Discovery Of Quantitative Trait Loci Research Articles

A Quantitative Theory of War and Peace in the Gut Microbiota

The gut microbiota is regarded as a densely packed microbial ecosystem. Growing data have evidenced the critical role this microcommunity plays in determining human health through its internal interactions, but it is unclear how such interactions organize into complex networks to function. Here, we propose a quantitative theory to dissect, visualize and interpret microbial interactions by inferring a directed, signed and weighted ecological network. This theory can reveal how and why gut microbes compete for resource (war) or cooperate symbiotically (peace) to reform and maintain the stability of microecological communities and predict microbial diversification, dynamics and evolution across host niches. The new theory is further incorporated into genome-wide association studies to map and identify host quantitative trait loci (QTLs) that modulate the alternative transition state of war and peace among bacteria. The discovery of such QTLs helps in designing personalized therapies that prevent microbial dysfunction to improve human health.

Mapping of quantitative trait loci for grain yield and its components in a US popular winter wheat TAM 111 using 90K SNPs.

Stable quantitative trait loci (QTL) are important for deployment in marker assisted selection in wheat (Triticum aestivum L.) and other crops. We reported QTL discovery in wheat using a population of 217 recombinant inbred lines and multiple statistical approach including multi-environment, multi-trait and epistatic interactions analysis. We detected nine consistent QTL linked to different traits on chromosomes 1A, 2A, 2B, 5A, 5B, 6A, 6B and 7A. Grain yield QTL were detected on chromosomes 2B.1 and 5B across three or four models of GenStat, MapQTL, and QTLNetwork while the QTL on chromosomes 5A.1, 6A.2, and 7A.1 were only significant with yield from one or two models. The phenotypic variation explained (PVE) by the QTL on 2B.1 ranged from 3.3–25.1% based on single and multi-environment models in GenStat and was pleiotropic or co-located with maturity (days to heading) and yield related traits (test weight, thousand kernel weight, harvest index). The QTL on 5B at 211 cM had PVE range of 1.8–9.3% and had no significant pleiotropic effects. Other consistent QTL detected in this study were linked to yield related traits and agronomic traits. The QTL on 1A was consistent for the number of spikes m-2 across environments and all the four analysis models with a PVE range of 5.8–8.6%. QTL for kernels spike-1 were found in chromosomes 1A, 2A.1, 2B.1, 6A.2, and 7A.1 with PVE ranged from 5.6–12.8% while QTL for thousand kernel weight were located on chromosomes 1A, 2B.1, 5A.1, 6A.2, 6B.1 and 7A.1 with PVEranged from 2.7–19.5%. Among the consistent QTL, five QTL had significant epistatic interactions (additive × additive) at least for one trait and none revealed significant additive × additive × environment interactions. Comparative analysis revealed that the region within the confidence interval of the QTL on 5B from 211.4–244.2 cM is also linked to genes for aspartate-semialdehyde dehydrogenase, splicing regulatory glutamine/lysine-rich protein 1 isoform X1, and UDP-glucose 6-dehydrogenase 1-like isoform X1. The stable QTL could be important for further validation, high throughput SNP development, and marker-assisted selection (MAS) in wheat.

Discovery of Consistent QTLs of Wheat Spike-Related Traits under Nitrogen Treatment at Different Development Stages.

Spike-related traits such as spike length (Sl), fertile spikelet number (Fsn), sterile spikelet number (Ssn), grain number per spike (Gns), and thousand-kernel weight (Tkw) are important factors influencing wheat yield. However, reliably stable markers that can be used for molecular breeding in different environments have not yet been identified. In this study, a double haploid (DH) population was used for quantitative trait locus (QTL) mapping of five spike-related traits under four different nitrogen (N) supply dates in two locations and years. Seventy additive QTLs with phenotypic variation ranging from 4.12 to 34.74% and 10 major epistatic QTLs were identified. Eight important chromosomal regions on five chromosomes (1B, 2B, 2D, 5D, and 6A) were found. Sixteen stable QTLs were detected for which N application had little effect. Among those stable QTLs, QSl.sdau-2D-1, and QSl.sdau-2D-2, with phenotypic variation explained (PVE) of 10.4 and 24.2%, respectively, were flanked by markers Xwmc112 and Xcfd53 in the same order. The QTLs QSsn.sdau-2B-1, QFsn.sdau-2B-1, and QGns.sdau-2B, with PVE ranging from 4.37 to 28.43%, collocated in the Xwmc179-Xbarc373 marker interval. The consistent kernel wheat QTL (QTkw.sdau-6A) on the long arm of chromosome 6A, flanked by SSR markers Xbarc1055 and Xwmc553, showed PVE of 5.87–15.18%. Among these stable QTLs, the two flanking markers Xwmc112 and Xcfd53 have been validated using different varieties and populations for selecting Sl. Therefore, these results will be of great value for marker-assisted selection (MAS) in breeding programs and will accelerate the understanding of the genetic relationships among spike-related traits at the molecular level.

A first linkage map and downy mildew resistance QTL discovery for sweet basil (Ocimum basilicum) facilitated by double digestion restriction site associated DNA sequencing (ddRADseq).

Limited understanding of sweet basil (Ocimum basilicum L.) genetics and genome structure has reduced efficiency of breeding strategies. This is evidenced by the rapid, worldwide dissemination of basil downy mildew (Peronospora belbahrii) in the absence of resistant cultivars. In an effort to improve available genetic resources, expressed sequence tag simple sequence repeat (EST-SSR) and single nucleotide polymorphism (SNP) markers were developed and used to genotype the MRI x SB22 F2 mapping population, which segregates for response to downy mildew. SNP markers were generated from genomic sequences derived from double digestion restriction site associated DNA sequencing (ddRADseq). Disomic segregation was observed in both SNP and EST-SSR markers providing evidence of an O. basilicum allotetraploid genome structure and allowing for subsequent analysis of the mapping population as a diploid intercross. A dense linkage map was constructed using 42 EST-SSR and 1,847 SNP markers spanning 3,030.9 cM. Multiple quantitative trait loci (QTL) model (MQM) analysis identified three QTL that explained 37–55% of phenotypic variance associated with downy mildew response across three environments. A single major QTL, dm11.1 explained 21–28% of phenotypic variance and demonstrated dominant gene action. Two minor QTL dm9.1 and dm14.1 explained 5–16% and 4–18% of phenotypic variance, respectively. Evidence is provided for an additive effect between the two minor QTL and the major QTL dm11.1 increasing downy mildew susceptibility. Results indicate that ddRADseq-facilitated SNP and SSR marker genotyping is an effective approach for mapping the sweet basil genome.

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.

In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning–based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets.

Identification of Quantitative Trait Loci for Popping Traits and Kernel Characteristics in Sorghum Grain

Popped grain sorghum [Sorghum bicolor (L.) Moench] has developed a niche among specialty snack‐food consumers. In contrast with popcorn (Zea mays L.), sorghum has not benefitted from persistent selective breeding for popping efficiency and kernel expansion ratio. Although recent studies have already demonstrated that popping characteristics are heritable in sorghum, the discovery of quantitative trait loci (QTL) for these traits could help expedite and streamline the breeding process. To that end, the objective of this study was to identify regions of the sorghum genome associated with kernel popping traits. Using digital genotyping technology in conjunction with linkage mapping methodology, QTL were identified for kernel popping efficiency and kernel expansion ratio. Additionally, sets of QTL were identified for desirable traits including kernel weight, kernel diameter, kernel hardness, and kernel color. These results provide an initial assessment of regions of the sorghum genome that harbor trait loci controlling the popping quality of sorghum kernels and provide a basis for further detailed genetic studies of this relatively new use for food‐grade sorghum grain.

Registration of Wyandot × PI 567301B Soybean Recombinant Inbred Line Population

A soybean [Glycine max (L.) Merr] mapping population, Wyandot × PI 567301B (Reg. No. MP‐5, NSL 519221 MAP), consisting of 357 F7–derived recombinant inbred lines (RILs) was jointly developed by the USDA‐ARS and the Ohio Agricultural Research and Development Center in Wooster, OH. The population was developed by single seed descent from a cross between ‘Wyandot’ and PI 567301B. Wyandot is a food‐grade cultivar released by The Ohio State University, and PI 567301B is a plant introduction from China that differs from Wyandot for many morphological traits. PI 567301B was also confirmed to carry resistance to seed and seedling root rot, powdery mildew, and the soybean aphid. We have used this population to map quantitative trait loci (QTL)/genes for nine traits. The 357 RILs were genotyped using BARCSoySNP6K BeadChips, and a high density linkage map with 2545 single nucleotide polymorphism (SNP) markers well distributed on the 20 chromosomes of soybean was constructed and recently published. This mapping population is among the few biparental soybean mapping populations featuring high‐resolution genetic maps. We believe that the genetic map, population, and phenotypic data we provide will be an excellent resource for teaching labs in plant breeding and genetics as well as in discovery of new QTL.

Leveraging genetically simple traits to identify small-effect variants for complex phenotypes.

BackgroundPolymorphisms underlying complex traits often explain a small part (less than 1 %) of the phenotypic variance (σ2 P). This makes identification of mutations underling complex traits difficult and usually only a subset of large-effect loci are identified. One approach to identify more loci is to increase sample size of experiments but here we propose an alternative. The aim of this paper is to use secondary phenotypes for genetically simple traits during the QTL discovery phase for complex traits. We demonstrate this approach in a dairy cattle data set where the complex traits were milk production phenotypes (fat, milk and protein yield; fat and protein percentage in milk) measured on thousands of individuals while secondary (potentially genetically simpler) traits are detailed milk composition traits (measurements of individual protein abundance, mineral and sugar concentrations; and gene expression).ResultsQuantitative trait loci (QTL) were identified using 11,527 Holstein cattle with milk production records and up to 444 cows with milk composition traits. There were eight regions that contained QTL for both milk production and a composition trait, including four novel regions. One region on BTAU1 affected both milk yield and phosphorous concentration in milk. The QTL interval included the gene SLC37A1, a phosphorous antiporter. The most significant imputed sequence variants in this region explained 0.001 σ2 P for milk yield, and 0.11 σ2 P for phosphorus concentration. Since the polymorphisms were non-coding, association mapping for SLC37A1 gene expression was performed using high depth mammary RNAseq data from a separate group of 371 lactating cows. This confirmed a strong eQTL for SLC37A1, with peak association at the same imputed sequence variants that were most significant for phosphorus concentration. Fitting any of these variants as covariables in the association analysis removed the QTL signal for milk production traits. Plausible causative mutations in the casein complex region were also identified using a similar strategy.ConclusionsMilk production traits in dairy cows are typical complex traits where polymorphisms explain only a small portion of the phenotypic variance. However, here we show that these mutations can have larger effects on secondary traits, such as concentrations of minerals, proteins and sugars in the milk, and expression levels of genes in mammary tissue. These larger effects were used to successfully map variants for milk production traits. Genetically simple traits also provide a direct biological link between possible causal mutations and the effect of these mutations on milk production.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3175-3) contains supplementary material, which is available to authorized users.

Expanding Maize Genetic Resources with Predomestication Alleles: Maize-Teosinte Introgression Populations.

Teosinte ( subsp. H. H. Iltis & Doebley) has greater genetic diversity than maize inbreds and landraces ( subsp. ). There are, however, limited genetic resources to efficiently evaluate and tap this diversity. To broaden resources for genetic diversity studies in maize, we developed and evaluated 928 near-isogenic introgression lines (NILs) from 10 teosinte accessions in the B73 background. Joint linkage analysis of the 10 introgression populations identified several large-effect quantitative trait loci (QTL) for days to anthesis (DTA), kernel row number (KRN), and 50-kernel weight (Wt50k). Our results confirm prior reports of kernel domestication loci and identify previously uncharacterized QTL with a range of allelic effects enabling future research into the genetic basis of these traits. Additionally, we used a targeted set of NILs to validate the effects of a KRN QTL located on chromosome 2. These introgression populations offer novel tools for QTL discovery and validation as well as a platform for initiating fine mapping.

Genome-wide dissection of the maize ear genetic architecture using multiple populations.

Improvement of grain yield is an essential long-term goal of maize (Zea mays) breeding to meet continual and increasing food demands worldwide, but the genetic basis remains unclear. We used 10 different recombination inbred line (RIL) populations genotyped with high-density markers and phenotyped in multiple environments to dissect the genetic architecture of maize ear traits. Three methods were used to map the quantitative trait loci (QTLs) affecting ear traits. We found 17-34 minor- or moderate-effect loci that influence ear traits, with little epistasis and environmental interactions, totally accounting for 55.4-82% of the phenotypic variation. Four novel QTLs were validated and fine mapped using candidate gene association analysis, expression QTL analysis and heterogeneous inbred family validation. The combination of multiple different populations is a flexible and manageable way to collaboratively integrate widely available genetic resources, thereby boosting the statistical power of QTL discovery for important traits in agricultural crops, ultimately facilitating breeding programs.

Genetic approaches to identify genomic regions associated with decreased susceptibility to bovine respiratory disease complex

One approach to reducing losses from BRDC is the implementation of selection of cattle that are less likely to be affected by disease by the use of dairy predicted transmitting ability (PTA) or beef expected progeny differences (EPD) for susceptibility to BRDC. The PTAs or EPDs would be based on genotypes associated with animals less susceptible to disease and incorporated into commercial genotyping platforms for industry use. The discovery of quantitative trait loci (QTL) associated with BRDC susceptibility is the first step in implementing the selection of cattle for enhanced resistance to BRDC. Feedlot crossbred Bos taurus (n=408) heifers from Washington were used to illustrate how genetic approaches identify BRDC susceptibility QTL that will form the basis for selection of cattle with increased resistance to the disease. The crossbred cattle consisted of 234 cases and 174 controls as defined by the McGuirk Health scoring system. Cases and controls remained in the same pens together throughout the study. The incidence of BRDC in the feedlot was 2.5% over a 15-month period. The distinction between cases and controls was based on the McGuirk health scoring system with possible integer scores ranging between 0 and 12; calves with scores <4 were identified as controls and those with a score >5 were identified as a case. Of the 174 controls, 4 (1.7%) subsequently became ill with BRDC and were reclassified as a case. The mean health score was 9.44 ± 1.19 for cases and 2.36 ± 0.61 for controls. Deeppharyngeal and mid-nasal swabs were taken for bacteriology and virology diagnostics. Histophilus somni had the greatest difference in prevalence between cases and controls (odds ratio = 1.65, 95% confidence interval 1.05 - 2.58, p < 0.05) among the detected pathogens. A genome-wide association analysis (GWAA) and genomic heritability estimate was computed using Illumina BovineHD BeadChip genotypes. Heritability of susceptibility to BRDC was estimated at 37%. After correction for population stratification using 30 principal components, the genomic inflation factor was 1.02. Single nucleotide polymorphisms (SNPs) were filtered for low call rate (<90%), Hardy-Weinberg disequilibrium (p < 1x10-50) and low minor allele frequency (<1%) which left 669,933 SNPs for the GWAA. Animals were also filtered for low genotyping call rate (<90%) leaving 220 cases and 164 controls. The GWAA identified 43 QTL (p < 5x10-5) on 15 chromosomes. The identification of QTL associated with BRDC susceptibility is the first step towards implementing selection of cattle that are less likely to be affected by BRDC through the use of genomic PTAs or EPDs.

WASP: allele-specific software for robust molecular quantitative trait locus discovery.

Allele-specific sequencing reads provide a powerful signal for identifying molecular quantitative trait loci (QTLs), however they are challenging to analyze and prone to technical artefacts. Here we describe WASP, a suite of tools for unbiased allele-specific read mapping and discovery of molecular QTLs. Using simulated reads, RNA-seq reads and ChIP-seq reads, we demonstrate that WASP has a low error rate and is far more powerful than existing QTL mapping approaches.

Discovery of QTL for stay-green and heat-stress in barley (Hordeum vulgare) grown under simulated abiotic stress conditions

This study maps genomic regions associated with terminal heat- and drought-stress tolerance in barley (Hordeum vulgare L.). One hundred lines were randomly sampled from a ND24260 × Flagship doubled haploid population and evaluated for stay-green (SG) expression. SG expression including that of parental controls and commercial check varieties was evaluated in two controlled environments; one simulating terminal heat-stress, the other terminal water-stress. During grain-fill the greenness of the spikes (S), flag leaf (FL) and the first leaf under the flag leaf (FL-1) were phenotyped; visually (using a 0–9 scale) and via single-photon avalanche diode measurements. From the visual assessments, the green leaf area of the plant was determined, by using the difference in green area of the S and FL. Composite interval mapping detected 10 quantitative trait loci (QTL) for SG, positioned on chromosomes 3H, 4H, 5H, 6H and 7H; six of which were associated with terminal heat-stress and four with terminal water-stress. None were co-located with previously reported barley stress-response QTL and thus represent novel barley QTL. Although novel, some SG QTL mapped near chromosomal regions previously reported; such as the two heat-stress QTL mapped to bPb-5529 on 5H, adjacent to QTL reported for root length and root-shoot ratio. Detection of SG QTL in barley grown under simulated heat- and water-stressed conditions offers the potential of high through put screening for these traits. If confirmed in field trials, these genomic regions will be candidates for barley breeding programs targeting improved abiotic stress tolerance via marker-assisted selection.

Host genetic resistance to root-knot nematodes, Meloidogyne spp., in Solanaceae: from genes to the field.

Root-knot nematodes (RKNs) heavily damage most solanaceous crops worldwide. Fortunately, major resistance genes are available in a number of plant species, and their use provides a safe and economically relevant strategy for RKN control. From a structural point of view, these genes often harbour NBS-LRR motifs (i.e. a nucleotide binding site and a leucine rich repeat region near the carboxy terminus) and are organised in syntenic clusters in solanaceous genomes. Their introgression from wild to cultivated plants remains a challenge for breeders, although facilitated by marker-assisted selection. As shown with other pathosystems, the genetic background into which the resistance genes are introgressed is of prime importance to both the expression of the resistance and its durability, as exemplified by the recent discovery of quantitative trait loci conferring quantitative resistance to RKNs in pepper. The deployment of resistance genes at a large scale may result in the emergence and spread of virulent nematode populations able to overcome them, as already reported in tomato and pepper. Therefore, careful management of the resistance genes available in solanaceous crops is crucial to avoid significant reduction in the duration of RKN genetic control in the field. From that perspective, only rational management combining breeding and cultivation practices will allow the design and implementation of innovative, sustainable crop production systems that protect the resistance genes and maintain their durability.

Genetical genomics of Populus leaf shape variation.

BackgroundLeaf morphology varies extensively among plant species and is under strong genetic control. Mutagenic screens in model systems have identified genes and established molecular mechanisms regulating leaf initiation, development, and shape. However, it is not known whether this diversity across plant species is related to naturally occurring variation at these genes. Quantitative trait locus (QTL) analysis has revealed a polygenic control for leaf shape variation in different species suggesting that loci discovered by mutagenesis may only explain part of the naturally occurring variation in leaf shape. Here we undertook a genetical genomics study in a poplar intersectional pseudo-backcross pedigree to identify genetic factors controlling leaf shape. The approach combined QTL discovery in a genetic linkage map anchored to the Populus trichocarpa reference genome sequence and transcriptome analysis.ResultsA major QTL for leaf lamina width and length:width ratio was identified in multiple experiments that confirmed its stability. A transcriptome analysis of expanding leaf tissue contrasted gene expression between individuals with alternative QTL alleles, and identified an ADP-ribosylation factor (ARF) GTPase (PtARF1) as a candidate gene for regulating leaf morphology in this pedigree. ARF GTPases are critical elements in the vesicular trafficking machinery. Disruption of the vesicular trafficking function of ARF by the pharmacological agent Brefeldin A (BFA) altered leaf lateral growth in the narrow-leaf P. trichocarpa suggesting a molecular mechanism of leaf shape determination. Inhibition of the vesicular trafficking processes by BFA interferes with cycling of PIN proteins and causes their accumulation in intercellular compartments abolishing polar localization and disrupting normal auxin flux with potential effects on leaf expansion.ConclusionsIn other model systems, ARF proteins have been shown to control the localization of auxin efflux carriers, which function to establish auxin gradients and apical-basal cell polarity in developing plant organs. Our results support a model where PtARF1 transcript abundance changes the dynamics of endocytosis-mediated PIN localization in leaf cells, thus affecting lateral auxin flux and subsequently lamina leaf expansion. This suggests that evolution of differential cellular polarity plays a significant role in leaf morphological variation observed in subgenera of genus Populus.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-015-0557-7) contains supplementary material, which is available to authorized users.

Genome-wide epistatic expression quantitative trait loci discovery in four human tissues reveals the importance of local chromosomal interactions governing gene expression.

BackgroundEpistasis (synergistic interaction) among SNPs governing gene expression is likely to arise within transcriptional networks. However, the power to detect it is limited by the large number of combinations to be tested and the modest sample sizes of most datasets. By limiting the interaction search space firstly to cis-trans and then cis-cis SNP pairs where both SNPs had an independent effect on the expression of the most variable transcripts in the liver and brain, we greatly reduced the size of the search space.ResultsWithin the cis-trans search space we discovered three transcripts with significant epistasis. Surprisingly, all interacting SNP pairs were located nearby each other on the chromosome (within 290 kb-2.16 Mb). Despite their proximity, the interacting SNPs were outside the range of linkage disequilibrium (LD), which was absent between the pairs (r2 < 0.01). Accordingly, we redefined the search space to detect cis-cis interactions, where a cis-SNP was located within 10 Mb of the target transcript. The results of this show evidence for the epistatic regulation of 50 transcripts across the tissues studied. Three transcripts, namely, HLA-G, PSORS1C1 and HLA-DRB5 share common regulatory SNPs in the pre-frontal cortex and their expression is significantly correlated. This pattern of epistasis is consistent with mediation via long-range chromatin structures rather than the binding of transcription factors in trans. Accordingly, some of the interactions map to regions of the genome known to physically interact in lymphoblastoid cell lines while others map to known promoter and enhancer elements. SNPs involved in interactions appear to be enriched for promoter markers.ConclusionsIn the context of gene expression and its regulation, our analysis indicates that the study of cis-cis or local epistatic interactions may have a more important role than interchromosomal interactions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1300-3) contains supplementary material, which is available to authorized users.

Using the UK reference population Avalon×Cadenza as a platform to compare breeding strategies in elite Western European bread wheat.

Wheat breeders select for qualitative and quantitative traits, the latter often detected as quantitative trait loci (QTL). It is, however, a long procedure from QTL discovery to the successful introduction of favourable alleles into new elite varieties and finally into farmers’ crops. As a proof of principle for this process, QTL for grain yield (GY), yield components, plant height (PH), ear emergence (EM), solid stem (SS) and yellow rust resistance (Yr) were identified in segregating UK bread wheat reference population, Avalon × Cadenza. Among the 163 detected QTL were several not reported before: 17 for GY, the major GY QTL on 2D; a major SS QTL on 3B; and Yr6 on 7B. Common QTL were identified on ten chromosomes, most interestingly, grain number (GN) was found to be associated with Rht-D1b; and GY and GN with a potential new allele of Rht8. The interaction of other QTL with GY and yield components was discussed in the context of designing a UK breeding target genotype. Desirable characteristics would be: similar PH and EM to Avalon; Rht-D1b and Vrn-A1b alleles; high TGW and GN; long and wide grains; a large root system, resistance to diseases; and maximum GY. The potential of the identified QTL maximising transgressive segregation to produce a high-yielding and resilient genotype was demonstrated by simulation. Moreover, simulating breeding strategies with F2 enrichment revealed that the F2–DH procedure was superior to the RIL and the modified SSD procedure to achieve that genotype. The proposed strategies of parent selection and breeding methodology can be used as guidance for marker-assisted wheat breeding.Electronic supplementary materialThe online version of this article (doi:10.1007/s11032-015-0268-7) contains supplementary material, which is available to authorized users.

Spot form of net blotch resistance in barley is under complex genetic control.

Evaluation of resistance to Pyrenophora teres f. maculata in barley breeding populations via association mapping revealed a complex genetic architecture comprising a mixture of major and minor effect genes. In the search for stable resistance to spot form of net blotch (Pyrenophora teres f. maculata, SFNB), association mapping was conducted on four independent barley (Hordeum vulgare L.) breeding populations comprising a total of 898 unique elite breeding lines from the Northern Region Barley Breeding Program in Australia for discovery of quantitative trait loci (QTL) influencing resistance at seedling and adult plant growth stages. A total of 29 significant QTL were validated across multiple breeding populations, with 22 conferring resistance at both seedling and adult plant growth stages. The remaining 7 QTL conferred resistance at either seedling (2 QTL) or adult plant (5 QTL) growth stages only. These 29 QTL represented 24 unique genomic regions, of which five were found to co-locate with previously identified QTL for SFNB. The results indicated that SFNB resistance is controlled by a large number of QTL varying in effect size with large effects QTL on chromosome 7H. A large proportion of the QTL acted in the same direction for both seedling and adult responses, suggesting that phenotypic selection for SFNB resistance performed at either growth stage could achieve adequate levels of resistance. However, the accumulation of specific resistance alleles on several chromosomes must be considered in molecular breeding selection strategies.

Plant Comparative and Functional Genomics.

Plant Comparative and Functional Genomics.

Challenges and prospects in genome‐wide quantitative trait loci mapping of standing genetic variation in natural populations

A considerable challenge in evolutionary genetics is to understand the genetic mechanisms that facilitate or impede evolutionary adaptation in natural populations. For this, we must understand the genetic loci contributing to trait variation and the selective forces acting on them. The decreased costs and increased feasibility of obtaining genotypic data on a large number of individuals have greatly facilitated gene mapping in natural populations, particularly because organisms whose genetics have been historically difficult to study are now within reach. Here we review the methods available to evolutionary ecologists interested in dissecting the genetic basis of traits in natural populations. Our focus lies on standing genetic variation in outbred populations. We present an overview of the current state of research in the field, covering studies on both plants and animals. We also draw attention to particular challenges associated with the discovery of quantitative trait loci and discuss parallels to studies on crops, livestock, and humans. Finally, we point to some likely future developments in genetic mapping studies.