title: Estimating relationships between phenotypes and subjects drawn from admixed families.
abstract: BACKGROUND
  Estimating relationships among subjects in a sample, within family structures or caused by population substructure, is complicated in admixed populations. Inaccurate allele frequencies can bias both kinship estimates and tests for association between subjects and a phenotype. We analyzed the simulated and real family data from Genetic Analysis Workshop 19, and were aware of the simulation model.
  
  
  RESULTS
  We found that kinship estimation is more accurate when marker data include common variants whose frequencies are less variable across populations. Estimates of heritability and association vary with age for longitudinally measured traits. Accounting for local ancestry identified different true associations than those identified by a traditional approach. Principal components aid kinship estimation and tests for association, but their utility is influenced by the frequency of the markers used to generate them.
  
  
  CONCLUSIONS
  Admixed families can provide a powerful resource for detecting disease loci, as well as analytical challenges. Allele frequencies, although difficult to adequately estimate in admixed populations, have a strong impact on the estimation of kinship, ancestry, and association with phenotypes. Approaches that acknowledge population structure in admixed families outperform those which ignore it.
authors.affiliation: Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195 USA.
authors.forename: Elizabeth M
authors.initials: EM
authors.lastname: Blue
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA 98195 USA.
authors.forename: Lisa A
authors.initials: LA
authors.lastname: Brown
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA 98195 USA.
authors.forename: Matthew P
authors.initials: MP
authors.lastname: Conomos
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA 98195 USA.
authors.forename: Jennifer L
authors.initials: JL
authors.lastname: Kirk
authors.affiliation: Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195 USA.
authors.forename: Alejandro Q
authors.initials: AQ
authors.lastname: Nato
authors.affiliation: Institute for Public Health Genetics, University of Washington, Seattle, WA 98195 USA.
authors.forename: Alice B
authors.initials: AB
authors.lastname: Popejoy
authors.affiliation: Department of Statistics, University of Washington, Seattle, WA 98195 USA.
authors.forename: Jesse
authors.initials: J
authors.lastname: Raffa
authors.affiliation: Department of Statistics, University of Washington, Seattle, WA 98195 USA.
authors.forename: John
authors.initials: J
authors.lastname: Ranola
authors.affiliation: Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195 USA ; Department of Biostatistics, University of Washington, Seattle, WA 98195 USA.
authors.forename: Ellen M
authors.initials: EM
authors.lastname: Wijsman
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA 98195 USA.
authors.forename: Timothy
authors.initials: T
authors.lastname: Thornton