INVITED EDITORIAL Statistical Approaches to Gene Mapping

In this brief primer, we hope to provide a general overview on statistical methods for disease-gene mapping. Of course, this cannot be complete—our apologies to researchers whose methods are not mentioned below. More-detailed information may be found in relevant textbooks (Ott 1999) and at the Web Resources of Genetic Linkage Analysis site (Laboratory of Statistical Genetics, Rockefeller University). The main purpose of this primer is to present, in a nontechnical manner, the methodological background and rationale of genetic mapping and to relate the various approaches to each other. In addition, current analysis methods for analysis of microarray data are discussed. Microarray data represent a new type of information that can provide important insight about the interaction of genes and that thus can complement the statistical approaches to gene mapping. Statistical genetic-mapping methods all rest on one biological phenomenon, recombination (crossing-over), which is exploited for the purposes of determining the genetic distance—or at least the closeness—between two loci. Crossovers between homologous chromosome strands occur semirandomly. Loci in close proximity to each other will rarely be separated by a recombination, whereas, for distant loci, recombinations occur as often as not. This phenomenon is used to derive a statistical measure of genetic distance. In family pedigrees, recombinations may be seen more or less directly; on the other hand, the consequences of recombinations in past generations can be observed in the form of linkage disequilibrium—that is, the preferential occurrence, in one gamete, of specific alleles at different loci.