Evidence map›Paper›PMID 33193620›Full record

ArticleFrontiers in genetics2020

A Causality Perspective of Genomic Breed Composition for Composite Animals.

Xiao-Lin Wu, Zhi Li, Yangfan Wang, Jun He, Guilherme J M Rosa, Ryan Ferretti, John Genho, Richard G Tait, Jamie Parham, Tom Schultz and 1 more

Abstract read
In one paragraph

Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Genomic analysis of breed composition and population structure in Montana composite cattle.Mammalian genome : official journal of the International Mammalian Genome Society · 2026
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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

11 authors.

Xiao-Lin WuBiostatistics and Bioinformatics, Neogen GeneSeek Operations, Lincoln, NE, United States.
Zhi LiBiostatistics and Bioinformatics, Neogen GeneSeek Operations, Lincoln, NE, United States.
Yangfan WangDepartment of Animal Sciences, University of Wisconsin, Madison, WI, United States.
Jun HeBiostatistics and Bioinformatics, Neogen GeneSeek Operations, Lincoln, NE, United States.
Guilherme J M RosaDepartment of Animal Sciences, University of Wisconsin, Madison, WI, United States.
Ryan FerrettiBiostatistics and Bioinformatics, Neogen GeneSeek Operations, Lincoln, NE, United States.
John GenhoBiostatistics and Bioinformatics, Neogen GeneSeek Operations, Lincoln, NE, United States.
Richard G TaitBiostatistics and Bioinformatics, Neogen GeneSeek Operations, Lincoln, NE, United States.
Jamie ParhamBiostatistics and Bioinformatics, Neogen GeneSeek Operations, Lincoln, NE, United States.
Tom SchultzBiostatistics and Bioinformatics, Neogen GeneSeek Operations, Lincoln, NE, United States.
Stewart BauckBiostatistics and Bioinformatics, Neogen GeneSeek Operations, Lincoln, NE, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genomic breed composition (GBC) of an individual animal refers to the partition of its genome according to the inheritance from its ancestors or ancestral breeds. For crossbred or composite animals, knowing their GBC is useful to estimate heterosis, to characterize their actual inheritance from foundation breeds, and to make management decisions for crossbreeding programs. Various statistical approaches have been proposed to estimate GBC in animals, but the interpretations of estimates have varied with these methods. In the present study, we proposed a causality interpretation of GBC based on path analysis. We applied this method to estimating GBC in two composite breeds of beef cattle, namely Brangus and Beefmaster. Three SNP panels were used to estimate GBC: a 10K SNP panel consisting of 10,226 common SNPs across three GeneSeek Genomic Profiler (GGP) bovine SNP arrays (GGP 30K, GGP 40K, and GGP 50K), and two subsets (1K and 5K) of uniformly distributed SNPs. The path analysis decomposed the relationships between the ancestors and the composite animals into direct and indirect path effects, and GBC was measured by the relative ratio of the coefficients of direct (D-GBC) and combined (C-GBC) effects from each ancestral breed to the progeny, respectively. Estimated GBC varied only slightly between different genotyping platforms and between the three SNP panels. In the Brangus cattle, because the two ancestral breeds had a very distant relationship, the estimated D-GBC and C-GBC were comparable to each other in the path analysis, and they corresponded roughly to the estimated GBC from the linear regression and the admixture model. In the Beefmaster, however, the strong relationship in allelic frequencies between Hereford and Shorthorn imposed a challenge for the linear regression and the admixture model to estimated GBC reliably. Instead, D-GBC by the path analysis included only direct ancestral effects, and it was robust to bias due to high genomic correlations between reference (ancestral) breeds.

Indexed as

beef cattlecrossbred animalsgenomic compositionpath analysisSNP arrays

Identifiers

PMID33193620
PMCPMC7662449

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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.