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Genetic Epidemiology 35 : 557567 (2011) Identity by Descent Estimation With Dense Genome-Wide

Summary: Genetic Epidemiology 35 : 557­567 (2011)
Identity by Descent Estimation With Dense Genome-Wide
Genotype Data
Lide Han and Mark AbneyĆ
Department of Human Genetics, University of Chicago, Chicago, Illinois
We present a novel method, IBDLD, for estimating the probability of identity by descent (IBD) for a pair of related
individuals at a locus, given dense genotype data and a pedigree of arbitrary size and complexity. IBDLD overcomes the
challenges of exact multipoint estimation of IBD in pedigrees of potentially large size and eliminates the difficulty of
accommodating the background linkage disequilibrium (LD) that is present in high-density genotype data. We show that
IBDLD is much more accurate at estimating the true IBD sharing than methods that remove LD by pruning SNPs and is
highly robust to pedigree errors or other forms of misspecified relationships. The method is fast and can be used to estimate
the probability for each possible IBD sharing state at every SNP from a high-density genotyping array for hundreds of
thousands of pairs of individuals. We use it to estimate point-wise and genomewide IBD sharing between 185,745 pairs
of subjects all of whom are related through a single, large and complex 13-generation pedigree and genotyped
with the Affymetrix 500 k chip. We find that we are able to identify the true pedigree relationship for individuals who
were misidentified in the collected data and estimate empirical kinship coefficients that can be used in follow-up QTL
mapping studies. IBDLD is implemented as an open source software package and is freely available. Genet. Epidemiol.
35:557­567, 2011. r 2011 Wiley-Liss, Inc.
Key words: linkage disequilibrium; IBD; pedigrees; Hidden Markov Models; SNP; relatedness
Additional Supporting Information may be found in the online version of this article.


Source: Abney, Mark - Department of Human Genetics, University of Chicago


Collections: Computer Technologies and Information Sciences; Biology and Medicine