AraPheno: a Public Database for Arabidopsis Thaliana Phenotypes

Overview

Journal Nucleic Acids Res

Publisher Oxford University Press

Specialty Biochemistry

Date 2016 Dec 8

PMID 27924043

Citations 42

Authors

Umit Seren

Dominik Grimm

Joffrey Fitz

Detlef Weigel

Magnus Nordborg

Karsten Borgwardt

Arthur Korte

Affiliations

Soon will be listed here.

Abstract

Natural genetic variation makes it possible to discover evolutionary changes that have been maintained in a population because they are advantageous. To understand genotype-phenotype relationships and to investigate trait architecture, the existence of both high-resolution genotypic and phenotypic data is necessary. Arabidopsis thaliana is a prime model for these purposes. This herb naturally occurs across much of the Eurasian continent and North America. Thus, it is exposed to a wide range of environmental factors and has been subject to natural selection under distinct conditions. Full genome sequencing data for more than 1000 different natural inbred lines are available, and this has encouraged the distributed generation of many types of phenotypic data. To leverage these data for meta analyses, AraPheno (https://arapheno.1001genomes.org) provide a central repository of population-scale phenotypes for A. thaliana inbred lines. AraPheno includes various features to easily access, download and visualize the phenotypic data. This will facilitate a comparative analysis of the many different types of phenotypic data, which is the base to further enhance our understanding of the genotype-phenotype map.

Citing Articles

The effectiveness of large language models with RAG for auto-annotating trait and phenotype descriptions.

Kainer D Biol Methods Protoc. 2025; 10(1):bpaf016.

PMID: 40040835 PMC: 11879556. DOI: 10.1093/biomethods/bpaf016.

Comparison of machine learning methods for genomic prediction of selected Arabidopsis thaliana traits.

Kelly C, McLaughlin R PLoS One. 2024; 19(8):e0308962.

PMID: 39196916 PMC: 11355539. DOI: 10.1371/journal.pone.0308962.

The benefits of permutation-based genome-wide association studies.

John M, Korte A, Grimm D J Exp Bot. 2024; 75(17):5377-5389.

PMID: 38954539 PMC: 11389838. DOI: 10.1093/jxb/erae280.

Epiallelic variation of non-coding RNA genes and their phenotypic consequences.

Liu J, Zhong X Nat Commun. 2024; 15(1):1375.

PMID: 38355746 PMC: 10867003. DOI: 10.1038/s41467-024-45771-5.

Crop-GPA: an integrated platform of crop gene-phenotype associations.

Gao Y, Zhou Q, Luo J, Xia C, Zhang Y, Yue Z NPJ Syst Biol Appl. 2024; 10(1):15.

PMID: 38346982 PMC: 10861494. DOI: 10.1038/s41540-024-00343-7.

References

Atwell S, Huang Y, Vilhjalmsson B, Willems G, Horton M, Li Y . Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature. 2010; 465(7298):627-31. PMC: 3023908. DOI: 10.1038/nature08800. View

Shindo C, Bernasconi G, Hardtke C . Natural genetic variation in Arabidopsis: tools, traits and prospects for evolutionary ecology. Ann Bot. 2007; 99(6):1043-54. PMC: 3243570. DOI: 10.1093/aob/mcl281. View

Hirschhorn J, Daly M . Genome-wide association studies for common diseases and complex traits. Nat Rev Genet. 2005; 6(2):95-108. DOI: 10.1038/nrg1521. View

Llinares-Lopez F, Grimm D, Bodenham D, Gieraths U, Sugiyama M, Rowan B . Genome-wide detection of intervals of genetic heterogeneity associated with complex traits. Bioinformatics. 2015; 31(12):i240-9. PMC: 4559912. DOI: 10.1093/bioinformatics/btv263. View

Segura V, Vilhjalmsson B, Platt A, Korte A, Seren U, Long Q . An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat Genet. 2012; 44(7):825-30. PMC: 3386481. DOI: 10.1038/ng.2314. View

. 1,135 Genomes Reveal the Global Pattern of Polymorphism in Arabidopsis thaliana. Cell. 2016; 166(2):481-491. PMC: 4949382. DOI: 10.1016/j.cell.2016.05.063. View

Koornneef M, Meinke D . The development of Arabidopsis as a model plant. Plant J. 2010; 61(6):909-21. DOI: 10.1111/j.1365-313X.2009.04086.x. View

Rakitsch B, Lippert C, Stegle O, Borgwardt K . A Lasso multi-marker mixed model for association mapping with population structure correction. Bioinformatics. 2012; 29(2):206-14. DOI: 10.1093/bioinformatics/bts669. View

Krajewski P, Chen D, Cwiek H, van Dijk A, Fiorani F, Kersey P . Towards recommendations for metadata and data handling in plant phenotyping. J Exp Bot. 2015; 66(18):5417-27. DOI: 10.1093/jxb/erv271. View

10.

Seren U, Vilhjalmsson B, Horton M, Meng D, Forai P, Huang Y . GWAPP: a web application for genome-wide association mapping in Arabidopsis. Plant Cell. 2013; 24(12):4793-805. PMC: 3556958. DOI: 10.1105/tpc.112.108068. View

11.

Zhou J, Cho M, Lange C, Lutz S, Silverman E, Laird N . Integrating Multiple Correlated Phenotypes for Genetic Association Analysis by Maximizing Heritability. Hum Hered. 2015; 79(2):93-104. PMC: 4508328. DOI: 10.1159/000381641. View

12.

Horton M, Hancock A, Huang Y, Toomajian C, Atwell S, Auton A . Genome-wide patterns of genetic variation in worldwide Arabidopsis thaliana accessions from the RegMap panel. Nat Genet. 2012; 44(2):212-6. PMC: 3267885. DOI: 10.1038/ng.1042. View

13.

Suo C, Toulopoulou T, Bramon E, Walshe M, Picchioni M, Murray R . Analysis of multiple phenotypes in genome-wide genetic mapping studies. BMC Bioinformatics. 2013; 14:151. PMC: 3655878. DOI: 10.1186/1471-2105-14-151. View

14.

Korte A, Vilhjalmsson B, Segura V, Platt A, Long Q, Nordborg M . A mixed-model approach for genome-wide association studies of correlated traits in structured populations. Nat Genet. 2012; 44(9):1066-71. PMC: 3432668. DOI: 10.1038/ng.2376. View