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The effect of rare alleles on estimated genomic relationships from whole genome sequence data

Overview of attention for article published in BMC Genomic Data, March 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

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Title
The effect of rare alleles on estimated genomic relationships from whole genome sequence data
Published in
BMC Genomic Data, March 2015
DOI 10.1186/s12863-015-0185-0
Pubmed ID
Authors

Sonia E Eynard, Jack J Windig, Grégoire Leroy, Rianne van Binsbergen, Mario PL Calus

Abstract

Relationships between individuals and inbreeding coefficients are commonly used for breeding decisions, but may be affected by the type of data used for their estimation. The proportion of variants with low Minor Allele Frequency (MAF) is larger in whole genome sequence (WGS) data compared to Single Nucleotide Polymorphism (SNP) chips. Therefore, WGS data provide true relationships between individuals and may influence breeding decisions and prioritisation for conservation of genetic diversity in livestock. This study identifies differences between relationships and inbreeding coefficients estimated using pedigree, SNP or WGS data for 118 Holstein bulls from the 1000 Bull genomes project. To determine the impact of rare alleles on the estimates we compared three scenarios of MAF restrictions: variants with a MAF higher than 5%, variants with a MAF higher than 1% and variants with a MAF between 1% and 5%. We observed significant differences between estimated relationships and, although less significantly, inbreeding coefficients from pedigree, SNP or WGS data, and between MAF restriction scenarios. Computed correlations between pedigree and genomic relationships, within groups with similar relationships, ranged from negative to moderate for both estimated relationships and inbreeding coefficients, but were high between estimates from SNP and WGS (0.49 to 0.99). Estimated relationships from genomic information exhibited higher variation than from pedigree. Inbreeding coefficients analysis showed that more complete pedigree records lead to higher correlation between inbreeding coefficients from pedigree and genomic data. Finally, estimates and correlations between additive genetic (A) and genomic (G) relationship matrices were lower, and variances of the relationships were larger when accounting for allele frequencies than without accounting for allele frequencies. Using pedigree data or genomic information, and including or excluding variants with a MAF below 5% showed significant differences in relationship and inbreeding coefficient estimates. Estimated relationships and inbreeding coefficients are the basis for selection decisions. Therefore, it can be expected that using WGS instead of SNP can affect selection decision. Inclusion of rare variants will give access to the variation they carry, which is of interest for conservation of genetic diversity.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 1%
France 1 1%
Brazil 1 1%
Argentina 1 1%
Belgium 1 1%
United States 1 1%
Poland 1 1%
Unknown 68 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 28%
Student > Ph. D. Student 16 21%
Student > Master 13 17%
Student > Doctoral Student 6 8%
Other 5 7%
Other 5 7%
Unknown 9 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 51 68%
Biochemistry, Genetics and Molecular Biology 9 12%
Veterinary Science and Veterinary Medicine 2 3%
Mathematics 1 1%
Pharmacology, Toxicology and Pharmaceutical Science 1 1%
Other 2 3%
Unknown 9 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 01 January 2023.
All research outputs
#3,671,336
of 26,017,215 outputs
Outputs from BMC Genomic Data
#112
of 1,222 outputs
Outputs of similar age
#44,351
of 278,263 outputs
Outputs of similar age from BMC Genomic Data
#3
of 30 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,222 research outputs from this source. They receive a mean Attention Score of 4.4. This one has done particularly well, scoring higher than 90% of its peers.
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 278,263 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.