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Development of Targeting Induced Local Lesions IN Genomes (TILLING) Populations in Small Grain Crops by Ethyl Methanesulfonate Mutagenesis
Published on: July 16, 2019
Population genetics of genomics-based crop improvement methods
Martha T Hamblin1, Edward S Buckler, Jean-Luc Jannink
1Institute for Genomic Diversity, Biotechnology Building, Cornell University, Ithaca, NY 14853, USA. jeanluc.jannink@ars.usda.gov
Trends in Genetics : TIG
|January 14, 2011
Summary
Genome-wide association studies (GWAS) explain less of human complex traits than expected. However, plant GWAS are more successful due to distinct genetic histories and selection, making quantitative trait loci (QTL) detection more efficient in crops.
Area of Science:
- Genetics
- Plant Breeding
- Genomics
Background:
- Genome-wide association studies (GWAS) in humans often fail to explain the genetic basis of complex traits, even with large sample sizes.
- Recent plant GWAS research demonstrates greater success with fewer resources, indicating potential differences in genetic architecture.
Purpose of the Study:
- To explore why plant GWAS are more successful than human GWAS.
- To compare trait architectures and linkage disequilibrium (LD) patterns between domesticated plants and humans.
Main Methods:
- Comparative analysis of population genetic histories.
- Review of past selection for traits of interest in plants and humans.
- Examination of linkage disequilibrium (LD) patterns.
Main Results:
- Differences in population genetic histories and past selection create distinct trait architectures between plants and humans.
- Patterns of linkage disequilibrium (LD) differ significantly between domesticated plants and humans.
- These differences make quantitative trait loci (QTL) detection more rewarding and cost-effective in crops.
Conclusions:
- Plant GWAS are more successful due to unique evolutionary histories and selection pressures.
- Understanding these differences is crucial for optimizing genomic selection (GS) and breeding strategies in crops.
- The findings highlight the importance of considering species-specific genetic factors in GWAS and GS applications.
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