Genetic architecture of maize kernel row number and whole genome prediction

Abstract

Key messageMaize kernel row number might be dominated by a set of large additive or partially dominant loci and several small dominant loci and can be accurately predicted by fewer than 300 top KRN-associated SNPs.AbstractKernel row number (KRN) is an important yield component in maize and directly affects grain yield. In this study, we combined linkage and association mapping to uncover the genetic architecture of maize KRN and to evaluate the phenotypic predictability using these detected loci. A genome-wide association study revealed 31 associated single nucleotide polymorphisms (SNPs) representing 17 genomic loci with an effect in at least one of five individual environments and the best linear unbiased prediction (BLUP) over all environments. Linkage mapping in three F2:3 populations identified 33 KRN quantitative trait loci (QTLs) representing 21 QTLs common to several population/environments. The majority of these common QTLs that displayed a large effect were additive or partially dominant. We found 70 % KRN-associated genomic loci were mapped in KRN QTLs identified in this study, KRN-associated SNP hotspots detected in NAM population and/or previous identified KRN QTL hotspots. Furthermore, the KRN of inbred lines and hybrids could be predicted by the additive effect of the SNPs, which was estimated using inbred lines as a training set. The prediction accuracy using the top KRN-associated tag SNPs was obviously higher than that of the randomly selected SNPs, and approximately 300 top KRN-associated tag SNPs were sufficient for predicting the KRN of the inbred lines and hybrids. The results suggest that the KRN-associated loci and QTLs that were detected in this study show great potential for improving the KRN with genomic selection in maize breeding.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-015-2581-2) contains supplementary material, which is available to authorized users.

The CLAVATA receptor FASCIATED EAR2 responds to different CLE peptides by signaling through different downstream effectors

“…Natural variation in the CLV-WUS pathway underlies yield improvements in different crop species including tomato, maize and mustard (Bommert et al 2013b;Fan et al 2014;Xu et al 2015;Je et al 2016), and FEA2 is a quantitative trait locus (QTL) for kernel row number (KRN) (Bommert et al 2013b). In this study, we used a maize association panel of 368 diverse inbred lines to show that ZmCRN also has significant association with KRN under multiple environments Liu et al 2015), suggesting that ZmCRN contributes to quantitative variation in this trait. Therefore, ZmCRN could be manipulated for maize yield enhancement.…”

“…The maize crn (Zmcrn) mutants had larger vegetative to ask if it is also associated with this yield trait. We conducted a candidate gene association study using a maize association panel of 368 diverse inbred lines Liu et al 2015). We found that three SNPs in the 3'UTR region of CRN showed significant association with KRN in multiple environments, below the threshold P-value < 0.001 (Figure supplement 4 and Table supplement 1).…”

“…22 SNPs in the ZmCRN gene region were observed based on previously released genotypes in the association panel. This was combined with KRN phenotypic data from five environments and BLUP (Best Linear Unbiased Prediction) data, including in Ya'an (30°N, 103°E), Sanya (18°N, 109°E) and Kunming (25°N, 102°E) in 2009 and Wuhan (30°N, 114°E) and Kunming (25°N, 102°E) in 2010 (Liu et al 2015). The association between ZmCRN and KRN was established by a mixed linear model corrected by population structure, with P-value < 0.001 as threshold Li et al 2013).…”

Genomic Prediction using Existing Historical Data Contributing to Selection in Biparental Populations: A Study of Kernel Oil in Maize

“…In previous simulations or empirical studies of prediction, the GS model has consistently outperformed an MAS model as it captured the genetic variance of a trait over the entire genome rather than a few significant markers (Jiang et al, 2017; Liu et al, 2015). In a cross‐validation study, the prediction accuracy of GS in the association population was consistently higher than that of the MAS model.…”

Quantitative trait loci mapping for yield‐related traits under low and high planting densities in maize (Zea mays)

“…Numerous quantitative trait locus (QTL) studies for yield‐related traits in maize were conducted by linkage mapping (Martinez et al, ; Pan et al, ) and association mapping (Liu et al, ; Millet et al, ; Xiao et al, ). These studies used many different populations, including F 2:3 (Guo et al, ), BC 2 F 2 (Li, Zhang, Li, Wang, & Zhou, ), recombinant inbred lines (RILs; Liu et al, ; Pan et al, ), IF 2 (immortalized F 2 ; Tang et al, ), IAP (inbred association panel; Liu et al, ), MAGIC (multi‐parent advanced generation intercross population (Dell’Acqua et al, ) and ROAM (random‐open‐parent association mapping; Xiao et al, ) and reported numerous QTLs and significant single‐nucleotide polymorphisms (SNPs) associated with grain yield‐related traits. Earlier studies (Gonzalo, Holland, Vyn, & McIntyre, ; Gonzalo, Vyn, Holland, & McIntyre, ; Guo et al, ) to map QTLs for grain yield were evaluated under different planting densities, though little attention has been paid to QTLs for grain yield relative to density tolerance.…”