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Article

Abstract

In the 20th century geneticists began to unravel some of the simpler aspects of the etiology of inherited diseases in humans. The theory of linkage analysis was developed and applied long before the advent of molecular biology, but only the technological advances of the second half of the 20th century made large-scale gene mapping with a dense genome-spanning set of markers a reality. More recently, the primary topic of interest has shifted from simple Mendelian diseases, for which genotypes of some gene are the cause of disease, to more complex diseases, for which genotypes of some set of genes together with environmental factors merely alter the probability that an individual gets the disease, although individual factors are typically insufficient to cause the disease outright. To this end, a great deal of dogma has evolved about the best way to skin this cat, although to date success has been minimal with any approach. We postulate that the main reason for this is a lack of attention to experimental design. Once the data have been ascertained, the most powerful statistical methods will not be able to salvage an inappropriately designed study (Andersen 1990). Each phenotype and/or population mandates its own individually tailored study design to maximize the chances of successful gene mapping. We suggest that careful consideration of the available data from real genotype-phenotype correlation studies (as opposed to oversimplified theoretically tractable models), and the practical feasibility of different ascertainment schemes dictate how one should proceed. In this review we review the theory and practice of gene mapping at the close of the 20th century, showing that most methods of linkage and linkage disequilibrium analysis are similar in a fundamental sense, with the differences being related more to study design and ascertainment than to technical details of the underlying statistical analysis. To this end, we propose a new focus in the field of statistical genetics that more explicitly highlights the primacy of study design as the means to increase power for gene mapping.

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