Limits to Prediction of Phenotypes from Knowledge of Genotypes
The fact that natural selection acts on phenotypes but the transmission of traits to the next generation is indirectly accomplished through genes gives rise to a challenging set of problems in evolutionary Biology. In order to understand adaptive evolution, it appears to be essential to first understand how genotypes give rise to observed phenotypes, or more precisely, how variation in phenotypes is mediated by underlying variation in genotypes. As the tools of molecular genetics give an increasingly detailed view of the underlying genetic variation, one would hope that this problem would be solved by the sheer volume of genetic data. Human molecular genetics has produced many significant successes recently, particularly in identifying genes that cause Mendelian disorders. In stark contrast, chronic diseases that exhibit familial clustering but do not segregate like a Mendelian gene have been remarkably difficult to analyze genetically. The focus of this chapter is on the question, “What are the barriers to our understanding of the genetic basis for familiar clustering of chronic diseases?” We will focus on medical genetics rather than the more general problem of genotype-phenotype associations in evolutionary Biology, because knowledge of phenotypic variation is so extensive for humans and the quantity of data on genetic variation is soon going to eclipse that of all other species, if it has not already.
KeywordsQuantitative Trait Locus Epistatic Interaction ApoE Genotype Quantitative Trait Locus Study Allelic Substitution
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- Agresti, A., 1990, Categorical Data Analysis, John Wiley & Sons, New York.Google Scholar
- Beavis, W. D., 1994, The power and deceit of QTL experiments: Lessons from comparative QTL studies, in: Proceedings of the 49th Annual Corn and Sorghum Industry Research Conference, pp. 250–266, American Seed Trade Association, Chicago.Google Scholar
- Bishop, Y. M. M., Fienberg, S. E., and Holland, P. W., 1975, Discrete Multivariate Analysis: Theory and Practice, MIT Press, Cambridge, Massachusetts.Google Scholar
- Cavalli-Sforza, L. L., Menozzi, P., and Piazza, A., 1994, The History and Geography of Human Genes, Princeton University Press, Princeton, New Jersey.Google Scholar
- Kauffman, S. A., 1993, Origins of Order: Self-organization and Selection in Evolution, Oxford University Press, Oxford, England.Google Scholar
- Levins, R., and Lewontin, R., 1985, The Dialectical Biologist, Harvard University Press, Cambridge, Massachusetts.Google Scholar
- Malone, K. E., Daling, J. R., Thompson, J. D., O’Brien, C. A., Francisco, L. V., and Ostrander, E. A., 1998, BRCA1 mutations and breast cancer in the general population: Analyses in women before age 35 years and in women before age 45 years with first-degree family history, J. Am. Med. Assoc. 279:922–929.CrossRefGoogle Scholar
- Neter, J., Wasserman, W., and Kutner, M. H., 1983, Applied Linear Regression Models, Richard D. Irwin, Inc., Home wood, Illinois.Google Scholar
- Reilly, S. L., Farrell, R. E., Kottke, B. A., Kamboh, M. I., and Sing, G F., 1991, The gender-specific apolipoprotein E genotype influence on the distribution of lipids and apolipoproteins in the population of Rochester, MN. I. Pleiotropic effects on means and variances, Am. J. Hum. Genet. 49:1155–1166.PubMedGoogle Scholar
- Simpson, E. H., 1951,The interpretation of interaction in contingency tables, J. Roy. Stat. Soc. Ser. B 13:238–241.Google Scholar
- Sing, C. F., Haviland, M. B., and Reilly, S. L., 1996, Genetic architecture of common multifactorial diseases, in: Variation in the Human Genome (K. M. Weiss, ed.), pp. 211–229, John Wiley & Sons, Chichester, England.Google Scholar
- Sokal, R. R., and Rohlf, F. J., 1995, Biometry, 3rd ed., W. H. Freeman and Co., New York.Google Scholar