2009 Annual Report
1a.Objectives (from AD-416)
The proposed work is a joint effort to develop genomic and bioinformatic approaches to dissect maize kernel quality, aluminum tolerance, and nitrogen use efficiency. The goal is to identify specific genes and alleles that could be used in maize improvement for these traits.
1. Develop mapping germplasm that captures most of maize’s diversity.
2. Characterize germplasm for kernel quality, aluminum tolerance, and nitrogen use efficiency.
3. Develop high efficiency SNP genotyping to help analyze thousands of candidate loci.
4. Develop statistical analysis approaches for relating genotype and phenotype. Deploy these analysis approaches in easy to use software for the entire community.
5. Evaluate candidate genes and alleles and make these available to plant breeders.
6. Develop bioinformatic tools to aid plant breeders in breeding decision analyses and to deal with next generation scale data
1b.Approach (from AD-416)
With respect to trait dissection, linkage mapping and candidate association approaches will be used. Diverse profiling and high throughput phenotyping approaches will be used to characterize these populations and related molecular aspects with agronomic phenotypes. Additionally, bioinformatic solutions will be developed to relate these genomic and agronomic datasets.
The grape genotyping project will use the following approaches:
1) Bioinformatically identify 500 SNPS for Vitis, using publically available sequence;
2) Validate a subset of these SNPS;
3) Design SNP arrays to genotypes 2500 samples,
4) Run the samples on an Illumina Beadstation; and
5) Estimate diversity, population structure, breeding values and associate genetic variation with agronomic rates.
In 2009, researchers at Cornell have identified the molecular basis of genes controlling maize flowering time. They identified a key region playing an important role in the domestication of maize. Cornell provided bioinformatic support and extensive phenotyping efforts to start characterizing 5000 diverse maize lines for multiple traits. A Cornell statistical geneticist has developed a creative and novel approach for accelerating computation of the mixed model by up to 1 million fold. This will allow complex traits to be analyzed much more rapidly and whole genomes can be analyzed.
In the grape project, researchers with collaboration with Ware (USDA-ARS) discovered several hundred thousand SNPs using next generation technologies. An array was developed to genotype the USDA grape collection, and over 2000 samples were genotyped with 9000 SNPs. Analysis is currently underway to understand how all grapes are related to one another and how genetic diversity is partitioned.
Additionally, they have collaborated extensively in the integration of the mixed model and the creation of TASSEL into a pipeline. This project is monitored through weekly meetings.