Characterization of a major QTL and Genome-Wide Epistatic Interactions for the Transformation of Single Spikelet in Teosinte Ears into Paired Spikelets in Maize Ears During Maize Domestication

Maize ear carries paired spikelets, whereas the ear of its wild ancestor, teosinte, bears single spikelets. However, little is known about the genetic basis of the processes of transformation of single spikelets in teosinte ear to paired spikelets in maize ear. In this study, a two-ranked, paired-spikelets primitive maize and a two-ranked, single-spikelet teosinte were utilized to develop an F2 population, and QTL mapping for single vs. paired spikelets (PEDS) was performed. Two QTL (qPEDS1.1 and qPEDS3.1) for PEDS located on chromosomes 1L and 3S were identified in the 162 F2 plants using the inclusive composite interval mapping of additive (ICIM-ADD) module, explaining 1.93% and 23.79% of the phenotypic variance, respectively. Out of the 409 F2 plants, 43 plants with PEDS = 0% and 43 plants with PEDS > 20% were selected for selective genotyping; the QTL (qPEDS3.1) accounting for 64.01% of the phenotypic variance for PEDS was also detected. Moreover, the QTL (qPEDS3.1) was validated in three environments, which explained 31.05%, 38.94% and 23.16% of the phenotypic variance, respectively. In addition, 50 epistatic QTLs were detected in 162 F2 plants using the two-locus epistatic QTL (ICIM-EPI) module; they were distributed on all 10 chromosomes and explained 94.40% of the total phenotypic variance. The results contribute to a better understanding of the genetic basis of domestication of paired spikelets and provide a genetic resource for future map-based cloning; in addition, the systematic dissection of epistatic interactions underlies a theoretical framework for overcoming epistatic effects on QTL fine mapping.

Mapping of QTLs Detected in a Broccoli Double Diploid Population for Plant Height and Leaf Characteristics

Background The planting density of broccoli can directly affect the yield and overall health of plants. So there is necessary to reveal the regulatory genes of planting density in broccoli by QTL mapping. In this study, the important planting density-dependent factors of broccoli, plant height (PH), maximum outer petiole length (PL) and leaf width (LW), were investigated during 2017 and 2018. The mapping of QTLs for PH, PL and LW were performed, and the interaction between QTLs and the environment was also analyzed by a DH population constructed with 176 genotypes derived from F1 obtained by crossing the broccoli inbred lines 86101 (P1) and 90196 (P2).Results A linkage group including a total of 438 SSR markers were constructed covering a length of 1168.26 cM using QTL IciMapping 4.0 software. Finally, there were mainly four QTLs (phc1, phc2, phc4-1, phc4-2), one QTL (plc6), and two QTLs (lwc1, lwc3) corresponding for PH, PL and LW recurred during the two years. In three environments, inclusive composite interval mapping (ICIM) analysis showed that there was a major QTL for PH at 7.20 cM on chromosome 1 between molecular markers 8C024 and sf4482 with a high explanatory contribution rate of 20.05%. The QTL at the 11.10 cM position of chromosome 6 was located for the PL with a high explanatory contribution rate of 20.02% between the molecular markers sc2170 and sf43960. The QTL at the 147.00 cM position of chromosome 3 was located on LW with a high explanatory contribution rate of 19.97% between molecular markers of Sc52751 and RA2-E12.Conclusions According to the QTL results of planting density in broccoli by a DH population, the possible positions of candidate genes were screened to provide a basis for further locating and cloning genes for plant height, maximum outer petiole and leaf width.

Inclusive composite-interval mapping reveals quantitative trait loci for plant architectural traits in sorghum (Sorghum bicolor)

Architecture-efficient sorghum (Sorghum bicolor (L.) Moench) has erect leaves forming a compact canopy that enables highly effective utilisation of solar radiation; it is suitable for high-density planting, resulting in an elevated overall production. Development of sorghum ideotypes with optimal plant architecture requires knowledge of the genetic basis of plant architectural traits. The present study investigated seven production-related architectural traits by using 181 sorghum recombinant inbred lines (RILs) with contrasting architectural phenotypes developed from the cross Shihong 137 × L-Tian. Parents along with RILs were phenotyped for plant architectural traits for two consecutive years (2012, 2013) at two locations in the field. Analysis of variance revealed significant (P ≤ 0.05) differences among RILs for architectural traits. All traits showed medium to high broad-sense heritability estimates (0.43–0.94) and significant (P ≤ 0.05) genotype × environment effects. We employed 181 simple sequence repeat markers to identify quantitative trait loci (QTLs) and the effects of QTL × environment interaction based on the inclusive composite interval mapping algorithm. In total, 53 robust QTLs (log of odds ≥4.68) were detected for these seven traits and explained 2.11–12.11% of phenotypic variation. These QTLs had small effects of QTL × environment interaction and yet significant epistatic effects, indicating that they could stably express across environments but influence phenotypes through strong interaction with non-allelic loci. The QTLs and linked markers need to be verified through function and candidate-gene analyses. The new knowledge of the genetic regulation of architectural traits in the present study will provide a theoretical basis for the genetic improvement of architectural traits in sorghum.

Detection of QTLs for cold tolerance at the booting stage in near-isogenic lines derived from rice landrace Lijiangxintuanheigu

Chilling damage significantly reduces grain yield in rice, while exploring major quantitative trait loci (QTLs) has the potential to improve rice production. Mapping of QTLs for 5 cold tolerance-related traits at the booting stage was conducted with SSR markers and inclusive composite interval mapping (ICIM) approach, based on 105 near-isogenic lines derived from a backcross between Lijiangxintuanheigu (LTH, cold-tolerant landrace) and Towada (cold-sensitive cultivar). Phenotype values were investigated under five cold-stress environments and analysed by the best linear unbiased prediction (BLUP). Twenty-one QTLs were identified on chromosomes 1, 2, 3, 4, 6, 7, 10 and 11, and the amount of variation (R²) explained by each QTL ranged from 7.71 to 29.66%, with five co-located QTL regions. Eight novel major loci (qSF-2, qSF-6a, qSF-7, qGW-6, qDGWP-4, qDSWPP-4, qDWPP-1 and qDWPP-4b) were detected in several environments and BLUP, and their alleles were contributed by LTH with R²variance from 12.24 to 29.66%. These favourable QTLs would facilitate elucidation of the genetic mechanism of cold tolerance and provide strategies for breeding high-productive rice.

Inheritance Analysis and Quantitative Trait Loci Detection of Head Splitting Resistance in Cabbage (Brassica oleracea L. var. capitata)

Head splitting resistance (HSR) in cabbage is an important trait closely related to appearance, yield, storability, and mechanical harvestability. In this study, a doubled haploid (DH) population derived from a cross between head splitting-susceptible inbred cabbage line 79-156 and resistant line 96-100 was used to analyze inheritance and detect quantitative trait loci (QTLs) for HSR during 2011–12 in Beijing, China. The analysis was performed using a mixed major gene/polygene inheritance method and QTL mapping. This approach, which uncovered no cytoplasmic effect, indicated that HSR can be attributed to additive-epistatic effects of three major gene pairs combined with those of polygenes. Major gene and polygene heritabilities were estimated to be 88.03% to 88.22% and 5.65% to 7.60%, respectively. Using the DH population, a genetic map was constructed with simple sequence repeat (SSR) markers anchored on nine linkage groups spanning 906.62 cM. Eight QTLs for HSR were located on chromosomes C4, C5, C7, and C9 based on 2 years of phenotypic data using both multiple-QTL mapping and inclusive composite interval mapping. The identified QTLs collectively explained 37.6% to 46.7% of phenotypic variation. Three or four major QTLs (Hsr 4.2, 7.2, 9.3, and/or 9.1) showing a relatively larger effect were robustly detected in different years or with different mapping methods. The HSR trait was shown to have a complex genetic basis. Results from QTL mapping and classical genetic analysis were consistent. Our results provide a foundation for further research on HSR genetic regulation and molecular marker-assisted selection (MAS) for HSR in cabbage.

QTL mapping for plant height and yield components in common wheat under water-limited and full irrigation environments

Plant height (PH) and yield components are important traits for yield improvement in wheat breeding. In this study, 207 F2:4 recombinant inbred lines (RILs) derived from the cross Jingdong 8/Aikang 58 were investigated under limited and full irrigation environments at Beijing and Gaoyi, Hebei province, during the 2011–12 and 2012–13 cropping seasons. The RILs were genotyped with 149 polymorphic simple sequence repeat (SSR) markers, and quantitative trait loci (QTLs) for PH and yield components were analysed by inclusive composite interval mapping. All traits in the experiment showed significant genetic variation and interaction with environments. The range of broad-sense heritabilities of PH, 1000-kernel weight (TKW), number of kernels per spike (KNS), number of spikes per m2 (NS), and grain yield (GY) were 0.97–0.97, 0.87–0.89, 0.59–0.61, 0.58–0.68, and 0.23–0.48. The numbers of QTLs detected for PH, TKW, KNS, NS, and GY were 3, 10, 8, 7 and 9, respectively, across all eight environments. PH QTLs on chromosomes 4D and 6A, explaining 61.3–80.2% of the phenotypic variation, were stably expressed in all environments. QPH.caas-4D is assumed to be the Rht-D1b locus, whereas QPH.caas-6A is likely to be a newly discovered gene. The allele from Aikang 58 at QPH.caas-4D reduced PH by 11.5–18.2% and TKW by 2.6–3.8%; however, KNS increased (1.2–3.7%) as did NS (2.8–4.1%). The QPH.caas-6A allele from Aikang 58 reduced PH by 8.0–11.5% and TKW by 6.9–8.5%, whereas KNS increased by 1.2–3.6% and NS by 0.9–4.5%. Genotypes carrying both QPH.caas-4D and QPH.caas-6A alleles from Aikang 58 showed reduced PH by 28.6–30.6%, simultaneously reducing TKW (13.8–15.2%) and increasing KNS (3.4–4.9%) and NS (6.5–10%). QTKW.caas-4B and QTKW.caas-5B.1 were stably detected and significantly associated with either KNS or NS. Major KNS QTLs QKNS.caas-4B and QKNS.caas-5B.1 and the GY QTL QGY.caas-3B.2 were detected only in water-limited environments. The major TKW QTKW.caas-6D had no significant effect on either KNS or NS and it could have potential for improving yield.