Skip to main content

Advertisement

Log in

Complex phenotypes and complex genetics: An introduction to genetic studies of complex traits

  • Published:
Current Atherosclerosis Reports Aims and scope Submit manuscript

Abstract

There is currently intense interest in the genetic factors contributing to many diseases with cardiovascular complications. Diseases like atherosclerosis, diabetes, and hypertension are referred to as complex traits because multiple genes contribute to the phenotype either individually or through interactions with each other or the environment. Enabled and energized by the striking successes over the past 20 years in identifying genes that are responsible for single gene traits, many geneticists have turned to the investigation of methods that will allow for the dissection of complex traits. There have already been some successes, so there is no reason to consider the problem as inherently intractable. However, it is important to reflect on what conditions are necessary for the identification of genes that operate in complex traits. A recurring theme in this research area has been difficulty in repeating and validating research findings, and this most often can be attributed to limitations in study design. It is also important to consider that any particular research strategy can only hope to describe a portion of factors that contribute to variation in the population; therefore, the genetic approach cannot be a panacea. New efficient technologies for genotyping and public databases describing the fine structure of genetic correlations in the genome should aid many aspects of the gene discovery process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

References and Recommended Reading

  1. Altshuler JS, Altshuler D: Organizational challenges in clinical genomic research. Nature 2004, 429:478–481.

    Article  PubMed  CAS  Google Scholar 

  2. Hardenbol P, Yu F, Belmont J, et al.: Highly multiplexed molecular inversion probe genotyping: over 10,000 targeted SNPs genotyped in a single tube assay. Genome Res 2005, 15:269–275.

    Article  PubMed  CAS  Google Scholar 

  3. Kennedy GC, Matsuzaki H, Dong S, et al.: Large-scale genotyping of complex DNA. Nat Biotechnol 2003, 21:1233–1237.

    Article  PubMed  CAS  Google Scholar 

  4. McVean GA, Myers SR, Hunt S, et al.: The fine-scale structure of recombination rate variation in the human genome. Science 2004, 304:581–584.

    Article  PubMed  CAS  Google Scholar 

  5. Hinds DA, Stuve LL, Nilsen GB, et al.: Whole-genome patterns of common DNA variation in three human populations. Science 2005, 307:1072–1079.

    Article  PubMed  CAS  Google Scholar 

  6. Smith JD, James D, Dansky HM, et al.: In silico quantitative trait locus map for atherosclerosis susceptibility in apolipoprotein E-deficient mice. Arterioscler Thromb Vasc Biol 2003, 23:117–122.

    Article  PubMed  CAS  Google Scholar 

  7. Long AD, Langley CH: The power of association studies to detect the contribution of candidate genetic loci to variation in complex traits. Genome Res 1999, 9:720–731.

    PubMed  CAS  Google Scholar 

  8. Broman KW: Mapping quantitative trait loci in the case of a spike in the phenotype distribution. Genetics 2003, 163:1169–1175.

    PubMed  Google Scholar 

  9. Grissom RJ, Kim JJ: Review of assumptions and problems in the appropriate conceptualization of effect size. Psychol Methods 2001, 6:135–146.

    Article  PubMed  CAS  Google Scholar 

  10. Abecasis GR, Cookson WO, Cardon LR: The power to detect linkage disequilibrium with quantitative traits in selected samples. Am J Hum Genet 2001, 68:1463–1474.

    Article  PubMed  CAS  Google Scholar 

  11. Zondervan KT, Cardon LR: The complex interplay among factors that influence allelic association. Nat Rev Genet 2004, 5:89–100.

    Article  PubMed  CAS  Google Scholar 

  12. Almasy L, Blangero J: Endophenotypes as quantitative risk factors for psychiatric disease: rationale and study design. Am J med Genet 2001, 105:42–44.

    Article  PubMed  CAS  Google Scholar 

  13. Gottesman II, Gould TD: The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry 2003, 160:636–645.

    Article  PubMed  Google Scholar 

  14. Soro A, Pajukanta P, Lilja HE, et al.: Genome scans provide evidence for low-HDL-C loci on chromosomes 8q23, 16q24.1–24.2, and 20q13.11 in Finnish families. Am J Hum Genet 2002, 70:1333–1340.

    Article  PubMed  CAS  Google Scholar 

  15. Cheung VG, Jen KY, Weber T, et al.: Genetics of quantitative variation in human gene expression. Cold Spring Harb Symp Quant Biol 2003, 68:403–407.

    PubMed  CAS  Google Scholar 

  16. Schliekelman P, Slatkin M: Multiplex relative risk and estimation of the number of loci underlying an inherited disease. Am J Hum Genet 2002, 71:1369–1385.

    Article  PubMed  CAS  Google Scholar 

  17. Risch N: Linkage strategies for genetically complex traits. I. Multilocus models. Am J Hum Genet 1990, 46:222–228.

    PubMed  CAS  Google Scholar 

  18. Almasy L, Tierney C, Risch N: Use of sibling risk ratios and components of genetic variance in the characterization of a simulated oligogenic disease. Genet Epidemiol 1995, 12:565–570.

    Article  PubMed  CAS  Google Scholar 

  19. Elston RC, Yelverton KC: General models for segregation analysis. Am J Hum Genet 1975, 27:31–45.

    PubMed  CAS  Google Scholar 

  20. Elston RC, Gray-McGuire C: A review of the ‘Statistical Analysis for Genetic Epidemiology’ (S.A.G.E.) software package. Hum Genomics 2004, 1:456–459.

    PubMed  Google Scholar 

  21. Zhang W, Tapper W, Collins A, et al.: A tournament of linkage tests in complex inheritance. Hum Hered 2001, 52:140–148.

    Article  PubMed  CAS  Google Scholar 

  22. Blackwelder WC, Elston RC: A comparison of sib-pair linkage tests for disease susceptibility loci. Genet Epidemiol 1985, 2:85–97.

    Article  PubMed  CAS  Google Scholar 

  23. Elston RC: Linkage and association. Genet Epidemiol 1998, 15:565–576.

    Article  PubMed  CAS  Google Scholar 

  24. Risch N: Linkage strategies for genetically complex traits. II. The power of affected relative pairs. Am J Hum Genet 1990, 46:229–241.

    PubMed  CAS  Google Scholar 

  25. Elston RC, Song D, Iyengar SK: Mathematical assumptions versus biological reality: myths in affected sib pair linkage analysis. Am J Hum Genet 2005, 76:152–156.

    Article  PubMed  CAS  Google Scholar 

  26. Knapp M, Seuchter SA, Baur MP: Linkage analysis in nuclear families. 1: Optimality criteria for affected sib-pair tests. Hum Hered 1994, 44:37–43.

    PubMed  CAS  Google Scholar 

  27. Knapp M, Seuchter SA, Baur MP: Linkage analysis in nuclear families. 2: Relationship between affected sib-pair tests and lod score analysis. Hum Hered 1994, 44:44–51.

    Article  PubMed  CAS  Google Scholar 

  28. Kong A, Cox NJ: Allele-sharing models: LOD scores and accurate linkage tests. Am J Hum Genet 1997, 61:1179–1188.

    Article  PubMed  CAS  Google Scholar 

  29. Kruglyak L, Lander ES: Complete multipoint sib-pair analysis of qualitative and quantitative traits. Am J Hum Genet 1995, 57:439–454.

    PubMed  CAS  Google Scholar 

  30. Kruglyak L, Daly MJ, Reeve-Daly MP, Lander ES: Parametric and nonparametric linkage analysis: a unified multipoint approach. Am J Hum Genet 1996, 58:1347–1363.

    PubMed  CAS  Google Scholar 

  31. McCarthy MI, Kruglyak L, Lander ES: Sib-pair collection strategies for complex diseases. Genet Epidemiol 1998, 15:317–340.

    Article  PubMed  CAS  Google Scholar 

  32. Almasy L, Blangero J: Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet 1998, 62:1198–1211.

    Article  PubMed  CAS  Google Scholar 

  33. Abecasis GR, Cherny SS, Cookson WO, Cardon LR: Merlin—rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet 2002, 30:97–101.

    Article  PubMed  CAS  Google Scholar 

  34. Blangero J, Williams JT, Almasy L: Variance component methods for detecting complex trait loci. Adv Genet 2001, 42:151–181.

    Article  PubMed  CAS  Google Scholar 

  35. Abecasis GR, Cardon LR, Cookson WO, et al.: Association analysis in a variance components framework. Genet Epidemiol 2001, 21(Suppl 1):S341-S346.

    PubMed  Google Scholar 

  36. Cardon LR, Bell JI: Association study designs for complex diseases. Nat Rev Genet 2001, 2:91–99.

    Article  PubMed  CAS  Google Scholar 

  37. Ardlie KG, Lunetta KL, Seielstad M: Testing for population subdivision and association in four case-control studies. Am J Hum Genet 2002, 71:304–311.

    Article  PubMed  CAS  Google Scholar 

  38. Cardon LR, Palmer LJ: Population stratification and spurious allelic association. Lancet 2003, 361:598–604.

    Article  PubMed  Google Scholar 

  39. Freedman ML, Reich D, Penney KL, et al.: Assessing the impact of population stratification on genetic association studies. Nat Genet 2004, 36:388–393.

    Article  PubMed  CAS  Google Scholar 

  40. Marchini J, Cardon LR, Phillips MS, Donnelly P: The effects of human population structure on large genetic association studies. Nat Genet 2004, 36:512–517.

    Article  PubMed  CAS  Google Scholar 

  41. Pritchard JK, Stephens M, Rosenberg NA, Donnelly P: Association mapping in structured populations. Am J Hum Genet 2000, 67:170–181.

    Article  PubMed  CAS  Google Scholar 

  42. Spielman RS, McGinnis RE, Ewens WJ: Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am J Hum Genet 1993, 52:506–516.

    PubMed  CAS  Google Scholar 

  43. Spielman RS, Ewens WJ: The TDT and other family-based tests for linkage disequilibrium and association. Am J Hum Genet 1996, 59:983–989.

    PubMed  CAS  Google Scholar 

  44. Horvath S, Xu X, Laird NM: The family based association test method: strategies for studying general genotype—phenotype associations. Eur J Hum Genet 2001, 9:301–306.

    Article  PubMed  CAS  Google Scholar 

  45. Horvath S, Xu X, Lake SL, et al.: Family-based tests for associating haplotypes with general phenotype data: application to asthma genetics. Genet Epidemiol 2004, 26:61–69.

    Article  PubMed  Google Scholar 

  46. Teng J, Risch N: The relative power of family-based and case-control designs for linkage disequilibrium studies of complex human diseases. II. Individual genotyping. Genome Res 1999, 9:234–241.

    PubMed  CAS  Google Scholar 

  47. Risch N, Teng J: The relative power of family-based and case-control designs for linkage disequilibrium studies of complex human diseases I. DNA pooling. Genome Res 1998, 8:1273–1288.

    PubMed  CAS  Google Scholar 

  48. Montana G, Pritchard JK: Statistical tests for admixture mapping with case-control and cases-only data. Am J Hum Genet 2004, 75:771–789.

    Article  PubMed  CAS  Google Scholar 

  49. Pritchard JK, Donnelly P: Case-control studies of association in structured or admixed populations. Theor Popul Biol 2001, 60:227–237.

    Article  PubMed  CAS  Google Scholar 

  50. Oliphant A, Barker DL, Stuelpnagel JR, Chee MS: BeadArray technology: enabling an accurate, cost-effective approach to high-throughput genotyping. Biotechniques 2002, Suppl:56–58, 60–61.

    Google Scholar 

  51. Hardenbol P, Baner J, Jain M, et al.: Multiplexed genotyping with sequence-tagged molecular inversion probes. Nat Biotechnol 2003, 21:673–678.

    Article  PubMed  CAS  Google Scholar 

  52. Evans DM, Cardon LR: Guidelines for genotyping in genome-wide linkage studies: single-nucleotide-polymorphism maps versus microsatellite maps. Am J Hum Genet 2004, 75:687–692.

    Article  PubMed  CAS  Google Scholar 

  53. John S, Shephard N, Liu G, et al.: Whole-genome scan, in a complex disease, using 11,245 single-nucleotide polymorphisms: comparison with microsatellites. Am J Hum Genet 2004, 75:54–64.

    Article  PubMed  CAS  Google Scholar 

  54. Schaid DJ, Guenther JC, Christensen GB, et al.: Comparison of microsatellites versus single-nucleotide polymorphisms in a genome linkage screen for prostate cancer-susceptibility Loci. Am J Hum Genet 2004, 75:948–965.

    Article  PubMed  CAS  Google Scholar 

  55. Huang Q, Shete S, Amos CI: Ignoring linkage disequilibrium among tightly linked markers induces false-positive evidence of linkage for affected sib pair analysis. Am J Hum Genet 2004, 75:1106–1112.

    Article  PubMed  CAS  Google Scholar 

  56. Stumpf MP, McVean GA: Estimating recombination rates from population-genetic data. Nat Rev Genet 2003, 4:959–968.

    Article  PubMed  CAS  Google Scholar 

  57. Crawford DC, Carlson CS, Rieder MJ, et al.: Haplotype diversity across 100 candidate genes for inflammation, lipid metabolism, and blood pressure regulation in two populations. Am J Hum Genet 2004, 74:610–622.

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  59. Daly MJ, Rioux JD, Schaffner SF, et al.: High-resolution haplotype structure in the human genome. Nat Genet 2001, 29:229–232.

    Article  PubMed  CAS  Google Scholar 

  60. Reich DE, Schaffner SF, Daly MJ, et al.: Human genome sequence variation and the influence of gene history, mutation and recombination. Nat Genet 2002, 32:135–142.

    Article  PubMed  CAS  Google Scholar 

  61. Wall JD, Pritchard JK: Haplotype blocks and linkage disequilibrium in the human genome. Nat Rev Genet 2003, 4:587–597.

    Article  PubMed  CAS  Google Scholar 

  62. Wall JD, Pritchard JK: Assessing the performance of the haplotype block model of linkage disequilibrium. Am J Hum Genet 2003, 73:502–515.

    Article  PubMed  CAS  Google Scholar 

  63. The International HapMap Project. Nature 2003, 426:789–796.

    Google Scholar 

  64. Lin S, Chakravarti A, Cutler DJ: Exhaustive allelic transmission disequilibrium tests as a new approach to genome-wide association studies. Nat Genet 2004, 36:1181–1188.

    Article  PubMed  CAS  Google Scholar 

  65. Rybicki BA, Iyengar SK, Harris T, et al.: The distribution of long range admixture linkage disequilibrium in an African-American population. Hum Hered 2002, 53:187–196.

    Article  PubMed  Google Scholar 

  66. Smith MW, Patterson N, Lautenberger JA, et al.: A high-density admixture map for disease gene discovery in african americans. Am J Hum Genet 2004, 74:1001–1013.

    Article  PubMed  CAS  Google Scholar 

  67. Zhu X, Luke A, Cooper RS, et al.: Admixture mapping for hypertension loci with genome-scan markers. Nat Genet 2005, 37:177–181.

    Article  PubMed  CAS  Google Scholar 

  68. Pritchard JK, Cox NJ: The allelic architecture of human disease genes: common disease-common variant...or not? Hum Mol Genet 2002, 11:2417–2423.

    Article  PubMed  CAS  Google Scholar 

  69. Botstein D, Risch N: Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nat Genet 2003, 33(Suppl):228–237.

    Article  PubMed  CAS  Google Scholar 

  70. Faham M, Baharloo S, Tomitaka S, et al.: Mismatch repair detection (MRD): high-throughput scanning for DNA variations. Hum Mol Genet 2001, 10:1657–1664.

    Article  PubMed  CAS  Google Scholar 

  71. Cohen J, Pertsemlidis A, Kotowski IK, et al.: Low LDL cholesterol in individuals of African descent resulting from frequent nonsense mutations in PCSK9. Nat Genet 2005, 37:161–165.

    Article  PubMed  CAS  Google Scholar 

  72. Cohen JC, Kiss RS, Pertsemlidis A, et al.: Multiple rare alleles contribute to low plasma levels of HDL cholesterol. Science 2004, 305:869–872.

    Article  PubMed  CAS  Google Scholar 

  73. Ishkanian AS, Malloff CA, Watson SK, et al.: A tiling resolution DNA microarray with complete coverage of the human genome. Nat Genet 2004, 36:299–303.

    Article  PubMed  CAS  Google Scholar 

  74. Snijders AM, Segraves R, Blackwood S, et al.: BAC microarray-based comparative genomic hybridization. Methods Mol Biol 2004, 256:39–56.

    PubMed  CAS  Google Scholar 

  75. Sebat J, Lakshmi B, Troge J, et al.: Large-scale copy number polymorphism in the human genome. Science 2004, 305:525–528.

    Article  PubMed  CAS  Google Scholar 

  76. Lucito R, Healy J, Alexander J, et al.: Representational oligonucleotide microarray analysis: a high-resolution method to detect genome copy number variation. Genome Res 2003, 13:2291–2305.

    Article  PubMed  CAS  Google Scholar 

  77. Lucito R, West J, Reiner A, et al.: Detecting gene copy number fluctuations in tumor cells by microarray analysis of genomic representations. Genome Res 2000, 10:1726–1736.

    Article  PubMed  CAS  Google Scholar 

  78. Huang J, Wei W, Zhang J, et al.: Whole genome DNA copy number changes identified by high density oligonucleotide arrays. Hum Genomics 2004, 1:287–299.

    PubMed  CAS  Google Scholar 

  79. Wong KK, Tsang YT, Shen J, et al.: Allelic imbalance analysis by high-density single-nucleotide polymorphic allele (SNP) array with whole genome amplified DNA. Nucleic Acids Res 2004, 32:e69.

    Google Scholar 

  80. Zhou X, Mok SC, Chen Z, et al.: Concurrent analysis of loss of heterozygosity (LOH) and copy number abnormality (CNA) for oral premalignancy progression using the Affymetrix 10K SNP mapping array. Hum Genet 2004, 115:327–330.

    Article  PubMed  CAS  Google Scholar 

  81. Merikangas KR, Risch N: Genomic priorities and public health. Science 2003, 302:599–601.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Belmont, J.W., Leal, S.M. Complex phenotypes and complex genetics: An introduction to genetic studies of complex traits. Curr Atheroscler Rep 7, 180–187 (2005). https://doi.org/10.1007/s11883-005-0004-6

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11883-005-0004-6

Keywords

Navigation