The genealogic approach to human genetics of disease

Cancer J. 2001 Jan-Feb;7(1):61-8.


The goal of modern human genetics is to correlate genes with disease or, more specifically, relate genetic variation to phenotypic variation. Although this correlation is usually straightforward in the Mendelian disorders, it has proved to be much more difficult to find in the common diseases because they appear to be more complex, likely involving an interplay among multiple genes and between genes and the environment. Although the strategy of linkage mapping of families was very successful when it was applied to the rare monogenic diseases, few common diseases have been mapped to statistical significance. Many investigators are now abandoning linkage analysis altogether and are moving to a candidate gene case-control strategy. In this article, we describe a genealogic approach to mapping human disease genes and provide three examples of how we have used it to map common diseases to statistical significance. We focus on a simple population with little historic migration and use a computerized genealogy database to increase the number of patients who can be compared with other affected relatives through high-density microsatellite genotyping. The genealogy helps determine which phenotypic classification is inherited and therefore possible to map. It may represent a more efficient strategy than candidate gene case-control studies for determination of what alleles or haplotypes are shared by patients in a population. We suggest that the genetics community not give up on linkage analysis, nor should it assume that the common diseases are too complex to map.

Publication types

  • Review

MeSH terms

  • Alzheimer Disease / genetics*
  • Chromosome Mapping / methods
  • Genealogy and Heraldry
  • Genetic Linkage
  • Genetic Markers
  • Genetics, Medical / methods*
  • Genetics, Population
  • Humans
  • Osteoarthritis / genetics*
  • Pedigree
  • Phenotype
  • Polymorphism, Single Nucleotide / genetics
  • Stroke / genetics*


  • Genetic Markers