Genetic architecture of the maize kernel row number revealed by combining QTL mapping using a high-density genetic map and bulked segregant RNA sequencing

GWAS and Meta-QTL Analysis of Yield-Related Ear Traits in Maize

Maize ear traits are an important component of yield, and the genetic basis of ear traits facilitates further yield improvement. In this study, a panel of 580 maize inbred lines were used as the study material, eight ear-related traits were measured through three years of planting, and whole genome sequencing was performed using the maize 40 K breeding chip based on genotyping by targeted sequencing (GBTS) technology. Five models were used to conduct a genome-wide association study (GWAS) on best linear unbiased estimate (BLUE) of ear traits to find the best model. The FarmCPU (Fixed and random model Circulating Probability Unification) model was the best model for this study; a total of 104 significant single nucleotide polymorphisms (SNPs) were detected, and 10 co-location SNPs were detected simultaneously in more than two environments. Through gene function annotation and prediction, a total of nine genes were identified as potentially associated with ear traits. Moreover, a total of 760 quantitative trait loci (QTL) associated with yield-related traits reported in 37 different articles were collected. Using the collected 760 QTL for meta-QTL analysis, a total of 41 MQTL (meta-QTL) associated with yield-related traits were identified, and 19 MQTL detected yield-related ear trait functional genes and candidate genes that have been reported in maize. Five significant SNPs detected by GWAS were located within these MQTL intervals, and another three significant SNPs were close to MQTL (less than 1 Mb). The results provide a theoretical reference for the analysis of the genetic basis of ear-related traits and the improvement of maize yield.

The Genetic Structures and Molecular Mechanisms Underlying Ear Traits in Maize (Zea mays L.)

Maize (Zea mays L.) is one of the world's staple food crops. In order to feed the growing world population, improving maize yield is a top priority for breeding programs. Ear traits are important determinants of maize yield, and are mostly quantitatively inherited. To date, many studies relating to the genetic and molecular dissection of ear traits have been performed; therefore, we explored the genetic loci of the ear traits that were previously discovered in the genome-wide association study (GWAS) and quantitative trait locus (QTL) mapping studies, and refined 153 QTL and 85 quantitative trait nucleotide (QTN) clusters. Next, we shortlisted 19 common intervals (CIs) that can be detected simultaneously by both QTL mapping and GWAS, and 40 CIs that have pleiotropic effects on ear traits. Further, we predicted the best possible candidate genes from 71 QTL and 25 QTN clusters that could be valuable for maize yield improvement.

Genome wide association analysis for yield related traits in maize

BACKGROUND

Understanding the genetic basis of yield related traits contributes to the improvement of grain yield in maize.

RESULTS

Using 291 excellent maize inbred lines as materials, six yield related traits of maize, including grain yield per plant (GYP), grain length (GL), grain width (GW), kernel number per row (KNR), 100 kernel weight (HKW) and tassel branch number (TBN) were investigated in Jinan, in 2017, 2018 and 2019. The average values of three environments were taken as the phenotypic data of yield related traits, and they were statistically analyzed. Based on 38,683 high-quality SNP markers in the whole genome of the association panel, the MLM with PCA model was used for genome-wide association analysis (GWAS) to obtain 59 significantly associated SNP sites. Moreover, 59 significantly associated SNPs (P < 0.0001) referring to GYP, GL, GW, KNR, HKW and TBN, of which 14 SNPs located in yield related QTLs/QTNs previously reported. A total of 66 candidate genes were identified based on the 59 significantly associated SNPs, of which 58 had functional annotation.

CONCLUSIONS

Using genome-wide association analysis strategy to identify genetic loci related to maize yield, a total of 59 significantly associated SNP were detected. Those results aid in our understanding of the genetic architecture of maize yield and provide useful SNPs for genetic improvement of maize.

Linkage mapping combined with GWAS revealed the genetic structural relationship and candidate genes of maize flowering time-related traits

BACKGROUND

Flowering time is an important agronomic trait of crops and significantly affects plant adaptation and seed production. Flowering time varies greatly among maize (Zea mays) inbred lines, but the genetic basis of this variation is not well understood. Here, we report the comprehensive genetic architecture of six flowering time-related traits using a recombinant inbred line (RIL) population obtained from a cross between two maize genotypes, B73 and Abe2, and combined with genome-wide association studies to identify candidate genes that affect flowering time.

RESULTS

Our results indicate that these six traits showed extensive phenotypic variation and high heritability in the RIL population. The flowering time of this RIL population showed little correlation with the leaf number under different environmental conditions. A genetic linkage map was constructed by 10,114 polymorphic markers covering the whole maize genome, which was applied to QTL mapping for these traits, and identified a total of 82 QTLs that contain 13 flowering genes. Furthermore, a combined genome-wide association study and linkage mapping analysis revealed 17 new candidate genes associated with flowering time.

CONCLUSIONS

In the present study, by using genetic mapping and GWAS approaches with the RIL population, we revealed a list of genomic regions and candidate genes that were significantly associated with flowering time. This work provides an important resource for the breeding of flowering time traits in maize.

Fine mapping qKRN5.04 provides a functional gene negatively regulating maize kernel row number

Zm00001d016075 was identified by fine mapping qKRN5.04. The function of Zm00001d016075, negatively modulating maize (Zea Mays L.) kernel row number (KRN), was verified by CRISPR-Cas9. InDel308 located in the promoter of Zm00001d016075 has potential for use as a molecular marker to identify KRN in maize breeding. Kernel row number (KRN), controlled by multiple quantitative trait loci (QTLs), is one of the most important traits that relate to maize production and domestication. Here, fine mapping was conducted to study a major QTL, qKRN5.04, to a 65-kb genomic region using a progeny test strategy in an advanced backcross population, in which Nong531 (N531) and the inbred line of Silunuo (SLN) were employed as the recurrent and the donor parent, respectively. Within this region, there was only one gene (Zm00001d016075) based on the B73 reference genome. Furthermore, we performed regional association mapping using a panel of 236 diverse inbred lines and observed that all significant SNPs were located within Zm00001d016075. The expression of Zm00001d016075 was significantly higher in N531 and qKRN5.04^N531 than qKRN5.04^SLN, resulting from the different promoter activity of Zm00001d016075. Sequence analysis revealed that InDel308, located in the promoter of Zm00001d016075, was related to the KRN variation in different maize inbred lines. Using the CRISPR-Cas9 strategy, we determined Zm00001d016075 played a role in negatively regulating KRN and had a moderate effect on 10-kernel width, 100-kernel weight, kernels per ear, and grain yield per ear. These results provide critical insights on the genetic basis and quantitative variation for KRN.

A major yellow-seed QTL on chromosome A09 significantly increases the oil content and reduces the fiber content of seed in Brassica napus

A major yellow-seed QTL on chromosome A09 significantly increases the oil content and reduces the fiber content of seed in Brassica napus. The yellow-seed trait (YST) has always been a main breeding objective for rapeseed because yellow-seeded B. napus generally contains higher oil contents, fewer pigments and polyphenols and lower fiber content than black-seeded B. napus, although the mechanism controlling this correlation remains unclear. In this study, QTL mapping was implemented for YST based on a KN double haploid population derived from the hybridization of yellow-seeded B. napus N53-2 with a high oil content and black-seeded Ken-C8 with a relatively low oil content. Ten QTLs were identified, including four stable QTLs that could be detected in multiple environments. A major QTL, cqSC-A09, on chromosome A09 was identified by both QTL mapping and BSR-Seq technology, and explained more than 41% of the phenotypic variance. The major QTL cqSC-A09 for YST not only controls the seed color but also affects the oil and fiber contents in seeds. More importantly, the advantageous allele could increase the oil content and reduce the pigment and fiber content at the same time. This is the first QTL reported to control seed color, oil content and fiber content simultaneously with a large effect and has great application value for breeding high oil varieties with high seed quality. Important candidate genes, including BnaA09. JAZ1, BnaA09. GH3.3 and BnaA09. LOX3, were identified for cqSC-A09 by combining sequence variation annotation, expression differences and an interaction network, which lays a foundation for further cloning and breeding applications in the future.

Genetic Architecture of Grain Yield-Related Traits in Sorghum and Maize

Grain size, grain number per panicle, and grain weight are crucial determinants of yield-related traits in cereals. Understanding the genetic basis of grain yield-related traits has been the main research object and nodal in crop science. Sorghum and maize, as very close C4 crops with high photosynthetic rates, stress tolerance and large biomass characteristics, are extensively used to produce food, feed, and biofuels worldwide. In this review, we comprehensively summarize a large number of quantitative trait loci (QTLs) associated with grain yield in sorghum and maize. We placed great emphasis on discussing 22 fine-mapped QTLs and 30 functionally characterized genes, which greatly hinders our deep understanding at the molecular mechanism level. This review provides a general overview of the comprehensive findings on grain yield QTLs and discusses the emerging trend in molecular marker-assisted breeding with these QTLs.

Quantitative Trait Loci Mapping and Development of KASP Marker Smut Screening Assay Using High-Density Genetic Map and Bulked Segregant RNA Sequencing in Sugarcane (Saccharum spp.)

Sugarcane is one of the most important industrial crops globally. It is the second largest source of bioethanol, and a major crop for biomass-derived electricity and sugar worldwide. Smut, caused by Sporisorium scitamineum, is a major sugarcane disease in many countries, and is managed by smut-resistant varieties. In China, smut remains the single largest constraint for sugarcane production, and consequently it impacts the value of sugarcane as an energy feedstock. Quantitative trait loci (QTLs) associated with smut resistance and linked diagnostic markers are valuable tools for smut resistance breeding. Here, we developed an F1 population (192 progeny) by crossing two sugarcane varieties with contrasting smut resistance and used for genome-wide single nucleotide polymorphism (SNP) discovery and mapping, using a high-throughput genotyping method called "specific locus amplified fragment sequencing (SLAF-seq) and bulked-segregant RNA sequencing (BSR-seq). SLAF-seq generated 148,500 polymorphic SNP markers. Using SNP and previously identified SSR markers, an integrated genetic map with an average 1.96 cM marker interval was produced. With this genetic map and smut resistance scores of the F1 individuals from four crop years, 21 major QTLs were mapped, with a phenotypic variance explanation (PVE) > 8.0%. Among them, 10 QTLs were stable (repeatable) with PVEs ranging from 8.0 to 81.7%. Further, four QTLs were detected based on BSR-seq analysis. aligning major QTLs with the genome of a sugarcane progenitor Saccharum spontaneum, six markers were found co-localized. Markers located in QTLs and functional annotation of BSR-seq-derived unigenes helped identify four disease resistance candidate genes located in major QTLs. 77 SNPs from major QTLs were then converted to Kompetitive Allele-Specific PCR (KASP) markers, of which five were highly significantly linked to smut resistance. The co-localized QTLs, candidate resistance genes, and KASP markers identified in this study provide practically useful tools for marker-assisted sugarcane smut resistance breeding.

Comparative Transcriptomics and RNA-Seq-Based Bulked Segregant Analysis Reveals Genomic Basis Underlying Cronartium ribicola vcr2 Virulence

Breeding programs of five-needle pines have documented both major gene resistance (MGR) and quantitative disease resistance (QDR) to Cronartium ribicola (Cri), a non-native, invasive fungal pathogen causing white pine blister rust (WPBR). WPBR is one of the most deadly forest diseases in North America. However, Cri virulent pathotypes have evolved and can successfully infect and kill trees carrying resistance (R) genes, including vcr2 that overcomes MGR conferred by the western white pine (WWP, Pinus monticola) R gene (Cr2). In the absence of a reference genome, the present study generated a vcr2 reference transcriptome, consisting of about 20,000 transcripts with 1,014 being predicted to encode secreted proteins (SPs). Comparative profiling of transcriptomes and secretomes revealed vcr2 was significantly enriched for several gene ontology (GO) terms relating to oxidation-reduction processes and detoxification, suggesting that multiple molecular mechanisms contribute to pathogenicity of the vcr2 pathotype for its overcoming Cr2. RNA-seq-based bulked segregant analysis (BSR-Seq) revealed genome-wide DNA variations, including about 65,617 single nucleotide polymorphism (SNP) loci in 7,749 polymorphic genes shared by vcr2 and avirulent (Avcr2) pathotypes. An examination of the distribution of minor allele frequency (MAF) uncovered a high level of genomic divergence between vcr2 and Avcr2 pathotypes. By integration of extreme-phenotypic genome-wide association (XP-GWAS) analysis and allele frequency directional difference (AFDD) mapping, we identified a set of vcr2-associated SNPs within functional genes, involved in fungal virulence and other molecular functions. These included six SPs that were top candidate effectors with putative activities of reticuline oxidase, proteins with common in several fungal extracellular membrane (CFEM) domain or ferritin-like domain, polysaccharide lyase, rds1p-like stress responsive protein, and two Cri-specific proteins without annotation. Candidate effectors and vcr2-associated genes provide valuable resources for further deciphering molecular mechanisms of virulence and pathogenicity by functional analysis and the subsequent development of diagnostic tools for monitoring the virulence landscape in the WPBR pathosystems.

Identification and validation of genetic locus Rfk1 for wheat fertility restoration in the presence of Aegilops kotschyi cytoplasm

Major fertility restorer locus for Aegilops kotschyi cytoplasm in wheat, Rfk1, was mapped to chromosome arm 1BS. Most likely candidate gene is TraesCS1B02G197400LC, which is predicted to encode a pectinesterase/pectinesterase inhibitor. Cytoplasmic male sterility (CMS) is widely used for heterosis and hybrid seed production in wheat. Genes related to male fertility restoration in the presence of Aegilops kotschyi cytoplasm have been reported, but the fertility restoration-associated gene loci have not been investigated systematically. In this study, a BC₁F₁ population derived from a backcross between KTP116A, its maintainer line TP116B, and its restorer line LK783 was employed to map fertility restoration by bulked segregant RNA-Seq (BSR-Seq). A major fertility allele restorer locus for Ae. kotschyi cytoplasm in wheat, Rfk1, was mapped to chromosome arm 1BS, and it was contributed by LK783. Morphological and cytological studies showed that male fertility restoration occurred mainly after the late uninucleate stage. Based on simple sequence repeat and single-nucleotide polymorphism genotyping, the gene locus was located between Xnwafu_6 and Xbarc137 on chromosome arm 1BS. To further isolate the specific region, six Kompetitive allele-specific polymerase chain reaction markers derived from BSR-Seq were developed to delimit Rfk1 within physical intervals of 26.0 Mb. After searching for differentially expressed genes within the candidate interval in the anthers and sequencing analysis, TraesCS1B02G197400LC was identified as a candidate gene for Rfk1 and it was predicted to encode a pectinesterase/pectinesterase inhibitor. Expression analysis also confirmed that it was specifically expressed in the anthers, and its expression level was higher in fertile lines compared with sterile lines. Thus, TraesCS1B02G197400LC was identified as the most likely candidate gene for Rfk1, thereby providing insights into the fertility restoration mechanism for K-type CMS in wheat.

Identification of a quantitative trait loci (QTL) associated with ammonia tolerance in the Pacific white shrimp (Litopenaeus vannamei)

Background

Ammonia is one of the most common toxicological environment factors affecting shrimp health. Although ammonia tolerance in shrimp is closely related to successful industrial production, few genetic studies of this trait are available.

Results

In this study, we constructed a high-density genetic map of the Pacific white shrimp (Litopenaeus vannamei) using specific length amplified fragment sequencing (SLAF-seq). The constructed genetic map contained 17,338 polymorphic markers spanning 44 linkage groups, with a total distance of 6360.12 centimorgans (cM) and an average distance of 0.37 cM. Using this genetic map, we identified a quantitative trait locus (QTL) that explained 7.41–8.46% of the phenotypic variance in L. vannamei survival time under acute ammonia stress. We then sequenced the transcriptomes of the most ammonia-tolerant and the most ammonia-sensitive individuals from each of four genetically distinct L. vannamei families. We found that 7546 genes were differentially expressed between the ammonia-tolerant and ammonia-sensitive individuals. Using QTL analysis and the transcriptomes, we identified one candidate gene (annotated as an ATP synthase g subunit) associated with ammonia tolerance.

Conclusions

In this study, we constructed a high-density genetic map of L. vannamei and identified a QTL for ammonia tolerance. By combining QTL and transcriptome analyses, we identified a candidate gene associated with ammonia tolerance. Our work provides the basis for future genetic studies focused on molecular marker-assisted selective breeding.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-020-07254-x.

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.

Maize BIG GRAIN1 homolog overexpression increases maize grain yield

The Zea Mays BIG GRAIN 1 HOMOLOG 1 (ZM-BG1H1) was ectopically expressed in maize. Elite commercial hybrid germplasm was yield tested in diverse field environment locations representing commercial models. Yield was measured in 101 tests across all 4 events, 26 locations over 2 years, for an average yield gain of 355 kg/ha (5.65 bu/ac) above control, with 83% tests broadly showing yield gains (range +2272 kg/ha to -1240 kg/ha), with seven tests gaining more than one metric ton per hectare. Plant and ear height were slightly elevated, and ear and tassel flowering time were delayed one day, but ASI was unchanged, and these traits did not correlate to yield gain. ZM-BG1H1 overexpression is associated with increased ear kernel row number and total ear kernel number and mass, but individual kernels trended slightly smaller and less dense. The ZM-BG1H1 protein is detected in the plasma membrane like rice OS-BG1. Five predominant native ZM-BG1H1 alleles exhibit little structural and expression variation compared to the large increased expression conferred by these ectopic alleles.

Identification of fruit size associated quantitative trait loci featuring SLAF based high-density linkage map of goji berry (Lycium spp.)

BACKGROUND

Goji (Lycium spp., 2n = 24) is a fruit bearing woody plant popular as a superfood for extensive medicinal and nutritional advantages. Fruit size associated attributes are important for evaluating small-fruited goji berry and plant architecture. The domestication traits are regulated quantitatively in crop plants but few studies have attempted on genomic regions corresponding to fruit traits.

RESULTS

In this study, we established high-resolution map using specific locus amplified fragment (SLAF) sequencing for de novo SNPs detection based on 305 F₁ individuals derived from L. chinense and L. barbarum and performed quantitative trait loci (QTL) analysis of fruit size related traits in goji berry. The genetic map contained 3495 SLAF markers on 12 LGs, spanning 1649.03 cM with 0.47 cM average interval. Female and male parents and F₁ individuals` sequencing depth was 111.85-fold and 168.72-fold and 35.80-fold, respectively. The phenotype data were collected for 2 successive years (2018-2019); however, two-year mean data were combined in an extra year (1819). Total 117 QTLs were detected corresponding to multiple traits, of which 78 QTLs in 2 individual years and 36 QTLs in extra year. Six Promising QTLs (qFW10-6.1, qFL10-2.1, qLL10-2.1, qLD10-2.1, qLD12-4.1, qLA10-2.1) were discovered influencing fruit weight, fruit length and leaf related attributes covering an interval ranged from 27.32-71.59 cM on LG10 with peak LOD of 10.48 and 14.6% PVE. Three QTLs targeting fruit sweetness (qFS3-1, qFS5-2) and fruit firmness (qFF10-1) were also identified. Strikingly, various traits QTLs were overlapped on LG10, in particular, qFL10-2.1 was co-located with qLL10-2.1, qLD10-2.1 and qLA10-2.1 among stable QTLs, harbored tightly linked markers, while qLL10-1 was one major QTL with 14.21 highest LOD and 19.3% variance. As LG10 harbored important traits QTLs, we might speculate that it could be hotspot region regulating fruit size and plant architectures.

CONCLUSIONS

This report highlights the extremely saturated linkage map using SLAF-seq and novel loci contributing fruit size-related attributes in goji berry. Our results will shed light on domestication traits and further strengthen molecular and genetic underpinnings of goji berry; moreover, these findings would better facilitate to assemble the reference genome, determining potential candidate genes and marker-assisted breeding.

A combination of linkage mapping and GWAS brings new elements on the genetic basis of yield-related traits in maize across multiple environments

Using GWAS and QTL mapping identified 100 QTL and 138 SNPs, which control yield-related traits in maize. The candidate gene GRMZM2G098557 was further validated to regulate ear row number by using a segregation population. Understanding the genetic basis of yield-related traits contributes to the improvement of grain yield in maize. This study used an inter-mated B73 × Mo17 (IBM) Syn10 doubled-haploid (DH) population and an association panel to identify the genetic loci responsible for nine yield-related traits in maize. Using quantitative trait loci (QTL) mapping, 100 QTL influencing these traits were detected across different environments in the IBM Syn10 DH population, with 25 co-detected in multiple environments. Using a genome-wide association study (GWAS), 138 single-nucleotide polymorphisms (SNPs) were identified as correlated with these traits (P < 2.04E-06) in the association panel. Twenty-one pleiotropic QTL/SNPs were identified to control different traits in both populations. A combination of QTL mapping and GWAS uncovered eight significant SNPs (PZE-101097575, PZE-103169263, ZM011204-0763, PZE-104044017, PZE-104123110, SYN8062, PZE-108060911, and PZE-102043341) that were co-located within seven QTL confidence intervals. According to the eight co-localized SNPs by the two populations, 52 candidate genes were identified, among which the ear row number (ERN)-associated SNP SYN8062 was closely linked to SBP-transcription factor 7 (GRMZM2G098557). Several SBP-transcription factors were previously demonstrated to modulate maize ERN. We then validated the phenotypic effects of SYN8062 in the IBM Syn10 DH population, indicating that the ERN of the lines with the A-allele in SYN8062 was significantly (P < 0.05) larger than that of the lines with the G-allele in SYN8062 in each environment. These findings provide valuable information for understanding the genetic mechanisms of maize grain yield formation and for improving molecular marker-assisted selection for the high-yield breeding of maize.

Identification of a genomic region controlling thermotolerance at flowering in maize using a combination of whole genomic re-sequencing and bulked segregant analysis

A novel genomic region controlling thermotolerance at flowering was identified by the combination of whole genomic re-sequencing and bulked segregant analysis in maize. The increasing frequency of extreme high temperature has brought a great threat to the development of maize throughout its life cycle, especially during the flowering phase. However, the genetic basis of thermotolerance at flowering in maize remains poorly understood. Here, we characterized a thermotolerant maize ecotype Abe2 and dissected its genetic basis using a F_2:8 recombinant inbred line (RIL) population generated from a cross between Abe2 and B73. After continuous high temperature stress above 35 °C for 17 days, Abe2 and B73 show distinct leaf scorching phenotype under field conditions. To identify the genomic regions associated with the phenotypic variation, we applied a combination of whole genomic re-sequencing and bulked segregant analysis, and revealed 10,316,744 SNPs and 1,488,302 InDels between the two parental lines, and 2,693,054 SNPs and 313,757 InDels between the two DNA pools generated from the thermos-tolerant and the sensitive individuals of the RIL, of which, 108,655 and 17,853 SNPs may cause nonsynonymous variations. Finally, a 7.41 Mb genomic region on chromosome 1 was identified, and 7 candidate genes were annotated to participate in high temperature-related stress response. A candidate gene Zm00001d033339 encoding a serine/threonine protein kinase was proposed to be the most likely causative gene contributing to the thermotolerance at flowering by involving in stomatal movement (GO: 0010119) via Abscisic acid (ABA) pathway (KO04075). This work could provide an opportunity for gene cloning and pyramiding breeding to improve thermotolerance at flowering in maize.

Development of introgression lines in high yielding, semi-dwarf genetic backgrounds to enable improvement of modern rice varieties for tolerance to multiple abiotic stresses free from undesirable linkage drag

Occurrence of multiple abiotic stresses in a single crop season has become more frequent than before. Most of the traditional donors possessing tolerance to abiotic stresses are tall, low-yielding with poor grain quality. To facilitate efficient use of complex polygenic traits in rice molecular breeding research, we undertook development of introgression lines in background of high-yielding, semi-dwarf varieties with good grain quality. The study reports the development and evaluations of over 25,000 introgression lines in eleven elite rice genetic backgrounds for improvement of yield under multiple abiotic-stresses such as drought, flood, high/low temperature. The developed introgression lines within each genetic background are near isogenic/recombinant inbred lines to their recipient recurrent parent with 50 to 98% background recovery and additionally carry QTLs/genes for abiotic stresses. The multiple-stress tolerant pyramided breeding lines combining high yield under normal situation and good yield under moderate to severe reproductive-stage drought, semi-dwarf plant type with good grain quality traits have been developed. The introgression lines in dwarf backgrounds open new opportunity to improve other varieties without any linkage drag as well as facilitate cloning of QTLs, identification and functional characterization of candidate genes, mechanisms associated with targeted QTLs and the genetic networks underlying complex polygenic traits.

Predicting Zea mays Flowering Time, Yield, and Kernel Dimensions by Analyzing Aerial Images

Image analysis methods for measuring crop phenotypes may replace traditional measurements if they more efficiently and reliably capture similar or superior information. This study used a recreational-grade unmanned aerial vehicle carrying a spectrally-modified consumer-grade camera to collect images in which each pixel value is a vegetation index based on the normalized difference between the blue and near infrared wavelength bands (BNDVI). The subjects of the study were Zea mays hybrids with good yield potential grown in 4-row plots. Flights were conducted at least once per week during three successive growing seasons in south-central Wisconsin. Average BNDVI for each plot (genotype) rose steadily through June, peaked in July, and then declined as plants matured. BNDVI histograms changed shape over the season as the canopy concealed soil, became more uniformly green, then senesced. Principal Components Analysis (PCA) captured the change in histogram shape. PC1 represented canopy closure. PC2 represented the mean of the BNDVI distribution. PC3 represented the spread of the distribution. Correlation analysis showed that flowering time correlated with PC2 and PC3 best (r ≈ 0.5) a few days before the event (day in which 50% of the plants exhibited tassels). Three ears were picked from each plot to quantify kernel dimensions by image analysis before each plot was mechanically harvested to determine grain weight per plot. Correlations between this measurement of yield and PC2 were low in June but exceeded 0.4 within 10 days after flowering. Kernel length correlated similarly with PC2. The correlation between PC2 and kernel thickness displayed a similar but inverted time course. These results indicate that greater mid-season BNDVI values correlate positively with yield comprised of tall, thin kernels. Partial least squares regression performed on the BNDVI time courses predicted flowering time (r = 0.54-0.79) and yield (r = 0.4-0.69). This three-year experiment demonstrated that readily available hardware and software can create a phenotyping platform capable of predicting maize flowering time, yield, and kernel dimensions to a useful degree.

Genome-wide association study of vitamin E using genotyping by sequencing in sesame (Sesamum indicum)

BACKGROUND

At least eight structurally related forms of vitamin E occur in nature, four tocopherols and four tocotrienols, all of which are potent membrane-soluble antioxidants. In this study, we detected two major isoforms in sesame (Sesamum indicum L.) seed: γ-tocopherol and β-tocotrienol. The objective of this study is to investigate the genetic basis of these vitamin E isoforms.

METHODS

We  conducted a genome-wide association study (GWAS) using 5962 genome-wide markers, acquired from 96 core sesame accessions. The GWAS was performed using generalized linear (GLM) and mixed linear (MLM) models.

RESULTS

LG08_6621957, on chromosome 8, was detected as having a significant association with γ-tocopherol in both models. It explained 20.9% of γ-tocopherol variation in sesame. For β-tocotrienol, no significant loci were detected according to the two models, but one locus, SLG03_13104062, explained 17.8% of the phenotypic variation. Based on structure and phylogenetic studies, the 96 accessions were clearly clustered into two subpopulations.

CONCLUSION

This study on sesame demonstrates and provides an evidence that genotyping by sequencing (GBS) based GWAS can be used to identifying important loci for small growing crops. The significant SNPs or genes could be useful for improving the vitamin E content in sesame breeding programs.

High-density genetic map construction and QTL mapping of first flower node in pepper (Capsicum annuum L.)

BACKGROUND

First flower node (FFN) is an important trait for evaluating fruit earliness in pepper (Capsicum annuum L.). The trait is controlled by quantitative trait loci (QTL); however, studies have been limited on QTL mapping and genes contributing to the trait.

RESULTS

In this study, we developed a high density genetic map using specific-locus amplified fragment sequencing (SLAF-seq), a high-throughput strategy for de novo single nucleotide polymorphism discovery, based on 146 recombinant inbred lines (RILs) derived from an intraspecific cross between PM702 and FS871. The map contained 9328 SLAF markers on 12 linkage groups (LGs), and spanned a total genetic distance of 2009.69 centimorgan (cM) with an average distance of 0.22 cM. The sequencing depth for the map was 72.39-fold in the male parent, 57.04-fold in the female parent, and 15.65-fold in offspring. Using the genetic map, two major QTLs, named Ffn2.1 and Ffn2.2, identified on LG02 were strongly associated with FFN, with a phenotypic variance explanation of 28.62 and 19.56%, respectively. On the basis of the current annotation of C. annuum cv. Criollo de Morelos (CM334), 59 candidate genes were found within the Ffn2.1 and Ffn2.2 region, but only 3 of 59 genes were differentially expressed according to the RNA-seq results. Eventually we identified one gene associated with the FFN based on the function through GO, KEGG, and Swiss-prot analysis.

CONCLUSIONS

Our research showed that the construction of high-density genetic map using SLAF-seq is a valuable tool for fine QTL mapping. The map we constructed is by far the most saturated complete genetic map of pepper, and using it we conducted fine QTL mapping for the important trait, FFN. QTLs and candidate genes obtained in this study lay a good foundation for the further research on FFN-related genes and other genetic applications in pepper.

Genome-Wide Association Mapping and Genomic Prediction Analyses Reveal the Genetic Architecture of Grain Yield and Flowering Time Under Drought and Heat Stress Conditions in Maize

Drought stress (DS) is a major constraint to maize yield production. Heat stress (HS) alone and in combination with DS are likely to become the increasing constraints. Association mapping and genomic prediction (GP) analyses were conducted in a collection of 300 tropical and subtropical maize inbred lines to reveal the genetic architecture of grain yield and flowering time under well-watered (WW), DS, HS, and combined DS and HS conditions. Out of the 381,165 genotyping-by-sequencing SNPs, 1549 SNPs were significantly associated with all the 12 trait-environment combinations, the average PVE (phenotypic variation explained) by these SNPs was 4.33%, and 541 of them had a PVE value greater than 5%. These significant associations were clustered into 446 genomic regions with a window size of 20 Mb per region, and 673 candidate genes containing the significantly associated SNPs were identified. In addition, 33 hotspots were identified for 12 trait-environment combinations and most were located on chromosomes 1 and 8. Compared with single SNP-based association mapping, the haplotype-based associated mapping detected fewer number of significant associations and candidate genes with higher PVE values. All the 688 candidate genes were enriched into 15 gene ontology terms, and 46 candidate genes showed significant differential expression under the WW and DS conditions. Association mapping results identified few overlapped significant markers and candidate genes for the same traits evaluated under different managements, indicating the genetic divergence between the individual stress tolerance and the combined drought and HS tolerance. The GP accuracies obtained from the marker-trait associated SNPs were relatively higher than those obtained from the genome-wide SNPs for most of the target traits. The genetic architecture information of the grain yield and flowering time revealed in this study, and the genomic regions identified for the different trait-environment combinations are useful in accelerating the efforts on rapid development of the stress-tolerant maize germplasm through marker-assisted selection and/or genomic selection.

QTL Mapping Combined With Bulked Segregant Analysis Identify SNP Markers Linked to Leaf Shape Traits in Pisum sativum Using SLAF Sequencing

Leaf shape is an important trait that influences the utilization rate of light, and affects quality and yield of pea (Pisum sativum). In the present study, a joint method of high-density genetic mapping using specific locus amplified fragment sequencing (SLAF-seq) and bulked segregant analysis (BSA) was applied to rapidly detect loci with leaf shape traits. A total of 7,146 polymorphic SLAFs containing 12,213 SNP markers were employed to construct a high-density genetic map for pea. We conducted quantitative trait locus (QTL) mapping on an F2 population to identify QTLs associated with leaf shape traits. Moreover, SLAF-BSA was conducted on the same F2 population to identify the single nucleotide polymorphism (SNP) markers linked to leaf shape in pea. Two QTLs (qLeaf_or-1, qLeaf_or-2) were mapped on linkage group 7 (LG7) for pea leaf shape. Through alignment of SLAF markers with Cicer arietinum, Medicago truncatula, and Glycine max, the pea LGs were assigned to their corresponding homologous chromosomal groups. The comparative genetic analysis showed that pea is more closely related to M. truncatula. Based on the sequencing results of two pools with different leaf shape, 179 associated markers were obtained after association analysis. The joint analysis of SLAF-seq and BSA showed that the QTLs obtained from mapping on a high-density genetic map are convincing due to the closely associated map region with the BSA results, which provided more potential markers related to leaf shape. Thus, the identified QTLs could be used in marker-assisted selection for pea breeding in the future. Our study revealed that joint analysis of QTL mapping on a high-density genetic map and BSA-seq is a cost-effective and accurate method to reveal genetic architecture of target traits in plant species without a reference genome.

Linkage mapping combined with association analysis reveals QTL and candidate genes for three husk traits in maize

Key message Combined linkage and association mapping analyses facilitate the emphasis on the candidate genes putatively involved in maize husk growth. The maize (Zea mays L.) husk consists of multiple leafy layers and plays important roles in protecting the ear from pathogen infection and in preventing grain dehydration. Although husk morphology varies widely among different maize inbred lines, the genetic basis of such variation is poorly understood. In this study, we used three maize recombinant inbred line (RIL) populations to dissect the genetic basis of three husk traits: i.e., husk length (HL), husk width (HW), and the number of husk layers (HN). Three husk traits in all three RIL populations showed wide phenotypic variation and high heritability. The HL showed stronger correlations with ear traits than did HW and HN. A total of 21 quantitative trait loci (QTL) were identified for the three traits in three RIL populations, and some of them were commonly observed for the same trait in different populations. The proportions of total phenotypic variation explained by QTL in three RIL populations were 31.8, 35.3, and 44.5% for HL, HW, and HN, respectively. The highest proportions of phenotypic variation explained by a single QTL were 14.7% for HL in the By815/K22 RIL population (BYK), 13.5% for HW in the By815/DE3 RIL population (BYD), and 19.4% for HN in the BYD population. A combined analysis of linkage mapping with a previous genome-wide association study revealed five candidate genes related to husk morphology situated within three QTL loci. These five genes were related to metabolism, gene expression regulation, and signal transduction.

Differentially Expressed Genes Associated with the Cabbage Yellow-Green-Leaf Mutant in the ygl-1 Mapping Interval with Recombination Suppression

Although the genetics and preliminary mapping of the cabbage yellow-green-leaf mutant YL-1 has been extensively studied, transcriptome profiling associated with the yellow-green-leaf mutant of YL-1 has not been discovered. Positional mapping with two populations showed that the yellow-green-leaf gene ygl-1 is located in a recombination-suppressed genomic region. Then, a bulk segregant RNA-seq (BSR) was applied to identify differentially expressed genes (DEGs) using an F₃ population (YL-1 × 11-192) and a BC₂ population (YL-1 × 01-20). Among the 37,286 unique genes, 5730 and 4118 DEGs were detected between the yellow-leaf and normal-leaf pools from the F₃ and BC₂ populations. BSR analysis with four pools greatly reduced the number of common DEGs from 4924 to 1112. In the ygl-1 gene mapping region with suppressed recombination, 43 common DEGs were identified. Five of the DEGs were related to chloroplasts, including the down-regulated Bo1g087310, Bo1g094360, and Bo1g098630 and the up-regulated Bo1g059170 and Bo1g098440. The Bo1g098440 and Bo1g098630 genes were excluded by qRT-PCR. Hence, we inferred that these three DEGs (Bo1g094360, Bo1g087310, and Bo1g059170) in the mapping interval may be tightly associated with the development of the yellow-green-leaf mutant phenotype.

Characterization of a Powdery Mildew Resistance Gene in Wheat Breeding Line 10V-2 and Its Application in Marker-Assisted Selection

Powdery mildew, caused by Blumeria graminis f. sp. tritici, is a serious disease of wheat (Triticum aestivum L.) throughout the world. Host resistance is the most effective and preferred means for managing this disease. Line 10V-2, a wheat breeding line with superior agronomic performance, shows broad-spectrum seedling resistance to powdery mildew. Genetic analysis demonstrated that its resistance was controlled by a single dominant gene, tentatively designated Pm10V-2. This gene was localized near the documented Pm2 locus on chromosome 5DS using the simple sequence repeat (SSR) marker Cfd81. To saturate the marker map of Pm10V-2, more markers were developed using bulked segregant RNA-Seq. Two single-nucleotide polymorphism (SNP) markers (Swgi047 and Swgi064), three expressed sequence tag markers (Swgi007, Swgi029, and Swgi038), and one SSR marker (Swgi066) were polymorphic between the resistant and susceptible bulks and showed tightly linked to the Pm10V-2 gene. Pm10V-2 was flanked by the new developed markers Swgi064 and Swgi066 at genetic distances of 0.4 and 1.2 centimorgans (cM), respectively, and cosegregated with Swgi007 and Swgi038. The homologous sequence of Pm2a was cloned from 10V-2 based on a recent study. Although the sequence cloned from 10V-2 was completely identical to that of the reported Pm2a-related gene, they did not cosegregate but were separated at a genetic distance of 1.6 cM, indicating that Pm10V-2 was different from the reported of Pm2a-related gene. When inoculated with multiple B. graminis f. sp. tritici isolates, Pm10V-2 had a significantly different resistance spectrum from Pm2a and other powdery mildew (Pm) resistance genes at or near the Pm2 locus. Therefore, Pm10V-2 may be a new Pm2 allele or Pm2-linked gene. To use Pm10V-2 in marker-assisted selection (MAS) breeding, seven markers applicable for MAS were confirmed, including three newly developed markers (Swgi029, Swgi038, and Swgi064) in the present work. Using these markers, a great number of resistant lines with desirable agronomic performance were selected from crosses involving 10V-2, including the breeding line KM5016, which has been entered in the Regional trials in Hebei Province, China.

Screening of Genes Related to Early and Late Flowering in Tree Peony Based on Bulked Segregant RNA Sequencing and Verification by Quantitative Real-Time PCR

Tree peony (Paeonia suffruticosa Andrews) is a perennial woody shrub bearing large and colorful flowers. However, the flowering period is short and relatively uniform, which to an important extent hinders the cultivation and exploitation of ornamental peonies. In this study, the segregation of an F₁ population derived from P. ostti ‘Feng Dan’ (an early-flowering cultivar) × P. suffruticosa ‘Xin Riyuejin’ (a late-flowering cultivar) was used to screen and analyze candidate genes associated with flowering period of the two parents. Extreme early- and late-flowering genotypes of the F₁ population at full-bloom stage were sampled to establish an early-flowering mixed pool (T03), a late-flowering mixed pool (T04), a late-flowering male pool (T01), and an early-flowering female pool (T02), using the Sequencing By Synthesis (SBS) technology on the Illumina HiSeq TM2500 platform. A total of 56.51 Gb of clean reads data, comprising at least 87.62% of Quality30 (Q30), was generated, which was then combined into 173,960 transcripts (N50 = 1781) and 78,645 (N50 = 1282) unigenes, with a mean length of 1106.76 and 732.27 base pairs (bp), respectively. Altogether, 58,084 genes were annotated by comparison with public databases, based on an E-value parameter of less than 10⁻⁵ and 10⁻¹⁰ for BLAST and HMMER, respectively. In total, 291 unigene sequences were finally screened out by BSR-seq (bulked segregant RNA-seq) association analysis. To validate these unigenes, we finally confirmed seven unigenes that were related to early and late flowering, which were then verified by quantitative real-time PCR (qRT-PCR). This is the first reported study to screen genes associated with early and late flowering of tree peony by the BSA (bulked sample analysis) method of transcriptome sequencing, and to construct a high-quality transcriptome database. A set of candidate functional genes related to flowering time was successfully obtained, providing an important genetic resource for further studies of flowering in peony and the mechanism of regulation of flowering time in tree peony.

A multiple near isogenic line (multi-NIL) RNA-seq approach to identify candidate genes underpinning QTL

This study demonstrates how identification of genes underpinning disease-resistance QTL based on differential expression and SNPs can be improved by performing transcriptomic analysis on multiple near isogenic lines. Transcriptomic analysis has been widely used to understand the genetic basis of a trait of interest by comparing genotypes with contrasting phenotypes. However, these approaches identify such large sets of differentially expressed genes that it proves difficult to isolate which genes underpin the phenotype of interest. This study tests whether using multiple near isogenic lines (NILs) can improve the resolution of RNA-seq-based approaches to identify genes underpinning disease-resistance QTL. A set of NILs for a major effect Fusarium crown rot-resistance QTL in barley on the 4HL chromosome arm were analysed under Fusarium crown rot using RNA-seq. Differential gene expression and single nucleotide polymorphism detection analyses reduced the number of putative candidates from thousands within individual NIL pairs to only one hundred and two genes, which were differentially expressed or contained SNPs in common across NIL pairs and occurred on 4HL. Our findings support the value of performing RNA-seq analysis using multiple NILs to remove genetic background effects. The enrichment analyses indicated conserved differences in the response to infection between resistant and sensitive isolines suggesting that sensitive isolines are impaired in systemic defence response to Fusarium pseudograminearum.

Construction of a High-Density Genetic Map from RNA-Seq Data for an Arabidopsis Bay-0 × Shahdara RIL Population

High-density genetic maps are essential for high resolution mapping of quantitative traits. Here, we present a new genetic map for an Arabidopsis Bayreuth × Shahdara recombinant inbred line (RIL) population, built on RNA-seq data. RNA-seq analysis on 160 RILs of this population identified 30,049 single-nucleotide polymorphisms (SNPs) covering the whole genome. Based on a 100-kbp window SNP binning method, 1059 bin-markers were identified, physically anchored on the genome. The total length of the RNA-seq genetic map spans 471.70 centimorgans (cM) with an average marker distance of 0.45 cM and a maximum marker distance of 4.81 cM. This high resolution genotyping revealed new recombination breakpoints in the population. To highlight the advantages of such high-density map, we compared it to two publicly available genetic maps for the same population, comprising 69 PCR-based markers and 497 gene expression markers derived from microarray data, respectively. In this study, we show that SNP markers can effectively be derived from RNA-seq data. The new RNA-seq map closes many existing gaps in marker coverage, saturating the previously available genetic maps. Quantitative trait locus (QTL) analysis for published phenotypes using the available genetic maps showed increased QTL mapping resolution and reduced QTL confidence interval using the RNA-seq map. The new high-density map is a valuable resource that facilitates the identification of candidate genes and map-based cloning approaches.

The Beavis Effect in Next-Generation Mapping Panels in Drosophila melanogaster

A major goal in the analysis of complex traits is to partition the observed genetic variation in a trait into components due to individual loci and perhaps variants within those loci. However, in both QTL mapping and genetic association studies, the estimated percent variation attributable to a QTL is upwardly biased conditional on it being discovered. This bias was first described in two-way QTL mapping experiments by William Beavis, and has been referred to extensively as “the Beavis effect.” The Beavis effect is likely to occur in multiparent population (MPP) panels as well as collections of sequenced lines used for genome-wide association studies (GWAS). However, the strength of the Beavis effect is unknown—and often implicitly assumed to be negligible—when “hits” are obtained from an association panel consisting of hundreds of inbred lines tested across millions of SNPs, or in multiparent mapping populations where mapping involves fitting a complex statistical model with several d.f. at thousands of genetic intervals. To estimate the size of the effect in more complex panels, we performed simulations of both biallelic and multiallelic QTL in two major Drosophila melanogaster mapping panels, the GWAS-based Drosophila Genetic Reference Panel (DGRP), and the MPP the Drosophila Synthetic Population Resource (DSPR). Our results show that overestimation is determined most strongly by sample size and is only minimally impacted by the mapping design. When < 100, 200, 500, and 1000 lines are employed, the variance attributable to hits is inflated by factors of 6, 3, 1.5, and 1.1, respectively, for a QTL that truly contributes 5% to the variation in the trait. This overestimation indicates that QTL could be difficult to validate in follow-up replication experiments where additional individuals are examined. Further, QTL could be difficult to cross-validate between the two Drosophila resources. We provide guidelines for: (1) the sample sizes necessary to accurately estimate the percent variance to an identified QTL, (2) the conditions under which one is likely to replicate a mapped QTL in a second study using the same mapping population, and (3) the conditions under which a QTL mapped in one mapping panel is likely to replicate in the other (DGRP and DSPR).