Abstract
BackgroundMeasuring the extent of shared ancestry between individuals or organisms is important in many fields, including forensic science, conservation genetics and animal breeding. The traditional approach is to calculate the expected degree of relatedness between individuals in a pedigree. This assumes that the founders of the pedigree are non-inbred and unrelated to each other, which is rarely the case. In contrast, molecular data allow measurement of actual relatedness without knowledge of a pedigree. Methods to do this have been proposed, but generally do not take the lengths of the genomic regions shared between individuals into account.ResultsTwo measures based on the extent of haplotype sharing between genomes are proposed. The intercept measure B estimates the fraction of shared genome between individuals, and the product measure C is closely related to the numerator relationship matrix A. Both are based on a model for the joint distribution of markers at the haplotype level. The two measures are compared to the pedigree-based measure A and to vanRaden’s G, a frequently used molecular measure, using a set of data comprising 5037 dairy cattle. The comparison criteria include the ability to capture genealogical relatedness and the prediction accuracy obtained when used in genomic prediction. Both B and C explain around 95 % of the variation in A, whereas G explains around 6 %. G captures genealogical relatedness poorly, particularly for distantly related individuals (second cousins or farther). Both B and C tend to be larger than A but this can be ascribed to the assumption of non-inbred unrelated founders. Using C in linear mixed models results in slightly higher prediction accuracy than G, and using B results in slightly lower prediction accuracy.ConclusionsThe two proposed measures of relatedness capture genealogical relatedness well, outperforming vanRaden’s G in this respect. When used in genomic prediction models, the product measure leads to slightly improved prediction accuracy.
Highlights
Measuring the extent of shared ancestry between individuals or organisms is important in many fields, including forensic science, conservation genetics and animal breeding
Examples range from forensic science [1], studies of population structure [2, 3], and conservation genetics [4], to the mixed linear models used in genomic prediction and genome-wide association studies [5, 6]
With the advent of high-scale genotyping technologies such as single nucleotide polymorphism (SNP) arrays, it is possible to estimate realized relatedness directly from molecular data without knowledge of genealogy, and a variety of ways to do this are available [8, 9]. These generally take the form of genome-wise averages of single-SNP statistics, which have the disadvantage of not taking the lengths of genomic regions shared between two individuals into account [8]
Summary
Measuring the extent of shared ancestry between individuals or organisms is important in many fields, including forensic science, conservation genetics and animal breeding. Molecular data allow measurement of actual relatedness without knowledge of a pedigree Methods to do this have been proposed, but generally do not take the lengths of the genomic regions shared between individuals into account. With the advent of high-scale genotyping technologies such as single nucleotide polymorphism (SNP) arrays, it is possible to estimate realized relatedness directly from molecular data without knowledge of genealogy, and a variety of ways to do this are available [8, 9] These generally take the form of genome-wise averages of single-SNP statistics, which have the disadvantage of not taking the lengths of genomic regions shared between two individuals into account [8]. Section “Software” describes the software used and Section “Discussion” gives a brief discussion
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