TY - JOUR

T1 - A method for detecting IBD regions simultaneously in multiple individuals--with applications to disease genetics

AU - Moltke, Ida

AU - Albrechtsen, Anders

AU - Hansen, Thomas V O

AU - Nielsen, Finn C

AU - Nielsen, Rasmus

PY - 2011

Y1 - 2011

N2 - All individuals in a finite population are related if traced back long enough and will, therefore, share regions of their genomes identical by descent (IBD). Detection of such regions has several important applications-from answering questions about human evolution to locating regions in the human genome containing disease-causing variants. However, IBD regions can be difficult to detect, especially in the common case where no pedigree information is available. In particular, all existing non-pedigree based methods can only infer IBD sharing between two individuals. Here, we present a new Markov Chain Monte Carlo method for detection of IBD regions, which does not rely on any pedigree information. It is based on a probabilistic model applicable to unphased SNP data. It can take inbreeding, allele frequencies, genotyping errors, and genomic distances into account. And most importantly, it can simultaneously infer IBD sharing among multiple individuals. Through simulations, we show that the simultaneous modeling of multiple individuals makes the method more powerful and accurate than several other non-pedigree based methods. We illustrate the potential of the method by applying it to data from individuals with breast and/or ovarian cancer, and show that a known disease-causing mutation can be mapped to a 2.2-Mb region using SNP data from only five seemingly unrelated affected individuals. This would not be possible using classical linkage mapping or association mapping.

AB - All individuals in a finite population are related if traced back long enough and will, therefore, share regions of their genomes identical by descent (IBD). Detection of such regions has several important applications-from answering questions about human evolution to locating regions in the human genome containing disease-causing variants. However, IBD regions can be difficult to detect, especially in the common case where no pedigree information is available. In particular, all existing non-pedigree based methods can only infer IBD sharing between two individuals. Here, we present a new Markov Chain Monte Carlo method for detection of IBD regions, which does not rely on any pedigree information. It is based on a probabilistic model applicable to unphased SNP data. It can take inbreeding, allele frequencies, genotyping errors, and genomic distances into account. And most importantly, it can simultaneously infer IBD sharing among multiple individuals. Through simulations, we show that the simultaneous modeling of multiple individuals makes the method more powerful and accurate than several other non-pedigree based methods. We illustrate the potential of the method by applying it to data from individuals with breast and/or ovarian cancer, and show that a known disease-causing mutation can be mapped to a 2.2-Mb region using SNP data from only five seemingly unrelated affected individuals. This would not be possible using classical linkage mapping or association mapping.

KW - Alleles

KW - Breast Neoplasms

KW - Chromosome Mapping

KW - Computer Simulation

KW - Databases, Genetic

KW - Female

KW - Genetic Linkage

KW - Genome, Human

KW - Genome-Wide Association Study

KW - Genotype

KW - Humans

KW - Markov Chains

KW - Models, Genetic

KW - Monte Carlo Method

KW - Mutation

KW - Ovarian Neoplasms

KW - Pedigree

KW - Polymorphism, Single Nucleotide

KW - Ubiquitin-Protein Ligases

U2 - 10.1101/gr.115360.110

DO - 10.1101/gr.115360.110

M3 - Journal article

C2 - 21493780

VL - 21

SP - 1168

EP - 1180

JO - Genome Research

JF - Genome Research

SN - 1088-9051

IS - 7

ER -