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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 Genomic Data, 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 Genomic Data, 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

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The data shown below were compiled from readership statistics for 185 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 180 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 17%
Student > Master 31 17%
Researcher 29 16%
Student > Bachelor 18 10%
Other 13 7%
Other 35 19%
Unknown 28 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 93 50%
Biochemistry, Genetics and Molecular Biology 19 10%
Veterinary Science and Veterinary Medicine 15 8%
Environmental Science 5 3%
Medicine and Dentistry 3 2%
Other 9 5%
Unknown 41 22%
Attention Score in Context

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
#17,285,036
of 25,371,288 outputs
Outputs from BMC Genomic Data
#668
of 1,204 outputs
Outputs of similar age
#164,207
of 275,271 outputs
Outputs of similar age from BMC Genomic Data
#24
of 44 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,204 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 34th percentile – i.e., 34% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 275,271 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.