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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Soybean Genomics & Improvement Laboratory » Research » Publications at this Location » Publication #316803
Title: Estimation of genetic parameters and their sampling variances of quantitative traits in the type 2 modified augmented design

Author
item YOU, FRANK - Agriculture And Agri-Food Canada
item Song, Qijian
item YANZHAO, CHENG - Agriculture And Agri-Food Canada
item DUGUID, SCOTT - Agriculture And Agri-Food Canada
item BOOKER, HELEN - University Of Saskatchewan
item CLOUTIER, SYLVIE - University Of Manitoba

Submitted to: The Crop Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/16/2015
Publication Date: 2/2/2016
Citation: You, F.M., Song, Q., Yanzhao, C., Duguid, S., Booker, H., Cloutier, S. 2016. Estimation of genetic parameters and their sampling variances of quantitative traits in the type 2 modified augmented design. The Crop Journal. doi: 10.1016/j.cj.2016.01.003.

Interpretive Summary: Scientists like to perform experiments several times to be sure that what they observe is reproducible and true. Unfortunately, it is not always possible in plant breeding to perform replicate trials under field growing conditions. So scientists developed what is called the type 2 modified augmented design to evaluate plants in non-replicated field experiments. Although a statistical method to estimate the performance of new plants was described previously for the type 2 modified augmented design, a method to estimate variation of traits and has not been reported. Here, we derived formulas and developed a computer program to estimate these parameters. We also validated the method by computer simulation and application of the method to a population of flax plants. This method could help breeders evaluate a large number of lines from their breeding program without replication and would facilitate more efficient selection of the best lines from a breeding program.

Technical Abstract: We proposed a method to estimate the error variance among non-replicated genotypes, thus to estimate the genetic parameters by using replicated controls. We derived formulas to estimate sampling variances of the genetic parameters. Computer simulation indicated that the proposed methods of estimating genetic parameters and their sampling variances were feasible and the reliability of the estimates was positively associated with the level of heritability of the trait. As an example, the genetic variation of three quantitative traits including iodine value, oil content, and linolenic acid content, in a bi-parental recombinant inbreeding line population of flax with 243 individuals was evaluated using our statistical models and a joint analysis of data over multiple environments is suggested for genetic parameter estimation. A pipeline using SAS and Perl was developed to facilitate data analysis and appended to the previously developed MAD data analysis pipeline (http://probes.pw.usda.gov/bioinformatics_tools/MADPipeline/index.html).