Genetic Epidemiology of Complex Traits

Abstract

Genetic epidemiology is a research discipline that combines genetics, molecular biology, epidemiology, statistics and bioinformatics to investigate the role of genes, the environment and their interaction in the expression of quantitative and qualitative traits and diseases in the population at large. Genetic epidemiologists often leverage population‐based family, case–control, and cohort research designs, including many with a longitudinal data collection scheme. However, characterising the influence of genes and the environment traits and diseases is complicated because most traits and diseases are influenced by a number of genetic and nongenetic factors. In addition to the problems associated with the characterisation of the genetic and environmental factors influencing traits and diseases due to the complex, multifactorial and context‐dependent pathophysiological basis of most traits and diseases, a number of issues associated with the genetic structure of human populations also complicate relevant analyses. Genetic epidemiologists attempt to deal with these issues in many ways. As the current biomedical research area era involving high‐throughput and data‐intensive assays evolves, new fields (e.g. metabolomics, epigenomics and megagenomics), technologies (e.g. high‐throughput sequencing, microarrays and mass spectrometry) and statistical approaches will be brought into genetic epidemiology, enabling more comprehensive ‘system‐level’ approaches to characterising the roles of the environment and inherited factors in disease risk, incidence, prevalence and response to therapy.

Key Concepts

  • Genetic epidemiologists investigate the role of genes, the environment and their interaction between the aetiology and population‐level incidence and prevalence of health‐related traits and diseases within families and in the population at large.
  • Factors at both the population and the individual or pathophysiological level contribute to the complexity of complex diseases, making it difficult to identify and characterise their determinants.
  • The main approaches taken by genetic epidemiologists for discovering genes influencing diseases and traits are family‐based linkage and population‐based association studies.
  • Once a variant gene has been identified, genetic epidemiologists characterise the effect of that gene on the disease burden or trait distribution in the population using a number of approaches, such as cataloguing its frequency in different populations, examining environmental factors that exacerbate or ameliorate its effects and assessing its origins and its typical effect on the time course of, for example, disease expression among individuals.
  • By integrating data from multiple high‐throughput biomedical assays (e.g. data from epigenomics, metabolomics, proteomics and metabolomics), genetic epidemiologists can apply ‘system‐level’ approaches to understand how various factors contribute to interaction in the expression of health‐related traits and diseases.

Keywords: polygenic traits; epistasis; linkage; genetic association; penetrance

Figure 1. Representation of coinheritance (i.e. genetic transmission) of a disease‐causing mutation or genetic variant and surrounding ‘founder’ chromosomal material over a number of generations. Homologous chromosomes are depicted by solid vertical lines. The numbers represent repeat lengths (alleles) associated with a microsatellite locus. The letters designate bases (alleles) at three single nucleotide polymorphic loci adjacent to the microsatellite locus. The circled ‘G’ base is a dominant and fully penetrant disease‐causing mutation. The arrows identify sites where recombination has occurred. In generation 1, two individuals produced four offspring and two that received the disease mutation (i.e. the inner two offspring). One of these offspring did not receive the entire chromosomal segment or ‘haplotype’ (123‐C–G–T) associated with the parent introducing the mutation into the population due to a recombination event that shuffled the 126 allele at the microsatellite locus with the C–G–T subhaplotype. The individuals possessing the mutation produce their own offspring and these offspring produce subsequent lines of descent (denoted by dashed lines). Two descendants of these individuals (in generation N − 1) mate with others and produce two offspring each. The two offspring from both matings receive the disease mutation, G. Note that all the diseased individuals share the basic core C–G–T haplotype, but in the first mating, the repeat allele 126 is coinherited with this haplotype, whereas the repeat allele 123 is coinherited with this haplotype in the second mating.
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References

Ala‐Korpela M, Kangas AJ and Inouye M (2011) Genome‐wide association studies and systems biology: together at last. Trends in Genetics 27: 493–498.

Collins FS (1992) Positional cloning: let's not call it reverse anymore. Nature Genetics 1: 3–6.

Crosby J, Peloso GM, Auer PL, et al. (2014) Loss‐of‐function mutations in APOC3, triglycerides, and coronary disease. New England Journal of Medicine 371: 22–31.

Foxman B and Rosenthal M (2013) Implications of the human microbiome project for epidemiology. American Journal of Epidemiology 177: 197–201.

Gabriel SB, Schaffner SF, Nguyen H, et al. (2002) The structure of haplotype blocks in the human genome. Science 296 (5576): 2225–2229.

Goetz LH, Uribe‐Bruce L, Quarless D, Libiger O and Schork NJ (2014) Admixture and clinical phenotypic variation. Human Heredity 77 (1–4): 73–86.

Gonzalez E, Dhanda R, Bamshad M, et al. (2001) Global survey of genetic variation in CCR5, RANTES and MIP‐1alpha: impact on the epidemiology of the HIV‐1 pandemic. Proceedings of the National Academy of Sciences of the United States of America 98 (9): 5199–5204.

Goodrich JK, Waters JL, Poole AC, et al. (2014) Human genetics shape the gut microbiome. Cell 159: 789–799.

Lander E and Schork NJ (1994) The genetic dissection of complex traits. Science 265: 2037–2048.

Nadeau J (2001) Modifier genes in mouse and humans. Nature Reviews. Genetics 2: 165–174.

Pan LL, Huang YM, Wang M, et al. (2015) Positional cloning and next‐generation sequencing identified a TGM6 mutation in a large Chinese pedigree with acute myeloid leukaemia. European Journal of Human Genetics 23: 218–223.

Schaefer S and Nadeau JH (2015) The genetics of epigenetic inheritance: modes, molecules, and mechanisms. Quarterly Reviews of Biology 90: 381–415.

Schork NJ, Xu X and Cardon LR (1998) The future of genetic epidemiology. Trends in Genetics 14: 266–272.

Schork NJ, Fallin D, Thiel B, et al. (2001) The future of genetic case‐control studies. Advances in Genetics 42: 191–212.

Schuler GD, Boguski MS, Stewart EA, et al. (1996) A gene map of the human genome. Science 274: 540–546.

Singh SM, Murphy B and O'Reilly R (2002) Epigenetic contributors to the discordance of monzygotic twins. Clinical Genetics 62: 97–103.

Smith D and Hemani G (2014) Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Human Molecular Genetics 23: R89–R98.

Strohman R (2002) Maneuvering in the complex path from genotype to phenotype. Science 296: 701–703.

UK10K Consortium, Walter K, Min JL, et al. (2015) The UK10K project identifies rare variants in health and disease. Nature 526: 82–90.

Visscher PM, Wray NR, Zhang Q, et al. (2017) 10 years of GWAS discovery: biology, function, and translation. American Journal of Human Genetics 101: 5–22.

Weatherall DJ (2001) Phenotype–Genotype relationships in monogenic diseases: lessons from the thalassemias. Nature Reviews. Genetics 2: 245–255.

Weinberg RB (1999) Apolipoprotein A‐IV‐2 allele: association of its worldwide distribution with adult persistence of lactase and speculation on its function and origin. Genetic Epidemiology 17: 285–297.

Further Reading

Coughlin SS (2014) Toward a road map for global omics: a primer on ‐omic technologies. American Journal of Epidemiology 180: 1188–1195.

Jiang Y‐H, Bressler J and Beaudet AL (2004) Epigenetics and human disease. Annual Review of Genomics and Human Genetics 5: 479–510.

Jorde LB (2000) Linkage disequilibrium and the search for complex disease genes. Genome Research 10: 1435–1444.

Khoury MJ, Beaty TH and Cohen BH (1993) Fundamentals of Genetic Epidemiology. New York: Oxford University Press.

McKeigue PM, Carpenter JR, Parra EJ and Shriver MD (2000) Estimation of admixture and detection of linkage in admixed populations by a Bayesian approach: application to African‐American populations. Annals of Human Genetics 64 (Pt 2): 171–186.

Ordovas JM and Corella D (2004) Nutritional genomics. Annual Review of Genomics and Human Genetics 5: 71–118.

Ott J (1985) Analysis of Human Genetic Linkage. Baltimore, MD: Johns Hopkins University Press.

Pritchard JK and Przeworski M (2001) Linkage disequilibrium in humans: models and data. American Journal of Human Genetics 69: 1–14.

Schork NJ, Fallin D and Lanchbury JS (2000) Single nucleotide polymorphisms and the future of genetic epidemiology. Clinical Genetics 58 (4): 250–264.

Sun YV and Hu YJ (2016) Integrative analysis of multiomics data for discovery and functional studies of complex human diseases. Advances in Genetics 93: 147–190.

The International Schizophrenia Consortium (2009) Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460: 748–752.

Weiss KM (1993) Genetic Variation and Human Disease: Principles and Evolutionary Approach. New York: Cambridge University Press.

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Rana, Brinda K, De Vivo, Imaculata, and Schork, Nicholas J(Oct 2017) Genetic Epidemiology of Complex Traits. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0005412.pub3]