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Influence of technology in genetic epidemiology

  • Received: 29 April 2015 Accepted: 16 September 2015 Published: 25 January 2015
  • Genetic epidemiology is the study of genetic factors and their influence on health and disease. Traditionally, these studies have been based on familial aggregation, segregation, or linkage analysis, mainly allowing the study of monogenic disorders. Advances in biotechnology have made techniques such as genome-wide association studies and next-generation sequencing possible, allowing more complex studies. In addition to the completion of large consortia projects, such as the Human Genome Project, ENCODE, and the 1000 Genome Project, these techniques make it possible to explain a higher proportion of the heritability in polygenic disorders compared to previous techniques. Here, we provide an overview of approaches to genetic epidemiology and how technological improvements have influenced experimentation in this area. These improvements have led genetic epidemiology to unprecedented advances, being excellent tools for understanding the genetic variability underlying complex phenotypes.

    Citation: Marcos Morey, Ana Fernández-Marmiesse, Jose Angel Cocho, María L. Couce. Influence of technology in genetic epidemiology[J]. AIMS Genetics, 2015, 2(3): 219-229. doi: 10.3934/genet.2015.3.219

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  • Genetic epidemiology is the study of genetic factors and their influence on health and disease. Traditionally, these studies have been based on familial aggregation, segregation, or linkage analysis, mainly allowing the study of monogenic disorders. Advances in biotechnology have made techniques such as genome-wide association studies and next-generation sequencing possible, allowing more complex studies. In addition to the completion of large consortia projects, such as the Human Genome Project, ENCODE, and the 1000 Genome Project, these techniques make it possible to explain a higher proportion of the heritability in polygenic disorders compared to previous techniques. Here, we provide an overview of approaches to genetic epidemiology and how technological improvements have influenced experimentation in this area. These improvements have led genetic epidemiology to unprecedented advances, being excellent tools for understanding the genetic variability underlying complex phenotypes.


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    [1] Morton NE (1997) Genetic epidemiology. Ann Hum Genet 61: 1-13.
    [2] Morton NE (1994) Fundamentals of genetic epidemiology. Genet Epidemiol 11: 389-390.
    [3] Morton NE (1982) Outline of genetic epidemiology. S. Karger AG (Switzerland), 252.
    [4] Cohen BH (1980) Chronic obstructive pulmonary disease: A challenge in genetic epidemiology. Am J Epidemiol 112: 274-288.
    [5] Morey M, Fernández-Marmiesse A, Castiñeiras D, et al. (2013) A glimpse into past, present, and future DNA sequencing. Mol Genet Metab 110: 3-24.
    [6] Matullo G, Gaetano CD, Guarrera S (2013) Next generation sequencing and rare genetic variants: From human population studies to medical genetics. Environ Mol Mutagen 54: 518-532.
    [7] IJzerman RG, Stehouwer CDA, Boomsma DI (2000) Evidence for genetic factors explaining the birth Weight–Blood pressure relation: Analysis in twins. Hypertension 36: 1008-1012.
    [8] Ostern R, Fagerheim T, Hjellnes H, et al. (2014) Segregation analysis in families with charcot-marie-tooth disease allows reclassification of putative disease causing mutations. BMC Med Genet 15: 12.
    [9] Jorde LB (2000) Linkage disequilibrium and the search for complex disease genes. Genome Res 10: 1435-1444.
    [10] Guo SW (2001) Does higher concordance in monozygotic twins than in dizygotic twins suggest a genetic component?. Hum Hered 51: 121-132.
    [11] King RC, Mulligan P, Stansfield W (2013) A dictionary of genetics. Oxford University Press, 641.
    [12] Wong AHC, Gottesman II, Petronis A (2005) Phenotypic differences in genetically identical organisms: The epigenetic perspective. Hum Mol Genet 14: R11-18.
    [13] Chaganti RSK, Miller DR, Meyers PA, et al. (1979) Cytogenetic evidence of the intrauterine origin of acute leukemia in monozygotic twins. N Engl J Med 300: 1032-1034.
    [14] Bell JT, Saffery R (2012) The value of twins in epigenetic epidemiology. Int J Epidemiol 41: 140-150.
    [15] Elston RC (1981) Segregation analysis. In: Harris H and Hirschhorn K, eds. Springer US, 63-120.
    [16] Jarvik GP (1998) Complex segregation analyses: Uses and limitations. Am J Hum Genet 63: 942-946.
    [17] Terwilliger JD, Goring HH (2000) Gene mapping in the 20th and 21st centuries: Statistical methods, data analysis, and experimental design. Hum Biol 72: 63-132.
    [18] Bateson W, Waunders ER, Punnett RC (1909) Experimental studies in the physiology of heredity. Zeitschrift für Induktive Abstammungs- Und Vererbungslehre 2: 17-19.
    [19] Stevens WL (1939) Tables of the recombination fraction estimated from the product ratio. J Genet 39: 171-180.
    [20] Tan YD, Fu YX (2007) A new strategy for estimating recombination fractions between dominant markers from an F2 population. Genetics 175: 923-931.
    [21] Botstein D, White RL, Skolnick M, et al. (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet 32: 314-331.
    [22] Stocker AJ, Rusuwa BB, Blacket MJ, et al. (2012) Physical and linkage maps for drosophila serrata, a model species for studies of clinal adaptation and sexual selection. G3 (Bethesda) 2: 287-297. doi: 10.1534/g3.111.001354
    [23] Bailey-Wilson JE (2005) Parametric versus nonparametric and two-point versus multipoint: Controversies in gene mapping. In: Anonymous Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics. John Wiley & Sons, Ltd.
    [24] Hirschhorn JN, Lohmueller K, Byrne E, et al. (2002) A comprehensive review of genetic association studies. Genet Med 4: 45-61.
    [25] Cordell HJ, Clayton DG (2005) Genetic association studies. Lancet 366: 1121-1131.
    [26] McCarthy MI, Abecasis GR, Cardon LR, et al. (2008) Genome-wide association studies for complex traits: Consensus, uncertainty and challenges. Nat Rev Genet 9: 356-369.
    [27] St George-Hyslop PH, Haines JL, Farrer LA, et al. (1990) Genetic linkage studies suggest that alzheimer's disease is not a single homogeneous disorder. Nature 347: 194-197.
    [28] Klein RJ, Zeiss C, Chew EY, et al. (2005) Complement factor H polymorphism in age-related macular degeneration. Science 308: 385-389.
    [29] Welter D, MacArthur J, Morales J, et al. (2013) The NHGRI GWAS catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 42: D1001-1006.
    [30] Kooperberg C, LeBlanc M, Obenchain V (2010) Risk prediction using genome-wide association studies. Genet Epidemiol 34: 643-652.
    [31] Gusev A, Bhatia G, Zaitlen N, et al. (2013) Quantifying missing heritability at known GWAS loci. PLoS Genet 9: e1003993.
    [32] Stranger BE, Stahl EA, Raj T (2011) Progress and promise of genome-wide association studies for human complex trait genetics. Genetics 187: 367-383.
    [33] Visscher P, Brown M, McCarthy M, et al. (2012) Five years of GWAS discovery. Am J Hum Genet 90: 7-24.
    [34] Gibson G (2012) Rare and common variants: Twenty arguments. Nat Rev Genet 13: 135-145.
    [35] Slatkin M (2008) Linkage disequilibrium - understanding the evolutionary past and mapping the medical future. Nat Rev Genet 9: 477-485.
    [36] Cooper GM, Shendure J (2011) Needles in stacks of needles: Finding disease-causal variants in a wealth of genomic data. Nat Rev Genet 12: 628-640.
    [37] Manolio TA, Collins FS, Cox NJ, et al. (2009) Finding the missing heritability of complex diseases. Nature 461: 747-753.
    [38] Frazer KA, Murray SS, Schork NJ, et al. (2009) Human genetic variation and its contribution to complex traits. Nat Rev Genet 10: 241-251.
    [39] Johnson DS, Mortazavi A, Myers RM, et al. (2007) Genome-wide mapping of in vivo protein-DNA interactions. Science 316: 1497-1502.
    [40] Shen P, Wang W, Krishnakumar S, et al. (2011) High-quality DNA sequence capture of 524 disease candidate genes. Proc Natl Acad Sci U S A 108: 6549-6554.
    [41] Service RF (2006) The race for the $1000 genome. Science 311: 1544-1546.
    [42] Wetterstrand KA, DNA Sequencing Costs: Data from the NHGRI Large-Scale Genome Sequencing Program. 2015. Available from: www.genome.gov/sequencingcosts
    [43] Broadwith P (2012) Sequencing in the fast lane. Chem World 9: 54-58.
    [44] Feldman AL, Dogan A, Smith DI, et al. (2010) Massively parallel mate pair DNA library sequencing for translocation discovery: Recurrent t(6;7)(p25.3;q32.3) translocations in ALK-negative anaplastic large cell lymphomas. ASH Annual Meeting Abstracts 116: 633.
    [45] Green R, Malaspinas A, Krause J, et al. (2008) A complete neandertal mitochondrial genome sequence determined by high-throughput sequencing. Cell 134: 416-426.
    [46] Durbin RM, Altshuler DL, Durbin RM, et al. (2010) A map of human genome variation from population-scale sequencing. Nature 467: 1061-1073.
    [47] Peters BA, Kermani BG, Sparks AB, et al. (2012) Accurate whole-genome sequencing and haplotyping from 10 to 20 human cells. Nature 487: 190-195.
    [48] Butler J, MacCallum I, Kleber M, et al. (2008) ALLPATHS: De novo assembly of whole-genome shotgun microreads. Genome Res 18: 810-820.
    [49] Furlotte NA, Heckerman D, Lippert C (2014) Quantifying the uncertainty in heritability. J Hum Genet 59: 269-275.
    [50] Majewski J, Schwartzentruber J, Lalonde E, et al. (2011) What can exome sequencing do for you?. J Med Genet 48: 580-589.
    [51] Wooderchak-Donahue W, O’Fallon B, Furtado L, et al. (2012) A direct comparison of next generation sequencing enrichment methods using an aortopathy gene panel- clinical diagnostics perspective. BMC Medical Genomics 5: 1-10.
    [52] Kalender Atak Z, De Keersmaecker K, Gianfelici V, et al. (2012) High accuracy mutation detection in leukemia on a selected panel of cancer genes. PLoS One 7: e38463.
    [53] Ni T, Wu H, Song S, et al. (2009) Selective gene amplification for high-throughput sequencing. Recent Pat DNA Gene Seq 3: 29-38.
    [54] Gaugler T, Klei L, Sanders SJ, et al. (2014) Most genetic risk for autism resides with common variation. Nat Genet 46: 881-885.
    [55] Muona M, Berkovic SF, Dibbens LM, et al. (2015) A recurrent de novo mutation in KCNC1 causes progressive myoclonus epilepsy. Nat Genet 47: 39-46.
    [56] Xu B, Ionita-Laza I, Roos JL, et al. (2012) De novo gene mutations highlight patterns of genetic and neural complexity in schizophrenia. Nat Genet 44: 1365-1369.
    [57] Cardinale CJ, Kelsen JR, Baldassano RN, et al. (2013) Impact of exome sequencing in inflammatory bowel disease. World J Gastroenterol 19: 6721-6729.
    [58] Gilissen C, Arts HH, Hoischen A, et al. (2010) Exome sequencing identifies WDR35 variants involved in sensenbrenner syndrome. Am J Hum Genet 87: 418-423.
    [59] Boycott KM, Vanstone MR, Bulman DE, et al. (2013) Rare-disease genetics in the era of next-generation sequencing: Discovery to translation. Nat Rev Genet 14: 681-691.
    [60] Roach JC, Glusman G, Smit AF, et al. (2010) Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science 328: 636-639.
    [61] Fernandez-Marmiesse A, Morey M, Pineda M, et al. (2014) Assessment of a targeted resequencing assay as a support tool in the diagnosis of lysosomal storage disorders. Orphanet J Rare Dis 9: 59.
    [62] Audo I, Bujakowska KM, Leveillard T, et al. (2012) Development and application of a next-generation-sequencing (NGS) approach to detect known and novel gene defects underlying retinal diseases. Orphanet J Rare Dis 7: 8.
    [63] Mardis ER (2009) New strategies and emerging technologies for massively parallel sequencing: Applications in medical research. Genome Med 1: 40.
    [64] Wendl MC, Wilson RK (2009) The theory of discovering rare variants via DNA sequencing. BMC Genomics 10: 485.
    [65] Cooper DN, Krawczak M, Polychronakos C, et al. (2013) Where genotype is not predictive of phenotype: Towards an understanding of the molecular basis of reduced penetrance in human inherited disease. Hum Genet 132: 1077-1130.
    [66] Venter JC, Adams MD, Myers EW, et al. (2001) The sequence of the human genome. Science 291: 1304-1351.
    [67] Lander ES, Linton LM, Birren B, et al. (2001) Initial sequencing and analysis of the human genome. Nature 409: 860-921.
    [68] Durbin R, Altshuler D, Durbin R, et al. (2010) A map of human genome variation from population-scale sequencing. Nature 467: 1061-1073.
    [69] Panoutsopoulou K, Tachmazidou I, Zeggini E (2013) In search of low-frequency and rare variants affecting complex traits. Hum Mol Genet 22: R16-21.
    [70] Li J, Schmieder R, Ward RM, et al. (2012) SEQanswers: An open access community for collaboratively decoding genomes. Bioinformatics 28: 1272-1273.
    [71] Elgar G, Vavouri T (2008) Tuning in to the signals: Noncoding sequence conservation in vertebrate genomes. Trends Genet 24: 344-352.
    [72] Eisenberger T, Neuhaus C, Khan AO, et al. (2013) Increasing the yield in targeted next-generation sequencing by implicating CNV analysis, non-coding exons and the overall variant load: The example of retinal dystrophies. PLoS One 8: e78496.
    [73] Ward LD, Kellis M (2011) HaploReg: A resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res 40: D930-934.
    [74] Boyle AP, Hong EL, Hariharan M, et al. (2012) Annotation of functional variation in personal genomes using RegulomeDB. Genome Res 22: 1790-1797.
    [75] Kircher M, Witten DM, Jain P, et al. (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet 46: 310-315.
    [76] Vandeweyer G, Van Laer L, Loeys B, et al. (2014) VariantDB: A flexible annotation and filtering portal for next generation sequencing data. Genome Med 6: 74.
    [77] Ritchie GRS, Dunham I, Zeggini E, et al. (2014) Functional annotation of noncoding sequence variants. Nat Meth 11: 294-296.
    [78] Wang K, Li M, Hakonarson H (2010) ANNOVAR: Functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Research 38: e164-.
    [79] Henry VJ, Bandrowski AE, Pepin A, et al. (2014) OMICtools: An informative directory for multi-omic data analysis. Database (Oxford) bau069.
    [80] The ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489: 57-74.
    [81] The 1000 Genomes Project Consortium (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491: 56-65.
    [82] McEwen JE, Boyer JT, Sun KY, et al. (2014) The ethical, legal, and social implications program of the national human genome research institute: Reflections on an ongoing experiment. Annu Rev Genomics Hum Genet 15: 481-505.

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