Behavior Genetics

, Volume 31, Issue 6, pp 497–509 | Cite as

A Genome-Wide Scan of 1842 DNA Markers for Allelic Associations with General Cognitive Ability: A Five-Stage Design Using DNA Pooling and Extreme Selected Groups

  • Robert Plomin
  • Linzy Hill
  • Ian W. Craig
  • Peter McGuffin
  • Shaun Purcell
  • Pak Sham
  • David Lubinski
  • Lee A. Thompson
  • Paul J. Fisher
  • Dragana Turic
  • Michael J. Owen
Article

Abstract

All measures of cognitive processes correlate moderately at the phenotypic level and correlate substantially at the genetic level. General cognitive ability (g) refers to what diverse cognitive processes have in common. Our goal is to identify quantitative trait loci (QTLs) associated with high g compared with average g. In order to detect QTLs of small effect size, we used extreme selected samples and a five-stage design with nominal alpha levels that permit false positive results in early stages but remove false positives in later stages. As a first step toward a systematic genome scan for allelic association, we used DNA pooling to screen 1842 simple sequence repeat (SSR) markers approximately evenly spaced at 2 cM throughout the genome in a five-stage design: (1) case-control DNA pooling (101 cases with mean IQ of 136 and 101 controls with mean IQ of 100), (2) case-control DNA pooling (96 cases with IQ >160 and 100 controls with mean IQ of 102), (3) individual genotyping of Stage 1 sample, (4) individual genotyping of Stage 2 sample, (5) transmission disequilibrium test (TDT; 196 parent-child trios for offspring with IQ >160). The overall Type I error rate is 0.000125, which robustly protects against false positive results. The numbers of markers surviving each stage using a conservative allele-specific directional test were 108, 6, 4, 2, and 0, respectively, for the five stages. A genomic control test using DNA pooling suggested that the failure to replicate the positive case-control results in the TDT analysis was not due to ethnic stratification. Several markers that were close to significance at all stages are being investigated further. Relying on indirect association based on linkage disequilibrium between markers and QTLs means that 100,000 markers may be needed to exclude QTL associations. Because power drops off precipitously for indirect association approaches when a marker is not close to the QTL, we are not planning to genotype additional SSR markers. Instead we are using the same design to screen markers such as cSNPs and SNPs in regulatory regions that are likely to include functional polymorphisms in which the marker can be presumed to be the QTL.

Intelligence genome scan quantitative trait loci (QTLs) DNA pool association 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

REFERENCES

  1. Abecasis, G. R., Noguchi, E., Heinzmann, A., Traherne, J. A., Bhattacharyya, S., Leaves, N. I., Anderson, G. G., Zhang, Y., Lench, N. J., Carey, A., et al. (2001). Extent and distribution of linkage disequilibrium in three genomic regions. Am. J. Human Gen. 68: 191–197.Google Scholar
  2. Allison, D. B. (1997). Transmission-disequilibrium test for quantitative traits. Am. J. Human Gen. 60: 676–690.Google Scholar
  3. Barcellos, L. F., Klitz, W., Field, L. L., Tobias, R., Bowcock, A. M., Wilson, R., Nelson, M. P., Nagatomi, J., and Thomson, G. (1997). Association mapping of disease loci, by use of a pooled DNA genomic screen. Am. J. Human Gen. 61: 734–747.Google Scholar
  4. Cardon, L. R., and Bell, J. (2001). Association study designs for complex diseases. Nature Gen. 2: 91–99.Google Scholar
  5. Carroll, J. B. (1993). Human cognitive abilities. New York: Cambridge University Press.Google Scholar
  6. Carroll, J. B. (1997). Psychometrics, intelligence, and public policy. Intelligence 24: 25–52.Google Scholar
  7. Chorney, M. J., Chorney, K., Seese, N., Owen, M. J., Daniels, J., McGuffin, P., Thompson, L. A., Detterman, D. K., Benbow, C. P., Lubinski, D., Eley, T. C., and Plomin, R. (1998). A quantitative trait locus (QTL) associated with cognitive ability in children. Psychol. Science 9: 1–8.Google Scholar
  8. Daniels, J., Holmans, P., Plomin, R., McGuffin, P., and Owen, M. J. (1998). A simple method for analyzing microsatellite allele image patterns generated from DNA pools and its application to allelic association studies. Am. J. Human Gen. 62: 1189–1197.Google Scholar
  9. Deary, I. J., Whalley, L. J., St Clair, D., Breen, G., Leaper, S., Lemmon, H., and Starr, J. M. (in press). The influence of the ɛ4 allele of the apolipoprotein E gene on childhood IQ, intelligence in old age, and lifetime cognitive change. Intelligence. Google Scholar
  10. Deutsch, S., Iseli, C., Bucher, P., Antonarakis, S. E., and Scott, H. S. (2001). A cSNP map and database for human chromosome 21. Genome Res. 11: 300–307.Google Scholar
  11. Egen, M. F., Goldberg, T. E., Kolachana, B. S., Callicott, J. H., Mazzanti, C. M., Straub, R. E., Goldman, D., and Weinberger, D. R. (2001). Effect of COMT Val108/158 Met genotype on frontal lobe function and risk for schizophrenia. PNAS 98: 6917–6922.Google Scholar
  12. Ewens, W. J., and Spielman, R. S. (1995). The transmission/ disequilibrium test: History, subdivision, and admixture. Am. J. Human Gen. 57: 455–464.Google Scholar
  13. Fisher, P. J., Turic, D., McGuffin, P., Asherson, P. J., Ball, D. M., Craig, I. W., Eley, T. C., Hill, L., Chorney, K., Chorney, M. J., Benbow, C. P., Lubinski, D., Plomin, R., and Owen, M. J. (1999). DNA pooling identifies QTLs for general cognitive ability in children on chromosome 4. Human Mol. Gen. 8: 915–922.Google Scholar
  14. Freeman, B., Powell, J., Ball, D. M., Hill, L., Craig, I. W., and Plomin, R. (1997). DNA by mail: An inexpensive and noninvasive method for collecting DNA samples from widely dispersed populations. Behav. Gen. 27: 251–257.Google Scholar
  15. Gould, S. J. (1996). The mismeasure of man. (2nd ed.). New York: Norton.Google Scholar
  16. Hill, L., Chorney, M. J., and Plomin, R. (2001). A quantitative trait locus (not) associated with cognitive ability in children. Manuscript submitted for publication.Google Scholar
  17. Hill, L., Craig, I. W., Ball, D. M., Eley, T. C., Ninomiya, T., Fisher, P. J., McGuffin, P., Owen, M. J., Chorney, K., Chorney, M. J., Benbow, C. P., Lubinski, D., Thompson, L. A., and Plomin, R. (1999b). DNA pooling and dense marker maps: A systematic search for genes for cognitive ability. NeuroReport 10: 843–848.Google Scholar
  18. Hoogendoorn, B., Norton, N., Kirov, G., Williams, N., Hamshere, M. L., Spurlock, G., Austin, J., Stephens, M. K., Buckland, P. R., Owen, M. J., and O'Donovan, M. C. (2000). Cheap, accurate and rapid allele frequency estimation of single nucleotide polymorphisms by primer extension and DHPLC in DNA pools. Human Gen. 107: 488–493.Google Scholar
  19. Jensen, A. R. (1998). The puzzle of nongenetic variance. In R. J. Sternberg and E. L. Grigorenko (eds.), Intelligence, heredity and environment (pp. 42–88), Cambridge: Cambridge University Press.Google Scholar
  20. Kruglyak, L. (1999). Prospects for whole-genome linkage disequilibrium mapping of common disease genes. Nature Gen. 22: 139–144.Google Scholar
  21. LeDuc, C., Miller, P., Lichter, J., and Parry, P. (1995). Batched analysis of genotypes. PCR Meth. Appl. 4: 331–336.Google Scholar
  22. Lubinski, D., and Benbow, C. P. (1994). The first three decades of a planned 50-year study of intellectual talent. In R. Subotnik and K. Arnold (eds.), Beyond Terman: Longitudinal studies in contemporary gifted education ( pp. 255–281). Norwood, NJ: Ablex.Google Scholar
  23. Marth, G., Yeh, R., Minton, M., Donaldson, R., Li, Q., Duan, S., Davenport, R., Miller, R. D., and Kwok, P. Y. (2001). Singlenucleotide polymorphisms in the public domain: How useful are they? Nature Gen. 27: 371–372.Google Scholar
  24. Neisser, U., Boodoo, G., Bouchard, T. J., Jr., Boykin, A. W., Brody, N., Ceci, S. J., Halpern, D. F., Loehlin, J. C., Perloff, R., Sternberg, R. J., and Urbina, S. (1996). Intelligence: knowns and unknowns. Am. Psychologist 51: 77–101.Google Scholar
  25. Page, G. P., and Amos, C. I. (1999). Comparison of linkageequilibrium methods for localization of genes influencing quantitative traits in humans. Am. J. Human Gen. 64: 1194–1205.Google Scholar
  26. Perlin, M. W., Lancia, G., and Ng, S. K. (1995). Toward fully automated genotyping: Genotyping microsatellite markers. Am. J. Human Gen. 57: 1–12.Google Scholar
  27. Petrill, S. A. (1997). Molarity versus modularity of cognitive functioning? A behavioral genetic perspective. Curr. Dir. Psychological Sci. 6: 96–99.Google Scholar
  28. Petrill, S. A., Ball, D. M., Eley, T. C., Hill, L., and Plomin, R. (1998). Failure to replicate a QTL association between a DNA marker identified by EST00083 and IQ. Intelligence 25: 179–184.Google Scholar
  29. Petrill, S. A., Saudino, K. J., Cherny, S. C., Emde, R. N., Fulker, D. W., Hewitt, J. K., and Plomin, R. (1998). Exploring the genetic and environmental etiology of high general cognitive ability in 14 to 36 month-old twins. Child Dev. 69: 68–74.Google Scholar
  30. Plomin, R. (1999a). Genetics and general cognitive ability. Nature 402: C25-C29.Google Scholar
  31. Plomin, R. (1999b). Genetic research on general cognitive ability as a model for mild mental retardation. Int. Rev. Psychiat. 11: 34–36.Google Scholar
  32. Plomin, R., Owen, M. J., and McGuffin, P. (1994). The genetic basis of complex human behaviors. Science 264: 1733–1739.Google Scholar
  33. Plomin, R., and Price, T. (2001). Genetics and intelligence. In Handbook of gifted education (3rd ed.), Boston: Allyn & Bacon.Google Scholar
  34. Plomin, R., DeFries, J. C., McClearn, G. E., and McGuffin, P. (2001). Behavioral genetics (4th ed.) New York: Worth.Google Scholar
  35. Plomin, R., McClearn, G. E., Smith, D. L., Skuder, P., Vignetti, S., Chorney, M. J., Chorney, K., Kasarda, S., Thompson, L. A., Detterman, D. K., Petrill, S. A., Daniels, J., Owen, M. J., and McGuffin, P. (1995). Allelic associations between 100 DNA markers and high versus low IQ. Intelligence 21: 31–48.Google Scholar
  36. Pritchard, J. K., and Rosenberg, N. A. (1999). Use of unlinked genetic markers to detect population stratification in association studies. Am. J. Human Gen. 65: 220–228.Google Scholar
  37. Reich, D. E., Cargill, M., Bolk, S., Ireland, J., Sabeti, P. C., Richter, D. J., Lavery, T., Kouyoumjian, R., Farhadian, S. F., Ward, R., and Lander, E. S. (2001). Linkage disequilibrium in the human genome. Nature 41: 199–204.Google Scholar
  38. Risch, N. J. (2000). Searching for genetic determinants in the new millennium. Nature 405: 847–856.Google Scholar
  39. Risch, N., and Merikangas, K. R. (1996). The future of genetic studies of complex human diseases. Science 273: 1516–1517.Google Scholar
  40. Risch, N., and Teng, J. (1998). The relative power of family-based and case-control designs for linkage disequilibrium studies of complex human diseases I. DNA pooling. Genome Res. 8: 1273–1288.Google Scholar
  41. Rithidech, K. N., Dunn, J. F., and Gordon, C. R. (1997). Combining multiplex and touchdown PCR to screen murine microsatellite polymorphisms. Biotechniques 23: 36–44.Google Scholar
  42. Saudino, K. J., Plomin, R., Pedersen, N. L., and McClearn, G. E. (1994). The etiology of high and low cognitive ability during the second half of the life span. Intelligence 19: 353–371.Google Scholar
  43. Snyderman, M., and Rothman, S. (1987). Survey of expert opinion on intelligence and aptitude testing. Am. Psychol. 42: 137–144.Google Scholar
  44. Terwilliger, J. D., and Ott, J. (1992). A haplotype-based ‘haplotype relative risk’ approach to detecting allelic associations. Human Heredity 42: 337–346.Google Scholar
  45. Turic, D., Fisher, P. J., Plomin, R., and Owen, M. J. (2001). No association between apolipoprotein E polymorphisms and general cognitive ability in children. Neurosci. Lett. 299: 97–100.Google Scholar
  46. Wahlström, J. (1990). Gene map of mental retardation. J. Mental Def. Res. 34: 11–27.Google Scholar
  47. Wechsler, D. (1974). Manual for the Wechsler Intelligence Scale for Children-Revised. (Rev. ed.) New York: Psychological Corporation.Google Scholar
  48. Wechsler, D. (1997). Wechsler Adult Intelligence Scale-III. New York, Psychological Corporation.Google Scholar
  49. Wickelgren, I. (1998). Tracking insulin to the mind. Science 280: 517–519.Google Scholar
  50. Zavattari, P., Deidda, E., Whalen, M., Lampis, R., Mulargia, A., Loddo, M., Eaves, I., Mastio, G., Todd, J. A., and Cucca, F. (2000). Major factors influencing linkage disequilibrium by analysis of different chromosome regions in distinct populations: Demography, chromosome recombination frequency and selection. Human Mol. Gen. 9: 2947–2957.Google Scholar

Copyright information

© Plenum Publishing Corporation 2001

Authors and Affiliations

  • Robert Plomin
    • 1
  • Linzy Hill
    • 1
  • Ian W. Craig
    • 1
  • Peter McGuffin
    • 1
  • Shaun Purcell
    • 1
  • Pak Sham
    • 1
  • David Lubinski
    • 2
  • Lee A. Thompson
    • 3
  • Paul J. Fisher
    • 4
  • Dragana Turic
    • 4
  • Michael J. Owen
    • 4
  1. 1.Social, Genetic and Developmental Psychiatry Research CentreInstitute of Psychiatry, King's College LondonLondonUK
  2. 2.Department of Psychology and Human DevelopmentVanderbilt UniversityNashvilleUSA
  3. 3.Department of PsychologyCase Western Reserve UniversityClevelandUSA
  4. 4.Department of Psychological MedicineUniversity of Wales College of MedicineCardiffUK

Personalised recommendations