Effectiveness of selective genotyping for detection of quantitative trait loci: an analysis of grain and malt quality traits in three barley populations.

Marker genotype data and grain and malt quality phenotype data from three barley (Hordeum vulgare L.) mapping populations were used to investigate the feasibility of selective genotyping for detection of quantitative trait loci (QTLs). With selective genotyping, only individuals with high and low phenotypic values for the trait of interest are genotyped. Here, genotyping of 10 to 70% of each population (i.e., 5 to 35% in each tail of the phenotypic distribution) was considered. Genomic positions detected by selective genotyping were compared to QTL position estimates from interval mapping analysis using marker genotype data from the entire population. Selective genotyping reliably detected almost all of the mapped QTLs, often with only 10% of the population genotyped. Selective genotyping also detected spurious QTLs in regions of the genome where no significant QTL had been mapped. Even with additional genotyping to verify putative QTLs, the total genotyping effort for detection of QTLs for a single trait by selective genotyping was usually less than 30% of that required for conventional interval mapping. Simultaneous investigation of two or more traits by selective genotyping would require additional genotyping effort, but could still be worthwhile.     

茄子分子遗传图谱的构建及果实性状的QTL定位

以茄子(Solanum melongena)材料09-101-M和10TL-102-F4-1的重组自交系(RIL)为作图群体,构建总长度为991.7c M、共包含16个连锁群157个位点、平均图距为6.32 c M的遗传图谱。应用复合区间作图法(CIM),共检测到18个与茄子果实性状相关的QTLs,其中10个为主效QTLs,8个QTLs在两年两点的实验中能够被重复检测到。在所有QTLs中,控制果重的QTL fw1.1的效应值最大,为23.8%–31.6%,被定位在LG01(E09)上E25M34–E33M57b区域内;果长、果径与果重显著相关,且控制果长、果径与果重的QTL位于同一连锁群的相同区域。     

Locating genes associated with root morphology and drought avoidance in rice via linkage to molecular markers

This research was undertaken to identify and map quantitative trait loci (QTLs) associated with five parameters of rice root morphology and to determine if these QTLs are located in the same chromosomal regions as QTLs associated with drought avoidance/tolerance. Root thickness, root:shoot ratio, root dry weight per tiller, deep root dry weight per tiller, and maximum root length were measured in three replicated experiments (runs) of 203 recombinant inbred lines grown in a greenhouse. The lines were from a cross between indica cultivar Co39 andjaponica cultivar Moroberekan. The 203 RI lines were also grown in three replicated field experiments where they were drought-stressed at the seedling, early vegetative, and late-vegetative growth stage and assigned a visual rating based on leaf rolling as to their degree of drought avoidance/tolerance. The QTL analysis of greenhouse and field data was done using single-marker analysis (ANOVA) and interval analysis (Mapmaker QTL). Most QTLs that were identified were associated with root thickness, root/shoot ratio, and root dry weight per tiller, and only a few with deep root weight. None were reliably associated with maximum root depth due to genotype-by-experiment interaction. Root thickness and root dry weight per tiller were the characters found to be the least influenced by environmental differences between greenhouse runs. Correlations of root parameters measured in greenhouse experiments with field drought avoidance/tolerance were significant but not highly predictive. Twelve of the fourteen chromosomal regions containing putative QTLs associated with field drought avoidance/tolerance also contained QTLs associated with root morphology. Thus, selecting for Moroberekan alleles at marker loci associated with the putative root QTLs identified in this study may be an effective strategy for altering the root phenotype of rice towards that commonly associated with drought-resistant cultivars.     

Using molecular markers to map multiple quantitative trait loci: models for backcross,recombinant inbred,and doubled haploid progeny

Summary To maximize parameter estimation efficiency and statistical power and to estimate epistasis, the parameters of multiple quantitative trait loci (QTLs) must be simultaneously estimated. If multiple QTL affect a trait, then estimates of means of QTL genotypes from individual locus models are statistically biased. In this paper, I describe methods for estimating means of QTL genotypes and recombination frequencies between marker and quantitative trait loci using multilocus backcross, doubled haploid, recombinant inbred, and testcross progeny models. Expected values of marker genotype means were defined using no double or multiple crossover frequencies and flanking markers for linked and unlinked quantitative trait loci. The expected values for a particular model comprise a system of nonlinear equations that can be solved using an interative algorithm, e.g., the Gauss-Newton algorithm. The solutions are maximum likelihood estimates when the errors are normally distributed. A linear model for estimating the parameters of unlinked quantitative trait loci was found by transforming the nonlinear model. Recombination frequency estimators were defined using this linear model. Certain means of linked QTLs are less efficiently estimated than means of unlinked QTLs.     

Assessing the importance of genotype × environment interaction for root traits in rice using a mapping population II: conventional QTL analysis

Modifying plant root systems is considered a means of crop improvement targeted to low-resource environments, particularly low nutrient and drought-prone agriculture. The identification of quantitative trait loci (QTLs) for root traits has stimulated marker-assisted breeding to this end, but different QTLs have been detected in different populations of the same species, and importantly, in the same population when grown in different experimental environments. The presence of QTL × environment interaction is implicated, and this must be characterised if the utility of the target QTLs is to be realised. Previous attempts to do this suffer from a lack of control over replicate environments and inadequate statistical rigour. The Bala × Azucena mapping population was grown in two replicate experiments of four treatment environments, a control, a low light, a low soil nitrogen and a low soil water treatment. After a 4 weeks growth, maximum root length, maximum root thickness, root mass below 50 cm, total plant dry mass, % root mass and shoot length were measured. A summary of the overall results is presented in an accompanying paper. Here, QTL analysis by composite interval mapping is presented. A total of 145 QTLs were detected, mapping to 37 discrete loci on all chromosomes. Superficial evidence of QTL × E (great difference in LOD score) was tested by single-marker analysis which confirmed QTL × E for five loci representing only five individual trait-loci interactions. Some loci appeared to be stable across environments. Some QTLs were clearly more or less active under low light, low nitrogen or drought. A few notable loci on chromosomes 1, 2, 3, 5, 7 and 9 are briefly discussed. Also discussed are some remaining statistical shortcomings that will be addressed in another companion paper.     

Identification of QTL for increased fibrous roots in soybean

Drought stress adversely affects soybean at various developmental stages, which collectively results in yield reduction. Unpredictable rainfall has been reported to contribute about 36% to variation of yield difference between the rain-fed and irrigated fields. Among the drought resistance mechanisms, drought avoidance in genotypes with fibrous roots was recognized to be associated with drought resistance in soybean. Plant introduction PI416937 was shown to possess fibrous roots and has been used as a parent in breeding programs to improve soybean productivity. Little information is available on relative contribution and chromosomal location of quantitative trait loci (QTL) conditioning fibrous roots in soybean. To identify the genomic locations and genetic bases of this trait, a recombinant inbred line population was derived from a cross between PI416937 and ‘Benning’. To detect associated QTLs, phenotypic data were collected and analyzed for 2 years under rain-fed field conditions. The selective genotyping approach was used to reduce the costs and work associated with conducting the QTL analysis. A total of five QTLs were identified on chromosomes Gm01 (Satt383), Gm03 (Satt339), Gm04 (Sct_191), Gm08 (Satt429), and Gm20 (Sat_299), and together explained 51% of the variation in root score. Detected QTLs were co-localized with QTLs related to root morphology, suggesting that fibrous roots QTL may be associated with other morpho-physiological traits and seed yield in soybean. Genetic dissection of the fibrous roots trait at the individual marker loci will allow for marker-assisted selection to develop soybean genotypes with enhanced levels of fibrous roots.     

Quantitative trait loci for root-penetration ability and root thickness in rice: comparison of genetic backgrounds.

Drought is the major abiotic stress limiting rice (Oryza sativa) production and yield stability in rainfed lowland and upland ecosystems. Root systems play an important role in drought resistance. Incorporation of root selection criteria in drought resistance improvement is difficult due to lack of reliable and efficient screening techniques. Using a wax-petrolatum layer system simulated to compacted soil layers, root traits were evaluated in a doubled haploid (DH) population derived from the cross between 'IR64' and 'Azucena'. Twelve putative QTLs (quantitative trait loci) were detected by interval mapping comprising four QTLs for root-penetration ability, four QTLs for root thickness, two QTLs for penetrated root number, and two QTLs for total root number. These QTLs individually explained 8.4% to 16.4% of the phenotypic variation. No QTL was detected for maximum penetrated root length by interval mapping. One QTL located between RG104 and RG348 was found to influence both root-penetration ability and root thickness. QTLs for root-penetration ability and root thickness were compared across two populations, 'IR64'-'Azucena' and 'CO39'-'Moroberekan', and different testing conditions. The identified consistent QTLs could be used for marker-assisted selection for deep and thick roots with high root-penetration ability in rice.     

Genetic mapping of loci responsible for milk quality parameters in dairy cattle

The review presents a definition of loci controlling quantitative traits (quantitative trait loci, QTLs) and localization of all currently known QTLs responsible for milk production traits in dairy cattle. The QTL number and chromosome localization are verified, with special reference to chromosomes 1, 3, 6, 14, 20, and 23. In a number of cases, close location of QTLs for mastitis and for milk production traits was found. Some aspects of QTL pleiotropy and epistasis are discussed and mapping methods of major QTLs are listed.     

Mapping of yield-related QTLs in pepper in an interspecific cross of <Emphasis Type="Italic">Capsicum annuum</Emphasis> and <Emphasis Type="Italic">C. frutescens</Emphasis>

An advanced backcross QTL study was performed in pepper using a cross between the cultivated species Capsicum annuum cv. Maor and the wild C. frutescens BG 2816 accession. A genetic map from this cross was constructed, based on 248 BC(2) plants and 92 restriction fragment length polymorphism (RFLP) markers distributed throughout the genome. Ten yield-related traits were analyzed in the BC(2) and BC(2)S(1) generations, and a total of 58 quantitative trait loci (QTLs) were detected; the number of QTLs per trait ranged from two to ten. Most of the QTLs were found in 11 clusters, in which similar QTL positions were identified for multiple traits. Unlike the high percentage of favorable QTL alleles discovered in wild species of tomato and rice, only a few such QTL alleles were detected in BG 2816. For six QTLs (10%), alleles with effects opposite to those expected from the phenotype were detected in the wild species. The use of common RFLP markers in the pepper and tomato maps enabled possible orthologous QTLs in the two species to be determined. The degree of putative QTL orthology for the two main fruit morphology traits-weight and shape-varied considerably. While all eight QTLs identified for fruit weight in this study could be orthologous to tomato fruit weight QTLs, only one out of six fruit shape QTLs found in this study could be orthologous to tomato fruit shape QTLs.     

Quantitative trait loci for spawning date and body weight in rainbow trout: testing for conserved effects across ancestrally duplicated chromosomes

We incorporated 69 microsatellite loci into an existing data set of 132 markers to test for quantitative trait loci (QTLs) affecting spawning date and body weight in a backcross between two outbred strains of rainbow trout (Oncorhynchus mykiss). Twenty-six linkage groups were identified and synteny of duplicated microsatellite markers was used to confirm 13 homeologous chromosome pairs. Gene-centromere data were used to localize the centromeres for 13 linkage groups whose orientations were previously unknown. We applied a combination of interval mapping and single marker analysis to the segregating maternal and paternal alleles at 201 microsatellite loci. Four spawning date QTLs with suggestive evidence for an additional two QTLs were detected in female trout spawning at 3 and 4 years of age. Similarly we detected three QTLs for body weight in females at 2 years of age plus four suggestive QTLs for this trait. We found marginal evidence that three pairs of ancestral homeologues contained detectable QTLs for the same trait. In one of the three pairs of homeologues, the duplicated QTL regions mapped to the same relative chromosomal location, while the exact localization of the QTL position in one of the other pairs was difficult to infer since it was based on data from a male-derived map. The existing data were unable to refute a hypothesis that duplicated functional genes will be maintained within the telomeric regions of salmonids due to preferential male-mediated crossing over in this region. Two of the four spawning date QTLs were detected on linkage groups with unknown homeologous relationships. QTLs with possible pleiotropic effects on both spawning date and body size were localized to two linkage groups.     

Quantitative trait loci for body size components in mice

Do body size components, such as weights of internal organs and long bone lengths, with different functions and different developmental histories also have different genetic architectures and pleiotropic patterns? We examine murine quantitative trait loci (QTL) for necropsy weight, four long bone lengths, and four organ weights in the LG/J × SM/J intercross. Differences between trait categories were found in number of QTL, dominance, and pleiotropic patterns. Ninety-seven QTLs for individual traits were identified: 52 for long bone lengths, 30 for organ weights, and 15 for necropsy weight. Results for long bones are typically more highly significant than for organs. Organ weights were more frequently over- or underdominant than long bone lengths or necropsy weight. The single-trait QTLs map to 35 pleiotropic loci. Long bones are much more frequently affected in groups while organs tend to be affected singly or in pairs. Organs and long bones are found at the same locus in only 11 cases, 8 of which also include necropsy weight. Our results suggest mainly separate genetic modules for organ weights and long bone lengths, with a few loci that affect overall body size. Antagonistic pleiotropy, in which a locus has opposite effects on different characteristics, is uncommon.     

Quantitative trait loci for aluminum resistance in Chinese wheat landrace FSW

Aluminum (Al) toxicity is a major constraint for wheat production in acid soils worldwide. Chinese landrace FSW demonstrates a high level of Al resistance. A population of recombinant inbred lines (RILs) was developed from a cross between FSW and an Al-sensitive Chinese line, ND35, using single seed descent, to map quantitative trait loci (QTLs) for Al resistance. Wheat reaction to Al stress was measured by net root growth (NRG) in a nutrient solution culture containing Al(3+) and hematoxylin staining score (HSS) of root after Al stress. After 1,437 simple sequence repeats (SSRs) were screened using bulk segregant analysis, three QTLs were identified to control Al resistance in FSW. One major QTL (Qalt.pser-4DL) was mapped on chromosome 4DL that co-segregated with Xups4, a marker for the promoter of the Al-activated malate transporter (ALMT1) gene. The other two QTLs (Qalt.pser-3BL, Qalt.pser-2A) were located on chromosomes 3BL and 2A, respectively. Together, the three QTLs accounted for up to 81.9% of the phenotypic variation for HSS and 78.3% of the variation for NRG. The physical positions of flanking markers for Qalt.pser-4DL and Qalt.pser-3BL were determined by analyzing these markers in corresponding nulli-tetrasomic, ditelosomic, and 3BL deletion lines of Chinese Spring. Qalt.pser-3BL is a novel QTL with a major effect on Al resistance discovered in this study. The two major QTLs on 4DL and 3BL demonstrated an additive effect. The SSR markers closely linked to the QTLs have potential to be used for marker-assisted selection (MAS) to improve Al resistance of wheat cultivars in breeding programs.     

Molecular mechanism of adventitious root formation in rice

Adventitious roots account for the majority of the rice root system and play an irreplaceable role in rice growth and development. Rice adventitious roots are formed by division of the innermost ground meristem cells in the central cylinder, and some lateral roots are observable in the adventitious root system. Multiple hormones have been implicated in the regulation of root development. Auxin is involved in the initiation of adventitious roots, whereas cytokinin inhibits adventitious root initiation, but promotes adventitious root elongation. Other phytohormones such as nitric oxide, ethylene, brassinosteroid, jasmonic acid and gibberellin may be also involved in regulating adventitious root initiation and development. Additionally, more than 600 root development related quantitative trait loci (QTLs) have been located by QTL analysis of root traits.     

Identification of quantitative trait loci for agronomically important traits and their association with genic-microsatellite markers in sorghum

The identification of quantitative trait loci (QTLs) affecting agronomically important traits enable to understand their underlying genetic mechanisms and genetic basis of their complex interactions. The aim of the present study was to detect QTLs for 12 agronomic traits related to staygreen, plant early development, grain yield and its components, and some growth characters by analyzing replicated phenotypic datasets from three crop seasons, using the population of 168 F₇ RILs of the cross 296B × IS18551. In addition, we report mapping of a subset of genic-microsatellite markers. A linkage map was constructed with 152 marker loci comprising 149 microsatellites (100 genomic- and 49 genic-microsatellites) and three morphological markers. QTL analysis was performed by using MQM approach. Forty-nine QTLs were detected, across environments or in individual environments, with 1–9 QTLs for each trait. Individual QTL accounted for 5.2–50.4% of phenotypic variance. Several genomic regions affected multiple traits, suggesting the phenomenon of pleiotropy or tight linkage. Stable QTLs were identified for studied traits across different environments, and genetic backgrounds by comparing the QTLs in the study with previously reported QTLs in sorghum. Of the 49 mapped genic-markers, 18 were detected associating either closely or exactly as the QTL positions of agronomic traits. EST marker Dsenhsbm19, coding for a key regulator (EIL-1) of ethylene biosynthesis, was identified co-located with the QTLs for plant early development and staygreen trait, a probable candidate gene for these traits. Similarly, such exact co-locations between EST markers and QTLs were observed in four other instances. Collectively, the QTLs/markers identified in the study are likely candidates for improving the sorghum performance through MAS and map-based gene isolations.     

Quantitative trait locus analysis of symbiotic nitrogen fixation activity in the model legume <Emphasis Type="Italic">Lotus japonicus</Emphasis>

Many legumes form nitrogen-fixing root nodules. An elevation of nitrogen fixation in such legumes would have significant implications for plant growth and biomass production in agriculture. To identify the genetic basis for the regulation of nitrogen fixation, quantitative trait locus (QTL) analysis was conducted with recombinant inbred lines derived from the cross Miyakojima MG-20 × Gifu B-129 in the model legume Lotus japonicus. This population was inoculated with Mesorhizobium loti MAFF303099 and grown for 14 days in pods containing vermiculite. Phenotypic data were collected for acetylene reduction activity (ARA) per plant (ARA/P), ARA per nodule weight (ARA/NW), ARA per nodule number (ARA/NN), NN per plant, NW per plant, stem length (SL), SL without inoculation (SLbac−), shoot dry weight without inoculation (SWbac−), root length without inoculation (RLbac−), and root dry weight (RWbac−), and finally 34 QTLs were identified. ARA/P, ARA/NN, NW, and SL showed strong correlations and QTL co-localization, suggesting that several plant characteristics important for symbiotic nitrogen fixation are controlled by the same locus. QTLs for ARA/P, ARA/NN, NW, and SL, co-localized around marker TM0832 on chromosome 4, were also co-localized with previously reported QTLs for seed mass. This is the first report of QTL analysis for symbiotic nitrogen fixation activity traits.     

Genetic mapping of loci responsible for milk production traits in dairy cattle

The review presents a definition of loci controlling quantitative traits (quantitative trait loci, QTLs) and localization of all currently known QTLs responsible for milk production traits in dairy cattle. The QTL number and chromosome localization are verified, with special reference to chromosomes 1, 3, 6, 14, 20, and 23. In a number of cases, close location of QTLs for mastitis and for milk production traits was found. Some aspects of QTL pleiotropy and epistasis are discussed and mapping methods of major QTLs are listed. Original Russian Text Sc M.G. Smaragdov, 2006, published in Genetika, 2006, Vol. 42, No. 1, pp. 5–21.     

On the mapping of quantitative trait loci at marker and non-marker locations

Previous studies have noted that the estimated positions of a large proportion of mapped quantitative trait loci (QTLs) coincide with marker locations and have suggested that this indicates a bias in the mapping methodology. In this study we predict the expected proportion of QTLs with positions estimated to be at the location of a marker and further examine the problem using simulated data. The results show that the higher proportion of putative QTLs estimated to be at marker positions compared with non-marker positions is an expected consequence of the estimation methods. The study initially focused on a single interval with no QTLs and was extended to include multiple intervals and QTLs of large effect. Further, the study demonstrated that the larger proportion of estimated QTL positions at the location of markers was not unique to linear regression mapping. Maximum likelihood produced similar results, although the accumulation of positional estimates at outermost markers was reduced when regions outside the linkage group were also considered. The bias towards marker positions is greatest under the null hypothesis of no QTLs or when QTL effects are small. This study discusses the impact the findings could have on the calculation of thresholds and confidence intervals produced by bootstrap methods.     

Mapping QTLs for root morphology of a rice population adapted to rainfed lowland conditions

In the rainfed lowlands, rice ( Oryza sativa L.) develops roots under anaerobic soil conditions with ponded water, prior to exposure to water stress and aerobic soil conditions that arise later in the season. Constitutive root system development in anaerobic soil conditions has been reported to have a positive effect on subsequent expression of adaptive root traits and water extraction during progressive water stress in aerobic soil conditions. We examined quantitative trait loci (QTLs) for constitutive root morphology traits using a mapping population derived from a cross between two rice lines which were well-adapted to rainfed lowland conditions. The effects of phenotyping environment and genetic background on QTLs identification were examined by comparing the experimental data with published results from four other populations. One hundred and eighty-four recombinant inbred lines (RILs) from a lowland indica cross (IR58821/IR52561) were grown under anaerobic conditions in two experiments. Seven traits, categorized into three groups (shoot biomass, deep root morphology, root thickness) were measured during the tillering stage. Though parental lines showed consistent differences in shoot biomass and root morphology traits across the two seasons, genotype-by-environment interaction (GxE) and QTL-by-environment interaction were significant among the progeny. Two, twelve, and eight QTLs for shoot biomass, deep root morphology, and root thickness, respectively, were identified, with LOD scores ranging from 2.0 to 12.8. Phenotypic variation explained by a single QTL ranged from 6% to 30%. Only two QTLs for deep root morphology, in RG256-RG151 in chromosome 2 and in PC75M3-PC11M4 in chromosome 4, were identified in both experiments. Comparison of positions of QTLs across five mapping populations (the current population plus populations from four other studies) revealed that these two QTLs for deep root morphology were only identified in populations that were phenotyped under anaerobic conditions. Fourteen and nine chromosome regions overlapped across different populations as putative QTLs for deep root morphology and root thickness, respectively. PC41M2-PC173M5 in chromosome 2 was identified as an interval that had QTLs for deep root morphology in four mapping populations. The PC75M3-PC11M4 interval in chromosome 4 was identified as a QTL for root thickness in three mapping populations with phenotypic variation explained by a single QTL consistently as large as 20-30%. Three QTLs for deep root morphology were found only in japonica/indica populations but not in IR58821/IR52561. The results identifying chromosome regions that had putative QTLs for deep root morphology and root thickness over different mapping populations indicate potential for marker-assisted selection for these traits.