Quantitative Trait Loci Effects Research Articles

The genotype–phenotype relationship and evolutionary genetics in the light of the Metabolic Control Analysis

Metabolic control analysis has long been used as a systemic model of the genotype–phenotype (GP) relationship. By considering kinetic parameters and enzyme concentrations as reflecting the genotype level and metabolic fluxes or pools as phenotypes related to fitness, MCA has given a biological basis to the relationship between these two levels. The non-linear and concave relationship between enzymes and fluxes can account for common genetic effects that reductionist approaches have been powerless to explain, such as the dominance of active alleles over less active alleles, the various types of epistasis and heterosis, and reveals the structural links between these genetic effects. The summation property of the flux control coefficients accounts for the L-shaped distribution of Quantitative Trait Locus (QTL) effects, irrespective of other possible causes. Metabolic models of response to selection results in evolutionary scenarios that are markedly different from those derived from the classical infinitesimal model of quantitative genetics. In particular, evolution towards selective neutrality appears to be a consequence of the diminishing return of the flux-enzyme relationship. In this paper, we survey the historical and recent achievements of MCA in genetics, quantitative genetics and evolution, focusing on epistasis and the evolution of flux in relation to enzyme concentrations.

DeepGBLUP: joint deep learning networks and GBLUP framework for accurate genomic prediction of complex traits in Korean native cattle

BackgroundGenomic prediction has become widespread as a valuable tool to estimate genetic merit in animal and plant breeding. Here we develop a novel genomic prediction algorithm, called deepGBLUP, which integrates deep learning networks and a genomic best linear unbiased prediction (GBLUP) framework. The deep learning networks assign marker effects using locally-connected layers and subsequently use them to estimate an initial genomic value through fully-connected layers. The GBLUP framework estimates three genomic values (additive, dominance, and epistasis) by leveraging respective genetic relationship matrices. Finally, deepGBLUP predicts a final genomic value by summing all the estimated genomic values.ResultsWe compared the proposed deepGBLUP with the conventional GBLUP and Bayesian methods. Extensive experiments demonstrate that the proposed deepGBLUP yields state-of-the-art performance on Korean native cattle data across diverse traits, marker densities, and training sizes. In addition, they show that the proposed deepGBLUP can outperform the previous methods on simulated data across various heritabilities and quantitative trait loci (QTL) effects.ConclusionsWe introduced a novel genomic prediction algorithm, deepGBLUP, which successfully integrates deep learning networks and GBLUP framework. Through comprehensive evaluations on the Korean native cattle data and simulated data, deepGBLUP consistently achieved superior performance across various traits, marker densities, training sizes, heritabilities, and QTL effects. Therefore, deepGBLUP is an efficient method to estimate an accurate genomic value. The source code and manual for deepGBLUP are available at https://github.com/gywns6287/deepGBLUP.

Meta‐analysis of the genetics of resistance to Fusarium head blight and deoxynivalenol accumulation in barley and considerations for breeding

AbstractFusarium head blight (FHB) or scab is a devastating disease of barley that severely reduces the yield and quality of the grain. Additionally, mycotoxins produced by the causal Fusarium species can contaminate harvested grain, resulting in food safety concerns and further economic losses. In the Upper Midwest region of the United States, Fusarium graminearum is the primary causal agent, and deoxynivalenol (DON) is the main mycotoxin associated with Fusarium infection. Deployment of resistant cultivars is an important component of an integrated strategy to manage this disease. Unfortunately, few good sources of FHB resistance have been identified from the evaluation of large collections of Hordeum germplasm. Over the past 25 years, many barley mapping populations have been developed with selected resistance sources to identify the number, chromosomal position and allelic effect of quantitative trait loci (QTL) contributing to FHB resistance and DON accumulation. To consolidate the genetic data generated from 14 mapping studies that included 22 bi‐ or tri‐parental mapping populations and three genome‐wide association (GWAS) mapping panels, a consensus map was constructed that includes 4145 SNP, SSR, RFLP and AFLP markers. A meta‐analysis based on this consensus map revealed 96 QTL for FHB resistance and 57 for DON accumulation scattered across the barley genome. Many of the QTL explained a low percentage (<10%) of variation for the traits and were often found significant in only one or a few environments in multi‐year/multi‐location field trials. Moreover, many of the FHB/DON QTL mapped to chromosomal positions coincided with various agro‐morphological traits that could influence the level of disease (e.g. heading date, height, spike density, and spike angle), raising the important question of whether the former are true resistance factors or are simply the result of pleiotropy with the latter. Considering the magnitude of effect, consistency of detection across environments and independence from agro‐morphological traits, only three of 96 QTL for FHB and five of 57 QTL for DON were considered priority targets for marker‐assisted selection (MAS). In spite of the challenge for having a limited number of useful QTL for breeding, genomic selection holds promise for increasing the efficiency of developing FHB‐resistant barley cultivars, an essential component of the overall management strategy for the disease.

Genetics of evolved load resistance in the skeletons of unusually large mice from Gough Island.

A primary function of the skeleton is to resist the loads imparted by body weight. Genetic analyses have identified genomic regions that contribute to differences in skeletal load resistance between laboratory strains of mice, but these studies are usually restricted to 1 or 2 bones and leave open the question of how load resistance evolves in natural populations. To address these challenges, we examined the genetics of bone structure using the largest wild house mice on record, which live on Gough Island (GI). We measured structural traits connected to load resistance in the femur, tibia, scapula, humerus, radius, ulna, and mandible of GI mice, a smaller-bodied reference strain from the mainland, and 760 of their F2s. GI mice have bone geometries indicative of greater load resistance abilities but show no increase in bone mineral density compared to the mainland strain. Across traits and bones, we identified a total of 153 quantitative trait loci (QTL) that span all but one of the autosomes. The breadth of QTL detection ranges from a single bone to all 7 bones. Additive effects of QTL are modest. QTL for bone structure show limited overlap with QTL for bone length and width and QTL for body weight mapped in the same cross, suggesting a distinct genetic architecture for load resistance. Our findings provide a rare genetic portrait of the evolution of load resistance in a natural population with extreme body size.

Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits.

Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in order to be used in marker-assisted selection (MAS) programs. Genome-wide association study (GWAS) is one of the most common genetic approaches that is regularly used for detecting QTL associated with quantitative traits. However, the current approaches are mainly focused on estimating the main effects of QTL, and, therefore, a substantial statistical improvement in GWAS is required to detect associated QTL considering their interactions with other QTL as well. This study aimed to compare the support vector regression (SVR) algorithm as a common machine learning method to fixed and random model circulating probability unification (FarmCPU), a common conventional GWAS method in detecting relevant QTL associated with soybean seed quality traits such as protein, oil, and 100-seed weight using 227 soybean genotypes. The results showed a significant negative correlation between soybean seed protein and oil concentrations, with heritability values of 0.69 and 0.67, respectively. In addition, SVR-mediated GWAS was able to identify more relevant QTL underlying the target traits than the FarmCPU method. Our findings demonstrate the potential use of machine learning algorithms in GWAS to detect durable QTL associated with soybean seed quality traits suitable for genomic-based breeding approaches. This study provides new insights into improving the accuracy and efficiency of GWAS and highlights the significance of using advanced computational methods in crop breeding research.

Exploring the novel SNPs in neuroticism and birth weight based on GWAS datasets

ObjectivesEpidemiological studies have confirmed that low birth weight (BW) is related to neuroticism and they may have a common genetic mechanism based on phenotypic correlation research. We conducted our study on a European population with 159,208 neuroticism and 289,142 birth weight samples. In this study, we aimed to identify new neuroticism single nucleotide polymorphisms (SNPs) and pleiotropic SNPs associated with neuroticism and BW and to provide more theoretical basis for the pathogenesis of the disease.MethodsWe estimated the pleiotropic enrichment between neuroticism and BW in two independent Genome-wide association studies (GWAS) when the statistical thresholds were Conditional False Discovery Rate (cFDR) < 0.01 and Conjunctional Conditional False Discovery Rate (ccFDR) < 0.05. We performed gene annotation and gene functional analysis on the selected significant SNPs to determine the biological role of gene function and pathogenesis. Two-sample Mendelian Randomization (TSMR) analysis was performed to explore the causal relationship between the neuroticism and BW.ResultsThe conditional quantile–quantile plots (Q-Q plot) indicated that neuroticism and BW have strong genetic pleiotropy enrichment trends. With the threshold of cFDR < 0.001, we identified 126 SNPs related to neuroticism and 172 SNPs related to BW. With the threshold of ccFDR < 0.05, we identified 62 SNPs related to both neuroticism and BW. Among these SNPs, rs8039305 and rs35755513 have eQTL (expressed quantitative trait loci) and meQTL (methylation quantitative trait loci) effects simultaneously. Through GO enrichment analysis we also found that the two pathways of positive regulation of “mesenchymal cell proliferation” and “DNA-binding transcription factor activity” were significantly enriched in neuroticism and BW. Mendelian randomization analysis results indicate that there is no obvious causal relationship between neuroticism and birth weight.ConclusionWe found 126 SNPs related to neuroticism, 172 SNPs related to BW and 62 SNPs associated with both neuroticism and BW, which provided a theoretical basis for their genetic mechanism and novel potential targets for treatment/intervention development.

Genetic mapping identified major main-effect and three co-localized quantitative trait loci controlling high iron and zinc content in groundnut.

Malnutrition is a major challenge globally, and groundnut is a highly nutritious self-pollinated legume crop blessed with ample genomic resources, including the routine deployment of genomic-assisted breeding. This study aimed to identify genomic regions and candidate genes for high iron (Fe) and zinc (Zn) content, utilizing a biparental mapping population (ICGV 00440×ICGV 06040;). Genetic mapping and quantitative trait locus (QTL) analysis (474 mapped single-nucleotide polymorphism loci; 1536.33cM) using 2 seasons of phenotypic data together with genotypic data identified 5 major main-effect QTLs for Fe content. These QTLs exhibited log-of-odds (LOD) scores ranging from 6.5 to 7.4, explaining phenotypic variation (PVE) ranging from 22% (qFe-Ah01) to 30.0% (qFe-Ah14). Likewise, four major main effect QTLs were identified for Zn content, with LOD score ranging from 4.4 to 6.8 and PVE ranging from 21.8% (qZn-Ah01) to 32.8% (qZn-Ah08). Interestingly, three co-localized major and main effect QTLs (qFe-Ah01, qZn-Ah03, and qFe-Ah11) were identified for both Fe and Zn contents. These genomic regions harbored key candidate genes, including zinc/iron permease transporter, bZIP transcription factor, and vacuolar iron transporter which likely play pivotal roles in the accumulation of Fe and Zn contents in seeds. The findings of this study hold potential for fine mapping and diagnostic marker development for high Fe and Zn contents in groundnut.

KASP mapping of QTLs for yield components using a RIL population in Basmati rice (Oryza sativa L.)

Super Basmati is an elite variety with extra-long grains and superior quality but its yield is severely affected by water stress. Recombinant inbred lines (RILs) developed from the cross of Super Basmati and IR55419-04 (a coarse grained variety with high yield potential) were used to identify and map yield-related quantitative trait loci (QTLs) under normal field conditions. Genotypes for 244 KASP markers were obtained from 188 F9 RILs and a linkage map constructed of 1369.4 cM, with average marker interval of 10.5 cM. Eleven agronomic traits were phenotyped in RILs and parents. Composite interval mapping and inclusive composite interval mapping model identified 21 common QTLs related to nine agronomic traits; of the QTLs identified, twelve mapped at novel positions. LOD scores ranged from 2.51 for number of filled grains (qFG1.1) to 16.8 for plant height (qPH1.1). Four major effect QTLs could explain 20% of variation related for plant height, hundred-grain weight and grain width. Alleles from IR55419-04 improved grain filling, grain weight and grain width, while alleles from Super Basmati contributed to longer grains, panicles and flag leaves. Putative candidate genes were identified for 18 yield-related QTLs. These results validate the use of KASP genotyping for QTL mapping of yield-related traits in a bi-parental segregating population. SNPs in the QTLs identified in this study can be used in marker assisted selection for yield components to improve Basmati rice.

Systematic druggable genome-wide Mendelian randomisation identifies therapeutic targets for Alzheimer’s disease

BackgroundAlzheimer’s disease (AD) is the leading cause of dementia. Currently, there are no effective disease-modifying treatments for AD. Mendelian randomisation (MR) has been widely used to repurpose licensed drugs and...

Multi-parental fungal mapping population study to detect genomic regions associated with Pyrenophora teres f. teres virulence

In recent years multi-parental mapping populations (MPPs) have been widely adopted in many crops to detect quantitative trait loci (QTLs) as this method can compensate for the limitations of QTL analyses using bi-parental mapping populations. Here we report the first multi-parental nested association mapping (MP-NAM) population study used to detect genomic regions associated with host-pathogenic interactions. MP-NAM QTL analyses were conducted on 399 Pyrenophora teres f. teres individuals using biallelic, cross-specific and parental QTL effect models. A bi-parental QTL mapping study was also conducted to compare the power of QTL detection between bi-parental and MP-NAM populations. Using MP-NAM with 399 individuals detected a maximum of eight QTLs with a single QTL effect model whilst only a maximum of five QTLs were detected with an individual bi-parental mapping population of 100 individuals. When reducing the number of isolates in the MP-NAM to 200 individuals the number of QTLs detected remained the same for the MP-NAM population. This study confirms that MPPs such as MP-NAM populations can be successfully used in detecting QTLs in haploid fungal pathogens and that the power of QTL detection with MPPs is greater than with bi-parental mapping populations.

A Comparison of Methods to Estimate Additive-by-Additive-by-Additive of QTL×QTL×QTL Interaction Effects by Monte Carlo Simulation Studies.

The goal of the breeding process is to obtain new genotypes with traits improved over the parental forms. Parameters related to the additive effect of genes as well as their interactions (such as epistasis of gene-by-gene interaction effect and additive-by-additive-by-additive of gene-by-gene-by-gene interaction effect) can influence decisions on the suitability of breeding material for this purpose. Understanding the genetic architecture of complex traits is a major challenge in the post-genomic era, especially for quantitative trait locus (QTL) effects, QTL-by-QTL interactions and QTL-by-QTL-by-QTL interactions. With regards to the comparing methods for estimating additive-by-additive-by-additive of QTL×QTL×QTL interaction effects by Monte Carlo simulation studies, there are no publications in the open literature. The parameter combinations assumed in the presented simulation studies represented 84 different experimental situations. The use of weighted regression may be the preferred method for estimating additive-by-additive-by-additive of QTL-QTL-QTL triples interaction effects, as it provides results closer to the true values of total additive-by-additive-by-additive interaction effects than using unweighted regression. This is also indicated by the obtained values of the determination coefficients of the proposed models.

QTL mapping and improvement of pre-harvest sprouting resistance using japonica weedy rice.

The stability of cultivation and production in terms of crop yield has been threatened by climate change due to global warming. Pre-harvest sprouting (PHS) is a threat to crops, especially staple foods, including rice, because of reductions in yield and quality. To address the problem of precocious germination before harvest, we performed quantitative trait loci (QTL) analysis for PHS using F8 RILs populations derived from japonica weedy rice in Korea. QTL analysis revealed that two stable QTLs, qPH7 and qPH2, associated with PHS resistance were identified on chromosomes 7 and 2, respectively, explaining approximately 38% of the phenotypic variation. The QTL effect in the tested lines significantly decreased the degree of PHS, based on the number of QTLs included. Through fine mapping for main QTL qPH7, the region for the PHS was found to be anchored within 23.575-23.785 Mbp on chromosome 7 using 13 cleaved amplified sequence (CAPS) markers. Among 15 open reading frames (ORFs) within the detected region, one ORF, Os07g0584366, exhibited upregulated expression in the resistant donor, which was approximately nine times higher than that of susceptible japonica cultivars under PHS-inducing conditions. Japonica lines with QTLs related to PHS resistance were developed to improve the characteristics of PHS and design practical PCR-based DNA markers for marker-assisted backcrosses of many other PHS-susceptible japonica cultivars.

Variation in CD8 T cell IFNγ differentiation to strains of Toxoplasma gondii is characterized by small effect QTLs with contribution from ROP16.

Toxoplasma gondii induces a strong CD8 T cell response characterized by the secretion of IFNγ that promotes host survival during infection. The initiation of CD8 T cell IFNγ responses in vitro differs widely between clonal lineage strains of T. gondii, in which type I strains are low inducers, while types II and III strains are high inducers. We hypothesized this phenotype is due to a polymorphic "Regulator Of CD8 T cell Response" (ROCTR). Therefore, we screened F1 progeny from genetic crosses between the clonal lineage strains to identify ROCTR. Naïve antigen-specific CD8 T cells (T57) isolated from transnuclear mice, which are specific for the endogenous and vacuolar TGD057 antigen, were measured for their ability to become activated, transcribe Ifng and produce IFNγ in response to T. gondii infected macrophages. Genetic mapping returned four non-interacting quantitative trait loci (QTL) with small effect on T. gondii chromosomes (chr) VIIb-VIII, X and XII. These loci encompass multiple gene candidates highlighted by ROP16 (chrVIIb-VIII), GRA35 (chrX), TgNSM (chrX), and a pair of uncharacterized NTPases (chrXII), whose locus we report to be significantly truncated in the type I RH background. Although none of the chromosome X and XII candidates bore evidence for regulating CD8 T cell IFNγ responses, type I variants of ROP16 lowered Ifng transcription early after T cell activation. During our search for ROCTR, we also noted the parasitophorous vacuole membrane (PVM) targeting factor for dense granules (GRAs), GRA43, repressed the response suggesting PVM-associated GRAs are important for CD8 T cell activation. Furthermore, RIPK3 expression in macrophages was an absolute requirement for CD8 T cell IFNγ differentiation implicating the necroptosis pathway in T cell immunity to T. gondii. Collectively, our data suggest that while CD8 T cell IFNγ production to T. gondii strains vary dramatically, it is not controlled by a single polymorphism with strong effect. However, early in the differentiation process, polymorphisms in ROP16 can regulate commitment of responding CD8 T cells to IFNγ production which may have bearing on immunity to T. gondii.

Genetics of behavioural evolution in giant mice from Gough Island.

The evolution of behaviour on islands is a pervasive phenomenon that contributed to Darwin's theory of natural selection. Island populations frequently show increased boldness and exploration compared with their mainland counterparts. Despite the generality of this pattern, the genetic basis of island-associated behaviours remains a mystery. To address this gap in knowledge, we genetically dissected behaviour in 613 F2s generated by crossing inbred mouse strains from Gough Island (where they live without predators or human commensals) and a mainland conspecific. We used open field and light/dark box tests to measure seven behaviours related to boldness and exploration in juveniles and adults. Across all assays, we identified a total of 41 quantitative trait loci (QTL) influencing boldness and exploration. QTL have moderate effects and are often unique to specific behaviours or ages. Function-valued trait mapping revealed changes in estimated effects of QTL during assays, providing a rare dynamic window into the genetics of behaviour often missed by standard approaches. The genomic locations of QTL are distinct from those found in laboratory strains of mice, indicating different genetic paths to the evolution of similar behaviours. We combine our mapping results with extensive phenotypic and genetic information available for laboratory mice to nominate candidate genes for the evolution of behaviour on islands.

Genetic variants in Mammalian STE20-like protein kinase 2 were associated with risk of NSCL/P

AimMammalian STE20-like protein kinase 2 (MST2) plays an important role in apoptosis and the development of many disorders. Here, we aim to explore if genetic variants in MST2 are associated with the risk of non-syndromic cleft lip with or without palate (NSCL/P). Materials and methodsThe association study was performed in a two-stage study of 1,069 cases and 1,724 controls to evaluate the association between genetic variants in the MST2 and NSCL/P risk. The potential function of the candidate single nucleotide polymorphism (SNP) was predicted using HaploReg, RegulomeDB, and public craniofacial histone chromatin immunoprecipitation sequencing (ChIP-seq) data. Haploview was used to perform the haplotype of risk alleles. The expression quantitative trait loci (eQTL) effect was assessed using the Genotype-Tissue Expression (GTEx) project. Gene expression in mouse embryo tissue was performed using data downloaded from GSE67985. The potential role of candidate gene in the development of NSCL/P was assessed by correlation and enrichment analysis. ResultsAmong SNPs in MST2, rs2922070 C allele (Pmeta = 2.93E-04) and rs6988087 T allele (Pmeta = 1.57E-03) were linked with significantly increased risk of NSCL/P. Rs2922070, rs6988087 and their high linkage disequilibrium (LD) SNPs constituted a risk haplotype of NSCL/P. Individuals carrying 3–4 risk alleles had an elevated risk of NSCL/P compared to those who carried less risk alleles (P = 2.00E-04). The eQTL analysis revealed a significant association between these two variants and MST2 in muscle tissue of the body. The MST2 expressed during mouse craniofacial development and over-expressed in the human orbicularis oris muscle (OOM) of NSCL/P patients compared to controls. MST2 was involved in the development of NSCL/P by regulating the mRNA surveillance pathway, the MAPK signaling pathway, the neurotrophin signaling pathway, the FoxO signaling pathway and the VEGF signaling pathway. ConclusionMST2 was associated with the development of NSCL/P.

Comprehensive analysis of physiological and metabolomic responses to drought reveals specific modulation of acquired tolerance mechanisms in Rice.

Mild stresses induce "acquired tolerance traits" (ATT) that provide tolerance when stress becomes severe.Here, we identifiedthe genetic variability in ATTs among a panel of rice germplasm accessions and demonstrated their relevance in protecting growth and productivity under water-limited conditions. Diverse approaches, including physiological screens, association mapping and metabolomics, were adopted and revealed 43 significant marker-trait associations. Non-targeted metabolomic profiling of contrasting genotypes revealed 26 "Tolerance-related-induced" primary and secondary metabolites in the tolerant genotypes (AC-39000 and AC-39020) compared to the susceptible one (BPT-5204) under water-limited condition. Metabolites that help maintain cellular functions, especially Calvin cycle processes, significantly accumulated more in tolerant genotypes, which resulted in superior photosynthetic capacity and hence water use efficiency.Upregulation of the Glutathione cycle intermediates explains the ROS homeostasis among the tolerant genotypes, maintening spikelet fertility and grain yield under stress. Bioinformatic dissection of a major effect Quantitative trait locus (QTL) on chromosome 8 revealed genes controlling metabolic pathways leading to the production of osmolites and antioxidants, such as GABA and faffinose.The study also led to the identification of specific trait donor genotypes that can be effectively used in translational crop improvement activities. This article is protected by copyright. All rights reserved.

Single-cell analyses and host genetics highlight the role of innate immune cells in COVID-19 severity

Mechanisms underpinning the dysfunctional immune response in severe acute respiratory syndrome coronavirus 2 infection are elusive. We analyzed single-cell transcriptomes and T and B cell receptors (BCR) of >895,000 peripheral blood mononuclear cells from 73 coronavirus disease 2019 (COVID-19) patients and 75 healthy controls of Japanese ancestry with host genetic data. COVID-19 patients showed a low fraction of nonclassical monocytes (ncMono). We report downregulated cell transitions from classical monocytes to ncMono in COVID-19 with reduced CXCL10 expression in ncMono in severe disease. Cell–cell communication analysis inferred decreased cellular interactions involving ncMono in severe COVID-19. Clonal expansions of BCR were evident in the plasmablasts of patients. Putative disease genes identified by COVID-19 genome-wide association study showed cell type-specific expressions in monocytes and dendritic cells. A COVID-19-associated risk variant at the IFNAR2 locus (rs13050728) had context-specific and monocyte-specific expression quantitative trait loci effects. Our study highlights biological and host genetic involvement of innate immune cells in COVID-19 severity.

Meta-Analysis of Microarray Data and Their Utility in Dissecting the Mapped QTLs for Heat Acclimation in Rice.

In the current global warming scenario, it is imperative to develop crops with improved heat tolerance or acclimation, for which knowledge of major heat stress-tolerant genes or genomic regions is a prerequisite. Though several quantitative trait loci (QTLs) for heat tolerance have been mapped in rice, candidate genes from these QTLs have not been reported yet. The meta-analysis of microarray datasets for heat stress in rice can give us a better genomic resource for the dissection of QTLs and the identification of major candidate genes for heat stress tolerance. In the present study, a database, RiceMetaSys-H, comprising 4227 heat stress-responsive genes (HRGs), was created using seven publicly available microarray datasets. This included in-house-generated microarray datasets of Nagina 22 (N22) and IR64 subjected to 8 days of heat stress. The database has provisions for searching the HRGs through genotypes, growth stages, tissues, and physical intervals in the genome, as well as Locus IDs, which provide complete information on the HRGs with their annotations and fold changes, along with the experimental material used for the analysis. The up-regulation of genes involved in hormone biosynthesis and signalling, sugar metabolism, carbon fixation, and the ROS pathway were found to be the key mechanisms of enhanced heat tolerance. Integrating variant and expression analysis, the database was used for the dissection of the major effect of QTLs on chromosomes 4, 5, and 9 from the IR64/N22 mapping population. Out of the 18, 54, and 62 genes in these three QTLs, 5, 15, and 12 genes harboured non-synonymous substitutions. Fifty-seven interacting genes of the selected QTLs were identified by a network analysis of the HRGs in the QTL regions. Variant analysis revealed that the proportion of unique amino acid substitutions (between N22/IR64) in the QTL-specific genes was much higher than the common substitutions, i.e., 2.58:0.88 (2.93-fold), compared to the network genes at a 0.88:0.67 (1.313-fold) ratio. An expression analysis of these 89 genes showed 43 DEGs between IR64/N22. By integrating the expression profiles, allelic variations, and the database, four robust candidates (LOC_Os05g43870, LOC_Os09g27830, LOC_Os09g27650, andLOC_Os09g28000) for enhanced heat stress tolerance were identified. The database thus developed in rice can be used in breeding to combat high-temperature stress.

Genetic dissection of QTLs for oil content in four maize DH populations.

Oil is one of the main components in maize kernels. Increasing the total oil content (TOC) is favorable to optimize feeding requirement by improving maize quality. To better understand the genetic basis of TOC, quantitative trait loci (QTL) in four double haploid (DH) populations were explored. TOC exhibited continuously and approximately normal distribution in the four populations. The moderate to high broad-sense heritability (67.00-86.60%) indicated that the majority of TOC variations are controlled by genetic factors. A total of 16 QTLs were identified across all chromosomes in a range of 3.49-30.84% in term of phenotypic variation explained. Among them, six QTLs were identified as the major QTLs that explained phenotypic variation larger than 10%. Especially, qOC-1-3 and qOC-2-3 on chromosome 9 were recognized as the largest effect QTLs with 30.84% and 21.74% of phenotypic variance, respectively. Seventeen well-known genes involved in fatty acid metabolic pathway located within QTL intervals. These QTLs will enhance our understanding of the genetic basis of TOC in maize and offer prospective routes to clone candidate genes regulating TOC for breeding program to cultivate maize varieties with the better grain quality.

QTL×QTL×QTL Interaction Effects for Total Phenolic Content of Wheat Mapping Population of CSDH Lines under Drought Stress by Weighted Multiple Linear Regression

This paper proposes the use of weighted multiple linear regression to estimate the triple3interaction (additive×additive×additive) of quantitative trait loci (QTLs) effects. The use of unweighted regression yielded an improvement (in absolute value) in the QTL×QTL×QTL interaction effects compared to assessment based on phenotypes alone in three cases (severe drought in 2010, control in 2012 and severe drought in 2012). In contrast, weighted regression yielded an improvement (in absolute value) in the evaluation of the aaagw parameter compared to aaap in five cases, with the exception of severe drought in 2012. The results show that by using weighted regression on marker observations, the obtained estimates are closer to the ones obtained by the phenotypic method. The coefficients of determination for the weighted regression model were significantly higher than for the unweighted regression and ranged from 46.2% (control in 2010) to 95.0% (control in 2011). Considering this, it is clear that a three-way interaction had a significant effect on the expression of quantitative traits.