Genomic selection in maize (Zea mays L.) population improvement for waterlogging tolerance

Issue Date

4-2017

Abstract

Excess soil moisture stress or waterlogging in maize is increasingly becoming a serious problem in the Philippines as a result of climate change. Waterlogging tolerance is best expressed in terms of yield reduction. Yield is a quantitative and polygenic trait. Genomic selection promises a more efficient way of improving quantitative traits in crop plants. Genomic selection is a type of maker assisted selected which uses all available marker data, phenotype data and statistical models to predict performance. High variability was found in a population of 390 S₁ families extracted from 39 Philippine traditional maize varieties in terms of yield under normal and excess soil moisture conditions. Genotyping-by-sequencing was implemented on 92 families sampled from the 390 S₁ families tested for waterlogging tolerance. Genotype and phenotype data from the 92 lines were used to gauge the feasibility of using genomic selection in these traditional maize varieties and to perform a preliminary genome-wide association study. The prediction accuracies of the three genomic selection models RR-BLUP, Bayesian RR and Bayesian LASSO were close to zero for crop yield susceptibility index and ranged 0.16-.44 for yield per se under normal and stressed conditions. Larger populations size should be used to improve prediction accuracies in maize genomic selection. Genome-wide association study detected 14 putative QTL for crop yield susceptibility index and two for yield under excess soil moisture stress, with significance level of 9.7x10ˉ⁵ to 1.4x10ˉ⁵, and power of 0.71 to 0.92. All the protein coding regions within 15kb upstream and downstream of the QTL are not yet characterized, except for GRMZM2G179270 (putative S-locus receptor-like protein kinase family protein), GRMZM2G071986 (tetratricopeptide repeat-like superfamily) and GRMZM2G093705 (ATPase).

Source or Periodical Title

Philippine Journal of Crop Science

ISSN

0115-463x

Volume

42

Issue

1

Page

15-26

Document Type

Article

Frequency

tri-quarterly

Physical Description

tables, dendrograms, graphs

Language

English

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