Genetic architecture of nonadditive inheritance in Arabidopsis thaliana hybrids.
The ubiquity of nonparental hybrid phenotypes, such as hybrid vigor and hybrid inferiority, has interested biologists for over a century and is of considerable agricultural importance. Although examples of both phenomena have been subject to intense investigation, no general model for the molecular basis of nonadditive genetic variance has emerged, and prediction of hybrid phenotypes from parental information continues to be a challenge. Here we explore the genetics of hybrid phenotype in 435 Arabidopsis thaliana individuals derived from intercrosses of 30 parents in a half diallel mating scheme. We find that nonadditive genetic effects are a major component of genetic variation in this population and that the genetic basis of hybrid phenotype can be mapped using genome-wide association (GWA) techniques. Significant loci together can explain as much as 20% of phenotypic variation in the surveyed population and include examples that have both classical dominant and overdominant effects. One candidate region inherited dominantly in the half diallel contains the gene for the MADS-box transcription factor AGAMOUS-LIKE 50 (AGL50), which we show directly to alter flowering time in the predicted manner. Our study not only illustrates the promise of GWA approaches to dissect the genetic architecture underpinning hybrid performance but also demonstrates the contribution of classical dominance to genetic variance.
Department of Molecular Biology, Max Planck Institute for Developmental Biology, 72076 Tubingen, Germany.
Danelle K
DK
Seymour
Department of Molecular Biology, Max Planck Institute for Developmental Biology, 72076 Tubingen, Germany.
Eunyoung
E
Chae
Machine Learning and Computational Biology Research Group, Max Planck Institute for Developmental Biology, Max Planck Institute for Intelligent Systems, 72076 Tubingen, Germany.|Machine Learning and Computational Biology Laboratory, Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule Zürich, 4058 Basel, Switzerland.
Dominik G
DG
Grimm
Department of Molecular Biology, Max Planck Institute for Developmental Biology, 72076 Tubingen, Germany.
Carmen
C
Martín Pizarro
Department of Molecular Biology, Max Planck Institute for Developmental Biology, 72076 Tubingen, Germany.
Anette
A
Habring-Müller
Department of Molecular Biology, Max Planck Institute for Developmental Biology, 72076 Tubingen, Germany.
François
F
Vasseur
Machine Learning and Computational Biology Research Group, Max Planck Institute for Developmental Biology, Max Planck Institute for Intelligent Systems, 72076 Tubingen, Germany.|European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SD, United Kingdom.
Barbara
B
Rakitsch
Machine Learning and Computational Biology Research Group, Max Planck Institute for Developmental Biology, Max Planck Institute for Intelligent Systems, 72076 Tubingen, Germany.|Machine Learning and Computational Biology Laboratory, Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule Zürich, 4058 Basel, Switzerland.
Karsten M
KM
Borgwardt
Department of Molecular Biology, Max Planck Institute for Developmental Biology, 72076 Tubingen, Germany.
Daniel
0000-0002-1037-5346
D
Koenig
Department of Molecular Biology, Max Planck Institute for Developmental Biology, 72076 Tubingen, Germany; weigel@weigelworld.org.
Detlef
0000-0002-2114-7963
D
Weigel
D017360:Arabidopsis / Q000235:genetics*; D003433:Crosses, Genetic; D014644:Genetic Variation; D006823:Hybrid Vigor / Q000235:genetics*; D006824:Hybridization, Genetic; D010641:Phenotype
Arabidopsis thaliana; GWAS; half diallel; heterosis