Evidence map›Paper›PMID 25887220›Full record

ArticleBMC genetics2015

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

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

Abstract read
In one paragraph

Article in BMC genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed.

  1. Article
  2. Restructuring breeding programs 1: Integration of diversity.Genetics, selection, evolution : GSE · 2026
    Article
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  7. Genetic Diversity and Population Structure Analysis ofAnimals : an open access journal from MDPI · 2025
    Article
  8. Practical Considerations When Using Mendelian Sampling Variances for Selection Decisions in Genomic Selection Programs.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2025
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. 275 years of forestry meets genomics inEvolutionary applications · 2020
    Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Sonia E EynardAnimal Breeding and Genomics Centre, Wageningen UR Livestock Research, P.O. Box 338, Wageningen, 6700 AH, The Netherlands. sonia.eynard@wur.nl.
Jack J WindigAnimal Breeding and Genomics Centre, Wageningen UR Livestock Research, P.O. Box 338, Wageningen, 6700 AH, The Netherlands. jack.windig@wur.nl.
Grégoire LeroyAgroParisTech, UMR 1313 Génétique Animale et Biologie Intégrative, 16 rue Claude Bernard, Paris 05, 75231, France. gregoire.leroy@agroparistech.fr.
Rianne van BinsbergenAnimal Breeding and Genomics Centre, Wageningen UR Livestock Research, P.O. Box 338, Wageningen, 6700 AH, The Netherlands. rianne.vanbinsbergen@wur.nl.
Mario P L CalusAnimal Breeding and Genomics Centre, Wageningen UR Livestock Research, P.O. Box 338, Wageningen, 6700 AH, The Netherlands. mario.calus@wur.nl.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRelationships 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%.

resultsWe 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.

conclusionsUsing 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.

Indexed as

AllelesGenomeGenomicsAlgorithmsAnimalsCattleGene FrequencyGenome-Wide Association StudyGenotypeInbreedingModels, GeneticModels, StatisticalPedigreePolymorphism, Single Nucleotide

Identifiers

PMID25887220
PMCPMC4365517

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.