Mapping of Candidate Genes Involved in Bud Dormancy and Flowering Time in Sweet Cherry (Prunus avium)

The timing of flowering in perennial plants is crucial for their survival in temperate climates and is regulated by the duration of bud dormancy. Bud dormancy release and bud break depend on the perception of cumulative chilling during endodormancy and heat during the bud development. The objectives of this work were to identify candidate genes involved in dormancy and flowering processes in sweet cherry, their mapping in two mapping progenies ‘Regina’ × ‘Garnet’ and ‘Regina’ × ‘Lapins’, and to select those candidate genes which co-localized with quantitative trait loci (QTLs) associated with temperature requirements for bud dormancy release and flowering. Based on available data on flowering processes in various species, a list of 79 candidate genes was established. The peach and sweet cherry orthologs were identified and primers were designed to amplify sweet cherry candidate gene fragments. Based on the amplified sequences of the three parents of the mapping progenies, SNPs segregations in the progenies were identified. Thirty five candidate genes were genetically mapped in at least one of the two progenies and all were in silico mapped. Co-localization between candidate genes and QTLs associated with temperature requirements and flowering date were identified for the first time in sweet cherry. The allelic composition of the candidate genes located in the major QTL for heat requirements and flowering date located on linkage group 4 have a significant effect on these two traits indicating their potential use for breeding programs in sweet cherry to select new varieties adapted to putative future climatic conditions.     

Mapping single-locus and epistatic quantitative trait loci for plant architectural traits in chrysanthemum

Plant architecture is important for chrysanthemum cultivation and breeding. To determine the genetic basis of plant architectural traits in chrysanthemum, a population of 142 F1 plants derived from a cross between the creeping ground-cover chrysanthemum cultivar Yuhualuoying and the erect potted cultivar Aoyunhanxiao was used to detect quantitative trait loci (QTL) associated with plant height, plant width, inter-node length and flower neck length. The broad-sense heritability h _B ² for the four plant architectural traits ranged from 0.33 to 0.83, and transgressive segregation was observed. Single-locus QTL analysis revealed a total of five QTL, accounting for 6.0?C16.1% of the phenotypic variation. Additionally, 11 pairs of epistatic QTL were identified, explaining 3.5?C14.5% of the phenotypic variations. The majority of the interactions detected occurred between background loci. These results indicate that both additive and epistatic effects contribute to phenotypic variation in the plant architecture of chrysanthemum. It is expected that the identified markers associated with the additive QTL and epistatic QTL detected in this study will be of importance in future breeding programs to develop chrysanthemum cultivars exhibiting desirable plant architecture.     

Detection of QTL for flowering time in multiple families of elite maize

Flowering time is a fundamental quantitative trait in maize that has played a key role in the postdomestication process and the adaptation to a wide range of climatic conditions. Flowering time has been intensively studied and recent QTL mapping results based on diverse founders suggest that the genetic architecture underlying this trait is mainly based on numerous small-effect QTL. Here, we used a population of 684 progenies from five connected families to investigate the genetic architecture of flowering time in elite maize. We used a joint analysis and identified nine main effect QTL explaining approximately 50?% of the genotypic variation of the trait. The QTL effects were small compared with the observed phenotypic variation and showed strong differences between families. We detected no epistasis with the genetic background but four digenic epistatic interactions in a full 2-dimensional genome scan. Our results suggest that flowering time in elite maize is mainly controlled by main effect QTL with rather small effects but that epistasis may also contribute to the genetic architecture of the trait.     

Footprints in time: comparative quantitative trait loci mapping of the pitcher-plant mosquito,Wyeomyia smithii

Identifying regions of the genome contributing to phenotypic evolution often involves genetic mapping of quantitative traits. The focus then turns to identifying regions of ‘major’ effect, overlooking the observation that traits of ecological or evolutionary relevance usually involve many genes whose individual effects are small but whose cumulative effect is large. Herein, we use the power of fully interfertile natural populations of a single species of mosquito to develop three quantitative trait loci (QTL) maps: one between two post-glacially diverged populations and two between a more ancient and a post-glacial population. All demonstrate that photoperiodic response is genetically a highly complex trait. Furthermore, we show that marker regressions identify apparently ‘non-significant’ regions of the genome not identified by composite interval mapping, that the perception of the genetic basis of adaptive evolution is crucially dependent upon genetic background and that the genetic basis for adaptive evolution of photoperiodic response is highly variable within contemporary populations as well as between anciently diverged populations.     

Genetic Variation for Life History Sensitivity to Seasonal Warming in Arabidopsis thaliana

Climate change has altered life history events in many plant species; however, little is known about genetic variation underlying seasonal thermal response. In this study, we simulated current and three future warming climates and measured flowering time across a globally diverse set of Arabidopsis thaliana accessions. We found that increased diurnal and seasonal temperature (1°–3°) decreased flowering time in two fall cohorts. The early fall cohort was unique in that both rapid cycling and overwintering life history strategies were revealed; the proportion of rapid cycling plants increased by 3–7% for each 1° temperature increase. We performed genome-wide association studies (GWAS) to identify the underlying genetic basis of thermal sensitivity. GWAS identified five main-effect quantitative trait loci (QTL) controlling flowering time and another five QTL with thermal sensitivity. Candidate genes include known flowering loci; a cochaperone that interacts with heat-shock protein 90; and a flowering hormone, gibberellic acid, a biosynthetic enzyme. The identified genetic architecture allowed accurate prediction of flowering phenotypes (R² > 0.95) that has application for genomic selection of adaptive genotypes for future environments. This work may serve as a reference for breeding and conservation genetic studies under changing environments.     

Quantitative trait loci associated with red foliage in <Emphasis Type="Italic">Cornus florida</Emphasis> L.

The objective of our investigation was to acquire information on the association between molecular markers and foliage color in flowering dogwood in order to improve our understanding of the inheritance of this trait and to make possible early selection of red foliage genotypes in breeding programs. A segregating pseudo-F₂ population of 94 individuals of flowering dogwood (Cornus florida L.), together with 255 simple sequence repeat markers, was used to identify putative quantitative trait loci (QTL) for foliage color. Foliage color segregated into green- and red-leaved phenotypes and was visually rated for color on five spring dates over 3 years (2007–2009). Repeated measures single-marker categorical analysis of variance (ANOVA) identified four putative QTL (CF309C, CF792A, CF367B, and CF367C) on three linkage groups. Single-marker categorical ANOVA was then used to determine stability of QTL across dates. We identified different QTL, found a low percentage of phenotypic variance explained by the QTL, and detected QTL instability over time, providing evidence of the complex genetics for red pigment expression in flowering dogwood.     

Flower Colour Modification of Chrysanthemum by Suppression of F3'H and Overexpression of the Exogenous Senecio cruentus F3'5'H Gene

Chrysanthemum (Chrysanthemum × morifolium) is one of the most important ornamental plants in the world. They are typically used as cut flowers or potted plants. Chrysanthemum can exhibit red, purple, pink, yellow and white flowers, but lack bright red and blue flowers. In this study, we identified two chrysanthemum cultivars, C × morifolium ‘LPi’ and C × morifolium ‘LPu’, that only accumulate flavonoids in their ligulate flowers. Next, we isolated seven anthocyanin biosynthesis genes, namely CmCHS, CmF3H, CmF3’H, CmDFR, CmANS, CmCHI and Cm3GT in these cultivars. RT-PCR and qRT-PCR analyses showed that CmF3′H was the most important enzyme required for cyanidin biosynthsis. To rebuild the delphinidin pathway, we downregulated CmF3’H using RNAi and overexpressed the Senecio cruentus F3′5′H (PCFH) gene in chrysanthemum. The resultant chrysanthemum demonstrated a significantly increased content of cyanidin and brighter red flower petals but did not accumulate delphinidin. These results indicated that CmF3′H in chrysanthemum is important for anthocyanin accumulation, and Senecio cruentus F3′5′H only exhibited F3′H activity in chrysanthemum but did not rebuild the delphinidin pathway to form blue flower chrysanthemum.     

Genetic Mapping of QTLs Controlling Fatty Acids Provided Insights into the Genetic Control of Fatty Acid Synthesis Pathway in Peanut (Arachis hypogaea L.)

Peanut, a high-oil crop with about 50% oil content, is either crushed for oil or used as edible products. Fatty acid composition determines the oil quality which has high relevance to consumer health, flavor, and shelf life of commercial products. In addition to the major fatty acids, oleic acid (C18:1) and linoleic acid (C18:2) accounting for about 80% of peanut oil, the six other fatty acids namely palmitic acid (C16:0), stearic acid (C18:0), arachidic acid (C20:0), gadoleic acid (C20:1), behenic acid (C22:0), and lignoceric acid (C24:0) are accounted for the rest 20%. To determine the genetic basis and to improve further understanding on effect of FAD2 genes on these fatty acids, two recombinant inbred line (RIL) populations namely S-population (high oleic line ‘SunOleic 97R’ × low oleic line ‘NC94022’) and T-population (normal oleic line ‘Tifrunner’ × low oleic line ‘GT-C20’) were developed. Genetic maps with 206 and 378 marker loci for the S- and the T-population, respectively were used for quantitative trait locus (QTL) analysis. As a result, a total of 164 main-effect (M-QTLs) and 27 epistatic (E-QTLs) QTLs associated with the minor fatty acids were identified with 0.16% to 40.56% phenotypic variation explained (PVE). Thirty four major QTLs (>10% of PVE) mapped on five linkage groups and 28 clusters containing more than three QTLs were also identified. These results suggest that the major QTLs with large additive effects would play an important role in controlling composition of these minor fatty acids in addition to the oleic and linoleic acids in peanut oil. The interrelationship among these fatty acids should be considered while breeding for improved peanut genotypes with good oil quality and desired fatty acid composition.     

QTLs associated with dry matter intake,metabolic mid-test weight,growth and feed efficiency have little overlap across 4 beef cattle studies

Background

The identification of genetic markers associated with complex traits that are expensive to record such as feed intake or feed efficiency would allow these traits to be included in selection programs. To identify large-effect QTL, we performed a series of genome-wide association studies and functional analyses using 50 K and 770 K SNP genotypes scored in 5,133 animals from 4 independent beef cattle populations (Cycle VII, Angus, Hereford and Simmental × Angus) with phenotypes for average daily gain, dry matter intake, metabolic mid-test body weight and residual feed intake.

Results

A total of 5, 6, 11 and 10 significant QTL (defined as 1-Mb genome windows with Bonferroni-corrected P-value <0.05) were identified for average daily gain, dry matter intake, metabolic mid-test body weight and residual feed intake, respectively. The identified QTL were population-specific and had little overlap across the 4 populations. The pleiotropic or closely linked QTL on BTA 7 at 23 Mb identified in the Angus population harbours a promising candidate gene ACSL6 (acyl-CoA synthetase long-chain family member 6), and was the largest effect QTL associated with dry matter intake and mid-test body weight explaining 10.39% and 14.25% of the additive genetic variance, respectively. Pleiotropic or closely linked QTL associated with average daily gain and mid-test body weight were detected on BTA 6 at 38 Mb and BTA 7 at 93 Mb confirming previous reports. No QTL for residual feed intake explained more than 2.5% of the additive genetic variance in any population. Marker-based estimates of heritability ranged from 0.21 to 0.49 for residual feed intake across the 4 populations.

Conclusions

This GWAS study, which is the largest performed for feed efficiency and its component traits in beef cattle to date, identified several large-effect QTL that cumulatively explained a significant percentage of additive genetic variance within each population. Differences in the QTL identified among the different populations may be due to differences in power to detect QTL, environmental variation, or differences in the genetic architecture of trait variation among breeds. These results enhance our understanding of the biology of growth, feed intake and utilisation in beef cattle.     

Metabolic control analysis as a mechanism that conserves genetic variance during advanced cycle breeding

The recycling of elite inbreds (i.e., advanced cycle breeding) has led to significant genetic gains but also to a narrow gene pool in plant breeding programs. Sustained yield improvements in many crops have suggested that genetic variance is not depleted at a rate predicted by an additive genetic model. Unlike the additive model in classical quantitative genetic theory, metabolic control analysis relates the variation in a biochemical process with the genetic variation in a quantitative trait. Our objective was to determine whether metabolic control analysis is a mechanism that slows the decrease in genetic variance during advanced cycle breeding. Three cycles of advanced cycle breeding were simulated with 10, 50, or 100 quantitative trait loci (QTL) controlling a trait. In metabolic control analysis, these QTL coded for enzymes involved in a linear metabolic pathway that converted a substrate into a product. In the absence of selection, both the additive model and the metabolic control analysis model led to about a 50% reduction in genetic variance from cycle to cycle. With selection, the additive model led to a 50–58% reduction in genetic variance, but the metabolic control analysis model generally led to only a 12–54% reduction. We suggest selection in a metabolic control analysis model as a mechanism that slows the decrease in genetic variance during advanced cycle breeding. This conservation of genetic variance would allow breeders to achieve genetic gains for a longer period than expected under the additive model.Communicated by H.C. Becker     

High-throughput root phenotyping screens identify genetic loci associated with root architectural traits in Brassica napus under contrasting phosphate availabilities

Background and Aims

Phosphate (Pi) deficiency in soils is a major limiting factor for crop growth worldwide. Plant growth under low Pi conditions correlates with root architectural traits and it may therefore be possible to select these traits for crop improvement. The aim of this study was to characterize root architectural traits, and to test quantitative trait loci (QTL) associated with these traits, under low Pi (LP) and high Pi (HP) availability in Brassica napus.

Methods

Root architectural traits were characterized in seedlings of a double haploid (DH) mapping population (n = 190) of B. napus [‘Tapidor’ × ‘Ningyou 7’ (TNDH)] using high-throughput phenotyping methods. Primary root length (PRL), lateral root length (LRL), lateral root number (LRN), lateral root density (LRD) and biomass traits were measured 12 d post-germination in agar at LP and HP.

Key Results

In general, root and biomass traits were highly correlated under LP and HP conditions. ‘Ningyou 7’ had greater LRL, LRN and LRD than ‘Tapidor’, at both LP and HP availability, but smaller PRL. A cluster of highly significant QTL for LRN, LRD and biomass traits at LP availability were identified on chromosome A03; QTL for PRL were identified on chromosomes A07 and C06.

Conclusions

High-throughput phenotyping of Brassica can be used to identify root architectural traits which correlate with shoot biomass. It is feasible that these traits could be used in crop improvement strategies. The identification of QTL linked to root traits under LP and HP conditions provides further insights on the genetic basis of plant tolerance to P deficiency, and these QTL warrant further dissection.     

Identification of QTLs Associated with Oil Content in a High-Oil Brassica napus Cultivar and Construction of a High-Density Consensus Map for QTLs Comparison in B. napus

Increasing seed oil content is one of the most important goals in breeding of rapeseed (B. napus L.). To dissect the genetic basis of oil content in B. napus, a large and new double haploid (DH) population containing 348 lines was obtained from a cross between ‘KenC-8’ and ‘N53-2’, two varieties with >10% difference in seed oil content, and this population was named the KN DH population. A genetic linkage map consisting of 403 markers was constructed, which covered a total length of 1783.9 cM with an average marker interval of 4.4 cM. The KN DH population was phenotyped in eight natural environments and subjected to quantitative trait loci (QTL) analysis for oil content. A total of 63 identified QTLs explaining 2.64–17.88% of the phenotypic variation were identified, and these QTLs were further integrated into 24 consensus QTLs located on 11 chromosomes using meta-analysis. A high-density consensus map with 1335 marker loci was constructed by combining the KN DH map with seven other published maps based on the common markers. Of the 24 consensus QTLs in the KN DH population, 14 were new QTLs including five new QTLs in A genome and nine in C genome. The analysis revealed that a larger population with significant differences in oil content gave a higher power detecting new QTLs for oil content, and the construction of the consensus map provided a new clue for comparing the QTLs detected in different populations. These findings enriched our knowledge of QTLs for oil content and should be a potential in marker-assisted breeding of B. napus.     

Molecular Characterization of Barley 3H Semi-Dwarf Genes

The barley chromosome 3H accommodates many semi-dwarfing genes. To characterize these genes, the two-rowed semi-dwarf Chinese barley landrace ‘TX9425’ was crossed with the Australian barley variety ‘Franklin’ to generate a doubled haploid (DH) population, and major QTLs controlling plant height have been identified in our previous study. The major QTL derived from ‘TX9425’ was targeted to investigate the allelism of the semi-dwarf gene uzu in barley. Twelve sets of near-isogenic lines and a large NILF₂ fine mapping population segregating only for the dwarfing gene from ‘TX9425’ were developed. The semi-dwarfing gene in ‘TX9425’ was located within a 2.8 cM region close to the centromere on chromosome 3H by fine mapping. Molecular cloning and sequence analyses showed that the ‘TX9425’-derived allele contained a single nucleotide substitution from A to G at position 2612 of the HvBRI1 gene. This was apparently the same mutation as that reported in six-rowed uzu barley. Markers co-segregating with the QTL were developed from the sequence of the HvBRI1 gene and were validated in the ‘TX9425’/‘Franklin’ DH population. The other major dwarfing QTL derived from the Franklin variety was distally located on chromosome 3HL and co-segregated with the sdw1 diagnostic marker hv20ox2. A third dwarfing gene, expressed only in winter-sown trials, was identified and located on chromosome 3HS. The effects and interactions of these dwarfing genes under different growing conditions are discussed. These results improve our understanding of the genetic mechanisms controlling semi-dwarf stature in barley and provide diagnostic markers for the selection of semi-dwarfness in barley breeding programs.     

High-Resolution Linkage Analyses to Identify Genes That Influence Varroa Sensitive Hygiene Behavior in Honey Bees

Varroa mites (V. destructor) are a major threat to honey bees (Apis melilfera) and beekeeping worldwide and likely lead to colony decline if colonies are not treated. Most treatments involve chemical control of the mites; however, Varroa has evolved resistance to many of these miticides, leaving beekeepers with a limited number of alternatives. A non-chemical control method is highly desirable for numerous reasons including lack of chemical residues and decreased likelihood of resistance. Varroa sensitive hygiene behavior is one of two behaviors identified that are most important for controlling the growth of Varroa populations in bee hives. To identify genes influencing this trait, a study was conducted to map quantitative trait loci (QTL). Individual workers of a backcross family were observed and evaluated for their VSH behavior in a mite-infested observation hive. Bees that uncapped or removed pupae were identified. The genotypes for 1,340 informative single nucleotide polymorphisms were used to construct a high-resolution genetic map and interval mapping was used to analyze the association of the genotypes with the performance of Varroa sensitive hygiene. We identified one major QTL on chromosome 9 (LOD score = 3.21) and a suggestive QTL on chromosome 1 (LOD = 1.95). The QTL confidence interval on chromosome 9 contains the gene ‘no receptor potential A’ and a dopamine receptor. ‘No receptor potential A’ is involved in vision and olfaction in Drosophila, and dopamine signaling has been previously shown to be required for aversive olfactory learning in honey bees, which is probably necessary for identifying mites within brood cells. Further studies on these candidate genes may allow for breeding bees with this trait using marker-assisted selection.     

The Nature of Genetic Variation for Complex Traits Revealed by GWAS and Regional Heritability Mapping Analyses

We use computer simulations to investigate the amount of genetic variation for complex traits that can be revealed by single-SNP genome-wide association studies (GWAS) or regional heritability mapping (RHM) analyses based on full genome sequence data or SNP chips. We model a large population subject to mutation, recombination, selection, and drift, assuming a pleiotropic model of mutations sampled from a bivariate distribution of effects of mutations on a quantitative trait and fitness. The pleiotropic model investigated, in contrast to previous models, implies that common mutations of large effect are responsible for most of the genetic variation for quantitative traits, except when the trait is fitness itself. We show that GWAS applied to the full sequence increases the number of QTL detected by as much as 50% compared to the number found with SNP chips but only modestly increases the amount of additive genetic variance explained. Even with full sequence data, the total amount of additive variance explained is generally below 50%. Using RHM on the full sequence data, a slightly larger number of QTL are detected than by GWAS if the same probability threshold is assumed, but these QTL explain a slightly smaller amount of genetic variance. Our results also suggest that most of the missing heritability is due to the inability to detect variants of moderate effect (∼0.03–0.3 phenotypic SDs) segregating at substantial frequencies. Very rare variants, which are more difficult to detect by GWAS, are expected to contribute little genetic variation, so their eventual detection is less relevant for resolving the missing heritability problem.     

The USDA Barley Core Collection: Genetic Diversity,Population Structure,and Potential for Genome-Wide Association Studies

New sources of genetic diversity must be incorporated into plant breeding programs if they are to continue increasing grain yield and quality, and tolerance to abiotic and biotic stresses. Germplasm collections provide a source of genetic and phenotypic diversity, but characterization of these resources is required to increase their utility for breeding programs. We used a barley SNP iSelect platform with 7,842 SNPs to genotype 2,417 barley accessions sampled from the USDA National Small Grains Collection of 33,176 accessions. Most of the accessions in this core collection are categorized as landraces or cultivars/breeding lines and were obtained from more than 100 countries. Both STRUCTURE and principal component analysis identified five major subpopulations within the core collection, mainly differentiated by geographical origin and spike row number (an inflorescence architecture trait). Different patterns of linkage disequilibrium (LD) were found across the barley genome and many regions of high LD contained traits involved in domestication and breeding selection. The genotype data were used to define ‘mini-core’ sets of accessions capturing the majority of the allelic diversity present in the core collection. These ‘mini-core’ sets can be used for evaluating traits that are difficult or expensive to score. Genome-wide association studies (GWAS) of ‘hull cover’, ‘spike row number’, and ‘heading date’ demonstrate the utility of the core collection for locating genetic factors determining important phenotypes. The GWAS results were referenced to a new barley consensus map containing 5,665 SNPs. Our results demonstrate that GWAS and high-density SNP genotyping are effective tools for plant breeders interested in accessing genetic diversity in large germplasm collections.     

Genotyping by Sequencing for SNP-Based Linkage Map Construction and QTL Analysis of Chilling Requirement and Bloom Date in Peach [Prunus persica (L.) Batsch]

Low-cost, high throughput genotyping methods are crucial to marker discovery and marker-assisted breeding efforts, but have not been available for many ‘specialty crops’ such as fruit and nut trees. Here we apply the Genotyping-By-Sequencing (GBS) method developed for cereals to the discovery of single nucleotide polymorphisms (SNPs) in a peach F₂ mapping population. Peach is a genetic and genomic model within the Rosaceae and will provide a template for the use of this method with other members of this family. Our F₂ mapping population of 57 genotypes segregates for bloom time (BD) and chilling requirement (CR) and we have extensively phenotyped this population. The population derives from a selfed F₁ progeny of a cross between ‘Hakuho’ (high CR) and ‘UFGold’ (low CR). We were able to successfully employ GBS and the TASSEL GBS pipeline without modification of the original methodology using the ApeKI restriction enzyme and multiplexing at an equivalent of 96 samples per Illumina HiSeq 2000 lane. We obtained hundreds of SNP markers which were then used to construct a genetic linkage map and identify quantitative trait loci (QTL) for BD and CR.     

Supermodels: sorghum and maize provide mutual insight into the genetics of flowering time

Nested association mapping (NAM) offers power to dissect complex, quantitative traits. This study made use of a recently developed sorghum backcross (BC)-NAM population to dissect the genetic architecture of flowering time in sorghum; to compare the QTL identified with other genomic regions identified in previous sorghum and maize flowering time studies and to highlight the implications of our findings for plant breeding. A subset of the sorghum BC-NAM population consisting of over 1,300 individuals from 24 families was evaluated for flowering time across multiple environments. Two QTL analysis methodologies were used to identify 40 QTLs with predominately small, additive effects on flowering time; 24 of these co-located with previously identified QTL for flowering time in sorghum and 16 were novel in sorghum. Significant synteny was also detected with the QTL for flowering time detected in a comparable NAM resource recently developed for maize (Zea mays) by Buckler et al. (Science 325:714–718, 2009). The use of the sorghum BC-NAM population allowed us to catalogue allelic variants at a maximal number of QTL and understand their contribution to the flowering time phenotype and distribution across diverse germplasm. The successful demonstration of the power of the sorghum BC-NAM population is exemplified not only by correspondence of QTL previously identified in sorghum, but also by correspondence of QTL in different taxa, specifically maize in this case. The unification across taxa of the candidate genes influencing complex traits, such as flowering time can further facilitate the detailed dissection of the genetic control and causal genes.     

Exploring Candidate Genes for Pericarp Russet Pigmentation of Sand Pear (Pyrus pyrifolia) via RNA-Seq Data in Two Genotypes Contrasting for Pericarp Color

Sand pear (Pyrus pyrifolia) russet pericarp is an important trait affecting both the quality and stress tolerance of fruits. This trait is controlled by a relative complex genetic process, with some fundamental biological questions such as how many and which genes are involved in the process remaining elusive. In this study, we explored differentially expressed genes between the russet- and green-pericarp offspring from the sand pear (Pyrus pyrifolia) cv. ‘Qingxiang’ × ‘Cuiguan’ F1 group by RNA-seq-based bulked segregant analysis (BSA). A total of 29,100 unigenes were identified and 206 of which showed significant differences in expression level (log₂fold values>1) between the two types of pericarp pools. Gene Ontology (GO) analyses detected 123 unigenes in GO terms related to ‘cellular_component’ and ‘biological_process’, suggesting developmental and growth differentiations between the two types. GO categories associated with various aspects of ‘lipid metabolic processes’, ‘transport’, ‘response to stress’, ‘oxidation-reduction process’ and more were enriched with genes with divergent expressions between the two libraries. Detailed examination of a selected set of these categories revealed repressed expressions of candidate genes for suberin, cutin and wax biosynthesis in the russet pericarps.Genes encoding putative cinnamoyl-CoA reductase (CCR), cinnamyl alcohol dehydrogenase (CAD) and peroxidase (POD) that are involved in the lignin biosynthesis were suggested to be candidates for pigmentation of sand pear russet pericarps. Nine differentially expressed genes were analyzed for their expressions using qRT-PCR and the results were consistent with those obtained from Illumina RNA-sequencing. This study provides a comprehensive molecular biology insight into the sand pear pericarp pigmentation and appearance quality formation.     

Adaptive divergence in flowering time among natural populations of Arabidopsis thaliana: Estimates of selection and QTL mapping

To identify the ecological and genetic mechanisms of local adaptation requires estimating selection on traits, identifying their genetic basis, and evaluating whether divergence in adaptive traits is due to conditional neutrality or genetic trade‐offs. To this end, we conducted field experiments for three years using recombinant inbred lines (RILs) derived from two ecotypes of Arabidopsis thaliana (Italy, Sweden), and at each parental site examined selection on flowering time and mapped quantitative trait loci (QTL). There was strong selection for early flowering in Italy, but weak selection in Sweden. Eleven distinct flowering time QTL were detected, and for each the Italian genotype caused earlier flowering. Twenty‐seven candidate genes were identified, two of which (FLC and VIN3) appear under major flowering time QTL in Italy. Seven of eight QTL in Italy with narrow credible intervals colocalized with previously reported fitness QTL, in comparison to three of four in Sweden. The results demonstrate that the magnitude of selection on flowering time differs strikingly between our study populations, that the genetic basis of flowering time variation is multigenic with some QTL of large effect, and suggest that divergence in flowering time between ecotypes is due mainly to conditional neutrality.