Estimation of inbreeding using pedigree, 50k SNP chip genotypes and full sequence data in three cattle breeds.

Bernt Guldbrandtsen,Mogens S Lund,Qianqian Zhang,Goutam Sahana,Mario Pl Calus

doi:10.1186/s12863-015-0227-7

Abstract

BackgroundLevels of inbreeding in cattle populations have increased in the past due to the use of a limited number of bulls for artificial insemination. High levels of inbreeding lead to reduced genetic diversity and inbreeding depression. Various estimators based on different sources, e.g., pedigree or genomic data, have been used to estimate inbreeding coefficients in cattle populations. However, the comparative advantage of using full sequence data to assess inbreeding is unknown. We used pedigree and genomic data at different densities from 50k to full sequence variants to compare how different methods performed for the estimation of inbreeding levels in three different cattle breeds.ResultsFive different estimates for inbreeding were calculated and compared in this study: pedigree based inbreeding coefficient (FPED); run of homozygosity (ROH)-based inbreeding coefficients (FROH); genomic relationship matrix (GRM)-based inbreeding coefficients (FGRM); inbreeding coefficients based on excess of homozygosity (FHOM) and correlation of uniting gametes (FUNI). Estimates using ROH provided the direct estimated levels of autozygosity in the current populations and are free effects of allele frequencies and incomplete pedigrees which may increase in inaccuracy in estimation of inbreeding. The highest correlations were observed between FROH estimated from the full sequence variants and the FROH estimated from 50k SNP (single nucleotide polymorphism) genotypes. The estimator based on the correlation between uniting gametes (FUNI) using full genome sequences was also strongly correlated with FROH detected from sequence data.ConclusionsEstimates based on ROH directly reflected levels of homozygosity and were not influenced by allele frequencies, unlike the three other estimates evaluated (FGRM, FHOM and FUNI), which depended on estimated allele frequencies. FPED suffered from limited pedigree depth. Marker density affects ROH estimation. Detecting ROH based on 50k chip data was observed to give estimates similar to ROH from sequence data. In the absence of full sequence data ROH based on 50k can be used to access homozygosity levels in individuals. However, genotypes denser than 50k are required to accurately detect short ROH that are most likely identical by descent (IBD).

Highlights

Levels of inbreeding in cattle populations have increased in the past due to the use of a limited number of bulls for artificial insemination
The inbreeding coefficient can be calculated as the proportion of genome covered by runs of homozygosity (ROH) and has been shown to be more informative than the inbreeding coefficient estimated from pedigree data or other estimators because ROH strongly correlate with homozygous mutation load [25]
To estimate the “true” state of ROH, whole-genome sequences should be used rather than Single Nucleotide Polymorphism (SNP) chip data, but, to date, there are only few studies doing this in cattle [32]

Summary

Introduction

Levels of inbreeding in cattle populations have increased in the past due to the use of a limited number of bulls for artificial insemination. Various estimators based on different sources, e.g., pedigree or genomic data, have been used to estimate inbreeding coefficients in cattle populations. We used pedigree and genomic data at different densities from 50k to full sequence variants to compare how different methods performed for the estimation of inbreeding levels in three different cattle breeds. VanRaden proposed a method to take into account unknown ancestors when estimating inbreeding coefficients, increasing the accuracy of inbreeding level estimates in incomplete pedigrees [19]. To estimate the “true” state of ROH, whole-genome sequences should be used rather than SNP chip data, but, to date, there are only few studies doing this in cattle [32]. It is possible to investigate and compare how well different commonly used estimators of inbreeding level correlate with ROH estimated using next-generation sequence (NGS) data

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