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Using association mapping in teosinte to investigate the function of maize selection-candidate genes
Allison L Weber1, Qiong Zhao, Michael D McMullen
1Laboratory of Genetics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America. alweber@ncsu.edu
Plos One
|December 17, 2009
Summary
Researchers identified 48 maize genes under artificial selection. Association mapping in teosinte revealed some significant genotype-phenotype links, but overall, these selection-candidate genes performed no better than random genes.
Area of Science:
- Plant genetics
- Genomics
- Evolutionary biology
Background:
- Large-scale maize genome screens identified 48 genes with signatures of artificial selection.
- These selection-candidate genes may control phenotypic differences between maize and teosinte.
- Candidate genes are located closer to domestication quantitative trait loci (QTL) than expected by chance.
Purpose of the Study:
- To define traits controlled by selection-candidate genes.
- To perform phenotype-genotype association mapping in teosinte for 35 selection-candidate genes.
- To investigate the role of these genes in maize domestication and improvement.
Main Methods:
- Phenotype-genotype association mapping was conducted in teosinte.
- 32 phenotypic traits, many altered during maize domestication, were assayed.
- Single nucleotide polymorphisms (SNPs) in selection-candidate genes were analyzed for trait association.
Main Results:
- Several significant associations were found between SNPs in selection-candidate genes and teosinte trait variation.
- Two associations surpassed the Bonferroni correction threshold.
- Five genes showed significant association with the same trait in multiple mapping panels, but overall performance did not exceed random genes.
Conclusions:
- Association analyses can aid in identifying traits controlled by selection-candidate genes.
- Evidence for novel functions of several selection-candidate genes was presented.
- Few significant associations were discovered overall, suggesting limitations in the current gene set or mapping strategy.
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