ASSOCIATION MAPPING IN CROP PLANTS
Plant, Soil and Nutrition Research
2012 Annual Report
1a.Objectives (from AD-416):
The proposed work is a joint effort to develop genomic and bioinformatic approaches to dissect maize, grape and other crops' agronomic traits. The goal is to identify specific genes and alleles that could be used in crop improvement.
1. Develop and molecularly characterize germplasm that capture high levels of genetic diversity.
2. Characterize germplasm for a wide range of agronomic and developmental traits.
3. Develop statistical analysis approaches for relating genotype and phenotype. Deploy these analysis approaches in easy to use software for the entire community.
1b.Approach (from AD-416):
1. Inbred lines and testcross germplasm will be created that will be efficient for trait dissection. The USDA maize and grape germplasm will be genotyped with high throughput next generation sequencing technologies.
2. Germplasm will be evaluated in replicated field trials for agronomic and developmental traits in both temperate and tropical environments.
3. Statistical approaches will be developed to deal with complex haplotypes, rare alleles, quantitative trait model building approaches, and breeding prediction. The software will be deployed in open source statistical scripts and in the TASSEL software package.
During FY 2012, researchers at Cornell have identified the molecular basis of genes controlling complex traits. Cornell developer and curator provided bioinformatic support and extensive phenotyping efforts for characterizing diverse maize lines for multiple traits from the US and with numerous global collaborators. Additionally, he supports the bioinformatics necessary to track 10,000s of samples using genotyping-by-sequencing technologies. A Cornell statistical geneticist has developed, led, and supported multiple large-scale genome wide association studies this year. He has also developed innovative approaches for combining linkage and association mapping populations in a single study. This researcher is also helping to coordinate collaboration between our USDA group and Chinese CAAS scientist to understand yield and drought tolerance in diverse maize. Finally, he released a very popular R statistical module for association mapping. A Cornell postdoc is analyzing the genetic relationships between all the USDA-ARS maize inbred lines, and providing a detailed and comprehensive understanding of all lines in the germplasm collection for the first time.
Additionally, they have collaborated extensively in the integration of the advanced statistics and bioinformatics into TASSEL.