Identity by descent estimation with dense genome-wide genotype data

Abstract

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.

Fig. 1

The condensed identity states. The 15 possible detailed identity states for individuals A and B, grouped according to their nine condensed states. Points represent alleles and lines indicate alleles that are IBD.

Fig. 2

Estimated average proportion of alleles shared IBD against the true average proportions for sibling pairs. For each method we consider the two cases where the genotype data (1) have neither missing data nor error, and (2) have 5% missing data and 2% error.

Fig. 3

Bias and RMSE for the different methods in a sibling pair. The genotype data (A) have neither missing data nor error, and (B) have 5% missing data and 2% error.

Fig. 4

Bias and RMSE for the different methods in the large pedigree pairs. The genotype data (A) have neither missing data nor error, and (B) have 5% missing data and 2% error.

Fig. 5

Estimated average proportion of alleles shared IBD against the true average proportions for large pedigree pairs. For each method we consider the two cases where the genotype data (1) have neither missing data nor error, and (2) have 5% missing data and 2% error.

Fig. 6

Estimated average proportion of alleles shared IBD across the genome against kinship coefficient for the Hutterite sample. (A) LD-20 using the CEU population to model background LD, (B) LD-20 using the Hutterites themselves to model background LD, (C) LD-RR using the Hutterites themselves to model background LD.

Fig. 7

EIBD versus kinship coefficient for the Hutterite sample. Deviations from the diagonal indicate possible pedigree errors. EIBD was computed using PREST.