Functional markers in wheat

Wheat (Triticum aestivum L.) is one of the most important staple crops world-wide. Gene-derived 'functional' markers (FMs, also called perfect or diagnostic markers elsewhere) would be the ideal tools for marker-assisted breeding of wheat but, at present, their utility is restricted by the limited availability of genes that control agronomic characters. This bottleneck will be overcome in the next decade by ongoing genomics projects. Another obstacle for assigning sequence to trait variation is the high level of linkage disequilibrium (LD) found in elite materials. Thus, although laborious, TILLING seems to be the most promising approach for targeting sites in genes of interest for FM development. Once larger numbers of FMs become available for wheat breeding, they might be useful in exploiting the fixed genetic variation that is present in regions of high LD.     

Functional markers in wheat: current status and future prospects

Functional markers (FM) are developed from sequence polymorphisms present in allelic variants of a functional gene at a locus. FMs accurately discriminate alleles of a targeted gene, and are ideal molecular markers for marker-assisted selection in wheat breeding. In this paper, we summarize FMs developed and used in common wheat. To date, more than 30 wheat loci associated with processing quality, agronomic traits, and disease resistance, have been cloned, and 97 FMs were developed to identify 93 alleles based on the sequences of those genes. A general approach is described for isolation of wheat genes and development of FMs based on in silico cloning and comparative genomics. The divergence of DNA sequences of different alleles that affect gene function is summarized. In addition, 14 molecular markers specific for alien genes introduced from common wheat relatives were also described. This paper provides updated information on all FMs and gene-specific STS markers developed so far in wheat and should facilitate their application in wheat breeding programs.     

Highly sensitive feature detection for high resolution LC/MS

Background  

Liquid chromatography coupled to mass spectrometry (LC/MS) is an important analytical technology for e.g. metabolomics experiments. Determining the boundaries, centres and intensities of the two-dimensional signals in the LC/MS raw data is called feature detection. For the subsequent analysis of complex samples such as plant extracts, which may contain hundreds of compounds, corresponding to thousands of features – a reliable feature detection is mandatory.     

From genomics to functional markers in the era of next-generation sequencing

The availability of complete genome sequences, along with other genomic resources for Arabidopsis, rice, pigeon pea, soybean and other crops, has revolutionized our understanding of the genetic make-up of plants. Next-generation DNA sequencing (NGS) has facilitated single nucleotide polymorphism discovery in plants. Functionally-characterized sequences can be identified and functional markers (FMs) for important traits can be developed at an ever-increasing ease. FMs are derived from sequence polymorphisms found in allelic variants of a functional gene. Linkage disequilibrium-based association mapping and homologous recombinants have been developed for identification of “perfect” markers for their use in crop improvement practices. Compared with many other molecular markers, FMs derived from the functionally characterized sequence genes using NGS techniques and their use provide opportunities to develop high-yielding plant genotypes resistant to various stresses at a fast pace.     

Some practical considerations for using RFLP markers to aid in selection during inbreeding of maize

Summary If molecular markers are to be routinely used in maize (Zea mays L.) breeding for selection of quantitative trait loci (QTL), then consistent marker-trait associations across breeding populations are needed, as are efficient methods for weighting information from different markers. Given 15 restriction fragment length polymorphism (RFLP) markers associated with grain yield in testcrosses of 220 [BS11(FR)C7 x FRMol7] F₂ individuals to FRB73, separate weighting schemes were attempted in order to maximize the frequency of favorable marker genotypes associated with increased grain yield in selected F₂ individuals and F₂:S₄ Unes. The following principles were apparent: (1) Differential weighting among markers, in addition to weighting individual marker genotypes on the basis of associated mean effects, should be emphasized when using markers to select in breeding populations. This is due to limited population sizes that can readily be handled. (2) Relatively few markers may need to be used to screen segregating populations (e.g., F₂) of limited size for loci affecting complex traits, such as combining ability for grain yield, assuming prior knowledge of marker-QTL associations. Markers given greatest weight (largest estimates of associated effects) will determine most selections. (3) When marker-based selection is among individuals at higher levels of inbreeding (e.g., S₄) within selected families, more markers need to be used in screening because those associated with relatively small effects have an increased chance of affecting selection.These results suggest a qualitative approach for utilizing RFLP markers to aid in selection of complex traits in commercial hybrid maize breeding programs. Commercial research programs produce thousands of crosses each year aimed at inbred line development. Discovery of molecular markers with consistent QTL associations across breeding populations and close QTL linkages would allow for rapid screening of new F₂ populations at a few key markers. Early elimination of individuals with undesirable genotypes would reduce the extent of hybrid performance testing necessary during later stages of inbreeding.     

Gain and diversity in advanced generation coastal Douglas-fir selections for seed production populations

Sublines are used in the third-generation breeding and testing of coastal Douglas-fir in British Columbia, with the original intent of selecting only one genotype per subline for production populations (e.g., seed orchards) to eliminate relatedness among parents (therein called “1/SL”). We evaluated three additional selection scenarios that did not consider the subline structure. One of the scenarios strictly selected on the basis of the highest breeding values of the trees (“TOP”); another scenario used the TOP selections, but assigned the number of ramets per selection proportionally to the selection breeding value (“LIND”); lastly, a simulated annealing technique was applied to maximize gain under explicit constraints on coancestry (“OPTS”). All three alternative selection scenarios resulted in some relatedness and coancestry among selections, but the last two provided increases in average breeding values compared to those obtained by the 1/SL scenario. Effective population sizes (and consequently inbreeding coefficients) varied among the three selection scenarios. Effects of the various selections on merchantable volume at rotation age were determined using a linear regression model based on an individual tree model (TASS), which was first run to determine the relationship between merchantable volume and inbreeding (f). LIND and TOP selections yielded the highest breeding values but, due to the increased coancestry among selections, paid a penalty in the merchantable volume determination. OPTS maximized merchantable volume at rotation age 60 after including more than 13 selections with an increase of around 3% over that obtained by the 1/SL selection scenario, with an associated increase in Ne of 50%. Other implications of the three alternative selection scenarios are discussed.     

Decomposition of sugarcane bagasse with lignocellulose-derived thermotolerant and thermoresistant Penicillia and Aspergilli

To promote the decomposition of sugarcane bagasse (SCB) for conversion into value-added products and to reduce waste, the capability of fungal mixes (FMs) to degrade SCB was examined. A total of 169 isolates from SCB and non-SCB were categorized as thermotolerant and thermoresistant. Thirty-six fungal candidates were screened for the presence of polyphenol oxidase, endoglucanase (EDN) and xylanase (XLN) activities, and EDN and XLN activities were quantitated. Five identified isolates (Aspergillus flavus AG10; Aspergillus niger AG68 & NB23; and Penicillium citrinum AG93 & AG140) were selected as the best enzyme producers, and 15 moderately to highly xylolytic, cellulolytic and ligninolytic isolates were added to construct FMs. Using a Taguchi design, the top ten reducing sugar-producing FMs (no. 12 showed the maximum amount of reducing sugar, at 2.11 mg g⁻¹, followed by no. 7, 15, 2, 16, 11, 13, 6, 4, & 8) were selected as potential agents for decomposition durations of 1, 2 and 3 months. The maximum decrease in SCB materials compared with the control was generated by FM 6 (9.08% cellulose reduction); FM 13 (21.03% hemicellulose reduction); and FM 16 (9.21% lignin reduction). These results indicate the potential use of SCB as a substrate for synergistic FMs. These FMs could be applied to the large-scale composting of SCB and other related agricultural residues, thus improving the biological pretreatment of lignocellulose.     

Comparing the physical demands of friendly matches and small-sided games in semiprofessional soccer players

This study compared the physical demands of friendly matches (FMs) and small-sided games (SGs) in semiprofessional soccer players by means of global positioning system technology. Twenty-seven semiprofessional soccer players were monitored during 7 FMs and 9 sessions involving different SGs. Their physical profile was described on the basis of 20 variables related to distances and frequencies at different running speeds, the number of accelerations, and through global indicators of workload such as the work:rest ratio, player workload, and the exertion index. Results showed significant differences (p < 0.01) between SGs and FMs for the following variables: overall workload (SG > FM); the distribution of the distance covered in the speed zones 7.0-12.9 km·h(-1) (SG > FM) and >21 km·h(-1) (FM > SG); the distribution of time spent in certain speed zones (FM > SG: 0.0-6.9 and >21 km·h(-1); FM > SG: 7.0-12.9 km·h(-1)). More sprints per hour of play were performed during FMs, with greater mean durations and distances, greater maximum durations and distances, and a greater frequency per hour of play for sprints of 10-40 and >40 m (p < 0.01). The frequency of repeated high-intensity efforts was higher during FM (p < 0.01). The results show that coaches and strength and conditioning professionals should consider FMs during their training routine to foster specific adaptations in the domain of high-intensity effort.     

Three-dimensional localization and quantification of PAF-induced gap formation in intact venular microvessels

Combining single-vessel perfusion technique with confocal microscopy, this study presents a new approach that allows three-dimensional visualization and quantification of endothelial gaps under experimental conditions identical to those used to measure permeability coefficients, endothelial calcium concentration, and nitric oxide production in individually perfused intact microvessels. This approach provides an efficient means for defining the transport pathways and cellular mechanisms of increased microvascular permeability during inflammation. Platelet-activating factor (PAF) was used to increase the permeability of individually perfused rat mesenteric venules. Fluorescent microspheres (FMs, 100 nm) were used as leakage markers, and confocal images were acquired at successive focal planes through the perfused microvessel. Perfusion of FMs under control conditions produced a thin, uniform layer of FMs in the vessel lumen, but in PAF-stimulated microvessels significant amounts of FMs accumulated at endothelial junctions. Reconstructed confocal images three-dimensionally delineated the temporal and spatial development of endothelial gaps in PAF-stimulated microvessels. The FM accumulation, quantified as the total fluorescence intensity per square micrometer of vessel wall, was 8.4 +/- 1.8 times the control value within 10 min of PAF perfusion and declined to 5.0 +/- 0.6 and 1.4 +/- 0.2 times the control value when FMs were applied 30 and 60 min after PAF perfusion. The changes in the magnitude of FM accumulation closely correlated with the time course of PAF-induced increases in hydraulic conductivity (L(p)), indicating that the opening and closing of endothelial gaps contributed to the transient increase in L(p) in PAF-stimulated microvessels. Electron microscopic evaluations confirmed PAF-induced gap formation and FM accumulation at endothelial clefts.     

An evaluation of the performance and acceptability of three LED fluorescent microscopes in Zambia: lessons learnt for scale-up

The World Health Organization recommends the roll-out of light-emitting diode (LED) fluorescent microscopes (FM) as an alternative to light microscopes in resource-limited settings. We evaluated the acceptability and performance of three LED FMs after a short orientation among laboratory technicians from government health centers in Zambia. Sixteen technicians with varied light microscopy experience were oriented to FMs and divided into groups; each group read a different set of 40 slides on each LED FM (Primo Star iLED?, Lumin?, FluoLED?) and on a reference mercury-vapor FM (Olympus BX41TF). Slide reading times were recorded. An experienced FM technician examined each slide on the Olympus BX41TF. Sensitivity and specificity compared to TB culture were calculated. Misclassification compared to the experienced technician and inter-rater reliability between trainees was assessed. Trainees rated microscopes on technical aspects. Primo Star iLED?, FluoLED? and Olympus BX41TF had comparable sensitivities (67%, 65% and 65% respectively), with the Lumin? significantly worse (56%; p<0.05). Specificity was low for trainees on all microscopes (75.9%) compared to the experienced technician on Olympus BX41TF (100%). Primo Star iLED? had significantly less misclassification (21.1% p<0.05) than FluoLED? (26.5%) and Lumin? (26.8%) and significantly higher inter-rater reliability (0.611; p<0.05), compared to FluoLED? (0.523) and Lumin? (0.492). Slide reading times for LED FMs were slower than the reference, but not significantly different from each other. Primo Star iLED? rated highest in acceptability measures, followed by FluoLED? then Lumin?. Primo Star iLED? was consistently better than FluoLED? and Lumin?, and performed comparably to the Olympus BX41TF in all analyses, except reading times. The Lumin? compared least favorably and was thought unacceptable for use. Specificity and inter-rater reliability were low for all microscopes suggesting that a brief orientation was insufficient in this setting. These results provide important data for resource-limited settings to consider as they scale-up LED FMs.     

Data analysis of assorted serum peptidome profiles

Discovery of biomarker patterns using proteomic techniques requires examination of large numbers of patient and control samples, followed by data mining of the molecular read-outs (e.g., mass spectra). Adequate signal processing and statistical analysis are critical for successful extraction of markers from these data sets. The protocol, specifically designed for use in conjunction with MALDI-TOF-MS-based serum peptide profiling, is a data analysis pipeline, starting with transfer of raw spectra that are interpreted using signal processing algorithms to define suitable features (i.e., peptides). We describe an algorithm for minimal entropy-based peak alignment across samples. Peak lists obtained in this way, and containing all samples, all peptide features and their normalized MS-ion intensities, can be evaluated, and results validated, using common statistical methods. We recommend visual inspection of the spectra to confirm all results, and have written freely available software for viewing and color-coding of spectral overlays.     

A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase

We present a statistical model for patterns of genetic variation in samples of unrelated individuals from natural populations. This model is based on the idea that, over short regions, haplotypes in a population tend to cluster into groups of similar haplotypes. To capture the fact that, because of recombination, this clustering tends to be local in nature, our model allows cluster memberships to change continuously along the chromosome according to a hidden Markov model. This approach is flexible, allowing for both "block-like" patterns of linkage disequilibrium (LD) and gradual decline in LD with distance. The resulting model is also fast and, as a result, is practicable for large data sets (e.g., thousands of individuals typed at hundreds of thousands of markers). We illustrate the utility of the model by applying it to dense single-nucleotide-polymorphism genotype data for the tasks of imputing missing genotypes and estimating haplotypic phase. For imputing missing genotypes, methods based on this model are as accurate or more accurate than existing methods. For haplotype estimation, the point estimates are slightly less accurate than those from the best existing methods (e.g., for unrelated Centre d'Etude du Polymorphisme Humain individuals from the HapMap project, switch error was 0.055 for our method vs. 0.051 for PHASE) but require a small fraction of the computational cost. In addition, we demonstrate that the model accurately reflects uncertainty in its estimates, in that probabilities computed using the model are approximately well calibrated. The methods described in this article are implemented in a software package, fastPHASE, which is available from the Stephens Lab Web site.     

Nested Association Mapping for Identification of Functional Markers

Identification of functional markers (FMs) provides information about the genetic architecture underlying complex traits. An approach that combines the strengths of linkage and association mapping, referred to as nested association mapping (NAM), has been proposed to identify FMs in many plant species. The ability to identify and resolve FMs for complex traits depends upon a number of factors including frequency of FM alleles, magnitudes of their genetic effects, disequilibrium among functional and nonfunctional markers, statistical analysis methods, and mating design. The statistical characteristics of power, accuracy, and precision to identify FMs with a NAM population were investigated using three simulation studies. The simulated data sets utilized publicly available genetic sequences and simulated FMs were identified using least-squares variable selection methods. Results indicate that FMs with simple additive genetic effects that contribute at least 5% to the phenotypic variability in at least five segregating families of a NAM population consisting of recombinant inbred progeny derived from 28 matings with a single reference inbred will have adequate power to accurately and precisely identify FMs. This resolution and power are possible even for genetic architectures consisting of disequilibrium among multiple functional and nonfunctional markers in the same genomic region, although the resolution of FMs will deteriorate rapidly if more than two FMs are tightly linked within the same amplicon. Finally, nested mating designs involving several reference parents will have a greater likelihood of resolving FMs than single reference designs.THE primary purpose for identifying functional markers (FMs) associated with complex traits in plant species is to provide molecular genetic information underlying variability upon which both artificial and natural selection are based. FMs are defined as polymorphic sites within genomes that causally affect phenotypic trait variability (Andersen and Lubberstedt 2003). This definition is a pragmatic recognition that phenotypic variability can be due to genomic variability located outside of open reading frames. Forward genetics approaches to associate naturally occurring structural genomic variants with phenotypic variability can be broadly categorized as (1) linkage mapping, also referred to as quantitative trait locus (QTL) mapping, (2) association genetic mapping, also known as linkage disequilibrium (LD) mapping, and (3) designs that combine linkage and LD mapping.The third approach based on the concept of combining LD with QTL mapping is a natural extension of the multifamily QTL approach and has been referred as joint linkage and linkage disequilibrium mapping (JLLDM) (Xiong and Jin 2000; Farnir et al. 2002; Wu et al. 2002; Perez-Enciso 2003; Jung et al. 2005) in samples from natural populations. The combined approach also has been applied to designed mapping families sampled from plant breeding populations (Xu 1998a; Jannink and Jansen 2000; Jannink and Wu 2003; Jansen et al. 2003). A special case of designed mapping families that are interconnected, known as nested association mapping (NAM), was proposed by Yu et al. (2008). As originally proposed, a NAM population consists of multiple families of recombinant inbred lines (RILs) derived from multiple inbred lines crossed to a single reference inbred line. Implicitly, genomic information is composed of high-density genotypes of parental inbred lines and low-density genotypes from segregating progeny. If the segregating progeny are RILs or doubled haploid lines (DHLs), then the genomic information can be “immortalized” for associations with phenotypes obtained through long-term longitudinal studies (Nordborg and Weigel 2008).A NAM population consisting of 25 families with 200 RILs for each family has been developed and released as a genetic resource for identification of FMs in maize (Yu et al. 2008). Other publicly available NAM populations are being developed for several species including Arabidopsis thaliana (Buckler and Gore 2007), barley (R. Wise, personal communication), sorghum (J. Yu, personal communication), and soybean (B. Diers, personal communication).The power, accuracy, and precision of identifying FMs in experimental NAM populations have not been investigated for complex genetic architectures. These statistical properties depend upon a number of factors including the following:

1. Data analysis method: Some methods are more powerful than others; however, experimental biologists prefer methods implemented in existing software packages. Are least-squares methods sufficiently powerful to identify FMs in established and developing NAM populations?
2. Frequency of functional markers and magnitudes of genetic effects: Development of a NAM population will change the allele frequencies of the FM relative to the reference population from which the lines are sampled. How will allele frequency and magnitude of genetic effects in a typical NAM population affect the ability to identify FMs?
3. Disequilibrium among functional and nonfunctional markers: Disequilibrium may exist among alleles within subpopulations even when there is no physical basis for genetic linkage. To what extent can the NAM design address consequences of gametic disequilibrium (population structure) in the reference population?
4. Multiple FMs in the same genomic region: If multiple FMs are physically located in the same genomic region, will equilibrium among the parental lines enable resolution of multiple FMs?
5. Mating design: An appropriate mating design can maximize the number of families that are informative for FMs. Will multiple-reference mating designs improve the probability of identifying FMs?

These five questions were addressed.     

Development of PCR-based allele-specific and InDel marker sets for nine rice blast resistance genes

Blast resistance is one of the most important traits in rice breeding, and application of molecular markers for blast resistance breeding is likely to allow the rapid screening for the trait during early growth stages, without the need for inoculation of pathogen and phenotyping. Allele-specific PCR markers and insertion/deletion (InDel) markers, which genotype single-nucleotide polymorphisms and InDel polymorphisms, respectively, are useful tools for marker-assisted selections. We developed sets of allele-specific PCR and InDel markers for nine rice blast resistance genes—Piz, Piz-t, Pit, Pik, Pik-m, Pik-p, Pita, Pita-2, and Pib—which are commonly used in Japanese blast resistance rice breeding programs. For each resistance gene, we used the segregation information from thousands of progeny in several crosses or published gene locations to generate a marker that cosegregated with the gene and markers that closely flanked the gene on either side. The developed cosegregating markers uniquely discriminated among each of the lines with the individual resistance genes (except for Pita and Pita-2). Therefore, these markers will likely facilitate the development of multiline cultivars carrying one or a combination of these nine blast resistance genes. In addition, the systems we developed may be valuable tools in the quality control of seed production from blast-resistant multiline cultivars.     

Genomic-assisted prediction of genetic value with semiparametric procedures

Semiparametric procedures for prediction of total genetic value for quantitative traits, which make use of phenotypic and genomic data simultaneously, are presented. The methods focus on the treatment of massive information provided by, e.g., single-nucleotide polymorphisms. It is argued that standard parametric methods for quantitative genetic analysis cannot handle the multiplicity of potential interactions arising in models with, e.g., hundreds of thousands of markers, and that most of the assumptions required for an orthogonal decomposition of variance are violated in artificial and natural populations. This makes nonparametric procedures attractive. Kernel regression and reproducing kernel Hilbert spaces regression procedures are embedded into standard mixed-effects linear models, retaining additive genetic effects under multivariate normality for operational reasons. Inferential procedures are presented, and some extensions are suggested. An example is presented, illustrating the potential of the methodology. Implementations can be carried out after modification of standard software developed by animal breeders for likelihood-based or Bayesian analysis.     

Genome-Wide Analysis of Yield in Europe: Allelic Effects Vary with Drought and Heat Scenarios

Assessing the genetic variability of plant performance under heat and drought scenarios can contribute to reduce the negative effects of climate change. We propose here an approach that consisted of (1) clustering time courses of environmental variables simulated by a crop model in current (35 years × 55 sites) and future conditions into six scenarios of temperature and water deficit as experienced by maize (Zea mays L.) plants; (2) performing 29 field experiments in contrasting conditions across Europe with 244 maize hybrids; (3) assigning individual experiments to scenarios based on environmental conditions as measured in each field experiment; frequencies of temperature scenarios in our experiments corresponded to future heat scenarios (+5°C); (4) analyzing the genetic variation of plant performance for each environmental scenario. Forty-eight quantitative trait loci (QTLs) of yield were identified by association genetics using a multi-environment multi-locus model. Eight and twelve QTLs were associated to tolerances to heat and drought stresses because they were specific to hot and dry scenarios, respectively, with low or even negative allelic effects in favorable scenarios. Twenty-four QTLs improved yield in favorable conditions but showed nonsignificant effects under stress; they were therefore associated with higher sensitivity. Our approach showed a pattern of QTL effects expressed as functions of environmental variables and scenarios, allowing us to suggest hypotheses for mechanisms and candidate genes underlying each QTL. It can be used for assessing the performance of genotypes and the contribution of genomic regions under current and future stress situations and to accelerate breeding for drought-prone environments.With climate changes, crops will be subjected to more frequent episodes of drought and high temperature that may threaten food security (IPCC, 2014). Reducing the impacts of these effects is an urgent priority that (not exclusively) involves the genetic progress of plant performance under heat and drought stresses (Tester and Langridge, 2010; Lobell et al., 2011). Because hundreds of new genotypes of most cereals are commercialized every year, a generic approach is needed to avoid an endless series of experiments assessing the performances of the newly released genotypes. A systematic exploration of the natural genetic diversity used in breeding can provide information usable for large groups of genotypes. This entails the identification, among the thousands of accessions existing in gene banks, of allelic variants exhibiting specific adaptation traits by addressing three questions: (1) Is there a genetic variability for yield and related traits in dry and hot environments? (2) Can this genetic variability be dissected into the effect of genomic regions (quantitative trait loci, QTLs), and (3) have these genomic regions differential effects depending on environmental conditions (QTL × environment interaction)? Advances in DNA marker analyses and sequencing technologies have decreased the cost of genotyping so the genome of thousands of plants can be densely characterized (Langridge and Fleury, 2011). Genome-wide association study (GWAS) allows associations of phenotypic traits with causal polymorphisms (Zhu et al., 2008) but, in our analysis, needs to be fine-tuned for plant responses to climatic scenarios associated with climate change. In particular, several options exist for the experimental strategy. (1) The comparison of genotype performances can be addressed in controlled infrastructures that simulate conditions in 2050—for instance, in phenotyping platforms (Tardieu and Tuberosa, 2010; Fiorani and Schurr, 2013) or in fields with managed environments (Salekdeh et al., 2009; Bishop et al., 2015). However, these possibilities do not address the diversity of environmental scenarios faced by plants in current and future conditions. (2) Panels of genotypes can be analyzed in a network of field experiments, resulting in the association of performances with genomic regions depending on environmental indices that best account for QTL×E interaction (Vargas et al., 2006; Malosetti et al., 2013; Bouffier et al., 2015). However, each network of experiments results in its own set of indices that cannot be easily compared between studies, nor extended to a whole geographic region.We propose here an approach that consists in utilizing the current year-to-year and site-to-site climatic variability for genetic analyses of plant performance in current climatic scenarios and in those predicted for the future. It consists of (1) clustering current and future environmental conditions into a limited number of scenarios as experienced by the studied crop; (2) performing a series of field experiments for a collection of scenarios across Europe; (3) assigning individual experiments to scenarios according to environmental conditions measured in each field experiment; and (4) analyzing the genetic variation for plant performances as a function of environmental scenarios. Here, we address these four steps for maize (Zea mays L.) in Europe. Maize was chosen as a case study because it is a C₄ species in which the increase of CO₂ has limited effect on photosynthesis.(1) The first step has been performed by running crop simulations over a large range of sites over tens of years and then clustering the simulated time courses of environmental variables into a limited number of environmental scenarios at key phenological stages of the crop (Chapman et al., 2000; Chenu et al., 2011). To address the case of maize grown in Europe, we have used the drought scenarios defined by Harrison et al., (2014) based on 55 European sites over 35 years. We have used the dataset collected in their paper to also identify three scenarios of temperature during the maize cropping cycle under current conditions. Future conditions have been simulated by using the model LARS-WG (Semenov and Stratonovitch, 2010).(2) The second step consisted in performing field experiments with a panel of genotypes over a range of conditions. This was done in 29 field experiments (defined as combinations of site × year × watering regime), in which a panel of 244 maize hybrids was analyzed along a climatic transect from west to east Europe, plus one experiment in Chile. This panel, genotyped with 515 000 single nucleotide polymorphism (SNP) markers, maximized the genetic variability in the dent maize group while restricting the range of flowering time to 10 d in order to avoid confounding the effects of phenology with intrinsic responses to drought and heat. It included first-cycle lines derived from historical landraces and more recent lines created by public institutions and breeding companies.(3) The third step ascribed each experiment to an environmental scenario defined in step 1. This required full environmental characterization of each individual experiment. We expected that the proportion of experiments belonging to each environmental scenario might appreciably differ from those calculated over 55 sites × 35 years. Hence, this step allowed us to give a weight to each experiment according to environmental conditions in this experiment and to frequencies of environmental scenarios. It was therefore not a simple classification of experiments of the network as performed by other groups (Vargas et al., 2006; Malosetti et al., 2013; Bouffier et al., 2015).(4) The fourth step consisted in evaluating the genetic variability of yield and of related variables within each climatic scenario, in identifying genomic regions associated with these traits in each scenario and in relating allelic effects to measured environmental conditions. Indeed, associations between markers and yield under stress pose a specific challenge because every significant marker may have opposite allelic effects depending on the timing and severity of drought or heat stresses (Vargas et al., 2006; Boer et al., 2007; Collins et al., 2008; Tardieu, 2012). The analysis of a network of dry field experiments has shown that a given allele at a QTL can have a markedly positive effect in one category of experiments, a markedly negative effect in another category, and nearly no effect in half of experimental fields (Bonneau et al., 2013). Hence, we have first performed single-environment GWAS that allows identification of QTLs strongly associated with specific experiments and multi-environment GWAS that allows identification of QTLs with both main effect and QTL × E effects (Boer et al., 2007; Maccaferri et al., 2008; Malosetti et al., 2008a; Maccaferri et al., 2016). We have then analyzed the effects of QTL alleles conditional on scenarios and measured environmental conditions.We could, in this way, estimate the frequencies of positive, negative, or null effects for each QTL in each climatic scenario, depending on measured environmental conditions in each field. This resulted in a pattern of QTL effects as a function of scenarios, environmental variables (e.g. temperature versus evaporative demand versus soil water potential) and traits (e.g. flowering time versus grain number versus grain size). We have deduced from these patterns hypotheses for the mechanisms underlying the QTLs, thereby helping in the selection of candidate genes among the small number of possible genes close to causal polymorphisms. Hence, this work aimed to bring together GWAS and ecophysiological analyses for modeling and providing biological/ecological interpretation of conditional QTL effects associated to ranges of soil water deficit, evaporative demand, and air temperature across Europe in current and future climatic scenarios.     

Tests for finding complex patterns of differential expression in cancers: towards individualized medicine

Background  

Microarray studies in cancer compare expression levels between two or more sample groups on thousands of genes. Data analysis follows a population-level approach (e.g., comparison of sample means) to identify differentially expressed genes. This leads to the discovery of 'population-level' markers, i.e., genes with the expression patterns A > B and B > A. We introduce the PPST test that identifies genes where a significantly large subset of cases exhibit expression values beyond upper and lower thresholds observed in the control samples.     

Alkaline methanolysis of lipid extracts extends shotgun lipidomics analyses to the low-abundance regime of cellular sphingolipids

Sphingolipids that contain a sphingoid base are composed of hundreds to thousands of distinct compounds, many of which serve as lipid regulators of biological functions. The global analysis of the large number of low-abundance sphingolipid molecular species has been hampered in many cases by the sphingolipid molecular species being overwhelmed by the quantity of other classes of lipid (e.g., glycerophospholipid) molecular species present, thereby imposing severe restrictions on the dynamic range of their measurement using shotgun lipidomics. Herein, we developed a facile approach in which the sphingolipids of cellular extracts were dramatically enriched by direct alkaline methanolysis of lipid extracts followed by extraction to remove the large majority of other endogenous lipid classes. Through direct infusion of the resultant enriched solution, we identified and quantitated a variety of very-low-abundance sphingolipid classes (e.g., sphingosine, psychosine, and lysosphingomyelin) and molecular species (e.g., sphingomyelin) using electrospray ionization mass spectrometry (i.e., shotgun sphingolipidomics). Accordingly, through utilization of these facile enrichment techniques, direct penetrance into the sphingolipidomes has been greatly extended, facilitating new insights into their metabolism and signaling functions in biological systems.     

Experimental designs for robust detection of effects in genome-wide case-control studies

In genome-wide association studies hundreds of thousands of loci are scanned in thousands of cases and controls, with the goal of identifying genomic loci underpinning disease. This is a challenging statistical problem requiring strong evidence. Only a small proportion of the heritability of common diseases has so far been explained. This "dark matter of the genome" is a subject of much discussion. It is critical to have experimental design criteria that ensure that associations between genomic loci and phenotypes are robustly detected. To ensure associations are robustly detected we require good power (e.g., 0.8) and sufficiently strong evidence [i.e., a high Bayes factor (e.g., 10(6), meaning the data are 1 million times more likely if the association is real than if there is no association)] to overcome the low prior odds for any given marker in a genome scan to be associated with a causal locus. Power calculations are given for determining the sample sizes necessary to detect effects with the required power and Bayes factor for biallelic markers in linkage disequilibrium with causal loci in additive, dominant, and recessive genetic models. Significantly stronger evidence and larger sample sizes are required than indicated by traditional hypothesis tests and power calculations. Many reported putative effects are not robustly detected and many effects including some large moderately low-frequency effects may remain undetected. These results may explain the dark matter in the genome. The power calculations have been implemented in R and will be available in the R package ldDesign.     

Cryptic plumage signaling in Aphelocoma Scrub-Jays

Recent studies of avian vision and plumage coloration have revealed a surprising degree of cryptic sexual dimorphism, with many examples of male–female differences in UV reflectance that are invisible to humans. We examined the potential for male–female and adult–subadult differences in plumage coloration in the genus Aphelocoma. This group of jays comprises 10 phylogenetic species, which are found across southern and central North America and include cooperatively breeding species, as well as species that form socially monogamous pair-bonds typical of most species of birds. Our goal was to determine whether male–female and adult–subadult differences in plumage coloration were more common in species with complex social systems (i.e., cooperative breeders). We collected a series of reflectance measurements from hundreds of museum specimens and analyzed the results using a model of an avian visual system. We found that age- and sex-related differences were not more frequent in species that practice cooperative breeding. Hence, plumage signaling relating sex and age may not be strongly associated with complex social systems. Rather, the relative lack of a stable and familiar social environment, as well as other selective pressures and constraints (e.g., habitat use and plumage complexity), may have favored a greater degree of age- and sex-related differences in plumage coloration in jays that practice simple biparental care.