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Genet Epidemiol. 2018 Sep;42(6):500-515. doi: 10.1002/gepi.22133. Epub 2018 Jun 3.

Analysis of pedigree data in populations with multiple ancestries: Strategies for dealing with admixture in Caribbean Hispanic families from the ADSP.

Author information

1
Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington.
2
Department of Statistics, University of Washington, Seattle, Washington.
3
Department of Biostatistics, University of Washington, Seattle, Washington.
4
Department of Neurology, Columbia University, New York, Washington.

Abstract

Multipoint linkage analysis is an important approach for localizing disease-associated loci in pedigrees. Linkage analysis, however, is sensitive to misspecification of marker allele frequencies. Pedigrees from recently admixed populations are particularly susceptible to this problem because of the challenge of accurately accounting for population structure. Therefore, increasing emphasis on use of multiethnic samples in genetic studies requires reevaluation of best practices, given data currently available. Typical strategies have been to compute allele frequencies from the sample, or to use marker allele frequencies determined by admixture proportions averaged over the entire sample. However, admixture proportions vary among pedigrees and throughout the genome in a family-specific manner. Here, we evaluate several approaches to model admixture in linkage analysis, providing different levels of detail about ancestral origin. To perform our evaluations, for specification of marker allele frequencies, we used data on 67 Caribbean Hispanic admixed families from the Alzheimer's Disease Sequencing Project. Our results show that choice of admixture model has an effect on the linkage analysis results. Variant-specific admixture proportions, computed for individual families, provide the most detailed regional admixture estimates, and, as such, are the most appropriate allele frequencies for linkage analysis. This likely decreases the number of false-positive results, and is straightforward to implement.

KEYWORDS:

Markov Chain Monte Carlo; complex trait; large pedigrees; late-onset disease; missing data

PMID:
29862559
PMCID:
PMC6160322
[Available on 2019-09-01]
DOI:
10.1002/gepi.22133

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