Estimation of genetic parameters and their sampling variances for quantitative traits in the type 2 modified augmented design

• Published in: The Crop journal Vol. 4; no. 2; pp. 107 - 118
• Main Authors: You, Frank M., Song, Qijian, Jia, Gaofeng, Cheng, Yanzhao, Duguid, Scott, Booker, Helen, Cloutier, Sylvie
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2016; KeAi Communications Co., Ltd
• Subjects: Broad-sense heritability; Genetic correlation; Modified augmented design; Sampling variance; Genetic correlation; Modified augmented design; Broad-sense heritability; Sampling variance
• ISSN: 2214-5141; 2095-5421; 2214-5141
• DOI: 10.1016/j.cj.2016.01.003

The type 2 modified augmented design (MAD2) is an efficient unreplicated experimental design used for evaluating large numbers of lines in plant breeding and for assessing genetic variation in a population. Statistical methods and data adjustment for soil heterogeneity have been previously described for this design. In the absence of replicated test genotypes in MAD2, their total variance cannot be partitioned into genetic and error components as required to estimate heritability and genetic correlation of quantitative traits, the two conventional genetic parameters used for breeding selection. We propose a method of estimating the error variance of unreplicated genotypes that uses replicated controls, and then of estimating the genetic parameters. Using the Delta method, we also derived formulas for estimating the sampling variances of the genetic parameters. Computer simulations indicated that the proposed method for estimating genetic parameters and their sampling variances was feasible and the reliability of the estimates was positively associated with the level of heritability of the trait. A case study of estimating the genetic parameters of three quantitative traits, iodine value, oil content, and linolenic acid content, in a biparental recombinant inbred line population of flax with 243 individuals, was conducted using our statistical models. A joint analysis of data over multiple years and sites was suggested for genetic parameter estimation. A pipeline module 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).