CRUMBLER: A tool for the prediction of ancestry in cattle

Abstract

In many beef and some dairy production systems, crossbreeding is used to take advantage of breed complementarity and heterosis. Admixed animals are frequently identified by their coat color and body conformation phenotypes, however, without pedigree information it is not possible to identify the expected breed composition of an admixed animal and in the presence of selection, the actual composition may differ from expectation. As the roles of DNA and genotype data become more pervasive in animal agriculture, a systematic method for estimating the breed composition (the proportions of an animal’s genome originating from ancestral pure breeds) has utility for a variety of downstream analyses including the estimation of genomic breeding values for crossbred animals, the estimation of quantitative trait locus effects, and heterosis and heterosis retention in advanced generation composite animals. Currently, there is no automated or semi-automated ancestry estimation platform for cattle and the objective of this study was to evaluate the utility of extant public software for ancestry estimation and determine the effects of reference population size and composition and number of utilized single nucleotide polymorphism loci on ancestry estimation. We also sought to develop an analysis pipeline that would simplify this process for members of the livestock genomics research community. We developed and tested a tool, “CRUMBLER”, to estimate the global ancestry of cattle using ADMIXTURE and SNPweights based on a defined reference panel. CRUMBLER, was developed and evaluated in cattle, but is a species agnostic pipeline that facilitates the streamlined estimation of breed composition for individuals with potentially complex ancestries using publicly available global ancestry software and a specified reference population SNP dataset. We developed the reference panel from a large cattle genotype data set and breed association pedigree information using iterative analyses to identify purebred individuals that were representative of each breed. We also evaluated the numbers of markers necessary for breed composition estimation and simulated genotypes for advanced generation composite animals to evaluate the precision of the developed tool. The developed CRUMBLER pipeline extracts a specified subset of genotypes that is common to all current commercially available genotyping platforms, processes these into the file formats required for the analysis software, and predicts admixture proportions using the specified reference population allele frequencies.

Genomic prediction using a reference population of multiple pure breeds and admixed individuals

“…Estimation of the breed composition of individuals with admixed genomic background is of relevance for genomic prediction, because if it is not accounted for, it may lead to spurious estimates of SNP effects [ 59 ]. In real life applications, pedigree records and/or parentage validation can be used to distinguish purebred and admixed animals, but any error in the pedigree may lead to inaccurate consideration of individuals as pure or admixed [ 18 ]. Nevertheless, genomic prediction should rely on local ancestry (i.e., breed of origin) for each of the SNP alleles, rather than a genome-wide (global) ancestry computed from pedigree or markers [ 60 ].…”

“…On the one hand, in rotational crossbreeding, for instance, where crossbred dams are mated to purebred sires from different pure breeds, the genetic composition of crossbred animals is an admixture of the breeds included in the rotation. At each rotation cycle, depending on the breed of the sires used, admixture proportions of crossbred individuals change considerably [ 18 ]. On the other hand, the gene pool of some “purebred” populations may also contain a fraction of the genome from other breeds, because bulls are used across the breeds to some extent [ 19 ].…”

A multi-breed reference panel and additional rare variants maximize imputation accuracy in cattle

“…A maximum of 50 individuals per breed that were genotyped with both the HD and F250 assays, were randomly chosen and masked to represent various commercial chip densities for testing imputation accuracy (Table 2). All test set individuals had their breed-composition estimated by the CRUMBLER pipeline [31]. To avoid depleting the reference panel of breeds with small numbers of research assay genotypes, no more than 50% of a breed’s F250 or HD genotyped animals were removed for testing.…”

“…larger values indicate that the individuals are more closely related to the animals in the reference panel. To observe the impact of within-breed genetic similarity on imputation accuracy, we created four breed-specific standardized GRM using test individuals and individuals in the reference with more than 50% Angus (number in test = 50, number in reference = 15,013), Holstein (number in test = 50, number of reference = 5127), Gelbvieh (number of testing = 49, number of reference = 470), and Brahman/Nelore (number of testing = 50, number of reference = 2043) ancestry reported from the CRUMBLER pipeline [31]. Row means were calculated for each individual to quantify the relationship between each test individual and the members of their breed.…”

Small SNP panels for breed proportion estimation in Indian crossbred dairy cattle

“…Most common exotic breeds were Holstein-Friesian (HFX), Jersey and Brown Swiss which were mostly live animals but more often semen for artificial insemination were imported. The exotic genetics were sourced from many countries, and guidelines to maintain a level of exotic ancestry of approximately 50% were issued by the National Commission for Agriculture (Chacko, 1994). Crossbred cattle in India were recently shown to be an average of 70.3% (SD 9.6%) exotic ancestry (Strucken et al, 2018), thus showing a much larger exotic ancestry than initially favoured.…”