Estimation of genotypic correlation and heritability of apricot traits, using restricted maximum likelihood in repeated measures data

• Published in: Canadian journal of plant science Vol. 96; no. 3; pp. 439 - 447
• Main Authors: Ismaili, Ahmad, Farhad Karami, Omidali Akbarpour, Abdolhossein Rezaei Nejad
• Format: Journal Article
• Language: English
• Published: Canadian Science Publishing 01.06.2016
• Subjects: analysis of variance; apricots; biplot; covariance; data collection; fruit set; genotype; genotype × year interaction; heritability; juvenility; longitudinal data; mixed model; pistil; potassium; Prunus armeniaca; statistical models; trees; United Nations; unstructured covariance
• ISSN: 1918-1833; 1918-1833
• DOI: 10.1139/CJPS-2015-0253

In estimation of genetic parameters in perennial tree species on the basis of analysis of variance (ANOVA), heterogeneity of years and genotype × environment interaction for data sets during the juvenility to maturity life period is ignored. Therefore, a linear mixed model based on restricted maximum likelihood (REML) approximation for modeling of covariance structure of longitudinal data can improve our ability to analyze repeated measures data. In the present research, a modeling of variance-covariance structure by mixed model based on the REML approach has been used for characteristics of 26 apricot genotypes recorded during three years. Fitting unstructured covariance (UN) models for all traits indicated a great heterogeneity of variances among repeated years and the trends of response variables in the genotypes (except for RWC) was due to imperfect correlation of subjects measured in different years. Based on the same structure, positive correlations were estimated among fruit set, potassium content, and yield of pistil in repetitive years, and most traits showed high heritability estimation. To our knowledge, this is the first report in plant that genotypic correlation and heritability and their standard errors are estimated in a repeated measures data over years using REML approximation.