Candidate Loci for Yield-Related Traits in Maize Revealed by a Combination of MetaQTL Analysis and Regional Association Mapping

Abstract

Maize grain yield and related traits are complex and are controlled by a large number of genes of small effect or quantitative trait loci (QTL). Over the years, a large number of yield-related QTLs have been identified in maize and deposited in public databases. However, integrating and re-analyzing these data and mining candidate loci for yield-related traits has become a major issue in maize. In this study, we collected information on QTLs conferring maize yield-related traits from 33 published studies. Then, 999 of these QTLs were iteratively projected and subjected to meta-analysis to obtain metaQTLs (MQTLs). A total of 76 MQTLs were found across the maize genome. Based on a comparative genomics strategy, several maize orthologs of rice yield-related genes were identified in these MQTL regions. Furthermore, three potential candidate genes (Gene ID: GRMZM2G359974, GRMZM2G301884, and GRMZM2G083894) associated with kernel size and weight within three MQTL regions were identified using regional association mapping, based on the results of the meta-analysis. This strategy, combining MQTL analysis and regional association mapping, is helpful for functional marker development and rapid identification of candidate genes or loci.

Maize Combined Insect Resistance Genomic Regions and Their Co-localization With Cell Wall Constituents Revealed by Tissue-Specific QTL Meta-Analyses

“…Furthermore, for MAS to be more efficient than phenotypic selection, several requirements are bound to the used QTL. These criteria pertain to the precision of the positions and the genotypic effects of the QTL, and the QTL explaining a sufficient portion of the genotypic variance, yet most of the QTL detected fall short of these prerequisites (Utz et al, 2000 ; Chen et al, 2017 ). Besides, some QTL go undetected due to their small size in the populations under consideration (Bohn et al, 1997 ).…”

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

“…Of these, four QTL clusters were specific for high‐density treatment and 16 genomic regions contained overlapping QTLs for different traits under low and high planting densities. Ten QTL clusters (QC1‐2, QC2‐1, QC3‐5, QC6‐2, QC6‐3, QC7‐2, QC7‐5, QC9‐1, QC9‐3 and QC10‐1) overlapped with meta‐QTLs presented by Chen et al () (Table ), and these meta‐QTLs were associated with grain yield, ear‐related traits and/or kernel‐related traits. Two QTLs at bin 5.04 shared the genomic region (QC5) for EL and KNPR across both densities, and this locus also overlapped with a pleiotropic region for EL and CWPE across low and high densities, RN under low density and KNPR under high density according to Guo et al ().…”

“…The cluster QC3‐1 for KW and RN under high planting density and QJRN3 for RN in the F 2:3 families derived from the same cross were mapped in the same position (Liu et al, ), and this cluster also co‐located with the genomic region that affected CWPE and ED under low‐ and high‐density conditions (Guo et al, ). QC9‐1 had an effect on CWPE (LD), CD (LD, HD) and ED (HD), co‐mapped with QJCD9 for CD in the F 2:3 families from the same parents (Liu et al, ) and MQTL‐63 for grain yield, ear‐related traits and kernel‐related traits reported by Chen et al (). Moreover, QC9‐2 for ED and RN under low density was found in the F 2:3 generation from the same cross (Liu et al, ).…”

The retromer protein ZmVPS29 regulates maize kernel morphology likely through an auxin‐dependent process(es)

“…Numerous studies have been devoted to the dissection of the genetic bases regulating maize kernel size and shape. Quantitative trait loci (QTLs) analyses have mapped hundreds of QTLs for kernel‐related traits, including kernel length, kernel width, kernel thickness and hundred‐kernel weight (HKW), using various genetic populations (Chen et al ., , ; Jiang et al ., ; Li et al ., ; Liu et al ., ; Martinez et al ., ; Peng et al ., ; Tang et al ., ). A recent study identified nine QTLs related to KM using a triple testcross population (Jiang et al ., ).…”