Mapping Genes for Common Diseases: Statistical Planning, Power, Efficiency and Informatics
After a decade or so of progress with the identification of major genes, the emphasis has shifted towards genes for common diseases (complex traits). Real successes have been few so far. There has been a gradual appreciation of the difficulties presented
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1.1 Introduction After a decade or so of progress with the identification of major genes, the emphasis has shifted towards genes for common diseases (complex traits). Real successes have been few so far. There has been a gradual appreciation of the difficulties presented by genes that have a relatively small individual phenotypic effect and which may show complex interactions. Furthermore, there may be different genetic determinants of the disease in different populations together with environmental effects and practical difficulties in obtaining sufficiently large samples of suitable material for analysis. At the same time, analytical methods have been in flux with many new tests and variations on existing methods appearing in the literature. The choice of analytical strategy is determined to a large extent by the nature of the disease and the DNA resources that can be obtained. Some assessment of power to detect a genetic locus can be useful; however, this may be of limited value given the ignorance of the nature of the genetic basis of the disease. Consideration of the efficiency of different approaches is also important and careful statistical planning should be undertaken. Figure 1.1 gives an overview of the relationship between effect on the phenotype (measured by a single parameter,~) and disease allele frequency. There have been many successes with major genes (~ of 1.5 or greater), rare alleles which have a large phenotypic effect. Examples of such single gene disorders are the cystic fibrosis gene (CFTR) (Kerem et al.1989) and Huntington's disease (HD) (Gusella et al. 1983). Genes of smaller individual effect (oligogenes) have higher frequency and show complex patterns of inheritance. For polygenes the individual effects may be so small as to make such genes undetectable with current technology. Progress in identifying polygenes involved in complex inheritance may come through the development of arrays of single nucleotide polymorphisms (SNPs) in which there is a representation of the polymorphism in every human gene (see, for example, Chakravarti 1999).
Principles and Practice Molecular Genetic Epidemiology - A Laboratory Perspective Ian N.M. Day (Ed.) © Springer-Verlag Berlin Heidelberg 2002
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Andy Collins
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Fig.l.l. Relationship between disease allele frequency and phenotypic effect (p) for three gene categories
1.2 Power and Sampling Considerations For diseases with a Mendelian pattern of inheritance, power calculations giving information about error rates and sample size requirements can be relatively straightforward. For common diseases with a complex mode of inheritance this is no longer true. In this case examination of power usually involves estimating the sample sizes required to detect a gene that requires simplifying assumptions about the (unknown) underlying mode of inheritance. There are two basic approaches to identification of the oligogenes involved in complex traits. The first is genetic linkage, which refers to the te
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