Drought tolerance (DT) and drought avoidance (DA) are two major mechanisms in drought resistance of higher plants. In this study, the genetic bases of DT and DA at reproductive stage in rice were analyzed using a recombinant inbred line population from a cross between an indica lowland and a tropical japonica upland cultivar. The plants were grown individually in PVC pipes and two cycles of drought stress were applied to individual plants with unstressed plants as the control. A total of 21 traits measuring fitness, yield, and the root system were investigated. Little correlation of relative yield traits with potential yield, plant size, and root traits was detected, suggesting that DT and DA were well separated in the experiment. A genetic linkage map consisting of 245 SSR markers was constructed for mapping QTL for these traits. A total of 27 QTL were resolved for 7 traits of relative performance of fitness and yield, 36 QTL for 5 root traits under control, and 38 for 7 root traits under drought stress conditions, suggesting the complexity of the genetic bases of both DT and DA. Only a small portion of QTL for fitness- and yield-related traits overlapped with QTL for root traits, indicating that DT and DA had distinct genetic bases.

Key message Maize 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. Abstract Kernel 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 F_2:3 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.

Soil waterlogging is one of the major abiotic stresses adversely affecting maize growth and yield. To identify dynamic expression of genes or quantitative trait loci (QTL), QTL associated with plant height, root length, root dry weight, shoot dry weight and total dry weight were identified via conditional analysis in a mixed linear model and inclusive composite interval mapping method at three respective periods under waterlogging and control conditions. A total of 13, 19 and 23 QTL were detected at stages 3D|0D (the period during 0–3 d of waterlogging), 6D|3D and 9D|6D, respectively. The effects of each QTL were moderate and distributed over nine chromosomes, singly explaining 4.14–18.88% of the phenotypic variation. Six QTL (ph6-1, rl1-2, sdw4-1, sdw7-1, tdw4-1 and tdw7-1) were identified at two consistent stages of seedling development, which could reflect a continuous expression of genes; the remaining QTL were detected at only one stage. Thus, expression of most QTL was influenced by the developmental status. In order to provide additional evidence regarding the role of corresponding genes in waterlogging tolerance, mapping of Expressed Sequence Tags markers and microRNAs were conducted. Seven candidate genes were observed to co-localize with the identified QTL on chromosomes 1, 4, 6, 7 and 9, and may be important candidate genes for waterlogging tolerance. These results are a good starting point for understanding the genetic basis for selectively expressing of QTL in different stress periods and the common genetic control mechanism of the co-localized traits.