Mapping genes underlying ethnic differences in disease risk by linkage disequilibrium in recently admixed populations

Am J Hum Genet. 1997 Jan;60(1):188-96.


Where recent admixture has occurred between two populations that have different disease rates for genetic reasons, family-based association studies can be used to map the genes underlying these differences, if the ancestry of the alleles at each locus examined can be assigned to one of the two founding populations. This article explores the statistical power and design requirements of this approach. Markers suitable for assigning the ancestry of genomic regions could be defined by grouping alleles at closely spaced microsatellite loci into haplotypes, or generated by representational difference analysis. For a given relative risk between populations, the sample size required to detect a disease locus that accounts for this relative risk by linkage-disequilibrium mapping in an admixed population is not critically dependent on assumptions about genotype penetrances or allele frequencies. Using the transmission-disequilibrium test to search the genome for a locus that accounts for a relative risk of between 2 and 3 in a high-risk population, compared with a low-risk population, generally requires between 150 and 800 case-parent pairs of mixed descent. The optimal strategy is to conduct an initial study using markers spaced at < or = 10 cM with cases from the second and third generations of mixed descent, and then to map the disease loci more accurately in a subsequent study of a population with a longer history of admixture. This approach has greater statistical power than allele-sharing designs and has obvious applications to the genetics of hypertension, non-insulin-dependent diabetes, and obesity.

MeSH terms

  • Alleles
  • Chromosome Mapping*
  • Ethnic Groups / genetics*
  • Genetic Markers
  • Genetic Predisposition to Disease*
  • Genetic Techniques
  • Genetics, Medical
  • Heterozygote
  • Humans
  • Linkage Disequilibrium*
  • Risk Factors
  • Sample Size
  • Statistics as Topic / methods


  • Genetic Markers