Genetic dissection of maize plant architecture using a novel nested association mapping population

Abstract

The leaf angle (LA), plant height (PH), and ear height (EH) are key plant architectural traits influencing maize (Zea mays L.) yield. However, their genetic determinants have not yet been well‐characterized. Here, we developed a maize advanced backcross‐nested association mapping population in Henan Agricultural University (HNAU‐NAM1) comprised of 1,625 BC1F4/BC2F4 lines. These were obtained by crossing a diverse set of 12 representative inbred lines with the common GEMS41 line, which were then genotyped using the MaizeSNP9.4K array. Genetic diversity and phenotypic distribution analyses showed considerable levels of genetic variation. We obtained 18–88 quantitative trait loci (QTLs) associated with LA, PH, and EH by using three complementary mapping methods, named as separate linkage mapping, joint linkage mapping, and genome‐wide association studies. Our analyses enabled the identification of ten QTL hot‐spot regions associated with the three traits, which were distributed on nine different chromosomes. We further selected 13 major QTLs that were simultaneously detected by three methods and deduced the candidate genes, of which eight were not reported before. The newly constructed HNAU‐NAM1 population in this study will further broaden our insights into understanding of genetic regulation of plant architecture, thus will help to improve maize yield and provide an invaluable resource for maize functional genomics and breeding research.

Genome-Wide Association Study and Genomic Prediction on Plant Architecture Traits in Sweet Corn and Waxy Corn

“…These QTL/QTNs contain 24 known genes cloned from mutants. In the genetic research of maize traits, scholars generally believe that PH is jointly controlled by major genes and minor polygenes, and the genetic basis is relatively complex, which is a typical quantitative trait inheritance [ 17 ]. Therefore, studying maize plant architecture-related traits can not only effectively improve the spatial distribution of maize plants and promote maize growth, but also support for breeding ideal plant architecture and molecular marker-assisted selection (MAS).…”

Identification of two new QTLs of maize (Zea mays L.) underlying kernel row number using the HNAU-NAM1 population

“…The maize NAM population, named HNAU-NAM1, contained 1,617 RILs that were derived from crosses of the common parent GEMS41 with each of 12 diverse inbred lines: CIMBL29, CIMBL83, CML304, CML360, CML454, CML470, CML486, CML496, DAN598, K22, P178 and TY1. The method of population construction was described in a previous study [ 36 ]. The 12 subpopulations were named Subpop CIMBL29, Subpop CIMBL83, Subpop CML304, Subpop CML360, Subpop CML454, Subpop CML470, Subpop CML486, Subpop CML496, Subpop DAN598, Subpop K22, Subpop P178, and Subpop TY1.…”

“…Next, the QTL supporting regions identified in the JLM or SLM methods were obtained based on the physical coordinates of flanking markers. The mapping results from the GWAS combined the 500 kb upstream and downstream regions of the significant SNPs as the QTL supporting regions, as described by Zhao et al [ 36 ]. Then, all the genes in the consensus QTL support interval were annotated according to the B73 reference genome v4 [ 69 ].…”

“…Through these mapping approaches, a large number of QTLs for complex traits such as flowering time, biological stress resistance and kernel composition have been identified in maize [33][34][35]. The NAM population in this study was developed in a former work and reflected a high QTL identification power in the maize plant architecture [36].…”

Statistical models and tools for QTL mapping in multi-parent populations

“…To the set of tools shown in Table 1, we add in Chapter 3 the R package statgenMPP that integrates IBD calculation for genetic predictors by a Hidden Markov Model with mixed model approaches for QTL mapping for a wide range of MPP designs. (Verbyla et al 2014a, b) IBD MAGIC (Verbyla et al 2014a, b) WGNAM (Paccapelo et al 2022) IBD NAM (Paccapelo et al 2022) GAPIT (Lipka et al 2012) IBS MAGIC (Islam et al 2016;Abdelraheem et al 2021); NAM (Altendorf et al 2021;Sandhu et al 2021) HAPPY (Mott et al 2000) IBD MAGIC (Kover et al 2009) mpMap (Huang and George 2011) IBD MAGIC (Rollar et al 2021b;Burgos et al 2021) GAPL (Zhang et al 2019) IBD MAGIC (Diaz et al 2021) IciMapping (Meng et al 2015) IBS NAM (Zhao et al 2022;Hu et al 2022) FarmCPU (Liu et al 2016) IBD MAGIC (Satturu et al 2020;Hautsalo et al 2021) BLINK (Huang et al 2019) IBS MAGIC (Hautsalo et al 2021…”