Mapping Architectural, Phenological, and Fruit Quality QTLs in Apricot

The F1 population “Harostar”?×?“Rouge de Mauves” was used to perform a quantitative trait loci (QTL) mapping for tree architecture traits (i.e., tree diameter, total branch number, tree shape); time to initial reproductive bud break; and fruit quality traits (i.e., ground color, fruit form, soluble solid content) using data collected from 2010 to 2012. For architectural traits, interval mapping detected QTLs only in “Rouge de Mauves” on linkage group 1 for trunk diameter in 2010, on LG6 for total branch number in 2010, and on LG1 and LG5 for tree shape for both years 2010 and 2011 combined. QTLs were detected only in “Harostar” for time to initial reproductive bud break on LG1 and LG4 in 2011. For fruit quality traits, data were collected in 2011 and 2012. QTLs were identified on LG1 in 2011 and on LG4 in 2012 for soluble solid content, on LG3 for ground color in both years, on LG7 only in 2011, and on LG3 for fruit form in both years. The QTLs that we identified were compared to those previously identified in other Prunus spp.     

Identification of Novel Strain-Specific and Environment-Dependent Minor QTLs Linked to Fire Blight Resistance in Apples

Since its first report almost 200 years ago, fire blight, caused by the gram-negative bacterium Erwinia amylovora, has threatened apple and pear production globally. Identifying novel genes and their functional alleles is a prerequisite to developing apple cultivars with enhanced fire blight resistance. Here, we report 13 strain-specific and environment-dependent minor QTLs linked to fire blight resistance from a segregating Malus sieversii × Malus × domestica mapping population. Interval mapping at 95% confidence and Kruskal–Wallis analysis at P value =?0.005 were used to identify QTLs for three strains of E. amylovora differing in virulence and pathogenicity. The QTLs identified explain a small to moderate part of resistance variability, and a majority was not common between years or E. amylovora strains. These QTLs are distributed in eight linkage groups of apples and comparison of their map position to previously identified fire blight resistance QTLs indicates that most are novel loci. Interaction between experimental conditions in the greenhouse and field, and between years, and differences in virulence levels of strains might be responsible for strain- and year-specific QTLs. The QTLs identified on LG10 for strain Ea273 in 2011 and strain LP101 in 2011, and on LG15 for strain LP101 could be the same QTLs identified previously with strain CFBP1430 in cultivar “Florina” and “Co-op16 × Co-op17” mapping population, respectively. We discuss the potential impact of newly identified minor fire blight QTLs and major gene-based resistance on the rate of mutation in pathogen populations to overcome resistance and durability of resistance.     

Mapping of quantitative trait loci (QTLs) for oil yield using SSRs and gene-based markers in African oil palm (<Emphasis Type="Italic">Elaeis guineensis</Emphasis> Jacq.)

The identification of quantitative trait loci (QTLs) based on anchor markers, especially candidate genes that control a trait of interest, has been noted to increase the power of QTL detection. Since these markers can be scored as co-dominant data, they are also valuable for comparing and integrating the QTL linkage maps from diverse mapping populations. To estimate the position and effects of QTLs linked to oil yield traits in African oil palm, co-dominant microsatellites (SSR) and candidate gene-based sequence polymorphisms were applied to construct a linkage map for a progeny showing large differences in oil yield components. The progeny was genotyped for 97 SSR markers, 93 gene-linked markers, and 12 non-gene-linked SNP markers. From these, 190 segregating loci could be arranged into 31 linkage groups while 12 markers remained unmapped. Using the single marker linkage, interval mapping and multiple QTL methods, 16 putative QTLs on seven linkage groups affecting important oil yield related traits such as fresh fruit bunch yield (FFB), ratio of oil per fruit (OF), oil per bunch (OB), fruit per bunch (FB) and wet mesocarp per fruit (WMF) could be identified in the segregating population with estimated values for explained variance ranging from 12.4 % to 54.5 %. Markers designed from some candidate genes involved in lipid biosynthesis were found to be mapped near significant QTLs for various economic yield traits. Associations between QTLs and potential candidate genes are discussed.     

Oil palm (Elaeis guineensis Jacq.) linkage map,and quantitative trait locus analysis for sex ratio and related traits

Sex ratio and shell-thickness type are among the main components determining yield in oil palm. An integrated linkage map of oil palm was constructed based on 208 offspring derived from a cross between two tenera palms differing in inherited sex ratio. The map consisted of 210 genomic simple sequence repeats (SSRs), 28 expressed sequence tag SSRs, 185 amplified fragment length polymorphism markers, and the Sh locus, which controls shell-thickness phenotype, distributed across 16 linkage groups covering 1,931 cM, with an average marker distance of 4.6 cM. Quantitative trait locus (QTL) analysis identified eight QTLs across six linkage groups associated with sex ratio and related traits. These QTLs explained 8.1–13.1 % of the total phenotypic variance. The QTL for sex ratio on linkage group 8 overlapped with a QTL for number of male inflorescences. In most cases a specific QTL allele combination was responsible for genotype class mean differences, suggesting that most QTLs in heterozygous oil palm are likely to be segregating for multiple alleles with different degrees of dominance. In addition, two new SSRs were shown to flank the major Sh locus controlling the fruit variety type in oil palm.     

QTL mapping for brown rot (Monilinia fructigena) resistance in an intraspecific peach (Prunus persica L. Batsch) F1 progeny

Brown rot (BR) caused by Monilinia spp. leads to significant post-harvest losses in stone fruit production, especially peach. Previous genetic analyses in peach progenies suggested that BR resistance segregates as a quantitative trait. In order to uncover genomic regions associated with this trait and identify molecular markers for assisted selection (MAS) in peach, an F1 progeny from the cross “Contender” (C, resistant)?×?“Elegant Lady” (EL, susceptible) was chosen for quantitative trait loci (QTL) analysis. Over two phenotyping seasons, skin (SK) and flesh (FL) artificial infections were performed on fruits using a Monilinia fructigena isolate. For each treatment, infection frequency (if) and average rot diameter (rd) were scored. Significant seasonal and intertrait correlations were found. Maturity date (MD) was significantly correlated with disease impact. Sixty-three simple sequence repeats (SSRs) plus 26 single-nucleotide polymorphism (SNP) markers were used to genotype the C?×?EL population and to construct a linkage map. C?×?EL map included the eight Prunus linkage groups (LG), spanning 572.92 cM, with an average interval distance of 6.9 cM, covering 78.73 % of the peach genome (V1.0). Multiple QTL mapping analysis including MD trait as covariate uncovered three genomic regions associated with BR resistance in the two phenotyping seasons: one containing QTLs for SK resistance traits near M1a (LG C?×?EL-2, R ²?=?13.1–31.5 %) and EPPISF032 (LG C?×?EL-4, R ²?=?11–14 %) and the others containing QTLs for FL resistance, near markers SNP_IGA_320761 and SNP_IGA_321601 (LG3, R ²?=?3.0–11.0 %). These results suggest that in the C?×?EL F1 progeny, skin resistance to fungal penetration and flesh resistance to rot spread are distinguishable mechanisms constituting BR resistance trait, associated with different genomic regions. Discovered QTLs and their associated markers could assist selection of new cultivars with enhanced resistance to Monilinia spp. in fruit.     

Quantitative trait loci for yield components in oil palm (Elaeis guineensis Jacq.)

The development of an oil palm RFLP marker map has enabled marker-based QTL mapping studies to be undertaken. Information from 153 RFLP markers was used in combination with phenotypic data from an F₂ population to estimate the position and effects of quantitative trait loci (QTLs) for traits including yield of fruit and its components and measures of vegetative growth. The mapping population consisted of 84 palms segregating for the major gene influencing shell thickness. Marker data were analysed to produce a linkage map consisting of 22 linkage groups. The QTL mapping analysis was carried out by interval mapping and single-marker analysis for the unlinked markers; significance thresholds were generated by permutation. Using both single-marker and interval-mapping analysis significant marker associated QTL effects were found for 11 of the 13 traits analysed. The results of interval-mapping analysis of fruit weight, petiole cross section and rachis length, and ratios of shell:fruit, mesocarp:fruit and kernel:fruit indicated significant (P<0.05) QTLs at the genome-wide threshold. The putative QTLs were associated with between 8.2% and 44.0% of the phenotypic variation, with an average of 27% for the single-marker analysis and 19% for the interval-mapping analysis. The higher percentage of phenotypic variation explained in the single-marker analysis, when compared to the interval-mapping analysis, is likely to be due to the lower stringency associated with the single-marker analysis. Large dominance deviations were associated with a sizeable proportion of the putative QTLs. The ultimate objective of mapping QTLs in commercial populations is to utilise novel breeding strategies such as marker-assisted selection (MAS). The potential impact of MAS in oil palm breeding programmes is discussed. Received: 26 June 2000 / Accepted: 24 October 2000     

The Sweet Passion Fruit (<Emphasis Type="Italic">Passiflora alata</Emphasis>) Crop: Genetic and Phenotypic Parameter Estimates and QTL Mapping for Fruit Traits

Despite their economic importance, some tropical crop species are largely neglected when it comes to conducting genetic studies characterizing target traits for breeding. Herein, genetic and phenotypic parameters as well quantitative trait loci (QTL) are described for the first time in a full-sib progeny of sweet passion fruit (Passiflora alata). A hundred F₁ individuals were evaluated in two locations for seven fruit traits: diameter of fruit (DF, in mm), length of fruit (LF, in mm), weight of fruit (WF, in g), thickness of fruit skin (TS, in mm), weight of fruit skin (WS, in g), weight of fruit pulp (WP, in g) and soluble solids (SS, in °Brix). Mixed models fitted complex, unstructured genetic variance-covariance matrices for all traits in phenotypic analysis. Because of important genetic correlations among skin and pulp traits, multiplicative index selection to select the most promising individuals was successfully applied. A previously reported integrated map supported composite interval mapping (CIM) analyses. In total, we found 22 QTLs mapped in seven out of nine linkage groups. Heritabilities (from 59.8 % to 82.7 %) and proportion of phenotypic variance explained by the QTLs (from 42.0 % to 64.3 %) were comparable for each trait. Principal component analysis on TS, WS and WP showed that the first two principal components (PCs) accounted for 93.6 % of the total variability. CIM analyses on these two PCs revealed five putative QTLs controlling variation for these three traits simultaneously. Thus, genetic improvement for sweet passion fruit should be based on correlations between traits and QTL-related information can be a useful tool.     

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.     

Genetic mapping of local adaptation along the altitudinal gradient in <Emphasis Type="Italic">Abies sachalinensis</Emphasis>

Understanding the genetic bases of local adaptation in dominant conifer species is critical in predicting the impacts of rapid climate change on forest ecosystems. However, the genetic basis of adaptation is not yet fully understood due to the huge and complex genomes of conifers and the unavailability to date of suitable crossing material. In this study, we constructed a linkage map for Abies sachalinensis (2n = 24) and investigated quantitative trait loci (QTLs) associated with local adaptation along an altitudinal gradient. A segregating population of 239 seedlings was produced from a cross between two F₁ hybrids (high-altitude × low-altitude genotypes). QTL mapping of phenological and growth traits was performed using a pseudo-testcross strategy with linkage maps based on 1251 single-nucleotide polymorphism (SNP) and three simple sequence repeat (SSR) markers. Two maps consisting of 12 linkage groups with an average marker interval of ca. 3 cM were constructed for each parent. The total lengths of the maps were 1861 and 1949 cM. A permutation test identified four significant QTLs and 11 additional suggestive QTLs, with high logarithm of odds (LOD) scores (> 3.0). This is the first highly saturated linkage map produced for Abies taxa. Our results suggest that spring bud phenology is controlled by several QTLs with moderate effects. The use of the mapping population created by crossing two hybrids (high × low altitude genotypes) and numerous SNP markers enabled us to investigate the genetic basis of adaptive traits in conifer species.     

Efficient QTL detection of flowering date in a soybean RIL population using the novel restricted two-stage multi-locus GWAS procedure

Key message

Eighty-six R1 QTLs accounting for 89.92% phenotypic variance in a soybean RIL population were identified using RTM-GWAS with SNPLDB marker which performed superior over CIM and MLM-GWAS with BIN/SNPLDB marker.

Abstract

A population (NJRIKY) composed of 427 recombinant inbred lines (RILs) derived from Kefeng-1?×?NN1138-2 (MGII?×?MGV, MG maturity group) was applied for detecting flowering date (R1) quantitative trait locus (QTL) system in soybean. From a low-depth re-sequencing (~?0.75?×), 576,874 SNPs were detected and organized into 4737 BINs (recombination breakpoint determinations) and 3683 SNP linkage disequilibrium blocks (SNPLDBs), respectively. Using the association mapping procedures “Restricted Two-stage Multi-locus Genome-wide Association Study” (RTM-GWAS), “Mixed Linear Model Genome-wide Association Study” (MLM-GWAS) and the linkage mapping procedure “Composite Interval Mapping” (CIM), 67, 36 and 10 BIN-QTLs and 86, 14 and 23 SNPLDB-QTLs were detected with their phenotypic variance explained (PVE) 88.70–89.92% (within heritability 98.2%), 146.41–353.62% (overflowing) and 88.29–172.34% (overflowing), respectively. The RTM-GWAS with SNPLDBs which showed to be more efficient and reasonable than the others was used to identify the R1 QTL system in NJRIKY. The detected 86 SNPLDB-QTLs with their PVE from 0.02 to 30.66% in a total of 89.92% covered 51 out of 104 R1 QTLs in 18 crosses in SoyBase and 26 out of 139 QTLs in a nested association mapping population, while the rest 29 QTLs were novel ones. From the QTL system, 52 candidate genes were annotated, including the verified gene E1, E2, E9 and J, and grouped into 3 categories of biological processes, among which 24 genes were enriched into three protein–protein interaction networks, suggesting gene networks working together. Since NJRIKY involves only MGII and MGV, the QTL/gene system among MG000–MGX should be explored further.

     

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.     

A QTL study on late leaf spot and rust revealed one major QTL for molecular breeding for rust resistance in groundnut (Arachis hypogaea L.)

Late leaf spot (LLS) and rust are two major foliar diseases of groundnut (Arachis hypogaea L.) that often occur together leading to 50–70% yield loss in the crop. A total of 268 recombinant inbred lines of a mapping population TAG 24 × GPBD 4 segregating for LLS and rust were used to undertake quantitative trait locus (QTL) analysis. Phenotyping of the population was carried out under artificial disease epiphytotics. Positive correlations between different stages, high to very high heritability and independent nature of inheritance between both the diseases were observed. Parental genotypes were screened with 1,089 simple sequence repeat (SSR) markers, of which 67 (6.15%) were found polymorphic. Segregation data obtained for these markers facilitated development of partial linkage map (14 linkage groups) with 56 SSR loci. Composite interval mapping (CIM) undertaken on genotyping and phenotyping data yielded 11 QTLs for LLS (explaining 1.70–6.50% phenotypic variation) in three environments and 12 QTLs for rust (explaining 1.70–55.20% phenotypic variation). Interestingly a major QTL associated with rust (QTL_rust01), contributing 6.90–55.20% variation, was identified by both CIM and single marker analysis (SMA). A candidate SSR marker (IPAHM 103) linked with this QTL was validated using a wide range of resistant/susceptible breeding lines as well as progeny lines of another mapping population (TG 26 × GPBD 4). Therefore, this marker should be useful for introgressing the major QTL for rust in desired lines/varieties of groundnut through marker-assisted backcrossing.     

Genetic diversity and association mapping of seed vigor in rice (Oryza sativa L.)

Key message

Twenty-seven QTLs were identified for rice seed vigor, in which 16 were novel QTLs. Fifteen elite parental combinations were designed for improving seed vigor in rice.

Abstract

Seed vigor is closely related to direct seeding in rice (Oryza sativa L.). Previous quantitative trait locus (QTL) studies for seed vigor were mainly derived from bi-parental segregating populations and no report from natural populations. In this study, association mapping for seed vigor was performed on a selected sample of 540 rice cultivars (419 from China and 121 from Vietnam). Population structure was estimated on the basis of 262 simple sequence repeat (SSR) markers. Seed vigor was evaluated by root length (RL), shoot length (SL) and shoot dry weight in 2011 and 2012. Abundant phenotypic and genetic diversities were found in the studied population. The population was divided into seven subpopulations, and the levels of linkage disequilibrium (LD) ranged from 10 to 80 cM. We identified 27 marker–trait associations involving 18 SSR markers for three traits. According to phenotypic effects for alleles of the detected QTLs, elite alleles were mined. These elite alleles could be used to design parental combinations and the expected results would be obtained by pyramiding or substituting the elite alleles per QTL (apart from possible epistatic effects). Our results demonstrate that association mapping can complement and enhance previous QTL information for marker-assisted selection and breeding by design.     

Genetic dissection of aroma volatile compounds from the essential oil of peach fruit: QTL analysis and identification of candidate genes using dense SNP maps

Volatile organic compounds (VOCs) in plants are involved in aroma and pest resistance. These compounds form a complex mixture whose composition is specific to species and often to varieties. Despite their importance as essential factors that determine peach fruit quality, understanding of molecular, genetic, and physiological mechanisms underlying aroma formation is limited. The aim of this study was the identification in peach of quantitative trait loci (QTLs) for fruit VOCs to understand their genetic basis using an F1 population of 126 seedlings deriving from the cross between “Bolero” (B) and “OroA” (O), two peach cultivars differing in their aroma profile. Dense single nucleotide polymorphism (SNP) and SSR maps covering the eight linkage groups of the peach genome were constructed by genotyping with the International Peach SNP Consortium peach SNP array v1, and data for 23 VOCs with high or unknown “odor activity value” were obtained by gas chromatography–mass spectrometry analysis of fruit essential oil in the years 2007 and 2008. A total of 72 QTLs were identified, most consistent in both years. QTLs were identified for the 23 VOCs studied, including three major QTLs for nonanal, linalool, and for p-menth-1-en-9-al stable in both years. Collocations between candidate genes and major QTLs were identified taking advantage of the peach genome sequence: genes encoding two putative terpene synthases and one lipoxygenase (Lox) might be involved in the biosynthesis of linalool and p-menth-1-en-9-al, and nonanal, respectively. Implications for marker-assisted selection and future research on the subject are discussed.     

Mapping quantitative trait loci associated with blush in peach [Prunus persica (L.) Batsch]

Blush is an important trait for marketing peaches. The red skin pigmentation develops through the flavonoid and anthocyanin pathways, and both genetic and environmental stimuli, and their interaction, control the regulation of these pathways. The molecular basis of blush development in peach is yet to be understood. An F₂ blush population (ZC²) derived from a cross between two peach cultivars with contrasting phenotypes for blush, “Zin Dai” (～30 % red) and “Crimson Lady” (～100 % red), was used for linkage map construction and quantitative trait loci (QTLs) mapping. The segregating population was phenotyped for blush for 4 years using a visual rating scale and quantified using a colorimeter (L*, a*, and b*) 1 year. The ZC² population was genotyped with the IPSC 9 K peach single-nucleotide polymorphism (SNP) array v1, and a high-density ZC² genetic linkage map was constructed. The map covers genetic a distance of ～452.51 cM with an average marker spacing of 2.38 cM/marker. Four QTLs were detected: one major QTL on LG3 (Blush.Pp.ZC-3.1) and three minor QTLs on LG 4 and 7 (Blush.Pp.ZC-4.1; Blush.Pp.ZC-4.2; Blush.Pp.ZC-7.1), indicating the presence of major and minor genes involved in blush development. Candidate genes involved in skin and flesh coloration of peach (PprMYB10), cherry (PavMYB10), and apple (MdMYB1/MdMYBA/MdMYB10) are located within the interval of the major QTL on LG3, suggesting the same genetic control for color development in the Rosaceae family. Marker-assisted selection (MAS) for blush is 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.     

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.     

An AFLP, SRAP, and SSR Genetic Linkage Map and Identification of QTLs for Fruit Traits in Pear (Pyrus L.)

In this study, a population of 97 F₁ seedlings from a cross between the interspecific hybrid (European × Chinese species) pear ‘Bayuehong’ (BYH) and the Chinese pear ‘Dangshansuli’ (DS) was used for establishing linkage maps and for quantitative trait loci (QTL) discovery. Using amplified length polymorphism (AFLP), simple sequence repeat (SSR), and sequence-related amplified polymorphism (SRAP) markers, along with the S locus for self-incompatibility, two parental linkage maps were constructed. The map of BYH consisted of 214 markers (143 AFLPs, 64 SRAPs, 6 SSRs, and S) mapped on all 17 linkage groups of the pear genome with a total length of 1,352.7 cM. The map of DS was comprised of 122 markers (83 AFLPs, 37 SRAPs, 1 SSR, and S) distributed along all 17 linkage groups and covering 1,044.3 cM. Based on phenotypic data from two successive years (2007 and 2008) for six fruit traits, including fruit weight (in grams), fruit diameter (in centimeters), fruit length (in centimeters), soluble solids content, fruit shape index, and maturity date, 19 QTLs were detected. These QTLs were mapped on LG 01, LG 02, LG 05, LG 07, LG 08, LG 10 of the BYH map and LG 02, LG 06, LG 15 of the DS map and accounting for 7.1 to 22.0 % of the observed phenotypic variance. Four QTLs, Pfi-8-1 for fruit shape index, Pfm-8-1 for fruit maturity date, Pfw-7-1 and Pfw-8-1 for fruit weight (in grams), with LOD scores ≥3.5, were deemed as major genes. QTLs Pfi-8-1, Pfm-8-1, and Pfw-8-1 were co-localized on LG 08 of the BYH map, along with Pfl-8-1 for fruit length. It was observed that on LG 07 of the BYH map, QTLs for fruit length, fruit shape index, and fruit weight were clustered. When QTL locations from both years were compared, Pfl-7-1 and Pfl-7-2 for fruit length, Pfi-2-1 and Pfi-2-2 for fruit shape index, and Pfm-8-1 and Pfm-8-2 for fruit maturity date were stably mapped onto the same linkage groups, respectively. Moreover, Pfm-8-1 and Pfm-8-2 were also located within the same region of LG 08 of the BYH map.     

Genetic mapping of QTLs for sugar-related traits in a RIL population of Sorghum bicolor L. Moench

The productivity of sorghum is mainly determined by quantitative traits such as grain yield and stem sugar-related characteristics. Substantial crop improvement has been achieved by breeding in the last decades. Today, genetic mapping and characterization of quantitative trait loci (QTLs) is considered a valuable tool for trait enhancement. We have investigated QTL associated with the sugar components (Brix, glucose, sucrose, and total sugar content) and sugar-related agronomic traits (flowering date, plant height, stem diameter, tiller number per plant, fresh panicle weight, and estimated juice weight) in four different environments (two locations) using a population of 188 recombinant inbred lines (RILs) from a cross between grain (M71) and sweet sorghum (SS79). A genetic map with 157 AFLP, SSR, and EST-SSR markers was constructed, and several QTLs were detected using composite interval mapping (CIM). Further, additive × additive interaction and QTL × environmental interaction were estimated. CIM identified more than five additive QTLs in most traits explaining a range of 6.0–26.1% of the phenotypic variation. A total of 24 digenic epistatic locus pairs were identified in seven traits, supporting the hypothesis that QTL analysis without considering epistasis can result in biased estimates. QTLs showing multiple effects were identified, where the major QTL on SBI-06 was significantly associated with most of the traits, i.e., flowering date, plant height, Brix, sucrose, and sugar content. Four out of ten traits studied showed a significant QTL × environmental interaction. Our results are an important step toward marker-assisted selection for sugar-related traits and biofuel yield in sorghum.     

基于F2群体的香菇遗传图谱构建及其在QTL定位中的应用

以171个F2双核体菌株为作图群体,通过相互配对的2个单核体的基因型推断双核体基因型,构建了第一张基于双核体群体的香菇遗传图谱。该图谱包含分布于15个连锁群的459个标记,覆盖长度为989.7cM,平均标记间隔为2.2cM。此外,以此双核体群体作为表型分离群体,定位了6个与香菇双核体菌丝生长速度相关的QTLs,位于5个连锁群上。采用全同胞单核体随机交配策略,易于构建相对大的双核体群体,用于连锁图构建和QTL定位。研究表明,在食用菌连锁图谱构建及QTL定位研究中,利用F2群体,可能为提高遗传作图效率,解决作图群体与表型分离群体间不一致问题提供新的途径。