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ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Research Project #434608

Research Project: Database Tools for Managing and Analyzing Big Data Sets to Enhance Small Grains Breeding

Location: Plant, Soil and Nutrition Research

Project Number: 8062-21000-045-00-D
Project Type: In-House Appropriated

Start Date: Apr 1, 2018
End Date: Mar 31, 2023

Objective:
Objective 1: Develop methods and analyses on the Triticeae Toolbox (T3) database that use data stored there to assign likelihood to genome segments of carrying trait associated variants. Sub-objective 1.A. Improve T3 upload, download, and quality control tools. Sub-objective 1.B. Implement the Genomics and Open-source Breeding Informatics Initiative (GOBII) genotype data storage on T3. Sub-objective 1.C. Automate imputation to high-density genotyping platforms. Sub-objective 1.D. Automate genome-wide association study implementation. Objective 2: Improve linkages between diversity data stored in T3 and knowledge gleaned from the literature based on biological experimentation. Sub-objective 2.A. Develop new linkages with KNetMiner. Sub-objective 2.B. Implement analyses to estimate between-trait genetic correlations using the whole database as the reference population. Objective 3: Enhance T3 facilities to analyze and manage multi-omic data and data from multi-state cooperative nurseries. Sub-objective 3.A. Functions for search and analysis of transcriptomic and metabolomic data. Sub-objective 3.B. Clustering and prediction using multi-omic data.

Approach:
ARS develop text file input methods and will implement Mendelian error checking when both parents and an offspring have marker data. Upon upload of a high-dimensional phenotype dataset, a relationship matrix will be constructed from it and compared to the marker-based and pedigree-based relationship matrices. It will be important to scale each phenotype according to the information it carries about the genotype, namely, its heritability. The method on both transcriptomic and metabolomic datasets will be developed and tested. The Genomics and Opensource Breeding Informatics Initiative (GOBII, www.gobiiproject.org) genotype data management system will be incorporated using the Breeding application program interface (BrAPI, www.brapi.org). For imputation, Beagle4 has been tested. We will collaborate with another ARS lab in bringing the Practical Haplotype Graph (PHG) to wheat. When new lines are uploaded to T3 with genotype data of adequate density, they will be imputed. Genome-wide association study (GWAS) analyses using imputed scores will take the reliability of those scores into account. For traits assayed in multiple trials, results are combined by meta-analysis. Genes will be sorted by cumulative evidence of association and automated links are made to external databases, and we will populate a JBrowse track with GWAS hits. T3 users will want to access the KNetMiner network after an association analysis in T3: having identified a variant associated with a trait, KNetMiner will provide access to information from the literature about it. KNetMiner has developed a beta application program interface (API) that takes a gene and a trait and displays the knowledge network connected to those will be used. Traits will be linked by a co-located association. In a focal dataset, users will query all associations that pass a user-defined threshold. Physical distance between associations in prior and focal datasets will be ranked and presented to enable the user to determine which traits she wants to link to. Traits will also be linked by the overall genetic correlation between them by correlating genomic predictions to traits measured in the focal dataset. All expression data of tens of thousands of genes will be stored in “materialized view” tables. JBrowse tracks will be created allowing gene expression of sets of individuals to be displayed. Clicking on a transcript will open a window with a link back to T3 enabling the selection of the transcript as a phenotype. The transcriptome sequences will be added as a T3 BLAST database. The challenge of metabolomics is that most metabolites detected in mass spectroscopy (MS) experiments are of unknown chemical composition. Metabolomic databases other than T3 allow metabolite identities to be explored. Metabolomic data will be stored in formats compatible with those databases to enable sharing. Users will be able to link back to T3 from them. As for gene expression, metabolites will be searchable based on the genetic correlation of their levels with other phenotypes.