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

Overview of attention for article published in BMC Genetics, July 2015
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Title
Estimation of inbreeding using pedigree, 50k SNP chip genotypes and full sequence data in three cattle breeds
Published in
BMC Genetics, July 2015
DOI 10.1186/s12863-015-0227-7
Pubmed ID
Authors

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

Abstract

Levels 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. Five 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. Estimates 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).

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Mendeley readers

The data shown below were compiled from readership statistics for 151 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Colombia 2 1%
United States 2 1%
Poland 1 <1%
Unknown 146 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 27 18%
Student > Ph. D. Student 25 17%
Researcher 23 15%
Student > Bachelor 15 10%
Student > Doctoral Student 10 7%
Other 31 21%
Unknown 20 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 79 52%
Biochemistry, Genetics and Molecular Biology 18 12%
Veterinary Science and Veterinary Medicine 13 9%
Medicine and Dentistry 3 2%
Environmental Science 2 1%
Other 6 4%
Unknown 30 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 August 2015.
All research outputs
#7,761,817
of 12,372,276 outputs
Outputs from BMC Genetics
#442
of 828 outputs
Outputs of similar age
#126,736
of 237,380 outputs
Outputs of similar age from BMC Genetics
#1
of 2 outputs
Altmetric has tracked 12,372,276 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 828 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
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