Premium
Prediction of Genetic Variance in Biparental Maize Populations: Genomewide Marker Effects versus Mean Genetic Variance in Prior Populations
Author(s) -
Lian Lian,
Jacobson Amy,
Zhong Shengqiang,
Bernardo Rex
Publication year - 2015
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2014.10.0729
Subject(s) - biology , population , explained variation , selection (genetic algorithm) , genetic variation , analysis of variance , statistics , genetics , demography , mathematics , gene , computer science , artificial intelligence , sociology
Good methods are lacking for predicting the genetic variance ( V G ) in biparental populations. Our objective was to determine whether genomewide marker effects and related populations could be used to predict the V G when two parents (A and B) are crossed to form a segregating population. For each of 85 A/B populations, 2 to 23 maize ( Zea mays L.) populations with A and B as one of the parents were used as the training population. In the genomewide selection model, the testcross V G in A/B was predicted as the variance among the predicted genotypic values of progeny from a simulated A/B population. In the mean variance model, V G in A/B was predicted as the mean of V G in a series of A/* populations and */B populations, where * denotes a random parent. The correlations between observed and predicted V G were significant ( P = 0.05) for both the genomewide selection model (0.18 for yield, 0.49 for moisture, and 0.52 for test weight) and the mean variance model (0.26 for yield, 0.46 for moisture, and 0.50 for test weight). The percentages of bias in estimates of V G were −28 to −60% for the genomewide selection model, but were only −1 to 5% for the mean variance model. Our results indicated that the V G in an A/B population could be predicted as the mean variance among populations with A and B as one of the parents. The mean variance model should be practical in breeding programs because it simply uses phenotypic data from prior, related populations.