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ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #208960

Title: TASSEL: Software for Association Mapping of Complex Traits in Diverse Samples

item Bradbury, Peter
item Buckler, Edward - Ed

Submitted to: Bioinformatics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/7/2007
Publication Date: 10/1/2007
Citation: Bradbury, P., Zhang, Z., Kroon, D., Casstevens, T., Ramdoss, Y., Buckler Iv, E.S. 2007. Tassel: software for association mapping of complex traits in diverse samples. Bioinformatics. 23:2633-2635.

Interpretive Summary: Association mapping is an important technique for identifying which genes or chromosome segments affect expressed traits. Those genes and segments are called quantitative trait loci (QTL). Unlike more traditional linkage mapping, association mapping is applied to a diverse population of individuals. Unfortunately, family relationships and substructure within a population can result in apparent QTLs which do not exist. To address that problem, TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) software was developed. TASSEL uses sophisticated statistical models that incorporate information about family structure and background QTLs to eliminate many false positive results while retaining the ability to identify true QTLs. In addition, TASSEL provides other statistics and graphs to help interpret the output from the models and provides functions for data management.

Technical Abstract: The association mapping of complex traits is becoming the method of choice in plant and animal genetics. In most samples, researchers have to deal with both population and family structure. TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) implements general linear model and mixed linear model approaches for controlling sample structure and background QTL. The software can also analyze diversity among groups of sequences, SNPs, or SSRs. Diversity and linkage disequilibrium statistics can be calculated and visualized graphically. Other features include analyzing insertions/deletions, an intuitive graphical interface, easy integration of phenotypic and genotypic data, methods for missing data and ability to interact with databases.