Pairs Of Quantitative Trait Loci Research Articles

QTL mapping for seed vigor-related traits under artificial aging in common wheat in two introgression line (IL) populations

Background Seed vigor recognized as a quantitative trait is of particular importance for agricultural production. However, limited knowledge is available for understanding genetic basis of wheat seed vigor. Methods The aim of this study was to identify quantitative trait loci (QTL) responsible for 10 seed vigor-related traits representing multiple aspects of seed-vigor dynamics during artificial aging with 6 different treatment times (0, 24, 36, 48, 60, and 72 h) under controlled conditions (48 °C, 95% humidity, and dark). The mapping populations were two wheat introgression lines (IL-1 and IL-2) derived from recipient parent (Lumai 14) and donor parent (Shaanhan 8675 or Jing 411). Results A total of 26 additive QTLs and 72 pairs of epistatic QTLs were detected for wheat seed-vigor traits. Importantly, chromosomes 1B and 7B contained several co-located QTLs, and chromosome 2A had a QTL-rich region near the marker Xwmc667, indicating that these QTLs may affect wheat seed vigor with pleiotropic effects. Furthermore, several possible consistent QTLs (hot-spot regions) were examined by comparison analysis of QTLs detected in this study and reported previously. Finally, a set of candidate genes for wheat seed vigor were predicted to be involved in transcription regulation, carbohydrate and lipid metabolism. Conclusion The present findings lay new insights into the mechanism underlying wheat seed vigor, providing valuable information for wheat genetic improvement especially marker-assisted breeding to increase seed vigor and consequently achieve high grain yield despite of further investigation required.

Mapping the genetic landscape establishing a tumor immune microenvironment favorable for anti-PD-1 response in mice and humans.

Identifying host genetic factors modulating immune checkpoint inhibitor (ICI) efficacy has been experimentally challenging because of variations in both host and tumor genomes, differences in the microbiome, and patient life exposures. Utilizing the Collaborative Cross (CC) multi-parent mouse genetic resource population, we developed an approach that fixes the tumor genomic configuration while varying host genetics. With this approach, we discovered that response to anti-PD-1 (aPD1) immunotherapy was significantly heritable in four distinct murine tumor models (H2 between 0.18-0.40). For the MC38 colorectal carcinoma system (H2 = 0.40), we mapped four significant ICI response quantitative trait loci (QTL) localized to mouse chromosomes (mChr) 5, 9, 15 and 17, and identified significant epistatic interactions between specific QTL pairs. Differentially expressed genes within these QTL were highly enriched for immune genes and pathways mediating allograft rejection and graft vs host disease. Using a cross species analytical approach, we found a core network of 48 genes within the four QTLs that showed significant prognostic value for overall survival in aPD1 treated human cohorts that outperformed all other existing validated immunotherapy biomarkers, especially in human tumors of the previously defined immune subtype 4. Functional blockade of two top candidate immune targets within the 48 gene network, GM-CSF and high affinity IL-2/IL-15 signaling, completely abrogated the MC38 tumor transcriptional response to aPD1 therapy in vivo. Thus, we have established a powerful cross species in vivo platform capable of uncovering host genetic factors that establish the tumor immune microenvironment configuration propitious for ICI response.

Genomic investigation of 18,421 lines reveals the genetic architecture of rice.

Understanding how numerous quantitative trait loci (QTL) shape phenotypic variation is an important question in genetics. To address this, we established a permanent population of 18,421 (18K) rice lines with reduced population structure. We generated reference-level genome assemblies of the founders and genotyped all 18K-rice lines through whole-genome sequencing. Through high-resolution mapping, 96 high-quality candidate genes contributing to variation in 16 traits were identified, including OsMADS22 and OsFTL1 verified as causal genes for panicle number and heading date, respectively. We identified epistatic QTL pairs and constructed a genetic interaction network with 19 genes serving as hubs. Overall, 170 masking epistasis pairs were characterized, serving as an important factor contributing to genetic background effects across diverse varieties. The work provides a basis to guide grain yield and quality improvements in rice.

Novel quantitative trait loci from an interspecific Brassica rapa derivative improve pod shatter resistance in Brassica napus.

Pod shatter is a trait of agricultural relevance that ensures plants dehisce seeds in their native environment and has been subjected to domestication and selection for non-shattering types in several broadacre crops. However, pod shattering causes a significant yield reduction in canola (Brassica napus L.) crops. An interspecific breeding line BC95042 derived from a B. rapa/B. napus cross showed improved pod shatter resistance (up to 12-fold than a shatter-prone B. napus variety). To uncover the genetic basis and improve pod shatter resistance in new varieties, we analysed F2 and F2:3 derived populations from the cross between BC95042 and an advanced breeding line, BC95041, and genotyped with 15,498 DArTseq markers. Through genome scan, interval and inclusive composite interval mapping analyses, we identified seven quantitative trait loci (QTLs) associated with pod rupture energy, a measure for pod shatter resistance or pod strength, and they locate on A02, A03, A05, A09 and C01 chromosomes. Both parental lines contributed alleles for pod shatter resistance. We identified five pairs of significant epistatic QTLs for additive x additive, additive dominance and dominance x dominance interactions between A01/C01, A03/A07, A07/C03, A03/C03, and C01/C02 chromosomes for rupture energy. QTL effects on A03/A07 and A01/C01 were in the repulsion phase. Comparative mapping identified several candidate genes (AG, ABI3, ARF3, BP1, CEL6, FIL, FUL, GA2OX2, IND, LATE, LEUNIG, MAGL15, RPL, QRT2, RGA, SPT and TCP10) underlying main QTL and epistatic QTL interactions for pod shatter resistance. Three QTLs detected on A02, A03, and A09 were near the FUL (FRUITFULL) homologues BnaA03g39820D and BnaA09g05500D. Focusing on the FUL, we investigated putative motifs, sequence variants and the evolutionary rate of its homologues in 373 resequenced B. napus accessions of interest. BnaA09g05500D is subjected to purifying selection as it had a low Ka/Ks ratio compared to other FUL homologues in B. napus. This study provides a valuable resource for genetic improvement for yield through an understanding of the genetic mechanism controlling pod shatter resistance in Brassica species.

Genetic Dissection of Extreme Seed-Flooding Tolerance in a Wild Soybean PI342618B by Linkage Mapping and Candidate Gene Analysis.

Seed-flooding stress is one of major abiotic constraints that adversely affects soybean production worldwide. Identifying tolerant germplasms and revealing the genetic basis of seed-flooding tolerance are imperative goals for soybean breeding. In the present study, high-density linkage maps of two inter-specific recombinant inbred line (RIL) populations, named NJIRNP and NJIR4P, were utilized to identify major quantitative trait loci (QTLs) for seed-flooding tolerance using three parameters viz., germination rate (GR), normal seedling rate (NSR), and electrical conductivity (EC). A total of 25 and 18 QTLs were detected by composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively, and 12 common QTLs were identified through both methods. All favorable alleles for the tolerance are notably from the wild soybean parent. Moreover, four digenic epistatic QTL pairs were identified, and three of them showed no main effects. In addition, the pigmented soybean genotypes exhibited high seed-flooding tolerance compared with yellow seed coat genotypes in both populations. Moreover, out of five identified QTLs, one major region containing multiple QTLs associated with all three traits was identified on Chromosome 8, and most of the QTLs within this hotspot were major loci (R2 > 10) and detectable in both populations and multiple environments. Based on the gene expression and functional annotation information, 10 candidate genes from QTL "hotspot 8-2" were screened for further analysis. Furthermore, the results of qRT-PCR and sequence analysis revealed that only one gene, GmDREB2 (Glyma.08G137600), was significantly induced under flooding stress and displayed a TTC tribasic insertion mutation of the nucleotide sequence in the tolerant wild parent (PI342618B). GmDREB2 encodes an ERF transcription factor, and the subcellular localization analysis using green fluorescent protein (GFP) revealed that GmDREB2 protein was localized in the nucleus and plasma membrane. Furthermore, overexpression of GmDREB2 significantly promoted the growth of soybean hairy roots, which might indicate its critical role in seed-flooding stress. Thus, GmDREB2 was considered as the most possible candidate gene for seed-flooding tolerance.

Genetic Analysis and Quantitative Trait Locus Mapping using the Major Gene Plus Polygene Model for Soybean [Glycine max (L.) Merr.] Main Quality Trait

Background: Soybean is an important oil and cash crop, soybean protein is the main source of protein for human consumption. Soybean oil is also major edible oil that is rich in unsaturated fatty acids and is more conducive to human long-term consumption than animal oil. An important research direction of current soybean breeding is improvement of the protein and fat content using marker assisted selection and many reports of associated quantitative trait locus (QTL) mapping have been documented. Until to now, a total of 241 QTLs associated with protein content and 315 related to fat content have been collected on the SoyBase website. Methods: In this study, 236 F2 generation plants and a derivative group were constructed by using Jiyu50 and Jinong18, obtained from Jilin Province. Combining three years of phenotypic and molecular detection data, using the ICIM method, one-dimensional scanning detected 24 QTLs related to protein and fat content, two-dimensional ICIM analysis of F2:3 families detected 7 pairs of epistatic QTLs associated with fat content. Result: Based on a mixed model for major genes and polygenes, the C model was determined the optimal genetic model for protein and fat content, with genetic rates of multiple genes of 71.15% and 79.15%, respectively. In the detection of high-oil molecular markers, the detection coincidence degree was Sat_238 greater than Satt 100 greater than Satt 150 greater than Satt 636 greater than Sat_287 = Sat_342, while this was Satt150 = Sat_342 greater than Satt100 greater than Satt 636 greater than Sat_287 greater than Sat_238 for high-protein markers. The QTL localization results showed that 9 microeffect QTLs related to protein content and 7 microeffect QTLs related to fat content were detected. Isolation analysis results were generally similar to those of QTL mapping. One stable QTL associated with protein content and two stable QTLs associated with fat content were identified as being of some application value in soybean molecular marker-assisted breeding.

QTL Analysis for Bread Wheat Seed Size, Shape and Color Characteristics Estimated by Digital Image Processing

The size, shape, and color of wheat seeds are important traits that are associated with yield and flour quality (size, shape), nutritional value, and pre-harvest sprouting (coat color). These traits are under multigenic control, and to dissect their molecular and genetic basis, quantitative trait loci (QTL) analysis is used. We evaluated 114 recombinant inbred lines (RILs) in a bi-parental RIL mapping population (the International Triticeae Mapping Initiative, ITMI/MP) grown in 2014 season. We used digital image analysis for seed phenotyping and obtained data for seven traits describing seed size and shape and 48 traits of seed coat color. We identified 212 additive and 34 pairs of epistatic QTLs on all the chromosomes of wheat genome except chromosomes 1A and 5D. Many QTLs were overlapping. We demonstrated that the overlap between QTL regions was low for seed size/shape traits and high for coat color traits. Using the literature and KEGG data, we identified sets of genes in Arabidopsis and rice from the networks controlling seed size and color. Further, we identified 29 and 14 candidate genes for seed size-related loci and for loci associated with seed coat color, respectively.

Phenotypic and Genetic Characterization of the Lentil Single Plant-Derived Core Collection for Resistance to Root Rot Caused by Fusarium avenaceum.

Lentil (Lens culinaris) is a pulse crop grown for its amino acid profile, moderate drought tolerance, and ability to fix nitrogen. As the global demand for lentils expands and new production regions emerge so too have the complement of diseases that reduce yield, including the root rot complex. Although the predominant causal pathogen varies based on growing region, Fusarium avenaceum is often found to be an important contributor to disease. This study screened part of the lentil single plant-derived core collection for resistance to F. avenaceum in a greenhouse. Plants were phenotyped for disease severity using three scoring scales and the differences in biomass traits due to pathogen presence were measured. Lentil accessions varied in disease severity and differences in biomass traits were found to be correlated with each visual severity estimate (r = -0.37 to -0.63, P < 0.001), however, heritability estimates were low to moderate among traits (H2 = 0.12 to 0.43). Results of a genome-wide association study (GWAS) using single nucleotide polymorphism (SNP) markers derived from genotyping-by-sequencing revealed 11 quantitative trait loci (QTL) across four chromosomes. Two pairs of QTL colocated for two traits and were found near putative orthologs that have been previously associated with plant disease resistance. The identification of lentil accessions that did not exhibit a difference in biomass traits may serve as parental material in breeding or in the development of biparental mapping populations to further validate and dissect the genetic control of resistance to root rot caused by F. avenaceum.

Dissection of the Genetic Architecture for Quantities of Gliadins Fractions in Wheat (Triticum aestivum L.)

Gliadin is a group of grain storage proteins that confers extensibility/viscosity to the dough and are vital to end-use quality in wheat. Moreover, gliadins are one of the important components for nutritional quality because they contain the nutritional unprofitable epitopes that cause chronic immune-mediated intestinal disorder in genetically susceptible individuals designated celiac disease (CD). The main genetic loci encoding the gliadins were revealed by previous studies; however, the genes related to the content of gliadins and their fractions were less elucidated. To illustrate the genetic basis of the content of gliadins and their fractions comprehensively, a recombinant inbred line (RIL) population that consisted of 196 lines was constructed from the two parents, Luozhen No.1 and Zhengyumai 9987. Quantitative trait loci (QTL) controlling the content of total gliadins and their fractions (ω-, α-, and γ-gliadin) were screened genome-widely under four environments across 2 years. Totally, thirty QTL which explained 1.97–12.83% of the phenotypic variation were detected to be distributed on 17 chromosomes and they were gathered into 12 clusters. One hundred and one pairs of epistatic QTL (E-QTL) were revealed, among which five were involved with the total gliadins and its fractions content QTL located on chromosome 1AS, 1DS, 4DS, 1DL, and 6AS. Three Kompetitive Allele-Specific PCR (KASP) markers were developed from three major QTL clusters located on chromosomes 6A, 6D, and 7D, respectively. The present research not only dissects the genetic loci for improving the content of gliadins and their three fractions, but may also contribute to marker-assisted selection of varieties with appropriate gliadin fractions content for end-use quality and health benefit at the early developmental stages and early breeding generations.

Detection of quantitative trait loci (QTL) associated with spring regrowth in alfalfa (Medicago sativa L.)

Spring regrowth is an important trait for perennial plants including alfalfa, the most cultivated forage legume worldwide. However, the genetic and genomic basis of the trait is largely unknown in alfalfa due to its complex genetic background of the tetroploid genome. The objective of this study was to identify quantitative trait loci (QTLs) associated with spring regrowth using high-resolution genetic linkage maps we constructed previously. In total, 36 significant additive effect QTLs for the trait were detected. Among them, 10 QTLs individually explained more than 10% of the phenotypic variation (PVE) with four in P1 and six in P2. Six overlapped QTLs intervals were detected with two and four intervals distributed in P1 and P2, respectively. In P1, both overlapped genomic regions were located on homolog 7D. In P2, the four QTLs with PVE>10% were co-localized on homolog 6D. Meanwhile, six pairs of significant epistatic QTLs were identified in P2. Screening of potential candidate genes associated with four overlapped QTLs (qCP2019-8, qLF2019-5, qLF2020-4, and qBLUP-3) narrowed down one candidate annotated as MAIL1. The Arabidopsis homolog gene has been reported to play an important role in plant growth. Therefore, the detected QTLs are valuable resources for genetic improvement of alfalfa spring vigor using marker-assisted selection (MAS), and further identification of the associated genes would provide insights into genetic control of spring regrowth in alfalfa.

Mapping of additive and epistatic QTLs linked to seed longevity in bread wheat (Triticum aestivum L.)

Plant genetic resources are stored and regenerated in > 1750 gene banks storing > 7,000,000 accessions. Since seeds are the primary storage units, research on seed longevity is of particular importance. Quantitative trait loci (QTL) analysis of 15 traits related to seed longevity and dormancy using 7584 high-quality SNPs recorded across 2 years and originated from five production years revealed a total of 46 additive QTLs. Exploration of the QTLs with epistatic effect resulted in the detection of 29 pairs of epistatic QTLs. To our information, this is only the second report of epistatic QTLs for seed longevity in bread wheat. We conclude that in addition to dense genetic maps, the epistatic interaction between loci should be considered to capture more variation which remained unnoticed in additive mapping.

Identification of quantitative trait loci underlying lodging of soybean across multiple environments

Lodging is an important agronomic trait related to crop yield and is easily susceptible to environmental influences. In this study, a recombinant inbred line population from soybean (Glycine max (L.) Merr.) Hefeng 25 × Dongnong L28 including 109 lines was used to identify quantitative trait loci (QTLs) related to soybean lodging. Seven QTLs were identified in the three environments (Harbin in 2017, 2018 and 2019), and these could explain 2.21–20.17% of the phenotypic variation. Among these QTLs, qLDG-I-1 (Chr20_24146101–Chr20_24297321) was stable for multiple environments. A residue heterozygous line, which was heterozygous at the qLDG-I-1 locus, was used to verify qLDG-I-1, and the results showed that this QTL could significantly improve lodging resistance of soybean. Meanwhile, 13 pairs of epistatic QTLs were detected, which could explain 3.26–18.24% of the phenotypic variation. QTL × environment interaction mapping was also used, and it detected 31 QTLs, which could explain 1.61–7.94% of the phenotypic variation. In total, 122 pairs of epistatic QTLs were detected, and they could explain 5.39–27.81% of the phenotypic variation. Additionally, candidate genes related to soybean lodging in the qLDG-I-1 interval were predicted, and Glyma.20g068000 was mined as a candidate gene based on quantitative real-time PCR analysis. The QTLs and candidate genes identified in this study are of great significance to position cloning, and could accelerate the progress of breeding resistance to lodging in soybean.

Quantitative traits loci mapping and molecular marker development for total glutenin and glutenin fraction contents in wheat

BackgroundGlutenin contents and compositions are crucial factors influencing the end-use quality of wheat. Although the composition of glutenin fractions is well known, there has been relatively little research on the genetic basis of glutenin fractions in wheat.ResultsTo elucidate the genetic basis for the contents of glutenin and its fractions, a population comprising 196 recombinant inbred lines (RILs) was constructed from two parents, Luozhen No.1 and Zhengyumai 9987, which differ regarding their total glutenin and its fraction contents (except for the By fraction). Forty-one additive Quantitative Trait Loci (QTL) were detected in four environments over two years. These QTL explained 1.3% - 53.4% of the phenotypic variation in the examined traits. Forty-three pairs of epistatic QTL (E-QTL) were detected in the RIL population across four environments. The QTL controlling the content of total glutenin and its seven fractions were detected in clusters. Seven clusters enriched with QTL for more than three traits were identified, including a QTL cluster 6AS-3, which was revealed as a novel genetic locus for glutenin and related traits. Kompetitive Allele-Specific PCR (KASP) markers developed from the main QTL cluster 1DL-2 and the previously developed KASP marker for the QTL cluster 6AS-3 were validated as significantly associated with the target traits in the RIL population and in natural varieties.ConclusionsThis study identified novel genetic loci related to glutenin and its seven fractions. Additionally, the developed KASP markers may be useful for the marker-assisted selection of varieties with high glutenin fraction content and for identifying individuals in the early developmental stages without the need for phenotyping mature plants. On the basis of the results of this study and the KASP markers described herein, breeders will be able to efficiently select wheat lines with favorable glutenin properties and develop elite lines with high glutenin subunit contents.

Genetic basis of maize kernel oil-related traits revealed by high-density SNP markers in a recombinant inbred line population

BackgroundMaize (Zea mays ssp. mays) is the most abundantly cultivated and highly valued food commodity in the world. Oil from maize kernels is highly nutritious and important for the diet and health of humans, and it can be used as a source of bioenergy. A better understanding of genetic basis for maize kernel oil can help improve the oil content and quality when applied in breeding.ResultsIn this study, a KUI3/SC55 recombinant inbred line (RIL) population, consisting of 180 individuals was constructed from a cross between inbred lines KUI3 and SC55. We phenotyped 19 oil-related traits and subsequently dissected the genetic architecture of oil-related traits in maize kernels based on a high-density genetic map. In total, 62 quantitative trait loci (QTLs), with 2 to 5 QTLs per trait, were detected in the KUI3/SC55 RIL population. Each QTL accounted for 6.7% (qSTOL1) to 31.02% (qBELI6) of phenotypic variation and the total phenotypic variation explained (PVE) of all detected QTLs for each trait ranged from 12.5% (OIL) to 52.5% (C16:0/C16:1). Of all these identified QTLs, only 5 were major QTLs located in three genomic regions on chromosome 6 and 9. In addition, two pairs of epistatic QTLs with additive effects were detected and they explained 3.3 and 2.4% of the phenotypic variation, respectively. Colocalization with a previous GWAS on oil-related traits, identified 19 genes. Of these genes, two important candidate genes, GRMZM2G101515 and GRMZM2G022558, were further verified to be associated with C20:0/C22:0 and C18:0/C20:0, respectively, according to a gene-based association analysis. The first gene encodes a kinase-related protein with unknown function, while the second gene encodes fatty acid elongase 2 (fae2) and directly participates in the biosynthesis of very long chain fatty acids in Arabidopsis.ConclusionsOur results provide insights on the genetic basis of oil-related traits and a theoretical basis for improving maize quality by marker-assisted selection.

Imaged‐based phenotyping accelerated QTL mapping and qtl × environment interaction analysis of testa colour in peanut (Arachis hypogaea)

AbstractTo explore the genetic mechanism underlying testa colour in peanut, three‐dimensional parameters, red (R), green (G) and blue (B) values were applied to define this trait. Image‐based phenotypic data of a recombinant inbred line population were collected from three environments and mostly exhibited continuous distributions and transgressive segregations. Significant and positive correlations, detected among RGB values and their broad‐sense heritabilities were 0.84, 0.94 and 0.87, respectively. Based on a previous high‐density genetic map, quantitative trait locus (QTL) mapping was performed. Individual environmental analysis yielded eight associated QTLs, explaining phenotypic variation from 3.29% to 35.94%. A stable and consistent region detected for R and G values was identified, covering a genomic region of 116.78–118.94 Mb on chromosome 3. Multi‐environmental joint analysis identified 12 additive QTLs, with phenotypic variation explained (PVE) of 1.88%‐34.65%. A total of 23 pairs of epistatic QTLs involving 30 loci were identified to regulate the RGB values of testa colour. Functional annotations facilitated mining the candidate genes for testa colour in the consistent QTL region.

Genetic basis of kernel starch content decoded in a maize multi-parent population.

SummaryStarch is the most abundant storage carbohydrate in maize kernels and provides calories for humans and other animals as well as raw materials for various industrial applications. Decoding the genetic basis of natural variation in kernel starch content is needed to manipulate starch quantity and quality via molecular breeding to meet future needs. Here, we identified 50 unique single quantitative trait loci (QTLs) for starch content with 18 novel QTLs via single linkage mapping, joint linkage mapping and a genome‐wide association study in a multi‐parent population containing six recombinant inbred line populations. Only five QTLs explained over 10% of phenotypic variation in single populations. In addition to a few large‐effect and many small‐effect additive QTLs, limited pairs of epistatic QTLs also contributed to the genetic basis of the variation in kernel starch content. A regional association study identified five non‐starch‐pathway genes that were the causal candidate genes underlying the identified QTLs for starch content. The pathway‐driven analysis identified ZmTPS9, which encodes a trehalose‐6‐phosphate synthase in the trehalose pathway, as the causal gene for the QTL qSTA4−2, which was detected by all three statistical analyses. Knockout of ZmTPS9 increased kernel starch content and, in turn, kernel weight in maize, suggesting potential applications for ZmTPS9 in maize starch and yield improvement. These findings extend our knowledge about the genetic basis of starch content in maize kernels and provide valuable information for maize genetic improvement of starch quantity and quality.

Genome-wide association analysis reveals quantitative trait loci and candidate genes involved in yield components under multiple field environments in cotton (Gossypium hirsutum)

BackgroundNumerous quantitative trait loci (QTLs) and candidate genes associated with yield-related traits have been identified in cotton by genome-wide association study (GWAS) analysis. However, most of the phenotypic data were from a single or few environments, and the stable loci remained to be validated under multiple field environments.ResultsHere, 242 upland cotton accessions collected from different origins were continuously investigated for phenotypic data of four main yield components, including boll weight (BW) and lint percentage (LP) under 13 field environments, and boll number per plant (BN) and seed index (SI) under 11 environments. Correlation analysis revealed a positive correlation between BN and LP, BW and SI, while SI had a negative correlation with LP and BN. Genetic analysis indicated that LP had the highest heritability estimates of 94.97%, followed by 92.08% for SI, 86.09% for BW, and 72.92% for BN, indicating LP and SI were more suitable traits for genetic improvement. Based on 56,010 high-quality single nucleotide polymorphisms (SNPs) and GWAS analysis, a total of 95 non-redundant QTLs were identified, including 12 of BN, 23 of BW, 45 of LP, and 33 of SI, respectively. Of them, 10 pairs of homologous QTLs were detected between A and D sub-genomes. We also found that 15 co-located QTLs with more than two traits and 12 high-confidence QTLs were detected under more than six environments, respectively. Further, two NET genes (GH_A08G0716 and GH_A08G0783), located in a novel QTL hotspot (qtl24, qtl25 and qlt26) were predominately expressed in early fiber development stages, exhibited significant correlation with LP and SI. The GH_A07G1389 in the stable qtl19 region encoded a tetratricopeptide repeat (TPR)-like superfamily protein and was a homologous gene involved in short fiber mutant ligon lintless-y (Liy), implying important roles in cotton yield.ConclusionsThe present study provides a foundation for understanding the regulatory mechanisms of yield components and may enhance yield improvement through molecular breeding in cotton.

Mapping QTLs for yield component traits using overwintering cultivated rice

The present study conducted QTL (quantitative trait locus) mapping using F2 and F2:3 generations derived from a cross between an overwintering cultivated rice Nuodao89-1 and Shuhui527, to identify potential yield component related QTLs. A total of 37 QTLs were detected across all chromosome except chromosome 7, with LOD values ranging from 3.10 to 12.67. Three QTLs including .qTGW3.1, qTGW6.1 and qNEG9.1 were repeatedly detected in both generations. qTGW3.1 was from Nuodao89-1, a total of 46 functional genes involved in 229 gene ontology terms were identified within its locus. Six QTL clusters were founded and corresponding agronomic traits of those QTLs showed highly significant correlation. Three types of epistatic interaction including 47 epistatic QTL pairs for eight yield component traits were detected, but the epistatic QTLs was not so important in controlling the genetic expression of the yield-related Nuodao89-1 as the additive QTLs. Overall, this research provides a theoretical basis for the mining of yield-related genes from overwintering cultivated rice.

Genetic analysis of ear‐related traits under different pollination treatments in maize (Zea mays)

AbstractEar‐related traits are crucial factors in grain yield, and understanding the genetic analysis of these traits provides important insights into maize breeding. This study derived a F5:6 recombinant inbred line (RIL) mapping population including 201 families from a cross between KA105 and KB020 lines. Genotyping by target sequencing was then used to construct a high‐density genetic map with 2,248 markers. Quantitative trait loci (QTLs) for the following six ear‐related traits were identified under four environmental conditions, including self‐pollination and open pollination, using the complete interval mapping method: ear length, fruit length, ear diameter, ear row number, kernel number per row and kernel number per ear. Forty‐eight QTLs were detected on all chromosomes, except chromosomes 7 and 9, with 4.13%–17.16% phenotypic variation explanation (PVE). Among them, five QTLs were identified across at least two environments and five QTLs were associated with more than two traits. Three pairs of epistatic QTLs were detected by single environment analysis. This research provides insights into genetic mechanisms and molecular marker‐assisted selection related to the improvement of ear‐related traits in maize.

Genetic architecture and adaptation of flowering time among environments.

The genetic basis of flowering time changes across environments, and pleiotropy may limit adaptive evolution of populations in response to local conditions. However, little information is known about how genetic architecture changes among environments. We used genome-wide association studies (GWAS) in Boechera stricta (Graham) Al-Shehbaz, a relative of Arabidopsis, to examine flowering variation among environments and associations with climate conditions in home environments. Also, we used molecular population genetics to search for evidence of historical natural selection. GWAS found 47 significant quantitative trait loci (QTLs) that influence flowering time in one or more environments, control plastic changes in phenology between experiments, or show associations with climate in sites of origin. Genetic architecture of flowering varied substantially among environments. We found that some pairs of QTLs showed similar patterns of pleiotropy across environments. A large-effect QTL showed molecular signatures of adaptive evolution and is associated with climate in home environments. The derived allele at this locus causes later flowering and predominates in sites with greater water availability. This work shows that GWAS of climate associations and ecologically important traits across diverse environments can be combined with molecular signatures of natural selection to elucidate ecological genetics of adaptive evolution.