Genome‐wide Association for Plant Height and Flowering Time across 15 Tropical Maize Populations under Managed Drought Stress and Well‐Watered Conditions in Sub‐Saharan Africa. Issue 5 (1st September 2016)

• Record Type: Journal Article
• Authors: Wallace, Jason G.; Zhang, Xuecai; Beyene, Yoseph; Semagn, Kassa; Olsen, Michael; Prasanna, Boddupalli M.; Buckler, Edward S.
• Is Part Of: Crop science. Volume 56:Issue 5(2016)
• Page Start: 2365
• Page End: 2378
• Publication Date: 2016-09-01
• Subjects: Crop science -- Periodicals; Cultures -- Périodiques; Cultures de plein champ -- Périodiques; Crop science; Nutzpflanzen; Zeitschrift; Pflanzenbau; Periodicals; 633
• DOI: 10.2135/cropsci2015.10.0632
• Languages: English
• ISSNs: 0011-183X

Abstract

Abstract : Genotyping breeding materials is now relatively inexpensive but phenotyping costs have remained the same. One method to increase gene mapping power is to use genome‐wide genetic markers to combine existing phenotype data for multiple populations into a unified analysis. We combined data from 15 biparental populations of maize ( Zea mays L.) (>2500 individual lines) developed under the Water‐Efficient Maize for Africa project to perform genome‐wide association analysis. Each population was phenotyped in multilocation trials under water‐stressed and well‐watered environments and genotyped via genotyping‐by‐sequencing. We focused on flowering time and plant height and identified clear associations between known genomic regions and the traits of interest. Out of ∼380, 000 single‐nucleotide polymorphisms (SNPs), we found 115 and 108 that were robustly associated with flowering time under well‐watered and drought stress conditions, respectively, and 143 and 120 SNPs, respectively, associated with plant height. These SNPs explained 36 to 80% of the genetic variance, with higher accuracy under well‐watered conditions. The same set of SNPs had phenotypic prediction accuracies equivalent to genome‐wide SNPs and were significantly better than an equivalent number of random SNPs, indicating that they captured most of the genetic variation for these phenotypes. These methods could potentially aid breeding efforts for maize in Sub‐Saharan Africa and elsewhere. The methods will also help in mapping drought tolerance and related traits in this germplasm. We expect that analyses combining data across multiple populations will become more common and we call for the development of algorithms and software to enable routine analyses of this nature.