Identification of Pedigree Relationship from Genome Sharing

Abstract

Determination of degree of relationship traditionally has been undertaken using genotypic data on individual loci, typically assumed to be independent. With dense marker data as now available, it is possible to identify the regions of the genome shared identical by descent (ibd). This information can be used to determine pedigree relationship (R), e.g., cousins vs. second cousins, and also to distinguish pedigrees that have the same Wright’s relationship (R) such as half-sibs and uncle–nephew. We use simulation to investigate the accuracy with which pedigree relationship can be inferred from genome sharing for uniparental relatives (a common ancestor on only one side of their pedigree), specifically the number, position (whether at chromosome ends), and length of shared regions ibd on each chromosome. Moments of the distribution of the likelihood ratio (including its expectation, the Kullback-Leibler distance) for alternative relationships are estimated for model human genomes, with the ratio of the mean to the SD of the likelihood ratio providing a useful reference point. Two relationships differing in R can be readily distinguished provided at least one has high R, e.g., approximately 98.5% correct assignment of cousins and half-cousins, but only approximately 75% for second cousins once removed and third cousins. Two relationships with the same R can be distinguished only if R is high, e.g., half-sibs and uncle–nephew, with probability of correct assignment being approximately 5/6.

On the use of dense SNP marker data for the identification of distant relative pairs

“…up to the level of second or halfsecond cousins, can be distinguished unambiguously from the a priori equally likely alternative hypothesis of 'unrelated' with 500K SNP data. This is consistent with other published findings [16,26,27,29,49] and the first three lines of Table 1 report posterior probability averages for 6 different HS-n-n relationships that are very similar to those reported in [49]. Table 1: Average posterior probabilities of six extended half-sibling relationships for different numbers of markers when the only alternative relationship considered is that the two individuals of interest are unrelated.…”

“…There is obviously a limit on what can be deduced, on average, from the amount of genome shared by distant relatives due to skewness and overlapping of distributions of the realised sharing [25,26]. While the pedigree likelihood approach that we propose here is not necessarily better than existing approaches, it competes well and has some obvious advantages.…”

“…The underlying assumptions of the ERSA approach based on the number and lengths of estimated IBD segments have been criticised in [26] who propose a simulation-based approach instead. Both methods require accurate detection of IBD segments however, and this may not be straightforward as we encountered with the real data from the MICROS study.…”

“…Firstly, there is a potentially enormous number of possible alternatives to consider, even in 'non-deficient' cases where all the pedigree members have been observed. Secondly, the observed genetic sharing amongst relatives may not reflect the expected sharing, due to the probabilistic nature of genetic inheritance [25,26,59].…”

Determination of genetic relatedness from low‐coverage human genome sequences using pedigree simulations

“…The simplest of these calculate genomewide averages across a panel of single‐SNP haploid or diploid genetic distances (Tal, ) or allele‐sharing coefficients (Pemberton, Wang, Li, & Rosenberg, ; Speed & Balding, ). More advanced methods identify IBD regions as shared haplotype segments within densely spaced, preferably unlinked genomic markers, and then infer relationships from the total proportion of IBD (Browning & Browning, , ; Gusev et al., ; Hill & White, ; Kong et al., ; Purcell et al., ). The most advanced methods can detect distant relationships up to the 9th degree, yielding probabilities of relatedness conditional on the total IBD, as in previous methods, as well as additional information including the number of IBD chromosomal segments, their lengths and the genotypes they contain (e.g., Albrechtsen et al., ; Huff et al., ; Li et al., ).…”