Review Article | Published:

Genetic architecture: the shape of the genetic contribution to human traits and disease

Nature Reviews Genetics volume 19, pages 110124 (2018) | Download Citation

Abstract

Genetic architecture describes the characteristics of genetic variation that are responsible for heritable phenotypic variability. It depends on the number of genetic variants affecting a trait, their frequencies in the population, the magnitude of their effects and their interactions with each other and the environment. Defining the genetic architecture of a complex trait or disease is central to the scientific and clinical goals of human genetics, which are to understand disease aetiology and aid in disease screening, diagnosis, prognosis and therapy. Recent technological advances have enabled genome-wide association studies and emerging next-generation sequencing studies to begin to decipher the nature of the heritable contribution to traits and disease. Here, we describe the types of genetic architecture that have been observed, how architecture can be measured and why an improved understanding of genetic architecture is central to future advances in the field.

Key points

  • The genetic architecture of common diseases is central to the scientific and clinical goals of human genetics because it directly impacts biology, disease screening, diagnosis, prognosis and treatment.

  • Genetic architecture is currently assessed by exploiting the differences in types of genetic variants ascertained through genome-wide association studies, whole-exome sequencing studies and whole-genome sequencing studies. Each of these has its own merits and disadvantages, but all are subject to the limitations of sample size. Gene mapping studies should thus be tailored to the unique contributions of each of these technologies.

  • To date, the observed genetic architecture of highly heritable diseases and traits differs markedly and cannot be reliably predicted. Where large sample sizes are available, differences in detectable architecture still exist.

  • The concept of variance explained is not always relevant to individual-level risk prediction or drug development, whereas the genetic architecture of a given trait or disease can be more pertinent.

  • Genetic architecture is variable in time and place and can be theoretically influenced by phenotypic measurement, selection and decanalization.

  • Interactions between genetic determinants of a trait or environmental influences contribute to genetic architecture. To date, few such interactions have been identified for most common diseases and traits, but this will likely change with increasing sample sizes.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    The genetic architecture of quantitative traits. Annu. Rev. Genet. 35, 303–339 (2001).

  2. 2.

    , , & Large-scale genomics unveils the genetic architecture of psychiatric disorders. Nat. Neurosci. 17, 782–790 (2014).

  3. 3.

    , & Heritability in the genomics era — concepts and misconceptions. Nat. Rev. Genet. 9, 255–266 (2008). This is an important Review of the concepts of heritability, a topic that often generates confusion.

  4. 4.

    The evolution of genetic architecture. Annu. Rev. Ecol. Evol. Syst. 37, 123–157 (2006).

  5. 5.

    , , & Human genetic variation and its contribution to complex traits. Nat. Rev. Genet. 10, 241–251 (2009).

  6. 6.

    , & Genome structural variation discovery and genotyping. Nat. Rev. Genet. 12, 363–376 (2011).

  7. 7.

    et al. 10 years of GWAS Discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017). This is an overview of the major lessons learned from the first decade of GWAS.

  8. 8.

    et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).

  9. 9.

    et al. All SNPs are not created equal: genome-wide association studies reveal a consistent pattern of enrichment among functionally annotated SNPs. PLOS Genet. 9, e1003449 (2013).

  10. 10.

    et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167, 1415–1429.e19 (2016).

  11. 11.

    et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

  12. 12.

    et al. Performance comparison of exome DNA sequencing technologies. Nat. Biotechnol. 29, 908–914 (2011).

  13. 13.

    , , & A copy number variation map of the human genome. Nat. Rev. Genet. 16, 172–183 (2015).

  14. 14.

    & Repetitive DNA and next-generation sequencing: computational challenges and solutions. Nat. Rev. Genet. 13, 36–46 (2011).

  15. 15.

    , , & UK biobank data: come and get it. Sci. Transl Med. 6, 224ed4 (2014).

  16. 16.

    National Heart Blood and Lung Institute. Trans-Omics for Precision Medicine (TOPMed) Program. National Institutes of Health (2017).

  17. 17.

    et al. Physical and neurobehavioral determinants of reproductive onset and success. Nat. Genet. 48, 617–623 (2016).

  18. 18.

    & The heritability of human disease: estimation, uses and abuses. Nat. Rev. Genet. 14, 139–149 (2013).

  19. 19.

    & Beyond Mendel: an evolving view of human genetic disease transmission. Nat. Rev. Genet. 3, 779–789 (2002).

  20. 20.

    , & An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017). This is a recent description and expansion of Fisher's infinitesimal model.

  21. 21.

    The correlation between relatives on the supposition of Mendelian inheritance. Proc. Roy. Soc. Edinburgh 52, 99–433 (1918).

  22. 22.

    & The molecular basis of dominance. Genetics 97, 639–666 (1981).

  23. 23.

    Impaired glucose tolerance in the U.S. population. Diabetes Care 12, 464–474 (1989).

  24. 24.

    & Understanding type 1 diabetes through genetics: advances and prospects. Nat. Rev. Genet. 12, 781–792 (2011).

  25. 25.

    DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).

  26. 26.

    et al. An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes 66, 2888–2902 (2017).

  27. 27.

    The genetic architecture of type 2 diabetes. Nature 536, 41–47 (2016). This is a large-scale search for low-frequency and rare variants associated with the risk of type 2 diabetes mellitus, which demonstrates, within the bounds of available statistical power, that most variants associated with this disease are common.

  28. 28.

    a et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA 106, 9362–9367 (2009).

  29. 29.

    et al. Heritability of cardiovascular and personality traits in 6,148 Sardinians. PLoS Genet. 2, e132 (2006).

  30. 30.

    et al. Genetic and non-genetic correlates of vitamins K and D. Eur. J. Clin. Nutr. 63, 458–464 (2009).

  31. 31.

    et al. Common genetic determinants of vitamin D insufficiency: a genome-wide association study. Lancet 376, 180–188 (2010).

  32. 32.

    et al. Low-frequency synonymous coding variation in CYP2R1 Has large effects on vitamin D levels and risk of multiple sclerosis. Am. J. Hum. Genet. 101, 227–238 (2017).

  33. 33.

    et al. The Genetic Architecture of 25-Hydroxyvitamin (poster abstract). American society of Human Genetics. (2017)

  34. 34.

    et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).

  35. 35.

    et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat. Genet. 41, 56–65 (2009).

  36. 36.

    et al. Identification of loci associated with schizophrenia by genome-wide association and follow-up. Nat. Genet. 40, 1053–1055 (2008).

  37. 37.

    et al. Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia. Nat. Genet. 49, 1576–1583 (2017). This study is a major contribution to the number of loci associated with schizophrenia, which at smaller sample sizes had few associated genetic variants.

  38. 38.

    et al. Interpretation and use of FRAX in clinical practice. Osteoporos. Int. 22, 2395–2411 (2011).

  39. 39.

    , , , & The heritability of bone mineral density, ultrasound of the calcaneus and hip axis length: a study of postmenopausal twins. J. Bone Miner. Res. 11, 530–534 (1996).

  40. 40.

    et al. WNT16 influences bone mineral density, cortical bone thickness, bone strength, and osteoporotic fracture risk. PLoS Genet. 8, e1002745 (2012).

  41. 41.

    et al. Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature 526, 112–117 (2015). This is one of the first descriptions of the use of WGS and WES to identify a protein not previously suspected to influence risk of a common disease.

  42. 42.

    et al. The UK10K project identifies rare variants in health and disease. Nature 526, 82–90 (2015). This is one of the first large-scale attempts to use WGS to identify genetic determinants of traits and common disease in the general population.

  43. 43.

    et al. Genome sequencing elucidates Sardinian genetic architecture and augments association analyses for lipid and blood inflammatory markers. Nat. Genet. 47, 1272–1281 (2015).

  44. 44.

    et al. Large-scale whole-genome sequencing of the Icelandic population. Nat. Genet. 47, 435–444 (2015).

  45. 45.

    et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat. Genet. 47, 1114–1120 (2015). This is an excellent demonstration of the variance component model to estimate heritability from many thousands of SNPs simultaneously.

  46. 46.

    , , & Loss-of-function mutations in APOC3 and risk of ischemic vascular disease. N. Engl. J. Med. 371, 32–41 (2014).

  47. 47.

    et al. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat. Genet. 40, 189–197 (2008).

  48. 48.

    The TG and HDL Working Group of the Exome Sequencing Project, National Heart, Lung, and Blood Institute. Loss-of-function mutations in APOC3, triglycerides, and coronary disease. N. Engl. J. Med. 371, 22–31 (2014).

  49. 49.

    et al. A rare variant in APOC3 is associated with plasma triglyceride and VLDL levels in Europeans. Nat. Commun. 5, 4871 (2014).

  50. 50.

    , & Validating therapeutic targets through human genetics. Nat. Rev. Drug Discov. 12, 581–594 (2013).

  51. 51.

    et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015). This paper provides a demonstration of the importance of human genetics to drug discovery.

  52. 52.

    , , , & Effect of naturally random allocation to lower low-density lipoprotein cholesterol on the risk of coronary heart disease mediated by polymorphisms in NPC1L1, HMGCR, or both: a 2 × 2 factorial Mendelian randomization study. J. Am. Coll. Cardiol. 65, 1552–1561 (2015).

  53. 53.

    et al. Antisense inhibition of apolipoprotein C-III in patients with hypertriglyceridemia. N. Engl. J. Med. 373, 438–447 (2015).

  54. 54.

    et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

  55. 55.

    , , , & The empirical power of rare variant association methods: results from Sanger sequencing in 1,998 individuals. PLOS Genet. 8, e1002496 (2012).

  56. 56.

    & Common and rare variants in multifactorial susceptibility to common diseases. Nat. Genet. 40, 695–701 (2008).

  57. 57.

    et al. Nonsense mutation in the LGR4 gene is associated with several human diseases and other traits. Nature 497, 517–520 (2013).

  58. 58.

    et al. Whole-genome sequencing coupled to imputation discovers genetic signals for anthropometric traits. Am. J. Hum. Genet. 100, 865–884 (2017).

  59. 59.

    et al. Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps. Nat. Genet. 48, 1303–1312 (2016).

  60. 60.

    , , , & Rare variants create synthetic genome-wide associations. PLoS Biol. 8, e1000294 (2010).

  61. 61.

    , , , & Synthetic associations created by rare variants do not explain most GWAS results. PLOS Biol. 9, e1000579 (2011).

  62. 62.

    , , , & Synthetic associations are unlikely to account for many common disease genome-wide association signals. PLOS Biol. 9, e1000580 (2011).

  63. 63.

    et al. Pitfalls of predicting complex traits from SNPs. Nat. Rev. Genet. 14, 507–515 (2013). This is a helpful review of the use of genetic variation to predict complex traits and disease.

  64. 64.

    , & The contribution of genetic variants to disease depends on the ruler. Nat. Rev. Genet. 15, 765–776 (2014).

  65. 65.

    , , & Detecting disease associations due to linkage disequilibrium using haplotype tags: a class of tests and the determinants of statistical power. Hum. Hered. 56, 18–31 (2003).

  66. 66.

    et al. Designing genome-wide association studies: sample size, power, imputation, and the choice of genotyping chip. PLOS Genet. 5, e1000477 (2009).

  67. 67.

    et al. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat. Genet. 44, 491–501 (2012).

  68. 68.

    Cystic fibrosis: molecular biology and therapeutic implications. Science 256, 774–779 (1992).

  69. 69.

    , , , & Cystic fibrosis adult care: consensus conference report. Chest 125, 1S–39S (2004).

  70. 70.

    et al. Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis. Nat. Genet. 49, 1468–1475 (2017). This is a demonstration of the effect of very large sample sizes to identify hundreds of loci for bone mineral density, a clinically relevant, highly polygenic trait.

  71. 71.

    & Structural mechanism for statin inhibition of HMG-CoA reductase. Science 292, 1160–1164 (2001).

  72. 72.

    et al. Comparative effects of lovastatin and niacin in primary hypercholesterolemia. A prospective trial. Arch. Intern. Med. 154, 1586–1595 (1994).

  73. 73.

    , & Genetics of osteoporosis from genome-wide association studies: advances and challenges. Nat. Rev. Genet. 13, 672–672 (2012).

  74. 74.

    et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat. Genet. 40, 161–169 (2008).

  75. 75.

    et al. Effect of a monoclonal antibody to PCSK9 on low-density lipoprotein cholesterol levels in statin-intolerant patients: the GAUSS randomized trial. JAMA 308, 2497–2506 (2012).

  76. 76.

    et al. Denosumab in postmenopausal women with low bone mineral density. N. Engl. J. Med. 354, 821–831 (2006).

  77. 77.

    & Emerging treatments in cystic fibrosis. Drugs 69, 1903–1910 (2009).

  78. 78.

    Trial watch: phase III and submission failures: 2007–2010. Nat. Rev. Drug Discov. 10, 87 (2011).

  79. 79.

    & 'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol. 32, 1–22 (2003).

  80. 80.

    , , , & Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants. Epidemiology 28, 30–42 (2017).

  81. 81.

    , , , & Pleiotropy in complex traits: challenges and strategies. Nat. Rev. Genet. 14, 483–495 (2013).

  82. 82.

    , & Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).

  83. 83.

    , & Mendelian randomization in cardiometabolic disease: challenges in evaluating causality. Nat. Rev. Cardiol. 14, 577–590 (2017).

  84. 84.

    & Regulation of glucose transport by insulin: traffic control of GLUT4. Nat. Rev. Mol. Cell Biol. 13, 383–396 (2012).

  85. 85.

    et al. Meta-analysis of genome-wide association studies in >80 000 subjects identifies multiple loci for C-reactive protein levels. Circulation 123, 731–738 (2011).

  86. 86.

    An update on the genetic architecture of hyperuricemia and gout. Arthritis Res. Ther. 17, 98 (2015).

  87. 87.

    et al. Common variation in three genes, including a noncoding variant in CFH, strongly influences risk of age-related macular degeneration. Nat. Genet. 38, 1055–1059 (2006).

  88. 88.

    , , & Genetics of coronary artery disease — a clinician's perspective. Indian Heart J. 66, 663–671 (2014).

  89. 89.

    , , & Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012).

  90. 90.

    et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340, 1467–1471 (2013).

  91. 91.

    Seven types of pleiotropy. Int. J. Dev. Biol. 42, 501–505 (1998).

  92. 92.

    et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

  93. 93.

    & Principles of Population Genetics 3rd edn (Sinauer Associates, 1997).

  94. 94.

    Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147, 915–925 (1997).

  95. 95.

    et al. Support from the relationship of genetic and geographic distance in human populations for a serial founder effect originating in Africa. Proc. Natl Acad. Sci. USA 102, 15942–15947 (2005).

  96. 96.

    et al. Genome-wide association studies in an isolated founder population from the Pacific island of Kosrae. PLoS Genet. 5, e1000365 (2009).

  97. 97.

    et al. Genetic characterization of Greek population isolates reveals strong genetic drift at missense and trait-associated variants. Nat. Commun. 5, 5345 (2014).

  98. 98.

    Seasonal variation in host susceptibility and cycles of certain infectious diseases. Emerg. Infect. Dis. 7, 369–374 (2001).

  99. 99.

    & Genetics of susceptibitlity to human infectious disease. Nat. Rev. Genet. 2, 967–977 (2001).

  100. 100.

    & Evolvability. Proc. Natl Acad. Sci. USA 95, 8420–8427 (1998).

  101. 101.

    & Immunogenetics of viral infections. Curr. Opin. Immunol. 17, 510–516 (2005).

  102. 102.

    Jeffreys, a J., & Intensely punctate meiotic recombination in the class II region of the major histocompatibility complex. Nat. Genet. 29, 217–222 (2001).

  103. 103.

    , , & The population genetics of the haemoglobinopathies. Baillieres. Clin. Haematol. 11, 1–51 (1998).

  104. 104.

    & Antagonistic pleiotropy as a widespread mechanism for the maintenance of polymorphic disease alleles. BMC Med. Genet. 12, 160 (2011).

  105. 105.

    , & How culture shaped the human genome: bringing genetics and the human sciences together. Nat. Rev. Genet. 11, 137–148 (2010). This paper discusses fundamental theoretical advances for human evolution and the important extension of niche construction as a valid theory.

  106. 106.

    et al. Positive natural selection in the human lineage. Science 312, 1614–1620 (2006).

  107. 107.

    et al. Evolution of lactase persistence: an example of human niche construction. Philos. Trans R. Soc. Lond. B Biol Sci. 366, 863–877 (2011).

  108. 108.

    Balancing selection and its effects on sequences in nearby genome regions. PLoS Genet. 2, 379–384 (2006).

  109. 109.

    & Detection of the signature of natural selection in humans: evidence from the Duffy blood group locus. Am. J. Hum. Genet. 66, 1669–1679 (2000).

  110. 110.

    et al. Molecular analysis of the β-globin gene cluster in the Niokholo Mandenka population reveals a recent origin of the βS Senegal mutation. Am. J. Hum. Genet. 70, 207–223 (2002).

  111. 111.

    et al. Detection of human adaptation during the past 2000 years. Science 354, 760–764 (2016).

  112. 112.

    & Population genetic signal of polygenic adaptation. PLoS Genet. 10, e1004412 (2014).

  113. 113.

    et al. Selection against variants in the genome associated with educational attainment. Proc. Natl Acad. Sci. USA 114, E727–E732 (2017).

  114. 114.

    et al. Evidence of widespread selection on standing variation in Europe at height-associated SNPs. Nat. Genet. 44, 1015–1019 (2012). This paper and reference 112 provide excellent insights into polygenic selection and how this shapes genetics architecture.

  115. 115.

    et al. Population genetic differentiation of height and body mass index across Europe. Nat. Genet. 47, 1357–1362 (2015).

  116. 116.

    Decanalization and the origin of complex disease. Nat. Rev. 10, 134–140 (2009).

  117. 117.

    The evolutionary genetics of canalization. Q. Rev. Biol. 80, 287–316 (2005).

  118. 118.

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

  119. 119.

    et al. Toward precision medicine: TBC1D4 disruption is common among the inuit and leads to underdiagnosis of type 2 diabetes. Diabetes Care 39, 1889–1895 (2016).

  120. 120.

    et al. A common Greenlandic TBC1D4 variant confers muscle insulin resistance and type 2 diabetes. Nature 512, 190–193 (2014). This paper and reference 119 demonstrate how isolated populations, which may have undergone canalization, can help to identify critical control points for disease aetiology and affect diagnosis and screening in these populations.

  121. 121.

    et al. A high-density admixture map for disease gene discovery in African Americans. Am. J. Hum. Genet. 74, 1001–1013 (2004).

  122. 122.

    Prediction and interaction in complex disease genetics: Experience in type 1 diabetes. PLoS Genet. 5, 1–6 (2009). This is an excellent overview of the difficulties in assessing prediction and interactions for complex diseases.

  123. 123.

    et al. Genotype-covariate interaction effects and the heritability of adult body mass index. Nat. Genet. 49, 1174–1181 (2017). This study is a demonstration of the lack of pervasive genotype–covariate interaction effects for a polygenic and highly powered trait, BMI.

  124. 124.

    & Body fat and obesity in Japanese Americans. Am. J. Clin. Nutr. 53, 1552S–1555S (1991).

  125. 125.

    , , , & Acculturation and obesity among migrant populations in high income countries — a systematic review. BMC Public Health 13, 458 (2013).

  126. 126.

    , & Obesity in international migrant populations. Curr. Obes. Rep. 6, 314–323 (2017).

  127. 127.

    et al. Impact of migration on coronary heart disease risk factors: comparison of Gujaratis in Britain and their contemporaries in villages of origin in India. Atherosclerosis 185, 297–306 (2006).

  128. 128.

    , & Epidemiological studies of migration and environmental risk factors in the inflammatory bowel diseases. World J. Gastroenterol. 20, 1238–1247 (2014).

  129. 129.

    , , , & The impact of migration on tuberculosis epidemiology and control in high-income countries: a review. BMC Med. 14, 48 (2016).

  130. 130.

    et al. Migration patterns and breast cancer risk in Asian-American women. J. Natl Cancer Inst. 85, 1819–1827 (1993).

  131. 131.

    , , , & Cancer incidence patterns among Vietnamese in the United States and Ha Noi, Vietnam. Int. J. Cancer 102, 412–417 (2002).

  132. 132.

    & Archibald Garrod and the development of the concept of inborn errors of metabolism. Bull. Hist. Med. 53, 315–328 (1979).

  133. 133.

    et al. Estimating genome-wide significance for whole-genome sequencing studies. Genet. Epidemiol. 38, 281–290 (2014).

  134. 134.

    et al. The power of gene-based rare variant methods to detect disease-associated variation and test hypotheses about complex disease. PLoS Genet. 11, e1005165 (2015).

  135. 135.

    , , & in Pacific Symposium on Biocomputing 2011 76–87 (Kohala Coast, Hawaii, 2011).

  136. 136.

    , , & Statistical analysis strategies for association studies involving rare variants. Nat. Rev. Genet. 11, 773–785 (2010).

  137. 137.

    & Comparison of statistical tests for disease association with rare variants. Genet. Epidemiol. 35, 606–619 (2011).

  138. 138.

    et al. Severe osteoarthritis of the hand associates with common variants within the ALDH1A2 gene and with rare variants at 1p31. Nat. Genet. 46, 498–502 (2014).

  139. 139.

    et al. Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction. Nature 518, 102–106 (2015).

  140. 140.

    , , & Empirical power of very rare variants for common traits and disease: results from sanger sequencing 1998 individuals. Eur. J. Hum. Genet. 21, 1027–1030 (2013).

  141. 141.

    et al. Integrating gene expression with summary association statistics to identify genes associated with 30 complex traits. Am. J. Hum. Genet. 100, 473–487 (2017).

  142. 142.

    The Emerging Risk Factors Collaboration. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet 375, 2215–2222 (2010).

  143. 143.

    & A study of the diet and metabolism of Eskimos undertaken in 1908 on an expedition to Greenland. Meddelelser Gronl. 41, 165–173 (1914).

  144. 144.

    , & Diabetes mellitus in Eskimos. JAMA 199, 107–112 (1967).

  145. 145.

    et al. Diabetes and impaired glucose tolerance among the inuit population of Greenland. Diabetes Care 25, 1766–1771 (2002).

  146. 146.

    et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445, 881–885 (2007).

  147. 147.

    et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat. Genet. 44, 991–1005 (2012).

  148. 148.

    et al. A genome-wide meta-analysis of six type 1 diabetes cohorts identifies multiple associated loci. PLoS Genet. 7, e1002293 (2011).

  149. 149.

    et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).

  150. 150.

    et al. Worldwide human relationships inferred from genome-wide patterns of variation. Science 319, 1100–1104 (2008).

  151. 151.

    et al. Genetic structure of human populations. Science 298, 2381–2385 (2002).

Download references

Acknowledgements

The authors wish to acknowledge V. Forgetta for his help drawing the figures. N.J.T. is a Wellcome Trust Investigator (202802/Z/16/Z), is a programme lead in the Medical Research Council (MRC) Integrative Epidemiology Unit (MC_UU_12013/3) and works within the University of Bristol National Institute for Health Research (NIHR) Biomedical Research Centre (BRC). C.M.T.G. has received funding from the Natural Sciences and Engineering Research Council (NSERC) and the Canadian Institutes of Health Research (CIHR). D.J.L. is funded by the Wellcome Trust under grant number WT104125MA. J.B.R. receives support from the CIHR, the Lady Davis Institute of the Jewish General Hospital and the Fonds de Recherche Santé Québec.

Author information

Affiliations

  1. MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Clifton, Bristol BS8 2BN, UK.

    • Nicholas J. Timpson
    •  & Daniel J. Lawson
  2. Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, 3755 Cote Ste Catherine, Montréal, Québec H3T 1E2, Canada.

    • Celia M. T. Greenwood
  3. Department of Oncology, McGill University, 3755 Cote Ste Catherine, Montréal, Québec H3T 1E2, Canada.

    • Celia M. T. Greenwood
  4. Departments of Human Genetics and Epidemiology, Biostatistics and Occupational Health, McGill University, 3755 Cote Ste Catherine, Montréal, Québec H3T 1E2, Canada.

    • Celia M. T. Greenwood
    •  & J. Brent Richards
  5. The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1HH, UK.

    • Nicole Soranzo
  6. Department of Haematology, University of Cambridge, Long Road, Cambridge CB2 0PT, UK.

    • Nicole Soranzo
  7. Department of Medicine, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, 3755 Cote Ste Catherine, Montréal, Québec H3T 1E2, Canada.

    • J. Brent Richards
  8. Department of Twin Research & Genetic Epidemiology, King's College London, St Thomas' Campus, Lambeth Palace Road, London SE1 7EH, UK.

    • J. Brent Richards

Authors

  1. Search for Nicholas J. Timpson in:

  2. Search for Celia M. T. Greenwood in:

  3. Search for Nicole Soranzo in:

  4. Search for Daniel J. Lawson in:

  5. Search for J. Brent Richards in:

Contributions

N.J.T., C.M.T.G., D.J.L. and J.B.R. researched data for the article, contributed to discussion of the content, wrote the article and reviewed and/or edited the manuscript before submission. N.S. contributed to discussion of the content and reviewed and/or edited the manuscript before submission.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Nicholas J. Timpson or J. Brent Richards.

Glossary

Broad-sense phenotypic heritability

The proportion of trait variance that is due to all genetic factors, including dominant and recessive factors, as well as the interactions between genetic factors. Narrow-sense heritability is the proportion of trait variance that is due to additive genetic factors.

Phenotype

A measurable characteristic of an individual.

Complex traits

Traits that do not follow Mendelian inheritance patterns and are derived from any combination of multiple genetic factors, environmental factors and their interactions.

Single nucleotide variants

(SNVs). Single base pair positions in the genome where there is variation across individuals. SNVs need not be biallelic or common.

Genome-wide association studies

(GWAS). Studies that test the association of all measured genetic variation across the genome with a trait or disease. GWAS usually test the association of a phenotype with genetic variants that have a minor allele frequency (MAF) ≥1%, but deep imputation methods allow GWAS to test associations with variants at a lower MAF.

Whole-exome sequencing studies

Studies that test the association between genetic variation (usually single nucleotide variants) across the measured coding sequence of the genome with a trait or disease. Whole-exome sequencing studies can measure most coding genetic variants, regardless of minor allele frequency.

Whole-genome sequencing studies

Studies that test the association of genetic variation across the entire variable genetic sequence of the genome with a trait or disease. Whole-genome sequencing studies can measure most genetic variants present in the genome, regardless of minor allele frequency. However, certain regions are not usually measurable via sequencing, such as highly repetitive regions.

Minor allele frequency

(MAF). The frequency of the less frequent allele at a genetic variant in a population. The less frequent allele is referred to as the minor allele.

Deep imputation

The use of large imputation reference panels to accurately estimate most low-frequency (minor allele frequency (MAF) ≥1% but ≤5%) and some rare (MAF <1%) unobserved genetic variation in individuals who have undergone genome-wide genotyping.

Single nucleotide polymorphisms

(SNPs). Single base pair positions in the genome where two or more nucleotides occur commonly in the population. 'Common' is usually defined as at least 1% of the population carrying an alternative allele. Most often, SNPs are biallelic, which means that the nucleotide will be one of two different alleles.

Heritable

A characteristic or trait that has a portion of variability that is accounted for by genetic factors.

Haplotypes

Sections of commonly varying or linked chromosomal material said to be in gametic phase, that is, not punctuated by recombination at an appreciable population-based frequency.

Imputation reference panel

A data set containing genetic information on a large number of individuals who have undergone whole-genome sequencing and had their haplotypes reconstructed. These haplotype panels enable accurate imputation of non-genotyped genetic variants in individuals who have undergone genome-wide genotyping.

Single SNV association test

A genetic association test that tests variation at a single nucleotide variant with variation in a phenotype. This is the most common genetic association test and is frequently used for genome-wide genotyping data.

Region-based testing

A single test of association between many genetic variants in a chosen region of the genome and a phenotype.

Burden test

A class of region-based testing that collapses genetic variation into a single genetic score by measuring the total number of minor alleles across a genomic region.

Variance component test

A single test of whether the phenotypic variance explained by a set of chosen genetic variants across a genomic region is zero. For example, a variance component test could be used to test whether all single nucleotide variants in a gene contribute to the variability in a phenotype.

Doubletons

Genetic variants that are observed twice within the population studied.

Variance explained

The proportion of variance in a phenotype that is explained by a mathematical model.

Linkage disequilibrium

The non-random association of alleles in a population.

Receiver operator curve

(ROC). A method to evaluate the performance of a diagnostic test for a binary outcome that plots the sensitivity of the test (the true positive rate) against one minus the specificity of the test (the false positive rate).

Phenotypic variance

The variance in a phenotype, which is often assumed to be a function of environmental and genetic factors as well as their interactions.

Confounding

When the association between an exposure and an outcome is distorted by their associations with a third variable. A confounding variable is a variable that is associated with both the exposure and the outcome but is not in the causal pathway between the two. A confounding variable could include a common cause of both the exposure and the outcome.

Reverse causation

The phenomenon whereby the outcome influences the exposure.

Horizontal pleiotropy

When the genetic variant in a Mendelian randomization study influences the outcome in a manner independent of the risk factor. This is a violation of Mendelian randomization assumptions.

Vertical pleiotropy

When the genetic variant in a Mendelian randomization study influences the outcome through multiple biomarkers in the same pathway. This is not a violation of Mendelian randomization assumptions.

Founder effect

The reduced genetic diversity that occurs when a population is descended from a small number of founders.

Lactase persistence

The continued activity of the enzyme lactase in adulthood in humans.

Singleton

Genetic variant that is observed only once within the population studied.

Admixture mapping

A method of genetic association testing that relies on the admixture of populations, which occurs when individuals from two or more historically isolated populations interbreed.

About this article

Publication history

Published

DOI

https://doi.org/10.1038/nrg.2017.101

Further reading