Estimating genome-wide IBD sharing from SNP data via an efficient hidden Markov model of LD with application to gene mapping

Abstract

Motivation: Association analysis is the method of choice for studying complex multifactorial diseases. The premise of this method is that affected persons contain some common genomic regions with similar SNP alleles and such areas will be found in this analysis. An important disadvantage of GWA studies is that it does not distinguish between genomic areas that are inherited from a common ancestor [identical by descent (IBD)] and areas that are identical merely by state [identical by state (IBS)]. Clearly, areas that can be marked with higher probability as IBD and have the same correlation with the disease status of identical areas that are more probably only IBS, are better candidates to be causative, and yet this distinction is not encoded in standard association analysis.

Results: We develop a factorial hidden Markov model-based algorithm for computing genome-wide IBD sharing. The algorithm accepts as input SNP data of measured individuals and estimates the probability of IBD at each locus for every pair of individuals. For two g-degree relatives, when g > or = 8, the computation yields a precision of IBD tagging of over 50% higher than previous methods for 95% recall. Our algorithm uses a first-order Markovian model for the linkage disequilibrium process and employs a reduction of the state space of the inheritance vector from being exponential in g to quadratic. The higher accuracy along with the reduced time complexity marks our method as a feasible means for IBD mapping in practical scenarios.

Availability: A software implementation, called IBDMAP, is freely available at http://bioinfo.cs.technion.ac.il/IBDmap.

Fig. 1.

Two slots of the g-degree relatives pedigree, where each node represents an individual at a specific location. The last generation is genotyped (measured). The full model has in addition two LD terms between the founder F_i in one slot and F_i in the next slot, for every founder in the pedigree. For clarity, these links are not shown. The model drawn has one LD term for each of the common founder B's alleles, and one additional LD term for each of the founders F_g and F^′_g.

Fig. 2.

The 4-track model achieves a significantly higher precision in comparison to the other methods under most tested scenarios, increasing the performance gap for g≥8 with over 50% improvement. Results correspond to over 95% recall for the 4-track model, and a lower recall for Plink and Merlin.

Fig. 3.

Finding a disease seeded at 22 cm (location indicated by the horizontal line) using IBD predictions. Pairs of individuals were simulated using g = 6,8,10, assuming a dominant disease with complete penetrance that originated from a common founder.