Composite Interval Mapping Analysis Research Articles

Genetic mapping and QTL analysis of multigenic resistance to powdery mildew (Podosphaera macularis) in hop (Humulus lupulus L.)

AbstractHop powdery mildew (PM) (Podosphaera macularis) causes substantial losses if left uncontrolled. Most resistant hop cultivars possess qualitative resistance based on R‐genes. One cultivar, Comet, has uncharacterized resistance that may be polygenic. This study focused on identifying genomic regions controlling PM resistance in Comet and ascertaining putative genetic mechanisms behind such resistance. A cross between Comet and susceptible male, USDA 64035M, was made. Offspring were screened for resistance under greenhouse conditions and genotyped using genotyping‐by‐sequencing. Genome‐wide analysis using mixed linear model analysis along with quantitative trait locus (QTL) analysis using either composite interval mapping or stepwise regression analyses was performed to identify QTLs. All analyses identified a region on chromosome 6 covering positions 308–314 Mb on the physical map. Analysis of the putative genes within this region identified 140 genes with 27 plant resistance‐like genes found in nine clusters. Six sulfur‐rich protein genes with homology to patatins, thionins, and agglutinins were identified in two clusters. Two glucan‐endo‐1,3‐beta‐glucosidase genes were identified bordering different R‐gene clusters. Finally, putative upregulators of transcription and stress‐response genes were identified. The 10 most highly associated single‐nucleotide polymorphisms for PM resistance were subsequently developed as KASP markers. The combination of R‐gene clusters, sulfur‐rich proteins, endo‐1,3‐beta‐glucosidase genes, and stress‐response genes may be responsible for resistance to PM in the cultivar Comet.

Quantitative Trait Mapping for Zebra-stem in Tomato Confirms a Genetic Cause Involving the Interaction of Unlinked Loci

Zebra-stem of tomato is a disorder characterized by leaf necrosis, wilting, and a stripped pattern on stems of mature plants. Wilting, necrosis, and death of seedlings are also observed. The physiological and genetic causes of zebra-stem are poorly characterized. Anecdotal evidence has suggested pedigrees with S. pimpinellifolium and bacterial speck resistance in the genetic background are often prone to this disorder. We demonstrate a genetic cause using composite interval mapping and association analysis approaches to define quantitative trait loci (QTLs) that contribute to the disorder. A biparental population of F4 partially inbred families was developed for the initial analysis, and four subsequent backcross or F2 populations were used for subsequent validation. Significant QTLs on chromosomes 5 and 10 were identified, explaining ∼60% and 40% of the variation, respectively. Polymorphisms in the Pto locus are strongly associated with the QTL on chromosome 5. The two loci were derived from different parentage, and a significant interaction effect was demonstrated, resulting in the characteristic zebra-stem symptoms when combined.

Novel quantitative trait loci from an interspecific Brassica rapa derivative improve pod shatter resistance in Brassica napus.

Pod shatter is a trait of agricultural relevance that ensures plants dehisce seeds in their native environment and has been subjected to domestication and selection for non-shattering types in several broadacre crops. However, pod shattering causes a significant yield reduction in canola (Brassica napus L.) crops. An interspecific breeding line BC95042 derived from a B. rapa/B. napus cross showed improved pod shatter resistance (up to 12-fold than a shatter-prone B. napus variety). To uncover the genetic basis and improve pod shatter resistance in new varieties, we analysed F2 and F2:3 derived populations from the cross between BC95042 and an advanced breeding line, BC95041, and genotyped with 15,498 DArTseq markers. Through genome scan, interval and inclusive composite interval mapping analyses, we identified seven quantitative trait loci (QTLs) associated with pod rupture energy, a measure for pod shatter resistance or pod strength, and they locate on A02, A03, A05, A09 and C01 chromosomes. Both parental lines contributed alleles for pod shatter resistance. We identified five pairs of significant epistatic QTLs for additive x additive, additive dominance and dominance x dominance interactions between A01/C01, A03/A07, A07/C03, A03/C03, and C01/C02 chromosomes for rupture energy. QTL effects on A03/A07 and A01/C01 were in the repulsion phase. Comparative mapping identified several candidate genes (AG, ABI3, ARF3, BP1, CEL6, FIL, FUL, GA2OX2, IND, LATE, LEUNIG, MAGL15, RPL, QRT2, RGA, SPT and TCP10) underlying main QTL and epistatic QTL interactions for pod shatter resistance. Three QTLs detected on A02, A03, and A09 were near the FUL (FRUITFULL) homologues BnaA03g39820D and BnaA09g05500D. Focusing on the FUL, we investigated putative motifs, sequence variants and the evolutionary rate of its homologues in 373 resequenced B. napus accessions of interest. BnaA09g05500D is subjected to purifying selection as it had a low Ka/Ks ratio compared to other FUL homologues in B. napus. This study provides a valuable resource for genetic improvement for yield through an understanding of the genetic mechanism controlling pod shatter resistance in Brassica species.

Mapping and identification of QTL for agro-physiological traits in rice (Oryza sativa L.) under drought stress

Exploring novel QTLs for drought tolerance traits using a new rice genetic resource would be valuable to dissect the mechanisms underlying the complexity of the trait. Here, we used a recombinant inbred line population to detect the QTL associated with agro-physiological traits under drought stress, in particular incorporating the SNPs and 147 phenotype data, analyzed the transcript expression of genes within QTL using differential gene expression meta-analysis and qRT-PCR technique. Composite interval mapping analysis allowed the detection of 154 QTLs distributed into 66 regions, which included a large QTL cluster called ‘hotspot QTL’ that strongly associated with drought tolerance on rice chromosome 8. We found several genes within the QTL-containing regions that were highly expressed based on the meta-analysis approach. In the future, the QTL reported here may be utilized for marker-assisted breeding and the candidate drought-responsive genes could be characterized for dissecting a comprehensive molecular mechanism of drought tolerance in rice.

Identification of Fruit Traits Related QTLs and a Candidate Gene, CaBRX, Controlling Locule Number in Pepper (Capsicum annuum L.)

Fruit traits are important in pepper (Capsicum annuum L.) and affect its quality and yield. These traits are controlled by quantitative trait loci (QTLs). In this study, we identified many major QTLs that control fruit length (Ftl), fruit diameter (Ftd), fruit shape (Fts), fruit weight (Ftw) and locule number (Lcn) in the F2 and F2:3 populations developed from the QTL mapping of GS6 (P1) and Qiemen (P2). A total of 111 simple sequence repeats and insertion/deletion markers were utilized to construct a linkage map with 12 linkage groups over a length of 1320.72 cM. An inclusive composite interval mapping analysis indicated that many QTLs were detected and included ftl2.1, ftd2.1, fts1.1, ftw2.1 and lcn1.1. As a novel QTL, lcn1.1 was located between HM1112 and EPMS709, and the genetic distance was 3.18 cM covering 60 predicted genes. Within the region, we identified Capana01g004285 as a candidate gene by functional annotation and expression analysis and found that it encodes the BREVIS RADIX (BRX) protein. Knockdown of CaBRX through the virus-induced gene silencing approach in GS6 reduced the number of locules and influenced the expressions of genes related to flower and locule development, suggesting that CaBRX plays an important function in the development of locules.

Mapping QTLs controlling the biosynthesis of maltose in soybean

The present study was carried out to identify genomic regions associated with maltose in 2 F2 populations through assessment of sugars using HPLC and genotyping using SSR markers across the genome. SSR markers, Sat_216 (chr 12) and Satt681 (chr 6) in F2 population I and Sat_105 (chr 20) in F2 population II showed significant (P< 0.5) association with maltose content through single marker analysis (SMA) with LOD score of 3.18 (R2 =9.7), 2.54 (R2 =6.8), and 3.54 (R2 =10.4), respectively. Composite interval mapping analysis (CIM) let to identify different QTLs (other than SMA) for maltose content on chr 11, chr 13 and chr 17 in F2 population I while chr 6 and chr15 in F2 population II. QTLs identified for maltose content are in proximity of known functional genes responsible for degradation of starch into maltose. QTLs identified for maltose in the study may be deployed for improving efficiency of marker assisted breeding for development of soybean genotypes with high levels of this sugar.

Quantitative trait loci and candidate genes associated with freezing tolerance of winter triticale (\xd7\u2009Triticosecale Wittmack)

Freezing tolerance of triticale is a major trait contributing to its winter hardiness. The identification of genomic regions — quantitative trait loci (QTL) and molecular markers associated with freezing tolerance in winter hexaploid triticale — was the aim of this study. For that purpose, a new genetic linkage map was developed for the population of 92 doubled haploid lines derived from ‘Hewo’ × ‘Magnat’ F1 hybrid. Those lines, together with parents were subjected to freezing tolerance test three times during two winter seasons. Plants were grown and cold-hardened under natural fall/winter conditions and then subjected to freezing in controlled conditions. Freezing tolerance was assessed as the plants recovery (REC), the electrolyte leakage (EL) from leaves and chlorophyll fluorescence parameters (JIP) after freezing. Three consistent QTL for several fluorescence parameters, electrolyte leakage, and the percentage of the survived plants were identified with composite interval mapping (CIM) and single marker analysis (SMA). The first locus Qfr.hm-7A.1 explained 9% of variation of both electrolyte leakage and plants recovery after freezing. Two QTL explaining up to 12% of variation in plants recovery and shared by selected chlorophyll fluorescence parameters were found on 4R and 5R chromosomes. Finally, main locus Qchl.hm-5A.1 was detected for chlorophyll fluorescence parameters that explained up to 19.6% of phenotypic variation. The co-located QTL on chromosomes 7A.1, 4R and 5R, clearly indicated physiological and genetic relationship of the plant survival after freezing with the ability to maintain optimal photochemical activity of the photosystem II and preservation of the cell membranes integrity. The genes located in silico within the identified QTL include those encoding BTR1-like protein, transmembrane helix proteins like potassium channel, and phosphoric ester hydrolase involved in response to osmotic stress as well as proteins involved in the regulation of the gene expression, chloroplast RNA processing, and pyrimidine salvage pathway. Additionally, our results confirm that the JIP test is a valuable tool to evaluate freezing tolerance of triticale under unstable winter environments.

Genetic analysis of end‐use quality traits in wheat

AbstractWheat (Triticum aestivum L.) is a major cereal food crop in the world. End‐use quality improvement is a major objective in wheat breeding programs. ‘Tiger’ is a hard white winter wheat cultivar with excellent bread baking qualities. The objective of this study was to identify the genetic loci underlying the quality traits in Tiger. A doubled‐haploid (DH) population derived from a cross between Tiger and ‘Danby’ was grown in two environments. A total of 17 quality traits together with grain yield (GY) and stripe rust (Puccinia striiformis f. sp. Tritici) were evaluated. The population was genotyped using single nucleotide polymorphism (SNP) markers generated through genotyping‐by‐sequencing (GBS). A linkage map was constructed with 1,507 GBS‐SNP markers that distributed over all 21 chromosomes. Most traits in this study showed normal or approximately normal distributions. Composite interval mapping analysis revealed a total of 72 quantitative trait loci (QTL), 67 for quality traits that were dispersed on all chromosomes except 3D, 5D, 6A, and 7A. Among the 67 QTL, some were novel QTL that have not been reported previously. Although many QTL were only significant in one environment, six genomic regions on chromosomes 1B, 1D, 2D, 5BL, 6B, and 6DL harbored major QTL for several important quality traits including kernel hardness (KH), grain and flour protein content (GPC, FPC), flour yield (FY), mix time (MT), mixing tolerance (MTOL), loaf volume (LV), and loaf volume regression (LVReg). At these major loci, Danby contributed favorable alleles for KH, GPC and FPC, while Tiger contributed favorable alleles for the other traits. The findings in this study will be useful for the genetic improvement of bread‐making quality in wheat.

Deciphering novel QTL for spectral reflectance indices in spring wheat

Drought is a common abiotic stress factor that seriously affects wheat production in many parts of the world. To identify stable QTLs associated with three morphophysiological traits and four spectral reflectance indices (SRIs) in F6 (previous research) and F7 generations, recombinant inbred line (RIL) population from the cross between drought-sensitive (Yecora Rojo) and drought-tolerant (Pavon 76) cultivars was used in a study conducted under two irrigation regimes at the King Saud University Agricultural, Research Station. The parents and RILs were genotyped using a set of microsatellite loci. The composite interval mapping (CIM) analysis detected 53 QTLs for the three morphophysiological traits and four SRIs. The phenotypic variance (PV) explained varied from 3 to 44%. Twelve co-located QTLs were identified on chromosomes: 1B, 1D, 2A, 2D, 3A, 3B, 4B, 5B and 5D. The highest number of co-located QTLs was identified on chromosomes 5B. The QTL on chromosome 5B flanked by barc109–barc59 showed significant effects for the morphophysiological traits and SRIs under the two irrigation regimes. This QTL explained the highest proportion of PV (14.0–44.0%) with LOD score (3.2–13.2). Seven stable QTLs were identified in both F6 and F7 generations. Of these QTLs, QWc.li-5B co-localized with QNdvi.li-5B and flanked by markers Barc109 and Barc59 was identified in F6 and F7 generations under reduced irrigation. QWc.li-5B explained the highest PV (43 and 44%) for relative water content in F6 and F7 RIL, respectively. The novel and consistent QTLs obtained can be used in marker-assisted selection to improve drought tolerance in wheat.

QTL mapping of adult-plant resistance to leaf rust based on SSR markers and SNP sequencing of Chinese wheat landrace Xu’ai (Triticum aestivum L.)

Leaf rust (Puccinia recondite f. sp. Tritici) is the prevalent wheat (Triticum aestivum) disease in many areas of the world. The preferred method of control is to introduce resistance genes against this disease into elite wheat germplasm, an approach that is more environmentally friendly than applying fungicides and that has underpinned sustained wheat production to date. Identifying new adult-plant resistance (APR) genes and quantitative trait loci (QTL) for leaf rust resistance can expand the sources of disease resistance and contribute to breeding for durable resistance, allowing the pyramiding of multiple APR genes in elite wheat lines. In this study, simple sequence repeat (SSR) markers, together with the bulked segregant and single nucleotide polymorphism analyses (BSA and SNP), were used to identify QTL in F2:3 lines derived from Xu’ai and Zhengzhou 5389. Yield data for the population lines were used to detect QTL by inclusive composite interval mapping analysis. Six QTL were identified, namely, QLr.hebau-1BL, QLr.hebau-2AS, QLr.hebau-2BS, QLr.hebau-2BL, QLr.hebau-4B and QLr.hebau-6AL. Both QLr.hebau-2BS and QLr.hebau-4B were detected in five environments, explaining 23.1–37.5% and 7.6–18.3% of the phenotypic variation for resistance, respectively. After analysis, QLr.hebau-1BL and QLr.hebau-4B were identified as Lr46 and Lr12, respectively. QLr.hebau-2BS is very close to the temperature-sensitive gene LrZH22, but requires further detection. QLr.hebau-2AS, QLr.hebau-2BL and QLr.hebau-6AL constitute new resistance loci. These genes or QTL for reduced disease severity were derived from the resistant parent Xu’ai. The QTL identified and their flanking markers could be useful for fine mapping and marker-assisted selection (MAS).

Mapping consistent additive and epistatic QTLs for plant production traits under drought in target populations of environment using locally adapted landrace in rice (Oryza sativa L.)

ABSTRACT A total of 36 colocated QTLs associated with plant production traits were mapped using IR62266/Norungan recombinant inbred lines under drought and non-stress conditions in target populations of environment. Multi-model composite interval mapping and multi-environment analysis detected seven consistent QTLs for plant production traits on chromosomes 1, 2, 3, 6, 10 and 11. QTLs for plant height, number of tillers, biomass, spikelet fertility and grain yield under drought identified on chr. 1 (RM5389-RM11943-RM5794), chr. 2 (RM324-RM5390 and RM12460-RM423-RM5345), chr. 3 (RM6329-RM5475-RM16030), chr. 6 (RM508-RM585-RM217), chr. 10 (RM7361-RM8207) and chr. 11 (RM209-RM6499) in this study have been reported as meta-QTLs for yield under drought. Consistent positive allelic contribution from the landrace, Norungan was shown at the two QTLs on chr. 2, while IR62266 showed consistent positive allelic effect at the QTL on chr. 10. At other four regions the effect was confounded with the stress condition, for instance the region on chr. 3 identified for straw yield showed additive effect from Norungan in all stress trials, while in the non-stress trials the positive allele came from IR62266. Epistasis for grain yield was detected in the regions of non-additive effects and its relative contribution was small. Relatively small percentage of grain yield variation was explained by additive x environment interaction (0.28 - 1.77%), but was larger than that explained by additive effect QTL across environments (0.05 - 0.35%). These consistent QTLs may have genes evolutionarily conserved in response to drought and could be ideal candidates for rice yield improvement in water-limited environments.

Mapping QTL hotspots associated with weed competitive traits in backcross population derived from Oryza sativa L. and O. glaberrima Steud.

To improve grain yield under direct seeded and aerobic conditions, weed competitive ability of a rice genotype is a key desirable trait. Hence, understanding and dissecting weed competitive associated traits at both morphological and molecular level is important in developing weed competitive varieties. In the present investigation, the QTLs associated with weed competitive traits were identified in BC1F2:3 population derived from weed competitive accession of O. glaberrima (IRGC105187) and O. sativa cultivar IR64. The mapping population consisting of 144 segregating lines were phenotyped for 33 weed competitive associated traits under direct seeded condition. Genetic analysis of weed competitive traits carried out in BC1F2:3 population showed significant variation for the weed competitive traits and predominance of additive gene action. The population was genotyped with 81 genome wide SSR markers and a linkage map covering 1423 cM was constructed. Composite interval mapping analysis identified 72 QTLs linked to 33 weed competitive traits which were spread on the 11 chromosomes. Among 72 QTLs, 59 were found to be major QTLs (> 10% PVE). Of the 59 major QTLs, 38 had favourable allele contributed from the O. glaberrima parent. We also observed nine QTL hotspots for weed competitive traits (qWCA2a, qWCA2b, qWCA2c, qWCA3, qWCA5, qWCA7, qWCA8, qWCA9, and qWCA10) wherein several QTLs co-localised. Our study demonstrates O. glaberrima species as potential source for improvement for weed competitive traits in rice and identified QTLs hotspots associated with weed competitive traits.

Mapping of QTLs detected in a broccoli double diploid population for planting density traits

The planting density of broccoli can directly affect the yield and overall health of plants. Nonetheless, studies on the genetic base of planting density in broccoli are rather limited. 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. In this study, mapping of QTL for PH, PL and LW based on a DH population in broccoli, and as well as the interaction between QTLs and the environment. The DH population was constructed with 176 genotypes obtained from F1 hybrid produced by crossing two different inbred lines 86101 (P1) and 90196 (P2). In our study, 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, phc4−2, phc4−3), one QTL (plc6−2), and two QTLs (lwc1, lwc3−1) 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 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 %. The QTL at the 147.00 cM position of chromosome 3 was located on LW with a high explanatory contribution rate of 19.97 %. Interestingly, we found an overlap region (pplc4) in the chromosome 4 containing three QLTs for traits PH, PL, and LW (phc4−2, plc4−3, and lwc4−2). According to this study, 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, which would be beneficial to breeding of broccoli and the Brassica crops, especially in high planting density.

P.523 Neuromelanin MRI as biomarker for treatment resistance in first episode schizophrenia patients

The planting density of broccoli can directly affect the yield and overall health of plants. Nonetheless, studies on the genetic base of planting density in broccoli are rather limited. 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. In this study, mapping of QTL for PH, PL and LW based on a DH population in broccoli, and as well as the interaction between QTLs and the environment. The DH population was constructed with 176 genotypes obtained from F1 hybrid produced by crossing two different inbred lines 86101 (P1) and 90196 (P2). In our study, 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, phc4−2, phc4−3), one QTL (plc6−2), and two QTLs (lwc1, lwc3−1) 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 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 %. The QTL at the 147.00 cM position of chromosome 3 was located on LW with a high explanatory contribution rate of 19.97 %. Interestingly, we found an overlap region (pplc4) in the chromosome 4 containing three QLTs for traits PH, PL, and LW (phc4−2, plc4−3, and lwc4−2). According to this study, 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, which would be beneficial to breeding of broccoli and the Brassica crops, especially in high planting density.

Map-based cloning of a novel QTL qBN-1 influencing branch number in soybean [Glycine max (L.) Merr.

Branch number (BN) is an important agronomic attribute related to the plant architecture, adaptability, and yield of soybean. To date, few studies of BN have been conducted to elucidate its genetic background. We aimed to localize genetic factors affecting BN using segregating populations derived from the high-branching cultivar ‘Kennong24’ (KN24) and the low-branching cultivar ‘Kenfeng19’ (KF19). Composite interval mapping analysis detected a QTL (qBN-1) on chromosome 6 between the SSR markers BARCSOYSSR_06_0993 and BARCSOYSSR_06_1070 using an F2 population. To fine-map qBN-1, a RIL population was developed and genotyped with 14 SSR markers located in the QTL region. qBN-1 was localized to a 115.67-kb interval flanked by markers BARCSOYSSR_06_1048 and BARCSOYSSR_06_1053. The QTL was further confirmed using backcross populations of size 1305 (BC2F2 with KN24 as a recurrent parent) and 1712 (BC3F2 with KF19 as a recurrent parent). The fine-mapping region of qBN-1 contained only two candidate genes, Glyma.06G208800 and Glyma.06G208900, whose expression patterns were investigated by qRT-PCR. Compared to Glyma.06G208800 gene expression, Glyma.06G208900 showed the highest expression of the two genes and showed a significant difference in expression between high- and low-branching genotypes in either axillary meristem or shoot apical meristem, and showed opposite expression patterns in the two tissues at V4 and R1 stages. These results identify Glyma.06G208900 as a novel candidate gene controlling BN. Taken together, the results of this study provide a foundation for cloning and functional analysis of the qBN-1 gene and for the improvement of BN by marker-assisted selection in soybean breeding.

Tagging, Mapping and Identification of the QTLs Associated With Phosphorous Utilization for Phosphorous- Limiting Soils in Tropical Maize

Phosphorus (P) deficiency is one of the major abiotic constraints leading to low maize productivity. Twenty- four SSR markers sequence information was obtained from maize databases (MaizeGDB). These SSRs were selected from targeted regions of the maize genome linked to Quantitative Trait Loci (QTLs) for Phosphorus utilization efficiency. The mapping population consisting 67 F2 maize plants, were obtained from advancing a bi-parental cross between an efficient line (L354) and an in-efficient maize line (L585). Out of 24 SSRs five identified polymorphic SSR markers were subsequently used for genotyping the 67 tagged F2 plants. Sixty seven F2:3 maize families were used for QTL localization. Inclusive composite interval mapping (ICIM) analysis identified seven QTLs associated with phosphorus utilization on chromosome 5. All the mapped QTLs were more than 5 cM from the nearest molecular marker utilized in the study. The closest mapped QTL, PeSB-5, 7 associated with shoot biomass was at 11.7 cM, from the nearest marker umc2136 on chromosome 5. Therefore, there is need to saturate the map which may prove useful to identify and test several markers near the mapped QTLs. The identified markers could serve as potential SSR markers for QTLs associated with phosphorous utilization for phosphorous- limiting soils in tropical maize.

A QTL hot spot region on chromosome 1B for nine important traits under terminal drought stress conditions in wheat

Drought tolerance is a polygenic trait and is considered as one of the most important limiting factors to productivity of wheat and other crops around the world. Identifying drought-responsive genes using quantitative trait loci (QTL) and association analysis is one of the ways of developing drought tolerance cultivars. In order to detect QTLs for terminal drought tolerance in wheat, QTL analyses were conducted using an F12 recombinant inbred line population. A total of 53 main effects QTLs on 15 different chromosomes and 12 QTL by QTL (QQ) interactions on 13 different chromosomes were detected for yield, yield components and some agronomic traits using composite interval mapping and mixed-model based composite interval mapping analysis. The most significant QTL for grain yield was QYld.1A1 on 1A1, explaining 19.7% of the phenotypic variance under terminal drought stress. In addition, a QTL hot-spot region including important QTLs for grain yield, biological yield, grain number per spike, thousand grain weight, spike length, spike weight, stem weight and plant height was identified in the same region on chromosome 1B and clustered within the marker interval between wpt-3465 and wpt-2019. The DArT marker Wpt-5801 at the position of 67.7 cM showed linkage with all traits using single marker regression analysis. The BLAST search of markers sequences on the wheat genome identified 390 gene stable identifiers. Gene ontology analysis showed that the most dominant terms were ‘binding’ and ‘single-organism catabolic process’ for molecular function and biological process categories, respectively. This QTL hot-spot region with nine important QTLs could be used to improve drought tolerance after validation.

Qtl mapping of some morphological traits of rice in a RILS population derived from Hashemi and modified Nemat varieties hybrid

In this study, QTLs controlling traits related to crop and grain yield were mapped using 140 recombinant inbred lines (F9 and F10) along with 4 control genotypes in anaugment experiment based on a randomized complete block design in 2consecutive years. Population linkage map was consisted of 170 SSR3 / 2132 centimorgan (cM) markers of rice genome, and mean distance between adjacent markers was equal to 12.47 centimorgan (cM). Composite Interval Mapping analysis was done for studying number of days to flowering in the first year, a QTL was identified on chromosome 3 and in the second year, three QTLs were identified on chromosomes 2, 3, and 7, which were common on chromosome 3 in both years. Regarding plant height in the first and second years, a QTL was mapped on chromosome 1, which was similar in both years. In terms of number of tillers in the first year, four QTLs were identified on chromosomes 4 and 12 and in the second year two QTLs were identified on chromosomes 11 and 12, and regarding panicle length in the first year, one QTL was identified on chromosome 6 and in the second year two QTLs were identified on chromosomes 2 and 4, and in terms of flag leaf length in the first year, two QTLs were identified on chromosomes 6 and 7 and in the second year two QTLs were identified on chromosome 7.With respect to flag leaf width in the first year, two QTLs were identified on chromosomes 1and 3, and in the second year, one QTL was identified on chromosome 11, and in relation to Panicle exertion in the first year, three QTLs were identified on chromosomes 1, 2, and 8, and in the second year, two QTLs were identified on chromosomes 1 and 2.In regard to yield in the first year, one QTL was identified on chromosome 2 and in the second year, two QTLs were identified on chromosome 2. Also, clusters of genes were identified by Interval Mapping in the population with respect to different traits on chromosomes 1 (two cases) and 12 (one case).

Mapping QTL conferring speckled snow mold resistance in winter wheat (Triticum aestivum L.).

Speckled snow mold caused by Typhula ishikariensis is one of the most devastating diseases of winter wheat in Hokkaido, Japan and parts of the Pacific Northwest region of USA. Münstertaler is a winter wheat landrace from Switzerland that has very high resistance to snow mold and superior freezing tolerance. Quantitative trait loci (QTL) for resistance to speckled snow mold were identified in a doubled haploid population derived from a cross between Münstertaler and susceptible variety Ibis, both under field conditions and controlled environment tests. Composite interval mapping analysis revealed a major QTL on chromosome 5D from Münstertaler, and on chromosome 6B from Ibis. Flanking microsatellite marker cfd 29 for the QTL on chromosome 5D was about 5 cM distant from vernalization requirement gene Vrn-D1, suggesting that the QTL on chromosome 5D is located on a cold-stress-related gene cluster along with Vrn-D1 and freezing tolerance gene Fr-D1. The QTL on chromosome 6B from Ibis was located on the centromere region flanking QTn.mst-6B, which is reported to increase plant tiller number.

Mapping of QTL for aluminum tolerance in tropical maize

Abstract The objectives of this study were to map genomic regions associated with QTL for aluminum (Al) tolerance in maize and determine the phenotypic effects of tolerance loci. QTL analysis for Al tolerance was carried out in a population of F2:3 progenies resulting from a cross between the contrasting lines LT 99-4 and LS 04-2. SSR (Simple Sequence Repeat) loci and AFLP (Amplified Fragment Length Polymorphism) were used to construct the linkage map and to detect QTL mapped by composite interval mapping analysis. Nine tolerance QTL among eight linkage groups (chromosomes 2, 4, 5, 6, 7, 8, 9, and 10) were mapped, which explained 70.3% of the phenotypic variance. The results confirmed three major QTL (bins 6.00, 8.05, and 10.01) that are described in the literature for Al tolerance, which accounted for most of the phenotypic variance (40.3%).