Mapping the genetic architecture of complex traits in experimental populations

Quantitative Trait Loci for Phenology, Yield, and Phosphorus Use Efficiency in Cowpea

BACKGROUND/OBJECTIVES

Cowpea is an important legume crop in sub-Saharan Africa (SSA) and beyond. However, access to phosphorus (P), a critical element for plant growth and development, is a significant constraint in SSA. Thus, it is essential to have high P-use efficiency varieties to achieve increased yields in environments where little-to- no phosphate fertilizers are applied.

METHODS

In this study, crop phenology, yield, and grain P efficiency traits were assessed in two recombinant inbred line (RIL) populations across ten environments under high- and low-P soil conditions to identify traits' response to different soil P levels and associated quantitative trait loci (QTLs). Single-environment (SEA) and multi-environment (MEA) QTL analyses were conducted for days to flowering (DTF), days to maturity (DTM), biomass yield (BYLD), grain yield (GYLD), grain P-use efficiency (gPUE) and grain P-uptake efficiency (gPUpE).

RESULTS

Phenotypic data indicated significant variation among the RILs, and inadequate soil P had a negative impact on flowering, maturity, and yield traits. A total of 40 QTLs were identified by SEA, with most explaining greater than 10% of the phenotypic variance, indicating that many major-effect QTLs contributed to the genetic component of these traits. Similarly, MEA identified 23 QTLs associated with DTF, DTM, GYLD, and gPUpE under high- and low-P environments. Thirty percent (12/40) of the QTLs identified by SEA were also found by MEA, and some of those were identified in more than one P environment, highlighting their potential in breeding programs targeting PUE. QTLs on chromosomes Vu03 and Vu08 exhibited consistent effects under both high- and low-P conditions. In addition, candidate genes underlying the QTL regions were identified.

CONCLUSIONS

This study lays the foundation for molecular breeding for PUE and contributes to understanding the genetic basis of cowpea response in different soil P conditions. Some of the identified genomic loci, many being novel QTLs, could be deployed in marker-aided selection and fine mapping of candidate genes.

Scrutinizing neurodegenerative diseases: decoding the complex genetic architectures through a multi-omics lens

Neurodegenerative diseases present complex genetic architectures, reflecting a continuum from monogenic to oligogenic and polygenic models. Recent advances in multi-omics data, coupled with systems genetics, have significantly refined our understanding of how these data impact neurodegenerative disease mechanisms. To contextualize these genetic discoveries, we provide a comprehensive critical overview of genetic architecture concepts, from Mendelian inheritance to the latest insights from oligogenic and omnigenic models. We explore the roles of common and rare genetic variants, gene-gene and gene-environment interactions, and epigenetic influences in shaping disease phenotypes. Additionally, we emphasize the importance of multi-omics layers including genomic, transcriptomic, proteomic, epigenetic, and metabolomic data in elucidating the molecular mechanisms underlying neurodegeneration. Special attention is given to missing heritability and the contribution of rare variants, particularly in the context of pleiotropy and network pleiotropy. We examine the application of single-cell omics technologies, transcriptome-wide association studies, and epigenome-wide association studies as key approaches for dissecting disease mechanisms at tissue- and cell-type levels. Our review introduces the OmicPeak Disease Trajectory Model, a conceptual framework for understanding the genetic architecture of neurodegenerative disease progression, which integrates multi-omics data across biological layers and time points. This review highlights the critical importance of adopting a systems genetics approach to unravel the complex genetic architecture of neurodegenerative diseases. Finally, this emerging holistic understanding of multi-omics data and the exploration of the intricate genetic landscape aim to provide a foundation for establishing more refined genetic architectures of these diseases, enhancing diagnostic precision, predicting disease progression, elucidating pathogenic mechanisms, and refining therapeutic strategies for neurodegenerative conditions.

Dissection of genetic architecture of nine hazardous component traits of mainstream smoke in tobacco (Nicotiana tabacum L.)

Tobacco (Nicotiana tabacum L.) use is the leading cause of preventable death, due to deleterious chemical components and smoke from tobacco products, and therefore reducing harmful chemical components in tobacco is one of the crucial tobacco breeding targets. However, due to complexity of tobacco smoke and unavailability of high-density genetic maps, the genetic architecture of representative hazardous smoke has not been fully dissected. The present study aimed to explore the genetic architecture of nine hazardous component traits of mainstream smoke through QTL mapping using 271 recombinant inbred lines (RILs) derived from K326 and Y3 in multiple environments. The analysis of genotype and genotype by environment interaction (GE) revealed substantially greater heritability over 95% contributed mostly by GE interaction effects. We also observed strong genetic correlations among most studied hazardous smoke traits, with the highest correlation coefficient of 0.84 between carbon monoxide and crotonaldehyde. Based on a published high-density genetic map, a total of 19 novel QTLs were detected for eight traits using a full QTL model, of which 17 QTLs showed significant additive effects, six showed significant additive-by-environment interaction effects, and one pair showed significant epistasis-by-environment interaction effect. Bioinformatics analysis of sequence in QTL region predicted six genes as candidates for four traits, of which Nt21g04598.1, Nt21g04600.1, and Nt21g04601.1 had pleiotropic effects on PHE and TAR.

The use of weighted multiple linear regression to estimate QTL × QTL × QTL interaction effects of winter wheat (Triticum aestivum L.) doubled-haploid lines

Knowledge of the magnitude of gene effects and their interactions, their nature, and contribution to determining quantitative traits is very important in conducting an effective breeding program. In traditional breeding, information on the parameter related to additive gene effect and additive-additive interaction (epistasis) and higher-order additive interactions would be useful. Although commonly overlooked in studies, higher-order interactions have a significant impact on phenotypic traits. Failure to account for the effect of triplet interactions in quantitative genetics can significantly underestimate additive QTL effects. Understanding the genetic architecture of quantitative traits is a major challenge in the post-genomic era, especially for quantitative trait locus (QTL) effects, QTL-QTL interactions, and QTL-QTL-QTL interactions. This paper proposes using weighted multiple linear regression to estimate the effects of triple interaction (additive-additive-additive) quantitative trait loci (QTL-QTL-QTL). The material for the study consisted of 126 doubled haploid lines of winter wheat (Mandub × Begra cross). The lines were analyzed for 18 traits, including percentage of necrosis leaf area, percentage of leaf area covered by pycnidia, heading data, and height. The number of genes (the number of effective factors) was lower than the number of QTLs for nine traits, higher for four traits and equal for five traits. The number of triples for unweighted regression ranged from 0 to 9, while for weighted regression, it ranged from 0 to 13. The total aaagu effect ranged from - 14.74 to 15.61, while aaagw ranged from - 23.39 to 21.65. The number of detected threes using weighted regression was higher for two traits and lower for four traits. Forty-nine statistically significant threes of the additive-by-additive-by-additive interaction effects were observed. The QTL most frequently occurring in threes was 4407404 (9 times). The use of weighted regression improved (in absolute value) the assessment of QTL-QTL-QTL interaction effects compared to the assessment based on unweighted regression. The coefficients of determination for the weighted regression model were higher, ranging from 0.8 to 15.5%, than for the unweighted regression. Based on the results, it can be concluded that the QTL-QTL-QTL triple interaction had a significant effect on the expression of quantitative traits. The use of weighted multiple linear regression proved to be a useful statistical tool for estimating additive-additive-additive (aaa) interaction effects. The weighted regression also provided results closer to phenotypic evaluations than estimator values obtained using unweighted regression, which is closer to the true values.

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.

Genetic mapping of some key plant architecture traits in Brassica juncea using a doubled haploid population derived from a cross between two distinct lines: vegetable type Tumida and oleiferous Varuna

Genetic mapping of some key plant architectural traits in a vegetable type and an oleiferous B. juncea cross revealed QTL and candidate genes for breeding more productive ideotypes. Brassica juncea (AABB, 2n = 36), commonly called mustard, is an allopolyploid crop of recent origin but contains considerable morphological and genetic variation. An F₁-derived doubled haploid population developed from a cross between an Indian oleiferous line, Varuna, and a Chinese stem type vegetable mustard, Tumida showed significant variability for some key plant architectural traits-four stem strength-related traits, stem diameter (Dia), plant height (Plht), branch initiation height (Bih), number of primary branches (Pbr), and days to flowering (Df). Multi-environment QTL analysis identified twenty Stable QTL for the above-mentioned nine plant architectural traits. Though Tumida is ill-adapted to the Indian growing conditions, it was found to contribute favorable alleles in Stable QTL for five architectural traits-press force, Dia, Plht, Bih, and Pbr; these QTL could be used to breed superior ideotypes in the oleiferous mustard lines. A QTL cluster on LG A10 contained Stable QTL for seven architectural traits that included major QTL (phenotypic variance ≥ 10%) for Df and Pbr, with Tumida contributing the trait-enhancing alleles for both. Since early flowering is critical for the cultivation of mustard in the Indian subcontinent, this QTL cannot be used for the improvement of Pbr in the Indian gene pool lines. Conditional QTL analysis for Pbr, however, identified other QTL which could be used for the improvement of Pbr without affecting Df. The Stable QTL intervals were mapped on the genome assemblies of Tumida and Varuna for the identification of candidate genes.

High-Throughput Association Mapping in Brassica napus L.: Methods and Applications

Oil seed rape (Braasica napus L.) is ranked second among oil seed crops cultivated globally for edible oil for human, and seed cake for animal consumption. Recent genetic and genomics advancements highlighted the diversity that exists within B. napus, which is largely discovered using the most promising genetic markers called single nucleotide polymorphism (SNP). Their calling rate is also enhanced to ~100 folds after the continuous advancements in the next generation sequencing (NGS) technologies. As the high throughput of NGS resulted in multi-Giga bases data, the detailed quality control (QC) prior to downstream analyses is a pre-requisite. It mainly involved the removal of false positives, missing proportions, filtering of low-quality SNPs, and adjustments of minor-allele frequency and heterozygosity. After marker-trait association, for conformation of target SNPs, validations of SNPs can be performed using various methods, especially allele-specific PCR assay-based methods have been utilized for SNP genotyping of genes targeting agronomic traits and somaclonal variations occurred during transgenic studies. In the present study, the authors mainly argue on the genotypic progress, and pipelines/methods that are being used for detection, calling, filtering, and validation of SNPs. Also, insight is provided into the application of SNPs in linkage and association mapping, including QTL mapping and genome-wide association studies targeting mainly developmental traits related to the root system and plant architecture, flowering time, silique, and oil quality. Briefly, the present study provides the recent information and recommendations on the SNP genotyping methods and its applications, which can be useful for marker-assisted breeding in B. napus and other crops.

Identification of major quantitative trait loci and candidate genes for seed weight in soybean

Four major quantitative trait loci for 100-seed weight were identified in a soybean RIL population under five environments, and the most likely candidate genes underlying these loci were identified. Seed weight is an important target of soybean breeding. However, the genes underlying the major quantitative trait loci (QTL) controlling seed weight remain largely unknown. In this study, a soybean population of 300 recombinant inbred lines (RILs) derived from a cross between PI595843 (PI) and WH was used to map the QTL and identify candidate genes for seed weight. The RIL population was genotyped through whole genome resequencing, and phenotyped for 100-seed weight under five environments. A total of 38 QTL were detected, and four major QTL, each explained at least 10% of the variation in 100-seed weight, were identified. Six candidate genes within these four major QTL regions were identified by analyses of their tissue expression patterns, gene annotations, and differential gene expression levels in soybean seeds during four developmental stages between two parental lines. Further sequence variation analyses revealed a C to T substitution in the first exon of the Glyma.19G143300, resulting in an amino acid change between PI and WH, and thus leading to a different predicted kinase domain, which might affect its protein function. Glyma.19G143300 is highly expressed in soybean seeds and encodes a leucine-rich repeat receptor-like protein kinase (LRR-RLK). Its predicted protein has typical domains of LRR-RLK family, and phylogenetic analyses reveled its similarity with the known LRR-RLK protein XIAO (LOC_Os04g48760), which is involved in controlling seed size. The major QTL and candidate genes identified in this study provide useful information for molecular breeding of new soybean cultivars with desirable seed weight.

Candidate loci for breeding compact plant-type soybean varieties

Plant height and node number are important agronomic traits that influence yield in soybean (Glycine max L.). Here, to better understand the genetic basis of the traits, we used two recombinant inbred line (RIL) populations to detect quantitative trait loci (QTLs) associated with plant height and node number in different environments. This analysis detected 9 and 21 QTLs that control plant height and node number, respectively. Among them, we identified two genomic regions that overlap with Determinate stem 1 (Dt1) and Dt2, which are known to influence both plant height and node number. Furthermore, different combinations of Dt1 and Dt2 alleles were enriched in distinct latitudes. In addition, we determined that the QTLs qPH-13-SE and qPH-13-DW in the two RIL populations overlap with genomic intervals associated with plant height and the QTL qNN-04-DW overlaps with an interval associated with node number. Combining the dwarf allele of qPH-13-SE/qPH-13-DW and the multiple-node allele of qNN-04-DW produced plants with ideal plant architecture, i.e., shorter main stems with more nodes. This plant type may help increase yield at high planting density. This study thus provides candidate loci for breeding elite soybean cultivars for plant height and node number.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01352-2.

Genome-wide association studies revealed complex genetic architecture and breeding perspective of maize ear traits

BACKGROUND

Maize (Zea Mays) is one of the world's most important crops. Hybrid maize lines resulted a major improvement in corn production in the previous and current centuries. Understanding the genetic mechanisms of the corn production associated traits greatly facilitate the development of superior hybrid varieties.

RESULT

In this study, four ear traits associated with corn production of Nested Association Mapping (NAM) population were analyzed using a full genetic model, and further, optimal genotype combinations and total genetic effects of current best lines, superior lines, and superior hybrids were predicted for each of the traits at four different locations. The analysis identified 21-34 highly significant SNPs (-log₁₀P > 5), with an estimated total heritability of 37.31-62.34%, while large contributions to variations was due to dominance, dominance-related epistasis, and environmental interaction effects ([Formula: see text] 14.06% ~ 49.28%), indicating these factors contributed significantly to phenotypic variations of the ear traits. Environment-specific genetic effects were also discovered to be crucial for maize ear traits. There were four SNPs found for three ear traits: two for ear length and weight, and two for ear row number and length. Using the Enumeration method and the stepwise tuning technique, optimum multi-locus genotype combinations for superior lines were identified based on the information obtained from GWAS.

CONCLUSIONS

Predictions of genetic breeding values showed that different genotype combinations in different geographical regions may be better, and hybrid-line variety breeding with homozygote and heterozygote genotype combinations may have a greater potential to improve ear traits.

Linkage mapping and association analysis to identify a reliable QTL for stigma exsertion rate in rice

The commercialization of hybrid rice has greatly contributed to the increase in rice yield, with the improvement of its seed production capacity having played an important role. The stigma exsertion rate (SER) is a key factor for improving the outcrossing of the sterile line and the hybrid rice seed production. We used the Zhenshan 97B × IRAT109 recombinant inbred population comprising 163 lines and a natural population of 138 accessions to decipher the genetic foundation of SER over 2 years in three environments. Additionally, we detected eight QTLs for SER on chromosomes 1, 2, and 8 via linkage mapping. We also identified seven and 19 significant associations for SER using genome-wide association study in 2016 and 2017, respectively. Interestingly, we located two lead SNPs (sf0803343504 and sf083344610) on chromosome 8 in the qTSE8 QTL region that were significantly associated with total SER. After transcriptomic analysis, quantitative real-time PCR, and haplotype analysis, we found 13 genes within this reliable region as important candidate genes. Our study results will be beneficial to molecular marker-assisted selection of rice lines with high outcrossing rate, thereby improving the efficiency of hybrid seed production.

Genetic mapping of powdery mildew resistance genes in wheat landrace Guizi 1 via genotyping by sequencing

BACKGROUND

Wheat (Triticum aestivum L.) powdery mildew (Pm), which caused by Blumeria graminis f. sp. tritici (Bgt), is a destructive disease worldwide that causes severe yield losses in wheat. Resistant wheat cultivars easily lose their ability to effectively resist newly emerged Bgt strains; therefore, identifying new resistance genes is necessary for breeding resistant cultivars.

METHODS AND RESULTS

Guizi 1 (GZ1) is a Chinese wheat cultivar with moderate and stable resistance to Pm. Genetic analysis indicated that the Pm resistance of GZ1 was controlled by a single dominant gene, designated PmGZ1. In total, 110 F₂ individual plants and their 2 parents were subjected to genotyping by sequencing (GBS), which yielded 23,134 high-quality single-nucleotide polymorphisms (SNPs). The SNP distributions across the 21 chromosomes ranged from 134 on chromosome 6D to 6288 on chromosome 3B. Chromosome 6A has 1866 SNPs, among which 16 are physically located between positions 307,802,221 and 309,885,836 in an approximate 2.3-cM region; this region also had the greatest SNP density. The average map distance between SNP markers was 0.1 cM. A quantitative trait locus (QTL) with a significant epistatic effect on Pm resistance was mapped to chromosome 6A. The logarithm of odds (LOD) value of PmGZ1 was 34.8, and PmGZ1 was located within the confidence interval marked by chr6a-307802221 and chr6a-309885836. Moreover, 74.7% of the phenotypic variance was explained by PmGZ1. Four candidate genes (which encoded two TaAP2-A and two actin proteins) were annotated maybe as resistance genes.

CONCLUSIONS

The present results provide valuable information for wheat genetic improvement, QTL fine mapping, and candidate gene validation.

A compressed variance component mixed model framework for detecting small and linked QTL-by-environment interactions

Detecting small and linked quantitative trait loci (QTLs) and QTL-by-environment interactions (QEIs) for complex traits is a difficult issue in immortalized F2 and F2:3 design, especially in the era of global climate change and environmental plasticity research. Here we proposed a compressed variance component mixed model. In this model, a parametric vector of QTL genotype and environment combination effects replaced QTL effects, environmental effects and their interaction effects, whereas the combination effect polygenic background replaced the QTL and QEI polygenic backgrounds. Thus, the number of variance components in the mixed model was greatly reduced. The model was incorporated into our genome-wide composite interval mapping (GCIM) to propose GCIM-QEI-random and GCIM-QEI-fixed, respectively, under random and fixed models of genetic effects. First, potentially associated QTLs and QEIs were selected from genome-wide scanning. Then, significant QTLs and QEIs were identified using empirical Bayes and likelihood ratio test. Finally, known and candidate genes around these significant loci were mined. The new methods were validated by a series of simulation studies and real data analyses. Compared with ICIM, GCIM-QEI-random had 29.77 ± 18.20% and 24.33 ± 10.15% higher average power, respectively, in 0.5–3.0% QTL and QEI detection, 43.44 ± 9.53% and 51.47 ± 15.70% higher average power, respectively, in linked QTL and QEI detection, and identified 30 more known genes for four rice yield traits, because GCIM-QEI-random identified more small genes/loci, being 2.69 ± 2.37% for additional genes. GCIM-QEI-random was slightly better than GCIM-QEI-fixed. In addition, the new methods may be extended into backcross and genome-wide association studies. This study provides effective methods for detecting small-effect and linked QTLs and QEIs.

Precise mapping of QTL for Hessian fly resistance in the hard winter wheat cultivar 'Overland'

A major QTL for Hessian fly resistance was precisely mapped to a 2.32 Mb region on chromosome 3B of the US hard winter wheat cultivar 'Overland'. The Hessian fly (HF, Mayetiola destructor) is a destructive insect pest of wheat in the USA and worldwide. Deploying HF-resistant cultivars is the most effective and economical approach to control this insect pest. A population of 186 recombinant inbred lines (RILs) was developed from 'Overland' × 'Overley' and phenotyped for responses to HF attack using the HF biotype 'Great Plains'. A high-density genetic linkage map was constructed using 1,576 single nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing (GBS). Two quantitative trait loci (QTLs) with a significant epistatic effect on HF resistance were mapped to chromosomes 3B (QHf.hwwg-3B) and 7A (QHf.hwwg-7A) in Overland, which are located in similar chromosome regions as found for H35 and H36 in the cultivar 'SD06165', respectively. QHf.hwwg-3B showed a much larger effect on HF resistance than QHf.hwwg-7A. Five and four GBS-SNPs, respectively, in the QHf.hwwg-3B and QHf.hwwg-7A QTL intervals were converted into Kompetitive allele specific polymerase chain reaction (KASP) markers. QHf.hwwg-3B was precisely mapped to a 2.32 Mb interval (2,479,314-4,799,538 bp) using near-isogenic lines (NILs) and RILs that have recombination within the QTL interval. The US winter wheat accessions carrying contrasting alleles at KASP markers KASP-3B4525164, KASP-7A47772047 and KASP-7A65090410 showed significant difference in HF resistance. The combination of the two KASP markers KASP-3B3797431 and KASP-3B4525164 is near-diagnostic for the detection of QHf.hwwg-3B in a US winter wheat panel and can be potentially used for screening the QTL in breeding programs.

Spontaneously Hypertensive Rat substrains show differences in model traits for addiction risk and cocaine self-administration: Implications for a novel rat reduced complexity cross

Forward genetic mapping of F2 crosses between closely related substrains of inbred rodents - referred to as a reduced complexity cross (RCC) - is a relatively new strategy for accelerating the pace of gene discovery for complex traits, such as drug addiction. RCCs to date were generated in mice, but rats are thought to be optimal for addiction genetic studies. Based on past literature, one inbred Spontaneously Hypertensive Rat substrain, SHR/NCrl, is predicted to exhibit a distinct behavioral profile as it relates to cocaine self-administration traits relative to another substrain, SHR/NHsd. Direct substrain comparisons are a necessary first step before implementing an RCC. We evaluated model traits for cocaine addiction risk and cocaine self-administration behaviors using a longitudinal within-subjects design. Impulsive-like and compulsive-like traits were greater in SHR/NCrl than SHR/NHsd, as were reactivity to sucrose reward, sensitivity to acute psychostimulant effects of cocaine, and cocaine use studied under fixed-ratio and tandem schedules of cocaine self-administration. Compulsive-like behavior correlated with the acute psychostimulant effects of cocaine, which in turn correlated with cocaine taking under the tandem schedule. Compulsive-like behavior also was the best predictor of cocaine seeking responses. Heritability estimates indicated that 22 %-40 % of the variances for the above phenotypes can be explained by additive genetic factors, providing sufficient genetic variance to conduct genetic mapping in F2 crosses of SHR/NCrl and SHR/NHsd. These results provide compelling support for using an RCC approach in SHR substrains to uncover candidate genes and variants that are of relevance to cocaine use disorders.

Genome-wide conditional association study reveals the influences of lifestyle cofactors on genetic regulation of body surface area in MESA population

Body surface area (BSA) is an important trait used for many clinical purposes. People's BSA may vary due to genetic background, race, and different lifestyle factors (such as walking, exercise, reading, smoking, transportation, etc.). GWAS of BSA was conducted on 5,324 subjects of four ethnic populations of European-American, African-American, Hispanic-American, and Chinese-American from the Multi-Ethnic Study of Atherocloris (MESA) data using unconditional and conditional full genetic models. In this study, fifteen SNPs were identified (Experiment-wise PEW < 1×10-5) using unconditional full genetic model, of which thirteen SNPs had individual genetic effects and seven SNPs were involved in four pairs of epistasis interactions. Seven single SNPs and eight pairs of epistasis SNPs were additionally identified using exercise, smoking, and transportation cofactor-conditional models. By comparing association analysis results from unconditional and cofactor conditional models, we observed three different scenarios: (i) genetic effects of several SNPs did not affected by cofactors, e.g., additive effect of gene CREB5 (a≙ -0.013 for T/T and 0.013 for G/G, -Log10 PEW = 8.240) did not change in the cofactor models; (ii) genetic effects of several SNPs affected by cofactors, e.g., the genetic additive effect (a≙ 0.012 for A/A and -0.012 for G/G, -Log10 PEW = 7.185) of SNP of the gene GRIN2A was not significant in transportation cofactor model; and (iii) genetic effects of several SNPs suppressed by cofactors, e.g., additive (a≙ -0.018 for G/G and 0.018 for C/C, -Log10 PEW = 19.737) and dominance (d≙ -0.038 for G/C, -Log10 PEW = 27.734) effects of SNP of gene ERBB4 was identified using only transportation cofactor model. Gene ontology analysis showed that several genes are related to the metabolic pathway of calcium compounds, coronary artery disease, type-2 Diabetes, Alzheimer disease, childhood obesity, sleeping duration, Parkinson disease, and cancer. This study revealed that lifestyle cofactors could contribute, suppress, increase or decrease the genetic effects of BSA associated genes.

Identification and Validation of Major QTLs, Epistatic Interactions, and Candidate Genes for Soybean Seed Shape and Weight Using Two Related RIL Populations

Understanding the genetic mechanism underlying seed size, shape, and weight is essential for enhancing soybean cultivars. High-density genetic maps of two recombinant inbred line (RIL) populations, LM6 and ZM6, were evaluated across multiple environments to identify and validate M-QTLs as well as identify candidate genes behind major and stable quantitative trait loci (QTLs). A total of 239 and 43 M-QTLs were mapped by composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM) approaches, from which 180 and 18, respectively, are novel QTLs. Twenty-two QTLs including four novel major QTLs were validated in the two RIL populations across multiple environments. Moreover, 18 QTLs showed significant AE effects, and 40 pairwise of the identified QTLs exhibited digenic epistatic effects. Thirty-four QTLs associated with seed flatness index (FI) were identified and reported here for the first time. Seven QTL clusters comprising several QTLs for seed size, shape, and weight on genomic regions of chromosomes 3, 4, 5, 7, 9, 17, and 19 were identified. Gene annotations, gene ontology (GO) enrichment, and RNA-seq analyses of the genomic regions of those seven QTL clusters identified 47 candidate genes for seed-related traits. These genes are highly expressed in seed-related tissues and nodules, which might be deemed as potential candidate genes regulating the seed size, weight, and shape traits in soybean. This study provides detailed information on the genetic basis of the studied traits and candidate genes that could be efficiently implemented by soybean breeders for fine mapping and gene cloning, and for marker-assisted selection (MAS) targeted at improving these traits individually or concurrently.

Advancing crop genomics from lab to field

Crop genomics remains a key element in ensuring scientific progress to secure global food security. It has been two decades since the sequence of the first plant genome, that of Arabidopsis thaliana, was released, and soon after that the draft sequencing of the rice genome was completed. Since then, the genomes of more than 100 crops have been sequenced, plant genome research has expanded across multiple fronts and the next few years promise to bring further advances spurred by the advent of new technologies and approaches. We are likely to see continued innovations in crop genome sequencing, genetic mapping and the acquisition of multiple levels of biological data. There will be exciting opportunities to integrate genome-scale information across multiple scales of biological organization, leading to advances in our mechanistic understanding of crop biological processes, which will, in turn, provide greater impetus for translation of laboratory results to the field.

Mapping chromosomal regions associated with anther indehiscence with exerted stigmas in CRI-48 and Jasmine 85 cross of rice (Oryza sativa L)

Anther indehiscence in certain wide crosses combines male sterility with stigma exertion, a phenomenon that is desirable for hybrid rice seed production. This study sought to identify chromosomal region(s) that combine anther indehiscence with exerted stigmas. A mapping population consisting of 189 BC1F1 plants was derived from a cross between CRI-48 and Jasmine 85 and backcrossing the resulting F1 to Jasmine 85. Contrary to the three complementary genes mode of inheritance reported earlier, a single locus (AI6-1) was mapped on chromosome 6 at 27.4 cM for anther indehiscence with exerted stigmas through a mixed model-based composite interval mapping (MCIM). This locus was flanked by two single nucleotide polymorphism (SNP) markers, K_ID6002884 and K_ID6003341 within a range of 23.1-28.9 cM. The allele at the locus was contributed by the CRI-48 parent which has Oryza glaberrima ancestry. This locus is suggested to control anther indehiscence and stigma exertion through pleiotropic gene action or cluster of genes.

Identification of Additive-Epistatic QTLs Conferring Seed Traits in Soybean Using Recombinant Inbred Lines

Seed weight and shape are important agronomic traits that affect soybean quality and yield. In the present study, we used image analysis software to evaluate 100-seed weight and seed shape traits (length, width, perimeter, projection area, length/width, and weight/projection area) of 155 novel recombinant inbred soybean lines (NJRISX) generated by crossing "Su88-M21" and "XYXHD". We examined quantitative trait loci (QTLs) associated with the six traits (except seed weight per projection area), and identified 42 additive QTLs (5-8 QTLs per trait) accounting for 24.9-37.5% of the phenotypic variation (PV). Meanwhile, 2-4 epistatic QTL pairs per trait out of a total of 18 accounted for 2.5-7.2% of the PV; and unmapped minor QTLs accounted for the remaining 35.0-56.7% of the PV. A total of 28 additive and 11 epistatic QTL pairs were concentrated in nine joint QTL segments (JQSs), indicating that QTLs associated with seed weight and shape are closely related and interacted. An interaction was also detected between additive and epistatic QTL pairs and environment, which made significant contributions of 1.4-9.5% and 0.4-0.8% to the PV, respectively. We annotated 18 candidate genes in the nine JQSs, which were important for interpreting the close relationships among the six traits. These findings indicate that examining the interactions between closely related traits rather than only analyzing individual trait provides more useful insight into the genetic system of the interrelated traits for which there has been limited QTL information.

Identification and Validation of New Stable QTLs for Grain Weight and Size by Multiple Mapping Models in Common Wheat

Improvement of grain weight and size is an important objective for high-yield wheat breeding. In this study, 174 recombinant inbred lines (RILs) derived from the cross between Jing 411 and Hongmangchun 21 were used to construct a high-density genetic map by specific locus amplified fragment sequencing (SLAF-seq). Three mapping methods, including inclusive composite interval mapping (ICIM), genome-wide composite interval mapping (GCIM), and a mixed linear model performed with forward-backward stepwise (NWIM), were used to identify QTLs for thousand grain weight (TGW), grain width (GW), and grain length (GL). In total, we identified 30, 15, and 18 putative QTLs for TGW, GW, and GL that explain 1.1-33.9%, 3.1%-34.2%, and 1.7%-22.8% of the phenotypic variances, respectively. Among these, 19 (63.3%) QTLs for TGW, 10 (66.7%) for GW, and 7 (38.9%) for GL were consistent with those identified by genome-wide association analysis in 192 wheat varieties. Five new stable QTLs, including 3 for TGW (Qtgw.ahau-1B.1, Qtgw.ahau-4B.1, and Qtgw.ahau-4B.2) and 2 for GL (Qgl.ahau-2A.1 and Qgl.ahau-7A.2), were detected by the three aforementioned mapping methods across environments. Subsequently, five cleaved amplified polymorphic sequence (CAPS) markers corresponding to these QTLs were developed and validated in 180 Chinese mini-core wheat accessions. In addition, 19 potential candidate genes for Qtgw.ahau-4B.2 in a 0.31-Mb physical interval were further annotated, of which TraesCS4B02G376400 and TraesCS4B02G376800 encode a plasma membrane H+-ATPase and a serine/threonine-protein kinase, respectively. These new QTLs and CAPS markers will be useful for further marker-assisted selection and map-based cloning of target genes.

Fine mapping of qKRN8, a QTL for maize kernel row number, and prediction of the candidate gene

KEY MESSAGE: qKRN8, a major QTL for kernel row number in maize, was fine mapped to an interval of ~ 520 kb on chromosome 8 and the key candidate gene was identified via expression analysis. Kernel row number (KRN) is one of the most important yield-influencing traits and is closely associated with female inflorescence development in maize (Zea mays L.). In this study, an F2:3 population derived from a cross between V54 (low KRN line) and Lian87 (high KRN line) was used to map quantitative trait loci (QTLs) conferring KRN in maize. We identified 12 QTLs for KRN in four environments, each explaining 1.40-14.95% of phenotypic variance. Among these, one novel major QTL (named qKRN8) was mapped to bin 8.03 in all four environments, explaining 8.79-14.95% of phenotypic variation. By combining map-based cloning with progeny testing of recombinants, we ultimately mapped qKRN8 to an ~ 520 kb genomic interval, harboring six putative candidate genes. Among them, one candidate gene showed contrasted expression level in immature ears of the near-isogenic lines qKRN8Lian87 and qKRN8V54. These findings should facilitate molecular breeding for KRN and the further identification of the polymorphism underlying this QTL.

Mapping Quantitative Trait Loci for Soybean Seedling Shoot and Root Architecture Traits in an Inter-Specific Genetic Population

Wild soybean species (Glycine soja Siebold & Zucc.) comprise a unique resource to widen the genetic base of cultivated soybean [Glycine max (L.) Merr.] for various agronomic traits. An inter-specific mapping population derived from a cross of cultivar Williams 82 and PI 483460B, a wild soybean accession, was utilized for genetic characterization of root architecture traits. The objectives of this study were to identify and characterize quantitative trait loci (QTL) for seedling shoot and root architecture traits, as well as to determine additive/epistatic interaction effects of identified QTLs. A total of 16,469 single nucleotide polymorphisms (SNPs) developed for the Illumina beadchip genotyping platform were used to construct a high resolution genetic linkage map. Among the 11 putative QTLs identified, two significant QTLs on chromosome 7 were determined to be associated with total root length (RL) and root surface area (RSA) with favorable alleles from the wild soybean parent. These seedling root traits, RL (BARC_020495_04641 ~ BARC_023101_03769) and RSA (SNP02285 ~ SNP18129_Magellan), could be potential targets for introgression into cultivated soybean background to improve both tap and lateral roots. The RL QTL region harbors four candidate genes with higher expression in root tissues: Phosphofructokinase (Glyma.07g126400), Snf7 protein (Glyma.07g127300), unknown functional gene (Glyma.07g127900), and Leucine Rich-Repeat protein (Glyma.07g127100). The novel alleles inherited from the wild soybean accession could be used as molecular markers to improve root system architecture and productivity in elite soybean lines.

Conditional and unconditional genome-wide association study reveal complicate genetic architecture of human body weight and impacts of smoking

To reveal the impacts of smoking on genetic architecture of human body weight, we conducted a genome-wide association study on 5,336 subjects in four ethnic populations from MESA (The Multi-Ethnic Study of Atherosclerosis) data. A full genetic model was applied to association mapping for analyzing genetic effects of additive, dominance, epistasis, and their ethnicity-specific effects. Both the unconditional model (base) and conditional model including smoking as a cofactor were investigated. There were 10 SNPs involved in 96 significant genetic effects detected by the base model, which accounted for a high heritability (61.78%). Gene ontology analysis revealed that a number of genetic factors are related to the metabolic pathway of benzopyrene, a main compound in cigarettes. Smoking may play important roles in genetic effects of dominance, dominance-related epistasis, and gene-ethnicity interactions on human body weight. Gene effect prediction shows that the genetic effects of smoking cessation on body weight vary from different populations.

Mapping QTLs for 15 morpho-metric traits in Arabidopsis thaliana using Col-0 × Don-0 population

Genome wide quantitative trait loci (QTL) mapping was conducted in Arabidopsis thaliana using F₂ mapping population (Col-0 × Don-0) and SNPs markers. A total of five linkage groups were obtained with number of SNPs varying from 45 to 59 per linkage group. The composite interval mapping detected a total of 36 QTLs for 15 traits and the number of QTLs ranged from one (root length, root dry biomass, cauline leaf width, number of internodes and internode distance) to seven (for bolting days). The range of phenotypic variance explained (PVE) and logarithm of the odds ratio of these 36 QTLs was found be 0.19-38.17% and 3.0-6.26 respectively. Further, the epistatic interaction detected one main effect QTL and four epistatic QTLs. Five major QTLs viz. Qbd.nbri.4.3, Qfd.nbri.4.2, Qrdm.nbri.5.1, Qncl.nbri.2.2, Qtd.nbri.4.1 with PVE > 15.0% might be useful for fine mapping to identify genes associated with respective traits, and also for development of specialized population through marker assisted selection. The identification of additive and dominant effect QTLs and desirable alleles of each of above mentioned traits would also be important for future research.

Mapping of QTL for Grain Yield Components Based on a DH Population in Maize

The elite maize hybrid Zhengdan 958 (ZD958), which has high and stable yield and extensive adaptability, is widely grown in China. To elucidate the genetic basis of yield and its related traits in this elite hybrid, a set of doubled haploid (DH) lines derived from ZD958 were evaluated in four different environments at two locations over two years, and a total of 49 quantitative trait loci (QTL) and 24 pairs of epistatic interactions related to yield and yield components were detected. Furthermore, 21 QTL for six investigated phenotypic traits were detected across two different sites. Combining the results of these QTL in each environment and across both sites, three main QTL hotspots were found in chromosomal bins 2.02, 2.05–2.06, and 6.05 between the simple sequence repeat (SSR) markers umc1165-bnlg1017, umc1065-umc1637, and nc012-bnlg345, respectively. The existence of three QTL hotspots associated with various traits across multiple environments could be explained by pleiotropic QTL or multiple tightly linked QTL. These genetic regions could provide targets for genetic improvement, fine mapping, and marker-assisted selection in future studies.

Mapping quantitative trait loci associated with common bunt resistance in a spring wheat (Triticum aestivum L.) variety Lillian

Based on their consistency over environments, two QTL identified in Lillian on chromosomes 5A and 7A could be useful targets for marker assisted breeding of common bunt resistance. Common bunt of wheat (Triticum aestivum L.) caused by Tilletia tritici and T. laevis is an economically important disease because of losses in grain yield and reduced grain quality. Resistance can be quantitative, under the control of multiple small effect genes. The Canada Western Red Spring wheat variety Lillian is moderately resistant to common bunt races found on the Canadian prairies. This study was conducted to identify and map quantitative trait loci (QTL) conferring resistance against common bunt in Lillian. A doubled haploid population comprising 280 lines was developed from F₁ plants of the cross of Lillian by Vesper. The lines were inoculated at seeding with the two races L16 (T. laevis) and T19 (T. tritici), grown in field near Swift Current, SK, in 2014, 2015 and 2016 and assessed for disease incidence. The lines were genotyped with the 90 K iSelect SNP genotyping assay, and a high-density genetic map was constructed. Quantitative trait locus analysis was performed with MapQTL.6^® software. Two relatively stable common bunt resistance QTL, detected in two of the 3 years, were identified on chromosomes 5A and 7A from Lillian. In addition, three less stable QTL, appearing in one out of 3 years, were identified: one was contributed by Lillian on chromosome 3D and two were contributed by Vesper on chromosomes 1D and 2A. Epistatic interaction was identified for the bunt incidence between 3D and 7A resulting in greater bunt resistance. Future bunt resistance breeding will benefit from combining these QTL through gene pyramiding.

Identifying a wild allele conferring small seed size, high protein content and low oil content using chromosome segment substitution lines in soybean

KEY MESSAGE

A wild soybean allele conferring 100-seed weight, protein content and oil content simultaneously was fine-mapped to a 329-kb region on Chromosome 15, in which Glyma.15g049200 was predicted a candidate gene. Annual wild soybean characterized with small 100-seed weight (100SW), high protein content (PRC), low oil content (OIC) may contain favourable alleles for broadening the genetic base of cultivated soybeans. To evaluate these alleles, a population composed of 195 chromosome segment substitution lines (SojaCSSLP4), with wild N24852 as donor and cultivated NN1138-2 as recurrent parent, was tested. In SojaCSSLP4, 10, 9 and 8 wild segments/QTL were detected for 100SW, PRC and OIC, respectively. Using a backcross-derived secondary population, one segment for the three traits (q100SW15, qPro15 and qOil15) and one for 100SW (q100SW18.2) were fine-mapped into a 329-kb region on chromosome 15 and a 286-kb region on chromosome 18, respectively. Integrated with the transcription data in SoyBase, 42 genes were predicted in the 329-kb region where Glyma.15g049200 showed significant expression differences at all seed development stages. Furthermore, the Glyma.15g049200 segments of the two parents were sequenced and compared, which showed two base insertions in CDS (coding sequence) in the wild N24852 comparing to the NN1138-2. Since only Glyma.15g049200 performed differential CDS between the two parents but related to the three traits, Glyma.15g049200 was predicted a pleiotropic candidate gene for 100SW, PRC and OIC. The functional annotation of Glyma.15g049200 indicated a bidirectional sucrose transporter belonging to MtN3/saliva family which might be the reason that this gene provides a same biochemical basis for 100SW, PRC and OIC, therefore, is responsible for the three traits. This result may facilitate isolation of the specific gene and provide prerequisite for understanding the other two pleiotropic QTL.

Identification of quantitative trait loci for kernel-related traits and the heterosis for these traits in maize (Zea mays L.)

Heterosis has been extensively applied for many traits during maize breeding, but there has been relatively little attention paid to the heterosis for kernel size. In this study, we evaluated a population of 301 recombinant inbred lines derived from a cross between 08-641 and YE478, as well as 298 hybrids from an immortalized F₂ (IF₂) population to detect quantitative trait loci (QTLs) for six kernel-related traits and the mid-parent heterosis (MPH) for these traits. A total of 100 QTLs, six pairs of loci with epistatic interactions, and five significant QTL × environment interactions were identified in both mapping populations. Seven QTLs accounted for over 10% of the phenotypic variation. Only four QTLs affected both the trait means and the MPH, suggesting the genetic mechanisms for kernel-related traits and the heterosis for kernel size are not completely independent. Moreover, more than half of the QTLs for each trait in the IF₂ population exhibited dominance, implying that dominance is more important than other genetic effects for the heterosis for kernel-related traits. Additionally, 20 QTL clusters comprising 46 QTLs were detected across ten chromosomes. Specific chromosomal regions (bins 2.03, 6.04-6.05, and 9.01-9.02) exhibited pleiotropy and congruency across diverse heterotic patterns in previous studies. These results may provide additional insights into the genetic basis for the MPH for kernel-related traits.

Genetic dissection of yield-related traits and mid-parent heterosis for those traits in maize (Zea mays L.)

BACKGROUND

Utilization of heterosis in maize could be critical in maize breeding for boosting grain yield. However, the genetic architecture of heterosis is not fully understood. To dissect the genetic basis of yield-related traits and heterosis in maize, 301 recombinant inbred lines derived from 08 to 641 × YE478 and 298 hybrids from the immortalized F₂ (IF₂) population were used to map quantitative trait loci (QTLs) for nine yield-related traits and mid-parent heterosis.

RESULTS

We observed 156 QTLs, 28 pairs of loci with epistatic interaction, and 10 significant QTL × environment interactions in the inbred and hybrid mapping populations. The high heterosis in F₁ and IF₂ populations for kernel weight per ear (KWPE), ear weight per ear (EWPE), and kernel number per row (KNPR) matched the high percentages of QTLs (over 50%) for those traits exhibiting overdominance, whereas a notable predominance of loci with dominance effects (more than 70%) was observed for traits that show low heterosis such as cob weight per ear (CWPE), rate of kernel production (RKP), ear length (EL), ear diameter (ED), cob diameter, and row number (RN). The environmentally stable QTL qRKP3-2 was identified across two mapping populations, while qKWPE9, affecting the trait mean and the mid-parent heterosis (MPH) level, explained over 18% of phenotypic variations. Nine QTLs, qEWPE9-1, qEWPE10-1, qCWPE6, qEL8, qED2-2, qRN10-1, qKWPE9, qKWPE10-1, and qRKP4-3, accounted for over 10% of phenotypic variation. In addition, QTL mapping identified 95 QTLs that were gathered together and integrated into 33 QTL clusters on 10 chromosomes.

CONCLUSIONS

The results revealed that (1) the inheritance of yield-related traits and MPH in the heterotic pattern improved Reid (PA) × Tem-tropic I (PB) is trait-dependent; (2) a large proportion of loci showed dominance effects, whereas overdominance also contributed to MPH for KNPR, EWPE, and KWPE; (3) marker-assisted selection for markers at genomic regions 1.09-1.11, 2.04, 3.08-3.09, and 10.04-10.05 contributed to hybrid performance per se and heterosis and were repeatedly reported in previous studies using different heterotic patterns is recommended.

Molecular Dissection of Quantitative Variation in Bermudagrass Hybrids (Cynodon dactylon x transvaalensis): Morphological Traits

Bermudagrass (Cynodon (L.)) is the most important warm-season grass grown for forage or turf. It shows extensive variation in morphological characteristics and growth attributes, but the genetic basis of this variation is little understood. Detection and tagging of quantitative trait loci (QTL) affecting above-ground morphology with diagnostic DNA markers would provide a foundation for genetic and molecular breeding applications in bermudagrass. Here, we report early findings regarding genetic architecture of foliage (canopy height, HT), stolon (stolon internode length, ILEN and length of the longest stolon LLS), and leaf traits (leaf blade length, LLEN and leaf blade width, LW) in 110 F1 individuals derived from a cross between Cynodon dactylon (T89) and C. transvaalensis (T574). Separate and joint environment analyses were performed on trait data collected across two to five environments (locations, and/or years, or time), finding significant differences (P < 0.001) among the hybrid progeny for all traits. Analysis of marker-trait associations detected 74 QTL and 135 epistatic interactions. Composite interval mapping (CIM) and mixed-model CIM (MCIM) identified 32 main effect QTL (M-QTL) and 13 interacting QTL (int-QTL). Colocalization of QTL for plant morphology partially explained significant correlations among traits. M-QTL qILEN-3-2 (for ILEN; R2 = 11-19%), qLLS-7-1 (for LLS; R2 = 13-27%), qLEN-1-1 (for LLEN; R2 = 10-11%), and qLW-3-2 (for LW; R2 = 10-12%) were 'stable' across multiple environments, representing candidates for fine mapping and applied breeding applications. QTL correspondence between bermudagrass and divergent grass lineages suggests opportunities to accelerate progress by predictive breeding of bermudagrass.

Genome-Wide Detection of Major and Epistatic Effect QTLs for Seed Protein and Oil Content in Soybean Under Multiple Environments Using High-Density Bin Map

Seed protein and oil content are the two important traits determining the quality and value of soybean. Development of improved cultivars requires detailed understanding of the genetic basis underlying the trait of interest. However, it is prerequisite to have a high-density linkage map for precisely mapping genomic regions, and therefore the present study used high-density genetic map containing 2267 recombination bin markers distributed on 20 chromosomes and spanned 2453.79 cM with an average distance of 1.08 cM between markers using restriction-site-associated DNA sequencing (RAD-seq) approach. A recombinant inbred line (RIL) population of 104 lines derived from a cross between Linhefenqingdou and Meng 8206 cultivars was evaluated in six different environments to identify main- and epistatic-effect quantitative trait loci (QTLs)as well as their interaction with environments. A total of 44 main-effect QTLs for protein and oil content were found to be distributed on 17 chromosomes, and 15 novel QTL were identified for the first time. Out of these QTLs, four were major and stable QTLs, viz., qPro-7-1, qOil-8-3, qOil-10-2 and qOil-10-4, detected in at least two environments plus combined environment with R² values >10%. Within the physical intervals of these four QTLs, 111 candidate genes were screened for their direct or indirect involvement in seed protein and oil biosynthesis/metabolism processes based on gene ontology and annotation information. Based on RNA sequencing (RNA-seq) data analysis, 15 of the 111 genes were highly expressed during seed development stage and root nodules that might be considered as the potential candidate genes. Seven QTLs associated with protein and oil content exhibited significant additive and additive × environment interaction effects, and environment-independent QTLs revealed higher additive effects. Moreover, three digenic epistatic QTLs pairs were identified, and no main-effect QTLs showed epistasis. In conclusion, the use of a high-density map identified closely linked flanking markers, provided better understanding of genetic architecture and candidate gene information, and revealed the scope available for improvement of soybean quality through marker assisted selection (MAS).

Genome-wide association study of maize plant architecture using F1 populations

Genome-wide association study of maize plant architecture using F₁ populations can better dissect various genetic effects that can provide precise guidance for genetic improvement in maize breeding. Maize grain yield has increased at least eightfold during the past decades. Plant architecture, including plant height, leaf angle, leaf length, and leaf width, has been changed significantly to adapt to higher planting density. Although the genetic architecture of these traits has been dissected using different populations, the genetic basis remains unclear in the F₁ population. In this work, we perform a genome-wide association study of the four traits using 573 F₁ hybrids with a mixed linear model approach and QTXNetwork mapping software. A total of 36 highly significant associated quantitative trait SNPs were identified for these traits, which explained 51.86-79.92% of the phenotypic variation and were contributed mainly by additive, dominance, and environment-specific effects. Heritability as a result of environmental interaction was more important for leaf angle and leaf length, while major effects (a, aa, and d) were more important for leaf width and plant height. The potential breeding values of the superior lines and superior hybrids were also predicted, and these values can be applied in maize breeding by direct selection of superior genotypes for the associated quantitative trait SNPs. A total of 108 candidate genes were identified for the four traits, and further analysis was performed to screen the potential genes involved in the development of maize plant architecture. Our results provide new insights into the genetic architecture of the four traits, and will be helpful in marker-assisted breeding for maize plant architecture.

A major QTL on chromosome C05 significantly reduces acid detergent lignin (ADL) content and increases seed oil and protein content in oilseed rape (Brassica napus L.)

A reduction in acid detergent lignin content in oilseed rape resulted in an increase in seed oil and protein content. Worldwide increasing demand for vegetable oil and protein requires continuous breeding efforts to enhance the yield of oil and protein crop species. The oil-extracted meal of oilseed rape is currently mainly used for feeding livestock, but efforts are undertaken to use the oilseed rape protein in food production. One limiting factor is the high lignin content of black-seeded oilseed rape that negatively affects digestibility and sensory quality of food products compared to soybean. Breeding attempts to develop yellow seeded oilseed rape with reduced lignin content have not yet resulted in competitive cultivars. The objective of this work was to investigate the inheritance of seed quality in a DH population derived from the cross of the high oil lines SGDH14 and cv. Express. The DH population of 139 lines was tested in field experiments in 14 environments in north-west Europe. Seeds harvested from open pollinated plants were used for extensive seed quality analysis. A molecular marker map based on the Illumina Infinium 60 K Brassica SNP chip was used to map QTL. Amongst others, one major QTL for acid detergent lignin content, explaining 81% of the phenotypic variance, was identified on chromosome C05. Lines with reduced lignin content nevertheless did not show a yellowish appearance, but showed a reduced seed hull content. The position of the QTL co-located with QTL for oil and protein content of the defatted meal with opposite additive effects, suggesting that the reduction in lignin content resulted in an increase in oil and protein content.

Dissecting genomic hotspots underlying seed protein, oil, and sucrose content in an interspecific mapping population of soybean using high-density linkage mapping

The cultivated [Glycine max (L) Merr.] and wild [Glycine soja Siebold & Zucc.] soybean species comprise wide variation in seed composition traits. Compared to wild soybean, cultivated soybean contains low protein, high oil, and high sucrose. In this study, an interspecific population was derived from a cross between G. max (Williams 82) and G. soja (PI 483460B). This recombinant inbred line (RIL) population of 188 lines was sequenced at 0.3× depth. Based on 91 342 single nucleotide polymorphisms (SNPs), recombination events in RILs were defined, and a high-resolution bin map was developed (4070 bins). In addition to bin mapping, quantitative trait loci (QTL) analysis for protein, oil, and sucrose was performed using 3343 polymorphic SNPs (3K-SNP), derived from Illumina Infinium BeadChip sequencing platform. The QTL regions from both platforms were compared, and a significant concordance was observed between bin and 3K-SNP markers. Importantly, the bin map derived from next-generation sequencing technology enhanced mapping resolution (from 1325 to 50 Kb). A total of five, nine, and four QTLs were identified for protein, oil, and sucrose content, respectively, and some of the QTLs coincided with soybean domestication-related genomic loci. The major QTL for protein and oil were mapped on Chr. 20 (qPro_20) and suggested negative correlation between oil and protein. In terms of sucrose content, a novel and major QTL were identified on Chr. 8 (qSuc_08) and harbours putative genes involved in sugar transport. In addition, genome-wide association using 91 342 SNPs confirmed the genomic loci derived from QTL mapping. A QTL-based haplotype using whole-genome resequencing of 106 diverse soybean lines identified unique allelic variation in wild soybean that could be utilized to widen the genetic base in cultivated soybean.

Study of QTLs linked to awn length and their relationships with chloroplasts under control and saline environments in bread wheat

INTRODUCTION

Some studies in wheat showed that awns may have a useful effect on yield, especially under drought stress. Up to this time few researches has identified the awn length QTLs with different effect in salinity stress.

OBJECTIVE

The primary objective of this study was to examine the additive (a) and the epistatic (aa) QTLs involve in wheat awns length in control and saline environments.

METHODS

A F7 RIL population consisting of 319 sister lines, derived from a cross between wheat cultivars Roshan and Falat (seri82), and the two parents were grown in two environments (control and Saline) based on an alpha lattice design with two replications in each environment. At flowering, awn length was measured for each line. For QTL analysis, the linkage map of the ''Roshan × Falat'' population was used, which included 748 markers including 719 DArT, 29 simple sequenced repeats (SSRs). Additive and pleiotropic QTLs were identified. In order to reveal the relationship between the identified QTL for awns length and the role of the gene or genes that it expresses, the awns length locus location and characteristics of its related CDS, gene, UTRs, ORF, exons and Introns were studied using ensemble plant ( http://plants.ensembl.org/Triticum_aestivum ). Furthermore, the promoter analysis has been done using NSITE-PL.

RESULTS

We identified 6 additive QTLs for awn length by QTL Cartographer program using single-environment phenotypical values. Also, we detected three additive and two epistatic QTLs for awn length by the QTLNetwork program using multi-environment phenotypical values. Our results showed that none of the additive and epistatic QTLs had interactions with environment. One of the additive QTLs located on chromosome 4A was co-located with QTLs for number of sterile spikelet per spike in both environment and number of seed per spike in control environment.

COCLUSION

Studies of the locus linked to the awns length QTL revealed the role of awn and its chloroplasts in grain filing during abiotic stress could be enhanced by over expression of some genes like GTP-Binding proteins which are enriched in chloroplasts encoded by genes included wPt-5730 locus.

Predictive ability of genome-assisted statistical models under various forms of gene action

Recent work has suggested that the performance of prediction models for complex traits may depend on the architecture of the target traits. Here we compared several prediction models with respect to their ability of predicting phenotypes under various statistical architectures of gene action: (1) purely additive, (2) additive and dominance, (3) additive, dominance, and two-locus epistasis, and (4) purely epistatic settings. Simulation and a real chicken dataset were used. Fourteen prediction models were compared: BayesA, BayesB, BayesC, Bayesian LASSO, Bayesian ridge regression, elastic net, genomic best linear unbiased prediction, a Gaussian process, LASSO, random forests, reproducing kernel Hilbert spaces regression, ridge regression (best linear unbiased prediction), relevance vector machines, and support vector machines. When the trait was under additive gene action, the parametric prediction models outperformed non-parametric ones. Conversely, when the trait was under epistatic gene action, the non-parametric prediction models provided more accurate predictions. Thus, prediction models must be selected according to the most probably underlying architecture of traits. In the chicken dataset examined, most models had similar prediction performance. Our results corroborate the view that there is no universally best prediction models, and that the development of robust prediction models is an important research objective.

Dominance and Epistasis Interactions Revealed as Important Variants for Leaf Traits of Maize NAM Population

Leaf orientation traits of maize (Zea mays) are complex traits controlling by multiple loci with additive, dominance, epistasis, and environmental interaction effects. In this study, an attempt was made for identifying the causal loci, and estimating the additive, non-additive, environmental specific genetic effects underpinning leaf traits (leaf length, leaf width, and upper leaf angle) of maize NAM population. Leaf traits were analyzed by using full genetic model and additive model of multiple loci. Analysis with full genetic model identified 38∼47 highly significant loci (-log₁₀P_EW > 5), while estimated total heritability were 64.32∼79.06% with large contributions due to dominance and dominance related epistasis effects (16.00∼56.91%). Analysis with additive model obtained smaller total heritability ( hT2 ≙ 18.68∼29.56%) and detected fewer loci (30∼36) as compared to the full genetic model. There were 12 pleiotropic loci identified for the three leaf traits: eight loci for leaf length and leaf width, and four loci for leaf length and leaf angle. Optimal genotype combinations of superior line (SL) and superior hybrid (SH) were predicted for each of the traits under four different environments based on estimated genotypic effects to facilitate maker-assisted selection for the leaf traits.

Genetic dissection of wheat panicle traits using linkage analysis and a genome-wide association study

Coincident regions on chromosome 4B for GW, on 5A for SD and TSS, and on 3A for SL and GNS were detected through an integration of a linkage analysis and a genome-wide association study (GWAS). In addition, six stable QTL clusters on chromosomes 2D, 3A, 4B, 5A and 6A were identified with high PVE% on a composite map. The panicle traits of wheat, such as grain number per spike and 1000-grain weight, are closely correlated with grain yield. Superior and effective alleles at loci related to panicles developments play a crucial role in the progress of molecular improvement in wheat yield breeding. Here, we revealed several notable allelic variations of seven panicle-related traits through an integration of genome-wide association mapping and a linkage analysis. The linkage analysis was performed using a recombinant inbred line (RIL) population (173 lines of F_8:9) with a high-density genetic map constructed with 90K SNP arrays, Diversity Arrays Technology (DArT) and simple sequence repeat (SSR) markers in five environments. Thirty-five additive quantitative trait loci (QTL) were discovered, including eleven stable QTLs on chromosomes 1A, 2D, 4B, 5B, 6B, and 6D. The marker interval between EX_C101685 and RAC875_C27536 on chromosome 4B exhibited pleiotropic effects for GW, SL, GNS, FSN, SSN, and TSS, with the phenotypic variation explained (PVE) ranging from 5.40 to 37.70%. In addition, an association analysis was conducted using a diverse panel of 205 elite wheat lines with a composite map (24,355 SNPs) based on the Illumina Infinium assay in four environments. A total of 73 significant marker-trait associations (MTAs) were detected for panicle traits, which were distributed across all wheat chromosomes except for 4D, 5D, and 6D. Consensus regions between RAC875_C27536_611 and Tdurum_contig4974_355 on chromosome 4B for GW in multiple environments, between QTSS5A.7-43 and BS00021805_51 on 5A for SD and TSS, and between QSD3A.2-164 and RAC875_c17479_359 on 3A for SL and GNS in multiple environments were detected through linkage analysis and a genome-wide association study (GWAS). In addition, six stable QTL clusters on chromosomes 2D, 3A, 4B, 5A, and 6A were identified with high PVE% on a composite map. This study provides potentially valuable information on the dissection of yield-component traits and valuable genetic alleles for molecular-design breeding or functional gene exploration.

Dynamic and epistatic QTL mapping reveals the complex genetic architecture of waterlogging tolerance in chrysanthemum

37 unconditional QTLs, 51 conditional QTLs and considerable epistatic QTLs were detected for waterlogging tolerance, and six favourable combinations were selected accelerating the possible application of MAS in chrysanthemum breeding. Chrysanthemum is seriously impacted by soil waterlogging. To determine the genetic characteristics of waterlogging tolerance (WAT) in chrysanthemum, a population of 162 F₁ lines was used to construct a genetic map to identify the dynamic and epistatic quantitative trait loci (QTLs) for four WAT traits: wilting index (WI), dead leaf ratio (DLR), chlorosis score (Score) and membership function value of waterlogging (MFVW). The h _B² for the WAT traits ranged from 0.49 to 0.64, and transgressive segregation was observed in both directions. A total of 37 unconditional consensus QTLs with 5.81-18.21% phenotypic variation explanation (PVE) and 51 conditional consensus QTLs with 5.90-24.56% PVE were detected. Interestingly, three unconditional consensus QTLs were consistently identified across different stages, whereas no conditional consensus QTLs were consistently expressed. In addition, considerable epistatic QTLs, all with PVE values ranging from 0.01 to 8.87%, were detected by a joint analysis of WAT phenotypes. These results illustrated that the QTLs (genes) controlling WAT were environmentally dependent and selectively expressed at different times and indicated that both additive and epistatic effects underlie the inheritance of WAT in chrysanthemum. The findings of the current study provide insights into the complex genetic architecture of WAT, and the identification of favourable alleles represents an important step towards the application of molecular marker-assisted selection (MAS) and QTL pyramiding in chrysanthemum WAT breeding programmes.

Chromosomes A07 and A05 associated with stable and major QTLs for pod weight and size in cultivated peanut (Arachis hypogaea L.)

Co-localized intervals and candidate genes were identified for major and stable QTLs controlling pod weight and size on chromosomes A07 and A05 in an RIL population across four environments. Cultivated peanut (Arachis hypogaea L.) is an important legume crops grown in > 100 countries. Hundred-pod weight (HPW) is an important yield trait in peanut, but its underlying genetic mechanism was not well studied. In this study, a mapping population (Xuhua 13 × Zhonghua 6) with 187 recombinant inbred lines (RILs) was developed to map quantitative trait loci (QTLs) for HPW together with pod length (PL) and pod width (PW) by both unconditional and conditional QTL analyses. A genetic map covering 1756.48 cM was constructed with 817 markers. Additive effects, epistatic interactions, and genotype-by-environment interactions were analyzed using the phenotyping data generated across four environments. Twelve additive QTLs were identified on chromosomes A05, A07, and A08 by unconditional analysis, and five of them (qPLA07, qPLA05.1, qPWA07, qHPWA07.1, and qHPWA05.2) showed major and stable expressions in all environments. Conditional QTL mapping found that PL had stronger influences on HPW than PW. Notably, qHPWA07.1, qPLA07, and qPWA07 that explained 17.93-43.63% of the phenotypic variations of the three traits were co-localized in a 5 cM interval (1.48 Mb in physical map) on chromosome A07 with 147 candidate genes related to catalytic activity and metabolic process. In addition, qHPWA05.2 and qPLA05.1 were co-localized with minor QTL qPWA05.2 to a 1.3 cM genetic interval (280 kb in physical map) on chromosome A05 with 12 candidate genes. This study provides a comprehensive characterization of the genetic components controlling pod weight and size as well as candidate QTLs and genes for improving pod yield in future peanut breeding.

Positive interactions of major-effect QTLs with genetic background that enhances rice yield under drought

To improve the grain yield of the lowland-adapted popular rice variety Samba Mahsuri under reproductive-stage drought (RS) and to understand the interactions between drought QTLs, two mapping populations were developed using marker-assisted selection (MAS) and marker-assisted recurrent selection (MARS). The mean grain yield of pyramided lines (PLs) with qDTY _2.2  + qDTY _4.1 in MAS is significantly higher under RS and irrigated control than lines with single QTLs. Among MARS PLs, lines with four qDTYs (qDTY _1.1  + qDTY _2.1  + qDTY _3.1  + qDTY _11.1 ) and two QTLs (qDTY _1.1  + qDTY _11.1 ) yielded higher than PLs with other qDTY combinations. The selected PLs showed a yield advantage of 0.3-2.0 t ha^-1 under RS. An allelic profile of MAS PLs having same qDTY combination but with different yields under drought was studied. Hierarchical clustering grouped together the selected lines with high yield under drought. Epistasis test showed the interaction of qDTY _4.1 and qDTY _9.1 loci with qDTY _7.1 significantly increased yield under drought and all the lines with higher yield under drought possessed the conserved region of qDTY _7.1 on chromosome 7. The positive interactions among QTLs, effectiveness of QTLs in different backgrounds, introgression of DTY QTLs together with resistance to biotic stresses shall help enhance grain yield under RS.

Main and epistatic QTL analyses for Sclerotinia Head Rot resistance in sunflower

Sclerotinia Head Rot (SHR), a disease caused by Sclerotinia sclerotiorum, is one of the most limiting factors in sunflower production. In this study, we identified genomic loci associated with resistance to SHR to support the development of assisted breeding strategies. We genotyped 114 Recombinant Inbred Lines (RILs) along with their parental lines (PAC2 -partially resistant-and RHA266 -susceptible-) by using a 384 single nucleotide polymorphism (SNP) Illumina Oligo Pool Assay to saturate a sunflower genetic map. Subsequently, we tested these lines for SHR resistance using assisted inoculations with S. sclerotiorum ascospores. We also conducted a randomized complete-block assays with three replicates to visually score disease incidence (DI), disease severity (DS), disease intensity (DInt) and incubation period (IP) through four field trials (2010-2014). We finally assessed main effect quantitative trait loci (M-QTLs) and epistatic QTLs (E-QTLs) by composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively. As a result of this study, the improved map incorporates 61 new SNPs over candidate genes. We detected a broad range of narrow sense heritability (h2) values (1.86-59.9%) as well as 36 M-QTLs and 13 E-QTLs along 14 linkage groups (LGs). On LG1, LG10, and LG15, we repeatedly detected QTLs across field trials; which emphasizes their putative effectiveness against SHR. In all selected variables, most of the identified QTLs showed high determination coefficients, associated with moderate to high heritability values. Using markers shared with previous Sclerotinia resistance studies, we compared the QTL locations in LG1, LG2, LG8, LG10, LG11, LG15 and LG16. This study constitutes the largest report of QTLs for SHR resistance in sunflower. Further studies focusing on the regions in LG1, LG10, and LG15 harboring the detected QTLs are necessary to identify causal alleles and contribute to unraveling the complex genetic basis governing the resistance.

An association study revealed substantial effects of dominance, epistasis and substance dependence co-morbidity on alcohol dependence symptom count

Alcohol dependence is a complex disease involving polygenes, environment and their interactions. Inadequate consideration of these interactions may have hampered the progress on genome-wide association studies of alcohol dependence. By using the dataset of the Study of Addiction: Genetics and Environment with 3838 subjects, we conducted a genome-wide association studies of alcohol dependence symptom count (ADSC) with a full genetic model considering additive, dominance, epistasis and their interactions with ethnicity, as well as conditions of co-morbid substance dependence. Twenty quantitative trait single nucleotide polymorphisms (QTSs) showed highly significant associations with ADSC, including four previously reported genes (ADH1C, PKNOX2, CPE and KCNB2) and the reported intergenic rs1363605, supporting the overall validity of the analysis. Two QTSs within or near ADH1C showed very strong association in a dominance inheritance mode and increased the phenotype value of ADSC when the effect of co-morbid opiate or marijuana dependence was controlled. Highly significant association was also identified in variants within four novel genes (RGS6, FMN1, NRM and BPTF), two non-coding RNA and two epistasis loci. QTS rs7616413, located near PTPRG encoding a protein tyrosine phosphatase receptor, interacted with rs10090742 within ANGPT1 encoding a protein tyrosine phosphatase in an additive × additive or dominance × additive manner. The detected QTSs contributed to about 20 percent of total heritability, in which dominance and epistasis effects accounted for over 50 percent. These results demonstrated that perturbations arising from gene-gene interaction and conditions of co-morbidity substantially influence the genetic architecture of complex trait.

Chromosome segment detection for seed size and shape traits using an improved population of wild soybean chromosome segment substitution lines

Size and shape of soybean seeds are closely related to seed yield and market value. Annual wild soybeans have the potential to improve cultivated soybeans, but their inferior seed characteristics should be excluded. To detect quantitative trait loci (QTLs)/segments of seed size and shape traits in annual wild soybean, its chromosome segment substitution lines (CSSLs) derived from NN1138-2 (recurrent parent, Glycine max) and N24852 (donor parent, Glycine soja) and then modified 2 iterations (coded SojaCSSLP3) were improved further to contain more lines (diagonal segments) and less heterozygous and missing portions. The new population (SojaCSSLP4) composed of 195 CSSLs was evaluated under four environments, and 11, 13, 7, 15 and 14 QTLs/segments were detected for seed length (SL), seed width (SW), seed roundness (SR), seed perimeter (SP) and seed cross section area (SA), respectively, with all 60 wild allele effects negative. Among them, 16 QTLs/segments were shared by 2-5 traits, respectively, but 0-3 segments for each of the 5 traits were independent. The non-shared Satt274 and shared Satt305, Satt540 and Satt239 were major segments, along with other segments composed of two different but related sets of genetic systems for SR and the other 4 traits, respectively. Compared with the literature, 7 SL, 5 SW and 2 SR QTLs/segments were also detected in cultivated soybeans; allele distinction took place between cultivated and wild soybeans, and also among cultivated parents. The present mapping is understood as macro-segment mapping, the segments may be further dissected into smaller segments as well as corresponding QTLs/genes.

Dissecting genetic architecture of startle response in Drosophila melanogaster using multi-omics information

Startle behavior is important for survival, and abnormal startle responses are related to several neurological diseases. Drosophila melanogaster provides a powerful system to investigate the genetic underpinnings of variation in startle behavior. Since mechanically induced, startle responses and environmental conditions can be readily quantified and precisely controlled. The 156 wild-derived fully sequenced lines of the Drosophila Genetic Reference Panel (DGRP) were used to identify SNPs and transcripts associated with variation in startle behavior. The results validated highly significant effects of 33 quantitative trait SNPs (QTSs) and 81 quantitative trait transcripts (QTTs) directly associated with phenotypic variation of startle response. We also detected QTT variation controlled by 20 QTSs (tQTSs) and 73 transcripts (tQTTs). Association mapping based on genomic and transcriptomic data enabled us to construct a complex genetic network that underlies variation in startle behavior. Based on principles of evolutionary conservation, human orthologous genes could be superimposed on this network. This study provided both genetic and biological insights into the variation of startle response behavior of Drosophila melanogaster, and highlighted the importance of genetic network to understand the genetic architecture of complex traits.

QTL mapping for nine drought-responsive agronomic traits in bread wheat under irrigated and rain-fed environments

In bread wheat, QTL interval mapping was conducted for nine important drought responsive agronomic traits. For this purpose, a doubled haploid (DH) mapping population derived from Kukri/Excalibur was grown over three years at four separate locations in India, both under irrigated and rain-fed environments. Single locus analysis using composite interval mapping (CIM) allowed detection of 98 QTL, which included 66 QTL for nine individual agronomic traits and 32 QTL, which affected drought sensitivity index (DSI) for the same nine traits. Two-locus analysis allowed detection of 19 main effect QTL (M-QTL) for four traits (days to anthesis, days to maturity, grain filling duration and thousand grain weight) and 19 pairs of epistatic QTL (E-QTL) for two traits (days to anthesis and thousand grain weight). Eight QTL were common in single locus analysis and two locus analysis. These QTL (identified both in single- and two-locus analysis) were distributed on 20 different chromosomes (except 4D). Important genomic regions on chromosomes 5A and 7A were also identified (5A carried QTL for seven traits and 7A carried QTL for six traits). Marker-assisted recurrent selection (MARS) involving pyramiding of important QTL reported in the present study, together with important QTL reported earlier, may be used for improvement of drought tolerance in wheat. In future, more closely linked markers for the QTL reported here may be developed through fine mapping, and the candidate genes may be identified and used for developing a better understanding of the genetic basis of drought tolerance in wheat.

Genetic Mapping of Quantitative Trait Loci for Yield-Affecting Traits in a Barley Doubled Haploid Population Derived from Clipper × Sahara 3771

Many traits play essential roles in determining crop yield. Wide variation for morphological traits exists in Hordeum vulgare L., but the genetic basis of this morphological variation is largely unknown. To understand genetic basis controlling morphological traits affecting yield, a barley doubled haploid population (146 individuals) derived from Clipper × Sahara 3771 was used to map chromosome regions underlying days to awn appearance, plant height, fertile spike number, flag leaf length, spike length, harvest index, seed number per plant, thousands kernel weight, and grain yield. Twenty-seven QTLs for nine traits were mapped to the barley genome that described 3-69% of phenotypic variations; and some genomic regions harbor a given QTL for more than one trait. Out of 27 QTLs identified, 19 QTLs were novel. Chromosomal regions on 1H, 2H, 4H, and 6H associated with seed grain yield, and chromosome regions on 2H and 6H had major effects on grain yield (GY). One major QTL for seed number per plant was flanked by marker VRS1-KSUF15 on chromosome 2H. This QTL was also associated with GY. Some loci controlling thousands kernel weight (TKW), fertile spike number (FSN), and GY were the same. The major grain yield QTL detected on linkage PSR167 co-localized with TAM10. Two major QTLs controlling TKW and FSN were also mapped at this locus. Eight QTLs on chromosomes 1H, 2H, 3H, 4H, 5H, 6H, and 7H consistently affected spike characteristics. One major QTL (ANIONT1A-TACMD) on 4H affected both spike length (SL) and spike number explained 9 and 5% of the variation of SL and FSN, respectively. In conclusion, this study could cast some light on the genetic basis of the studied pivotal traits. Moreover, fine mapping of the identified major effect markers may facilitate the application of molecular markers in barley breeding programs.

Quantitative Trait Loci Mapping for Spike Characteristics in Hexaploid Wheat

Wheat ( L.) spike characteristics determine the number of grains produced on each spike and constitute key components of grain yield. Understanding of the genetic basis of spike characteristics in wheat, however, is limited. In this study, genotyping-by-sequencing (GBS) and the iSelect 9K assay were used on a doubled-haploid (DH) soft red winter wheat population that showed a wide range of phenotypic variation for spike traits. A genetic map spanning 2934.1 cM with an average interval length of 3.4 cM was constructed. Quantitative trait loci (QTL) analysis involving additive effects, epistasis (QQ) and QTL × environment (QE), and epistasis × environment (QQE) interactions detected a total of 109 QTL, 13 QE, and 20 QQ interactions in five environments. Spike characteristics were mainly determined by additive effects and were fine-tuned by QQ, QE, and QQE. Major QTL / explained up to 30.9% of the phenotypic variation for spike length (SL) and fertile spikelet number, .1 explained up to 15.6% of the phenotypic variation of grain number per spikelet, and explained up to 80.2% of the phenotypic variation for spike compactness. Additionally, QTL for correlated spike characteristics formed QTL clusters on chromosomes 1A, 5A, 2B, 3B, 5B, 1D, and 5D. This study expands the understanding of the genetic basis of spike characteristics in hexaploid wheat. A number of stable QTL detected in this study have potential to be used in marker-assisted selection. Additionally, the genetic map generated in this study could be used to study other traits of economic importance.