Meta-quantitative Trait Loci Research Articles

Meta-QTL s and haplotypes for efficient zinc biofortification of rice.

Biofortification of rice with improved grain zinc (Zn)content is the most sustainable and cost-effective approach to address Zn malnutrition in Asia. Genomics-assisted breeding using precise and consistent Zn quantitative trait loci (QTLs), genes, and haplotypes can fast-track the development of Zn biofortified rice varieties. We conducted the meta-analysis of 155 Zn QTLs reported from 26 different studies. Results revealed 57 meta-QTLs with a significant reduction of 63.2% and 80% in the number and confidence interval of the Zn QTLs, respectively. Meta-quantitative trait loci (MQTLs) regions were found to be enriched with diverse metal homeostasis genes; at least 11 MQTLs were colocated with 20 known major genes involved in the production of root exudates, metal uptake, transport, partitioning, and loading into grains in rice. These genes were differentially expressed in vegetative and reproductive tissues, and a complex web of interactions were observed among them. We identified superior haplotypes and their combinations for nine candidate genes (CGs), and the frequency and allelic effects of superior haplotypes varied in different subgroups. The precise MQTLs with high phenotypic variance, CGs, and superior haplotypes identified in our study are useful for an efficient Zn biofortification of rice and to ensure Zn as an essential component of all the future rice varieties through mainstreaming of Zn breeding.

Genome-wide association study to identify genomic loci associated with early vigor in bread wheat under simulated water deficit complemented with quantitative trait loci meta-analysis.

A genome-wide association study (GWAS) was used to identify associated loci with early vigor under simulated water deficit and grain yield under field drought in a diverse collection of Iranian bread wheat landraces. In addition, a meta-quantitative trait loci (MQTL) analysis was used to further expand our approach by retrieving already published quantitative trait loci (QTL) from recombinant inbred lines, double haploids, back-crosses, and F2 mapping populations. In the current study, around 16%, 14%, and 16% of SNPs were in significant linkage disequilibrium (LD) in the A, B, and D genomes, respectively, and varied between 5.44% (4A) and 21.85% (6A). Three main subgroups were identified among the landraces with different degrees of admixture, and population structure was further explored through principal component analysis. Our GWAS identified 54 marker-trait associations (MTAs) that were located across the wheat genome but with the highest number found in the B sub-genome. The gene ontology (GO) analysis of MTAs revealed that around 75% were located within or closed to protein-coding genes. In the MQTL analysis, 23 MQTLs, from a total of 215 QTLs, were identified and successfully projected onto the reference map. MQT-YLD4, MQT-YLD9, MQT-YLD13, MQT-YLD17, MQT-YLD18, MQT-YLD19, and MQTL-RL1 contributed to the highest number of projected QTLs and were therefore regarded as the most reliable and stable QTLs under water deficit conditions. These MQTLs greatly facilitate the identification of putative candidate genes underlying at each MQTL interval due to the reduced confidence of intervals associated with MQTLs. These findings provide important information on the genetic basis of early vigor traits and grain yield under water deficit conditions and set the foundation for future investigations into adaptation to water deficit in bread wheat.

A meta-quantitative trait loci analysis identified consensus genomic regions and candidate genes associated with grain yield in rice.

Improving grain yield potential in rice is an important step toward addressing global food security challenges. The meta-QTL analysis offers stable and robust QTLs irrespective of the genetic background of mapping populations and phenotype environment and effectively narrows confidence intervals (CI) for candidate gene (CG) mining and marker-assisted selection improvement. To achieve these aims, a comprehensive bibliographic search for grain yield traits (spikelet fertility, number of grains per panicle, panicles number per plant, and 1000-grain weight) QTLs was conducted, and 462 QTLs were retrieved from 47 independent QTL research published between 2002 and 2022. QTL projection was performed using a reference map with a cumulative length of 2,945.67 cM, and MQTL analysis was conducted on 313 QTLs. Consequently, a total of 62 MQTLs were identified with reduced mean CI (up to 3.40 fold) compared to the mean CI of original QTLs. However, 10 of these MQTLs harbored at least six of the initial QTLs from diverse genetic backgrounds and environments and were considered the most stable and robust MQTLs. Also, MQTLs were compared with GWAS studies and resulted in the identification of 16 common significant loci modulating the evaluated traits. Gene annotation, gene ontology (GO) enrichment, and RNA-seq analyses of chromosome regions of the stable MQTLs detected 52 potential CGs including those that have been cloned in previous studies. These genes encode proteins known to be involved in regulating grain yield including cytochrome P450, zinc fingers, MADs-box, AP2/ERF domain, F-box, ubiquitin ligase domain protein, homeobox domain, DEAD-box ATP domain, and U-box domain. This study provides the framework for molecular dissection of grain yield in rice. Moreover, the MQTLs and CGs identified could be useful for fine mapping, gene cloning, and marker-assisted selection to improve rice productivity.

Genome-Wide Expression Analysis of Root Tips in Contrasting Rice Genotypes Revealed Novel Candidate Genes for Water Stress Adaptation.

Root system architecture (RSA) is an important agronomic trait with vital roles in plant productivity under water stress conditions. A deep and branched root system may help plants to avoid water stress by enabling them to acquire more water and nutrient resources. Nevertheless, our knowledge of the genetics and molecular control mechanisms of RSA is still relatively limited. In this study, we analyzed the transcriptome response of root tips to water stress in two well-known genotypes of rice: IR64, a high-yielding lowland genotype, which represents a drought-susceptible and shallow-rooting genotype; and Azucena, a traditional, upland, drought-tolerant and deep-rooting genotype. We collected samples from three zones (Z) of root tip: two consecutive 5 mm sections (Z1 and Z2) and the following next 10 mm section (Z3), which mainly includes meristematic and maturation regions. Our results showed that Z1 of Azucena was enriched for genes involved in cell cycle and division and root growth and development whereas in IR64 root, responses to oxidative stress were strongly enriched. While the expansion of the lateral root system was used as a strategy by both genotypes when facing water shortage, it was more pronounced in Azucena. Our results also suggested that by enhancing meristematic cell wall thickening for insulation purposes as a means of confronting stress, the sensitive IR64 genotype may have reduced its capacity for root elongation to extract water from deeper layers of the soil. Furthermore, several members of gene families such as NAC, AP2/ERF, AUX/IAA, EXPANSIN, WRKY, and MYB emerged as main players in RSA and drought adaptation. We also found that HSP and HSF gene families participated in oxidative stress inhibition in IR64 root tip. Meta-quantitative trait loci (QTL) analysis revealed that 288 differentially expressed genes were colocalized with RSA QTLs previously reported under drought and normal conditions. This finding warrants further research into their possible roles in drought adaptation. Overall, our analyses presented several major molecular differences between Azucena and IR64, which may partly explain their differential root growth responses to water stress. It appears that Azucena avoided water stress through enhancing growth and root exploration to access water, whereas IR64 might mainly rely on cell insulation to maintain water and antioxidant system to withstand stress. We identified a large number of novel RSA and drought associated candidate genes, which should encourage further exploration of their potential to enhance drought adaptation in rice.

QTL analysis in multiple sorghum mapping populations facilitates dissection of the genetic control of agronomic and yield-related traits in sorghum [Sorghum bicolor (Moench)

The genetic architectures of agronomic and yield-related traits are expected to involve multiple loci that are unlikely all to segregate for alternative alleles in a single biparental population. Therefore, the identification of quantitative trait loci (QTL) that are expressed in diverse genetic backgrounds of multiple bi-parental populations provides evidence about both background-specific and common genetic variants. The purpose of this study was to map QTLs for agronomic and yield related traits using three connected mapping populations of different genetic backgrounds, to gain insight into the genomic landscape of these important traits in elite Ethiopian sorghum germplasm. The three bi-parental populations, each with 207 F 2:3 lines were evaluated using an alpha lattice design with two replications under two moisture stress environments. Data analysis was done separately for each population using composite interval mapping, finding a total of 105 QTLs. All the QTLs identified from individual populations were projected on a combined consensus map, comprising a total of 25 meta QTLs for seven traits. The consensus map allowed us to deduce locations of a larger number of markers than possible in any individual map, providing a reference for genetic studies in different genetic backgrounds. The meta QTLs identified in this study could be used for marker-assisted breeding programs in sorghum after validation. Only one trait, reduced leaf senescence, showed a striking bias of allele distribution, indicating substantial standing variation among the lines that might be employed in improving drought tolerance of sorghum.

Comparative Genomic Analysis of Quantitative Trait Loci Associated With Micronutrient Contents, Grain Quality, and Agronomic Traits in Wheat (Triticum aestivum L.).

Comparative genomics and meta-quantitative trait loci (MQTLs) analysis are important tools for the identification of reliable and stable QTLs and functional genes controlling quantitative traits. We conducted a meta-analysis to identify the most stable QTLs for grain yield (GY), grain quality traits, and micronutrient contents in wheat. A total of 735 QTLs retrieved from 27 independent mapping populations reported in the last 13 years were used for the meta-analysis. The results showed that 449 QTLs were successfully projected onto the genetic consensus map which condensed to 100 MQTLs distributed on wheat chromosomes. This consolidation of MQTLs resulted in a three-fold reduction in the confidence interval (CI) compared with the CI for the initial QTLs. Projection of QTLs revealed that the majority of QTLs and MQTLs were in the non-telomeric regions of chromosomes. The majority of micronutrient MQTLs were located on the A and D genomes. The QTLs of thousand kernel weight (TKW) were frequently associated with QTLs for GY and grain protein content (GPC) with co-localization occurring at 55 and 63%, respectively. The co- localization of QTLs for GY and grain Fe was found to be 52% and for QTLs of grain Fe and Zn, it was found to be 66%. The genomic collinearity within Poaceae allowed us to identify 16 orthologous MQTLs (OrMQTLs) in wheat, rice, and maize. Annotation of promising candidate genes (CGs) located in the genomic intervals of the stable MQTLs indicated that several CGs (e.g., TraesCS2A02G141400, TraesCS3B02G040900, TraesCS4D02G323700, TraesCS3B02G077100, and TraesCS4D02G290900) had effects on micronutrients contents, yield, and yield-related traits. The mapping refinements leading to the identification of these CGs provide an opportunity to understand the genetic mechanisms driving quantitative variation for these traits and apply this information for crop improvement programs.

Whole-genome mining of abiotic stress gene loci in rice.

We projected meta-QTL (MQTL) for drought, salinity, cold state, and high metal ion tolerance in rice using a meta-analysis based on high-density consensus maps. In addition, a genome-wide association analysis was used to validate the results of the meta-analysis, and four new chromosome intervals for mining abiotic stress candidate genes were obtained. Drought, severe cold, high salinity, and high metallic ion concentrations severely restrict rice production. Consequently, the breeding of abiotic stress-tolerant variety is being paid increasingly more attention. This study aimed to identify meta-quantitative trait loci (MQTL) for abiotic stress tolerance in rice, as well as the molecular markers and potential candidate genes of the MQTL regions. We summarized 2785 rice QTL and conducted a meta-analysis of 159 studies. We found 82 drought tolerance (DT), 70 cold tolerance (CT), 70 salt tolerance (ST), and 51 heavy metal ion tolerance (IT) meta-QTL, as well as 20 DT, 11 CT, 22 ST, and 5 IT candidate genes in the MQTL interval. Thirty-one multiple-tolerance related MQTL regions, which were highly enriched, were also detected, and 13 candidate genes related to multiple-tolerance were obtained. In addition, the correlation between DT, CT, and ST was significant in the rice genome. Four candidate genes and four MM-QTL regions were detected simultaneously by GWAS and meta-analysis. The four candidate genes showed distinct genetic differentiation and substantial genetic distance between indica and japonica rice, and the four MM-QTL are potential intervals for mining abiotic stress-related candidate genes. The candidate genes identified in this study will not only be useful for marker-assisted selection and pyramiding but will also accelerate the fine mapping and cloning of the candidate genes associated with abiotic stress-tolerance mechanisms in rice.

High Resolution Melting and Insertion Site-Based Polymorphism Markers for Wheat Variability Analysis and Candidate Genes Selection at Drought and Heat MQTL Loci

The practical use of molecular markers is facilitated by cost-effective detection techniques. In this work, wheat insertion site-based polymorphisms (ISBP) markers were set up for genotyping using high-resolution melting analysis (HRM). Polymorphic HRM-ISBP assays were developed for wheat chromosomes 4A and 3B and used for wheat variability assessment. The marker sequences were mapped against the wheat genome reference sequence, targeting interesting genes. Those genes were located within or in proximity to previously described quantitative trait loci (QTL) or meta-quantitative trait loci (MQTL) for drought and heat stress tolerance, and also yield and yield related traits. Eighteen of the markers used tagged drought related genes and, interestingly, eight of the genes were differentially expressed under different abiotic stress conditions. These results confirmed HRM as a cost-effective and efficient tool for wheat breeding programs.

Accelerating Breeding for Heat Tolerance in Tomato (Solanum lycopersicum L.): An Integrated Approach

Heat stress is a major limiting factor for crop productivity. Tomato is highly sensitive to heat stress, which can result in a total yield loss. To adapt to current and future heat stress, there is a dire need to develop heat tolerant cultivars. Here, we review recent attempts to improve screening for heat tolerance and to exploit genetic and genomic resources in tomatoes. We provide key factors related to phenotyping environments and traits (morphological, physiological, and metabolic) to be considered to identify and breed thermo-tolerant genotypes. There is significant variability in tomato germplasm that can be harnessed to breed for thermo-tolerance. Based on our review, we propose that the use of advanced backcross populations and chromosome segments substitution lines is the best means to exploit variability for heat tolerance in non-cultivated tomato species. We applied a meta quantitative trait loci (MQTL) analysis on data from four mapping experiments to co-localize QTL associated with heat tolerance traits (e.g., pollen viability, number of pollen, number of flowers, style protrusion, style length). The analysis revealed 13 MQTL of which 11 were composed of a cluster of QTL. Overall, there was a reduction of about 1.5-fold in the confidence interval (CI) of the MQTL (31.82 cM) compared to the average CI of individual QTL (47.4 cM). This confidence interval is still large and additional mapping resolution approaches such as association mapping and multi-parent linkage mapping are needed. Further investigations are required to decipher the genetic architecture of heat tolerance surrogate traits in tomatoes. Genomic selection and new breeding techniques including genome editing and speed breeding hold promise to fast-track development of improved heat tolerance and other farmer- and consumer-preferred traits in tomatoes.

Meta-QTL analysis of seed iron and zinc concentration and content in common bean (Phaseolus vulgaris L.).

Twelve meta-QTL for seed Fe and Zn concentration and/or content were identified from 87 QTL originating from seven population grown in sixteen field trials. These meta-QTL include 2 specific to iron, 2 specific to zinc and 8 that co-localize for iron and zinc concentrations and/or content. Common bean (Phaseolus vulgaris L.) is the most important legume for human consumption worldwide and it is an important source of microelements, especially iron and zinc. Bean biofortification breeding programs develop new varieties with high levels of Fe and Zn targeted for countries with human micronutrient deficiencies. Biofortification efforts thus far have relied on phenotypic selection of raw seed mineral concentrations in advanced generations. While numerous quantitative trait loci (QTL) studies have been conducted to identify genomic regions associated with increased Fe and Zn concentration in seeds, these results have yet to be employed for marker-assisted breeding. The objective of this study was to conduct a meta-analysis from seven QTL studies in Andean and Middle American intra- and inter-gene pool populations to identify the regions in the genome that control the Fe and Zn levels in seeds. Two meta-QTL specific to Fe and two meta-QTL specific to Zn were identified. Additionally, eight Meta QTL that co-localized for Fe and Zn concentration and/or content were identified across seven chromosomes. The Fe and Zn shared meta-QTL could be useful candidates for marker-assisted breeding to simultaneously increase seed Fe and Zn. The physical positions for 12 individual meta-QTL were identified and within five of the meta-QTL, candidate genes were identified from six gene families that have been associated with transport of iron and zinc in plants.

Integrating CNVs into meta-QTL identified GBP4 as positional candidate for adult cattle stature.

Copy number variation (CNV) of DNA sequences, functionally significant but yet fully ascertained, is believed to confer considerable increments in unexplained heritability of quantitative traits. Identification of phenotype-associated CNVs (paCNVs) therefore is a pressing need in CNV studies to speed up their exploitation in cattle breeding programs. Here, we provided a new avenue to achieve this goal that is to project the published CNV data onto meta-quantitative trait loci (meta-QTL) map which connects causal genes with phenotypes. Any CNVs overlapping meta-QTL therefore will be potential paCNVs. This study reported potential paCNVs in Bos taurus autosome 3 (BTA3). Notably, overview indexes and CNVs both highlighted a narrower region (BTA3 54,500,000-55,000,000bp, named BTA3_INQTL_6) within one constructed meta-QTL. Then, we ascertained guanylate-binding protein 4 (GBP4) among the nine positional candidate genes was significantly associated with adult cattle stature, including body weight (BW, P < 0.05) and withers height (WHT, P < 0.05), fitting GBP4 CNV either with three levels or with six levels in the model. Although higher copy number downregulated the mRNA levels of GBP2 (P < 0.05) and GBP4 (P < 0.05) in 1-Mb window (54.0-55.0Mb) in muscle and adipose, additional analyses will be needed to clarify the causality behind the ascertained association.

QTL Mapping in Three Rice Populations Uncovers Major Genomic Regions Associated with African Rice Gall Midge Resistance.

African rice gall midge (AfRGM) is one of the most destructive pests of irrigated and lowland African ecologies. This study aimed to identify the quantitative trait loci (QTL) associated with AfRGM pest incidence and resistance in three independent bi-parental rice populations (ITA306xBW348-1, ITA306xTOG7106 and ITA306xTOS14519), and to conduct meta QTL (mQTL) analysis to explore whether any genomic regions are conserved across different genetic backgrounds. Composite interval mapping (CIM) conducted on the three populations independently uncovered a total of 28 QTLs associated with pest incidence (12) and pest severity (16). The number of QTLs per population associated with AfRGM resistance varied from three in the ITA306xBW348-1 population to eight in the ITA306xTOG7106 population. Each QTL individually explained 1.3 to 34.1% of the phenotypic variance. The major genomic region for AfRGM resistance had a LOD score and R2 of 60.0 and 34.1% respectively, and mapped at 111 cM on chromosome 4 (qAfrGM4) in the ITA306xTOS14519 population. The meta-analysis reduced the number of QTLs from 28 to 17 mQTLs, each explaining 1.3 to 24.5% of phenotypic variance, and narrowed the confidence intervals by 2.2 cM. There was only one minor effect mQTL on chromosome 1 that was common in the TOS14519 and TOG7106 genetic backgrounds; all other mQTLs were background specific. We are currently fine-mapping and validating the major effect genomic region on chromosome 4 (qAfRGM4). This is the first report in mapping the genomic regions associated with the AfRGM resistance, and will be highly useful for rice breeders.

Quantitative trait loci identification and meta-analysis for rice panicle-related traits.

Rice yield is a complex trait controlled by quantitative trait loci (QTLs). In the past three decades, thousands of QTLs for rice yield traits have been detected, but only a very small percentage has been cloned to date, as identifying the QTL genes requires a substantial investment of time and money. Meta-analysis provides a simple, reliable, and economical method for integrating information from multiple QTL studies across various environmental and genetic backgrounds, detecting consistent QTLs powerfully and estimating their genetic positions precisely. In this study, we aimed to locate consistent QTL regions associated with rice panicle traits by applying a genome-wide QTL meta-analysis approach. We first conducted a QTL analysis of 5 rice panicle traits using 172 plants in 2011 and 138 plants in 2012 from an F2 population derived from a cross between Nipponbare and H71D rice cultivators. A total of 54 QTLs were detected, and these were combined with 1085 QTLs collected from 82 previous studies to perform a meta-analysis using BioMercator v4.2. The integration of 82 maps resulted in a consensus map with 6970 markers and a total map length of 1823.1 centimorgan (cM), on which 837 QTLs were projected. These QTLs were then integrated into 87 meta-quantitative trait loci (MQTLs) by meta-analysis, and the 95% confidence intervals (CI) of them were smaller than the mean value of the original QTLs. Also, 30 MQTLs covered 47 of the 54 QTLs detected from the cross between Nipponbare and H71D in this study. Among them, the two major and stable QTLs, spp10.1 and sd10.1, were found to be included in MQTL10.4. The three other major QTLs, pl3.1, sb2.1, and sb10.1, were included in MQTL3.3, MQTL2.2, and MQTL10.3, respectively. A total of 21 of the 87 MQTLs' phenotypic variation were>20%. In total, 24 candidate genes were found in 15 MQTLs that spanned physical intervals<0.2Mb, including genes that have been cloned previously, e.g., EP3, LP, MIP1, HTD1, DSH1, and OsPNH1. However, it would be beneficial to identify a greater number of candidate genes from these MQTLs. Mining new genes that modulate yield and its related traits would assist researchers to better understand the relevant molecular mechanisms. The MQTLs found in this study that have small physical and genetic intervals are useful not only for marker-assisted selection and pyramiding, but they also provide important information of rice yield and related gene mining for future research.

Embracing new-generation 'omics' tools to improve drought tolerance in cereal and food-legume crops

Drought stress presents a considerable threat to the global crop production. As a dominant source of vegetarian diet, cereals and grain-legumes remain crucial to meeting the growing dietary demands worldwide. Therefore, breeding cultivars of these staple crops with enhanced drought tolerance stands to be one of the most sustainable solutions to enhance food production in changing climate. Given the context, a more focused survey of environment-defined germplasm sets is imperative to comprehend such adaptive traits. In parallel, uncovering the genetic architecture and the molecular networks that collectively contribute towards drought tolerance is urgently required through rationally combining large-scale genomics, proteomics, and metabolomics data. Also, attention needs to be directed to reasonably quantify the epistatic as well as environmental influences, thereby warranting deployment of analyses like metaquantitative trait loci (QTL) that encompass multiple environments and diverse genetic backgrounds. Further, innovative techniques like genomic selection (GS) and genome wide association study (GWAS) would help to capture the quantitative variation underlying drought tolerance. Equally importantly, integration of physiological traits-based techniques with ever-evolving ‘omics’ technologies and the new-generation phenotyping platforms will be of immense importance in advancing our existing knowledge about the genetically-complex and poorly-understood phenomena, such as plant drought response, and a deeper understanding would likely to provide a great impetus to the progress of crop breeding for drought tolerance.

MetaQTL analysis provides a compendium of genomic loci controlling fruit quality traits in apple

Fruit quality represents an important aspect of any fruit species, due to its economical importance and direct impact on consumers’ appreciation. In order to generate a compendium about the genomic intervals putatively involved in the control of the several fruit quality components, a Meta-quantitative trait loci (QTL) analysis was performed starting from a QTL mapping survey individually conducted on four full-sib populations. These progenies were simultaneously genotyped with 1289 SNP markers, of which 52 % were in common for at least two maps. The combination of the genotypic and phenotypic datasets allowed the identification of 56 QTLs, which were subsequently projected into a consensus map, reducing the total number of genomic intervals to 27 MetaQTLs. The majority of these regions, associated to fruit quality traits such as fruit skin color and flesh firmness, resulted also consistent with previous reports presented to date to the scientific community. This MetaQTL overview would represents a valuable source for genome anchoring and data mining investigation, suitable for a further in silico identification of relevant causal genes. As example is reported the case of Md-PG1, a gene known to control fruit firmness in apple and retrieved within the confidence interval of a MetaQTL associated to fruit firmness.

A comprehensive meta QTL analysis for fiber quality, yield, yield related and morphological traits, drought tolerance, and disease resistance in tetraploid cotton

BackgroundThe study of quantitative trait loci (QTL) in cotton (Gossypium spp.) is focused on traits of agricultural significance. Previous studies have identified a plethora of QTL attributed to fiber quality, disease and pest resistance, branch number, seed quality and yield and yield related traits, drought tolerance, and morphological traits. However, results among these studies differed due to the use of different genetic populations, markers and marker densities, and testing environments. Since two previous meta-QTL analyses were performed on fiber traits, a number of papers on QTL mapping of fiber quality, yield traits, morphological traits, and disease resistance have been published. To obtain a better insight into the genome-wide distribution of QTL and to identify consistent QTL for marker assisted breeding in cotton, an updated comparative QTL analysis is needed.ResultsIn this study, a total of 1,223 QTL from 42 different QTL studies in Gossypium were surveyed and mapped using Biomercator V3 based on the Gossypium consensus map from the Cotton Marker Database. A meta-analysis was first performed using manual inference and confirmed by Biomercator V3 to identify possible QTL clusters and hotspots. QTL clusters are composed of QTL of various traits which are concentrated in a specific region on a chromosome, whereas hotspots are composed of only one trait type. QTL were not evenly distributed along the cotton genome and were concentrated in specific regions on each chromosome. QTL hotspots for fiber quality traits were found in the same regions as the clusters, indicating that clusters may also form hotspots.ConclusionsPutative QTL clusters were identified via meta-analysis and will be useful for breeding programs and future studies involving Gossypium QTL. The presence of QTL clusters and hotspots indicates consensus regions across cultivated tetraploid Gossypium species, environments, and populations which contain large numbers of QTL, and in some cases multiple QTL associated with the same trait termed a hotspot. This study combines two previous meta-analysis studies and adds all other currently available QTL studies, making it the most comprehensive meta-analysis study in cotton to date.

Meta - and combined - QTL analysis of different experiments on immune traits in chickens

Meta and/or combined QTL analysis from multiple studies can improve quantitative trait loci (QTL) position estimates compared to the individual experiments. Hereby we present results of a meta-analysis of QTL on chicken chromosome 9, 14 and 18 using data from three separate experiments and joint QTL analysis for chromosome 14 and 18. Meta QTL analysis uses information from multiple QTLs studies. Joint QTL analysis is based on combining raw data from different QTL experimental populations. QTLs under the study were related to specific antibody response to keyhole lymphet hemocyanin (KLH), and natural antibodies to environmental antigens, lipopolisaccharide (LPS) and lipoteichoic acid (LTA). Meta QTL analysis resulted in narrowing down the confidence interval for two QTLs on GGA14. The first one for natural antibodies against LTA and the second one for specific antibody response toward KLH. Also, a confidence interval of a QTL for natural antibodies against LPS located on GGA18 was narrowed down. Combined QTL analysis was successful for two QTLs: for specific antibody response toward KLH on GGA14, and for natural antibodies against LPS on GGA18. The greatest statistical power for QTL detection in joint analysis was achieved when raw data from segregating half–sib families from different populations under the study was used.

A Common Genetic Determinism for Sensitivities to Soil Water Deficit and Evaporative Demand: Meta-Analysis of Quantitative Trait Loci and Introgression Lines of Maize

Evaporative demand and soil water deficit equally contribute to water stress and to its effect on plant growth. We have compared the genetic architectures of the sensitivities of maize (Zea mays) leaf elongation rate with evaporative demand and soil water deficit. The former was measured via the response to leaf-to-air vapor pressure deficit in well-watered plants, the latter via the response to soil water potential in the absence of evaporative demand. Genetic analyses of each sensitivity were performed over 21 independent experiments with (1) three mapping populations, with temperate or tropical materials, (2) one population resulting from the introgression of a tropical drought-tolerant line in a temperate line, and (3) two introgression libraries genetically independent from mapping populations. A very large genetic variability was observed for both sensitivities. Some lines maintained leaf elongation at very high evaporative demand or water deficit, while others stopped elongation in mild conditions. A complex architecture arose from analyses of mapping populations, with 19 major meta-quantitative trait loci involving strong effects and/or more than one mapping population. A total of 68% of those quantitative trait loci affected sensitivities to both evaporative demand and soil water deficit. In introgressed lines, 73% of the tested genomic regions affected both sensitivities. To our knowledge, this study is the first genetic demonstration that hydraulic processes, which drive the response to evaporative demand, also have a large contribution to the genetic variability of plant growth under water deficit in a large range of genetic material.