Evidence map›Paper›PMID 35961029›Full record

ArticleGenetics2022

Maintenance of quantitative genetic variance in complex, multitrait phenotypes: the contribution of rare, large effect variants in 2 Drosophila species.

Emma Hine, Daniel E Runcie, Scott L Allen, Yiguan Wang, Stephen F Chenoweth, Mark W Blows, Katrina McGuigan

Open access · hybridAbstract read
In one paragraph

Article in Genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 39% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Review
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

7 authors at 2 institutions in 3 countries.

Emma HineSchool of Biological Sciences, The University of Queensland, Brisbane, QLD 4072, Australia.ORCID 0000-0002-2257-4430
Daniel E RuncieDepartment of Plant Sciences, University of California Davis, Davis, CA 95616, USA.ORCID 0000-0002-3008-9312
Scott L AllenSchool of Biological Sciences, The University of Queensland, Brisbane, QLD 4072, Australia.ORCID 0000-0003-3933-0632
Yiguan WangSchool of Biological Sciences, The University of Queensland, Brisbane, QLD 4072, Australia.ORCID 0000-0003-0550-9511
Stephen F ChenowethSchool of Biological Sciences, The University of Queensland, Brisbane, QLD 4072, Australia.ORCID 0000-0002-8303-9159
Mark W BlowsSchool of Biological Sciences, The University of Queensland, Brisbane, QLD 4072, Australia.ORCID 0000-0002-1065-5524
Katrina McGuiganSchool of Biological Sciences, The University of Queensland, Brisbane, QLD 4072, Australia.ORCID 0000-0002-0525-2202
The University of Queensland · AUUniversity of California, Davis · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The interaction of evolutionary processes to determine quantitative genetic variation has implications for contemporary and future phenotypic evolution, as well as for our ability to detect causal genetic variants. While theoretical studies have provided robust predictions to discriminate among competing models, empirical assessment of these has been limited. In particular, theory highlights the importance of pleiotropy in resolving observations of selection and mutation, but empirical investigations have typically been limited to few traits. Here, we applied high-dimensional Bayesian Sparse Factor Genetic modeling to gene expression datasets in 2 species, Drosophila melanogaster and Drosophila serrata, to explore the distributions of genetic variance across high-dimensional phenotypic space. Surprisingly, most of the heritable trait covariation was due to few lines (genotypes) with extreme [>3 interquartile ranges (IQR) from the median] values. Intriguingly, while genotypes extreme for a multivariate factor also tended to have a higher proportion of individual traits that were extreme, we also observed genotypes that were extreme for multivariate factors but not for any individual trait. We observed other consistent differences between heritable multivariate factors with outlier lines vs those factors without extreme values, including differences in gene functions. We use these observations to identify further data required to advance our understanding of the evolutionary dynamics and nature of standing genetic variation for quantitative traits.

Indexed as

DrosophilaModels, GeneticAnimalsBayes TheoremDrosophila melanogasterGenetic VariationPhenotypeSelection, GeneticDrosophila melanogasterDrosophila serratagene expressiongenetic covarianceHouse of Cardsmutation–selection balancesparse factor analysisstanding genetic variance

Identifiers

PMID35961029
PMCPMC9526065
OpenAlexW4290930092

What OpenQuestion holds

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LicenceCC BY
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.