Evidence map›Paper›PMID 34344180›Full record

ArticleProceedings. Biological sciences2021

The utility of genomic prediction models in evolutionary genetics.

Suzanne E McGaugh, Aaron J Lorenz, Lex E Flagel

Open access · greenAbstract read
In one paragraph

Article in Proceedings. Biological sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
4.3field-weighted citation impact, top 5% of its field
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

15 citing papers in PubMed, 36 citations in OpenAlex.

  1. Review
  2. Review
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  4. Optimizing resource allocation inFrontiers in plant science · 2026
    Article
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  13. Taking quantitative genomics into the wild.Proceedings. Biological sciences · 2022
    Article
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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

3 authors at 1 institution in 1 country.

Suzanne E McGaughEcology, Evolution, and Behavior, University of Minnesota, 140 Gortner Lab, 1479 Gortner Avenue, Saint Paul, MN 55108, USA.
Aaron J LorenzAgronomy and Plant Genetics, University of Minnesota, 411 Borlaug Hall, 1991 Upper Buford Circle, Saint Paul, MN 55108, USA.
Lex E FlagelPlant and Microbial Biology, University of Minnesota, 140 Gortner Lab, 1479 Gortner Avenue, Saint Paul, MN 55108, USA.
University of Minnesota · US

Funding

Understanding complex trait architecture through population genomicsR01GM127872 · NIGMS · UNIVERSITY OF MINNESOTA · PI KEENE, ALEX C, MCGAUGH, SUZANNE · 2018 to 2021
$1.7M
NIGMS NIH HHS R01 GM127872
6 · The paper itself

Abstract

Variation in complex traits is the result of contributions from many loci of small effect. Based on this principle, genomic prediction methods are used to make predictions of breeding value for an individual using genome-wide molecular markers. In breeding, genomic prediction models have been used in plant and animal breeding for almost two decades to increase rates of genetic improvement and reduce the length of artificial selection experiments. However, evolutionary genomics studies have been slow to incorporate this technique to select individuals for breeding in a conservation context or to learn more about the genetic architecture of traits, the genetic value of missing individuals or microevolution of breeding values. Here, we outline the utility of genomic prediction and provide an overview of the methodology. We highlight opportunities to apply genomic prediction in evolutionary genetics of wild populations and the best practices when using these methods on field-collected phenotypes.

Indexed as

Models, GeneticPolymorphism, Single NucleotideAnimalsBreedingGenomeGenomicsGenotypeHumansPhenotypeBLUPbreeding valuequantitative genetics

Identifiers

PMID34344180
PMCPMC8334854
OpenAlexW3188915358

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.