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Missing data in haplotype analysis: a study on the MILC method
Author(s) -
BOURGAIN C.,
GENIN E.,
OBER C.,
CLERGETDARPOUX F.
Publication year - 2002
Publication title -
annals of human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 77
eISSN - 1469-1809
pISSN - 0003-4800
DOI - 10.1046/j.1469-1809.2002.6610099.x
Subject(s) - haplotype , genome , identification (biology) , allele , biology , data set , genetics , missing data , haplotype estimation , computational biology , computer science , gene , artificial intelligence , machine learning , botany
Given the enormous progress in the knowledge of the human genome, genetic markers are now available throughout the genome. Haplotype analysis, allowing the simultaneous use of information from several markers, has thus become increasingly popular. However, we often face the problem of missing data and of haplotype identification. We have proposed a haplotype based method for the genetic study of multifactorial diseases in founder populations, the MILC method (Bourgain et al . 2000). MILC is based on the contrast of identity length between haplotypes transmitted to affected offspring and haplotypes non‐transmitted. In this study, the impact of different strategies, regarding missing data, on the MILC method are evaluated. A real situation is considered where data are derived from a genome screen for asthma susceptibility alleles in the Hutterites. Results are illustrated on this asthma data set.