Fine mapping of grain length QTLs on chromosomes 1 and 7 in Basmati rice (Oryza sativa L.)

Grain dimensions (length, breadth and length/breadth ratio) are important quality attributes of Basmati rice for its high consumer acceptance. Earlier we identified two significant quantitative trait loci (QTL) intervals on chromosomes 1 and 7 for grain dimensions in Basmati rice using a population of recombinant inbred lines (RILs) from cross between Basmati variety Pusa 1121 and a short grain non-aromatic variety Pusa 1342. For fine mapping of these QTLs, 184 F₆ RILs were grown and phenotyped in the normal rice growing season at two different locations. Forty-nine new SSR markers targeting these QTL intervals were tested and nine were found polymorphic between the parents. Using revised genetic maps adding new markers, the grain length QTL qGRL1.1 on chromosome 1 was narrowed down to 108?kbp from the earlier reported 6,133?kbp. There were total 13 predicted gene models in this interval which includes the probable candidate gene for the exceptionally high grain length of Basmati variety Pusa 1121. Similarly, two tandem QTL intervals qGRL7.1 and qGRL7.2 on chromosome 7 were merged into a single one narrowed down to 2,390?kbp from the earlier reported length of 5,269?kbp. This region of chromosome 7 also has co-localized QTLs for grain breadth and length to breadth ratio. SSR markers tightly linked to the QTL at a map distance of ??0.2?cM were developed for the qGRL1.1 and qGRL7.1 loci that could be used for marker-assisted breeding. Validation of earlier published markers for the grain length gene GS3 on chromosome 3 showed no difference between Pusa 1121 and Pusa 1342, highlighting the significance of qGRL1.1 and qGRL7.1 for the extra grain length of Basmati variety Pusa 1121.     

Mapping of quantitative trait loci for basmati quality traits in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

Traditional basmati rice varieties are very low yielding due to their poor harvest index, tendency to lodging and increasing susceptibility to foliar diseases; hence there is a need to develop new varieties combining the grain quality attributes of basmati with high yield potential to fill the demand gap. Genetic control of basmati grain and cooking quality traits is quite complex, but breeding work can be greatly facilitated by use of molecular markers tightly linked to these traits. A set of 209 recombinant inbred lines (RILs) developed from a cross between basmati quality variety Pusa 1121 and a contrasting quality breeding line Pusa 1342, were used to map the quantitative trait loci (QTLs) for seven important quality traits namely grain length (GL), grain breadth (GB), grain length to breadth ratio (LBR), cooked kernel elongation ratio (ELR), amylose content (AC), alkali spreading value (ASV) and aroma. A framework molecular linkage map was constructed using 110 polymorphic simple sequence repeat (SSR) markers distributed over the 12 rice chromosomes. A number of QTLs, including three for GL, two for GB, two for LBR, three for aroma and one each for ELR, AC and ASV were mapped on seven different chromosomes. While location of majority of these QTLs was consistent with the previous reports, one QTL for GL on chromosomes 1, and one QTL each for ELR and aroma on chromosomes 11 and 3, respectively, are being reported here for the first time. Contrary to the earlier reports of monogenic recessive inheritance, the aroma in Pusa 1121 is controlled by at least three genes located on chromosomes 3, 4 and 8, and similar to the reported association of badh2 gene with aroma QTL on chromosome 8, we identified location of badh1 gene in the aroma QTL interval on chromosome 4. A discontinuous 5 + 3 bp deletion in the seventh exon of badh2 gene, though present in all the RILs with high aroma, was not sufficient to impart this trait to the rice grains as many of the RILs possessing this deletion showed only mild or no aroma expression. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Identification and analysis of QTLs controlling cold tolerance at the reproductive stage and validation of effective QTLs in cold-tolerant genotypes of rice (Oryza sativa L.)

Low temperature or cold stress is one of the major constraints of rice production and productivity in temperate rice-growing countries and high-altitude areas in the tropics. Even though low temperature affects the rice plant in all stages of growth, the percent seed set is damaged severely by cold and this reduces the yield potential of cultivars significantly. In this study, a new source of cold-tolerant line, IR66160-121-4-4-2, was used as a donor parent with a cold-sensitive cultivar, Geumobyeo, to produce 153 F₈ recombinant inbred lines (RILs) for quantitative trait locus (QTL) analysis. QTL analysis with 175 polymorphic simple sequence repeat (SSR) markers and composite interval mapping identified three main-effect QTLs (qPSST-3, qPSST-7, and qPSST-9) on chromosomes 3, 7, and 9. The SSR markers RM569, RM1377, and RM24545 were linked to the identified QTLs for cold tolerance with respect to percent seed set using cold-water (18–19°C) irrigation in the field and controlled air temperature (17°C) in the greenhouse. The total phenotypic variation for cold tolerance contributed by the three QTLs was 27.4%. RILs with high percent seed set under cold stress were validated with linked DNA markers and by haplotype analysis that revealed the contribution of progenitor genomes from the tropical japonica cultivar Jimbrug (Javanica) and temperate japonica cultivar Shen-Nung89-366. Three QTLs contributed by the cold-tolerant parent were identified which showed additive effect on percent seed set under cold treatment. This study demonstrated the utility of a new phenotyping method as well as the identification of SSR markers associated with QTLs for selection of cold-tolerant genotypes to improve temperate rice production.     

Quantitative trait loci analysis of fiber quality traits using a random-mated recombinant inbred population in Upland cotton (Gossypium hirsutum L.)

Background

Upland cotton (Gossypium hirsutum L.) accounts for about 95% of world cotton production. Improving Upland cotton cultivars has been the focus of world-wide cotton breeding programs. Negative correlation between yield and fiber quality is an obstacle for cotton improvement. Random-mating provides a potential methodology to break this correlation. The suite of fiber quality traits that affect the yarn quality includes the length, strength, maturity, fineness, elongation, uniformity and color. Identification of stable fiber quantitative trait loci (QTL) in Upland cotton is essential in order to improve cotton cultivars with superior quality using marker-assisted selection (MAS) strategy.

Results

Using 11 diverse Upland cotton cultivars as parents, a random-mated recombinant inbred (RI) population consisting of 550 RI lines was developed after 6 cycles of random-mating and 6 generations of self-pollination. The 550 RILs were planted in triplicates for two years in Mississippi State, MS, USA to obtain fiber quality data. After screening 15538 simple sequence repeat (SSR) markers, 2132 were polymorphic among the 11 parents. One thousand five hundred eighty-two markers covering 83% of cotton genome were used to genotype 275 RILs (Set 1). The marker-trait associations were analyzed using the software program TASSEL. At p < 0.01, 131 fiber QTLs and 37 QTL clusters were identified. These QTLs were responsible for the combined phenotypic variance ranging from 62.3% for short fiber content to 82.8% for elongation. The other 275 RILs (Set 2) were analyzed using a subset of 270 SSR markers, and the QTLs were confirmed. Two major QTL clusters were observed on chromosomes 7 and 16. Comparison of these 131 QTLs with the previously published QTLs indicated that 77 were identified before, and 54 appeared novel.

Conclusions

The 11 parents used in this study represent a diverse genetic pool of the US cultivated cotton, and 10 of them were elite commercial cultivars. The fiber QTLs, especially QTL clusters reported herein can be readily implemented in a cotton breeding program to improve fiber quality via MAS strategy. The consensus QTL regions warrant further investigation to better understand the genetics and molecular mechanisms underlying fiber development.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-397) contains supplementary material, which is available to authorized users.     

Exploring the Genetic Characteristics of Two Recombinant Inbred Line Populations via High-Density SNP Markers in Maize

Understanding genetic characteristics can reveal the genetic diversity in maize and be used to explore evolutionary mechanisms and gene cloning. A high-density linkage map was constructed to determine recombination rates (RRs), segregation distortion regions (SDRs), and recombinant blocks (RBs) in two recombinant inbred line populations (RILs) (B73/By804 and Zong3/87-1) generated by the single seed descent method. Population B73/By804 containing 174 lines were genotyped with 198 simple sequence repeats (SSRs) markers while population Zong3/87-1 comprised of 175 lines, were genotyped with 210 SSR markers along with 1536 single nucleotide polymorphism (SNP) markers for each population, spanning 1526.7 cM and 1996.2 cM in the B73/By804 and Zong3/87-1 populations, respectively. The total variance of the RR in the whole genome was nearly 100 fold, and the maximum average was 10.43–11.50 cM/Mb while the minimum was 0.08–0.10 cM/Mb in the two populations. The average number of RB was 44 and 37 in the Zong3/87-1 and B73/By804 populations, respectively, whereas 28 SDRs were observed in both populations. We investigated 11 traits in Zong3/87-1 and 10 traits in B73/By804. Quantitative trait locus (QTLs) mapping of SNP+SSR with SNP and SSR marker sets were compared to showed the impact of different density markers on QTL mapping and resolution. The confidence interval of QTL Pa19 (FatB gene controlling palmitic acid content) was reduced from 3.5 Mb to 1.72 Mb, and the QTL Oil6 (DGAT1-2 gene controlling oil concentration) was significantly reduced from 10.8 Mb to 1.62 Mb. Thus, the use of high-density markers considerably improved QTL mapping resolution. The genetic information resulting from this study will support forthcoming efforts to understand recombination events, SDRs, and variations among different germplasm. Furthermore, this study will facilitate gene cloning and understanding of the fundamental sources of total variation and RR in maize, which is the most widely cultivated cereal crop.     

Identification of QTL for maize grain yield and kernel-related traits

Grain yield (GY) is one of the most important and complex quantitative traits in maize (Zea mays L.) breeding practice. Quantitative trait loci (QTLs) for GY and three kernel-related traits were detected in a set of recombinant inbred lines (RILs). One hundred and seven simple sequence repeats (SSRs) and 168 insertion/deletion polymorphism markers (Indels) were used to genotype RILs. Eight QTLs were found to be associated with four yield-related traits: GY, 100-kernel weight (HKW), 10-kernel length (KL), and 10-kernel length width (KW). Each QTL explained between 5.96 (qKL2-1) and 13.05 (qKL1-1) per cent of the phenotypic variance. Notably, one common QTL, located at the marker interval between bnlg1893 and chr2-236477 (chromosomal bin 2.09) simultaneously controlled GY and HKW; another common QTL, at bin 2.03 was simultaneously responsible for HKW and KW. Of the QTLs identified, only one pair of significant epistatic interaction involved in chromosomal region at bin 2.03 was detected for HKW; no significant QTL × environment interactions were observed. These results provide the common QTLs and for marker-assisted breeding.     

A haplotype inference algorithm for trios based on deterministic sampling

Background

The three-dimensional shape of grain, measured as grain length, width, and thickness (GL, GW, and GT), is one of the most important components of grain appearance in rice. Determining the genetic basis of variations in grain shape could facilitate efficient improvements in grain appearance. In this study, an F_7:8 recombinant inbred line population (RIL) derived from a cross between indica and japonica cultivars (Nanyangzhan and Chuan7) contrasting in grain size was used for quantitative trait locus (QTL) mapping. A genetic linkage map was constructed with 164 simple sequence repeat (SSR) markers. The major aim of this study was to detect a QTL for grain shape and to fine map a minor QTL, qGL7.

Results

Four QTLs for GL were detected on chromosomes 3 and 7, and 10 QTLs for GW and 9 QTLs for GT were identified on chromosomes 2, 3, 5, 7, 9 and 10, respectively. A total of 28 QTLs were identified, of which several are reported for the first time; four major QTLs and six minor QTLs for grain shape were also commonly detected in both years. The minor QTL, qGL7, exhibited pleiotropic effects on GL, GW, GT, 1000-grain weight (TGW), and spikelets per panicle (SPP) and was further validated in a near isogenic F₂ population (NIL-F₂). Finally, qGL7 was narrowed down to an interval between InDel marker RID711 and SSR marker RM6389, covering a 258-kb region in the Nipponbare genome, and cosegregated with InDel markers RID710 and RID76.

Conclusion

Materials with very different phenotypes were used to develop mapping populations to detect QTLs because of their complex genetic background. Progeny tests proved that the minor QTL, qGL7, could display a single mendelian characteristic. Therefore, we suggested that minor QTLs for traits with high heritability could be isolated using a map-based cloning strategy in a large NIL-F₂ population. In addition, combinations of different QTLs produced diverse grain shapes, which provide the ability to breed more varieties of rice to satisfy consumer preferences.     

Mapping and validation of simple sequence repeat markers linked to a major gene controlling seed cadmium accumulation in soybean [Glycine max (L.) Merr]

Daily consumption of cadmium (Cd) contaminated foods poses a risk to human health. Cultivar selection is an important method to limit Cd uptake and accumulation, however, analyzing grain Cd concentration is costly and time-consuming. Developing markers for low Cd accumulation will facilitate marker assisted selection (MAS). Inheritance studies using a threshold value of 0.2 mg kg^?1 for low and high and an F_2:3 population showed that low Cd accumulation in soybean seed is under the control of a major gene (Cda1, proposed name) with the allele for low accumulation being dominant. A recombinant inbred line (RIL) population (F_6:8) derived from the cross AC Hime (high Cd accumulation) and Westag-97 (low Cd accumulation) was used to identify the DNA markers linked to Cda gene(s) or quantitative trait loci (QTLs) controlling low Cd accumulation. We screened 171 simple sequence repeat (SSR) primers that showed polymorphism between parents on the 166 RILs. Of these, 40 primers were newly developed from the soybean genomic DNA sequence. Seven SSR markers, SatK138, SatK139, SatK140 (0.5 cM), SatK147, SacK149, SaatK150 and SattK152 (0.3 cM), were linked to Cda1 in soybean seed. All the linked markers were mapped to the same linkage group (LG) K. The closest flanking SSR markers linked to Cda1 were validated using a parallel population (RILs) involving Leo × Westag-97. Linked markers were also validated with diverse soybean genotypes differing in their seed Cd concentration and showed that SSR markers SatK147, SacK149, and SattK152 clearly differentiated the high and low Cd accumulating genotypes tested. To treat Cd uptake as a quantitative trait, QTL analysis using a linkage map constructed with 161 markers identified a major QTL associated with low Cd concentration in the seeds. The QTL was also mapped to the same location as Cda1 on LG-K. This QTL accounted for 57.3% of the phenotypic variation. Potential candidate genes (genes with known or predicted function that could influence the seed Cd concentration) like protein kinase, putative Adagio-like protein, and plasma membrane H⁺-ATPase were found to be located in the locus of interest. Of the four SSR markers located in the region, SattK152 was localized in the plasma membrane H⁺-ATPase gene. SSR markers closely linked to Cda1 in seeds of soybean were identified and have potential to be used for MAS to develop low Cd accumulating cultivars in a breeding program.     

Enrichment of a common wheat genetic map and QTL mapping for fatty acid content in grain

DArT and SSR markers were used to saturate and improve a previous genetic map of RILs derived from the cross Chuan35050 × Shannong483. The new map comprised 719 loci, 561 of which were located on specific chromosomes, giving a total map length of 4008.4 cM; the rest 158 loci were mapped to the most likely intervals. The average chromosome length was 190.9 cM and the marker density was 7.15 cM per marker interval. Among the 719 loci, the majority of marker loci were DArTs (361); the rest included 170 SSRs, 100 EST-SSRs, and 88 other molecular and biochemical loci. QTL mapping for fatty acid content in wheat grain was conducted in this study. Forty QTLs were detected in different environments, with single QTL explaining 3.6-58.1% of the phenotypic variations. These QTLs were distributed on 16 chromosomes. Twenty-two QTLs showed positive additive effects, with Chuan35050 increasing the QTL effects, whereas 18 QTLs were negative with increasing effects from Shannong483. Six sets of co-located QTLs for different traits occurred on chromosomes 1B, 1D, 2D, 5D, and 6B.     

Quantitative trait loci for aluminum resistance in wheat

Quantitative trait loci (QTL) for wheat resistance to aluminum (Al) toxicity were analyzed using simple sequence repeats (SSRs) in a population of 192 F₆ recombinant inbred lines (RILs) derived from a cross between an Al-resistant cultivar, Atlas 66 and an Al-sensitive cultivar, Chisholm. Wheat reaction to Al was measured by relative root growth and root response to hematoxylin stain in nutrient-solution culture. After screening 1,028 SSR markers for polymorphisms between the parents and bulks, we identified two QTLs for Al resistance in Atlas 66. One major QTL was mapped on chromosome 4D that co-segregated with the Al-activated malate transporter gene (ALMT1). Another minor QTL was located on chromosome 3BL. Together, these two QTLs accounted for about 57% of the phenotypic variation in hematoxylin staining score and 50% of the variation in net root growth (NRG). Expression of the minor QTL on 3BL was suppressed by the major QTL on 4DL. The two QTLs for Al resistance in Atlas 66 were also verified in an additional RIL population derived from Atlas 66/Century. Several SSR markers closely linked to the QTLs were identified and have potential to be used for marker-assisted selection (MAS) to improve Al-resistance of wheat cultivars in breeding programs.     

Genetic analysis of shoot fresh weight in a cross of wild (<Emphasis Type="Italic">G. soja</Emphasis>) and cultivated (<Emphasis Type="Italic">G. max</Emphasis>) soybean

Shoot fresh weight (SFW) is one of the parameters, used to estimate the total plant biomass yield in soybean. In the present study, a total of 188 F_5:8 recombinant inbred lines (RIL) derived from an interspecific cross of PI 483463 (Glycine soja) and Hutcheson (Glycine max) were investigated for SFW variation in the field for three consecutive years. The parental lines and RILs were phenotyped in the field at the R6 stage by measuring total biomass in kg/plot to identify the QTLs for SFW. Three QTLs qSFW6_1, qSFW15_1, and qSFW19_1 influencing SFW were identified on chromosome 6, 15, and 19, respectively. The QTL qSFW19_1 flanked between the markers BARC-044913-08839 and BARC-029975-06765 was the stable QTL expressed in all the three environments. The phenotypic variation explained by the QTLs across all environments ranged from 6.56 to 21.32 %. The additive effects indicated contribution of alleles from both the parents and additive × environment interaction effects affected the expression of SFW QTL. Screening of the RIL population with additional SSRs from the qSFW19_1 region delimited the QTL between the markers SSR19-1329 and BARC-29975-06765. QTL mapping using bin map detected two QTLs, qSFW19_1A and qSFW19_1B. The QTL qSFW19_1A mapped close to the Dt1 gene locus, which affects stem termination, plant height, and floral initiation in soybean. Potential candidate genes for SFW were pinpointed, and sequence variations within their sequences were detected using high-quality whole-genome resequencing data. The findings in this study could be useful for understanding genetic basis of SFW in soybean.     

Genetic analysis of Verticillium wilt resistance in a backcross inbred line population and a meta-analysis of quantitative trait loci for disease resistance in cotton

Background

Verticillium wilt (VW) and Fusarium wilt (FW), caused by the soil-borne fungi Verticillium dahliae and Fusarium oxysporum f. sp. vasinfectum, respectively, are two most destructive diseases in cotton production worldwide. Root-knot nematodes (Meloidogyne incognita, RKN) and reniform nematodes (Rotylenchulus reniformis, RN) cause the highest yield loss in the U.S. Planting disease resistant cultivars is the most cost effective control method. Numerous studies have reported mapping of quantitative trait loci (QTLs) for disease resistance in cotton; however, very few reliable QTLs were identified for use in genomic research and breeding.

Results

This study first performed a 4-year replicated test of a backcross inbred line (BIL) population for VW resistance, and 10 resistance QTLs were mapped based on a 2895 cM linkage map with 392 SSR markers. The 10 VW QTLs were then placed to a consensus linkage map with other 182 VW QTLs, 75 RKN QTLs, 27 FW QTLs, and 7 RN QTLs reported from 32 publications. A meta-analysis of QTLs identified 28 QTL clusters including 13, 8 and 3 QTL hotspots for resistance to VW, RKN and FW, respectively. The number of QTLs and QTL clusters on chromosomes especially in the A-subgenome was significantly correlated with the number of nucleotide-binding site (NBS) genes, and the distribution of QTLs between homeologous A- and D- subgenome chromosomes was also significantly correlated.

Conclusions

Ten VW resistance QTL identified in a 4-year replicated study have added useful information to the understanding of the genetic basis of VW resistance in cotton. Twenty-eight disease resistance QTL clusters and 24 hotspots identified from a total of 306 QTLs and linked SSR markers provide important information for marker-assisted selection and high resolution mapping of resistance QTLs and genes. The non-overlapping of most resistance QTL hotspots for different diseases indicates that their resistances are controlled by different genes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1682-2) contains supplementary material, which is available to authorized users.     

Locating QTLs controlling overwintering seedling rate in perennial glutinous rice 89-1 (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

A new cold tolerant germplasm resource named glutinous rice 89-1 (Gr89-1, Oryza sativa L.) can overwinter using axillary buds, with these buds being ratooned the following year. The overwintering seedling rate (OSR) is an important factor for evaluating cold tolerance. Many quantitative trait loci (QTLs) controlling cold tolerance at different growth stages in rice have been identified, with some of these QTLs being successfully cloned. However, no QTLs conferring to the OSR trait have been located in the perennial O. sativa L. To identify QTLs associated with OSR and to evaluate cold tolerance. 286 F₁₂ recombinant inbred lines (RILs) derived from a cross between the cold tolerant variety Gr89-1 and cold sensitive variety Shuhui527 (SH527) were used. A total of 198 polymorphic simple sequence repeat (SSR) markers that were distributed uniformly on 12 chromosomes were used to construct the linkage map. The gene ontology (GO) annotation of the major QTL was performed through the rice genome annotation project system. Three main-effect QTLs (qOSR2, qOSR3, and qOSR8) were detected and mapped on chromosomes 2, 3, and 8, respectively. These QTLs were located in the interval of RM14208 (35,160,202 base pairs (bp))–RM208 (35,520,147 bp), RM218 (8,375,236 bp)–RM232 (9,755,778 bp), and RM5891 (24,626,930 bp)–RM23608 (25,355,519 bp), and explained 19.6%, 9.3%, and 11.8% of the phenotypic variations, respectively. The qOSR2 QTL displayed the largest effect, with a logarithm of odds score (LOD) of 5.5. A total of 47 candidate genes on the qOSR2 locus were associated with 219 GO terms. Among these candidate genes, 11 were related to cell membrane, 7 were associated with cold stress, and 3 were involved in response to stress and biotic stimulus. OsPIP1;3 was the only one candidate gene related to stress, biotic stimulus, cold stress, and encoding a cell membrane protein. After QTL mapping, a total of three main-effect QTLs—qOSR2, qOSR3, and qOSR8—were detected on chromosomes 2, 3, and 8, respectively. Among these, qOSR2 explained the highest phenotypic variance. All the QTLs elite traits come from the cold resistance parent Gr89-1. OsPIP1;3 might be a candidate gene of qOSR2.     

A Genome Scan for Quantitative Trait Loci Associated with Vibrio anguillarum Infection Resistance in Japanese Flounder (Paralichthys olivaceus) by Bulked Segregant Analysis

A recent genetic linkage map was employed to detect quantitative trait loci (QTLs) associated with Vibrio anguillarum resistance in Japanese flounder. An F1 family established and challenged with V. anguillarum in 2009 was used for QTL mapping. Of the 221 simple sequence repeat (SSR) markers used to detect polymorphisms in the parents of F1, 170 were confirmed to be polymorphic. The average distance between the markers was 10.6 cM. Equal amounts of genomic DNA from 15 fry that died early and from 15 survivors were pooled separately to constitute susceptible bulk and resistance bulk DNA. Bulked segregant analysis and QTL mapping were combined to detect candidate SSR markers and regions associated with the disease. A genome scan identified four polymorphic SSR markers, two of which were significantly different between susceptible and resistance bulk (P?=?0.008). These two markers were located in linkage group (LG) 7; therefore, all the SSR markers in LG7 were genotyped in all the challenged fry by single marker analysis. Using two different models, 11–17 SSR markers were detected with different levels of significance. To confirm the associations of these markers with the disease, composite interval mapping was employed to genotype all the challenged individuals. One and three QTLs, which explained more than 60 % of the phenotypic variance, were detected by the two models. Two of the QTLs were located at 48.6 cM. The common QTL may therefore be a major candidate region for disease resistance against V. anguillarum infection.     

QTLs of cold tolerance-related traits at the booting stage for NIL-RILs in rice revealed by SSR

QTLs for cold tolerance-related traits at the booting stage using balanced population for 1525 recombinant inbred lines of near-isogenic lines (viz.NIL-RILs for BC5F3 and BC₅F₄ and BC₅F₅) over 3 years and two locations by backcrossing the strongly cold-tolerant landrace (Kunmingxiaobaigu) and a cold-sensitive cultivar (Towada) was analyzed. In this study, 676 microsatellite markers were employed to identify QTLs conferring cold tolerance at booting stage. Single marker analysis revealed that 12 markers associated with cold tolerance on chromosome 1, 4 and 5. Using a LOD significance threshold of 3.0,compositive interval mapping based on a mixed linear model revealed eight QTLs for 10 cold tolerance-related traits on chromosomes 1, 4, and 5. They were tentatively designatedqCTB-1-1, qCTB-4-1, qCTB-4-2, qCTB-4-3, qCTB-4-4, qCTB-4-5, qCTB-4-6, andqCTB-5-1. The marker intervals of them were narrowed to 0.3-6.8 cM. Genetic distances between the peaks of the QTL and nearest markers varied from 0 to 0.04 cM. We were noticed in some traits associated cold tolerance, such asqCTB-1-1 for 5 traits (plant height, panicle exsertion, spike length, blighted grains per spike and spikelet fertility),qCTB-4-1 for 8 traits (plant height, node length under spike, leaf length, leaf width, spike length, full grains per spike, total grains per spike and spikelet fertility),qCTB-4-2 for 3 traits (spike length, full grains per spike and spikelet fertility),qCTB-5-1 for 5 traits (plant height, panicle exsertion, blighted grains per spike, full grains per spike and spikelet fertility). The variance explained by a single QTL ranged from 0.80 to 16.80%. Three QTLs (qCTB-1-1, qCTB-4-1, qCTB-4-2) were detected in two or more trials. Our study sets a foundation for cloning cold-tolerance genes and provides opportunities to understand the mechanism of cold tolerance at the booting stage.     

Validation and high-resolution mapping of a major quantitative trait locus for alkaline salt tolerance in soybean using residual heterozygous line

Alkaline soil restricts soybean plant growth and yield. In our previous study, a major alkaline salt tolerance quantitative trait locus (QTL) was identified in soybean on chromosome 17. In this study, the residual heterozygous line (RHL46), which was selected from a population of F6 recombinant inbred lines (RILs) derived from a cross between an alkaline salt-sensitive soybean cultivar Jackson and a tolerant wild soybean accession JWS156-1, was used for validation and high-resolution mapping of the QTL. In a large segregating population (n = 1,109), which was produced by self-pollinating heterozygotes of RHL46, segregation of alkaline salt tolerance showed a continuous distribution, and the tolerant plants were predominant. Linkage mapping analysis revealed a major QTL with a large dominant effect for alkaline salt tolerance, and the highest LOD score was detected between the single sequence repeat (SSR) markers GM17-12.2 and Satt447. Furthermore, 10 fixed recombinant lines carrying chromosome fragments of different lengths in the QTL region were selected from the RHL46 progeny. Phenotype evaluation and SSR marker analysis of the recombinant lines narrowed down the QTL to a 3.33-cM interval region between the markers GM17-11.6 and Satt447 with a physical map length of approximately 771 kb. High-resolution mapping of the alkaline salt tolerance QTL will be useful not only for marker-assisted selection in soybean breeding programs but also for map-based cloning of the alkaline salt tolerance gene in order to understand alkaline salt tolerance in soybean and other plant species.     

High-Density Genetic Linkage Map Construction and QTL Mapping of Grain Shape and Size in the Wheat Population Yanda1817 × Beinong6

High-density genetic linkage maps are necessary for precisely mapping quantitative trait loci (QTLs) controlling grain shape and size in wheat. By applying the Infinium iSelect 9K SNP assay, we have constructed a high-density genetic linkage map with 269 F ₈ recombinant inbred lines (RILs) developed between a Chinese cornerstone wheat breeding parental line Yanda1817 and a high-yielding line Beinong6. The map contains 2431 SNPs and 128 SSR & EST-SSR markers in a total coverage of 3213.2 cM with an average interval of 1.26 cM per marker. Eighty-eight QTLs for thousand-grain weight (TGW), grain length (GL), grain width (GW) and grain thickness (GT) were detected in nine ecological environments (Beijing, Shijiazhuang and Kaifeng) during five years between 2010–2014 by inclusive composite interval mapping (ICIM) (LOD≥2.5). Among which, 17 QTLs for TGW were mapped on chromosomes 1A, 1B, 2A, 2B, 3A, 3B, 3D, 4A, 4D, 5A, 5B and 6B with phenotypic variations ranging from 2.62% to 12.08%. Four stable QTLs for TGW could be detected in five and seven environments, respectively. Thirty-two QTLs for GL were mapped on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6B, 7A and 7B, with phenotypic variations ranging from 2.62% to 44.39%. QGl.cau-2A.2 can be detected in all the environments with the largest phenotypic variations, indicating that it is a major and stable QTL. For GW, 12 QTLs were identified with phenotypic variations range from 3.69% to 12.30%. We found 27 QTLs for GT with phenotypic variations ranged from 2.55% to 36.42%. In particular, QTL QGt.cau-5A.1 with phenotypic variations of 6.82–23.59% was detected in all the nine environments. Moreover, pleiotropic effects were detected for several QTL loci responsible for grain shape and size that could serve as target regions for fine mapping and marker assisted selection in wheat breeding programs.     

QTL Analysis of Popping Fold and the Consistency of QTLs Under Two Environments in Popcorn

Popping fold (PF) is the most important quality trait in popcorn. In this study, a total of 259 F_2:3 families, derived from the cross between a dent corn inbred Dan232 and a popcorn inbred N04, were evaluated for their popping folds in replicated experiments under two environments. Of 613 simple sequence repeat (SSR) primer pairs screened, 183 pairs were selected to construct a genetic linkage map with the genetic distance of 1 762.2 cM (centimorgan) and on average 9.63 cM every marker. Quantative trait loci (QTL) were identified, and their genetic effects were estimated using CIM (composite interval mapping) method. The interactions among QTLs detected were calculated using MIM (multiple interval mapping) method. In all, 22 QTLs were detected, and only 5 of them were common under two environments. Contribution to phenotypic variation of a single QTL varied from 3.07% to 12.84%, and total contributions of all QTLs under two environments were 66.46% and 51.90%, respectively. Three QTLs (qPF-6-1, qPF-8-1 and qPF-1-3) with more than 10% contributions were observed. The additive effects were larger than dominant effects for most QTLs. The amount of QTLs showing additive, partially dominant, dominant and over-dominant effects were 4, 5, 0, 2 in spring sowing and 2, 5, 2, 2 in summer sowing, respectively. There were only 2.60% pairs of QTLs or maker intervals expressing AA, DA or DD interactions.     

Molecular mapping of four blast resistance genes using recombinant inbred lines of 93-11 and nipponbare

Molecular mapping of new blast resistance genes is important for developing resistant rice cultivars using marker-assisted selection. In this study, 259 recombinant inbred lines (RILs) were developed from a cross between Nipponbare and 93-11, and were used to construct a 1165.8-cM linkage map with 131 polymorphic simple sequence repeat (SSR) markers. Four major quantitative trait loci (QTLs) for resistance to six isolates of Magnaporthe oryzae were identified: qPi93-1, qPi93-2, qPi93-3, and qPiN-1. For the three genes identified in 93-11, qPi93-1 is linked with SSR marker RM116 on the short arm of chromosome 11 and explains 33% of the phenotypic variation in resistance to isolate CHE86. qPi93-2 is linked with SSR marker RM224 on the long arm of chromosome 11 and accounts for 31% and 25% of the phenotypic variation in resistance to isolates 162-8B and ARB50, respectively. qPi93-3 is linked with SSR marker RM7102 on chromosome 12 and explains 16%, 53%, and 28% of the phenotypic variation in resistance to isolates CHE86, ARB52, and ARB94, respectively. QTL qPiN-1 from Nipponbare is associated with SSR marker RM302 on chromosome 1 and accounts for 34% of the phenotypic variation in resistance to isolate PO6-6. These new genes can be used to develop new varieties with blast resistance via marker-aided selection and to explore the molecular mechanism of rice blast resistance.