Mapping soybean aphid resistance genes in PI 567598B

The soybean aphid (Aphis glycines Matsumura) has been a major pest of soybean [Glycine max (L.) Merr.] in North America since it was first reported in 2000. Our previous study revealed that the strong aphid resistance of plant introduction (PI) 567598B was controlled by two recessive genes. The objective of this study was to locate these two genes on the soybean genetic linkage map using molecular markers. A mapping population of 282 F_4:5 lines derived from IA2070 × E06902 was evaluated for aphid resistance in a field trial in 2009 and a greenhouse trial in 2010. Two quantitative trait loci (QTLs) were identified using the composite and multiple interval mapping methods, and were mapped on chromosomes 7 (linkage group M) and 16 (linkage group J), respectively. E06902, a parent derived from PI 567598B, conferred resistance at both loci. In the 2010 greenhouse trial, each of the two QTLs explained over 30 % of the phenotypic variation. Significant epistatic interaction was also found between these two QTLs. However, in the 2009 field trial, only the QTL on chromosome 16 was found and it explained 56.1 % of the phenotypic variation. These two QTLs and their interaction were confirmed with another population consisting of 94 F_2:5 lines in the 2008 and 2009 greenhouse trials. For both trials in the alternative population, these two loci explained about 50 and 80.4 % of the total phenotypic variation, respectively. Our study shows that soybean aphid isolate used in the 2009 field trial defeated the QTL found on chromosome 7. Presence of the QTL on chromosome 16 conferred soybean aphid resistance in all trials. The markers linked to the aphid-resistant QTLs in PI 567598B or its derived lines can be used in marker-assisted breeding for aphid resistance.     

Identification of a novel gene, H34, in wheat using recombinant inbred lines and single nucleotide polymorphism markers

Hessian fly (HF), Mayetiola destructor, is an important pest of wheat (Triticum aestivum L.) worldwide. Because it has multiple biotypes that are virulent to different wheat HF resistance genes, pyramiding multiple resistance genes in a cultivar can improve resistance durability, and finding DNA markers tightly linked to these genes is essential to this process. This study identified quantitative trait loci (QTLs) for Hessian fly resistance (HFR) in the wheat cultivar ‘Clark’ and tightly linked DNA markers for the QTLs. A linkage map was constructed with single nucleotide polymorphism and simple sequence repeat markers using a population of recombinant inbred lines (RILs) derived from the cross ‘Ning7840’ × ‘Clark’ by single-seed descent. Two QTLs associated with resistance to fly biotype GP were identified on chromosomes 6B and 1A, with the resistance alleles contributed from ‘Clark’. The QTL on 6B flanked by loci Xsnp921 and Xsnp2745 explained about 37.2 % of the phenotypic variation, and the QTL on 1A was flanked by Xgwm33 and Xsnp5150 and accounted for 13.3 % of phenotypic variation for HFR. The QTL on 6B has not been reported before and represents a novel wheat gene with resistance to HF, thus, it is designated H34. A significant positive epistasis was detected between the two QTLs that accounted for about 9.5 % of the mean phenotypic variation and increased HFR by 0.16. Our results indicated that different QTLs may contribute different degrees of resistance in a cultivar and that epistasis may play an important role in HFR.     

Dynamic quantitative trait locus analysis of seed dormancy at three development stages in rice

In this study, a rice population of recombinant inbred lines (RILs) was used to determine the genetic characteristics of seed dormancy (SD) at 4 (early), 5 (middle) and 6 (late) weeks after heading stages. Dynamic analysis showed that the indica IR28 variety tended to have deeper dormancy than the japonica Daguandao at the middle and late development stages. The level of SD decreased with the process of seed development. The significant interaction between heading date (HD) and SD occurred only in those seeds collected at the early development stage. A total of nine additive quantitative trait loci (QTLs) and eight epistatic QTLs for SD were identified at three seed development stages. Of them, one additive and four epistatic QTLs were identified for the early stage, six additive and one epistatic QTL for the middle stage and two additive and three epistatic QTLs for the late stage. The phenotypic variation explained by each additive and epistatic QTL ranged from 5.8 to 30.6 % and from 3.8 to 13.1 %, respectively. Compared with the additive QTLs, epistatic interactions were much more important for SD at the early and late development stages. Two major additive QTLs, qSD3.1 and qSD4.1, were identified; each QTL could explain more than 20 % of the total phenotypic variance and each dormancy-enhancing allele could decrease the germination percentage by about 10 %. By comparing the chromosomal positions of these additive QTLs with those previously identified, five additive QTLs, qSD1.2, qSD2.1, qSD3.2, qSD4.1 and qSD9.1, might represent novel genes. One QTL identified here, qHD1, and nine QTLs identified in previous studies for HD were co-located with our QTLs for SD, which indicated that the significant correlation between SD and HD might be due to the linkage of QTLs for SD and HD. Four RILs with deep dormancy at development stages but non-dormancy after post-ripening under different germination conditions were selected. Using the selected RILs, three cross combinations of SD for the development of RIL populations were predicted. The selected RILs and the identified QTLs might be applicable for the improvement of pre-harvest sprouting tolerance by marker-assisted selection in rice.     

Construction of high-density linkage map and identification of QTLs for resistance to sorghum downy mildew in maize (<Emphasis Type="Italic">Zea mays</Emphasis> L.)

Sorghum downy mildew (SDM), caused by obligate biotrophic fungi Peronosclerospora sorghi, is an economically important disease of maize. The genetics of resistance was reported to be polygenic thereby necessitating identification of QTLs for resistance to SDM to initiate effective marker-assisted selection programs. During post-rainy and winter season of 2012, 645 F_2:3 progeny families from the cross CML153 (susceptible) × CML226 (resistant) were screened for their reaction to SDM. Characterization of QTLs affecting resistance to SDM was undertaken using the genetic linkage map with 319 polymorphic SSR and SNP marker loci and the phenotypic data of F_2:3 families. Three QTLs conferring resistance to SDM were consistently identified on chromosomes 2, 3 and 6 in both seasons. The resistant parent CML226 contributed all the QTL alleles conferring resistance to SDM. The major QTL located on chromosome 2 explained 38.68% of total phenotypic variation in the combined analysis with a LOD score of 9.12. All the three QTL showed partially dominant gene effects in combined analysis. The detection of more than one QTL supports the hypothesis that quantitative genes control resistance to P. sorghi. The generation was advanced to F₆ using markers linked to major QTLs on chromosomes 2 and 3 to derive 33 SDM resistant maize inbred lines.     

Identification of QTLs with additive,epistatic and QTL × development interaction effects for seed dormancy in rice

Seed dormancy (SD) is an important agronomic trait affecting crop yield and quality. In this study, one rice population of recombinant inbred lines (RILs) was used to determine the genetic characteristics of SD at the early (4 weeks after heading), middle (5 weeks after heading) and late (6 weeks after heading) development stages. The level of SD decreased with the process of seed development, and the SD was significantly affected by the heading date (HD) and temperature at the early and middle development stages. A total of eight additive quantitative trait loci (QTLs) for SD were identified at three development stages, and more QTLs were expressed at the early and late development stages. Among them, four, one and three additive QTLs were identified at the early, middle and late development stages, respectively. Epistatic QTLs and QTL-by-development interactions were detected by the joint analysis of multi-development phenotypic values, and one additive and two epistatic QTLs were identified. The phenotypic variation of SD explained by each additive, epistatic QTL and QTL × development interaction ranged from 8.0 to 13.5 %, 0.7 to 3.9 % and 1.3 to 2.8 %, respectively. One major QTL qSD7.1 for SD was tightly linked to the major QTL qHD7.4 for HD, which might be applied to reveal the relationship of SD and HD. By comparing chromosomal positions of these additive QTLs with those previously identified, five additive QTLs qSD1.1, qSD2.1, qSD2.2, qSD4.1 and qSD4.2 might represent novel genes. The best three cross combinations for the development of RIL populations were predicted to improve SD. The selected RILs and the identified QTLs might be applicable to improve the rice pre-harvest sprouting tolerance by the marker-assisted selection approach.     

Introgression of homeologous quantitative trait loci (QTLs) for resistance to the root-knot nematode [Meloidogyne arenaria (Neal) Chitwood] in an advanced backcross-QTL population of peanut (Arachis hypogaea L.)

Resistance to root-knot nematodes [Meloidogyne arenaria (Neal) Chitwood] is needed for cultivation of peanut in major peanut-growing areas, but significant resistance is lacking in the cultivated species (Arachis hypogaea L.). Markers to two closely-linked genes introgressed from wild relatives of peanut have been identified previously, but phenotypic evidence for the presence of additional genes in wild species and introgression lines has eluded quantitative trait locus (QTL) identification. Here, to improve sensitivity to small-effect QTLs, an advanced backcross population from a cross between a Florunner component line and the synthetic amphidiploid TxAG-6 [Arachis batizocoi × (A. cardenasii × A. diogoi)]^4× was screened for response to root-knot nematode infection. Composite interval mapping results suggested a total of seven QTLs plus three putative QTLs. These included the known major resistance gene plus a second QTL on LG1, and a potentially homeologous B-genome QTL on LG11. Additional potential homeologs were identified on linkage group (LG) 8 and LG18, plus a QTL on LG9.2 and putative QTLs on LG9.1 and 19. A QTL on LG15 had no inferred resistance-associated homeolog. Contrary to expectation, two introgressed QTLs were associated with susceptibility, and QTLs at some homeologous loci were found to confer opposite phenotypic responses. Long-term functional conservation accompanied by rapid generation of functionally divergent alleles may be a singular feature of NBS-LRR resistance gene clusters, contributing to the richness of resistance alleles available in wild relatives of crops. The significance for peanut evolution and breeding is discussed.     

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.     

Mapping of the quantitative trait locus (QTL) conferring partial resistance to rice leaf blast disease

Malaysian rice, Pongsu Seribu 2, has wide-spectrum resistance against blast disease. Chromosomal locations conferring quantitative resistance were detected by linkage mapping with SSRs and quantitative trait locus (QTL) analysis. For the mapping population, 188 F₃ families were derived from a cross between the susceptible cultivar, Mahsuri, and a resistant variety, Pongsu Seribu 2. Partial resistance to leaf blast in the mapping population was assessed. A linkage map covering ten chromosomes and consisting of 63 SSR markers was constructed. 13 QTLs, including 6 putative and 7 putative QTLs, were detected on chromosomes 1, 2, 3, 5, 6, 10, 11 and 12. The resulting phenotypic variation due to a single QTL ranged from 2 to 13 %. These QTLs accounted for approx. 80 % of the total phenotypic variation within the F₃ population. Therefore, partial resistance to blast in Pongsu Seribu 2 is due to combined effects of multiple loci with major and minor effects.     

QTL Analysis of Na+ and K+ Concentrations in Roots and Shoots under Different Levels of NaCl Stress in Rice (Oryza sativa L.)

The key to plant survival under NaCl salt stress is maintaining a low Na⁺ level or Na⁺/K⁺ ratio in the cells. A population of recombinant inbred lines (RILs, F_2∶9) derived from a cross between the salt-tolerant japonica rice variety Jiucaiqing and the salt-sensitive indica variety IR26, was used to determine Na⁺ and K⁺ concentrations in the roots and shoots under three different NaCl stress conditions (0, 100 and 120 mM NaCl). A total of nine additive QTLs were identified by QTL Cartographer program using single-environment phenotypic values, whereas eight additive QTLs were identified by QTL IciMapping program. Among these additive QTLs, five were identified by both programs. Epistatic QTLs and QTL-by-environment interactions were detected by QTLNetwork program in the joint analyses of multi-environment phenotypic values, and one additive QTL and nine epistatic QTLs were identified. There were three epistatic QTLs identified for Na⁺ in roots (RNC), three additive QTLs and two epistatic QTLs identified for Na⁺ in shoots (SNC), four additive QTLs identified for K⁺ in roots (RKC), four additive QTLs and three epistatic QTLs identified for K⁺ in shoots (SKC) and one additive QTL and one epistatic QTL for salt tolerance rating (STR). The phenotypic variation explained by each additive, epistatic QTL and QTL×environment interaction ranged from 8.5 to 18.9%, 0.5 to 5.3% and 0.7 to 7.5%, respectively. By comparing the chromosomal positions of these additive QTLs with those previously identified, five additive QTLs, qSNC9, qSKC1, qSKC9, qRKC4 and qSTR7, might represent novel salt tolerance loci. The identification of salt tolerance in selected RILs showed that a major QTL qSNC11 played a significant role in rice salt tolerance, and could be used to improve salt tolerance of commercial rice varieties with marker-assisted selection (MAS) approach.     

Quantitative trait loci mapping of yield and related traits using a high-density genetic map of maize

Improving grain yield is the ultimate goal of the maize-breeding programs. In this study, analyses of conditional and unconditional quantitative trait locus (QTL) and epistatic interactions were used to elucidate the genetic architecture of yield and its related traits. A total of 15 traits of a recombinant inbred line population, including yield per plant (YPP), seven ear-related traits, and seven kernel-related traits, were measured in six different environments. Based on the genetic linkage map constructed using 2091 bins as markers, 56 main-effect QTLs for these traits were identified. These QTLs were distributed across eight genomic regions (bin 1.06, bin 4.02/4.05/4.08, bin 5.04, bin 7.04, bin 8.08, and bin 9.04), within the marker intervals of 85.45–6260.66 kb, and the phenotypic variance explained ranging from 5.69 to 11.56 %. One gene (GRMZM2G168229) encoding SBP-box domain protein was located in the small interval of qKRN4-3 and may be involved in patterning of kernel row number. Seventeen conditional QTLs identified for YPP were conditioned on its related traits and explained 6.18–23.15 % of the phenotypic variance. Conditional mapping analysis revealed that qYPP4-1, qYPP6-1, and qYPP8-1 are partially influenced by YPP-related traits at the individual QTL level. Digenic epistatic analysis identified 12 digenic interactions involving 22 loci across the whole genome. In addition, conditional digenic epistatic analysis identified 14 digenic interactions involving 21 loci. This study provides valuable information for understanding the genetic relationship between YPP and related traits and constitutes the first step toward the cloning of the relevant genes.     

Genetic dissection of drought tolerance in chickpea (Cicer arietinum L.)

Key message

Analysis of phenotypic data for 20 drought tolerance traits in 1–7 seasons at 1–5 locations together with genetic mapping data for two mapping populations provided 9 QTL clusters of which one present on CaLG04 has a high potential to enhance drought tolerance in chickpea improvement.

Abstract

Chickpea (Cicer arietinum L.) is the second most important grain legume cultivated by resource poor farmers in the arid and semi-arid regions of the world. Drought is one of the major constraints leading up to 50 % production losses in chickpea. In order to dissect the complex nature of drought tolerance and to use genomics tools for enhancing yield of chickpea under drought conditions, two mapping populations—ICCRIL03 (ICC 4958 × ICC 1882) and ICCRIL04 (ICC 283 × ICC 8261) segregating for drought tolerance-related root traits were phenotyped for a total of 20 drought component traits in 1–7 seasons at 1–5 locations in India. Individual genetic maps comprising 241 loci and 168 loci for ICCRIL03 and ICCRIL04, respectively, and a consensus genetic map comprising 352 loci were constructed (http://cmap.icrisat.ac.in/cmap/sm/cp/varshney/). Analysis of extensive genotypic and precise phenotypic data revealed 45 robust main-effect QTLs (M-QTLs) explaining up to 58.20 % phenotypic variation and 973 epistatic QTLs (E-QTLs) explaining up to 92.19 % phenotypic variation for several target traits. Nine QTL clusters containing QTLs for several drought tolerance traits have been identified that can be targeted for molecular breeding. Among these clusters, one cluster harboring 48 % robust M-QTLs for 12 traits and explaining about 58.20 % phenotypic variation present on CaLG04 has been referred as “QTL-hotspot”. This genomic region contains seven SSR markers (ICCM0249, NCPGR127, TAA170, NCPGR21, TR11, GA24 and STMS11). Introgression of this region into elite cultivars is expected to enhance drought tolerance in chickpea.     

Identification of genome regions controlling cotyledon,pod wall/seed coat and pod wall resistance to pea weevil through QTL mapping

Pea weevil, Bruchus pisorum, is one of the limiting factors for field pea (Pisum sativum) cultivation in the world with pesticide application the only available method for its control. Resistance to pea weevil has been found in an accession of Pisum fulvum but transfer of this resistance to cultivated pea (P. sativum) is limited due to a lack of easy-to-use techniques for screening interspecific breeding populations. To address this problem, an interspecific population was created from a cross between cultivated field pea and P. fulvum (resistance source). Quantitative trait locus (QTL) mapping was performed to discover the regions associated with resistance to cotyledon, pod wall/seed coat and pod wall resistance. Three major QTLs, located on linkage groups LG2, LG4 and LG5 were found for cotyledon resistance explaining approximately 80 % of the phenotypic variation. Two major QTLs were found for pod wall/seed coat resistance on LG2 and LG5 explaining approximately 70 % of the phenotypic variation. Co-linearity of QTLs for cotyledon and pod wall/seed coat resistance suggested that the mechanism of resistance for these two traits might act through the same pathways. Only one QTL was found for pod wall resistance on LG7 explaining approximately 9 % of the phenotypic variation. This is the first report on the development of QTL markers to probe Pisum germplasm for pea weevil resistance genes. These flanking markers will be useful in accelerating the process of screening when breeding for pea weevil resistance.     

QTLs for resistance to Phomopsis seed decay are associated with days to maturity in soybean (Glycine max)

Phomopsis seed decay (PSD), primarily caused by Phomopsis longicolla, is a major contributor to poor soybean seed quality and significant yield loss, particularly in early maturing soybean genotypes. However, it is not yet known whether PSD resistance is associated with early maturity. This study was conducted to identify quantitative trait loci (QTLs) for resistance to PSD and days to maturity using a recombinant inbred line (RIL) population derived from a cross between the PSD-resistant Taekwangkong and the PSD-susceptible SS2-2. Based on a genetic linkage map incorporating 117 simple sequence repeat markers, QTL analysis revealed two and three QTLs conferring PSD resistance and days to maturity, respectively, in the RIL population. Two QTLs (PSD-6-1 and PSD-10-2) for PSD resistance were identified in the intervals of Satt100–Satt460 and Sat_038–Satt243 on chromosomes 6 and 10, respectively. Two QTLs explained phenotypic variances in PSD resistance of 46.3 and 14.1 %, respectively. At the PSD-6-1 QTL, the PSD-resistant cultivar Taekwangkong contributed the allele with negative effect decreasing the infection rate of PSD and this QTL does not overlap with any previously reported loci for PSD resistance in other soybean genotypes. Among the three QTLs for days to maturity, two (Mat-6-2 and Mat-10-3) were located at positions similar to the PSD-resistance QTLs. The identification of the QTLs linked to both PSD resistance and days to maturity indicates a biological correlation between these two traits. The newly identified QTL for resistance to PSD associated with days to maturity in Taekwangkong will help improve soybean resistance to P. longicolla.     

Characterization of Yr54 and other genes associated with adult plant resistance to yellow rust and leaf rust in common wheat Quaiu 3

Leaf rust (LR) and yellow rust (YR), caused by Puccinia triticina and Puccinia striiformis f. sp. tritici, respectively, are important diseases of wheat. Quaiu 3, a common wheat line developed at the International Maize and Wheat Improvement Center (CIMMYT), is immune to YR in Mexico despite seedling susceptibility to predominant races. Quaiu 3 also shows immunity to LR in field trials and is known to possess the race-specific gene Lr42. A mapping population of 182 recombinant inbred lines (RILs) was developed by crossing Quaiu 3 with susceptible Avocet-YrA and phenotyped with LR and YR in field trials for 2 years in Mexico. Quantitative trait loci (QTL) associated with YR and LR resistance in the RILs were identified using Diversity Arrays Technology and simple sequence repeat markers. A large-effect QTL on the long arm of chromosome 2D explained 49–54 % of the phenotypic variation in Quaiu 3 and was designated as Yr54. Two additional loci on 1BL and 3BS explained 8–17 % of the phenotypic variation for YR and coincided with previously characterized adult plant resistance (APR) genes Lr46/Yr29 and Sr2/Yr30, respectively. QTL on 1DS and 1BL corresponding to Lr42 and Lr46/Yr29, respectively, contributed 60–71 % of the variation for LR resistance. A locus on 3D associated with APR to both diseases explained up to 7 % of the phenotypic variance. Additional Avocet-YrA-derived minor QTL were also detected for YR on chromosomes 1A, 3D, 4A, and 6A. Yr54 is a newly characterized APR gene which can be combined with other genes by using closely linked molecular markers.     

Strain-specific and recessive QTLs involved in the control of partial resistance to Fusarium oxysporum f. sp. melonis race 1.2 in a recombinant inbred line population of melon

Fusarium oxysporum f. sp. melonis (FOM) causes serious economic losses in melon (Cucumis melo L.). Two dominant resistance genes have been identified, Fom-1 and Fom-2, which provide resistance to races 0 and 2 and races 0 and 1, respectively, however FOM race 1.2 overcomes these resistance genes. A partial resistance to FOM race 1.2 that has been found in some Far East accessions is under polygenic control. A genetic map of melon was constructed to tag FOM race 1.2 resistance with DNA markers on a recombinant inbred line population derived from a cross between resistant (Isabelle) and susceptible (cv. Védrantais) lines. Artificial root inoculations on plantlets of this population using two strains, one that causes wilting (FOM 1.2w) and one that causes yellowing (FOM 1.2y), resulted in phenotypic and genotypic data that enabled the identification of nine quantitative trait loci (QTLs). These QTLs were detected on five linkage groups by composite interval mapping and explained between 41.9% and 66.4% of the total variation. Four digenic epistatic interactions involving seven loci were detected and increased the total phenotypic variation that was explained. Co-localizations between QTLs and resistance gene homologs or resistance genes, such as Fom-2 and Vat, were observed. A strain-specific QTL was detected, and some QTLs appeared to be recessive.     

Mapping QTLs for morpho-agronomic traits in proso millet (<Emphasis Type="Italic">Panicum miliaceum</Emphasis> L.)

Proso millet (Panicum miliaceum L.) is the cereal crop with the low water requirement and increasingly being used for human consumption. It is the most common rotational crop within wheat-based dryland production systems in the semiarid High Plains of the USA. However, there is no published genetic map for this species, which prevents the identification of quantitative trait loci (QTL). The objectives of the present study were (1) construction of a genetic linkage map and (2) identification of DNA markers linked to QTLs for morpho-agronomic traits. A total of 93 recombinant inbred lines derived from a single F₁ (“Huntsman” × “Minsum”) were genotyped with GBS-SNP markers and phenotyped for nine morpho-agronomic traits in the field during 2013 and 2014 at Scottsbluff and Sidney, NE. IciMapping v.4.0.6.0 was used for constructing a genetic linkage map and mapping QTL. The RILs exhibited significant variation for a wide range of traits, and several traits showed evidence of genotype × environment interactions. A total of 833 GBS-SNP markers formed 18 major and 84 minor linkage groups, whereas 519 markers remained ungrouped. A total of 117 GBS-SNP markers were distributed on the 18 major linkage groups spanning a genome length of 2137 cM of proso millet with an average distance of 18 cM between markers. The length and number of markers in each of the 18 major linkage groups ranged from 54.6 to 236 cM and 4 to 12, respectively. A total of 18 QTLs for eight morpho-agronomic traits were detected on 14 linkage groups, each of which explained 13.2–34.7 % phenotypic variance. DNA markers flanking the QTLs were identified, which will aid in marker-assisted selection of these traits. To our knowledge, this is the first genetic linkage map and QTL mapping in proso millet, which will support further genetic analysis and genomics-assisted genetic improvement of this crop.     

Mapping QTLs for physiological and biochemical traits related to grain yield under control and terminal heat stress conditions in bread wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

In order to detect genomic regions with different effects for some of the physiological and biochemical traits of wheat, four experiments were conducted at Research Farm of Agricultural and Natural Resources Research Center of Zabol in 2015–2016 and 2016–2017 growing seasons. The experiments were carried out using four alpha lattice designs with two replications under non-stress and terminal heat stress conditions. Plant materials used in this study included 167 recombinant inbred lines and their parents (‘SeriM82’ and ‘Babax’). Six traits including grain yield (GY), proline content (PRO), water soluble carbohydrates (WSC), maximum efficiency of photosystem II (Fv/Fm), cytoplasmic membrane stability (CMS) and chlorophyll content (CHL) were evaluated. Genetic linkage map consisted of 211 AFLP marker, 120 SSR marker and 144 DArT markers with 1864 cm length and 4.4 cm mean distance. QTL analysis was carried out using a mixed-model-based composite interval mapping (MCIM) method. By the combined analysis of normal phenotypic values, 27 additive QTLs and five pairs of epistatic effects were identified for studied traits, among which two additive and one epistatic QTL showed significant QTL?×?environment interactions. By the combined analysis of stress phenotypic values, a total of 26 QTLs with additive effects and 5 epistatic QTLs were detected, among which one additive and one epistatic QTL showed QTL?×?environment interactions. Six QTLs with major effects (QGY-2B, QGY-2D, QPro-5B, QWSC-4A, QFv/Fm-6A and QCMS-4B), which were common between two conditions could be useful for marker-assisted selection (MAS) in order to develop heat tolerant and high-performance wheat varieties.     

Analysis on additive effects and additive-by-additive epistatic effects of QTLs for yield traits in a recombinant inbred line population of rice

A linkage map consisting of 158 DNA markers were constructed by using a recombinant inbred line (RIL) population derived from the indica-indica rice cross Zhenshan 97B 2 Milyang 46. Quantitative trait loci (QTLs) conditioning grain yield and five yield component traits were determined at the one-locus and two-locus levels, and genotype-by-environment (GE) interactions were analyzed. Thirty-one QTLs were detected to have significant additive effects for yield traits, of which 12 also exhibited significant epistatic effects. Sixteen significant additive-by-additive (AA) interactions were detected, of which nine occurred between QTLs with own additive effects (M_epQTLs), four occurred between QTLs showing epistatic effects only (epQTLs), and three occurred between M_epQTLs and epQTLs. Significant GE interactions were found for six QTLs with additive effects and one AA interaction. Generally, the contributions to the phenotypic variation were higher due to QTL main effects than to epistatic effects. The detection of additive effects and AA effects of a QTL interfered with each other, indicating that the detection of QTLs with main effects, as well as the magnitude and directions of the additive effects, might vary depending on their interactions with other loci.     

Quantitative trait locus analysis of tuber dormancy in diploid potato (Solanum spp.)

Quantitative trait locus (QTL) analysis for tuber dormancy was performed in a diploid potato population (TRP133) consisting of 110 individuals. The female parent was a hybrid between haploid S. tuberosum (2x) and S. chacoense, while the male parent was a S. phureja clone. The population was characterized for ten isozyme loci, 44 restriction fragment length polymorphisms (RFLPs) and 63 random amplified polymorphic DNAs (RAPDs). Eighty-seven of these loci segregating from the female parent were utilized to develop a linkage map that comprised 10 of the 12 chromosomes in the genome. Dormancy, as measured by days-to-sprouting after harvest, ranged from 10 to 90 days, with a mean of 19 days. QTLs were mapped by conducting one-way analyses of variance for each marker locus by dormancy combination. Twenty-two markers had a significant association with dormancy, identifying six putative QTLs localized on each of chromosomes 2, 3, 4, 5, 7 and 8. The QTL with the strongest effect on dormancy was detected on chromosome 7. A multilocus model was developed using the locus with highest R² value in each QTL. This model explained 57.5% of the phenotypic variation for dormancy. Seven percent of possible epistatic interactions among significant markers were significant when tested through two-way analyses of variance. When these were included in the main-effects model, it explained 72.1% of the phenotypic variation for dormancy. QTL analysis in potato, the methodology to transfer traits and interactions into the 4x level, and QTLs of value for marker-assisted selection, are discussed.     

Identification of major QTLs and epistatic interactions for seed protein concentration in soybean under multiple environments based on a high-density map

The concentration of protein in soybean is an important trait that drives successful soybean quality. A recombinant inbred line derived from a cross between the Charleston and Dongnong594 cultivars was planted in one location across 10 years and two locations across 5 years in China (20 environments in total), and the genetic effects were partitioned into additive main effects, epistatic main effects and their environmental interaction effects using composite interval mapping and inclusive composite interval mapping models based on a high-density genetic map. Ten main-effect quantitative trait loci (QTLs) were identified on chromosomes 3, 6, 7, 13, 15 and 20 and detected in more than three environments, with each of the main-effect QTLs contributing a phenotypic variation of around 10 %. Between the intervals of the main-effect QTLs, 93 candidate genes were screened for their involvement in seed protein storage and/or amino acid biosynthesis and metabolism processes based on gene ontology and annotation information. Furthermore, an analysis of epistatic interactions showed that three epistatic QTL pairs were detected, and could explain approximately 50 % of the phenotypic variation. The additive main-effect QTLs and epistatic QTL pairs contributed to high phenotypic variation under multiple environments, and the results were also validated and corroborated with previous research, indicating that marker-assisted selection can be used to improve soybean protein concentrations and that the candidate genes can also be used as a foundation data set for research on gene function.