. Chest 149:380–89
    [Google Scholar]
  18. 18. 
    Chatterjee N 2004. A two-stage regression model for epidemiological studies with multivariate disease classification data. J. Am. Stat. Assoc. 99:127–38
    [Google Scholar]
  19. 19. 
    Chatterjee N, Carroll RC 2005. Semiparametric maximum likelihood estimation exploiting gene-environment independence in case-control studies. Biometrika 92:399–418
    [Google Scholar]
  20. 20. 
    Chen P, Lin JJ, Lu CS, Ong CT, Hsieh PF et al. 2011. Carbamazepine-induced toxic effects and HLA-B*1502 screening in Taiwan. N. Engl. J. Med. 364:1126–33
    [Google Scholar]
  21. 21. 
    Cho JH, Feldman M 2015. Heterogeneity of autoimmune diseases: pathophysiologic insights from genetics and implications for new therapies. Nat. Med. 21:730–38
    [Google Scholar]
  22. 22. 
    Cortes A, Dendrou CA, Motyer A, Jostins L, Vukcevic D et al. 2017. Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank. Nat. Genet. 49:1311–18
    [Google Scholar]
  23. 23. 
    Coulter C, Baker KK, Margolis RL 2019. Specialized consultation for suspected recent-onset schizophrenia: diagnostic clarity and the distorting impact of anxiety and reported auditory hallucinations. J. Psychiatr. Pract. 25:76–81
    [Google Scholar]
  24. 24. 
    Dahl A, Cai N, Ko A, Laakso M, Pajukanta P et al. 2019. Reverse GWAS: using genetics to identify and model phenotypic subtypes. PLOS Genet. 15:e1008009
    [Google Scholar]
  25. 25. 
    Dahl A, Guillemot V, Mefford J, Aschard H, Zaitlen N 2019. Adjusting for principal components of molecular phenotypes induces replicating false positives. Genetics 211:1179–89
    [Google Scholar]
  26. 26. 
    Dahl A, Iotchkova V, Baud A, Johansson Å, Gyllensten U et al. 2016. A multiple-phenotype imputation method for genetic studies. Nat. Genet. 48:466–72
    [Google Scholar]
  27. 27. 
    Dahl A, Nguyen K, Cai N, Gandal MJ, Flint J, Zaitlen N 2020. A robust method uncovers significant context-specific heritability in diverse complex traits. Am. J. Hum. Genet. 106:71–91
    [Google Scholar]
  28. 28. 
    DeJesus-Hernandez M, Mackenzie IR, Boeve BF, Boxer AL, Baker M et al. 2011. Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS. Neuron 72:245–56
    [Google Scholar]
  29. 29. 
    Dempster ER, Lerner IM 1950. Heritability of threshold characters. Genetics 35:212–36
    [Google Scholar]
  30. 30. 
    Dewey FE, Murray MF, Overton JD, Habegger L, Leader JB et al. 2016. Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study. Science 354:aaf6814
    [Google Scholar]
  31. 31. 
    Drumm ML, Ziady AG, Davis PB 2012. Genetic variation and clinical heterogeneity in cystic fibrosis. Annu. Rev. Pathol. 7:267–82
    [Google Scholar]
  32. 32. 
    Dudbridge F, Fletcher O 2014. Gene-environment dependence creates spurious gene-environment interaction. Am. J. Hum. Genet. 95:301–7
    [Google Scholar]
  33. 33. 
    Earl RK, Turner TN, Mefford HC, Hudac CM, Gerdts J et al. 2017. Clinical phenotype of ASD-associated DYRK1A haploinsufficiency. Mol. Autism 8:105
    [Google Scholar]
  34. 34. 
    Engelhardt BE, Stephens M 2010. Analysis of population structure: a unifying framework and novel methods based on sparse factor analysis. PLOS Genet. 6:e1001117
    [Google Scholar]
  35. 35. 
    Fairfax BP, Humburg P, Makino S, Naranbhai V, Wong D et al. 2014. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Nature 343:1246949
    [Google Scholar]
  36. 36. 
    Falconer DS 1967. The inheritance of liability to diseases with variable age of onset, with particular reference to diabetes mellitus. Ann. Hum. Genet. 31:1–20
    [Google Scholar]
  37. 37. 
    Ferrell PB, McLeod HL 2008. Carbamazepine, HLA-B*1502 and risk of Stevens–Johnson syndrome and toxic epidermal necrolysis: US FDA recommendations. Pharmacogenomics 9:1543–46
    [Google Scholar]
  38. 38. 
    Flint J, Kendler KS 2014. The genetics of major depression. Neuron 81:484–503
    [Google Scholar]
  39. 39. 
    Folca P, Glascock R, Irvine W 1961. Studies with tritium-labelled hexoestrol in advanced breast cancer: comparison of tissue accumulation of hexoestrol with response to bilateral adrenalectomy and oophorectomy. Lancet 278:796–98
    [Google Scholar]
  40. 40. 
    Freund MK, Burch KS, Shi H, Mancuso N, Kichaev G et al. 2018. Phenotype-specific enrichment of Mendelian disorder genes near GWAS regions across 62 complex traits. Am. J. Human Genet. 103:535–52
    [Google Scholar]
  41. 41. 
    Gabai-Kapara E, Lahad A, Kaufman B, Friedman E, Segev S et al. 2014. Population-based screening for breast and ovarian cancer risk due to BRCA1 and BRCA2. PNAS 111:14205–10
    [Google Scholar]
  42. 42. 
    Garcia-Closas M, Couch FJ, Lindström S, Michailidou K, Schmidt MK et al. 2013. Genome-wide association studies identify four ER negative–specific breast cancer risk loci. Nat. Genet. 45:392–98
    [Google Scholar]
  43. 43. 
    Geisheker MR, Heymann G, Wang T, Coe BP, Turner TN et al. 2017. Hotspots of missense mutation identify neurodevelopmental disorder genes and functional domains. Nat. Neurosci. 20:1043–51
    [Google Scholar]
  44. 44. 
    Ghaleb Y, Elbitar S, El Khoury P, Bruckert E, Carreau V et al. 2018. Usefulness of the genetic risk score to identify phenocopies in families with familial hypercholesterolemia?. Eur. J. Hum. Genet. 26:570–78
    [Google Scholar]
  45. 45. 
    Goldstein JL, Brown MS 2009. The LDL receptor. Arterioscler. Thromb. Vasc. Biol. 29:431–38
    [Google Scholar]
  46. 46. 
    Grath S, Parsch J 2016. Sex-biased gene expression. Annu. Rev. Genet. 50:29–44
    [Google Scholar]
  47. 47. 
    Greene WH 2003. Econometric Analysis New York: Pearson. 5th ed.
  48. 48. 
    Hall JM, Lee MK, Newman B, Morrow JE, Anderson LA et al. 1990. Linkage of early-onset familial breast cancer to chromosome 17q21. Science 250:1684–89
    [Google Scholar]
  49. 49. 
    Han B, Diogo D, Eyre S, Kallberg H, Zhernakova A et al. 2014. Fine mapping seronegative and seropositive rheumatoid arthritis to shared and distinct HLA alleles by adjusting for the effects of heterogeneity. Am. J. Hum. Genet. 94:522–32
    [Google Scholar]
  50. 50. 
    Han B, Pouget JG, Slowikowski K, Stahl E, Lee CH et al. 2016. A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases. Nat. Genet. 48:803–10
    [Google Scholar]
  51. 51. 
    Hancock DB, Artigas MS, Gharib SA, Henry A, Manichaikul A et al. 2012. Genome-wide joint meta-analysis of SNP and SNP-by-smoking interaction identifies novel loci for pulmonary function. PLOS Genet. 8:e1003098
    [Google Scholar]
  52. 52. 
    Hanson E, Bernier R, Porche K, Jackson FI, Goin-Kochel RP et al. 2015. The cognitive and behavioral phenotype of the 16p11.2 deletion in a clinically ascertained population. Biol. Psychiatry 77:785–93
    [Google Scholar]
  53. 53. 
    Hinks TSC, Brown T, Lau LCK, Rupani H, Barber C et al. 2016. Multidimensional endotyping in patients with severe asthma reveals inflammatory heterogeneity in matrix metalloproteinases and chitinase 3–like protein 1. J. Allergy Clin. Immunol. 138:61–75
    [Google Scholar]
  54. 54. 
    Hoffmann TJ, Theusch E, Haldar T, Ranatunga DK, Jorgenson E et al. 2018. A large electronic-health-record-based genome-wide study of serum lipids. Nat. Genet. 50:401–13
    [Google Scholar]
  55. 55. 
    Howrylak JA, Moll M, Weiss ST, Raby BA, Wu W, Xing EP 2016. Gene expression profiling of asthma phenotypes demonstrates molecular signatures of atopy and asthma control. J. Allergy Clin. Immunol. 137:1390–97.e6
    [Google Scholar]
  56. 56. 
    Huckins LM, Dobbyn A, Ruderfer DM, Hoffman G, Wang W et al. 2019. Gene expression imputation across multiple brain regions provides insights into schizophrenia risk. Nat. Genet. 51:659–74
    [Google Scholar]
  57. 57. 
    Iqbal J, Ginsburg O, Rochon PA, Sun P, Narod SA 2015. Differences in breast cancer stage at diagnosis and cancer-specific survival by race and ethnicity in the United States. JAMA 313:165–73
    [Google Scholar]
  58. 58. 
    Jayadev S, Bird TD 2013. Hereditary ataxias: overview. Genet. Med. 15:673–83
    [Google Scholar]
  59. 59. 
    Jeste SS, Geschwind DH 2014. Disentangling the heterogeneity of autism spectrum disorder through genetic findings. Nat. Rev. Neurol. 10:74–81
    [Google Scholar]
  60. 60. 
    Jiang J, Li C, Paul D, Yang C, Zhao H 2016. On high-dimensional misspecified mixed model analysis in genome-wide association study. Ann. Stat. 44:2127–60
    [Google Scholar]
  61. 61. 
    Katayama Y, Nishiyama M, Shoji H, Ohkawa Y, Kawamura A et al. 2016. CHD8 haploinsufficiency results in autistic-like phenotypes in mice. Nature 537:675–79
    [Google Scholar]
  62. 62. 
    Kendler KS, Kessler RC, Walters EE, MacLean C, Neale MC et al. 1995. Stressful life events, genetic liability, and onset of an episode of major depression in women. Am. J. Psychiatry 152:833–42
    [Google Scholar]
  63. 63. 
    Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C et al. 2018. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50:1219–24
    [Google Scholar]
  64. 64. 
    Khramtsova EA, Davis LK, Stranger BE 2019. The role of sex in the genomics of human complex traits. Nat. Rev. Genet. 20:173–90
    [Google Scholar]
  65. 65. 
    Klarin D, Damrauer SM, Cho K, Sun YV, Teslovich TM et al. 2018. Genetics of blood lipids among ∼300,000 multi-ethnic participants of the million veteran program. Nat. Genet. 50:1514–23
    [Google Scholar]
  66. 66. 
    Klein SL, Flanagan KL 2016. Sex differences in immune responses. Nat. Rev. Immunol. 16:626–38
    [Google Scholar]
  67. 67. 
    Krishnan ML, Wang Z, Aljabar P, Ball G, Mirza G et al. 2017. Machine learning shows association between genetic variability in PPARG and cerebral connectivity in preterm infants. PNAS 114:13744–49
    [Google Scholar]
  68. 68. 
    Krumm N, Turner TN, Baker C, Vives L, Mohajeri K et al. 2015. Excess of rare, inherited truncating mutations in autism. Nat. Genet. 47:582–88
    [Google Scholar]
  69. 69. 
    Laakso M 2019. Biomarkers for type 2 diabetes. Mol. Metab. 27:S139–46
    [Google Scholar]
  70. 70. 
    Lai MC, Lombardo MV, Chakrabarti B, Baron-Cohen S 2013. Subgrouping the autism “spectrum”: reflections on DSM-5. PLOS Biol. 11:e1001544
    [Google Scholar]
  71. 71. 
    Laursen TM, Agerbo E, Pedersen CB 2009. Bipolar disorder, schizoaffective disorder, and schizophrenia overlap: a new comorbidity index. J. Clin. Psychiatry 70:1432–38
    [Google Scholar]
  72. 72. 
    Lee MN, Ye C, Villani AC, Raj T, Li W et al. 2014. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science 343:1246980
    [Google Scholar]
  73. 73. 
    Leek JT, Storey JD 2007. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLOS Genet. 3:e161
    [Google Scholar]
  74. 74. 
    Levey AS, Coresh J 2012. Chronic kidney disease. Lancet 379:165–80
    [Google Scholar]
  75. 75. 
    Li L, Cheng WY, Glicksberg BS, Gottesman O, Tamler R et al. 2015. Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci. Transl. Med. 7:311ra174
    [Google Scholar]
  76. 76. 
    Liley J, Todd JA, Wallace C 2016. A method for identifying genetic heterogeneity within phenotypically defined disease subgroups. Nat. Genet. 49:310–16
    [Google Scholar]
  77. 77. 
    Lynch T, Price A 2007. The effect of cytochrome P450 metabolism on drug response, interactions, and adverse effects. Am. Fam. Physician 76:391–96
    [Google Scholar]
  78. 78. 
    Mangravite LM, Engelhardt BE, Medina MW, Smith JD, Brown CD et al. 2013. A statin-dependent QTL for GATM expression is associated with statin-induced myopathy. Nature 502:377–80
    [Google Scholar]
  79. 79. 
    Manna S, Holz MK 2016. Tamoxifen action in ER-negative breast cancer. Signal Transduct. Insights 5:1–7
    [Google Scholar]
  80. 80. 
    Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA et al. 2009. Finding the missing heritability of complex diseases. Nature 461:747–53
    [Google Scholar]
  81. 81. 
    Marchini J, Howie B 2010. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 11:499–511
    [Google Scholar]
  82. 82. 
    Mathieson I, McVean G 2012. Differential confounding of rare and common variants in spatially structured populations. Nat. Genet. 44:243–46
    [Google Scholar]
  83. 83. 
    Mavaddat N, Michailidou K, Dennis J, Lush M, Fachal L et al. 2019. Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. Am. J. Human Genet. 104:21–34
    [Google Scholar]
  84. 84. 
    McClellan J, King MC 2010. Genetic heterogeneity in human disease. Cell 141:210–17
    [Google Scholar]
  85. 85. 
    McCormack M, Alfirevic A, Bourgeois S, Farrell JJ, Kasperavičiūtė D et al. 2011. HLA-A*3101 and carbamazepine-induced hypersensitivity reactions in Europeans. N. Engl. J. Med. 364:1134–43
    [Google Scholar]
  86. 86. 
    McCullagh P, Nelder J 1989. Generalized Linear Models Boca Raton, FL: Chapman & Hall/CRC. 2nd ed.
  87. 87. 
    Mega JL, Simon T, Collet JP, Anderson JL, Antman EM et al. 2010. Reduced-function CYP2C19 genotype and risk of adverse clinical outcomes among patients treated with clopidogrel predominantly for PCI: a meta-analysis. JAMA 304:1821–30
    [Google Scholar]
  88. 88. 
    Milne RL, Kuchenbaecker KB, Michailidou K, Beesley J, Kar S et al. 2017. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer. Nat. Genet. 49:1767–78
    [Google Scholar]
  89. 89. 
    Mitra I, Tsang K, Ladd-Acosta C, Croen LA, Aldinger KA et al. 2016. Pleiotropic mechanisms indicated for sex differences in autism. PLOS Genet. 12:e1006425
    [Google Scholar]
  90. 90. 
    Moore R, Casale FP, Bonder MJ, Horta D, BIOS Consort. et al. 2019. A linear mixed-model approach to study multivariate gene-environment interactions. Nat. Genet. 51:180–86
    [Google Scholar]
  91. 91. 
    Morris AP, Lindgren CM, Zeggini E, Timpson NJ, Frayling TM et al. 2010. A powerful approach to sub-phenotype analysis in population-based genetic association studies. Genet. Epidemiol. 34:335–43
    [Google Scholar]
  92. 92. 
    Morrow EM, Yoo SY, Flavell SW, Kim TK, Lin Y et al. 2008. Identifying autism loci and genes by tracing recent shared ancestry. Nature 321:218–23
    [Google Scholar]
  93. 93. 
    Mukherjee S, Shukla S, Woodle J, Rosen AM, Olarte S 1983. Misdiagnosis of schizophrenia in bipolar patients: a multiethnic comparison. Am. J. Psychiatry 140:1571–74
    [Google Scholar]
  94. 94. 
    Myers RA, Scott NM, Gauderman WJ, Qiu W, Mathias RA et al. 2014. Genome-wide interaction studies reveal sex-specific asthma risk alleles. Hum. Mol. Genet. 23:5251–59
    [Google Scholar]
  95. 95. 
    Nicolau M, Levine AJ, Carlsson G 2011. Topology based data analysis identifies a subgroup of breast cancers with a unique mutational profile and excellent survival. PNAS 108:7265–70
    [Google Scholar]
  96. 96. 
    Onitilo AA, Engel JM, Greenlee RT, Mukesh BN 2009. Breast cancer subtypes based on ER/PR and Her2 expression: comparison of clinicopathologic features and survival. Clin. Med. Res. 7:4–13
    [Google Scholar]
  97. 97. 
    O'Roak BJ, Vives L, Girirajan S, Karakoc E, Krumm N et al. 2012. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485:246–50
    [Google Scholar]
  98. 98. 
    Paré G, Cook NR, Ridker PM, Chasman DI 2010. On the use of variance per genotype as a tool to identify quantitative trait interaction effects: a report from the Women's Genome Health Study. PLOS Genet. 6:e1000981
    [Google Scholar]
  99. 99. 
    Parikshak NN, Luo R, Zhang A, Won H, Lowe JK et al. 2013. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155:1008–21
    [Google Scholar]
  100. 100. 
    Parikshak NN, Swarup V, Belgard TG, Irimia M, Ramaswami G et al. 2016. Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature 540:423–27
    [Google Scholar]
  101. 101. 
    Patel CJ, Chen R, Kodama K, Ioannidis JPA, Butte AJ 2013. Systematic identification of interaction effects between genome- and environment-wide associations in type 2 diabetes mellitus. Hum. Genet. 132:495–508
    [Google Scholar]
  102. 102. 
    Peterson RE, Cai N, Dahl AW, Bigdeli TB, Edwards AC et al. 2018. Molecular genetic analysis subdivided by adversity exposure suggests etiologic heterogeneity in major depression. Am. J. Psychiatry 175:545–54
    [Google Scholar]
  103. 103. 
    Pharoah PD, Antoniou AC, Easton DF, Ponder BA 2008. Polygenes, risk prediction, and targeted prevention of breast cancer. N. Engl. J. Med. 358:2796–803
    [Google Scholar]
  104. 104. 
    Piccart-Gebhart MJ, Procter M, Leyland-Jones B, Goldhirsch A, Untch M et al. 2005. Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer. N. Engl. J. Med. 353:1659–72
    [Google Scholar]
  105. 105. 
    Preiss D, Seshasai SRK, Welsh P, Murphy SA, Ho JE et al. 2011. Risk of incident diabetes with intensive-dose compared with moderate-dose statin therapy: a meta-analysis. JAMA 305:2556–64
    [Google Scholar]
  106. 106. 
    Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D 2006. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38:904–9
    [Google Scholar]
  107. 107. 
    Pritchard JK, Stephens M, Rosenberg NA, Donnelly P 2000. Association mapping in structured populations. Am. J. Hum. Genet. 67:170–81
    [Google Scholar]
  108. 108. 
    Renton AE, Majounie E, Waite A, Simón-Sánchez J, Rollinson S et al. 2011. A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD. Neuron 72:257–68
    [Google Scholar]
  109. 109. 
    Robinson MR, English G, Moser G, Lloyd-Jones LR, Triplett MA et al. 2017. Genotype-covariate interaction effects and the heritability of adult body mass index. Nat. Genet. 49:1174–81
    [Google Scholar]
  110. 110. 
    Ruderfer DM, Ripke S, McQuillin A, Boocock J, Stahl EA et al. 2018. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell 173:1705–15.e16
    [Google Scholar]
  111. 111. 
    Sattar N, Preiss D, Murray HM, Welsh P, Buckley BM et al. 2010. Statins and risk of incident diabetes: a collaborative meta-analysis of randomised statin trials. Lancet 375:735–42
    [Google Scholar]
  112. 112. 
    Schmidt MK, Hogervorst F, van Hien R, Cornelissen S, Broeks A et al. 2016. Age- and tumor subtype–specific breast cancer risk estimates for CHEK2*1100delC carriers. J. Clin. Oncol. 34:2750–60
    [Google Scholar]
  113. 113. 
    Schrode N, Ho SM, Yamamuro K, Dobbyn A, Huckins L et al. 2019. Synergistic effects of common schizophrenia risk variants. Nat. Genet. 51:1475–85
    [Google Scholar]
  114. 114. 
    Schulz JB, Pandolfo M 2013. 150 years of Friedreich ataxia: from its discovery to therapy. J. Neurochem. 126:1–3
    [Google Scholar]
  115. 115. 
    Shi H, Mancuso N, Spendlove S, Pasaniuc B 2017. Local genetic correlation gives insights into the shared genetic architecture of complex traits. Am. J. Hum. Genet. 101:737–51
    [Google Scholar]
  116. 116. 
    Shorter E 2015. The history of nosology and the rise of the Diagnostic and Statistical Manual of Mental Disorders. Dialogues Clin. Neurosci. 17:59–67
    [Google Scholar]
  117. 117. 
    Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T, Locke AE et al. 2015. New genetic loci link adipose and insulin biology to body fat distribution. Nature 518:187–96
    [Google Scholar]
  118. 118. 
    Skol AD, Sasaki MM, Onel K 2016. The genetics of breast cancer risk in the post-genome era: thoughts on study design to move past BRCA and towards clinical relevance. Breast Cancer Res. 18:99
    [Google Scholar]
  119. 119. 
    Sladek R 2018. The many faces of diabetes: addressing heterogeneity of a complex disease. Lancet Diabetes Endocrinol. 383:1084–94
    [Google Scholar]
  120. 120. 
    Small KS, Todorčević M, Civelek M, Moustafa JSES, Wang X et al. 2018. Regulatory variants at KLF14 influence type 2 diabetes risk via a female-specific effect on adipocyte size and body composition. Nat. Genet. 50:572–80
    [Google Scholar]
  121. 121. 
    Sohail M, Maier RM, Ganna A, Bloemendal A, Martin AR et al. 2019. Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. eLife 8:e39702
    [Google Scholar]
  122. 122. 
    Steinsaltz D, Dahl A, Wachter KW 2018. Statistical properties of simple random-effects models for genetic heritability. Electron. J. Stat. 12:321–58
    [Google Scholar]
  123. 123. 
    Stessman HAF, Bernier R, Eichler EE 2014. A genotype-first approach to defining the subtypes of a complex disease. Cell 156:872–77
    [Google Scholar]
  124. 124. 
    Stessman HAF, Turner TN, Eichler EE 2016. Molecular subtyping and improved treatment of neurodevelopmental disease. Genome Med. 8:22
    [Google Scholar]
  125. 125. 
    Stessman HAF, Xiong B, Coe BP, Wang T, Hoekzema K et al. 2017. Targeted sequencing identifies 91 neurodevelopmental-disorder risk genes with autism and developmental-disability biases. Nat. Genet. 49:515–26
    [Google Scholar]
  126. 126. 
    Stewart C, Pepper MS 2016. Cystic fibrosis on the African continent. Genet. Med. 18:653–62
    [Google Scholar]
  127. 127. 
    Stewart C, Pepper MS 2017. Cystic fibrosis in the African diaspora. Ann. Am. Thorac. Soc. 14:1–7
    [Google Scholar]
  128. 128. 
    Stone G, Choi A, Meritxell O, Gorham J, Heydarpour M et al. 2019. Sex differences in gene expression in response to ischemia in the human left ventricular myocardium. Hum. Mol. Genet. 28:1682–93
    [Google Scholar]
  129. 129. 
    Sul JH, Bilow M, Yang WY, Kostem E, Furlotte N et al. 2016. Accounting for population structure in gene-by-environment interactions in genome-wide association studies using mixed models. PLOS Genet. 12:e1005849
    [Google Scholar]
  130. 130. 
    Sverdlov S, Thompson E 2017. Combinatorial methods for epistasis and dominance. J. Comput. Biol. 24:267–79
    [Google Scholar]
  131. 131. 
    Terao C, Brynedal B, Chen Z, Jiang X, Westerlind H et al. 2019. Distinct HLA associations with rheumatoid arthritis subsets defined by serological subphenotype. Am. J. Hum. Genet. 105:616–24
    [Google Scholar]
  132. 132. 
    Thyme SB, Pieper LM, Li EH, Pandey S, Wang Y et al. 2019. Phenotypic landscape of schizophrenia-associated genes defines candidates and their shared functions. Cell 177:478–91.e20
    [Google Scholar]
  133. 133. 
    Udler MS, Kim J, von Grotthuss M, Bonàs-Guarch S, Cole JB et al. 2018. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis. PLOS Med. 15:e1002654
    [Google Scholar]
  134. 134. 
    Uricchio LH, Kitano HC, Gusev A, Zaitlen NA 2019. An evolutionary compass for detecting signals of polygenic selection and mutational bias. Evol. Lett. 3:69–79
    [Google Scholar]
  135. 135. 
    van ’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AAM et al. 2002. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530–36
    [Google Scholar]
  136. 136. 
    Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y et al. 2011. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474:380–84
    [Google Scholar]
  137. 137. 
    Wang J, Zhao Q, Hastie T, Owen AB 2017. Confounder adjustment in multiple hypothesis testing. Ann. Stat. 45:1863–94
    [Google Scholar]
  138. 138. 
    Wang L, Liang R, Zhou T, Zheng J, Liang BM et al. 2017. Identification and validation of asthma phenotypes in Chinese population using cluster analysis. Ann. Allergy Asthma Immunol. 119:324–32
    [Google Scholar]
  139. 139. 
    Weiner DJ, Wigdor EM, Ripke S, Walters RK, Kosmicki JA et al. 2017. Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders. Nat. Genet. 49:978–85
    [Google Scholar]
  140. 140. 
    Weiss LA, Shen Y, Korn JM, Arking DE, Miller DT et al. 2008. Association between microdeletion and microduplication at 16p11.2 and autism. N. Engl. J. Med. 358:667–75
    [Google Scholar]
  141. 141. 
    Woodruff PG, Modrek B, Choy DF, Jia G, Abbas AR et al. 2009. T-helper type 2-driven inflammation defines major subphenotypes of asthma. Am. J. Respir. Crit. Care Med. 180:388–95
    [Google Scholar]
  142. 142. 
    Wray NR, Lee SH, Kendler KS 2012. Impact of diagnostic misclassification on estimation of genetic correlations using genome-wide genotypes. Eur. J. Hum. Genet. 20:668–74
    [Google Scholar]
  143. 143. 
    Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK et al. 2010. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42:565–69
    [Google Scholar]
  144. 144. 
    Yang J, Lee SH, Goddard ME, Visscher PM 2011. GCTA: a tool for genome-wide complex trait analysis. Am. J. Human Genet. 88:76–82
    [Google Scholar]
  145. 145. 
    Young AI, Wauthier FL, Donnelly P 2016. Multiple novel gene-by-environment interactions modify the effect of FTO variants on body mass index. Nat. Commun. 7:12724
    [Google Scholar]
  146. 146. 
    Young AI, Wauthier FL, Donnelly P 2018. Identifying loci affecting trait variability and detecting interactions in genome-wide association studies. Nat. Genet. 50:1608–14
    [Google Scholar]
  147. 147. 
    Zhang F, Gu W, Hurles ME, Lupski JR 2009. Copy number variation in human health, disease, and evolution. Annu. Rev. Genom. Hum. Genet. 10:451–81
    [Google Scholar]
  148. 148. 
    Zuk O, Hechter E, Sunyaev SR, Lander ES 2012. The mystery of missing heritability: Genetic interactions create phantom heritability. PNAS 109:1193–98
    [Google Scholar]
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