Multi-trait Quantitative Trait Loci Research Articles

Comparative quantitative trait loci analysis framework reveals relationships between salt stress responsive phenotypes and pathways.

Soil salinity is a complex abiotic stress that involves several biological pathways. Hence, focusing on a specific or a few salt-tolerant phenotypes is unlikely to provide comprehensive insights into the intricate and interwinding mechanisms that regulate salt responsiveness. In this study, we develop a heuristic framework for systematically integrating and comprehensively evaluating quantitative trait loci (QTL) analyses from multiple stress-related traits obtained by different studies. Making use of a combined set of 46 salinity-related traits from three independent studies that were based on the same chromosome segment substitution line (CSSL) population of rice (Oryza sativa), we demonstrate how our approach can address technical biases and limitations from different QTL studies and calling methods. This allows us to compile a comprehensive list of trait-specific and multi-trait QTLs, as well as salinity-related candidate genes. In doing so, we discover several novel relationships between traits that demonstrate similar trends of phenotype scores across the CSSLs, as well as the similarities between genomic locations that the traits were mapped to. Finally, we experimentally validate our findings by expression analyses and functional validations of several selected candidate genes from multiple pathways in rice and Arabidopsis orthologous genes, including OsKS7 (ENT-KAURENE SYNTHASE 7), OsNUC1 (NUCLEOLIN 1) and OsFRO1 (FERRIC REDUCTASE OXIDASE 1) to name a few. This work not only introduces a novel approach for conducting comparative analyses of multiple QTLs, but also provides a list of candidate genes and testable hypotheses for salinity-related mechanisms across several biological pathways.

QTL and Candidate Genes: Techniques and Advancement in Abiotic Stress Resistance Breeding of Major Cereals.

At least 75% of the world's grain production comes from the three most important cereal crops: rice (Oryza sativa), wheat (Triticum aestivum), and maize (Zea mays). However, abiotic stressors such as heavy metal toxicity, salinity, low temperatures, and drought are all significant hazards to the growth and development of these grains. Quantitative trait locus (QTL) discovery and mapping have enhanced agricultural production and output by enabling plant breeders to better comprehend abiotic stress tolerance processes in cereals. Molecular markers and stable QTL are important for molecular breeding and candidate gene discovery, which may be utilized in transgenic or molecular introgression. Researchers can now study synteny between rice, maize, and wheat to gain a better understanding of the relationships between the QTL or genes that are important for a particular stress adaptation and phenotypic improvement in these cereals from analyzing reports on QTL and candidate genes. An overview of constitutive QTL, adaptive QTL, and significant stable multi-environment and multi-trait QTL is provided in this article as a solid framework for use and knowledge in genetic enhancement. Several QTL, such as DRO1 and Saltol, and other significant success cases are discussed in this review. We have highlighted techniques and advancements for abiotic stress tolerance breeding programs in cereals, the challenges encountered in introgressing beneficial QTL using traditional breeding techniques such as mutation breeding and marker-assisted selection (MAS), and the in roads made by new breeding methods such as genome-wide association studies (GWASs), the clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 system, and meta-QTL (MQTL) analysis. A combination of these conventional and modern breeding approaches can be used to apply the QTL and candidate gene information in genetic improvement of cereals against abiotic stresses.

Trade-offs in the genetic control of functional and nutritional quality traits in UK winter wheat.

A complex network of trade-offs exists between wheat quality and nutritional traits. We investigated the correlated relationships among several milling and baking traits as well as mineral density in refined white and whole grain flour. Our aim was to determine their pleiotropic genetic control in a multi-parent population over two trial years with direct application to practical breeding. Co-location of major quantitative trait loci (QTL) and principal component based multi-trait QTL mapping increased the power to detect QTL and revealed pleiotropic effects explaining many complementary and antagonistic trait relationships. High molecular weight glutenin subunit genes explained much of the heritable variation in important dough rheology traits, although additional QTL were detected. Several QTL, including one linked to the TaGW2 gene, controlled grain size and increased flour extraction rate. The semi-dwarf Rht-D1b allele had a positive effect on Hagberg falling number, but reduced grain size, specific weight, grain protein content and flour water absorption. Mineral nutrient concentrations were lower in Rht-D1b lines for many elements, in wholemeal and white flour, but potassium concentration was higher in Rht-D1b lines. The presence of awns increased calcium content without decreasing extraction rate, despite the negative correlation between these traits. QTL were also found that affect the relative concentrations of key mineral nutrients compared to phosphorus which may help increase bioavailability without associated anti-nutritional effects of phytic acid. Taken together these results demonstrate the potential for marker-based selection to optimise trait trade-offs and enhance wheat nutritional value by considering pleiotropic genetic effects across multiple traits.

Robust regressionbased genome-wide multi-trait QTL analysis.

In genome-wide quantitative trait locus (QTL) mapping studies, multiple quantitative traits are often measured along with the marker genotypes. Multi-trait QTL (MtQTL) analysis, which includes multiple quantitative traits together in a single model, is an efficient technique to increase the power of QTL identification. The two most widely used classical approaches for MtQTL mapping are Gaussian Mixture Model-based MtQTL (GMM-MtQTL) and Linear Regression Model-based MtQTL (LRM-MtQTL) analyses. There are two types of LRM-MtQTL approach known as least squares-based LRM-MtQTL (LS-LRM-MtQTL) and maximum likelihood-based LRM-MtQTL (ML-LRM-MtQTL). These three classical approaches are equivalent alternatives for QTL detection, but ML-LRM-MtQTL is computationally faster than GMM-MtQTL and LS-LRM-MtQTL. However, one major limitation common to all the above classical approaches is that they are very sensitive to outliers, which leads to misleading results. Therefore, in this study, we developed an LRM-based robust MtQTL approach, called LRM-RobMtQTL, for the backcross population based on the robust estimation of regression parameters by maximizing the β-likelihood function induced from the β-divergence with multivariate normal distribution. When β = 0, the proposed LRM-RobMtQTL method reduces to the classical ML-LRM-MtQTL approach. Simulation studies showed that both ML-LRM-MtQTL and LRM-RobMtQTL methods identified the same QTL positions in the absence of outliers. However, in the presence of outliers, only the proposed method was able to identify all the true QTL positions. Real data analysis results revealed that in the presence of outliers only our LRM-RobMtQTL approach can identify all the QTL positions as those identified in the absence of outliers by both methods. We conclude that our proposed LRM-RobMtQTL analysis approach outperforms the classical MtQTL analysis methods.

Quantitative trait locus mapping reveals an independent genetic basis for joint divergence in leaf function, life-history, and floral traits between scarlet monkeyflower (Mimulus cardinalis) populations.

Across taxa, vegetative and floral traits that vary along a fast-slow life-history axis are often correlated with leaf functional traits arrayed along the leaf economics spectrum, suggesting a constrained set of adaptive trait combinations. Such broad-scale convergence may arise from genetic constraints imposed by pleiotropy (or tight linkage) within species, or from natural selection alone. Understanding the genetic basis of trait syndromes and their components is key to distinguishing these alternatives and predicting evolution in novel environments. We used a line-cross approach and quantitative trait locus (QTL) mapping to characterize the genetic basis of twenty leaf functional/physiological, life history, and floral traits in hybrids between annualized and perennial populations of scarlet monkeyflower (Mimulus cardinalis). We mapped both single and multi-trait QTLs for life history, leaf function and reproductive traits, but found no evidence of genetic co-ordination across categories. A major QTL for three leaf functional traits (thickness, photosynthetic rate, and stomatal resistance) suggests that a simple shift in leaf anatomy may be key to adaptation to seasonally dry habitats. Our results suggest that the co-ordination of resource-acquisitive leaf physiological traits with a fast life-history and more selfing mating system results from environmental selection rather than functional or genetic constraint. Independent assortment of distinct trait modules, as well as a simple genetic basis to leaf physiological traits associated with drought escape, may facilitate adaptation to changing climates.

Regression based fast multi-trait genome-wide QTL analysis.

Multivariate simple interval mapping (SIM) is one of the most popular approaches for multiple quantitative trait locus (QTL) analysis. Both maximum likelihood (ML) and least squares (LS) multivariate regression (MVR) are widely used methods for multi-trait SIM. ML-based MVR (MVR-ML) is an expectation maximization (EM) algorithm based iterative and complex time-consuming approach. Although the LS-based MVR (MVR-LS) approach is not an iterative process, the calculation of likelihood ratio (LR) statistic in MVR-LS is also a time-consuming complex process. We have introduced a new approach (called FastMtQTL) for multi-trait QTL analysis based on the assumption of multivariate normal distribution of phenotypic observations. Our proposed method can identify almost the same QTL positions as those identified by the existing methods. Moreover, the proposed method takes comparatively less computation time because of the simplicity in the calculation of LR statistic by this method. In the proposed method, LR statistic is calculated only using the sample variance-covariance matrix of phenotypes and the conditional probability of QTL genotype given the marker genotypes. This improvement in computation time is advantageous when the numbers of phenotypes and individuals are larger, and the markers are very dense resulting in a QTL mapping with a bigger dataset.

Special issue in honour of Prof. Reto J. Strasser - Phenotyping with fast fluorescence sensors approximates yield component measurements in pepper (Capsicum annuum L.)

Molecular breeding, a powerful technique to increase crop yield, tries to predict yield by crop growth models with genotype specific, environment-independent yield components and environmental indices as inputs. A fluorescence-trait-based approach is presented to approximate some costly and time-consuming measurements of yield components. Temporal monitoring of chlorophyll a fluorescence resulted in fluorescence traits with high heritability (0.60-0.82) that could act as proxies for model inputs. Medium-sized Pearson's correlations were calculated between fluorescence traits, light-use efficiency (LUE), and fruit related parameters up to 0.53. Multi-trait quantitative trait locus (QTL) analyses identified genomic regions of pepper (Capsicum annuum L.) involved in the phenotypic variation of the fluorescence traits. Fluorescence QTLs found on linkage groups P6, P7, and P11 corresponded to QTLs for number of fruits, partitioning into fruits, and LUE. Fluorescence parameters within 1 min of the fluorescence response curve can thus be useful to approximate yield component traits.

Genome-Wide Association Study for Adult-Plant Resistance to Stripe Rust in Chinese Wheat Landraces (Triticum aestivum L.) From the Yellow and Huai River Valleys.

Stripe rust (also known as yellow rust), caused by the pathogen Puccinia striiformis f. sp. tritici (Pst), is a common and serious fungal disease of wheat (Triticum aestivum L.) worldwide. To identify effective stripe rust resistance loci, a genome-wide association study was performed using 152 wheat landraces from the Yellow and Huai River Valleys in China based on Diversity Arrays Technology and simple sequence repeat markers. Phenotypic evaluation of the degree of resistance to stripe rust at the adult-plant stage under field conditions was carried out in five environments. In total, 19 accessions displayed stable, high degrees of resistance to stripe rust development when exposed to mixed races of Pst at the adult-plant stage in multi-environment field assessments. A marker–trait association analysis indicated that 51 loci were significantly associated with adult-plant resistance to stripe rust. These loci included 40 quantitative trait loci (QTL) regions for adult-plant resistance. Twenty identified resistance QTL were linked closely to previously reported yellow rust resistance genes or QTL regions, which were distributed across chromosomes 1B, 1D, 2A, 2B, 3A, 3B, 4A, 4B, 5B, 6B, 7A, 7B, and 7D. Six multi-trait QTL were detected on chromosomes 1B, 1D, 2B, 3A, 3B, and 7D. Twenty QTL were mapped to chromosomes 1D, 2A, 2D, 4B, 5B, 6A, 6B, 6D, 7A, 7B, and 7D, distant from previously identified yellow rust resistance genes. Consequently, these QTL are potentially novel loci for stripe rust resistance. Among the 20 potentially novel QTL, five (QDS.sicau-2A, QIT.sicau-4B, QDS.sicau-4B.2, QDS.sicau-6A.3, and QYr.sicau-7D) were associated with field responses at the adult-plant stage in at least two environments, and may have large effects on stripe rust resistance. The novel effective QTL for adult-plant resistance to stripe rust will improve understanding of the genetic mechanisms that control the spread of stripe rust, and will aid in the molecular marker-assisted selection-based breeding of wheat for stripe rust resistance.

The genetic architecture of phosphorus efficiency in sorghum involves pleiotropic QTL for root morphology and grain yield under low phosphorus availability in the soil

BackgroundPhosphorus (P) fixation on aluminum (Al) and iron (Fe) oxides in soil clays restricts P availability for crops cultivated on highly weathered tropical soils, which are common in developing countries. Hence, P deficiency becomes a major obstacle for global food security. We used multi-trait quantitative trait loci (QTL) mapping to study the genetic architecture of P efficiency and to explore the importance of root traits on sorghum grain yield on a tropical low-P soil.ResultsP acquisition efficiency was the most important component of P efficiency, and both traits were highly correlated with grain yield under low P availability. Root surface area was positively associated with grain yield. The guinea parent, SC283, contributed 58% of all favorable alleles detected by single-trait mapping. Multi-trait mapping detected 14 grain yield and/or root morphology QTLs. Tightly linked or pleiotropic QTL underlying the surface area of fine roots (1–2 mm in diameter) and grain yield were detected at positions 1–7 megabase pairs (Mb) and 71 Mb on chromosome 3, respectively, and a root diameter/grain yield QTL was detected at 7 Mb on chromosome 7. All these QTLs were near sorghum homologs of the rice serine/threonine kinase, OsPSTOL1. The SbPSTOL1 genes on chromosome 3, Sb03g006765 at 7 Mb and Sb03g031690 at 60 Mb were more highly expressed in SC283, which donated the favorable alleles at all QTLs found nearby SbPSTOL1 genes. The Al tolerance gene, SbMATE, may also influence a grain yield QTL on chromosome 3. Another PSTOL1-like gene, Sb07g02840, appears to enhance grain yield via small increases in root diameter. Co-localization analyses suggested a role for other genes, such as a sorghum homolog of the Arabidopsis ubiquitin-conjugating E2 enzyme, phosphate 2 (PHO2), on grain yield advantage conferred by the elite parent, BR007 allele.ConclusionsGenetic determinants conferring higher root surface area and slight increases in fine root diameter may favor P uptake, thereby enhancing grain yield under low-P availability in the soil. Molecular markers for SbPSTOL1 genes and for QTL increasing grain yield by non-root morphology-based mechanisms hold promise in breeding strategies aimed at developing sorghum cultivars adapted to low-P soils.

Quantitative trait locus analysis of seed germination, seedling vigour and seedling‐regulated hormones in Brassica napus

AbstractGood germination and seedling vigour are major breeding targets in winter oilseed rape (Brassica napus), because seedling vigour and prewinter crop establishment are closely associated with postwinter growth and yield. Here, we identified quantitative trait loci (QTL) related to germination, seedling vigour and seedling‐regulated hormones in a doubled haploid (DH) mapping population from a cross between winter oilseed rape parents with high vigour (Express 617) and low vigour (1012‐98). By phenotyping in a climate‐controlled glasshouse, we identified a total of 13 QTL on nine chromosomes for germination and seedling‐related traits at 7 and 14 days after sowing (DAS), explaining up to 11.2% of the phenotypic variation for seedling vigour. Forty‐seven metabolic QTL on 15 chromosomes were identified for auxin, abscisic acid (ABA) and dihydrophaseic acid (DPA) at 5 and 12 DAS, explaining up to 49.4% of phenotypic variation in seedling hormone composition. Multitrait QTL hot spots contribute to our understanding of the genetics and metabolomics of germination and seeding vigour in B. napus, and represent potential targets to breed high‐vigour cultivars.

QTL mapping and analysis of epistatic interactions for grain yield and yield-related traits in Triticum turgidum L. var. durum

A quantitative trait loci (QTL) analysis of grain yield and yield-related traits was performed on 93 durum wheat recombinant inbred lines derived from the cross UC1113 × Kofa. The mapping population and parental lines were analyzed considering 19 traits assessed in different Argentine environments, namely grain yield, heading date, flowering time, plant height, biomass per plant, and spikelet number per ear, among others. A total of 224 QTL with logarithm of odds ratio (LOD) ≥ 3 and 47 additional QTL with LOD > 2.0 were detected. These QTL were clustered in 35 regions with overlapping QTL, and 12 genomic regions were associated with only one phenotypic trait. The regions with the highest number of multi-trait and stable QTL were 3BS.1, 3BS.2, 2BS.1, 1BL.1, 3AL.1, 1AS, and 4AL.3. The effects of epistatic QTL and QTL × environment interactions were also analyzed. QTL putatively located at major gene loci (Rht, Vrn, Eps, and Ppd) as well as additional major/minor QTL involved in the complex genetic basis of yield-related traits expressed in Argentine environments were identified. Interestingly, the 3AL.1 region was found to increase yield without altering grain quality or crop phenology.

Genome-Wide Association Mapping Reveals That Specific and Pleiotropic Regulatory Mechanisms Fine-Tune Central Metabolism and Growth in Arabidopsis.

Central metabolism is a coordinated network that is regulated at multiple levels by resource availability and by environmental and developmental cues. Its genetic architecture has been investigated by mapping metabolite quantitative trait loci (QTL). A more direct approach is to identify enzyme activity QTL, which distinguishes between cis-QTL in structural genes encoding enzymes and regulatory trans-QTL. Using genome-wide association studies, we mapped QTL for 24 enzyme activities, nine metabolites, three structural components, and biomass in Arabidopsis thaliana We detected strong cis-QTL for five enzyme activities. A cis-QTL for UDP-glucose pyrophosphorylase activity in the UGP1 promoter is maintained through balancing selection. Variation in acid invertase activity reflects multiple evolutionary events in the promoter and coding region of VAC-INVcis-QTL were also detected for ADP-glucose pyrophosphorylase, fumarase, and phosphoglucose isomerase activity. We detected many trans-QTL, including transcription factors, E3 ligases, protein targeting components, and protein kinases, and validated some by knockout analysis. trans-QTL are more frequent but tend to have smaller individual effects than cis-QTL. We detected many colocalized QTL, including a multitrait QTL on chromosome 4 that affects six enzyme activities, three metabolites, protein, and biomass. These traits are coordinately modified by different ACCELERATED CELL DEATH6 alleles, revealing a trade-off between metabolism and defense against biotic stress.

Flowering Locus C (FLC) Is a Potential Major Regulator of Glucosinolate Content across Developmental Stages of Aethionema arabicum (Brassicaceae).

The biochemical defense of plants can change during their life-cycle and impact herbivore feeding and plant fitness. The annual species Aethionema arabicum is part of the sister clade to all other Brassicaceae. Hence, it holds a phylogenetically important position for studying crucifer trait evolution. Glucosinolates (GS) are essentially Brassicales-specific metabolites involved in plant defense. Using two Ae. arabicum accessions (TUR and CYP) we identify substantial differences in glucosinolate profiles and quantities between lines, tissues and developmental stages. We find tissue specific side-chain modifications in aliphatic GS: methylthioalkyl in leaves, methylsulfinylalkyl in fruits, and methylsulfonylalkyl in seeds. We also find large differences in absolute glucosinolate content between the two accessions (up to 10-fold in fruits) that suggest a regulatory factor is involved that is not part of the quintessential glucosinolate biosynthetic pathway. Consistent with this hypothesis, we identified a single major multi-trait quantitative trait locus controlling total GS concentration across tissues in a recombinant inbred line population derived from TUR and CYP. With fine-mapping, we narrowed the interval to a 58 kb region containing 15 genes, but lacking any known GS biosynthetic genes. The interval contains homologs of both the sulfate transporter SULTR2;1 and FLOWERING LOCUS C. Both loci have diverse functions controlling plant physiological and developmental processes and thus are potential candidates regulating glucosinolate variation across the life-cycle of Aethionema. Future work will investigate changes in gene expression of the candidates genes, the effects of GS variation on insect herbivores and the trade-offs between defense and reproduction.

Multi-trait and multi-environment QTL analysis reveals the impact of seed colour on seed composition traits in Brassica napus

Brassica napus seed composition traits (fibre, protein, oil and fatty acid profiles), seed colour and yield-associated traits are regulated by a complex network of genetic factors. Although previous studies have attempted to dissect the underlying genetic basis for these traits, a more complete picture of the available quantitative trait loci (QTL) variation and any interaction between the different traits is required. In this study, QTL mapping for eleven seed composition traits, seed colour and a yield-related trait (TSW) was conducted in a spring-type canola-quality B. napus doubled haploid (DH) population from a cross between black-seeded (DH12075) and yellow-seeded (YN01-429) lines across five environments. A major QTL associated with fibre traits (acid detergent fibre, acid detergent lignin and neutral detergent fibre) and seed colour (whiteness index) was mapped on chromosome N9 across the five environments. Multi-trait analysis identified QTL which had pleiotropic effect for seed colour and other composition traits. Multi-environment analysis revealed genetic (QTL) × environment effects on most QTL. These findings provide a more detailed insight into the complex QTL networks controlling seed composition and yield-associated traits in canola-quality B. napus.

Identification of candidate genes involved in Witches' broom disease resistance in a segregating mapping population of Theobroma cacao L. in Brazil.

BackgroundWitches’ broom disease (WBD) caused by the fungus Moniliophthora perniciosa is responsible for considerable economic losses for cacao producers. One of the ways to combat WBD is to plant resistant cultivars. Resistance may be governed by a few genetic factors, mainly found in wild germplasm.ResultsWe developed a dense genetic linkage map with a length of 852.8 cM that contains 3,526 SNPs and is based on the MP01 mapping population, which counts 459 trees from a cross between the resistant ‘TSH 1188’ and the tolerant ‘CCN 51’ at the Mars Center for Cocoa Science in Barro Preto, Bahia, Brazil. Seven quantitative trait loci (QTL) that are associated with WBD were identified on five different chromosomes using a multi-trait QTL analysis for outbreeders. Phasing of the haplotypes at the major QTL region on chromosome IX on a diversity panel of genotypes clearly indicates that the major resistance locus comes from a well-known source of WBD resistance, the clone ‘SCAVINA 6’. Various potential candidate genes identified within all QTL may be involved in different steps leading to disease resistance. Preliminary expression data indicate that at least three of these candidate genes may play a role during the first 12 h after infection, with clear differences between ‘CCN 51’ and ‘TSH 1188’.ConclusionsWe combined the information from a large mapping population with very distinct parents that segregate for WBD, a dense set of mapped markers, rigorous phenotyping capabilities and the availability of a sequenced genome to identify several genomic regions that are involved in WBD resistance. We also identified a novel source of resistance that most likely comes from the ‘CCN 51’ parent. Thanks to the large population size of the MP01 population, we were able to pick up QTL and markers with relatively small effects that can contribute to the creation and selection of more tolerant/resistant plant material.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2415-x) contains supplementary material, which is available to authorized users.

Back to Acid Soil Fields: The Citrate Transporter SbMATE Is a Major Asset for Sustainable Grain Yield for Sorghum Cultivated on Acid Soils

Aluminum (Al) toxicity damages plant roots and limits crop production on acid soils, which comprise up to 50% of the world’s arable lands. A major Al tolerance locus on chromosome 3, AltSB, controls aluminum tolerance in sorghum [Sorghum bicolor (L.) Moench] via SbMATE, an Al-activated plasma membrane transporter that mediates Al exclusion from sensitive regions in the root apex. As is the case with other known Al tolerance genes, SbMATE was cloned based on studies conducted under controlled environmental conditions, in nutrient solution. Therefore, its impact on grain yield on acid soils remains undetermined. To determine the real world impact of SbMATE, multi-trait quantitative trait loci (QTL) mapping in hydroponics, and, in the field, revealed a large-effect QTL colocalized with the Al tolerance locus AltSB, where SbMATE lies, conferring a 0.6 ton ha–1 grain yield increase on acid soils. A second QTL for Al tolerance in hydroponics, where the positive allele was also donated by the Al tolerant parent, SC283, was found on chromosome 9, indicating the presence of distinct Al tolerance genes in the sorghum genome, or genes acting in the SbMATE pathway leading to Al-activated citrate release. There was no yield penalty for AltSB, consistent with the highly localized Al regulated SbMATE expression in the root tip, and Al-dependent transport activity. A female effect of 0.5 ton ha–1 independently demonstrated the effectiveness of AltSB in hybrids. Al tolerance conferred by AltSB is thus an indispensable asset for sorghum production and food security on acid soils, many of which are located in developing countries.

The genetic architecture of petal number in Cardamine hirsuta.

Invariant petal number is a characteristic of most flowers and is generally robust to genetic and environmental variation. We took advantage of the natural variation found in Cardamine hirsuta petal number to investigate the genetic basis of this trait in a case where robustness was lost during evolution. We used quantitative trait locus (QTL) analysis to characterize the genetic architecture of petal number. Αverage petal number showed transgressive variation from zero to four petals in five C.hirsuta mapping populations, and this variation was highly heritable. We detected 15 QTL at which allelic variation affected petal number. The effects of these QTL were relatively small in comparison with alleles induced by mutagenesis, suggesting that natural selection may act to maintain petal number within its variable range below four. Petal number showed a temporal trend during plant ageing, as did sepal trichome number, and multi-trait QTL analysis revealed that these age-dependent traits share a common genetic basis. Our results demonstrate that petal number is determined by many genes of small effect, some of which are age-dependent, and suggests a mechanism of trait evolution via the release of cryptic variation.

Whole-genome analysis of multienvironment or multitrait QTL in MAGIC.

Multiparent Advanced Generation Inter-Cross (MAGIC) populations are now being utilized to more accurately identify the underlying genetic basis of quantitative traits through quantitative trait loci (QTL) analyses and subsequent gene discovery. The expanded genetic diversity present in such populations and the amplified number of recombination events mean that QTL can be identified at a higher resolution. Most QTL analyses are conducted separately for each trait within a single environment. Separate analysis does not take advantage of the underlying correlation structure found in multienvironment or multitrait data. By using this information in a joint analysis—be it multienvironment or multitrait — it is possible to gain a greater understanding of genotype- or QTL-by-environment interactions or of pleiotropic effects across traits. Furthermore, this can result in improvements in accuracy for a range of traits or in a specific target environment and can influence selection decisions. Data derived from MAGIC populations allow for founder probabilities of all founder alleles to be calculated for each individual within the population. This presents an additional layer of complexity and information that can be utilized to identify QTL. A whole-genome approach is proposed for multienvironment and multitrait QTL analysis in MAGIC. The whole-genome approach simultaneously incorporates all founder probabilities at each marker for all individuals in the analysis, rather than using a genome scan. A dimension reduction technique is implemented, which allows for high-dimensional genetic data. For each QTL identified, sizes of effects for each founder allele, the percentage of genetic variance explained, and a score to reflect the strength of the QTL are found. The approach was demonstrated to perform well in a small simulation study and for two experiments, using a wheat MAGIC population.

Selecting Informative Traits for Multivariate Quantitative Trait Locus Mapping Helps to Gain Optimal Power

A major consideration in multitrait analysis is which traits should be jointly analyzed. As a common strategy, multitrait analysis is performed either on pairs of traits or on all of traits. To fully exploit the power of multitrait analysis, we propose variable selection to choose a subset of informative traits for multitrait quantitative trait locus (QTL) mapping. The proposed method is very useful for achieving optimal statistical power for QTL identification and for disclosing the most relevant traits. It is also a practical strategy to effectively take advantage of multitrait analysis when the number of traits under consideration is too large, making the usual multivariate analysis of all traits challenging. We study the impact of selection bias and the usage of permutation tests in the context of variable selection and develop a powerful implementation procedure of variable selection for genome scanning. We demonstrate the proposed method and selection procedure in a backcross population, using both simulated and real data. The extension to other experimental mapping populations is straightforward.

QTL clustering as a mechanism for rapid multi-trait evolution

Cave-dwelling animals exhibit remarkable convergence in multiple cave-related traits, yet the genetic mechanisms responsible for the evolution and integration of many such traits remain unclear. Astyanax mexicanus is a model cave-dwelling fish with sighted surface-dwelling (surface fish) and blind cave-dwelling (cavefish) forms. Using a genetic cross between these morphs, we discovered significant correlations among several cave-related traits, including reduced eyes, increased superficial neuromast receptors located within the eye orbit (EO SN) and a vibration-attraction behavior (VAB) that facilitates foraging in darkness. Furthermore, we discovered that the quantitative trait loci (QTL) underlying these traits are clustered within the Astyanax genome. Following an ablation experiment that demonstrated that the EO SN contribute to VAB, we concluded that the adaptive evolution of VAB and EO SN has likely contributed to eye loss in cavefish. In this addendum, we further discuss the possible role of multi-trait QTL clustering in facilitating rapid adaptation.