Characterization of the main effects, epistatic effects and their environmental interactions of QTLs on the genetic basis of yield traits in rice

Genomic introgressions from African rice (Oryza glaberrima) in Asian rice (O. sativa) lead to the identification of key QTLs for panicle architecture

BACKGROUND

Developing high yielding varieties is a major challenge for breeders tackling the challenges of climate change in agriculture. The panicle (inflorescence) architecture of rice is one of the key components of yield potential and displays high inter- and intra-specific variability. The genus Oryza features two different crop species: Asian rice (Oryza sativa L.) and the African rice (O. glaberrima Steud.). One of the main morphological differences between the two independently domesticated species is the structure (or complexity) of the panicle, with O. sativa displaying a highly branched panicle, which in turn produces a larger number of grains than that of O. glaberrima. The gene regulatory network that governs intra- and interspecific panicle diversity is still under-studied.

RESULTS

To identify genetic factors linked to panicle architecture diversity in the two species, we used a set of 60 Chromosome Segment Substitution Lines (CSSLs) issued from third generation backcross (BC3DH) and carrying genomic segments from O. glaberrima cv. MG12 in the genetic background of O. sativa Tropical Japonica cv. Caiapó. Phenotypic data were collected for rachis and primary branch length, primary, secondary and tertiary branch number and spikelet number. A total of 15 QTLs were localized on chromosomes 1, 2, 3, 7, 11 and 12, QTLs associated with enhanced secondary and tertiary branch numbers were detected in two CSSLs. Furthermore, BC4F3:5 lines carrying different combinations of substituted segments were produced to decipher the effects of the identified QTL regions on variations in panicle architecture. A detailed analysis of phenotypes versus genotypes was carried out between the two parental genomes within these regions in order to understand how O. glaberrima introgression events may lead to alterations in panicle traits.

CONCLUSION

Our analysis led to the detection of genomic variations between O. sativa cv. Caiapó and O. glaberrima cv. MG12 in regions associated with enhanced panicle traits in specific CSSLs. These regions contain a number of key genes that regulate panicle development in O. sativa and their interspecific genomic variations may explain the phenotypic effects observed.

Mining the candidate genes of rice panicle traits via a genome-wide association study

Panicle traits are important for improving the panicle architecture and grain yield of rice. Therefore, we performed a genome-wide association study (GWAS) to analyze and determine the genetic determinants of five panicle traits. A total of 1.29 million single nucleotide polymorphism (SNP) loci were detected in 162 rice materials. We carried out a GWAS of panicle length (PL), total grain number per panicle (TGP), filled grain number per panicle (FGP), seed setting rate (SSR) and grain weight per panicle (GWP) in 2019, 2020 and 2021. Four quantitative trait loci (QTLs) for PL were detected on chromosomes 1, 6, and 9; one QTL for TGP, FGP, and GWP was detected on chromosome 4; two QTLs for FGP were detected on chromosomes 4 and 7; and one QTL for SSR was detected on chromosome 1. These QTLs were detected via a general linear model (GLM) and mixed linear model (MLM) in both years of the study period. In this study, the genomic best linear unbiased prediction (BLUP) method was used to verify the accuracy of the GWAS results. There are nine QTLs were both detected by the multi-environment GWAS method and the BLUP method. Moreover, further analysis revealed that three candidate genes, LOC_Os01g43700, LOC_Os09g25784, and LOC_Os04g47890, may be significantly related to panicle traits of rice. Haplotype analysis indicated that LOC_Os01g43700 and LOC_Os09g25784 are highly associated with PL and that LOC_Os04g47890 is highly associated with TGP, FGP, and GWP. Our results offer essential genetic information for the molecular improvement of panicle traits. The identified candidate genes and elite haplotypes could be used in marker-assisted selection to improve rice yield through pyramid breeding.

Molecular mapping and characterization of QTLs for grain quality traits in a RIL population of US rice under high nighttime temperature stress

Elevated nighttime temperatures resulting from climate change significantly impact the rice crop worldwide. The rice (Oryza sativa L.) plant is highly sensitive to high nighttime temperature (HNT) during grain-filling (reproductive stage). HNT stress negatively affects grain quality traits and has a major impact on the value of the harvested rice crop. In addition, along with grain dimensions determining rice grain market classes, the grain appearance and quality traits determine the rice grain market value. During the last few years, there has been a major concern for rice growers and the rice industry over the prevalence of rice grains opacity and the reduction of grain dimensions affected by HNT stress. Hence, the improvement of heat-stress tolerance to maintain grain quality of the rice crop under HNT stress will bolster future rice value in the market. In this study, 185 F₁₂-recombinant inbred lines (RILs) derived from two US rice cultivars, Cypress (HNT-tolerant) and LaGrue (HNT-sensitive) were screened for the grain quality traits grain length (GL), grain width (GW), and percent chalkiness (%chalk) under control and HNT stress conditions and evaluated to identify the genomic regions associated with the grain quality traits. In total, there were 15 QTLs identified; 6 QTLs represented under control condition explaining 3.33% to 8.27% of the phenotypic variation, with additive effects ranging from - 0.99 to 0.0267 on six chromosomes and 9 QTLs represented under HNT stress elucidating 6.39 to 51.53% of the phenotypic variation, with additive effects ranging from - 8.8 to 0.028 on nine chromosomes for GL, GW, and % chalk. These 15 QTLs were further characterized and scanned for natural genetic variation in a japonica diversity panel (JDP) to identify candidate genes for GL, GW, and %chalk. We found 6160 high impact single nucleotide polymorphisms (SNPs) characterized as such depending on their type, region, functional class, position, and proximity to the gene and/or gene features, and 149 differentially expressed genes (DEGs) in the 51 Mbp genomic region comprising of the 15 QTLs. Out of which, 11 potential candidate genes showed high impact SNP associations. Therefore, the analysis of the mapped QTLs and their genetic dissection in the US grown Japonica rice genotypes at genomic and transcriptomic levels provide deep insights into genetic variation beneficial to rice breeders and geneticists for understanding the mechanisms related to grain quality under heat stress in rice.

Construction of a high-density genetic map and dissection of genetic architecture of six agronomic traits in tobacco (Nicotiana tabacum L.)

Tobacco (Nicotiana tabacum L.) is an economic crop and a model organism for studies on plant biology and genetics. A population of 271 recombinant inbred lines (RIL) derived from K326 and Y3, two elite flue-cured tobacco parents, has been constructed to investigate the genetic basis of agronomic traits in tobacco. Six agronomic traits including natural plant height (nPH), natural leaf number (nLN), stem girth (SG), inter-node length (IL), length of the largest leaf (LL) and width of the largest leaf (LW) were measured in seven environments, spanning the period between 2018 and 2021. We firstly developed an integrated SNP-indel-SSR linkage map with 43,301 SNPs, 2,086 indels and 937 SSRs, which contained 7,107 bin markers mapped on 24 LGs and covered 3334.88 cM with an average genetic distance of 0.469cM. Based on this high-density genetic map, a total of 70 novel QTLs were detected for six agronomic traits by a full QTL model using the software QTLNetwork, of which 32 QTLs showed significant additive effects, 18 QTLs showed significant additive-by-environment interaction effects, 17 pairs showed significant additive-by-additive epistatic effects and 13 pairs showed significant epistasis-by-environment interaction effects. In addition to additive effect as a major contributor to genetic variation, both epistasis effects and genotype-by-environment interaction effects played an important role in explaining phenotypic variation for each trait. In particular, qnLN6-1 was detected with considerably large main effect and high heritability ( h a 2 =34.80%). Finally, four genes including Nt16g00284.1, Nt16g00767.1, Nt16g00853.1, Nt16g00877.1 were predicted as pleiotropic candidate genes for five traits.

Characterization of qPL5: a novel quantitative trait locus (QTL) that controls panicle length in rice (Oryza sativa L.)

Panicle length (PL) is an important trait that determines panicle architecture and strongly affects grain yield and quality in rice. However, this trait has not been well characterized genetically, and its contribution to yield improvement is not well understood. Characterization of novel genes related to PL is of great significance for breeding high-yielding rice varieties. In our previous research, we identified qPL5, a quantitative trait locus for PL. In this study, we aimed to determine the exact position of qPL5 in the rice genome and identify the candidate gene. Through substitution mapping, we mapped qPL5 to a region of 21.86 kb flanked by the molecular marker loci STS5-99 and STS5-106 in which two candidate genes were predicted. By sequence analysis and relative expression analysis, LOC-Os05g41230, which putatively encodes a BRASSINOSTEROID INSENSITIVE 1-associated receptor kinase 1 precursor, was considered to be the most likely candidate gene for qPL5. In addition, we successfully developed a pair of near-isogenic lines (NILs) for qPL5 in different genetic backgrounds to evaluate the genetic effects of qPL5. Agronomic trait analysis of the NILs indicated that qPL5 positively contributes to plant height, grain number per panicle, panicle length, grain yield per plant, and flag leaf length, but it had no influence on heading date and grain-size-related traits. Therefore, qPL5 and the markers tightly linked to it should be available for molecular breeding of high-yielding varieties.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01339-z.

Genetic Dissection of Epistatic Interactions Contributing Yield-Related Agronomic Traits in Rice Using the Compressed Mixed Model

Rice (Oryza sativa) is one of the most important cereal crops in the world, and yield-related agronomic traits, including plant height (PH), panicle length (PL), and protein content (PC), are prerequisites for attaining the desired yield and quality in breeding programs. Meanwhile, the main effects and epistatic effects of quantitative trait nucleotides (QTNs) are all important genetic components for yield-related quantitative traits. In this study, we conducted genome-wide association studies (GWAS) for 413 rice germplasm resources, with 36,901 single nucleotide polymorphisms (SNPs), to identify QTNs, QTN-by-QTN interaction (QQI), and their candidate genes, using a multi-locus compressed variance component mixed model, 3VmrMLM. As a result, two significant QTNs and 56 paired QQIs were detected, amongst 5219 genes of these QTNs, and 26 genes were identified as the yield-related confirmed genes, such as LCRN1, OsSPL3, and OsVOZ1 for PH, and LOG and QsBZR1 for PL. To reveal the substantial contributions related to the variation of yield-related agronomic traits in rice, we further implemented an enrichment analysis and expression analysis. As the results showed, 114 genes, nearly all significant QQIs, were involved in 37 GO terms; for example, the macromolecule metabolic process (GO:0043170), intracellular part (GO:0044424), and binding (GO:0005488). It was revealed that most of the QQIs and the candidate genes were significantly involved in the biological process, molecular function, and cellular component of the target traits. The demonstrated genetic interactions play a critical role in yield-related agronomic traits of rice, and such epistatic interactions contributed to large portions of the missing heritability in GWAS. These results help us to understand the genetic basis underlying the inheritance of the three yield-related agronomic traits and provide implications for rice improvement.

Construction of a novel Wheat 55 K SNP array-derived genetic map and its utilization in QTL mapping for grain yield and quality related traits

Wheat is one of the most important staple crops for supplying nutrition and energy to people world. A new genetic map based on the Wheat 55 K SNP array was constructed using recombinant inbred lines derived from a cross between Zhongkemai138 and Kechengmai2 to explore the genetic foundation for wheat grain features. This new map covered 2,155.72 cM across the 21 wheat chromosomes with 11,455 markers. And 2,846 specific markers for this genetic map and 148 coincident markers among different maps were documented, which was helpful for improving and updating wheat genetic and genomic information. Using this map, a total of 68 additive QTLs and 82 pairs of epistatic QTLs were detected for grain features including yield, nutrient composition, and quality-related traits by QTLNetwork 2.1 and IciMapping 4.1 software. Fourteen additive QTLs and one pair of epistatic QTLs could be detected by both software programs and thus regarded as stable QTLs here, all of which explained higher phenotypic variance and thus could be utilized for wheat grain improvement. Additionally, thirteen additive QTLs were clustered into three genomic intervals (C4D.2, C5D, and C6D2), each of which had at least two stable QTLs. Among them, C4D.2 and C5D have been attributed to the famous dwarfing gene Rht2 and the hardness locus Pina, respectively, while endowed with main effects on eight grain yield/quality related traits and epistatically interacted with each other to control moisture content, indicating that the correlation of involved traits was supported by the pleotropic of individual genes but also regulated by the gene interaction networks. Additionally, the stable additive effect of C6D2 (QMc.cib-6D2 and QTw.cib-6D2) on moisture content was also highlighted, potentially affected by a novel locus, and validated by its flanking Kompetitive Allele-Specific PCR marker, and TraesCS6D02G109500, encoding aleurone layer morphogenesis protein, was deduced to be one of the candidate genes for this locus. This result observed at the QTL level the possible contribution of grain water content to the balances among yield, nutrients, and quality properties and reported a possible new locus controlling grain moisture content as well as its linked molecular marker for further grain feature improvement.

Genetic Mapping to Detect Stringent QTLs Using 1k-RiCA SNP Genotyping Platform from the New Landrace Associated with Salt Tolerance at the Seedling Stage in Rice

Rice is the world’s most important food crop, providing the daily calorie intake for more than half of the world’s population. Rice breeding has always been preoccupied with maximizing yield potential. However, numerous abiotic factors, such as salt, cold, drought, and heat, significantly reduce rice productivity. Salinity, one of the major abiotic stresses, reduces rice yield worldwide. This study was conducted to determine new quantitative trait loci (QTLs) that regulate salt tolerance in rice seedlings. One F_2:3 mapping population was derived from a cross between BRRI dhan49 (a popular but sensitive rainfed rice variety) and Akundi (a salt-tolerant rice landrace in Bangladesh used as a donor parent). The 1k-Rice Custom Amplicon (1k-RiCA) single-nucleotide polymorphism (SNP) markers were used to genotype this mapping population. After removing segregation distortion and monomorphic markers, 884 SNPs generated a 1526.8 cM-long genetic linkage map with a mean marker density of 1.7 cM for the 12 linkage groups. By exploiting QGene and ICIM-ADD, a sum of 15 QTLs for nine traits was identified in salt stress on seven chromosomes. Four important genomic loci were identified (qSES1, qSL1, qSUR1 and qRL1) on chromosome 1. Out of these 15 QTLs, 14 QTLs are unique, as no other study has mapped in the same chromosomal location. We also detected 15 putative candidate genes and their functions. The ICIM-EPI approach identified 43 significant pairwise epistasis interactions between regions associated with and unassociated with QTLs. Apart from more well-known donors, Akundi serves as an important new donor source for global salt tolerance breeding initiatives, including Bangladesh. The introgression of the novel QTLs identified in this study will accelerate the development of new salt-tolerant varieties that are highly resistant to salt stress using marker-enabled breeding.

Characterization of functional genes GS3 and GW2 and their effect on the grain size of various landraces of rice (Oryza sativa)

BACKGROUND

Grain size is an essential factor of grain quality and yield in rice. The genetic studies have substantially contributed to enhancing yield and maintaining a good quality of rice. The two major genes GS3 (a negative regulator of grain length) and GW2 (a negative regulator of grain width) with functional mutation play a significant role in controlling the grain size of rice. METHODS AND RESULTS: In the study, 17 different widely grown Pakistani landraces of various genetic and geographic backgrounds were evaluated for grain phenotypic traits (1000-grain weight, length, width, and thickness) and also screened for genotypic mutation in GS3 and GW2 genes. Phenotypic data revealed the range for grain weight from 16.86 g (Lateefy) to 26.91 g (PS2), grain length ranged from 7.27 mm (JP-5) to 12.18 mm (PS2), grain width ranged from 2.01 mm (Lateefy) to 3.51 mm (JP5), and grain thickness ranged from 1.79 mm to 2.19. Correlation revealed a negative and significant correlation between grain width and length. There was no significant correlation between grain length and 1000-grain weight and grain width. LSD test displayed that the means of three variables grain length, grain width, and 1000-grain weight were statistically different from one another except grain width and grain breadth. Fifteen accessions carried the domesticated allele of GS3 while JP5 and Fakhr-e-Malakand carried the dominant allele. Similarly, fifteen accessions carried the dominant allele of GW2 while JP-5 and Fakhr-e-Malakand carried the mutant allele.

CONCLUSIONS

The study shows that the mutant alleles of both genes are of significance to pyramid them in any breeding program. However, just incorporating favorable alleles is not the sole solution for improving the grain size. Therefore, further elucidation of GS3 and GW2 genes regulatory network, their interaction, trade-off, and pathways will better coordinate their marker-assisted selection in the future breeding program. Additionally, the study concluded that the selection of grain size was not dependent on 1000-grain weight in the selected germplasm.

Impact of early genomic prediction for recurrent selection in an upland rice synthetic population

Population breeding through recurrent selection is based on the repetition of evaluation and recombination among best-selected individuals. In this type of breeding strategy, early evaluation of selection candidates combined with genomic prediction could substantially shorten the breeding cycle length, thus increasing the rate of genetic gain. The objective of this study was to optimize early genomic prediction in an upland rice (Oryza sativa L.) synthetic population improved through recurrent selection via shuttle breeding in two sites. To this end, we used genomic prediction on 334 S0 genotypes evaluated with early generation progeny testing (S0:2 and S0:3) across two sites. Four traits were measured (plant height, days to flowering, grain yield, and grain zinc concentration) and the predictive ability was assessed for the target site. For days to flowering and plant height, which correlate well among sites (0.51-0.62), an increase of up to 0.4 in predictive ability was observed when the model was trained using the two sites. For grain zinc concentration, adding the phenotype of the predicted lines in the nontarget site to the model improved the predictive ability (0.51 with two-site and 0.31 with single-site model), whereas for grain yield the gain was less (0.42 with two-site and 0.35 with single-site calibration). Through these results, we found a good opportunity to optimize the genomic recurrent selection scheme and maximize the use of resources by performing early progeny testing in two sites for traits with best expression and/or relevance in each specific environment.

Identification of Markers for Root Traits Related to Drought Tolerance Using Traditional Rice Germplasm

Drought is one of the important constraints affecting rice productivity worldwide. The vigorous shoot and deep root system help to improve drought resistance. In present era, genome-wide association study (GWAS) is the preferred method for mapping of QTLs for complex traits such as root and drought tolerance traits. In the present study, 114 rice genotypes were evaluated for various root and shoot traits under water stress conditions. All genotypes showed a significant amount of variation for various root and shoot traits. Correlation analysis revealed that high dry shoot weight and fresh shoot weight is associated with root length, root volume, fresh root weight and dry root weight. A total of 11 significant marker-trait associations were detected for various root, shoot and drought tolerance traits with the coefficient of determination (R²) ranging from 18.99 to 53.41%. Marker RM252 and RM212 showed association with three root traits which suggests their scope for improvement of root system. In the present study, a novel QTL was detected for root length associated with RM127, explaining 19.30% of variation. The marker alleles with increasing phenotypic effects for root and drought-tolerant traits can be exploited for improvement of root and drought tolerance traits using marker-assisted selection.

A Genetic Linkage Map of BC2 Population Reveals QTL Associated with Plant Architecture Traits in Lagerstroemia

Plant architecture improvement is of great significance in influencing crop yield, harvesting efficiency and ornamental value, by changing the spatial structure of the canopy. However, the mechanism on plant architecture in woody plants is still unclear. In order to study the genetic control of plant architecture traits and promote marker-assisted selection (MAS), a genetic linkage map was constructed, and QTL mapping was performed. In this study, using 188 BC2 progenies as materials, a genetic map of Lagerstroemia was constructed using amplification fragment length polymorphisms (AFLP) and simple sequence repeats (SSR) markers, and the QTLs of four key plant architecture traits (plant height, crown width, primary lateral branch height and internode length) were analyzed. The genetic map contains 22 linkage groups, including 198 AFLP markers and 36 SSR markers. The total length of the genome covered by the map is 1272 cM, and the average distance between markers is 6.8 cM. Three QTLs related to plant height were located in LG1, LG4 and LG17 linkage groups, and the phenotypic variation rates were 32.36, 16.18 and 12.73%, respectively. A QTL related to crown width was located in LG1 linkage group, and the phenotypic variation rate was 18.07%. Two QTLs related to primary lateral branch height were located in the LG1 and LG7 linkage groups, and the phenotypic variation rates were 20.59 and 15.34%, respectively. Two QTLs related to internode length were located in the LG1 and LG20 linkage groups, and the phenotypic variation rates were 14.86 and 9.87%. The results provide a scientific basis for finely mapping genes of plant architecture traits and marker-assisted breeding in Lagerstroemia.

Genetic architecture to cause dynamic change in tiller and panicle numbers revealed by genome-wide association study and transcriptome profile in rice

Panicle number (PN) is one of the three yield components in rice. As one of the most unstable traits, the dynamic change in tiller number (DCTN) may determine the final PN. However, the genetic basis of DCTN and its relationship with PN remain unclear. Here, 377 deeply re-sequenced rice accessions were used to perform genome-wide association studies (GWAS) for tiller/PN. It was found that the DCTN pattern rather than maximum tiller number or effective tiller ratio is the determinant factor of high PN. The DCTN pattern that affords more panicles exhibits a period of stable tillering peak between 30 and 45 days after transplant (called DT30 and DT45, respectively), which was believed as an ideal pattern contributing to the steady transition from tiller development to panicle development (ST-TtP). Consistently, quantitative trait loci (QTL) expressed near DT30-DT45 were especially critical to the rice DCTN and in supporting the ST-TtP. The spatio-temporal expression analysis showed that the expression pattern of keeping relatively high expression in root at 24:00 (R24-P2) from about DT30 to DT45 is a typical expression pattern of cloned tiller genes, and the candidate genes with R24-P2 can facilitate the prediction of PN. Moreover, gene OsSAUR27 was identified by an integrated approach combining GWAS, bi-parental QTL mapping and transcription. These findings related to the genetic basis underlying the DCTN will provide the genetic theory in making appropriate decisions on field management, and in developing new varieties with high PN and ideal dynamic plant architecture.

Detecting CSSLs and yield QTLs with additive, epistatic and QTL×environment interaction effects from Oryza sativa × O. nivara IRGC81832 cross

Chromosome segment substitution lines (CSSLs) are useful tools for precise mapping of quantitative trait loci (QTLs) and the evaluation of gene action and interaction in inter-specific crosses. In this study, a set of 90 back cross lines at BC2F8 generation derived from Swarna x Oryza nivara IRGC81832 was evaluated for yield traits under irrigated conditions in wet seasons of 3 consecutive years. We identified a set of 70 chromosome segment substitution lines, using genotyping data from 140 SSR markers covering 94.4% of O. nivara genome. Among these, 23 CSSLs were significantly different for 7 traits. 22 QTLs were detected for 11 traits with 6.51 to 46.77% phenotypic variation in 90 BILs. Three pleiotropic genomic regions associated with yield traits were mapped on chromosomes 1, 8 and 11. The marker interval RM206-RM144 at chromosome 11 was recurrently detected for various yield traits. Ten QTLs were identified consistently in the three consecutive years of testing. Seventeen pairs of significant epistatic QTLs (E-QTLs) were detected for days to flowering, days to maturity and plant height. Chromosome segments from O. nivara contributed trait enhancing alleles. The significantly improved lines and the stable QTLs identified in this study are valuable resource for gene discovery and yield improvement.

A complex network of QTL for thousand-kernel weight in the rye genome

Here, QTL mapping for thousand-kernel weight carried out within a 541 × Ot1-3 population of recombinant inbred lines using high-density DArT-based map and three methods (single-marker analysis with F parametric test, marker analysis with the Kruskal–Wallis K* nonparametric test, and the recently developed analysis named genes interaction assorting by divergent selection with χ² test) revealed 28 QTL distributed over all seven rye chromosomes. The first two methods showed a high level of consistency in QTL detection. Each of 13 QTL revealed in the course of gene interaction assorting by divergent selection analysis coincided with those detected by the two other methods, confirming the reliability of the new approach to QTL mapping. Its unique feature of discriminating QTL classes might help in selecting positively acting QTL and alleles for marker-assisted selection. Also, interaction among seven QTL for thousand-kernel weight was analyzed using gene interaction assorting by the divergent selection method. Pairs of QTL showed a predominantly additive relationship, but epistatic and complementary types of two-loci interactions were also revealed.

Genome-Wide Association Study of Grain Size Traits in Indica Rice Multiparent Advanced Generation Intercross (MAGIC) Population

Rice grain size plays a crucial role in determining grain quality and yield. In this study, two multiparent advanced generation intercross (MAGIC) populations, DC1 and BIM, were evaluated for grain size across three environments and genotyped with 55K array-based SNP detection and genotype-by-sequencing (GBS), respectively, to identify QTLs and SNPs associated with grain length, grain width, grain length-width ratio, grain thickness, and thousand grain weight. A total of 18 QTLs were identified for the five grain size-related traits and explained 6.43-63.35% of the total phenotypic variance. Twelve of these QTLs colocalized with the cloned genes, GS3, GW5/qSW5, GW7/GL7/SLG7, and GW8/OsSPL16, of which the first two genes showed the strongest effect for grain length and grain width, respectively. Four potential new genes were also identified from the QTLs, which exhibited both genetic background independency and environment stability and could be validated in future studies. Moreover, the significant SNP markers identified are valuable for direct utilization in marker-assisted breeding to improve rice grain size.

Genome wide screening and comparative genome analysis for Meta-QTLs, ortho-MQTLs and candidate genes controlling yield and yield-related traits in rice

Background

Improving yield and yield-related traits is the crucial goal in breeding programmes of cereals. Meta-QTL (MQTL) analysis discovers the most stable QTLs regardless of populations genetic background and field trial conditions and effectively narrows down the confidence interval (CI) for identification of candidate genes (CG) and markers development.

Results

A comprehensive MQTL analysis was implemented on 1052 QTLs reported for yield (YLD), grain weight (GW), heading date (HD), plant height (PH) and tiller number (TN) in 122 rice populations evaluated under normal condition from 1996 to 2019. Consequently, these QTLs were confined into 114 MQTLs and the average CI was reduced up to 3.5 folds in compare to the mean CI of the original QTLs with an average of 4.85 cM CI in the resulted MQTLs. Among them, 27 MQTLs with at least five initial QTLs from independent studies were considered as the most stable QTLs over different field trials and genetic backgrounds. Furthermore, several known and novel CGs were detected in the high confident MQTLs intervals. The genomic distribution of MQTLs indicated the highest density at subtelomeric chromosomal regions. Using the advantage of synteny and comparative genomics analysis, 11 and 15 ortho-MQTLs were identified at co-linear regions between rice with barley and maize, respectively. In addition, comparing resulted MQTLs with GWAS studies led to identification of eighteen common significant chromosomal regions controlling the evaluated traits.

Conclusion

This comprehensive analysis defines a genome wide landscape on the most stable loci associated with reliable genetic markers and CGs for yield and yield-related traits in rice. Our findings showed that some of these information are transferable to other cereals that lead to improvement of their breeding programs.

Validation and Characterization of a Seed Number Per Silique Quantitative Trait Locus qSN.A7 in Rapeseed (Brassica napus L.)

Seed number is a key character/trait tightly related to the plant fitness/evolution and crop domestication/improvement. The seed number per silique (SNPS) shows a huge variation from several to more than 30, however the underlying regulatory mechanisms are poorly known, which has hindered its improvement. To answer this question, several representative lines with extreme SNPS were previously subjected to systematic genetic and cytological analyses. The results showed that the natural variation of seed number per silique is mainly controlled by maternal and embryonic genotype, which are co-determined by ovule number per ovary, fertile ovule ratio, ovule fertilization rate, and fertilized ovule development rate. More importantly, we also mapped two repeatable quantitative trait loci (QTLs) for SNPS using the F2:3 population derived from Zhongshuang11 and No. 73290, of which the major QTL qSN.A6 has been fine-mapped. In the current study, the near-isogenic lines (NILs) of qSN.A7 were successfully developed by the successive backcross of F1 with Zhongshuang11. First, the effect of qSN.A7 was validated by evaluating the SNPS of two types of homozygous NILs from BC3F2 population, which showed a significant difference of 2.23 on average. Then, qSN.A7 was successfully fine-mapped from the original 4.237 to 1.389 Mb, using a BC4F2 segregating population of 2,551 individuals. To further clarify the regulatory mechanism of qSN.A7, the two types of homologous NILs were subjected to genetic and cytological analyses. The results showed that the difference in SNPS between the two homologous NILs was determined by the embryonic genotypic effect. Highly accordant with this, no significant difference was observed in ovule number per ovary, ovule fertility, fertilization rate, and pollen fertility between the two homologous NILs. Therefore, the regulatory mechanism of qSN.A7 is completely different from the cloned qSS.C9 and qSN.A6. These results will advance the understanding of SNPS and facilitate gene cloning and molecular breeding in Brassica napus.

In Silico Identification of QTL-Based Polymorphic Genes as Salt-Responsive Potential Candidates through Mapping with Two Reference Genomes in Rice

Recent advances in next generation sequencing have created opportunities to directly identify genetic loci and candidate genes for abiotic stress responses in plants. With the objective of identifying candidate genes within the previously identified QTL-hotspots, the whole genomes of two divergent cultivars for salt responses, namely At 354 and Bg 352, were re-sequenced using Illumina Hiseq 2500 100PE platform and mapped to Nipponbare and R498 genomes. The sequencing results revealed approximately 2.4 million SNPs and 0.2 million InDels with reference to Nipponbare while 1.3 million and 0.07 million with reference to R498 in two parents. In total, 32,914 genes were reported across all rice chromosomes of this study. Gene mining within QTL hotspots revealed 1236 genes, out of which 106 genes were related to abiotic stress. In addition, 27 abiotic stress-related genes were identified in non-QTL regions. Altogether, 32 genes were identified as potential genes containing polymorphic non-synonymous SNPs or InDels between two parents. Out of 10 genes detected with InDels, tolerant haplotypes of Os01g0581400, Os10g0107000, Os11g0655900, Os12g0622500, and Os12g0624200 were found in the known salinity tolerant donor varieties. Our findings on different haplotypes would be useful in developing resilient rice varieties for abiotic stress by haplotype-based breeding studies.

QTL.gCIMapping.GUI v2.0: An R software for detecting small-effect and linked QTLs for quantitative traits in bi-parental segregation populations

The methodologies and software packages for mapping quantitative trait loci (QTLs) in bi-parental segregation populations are well established. However, it is still difficult to detect small-effect and linked QTLs. To address this issue, we proposed a genome-wide composite interval mapping (GCIM) in bi-parental segregation populations. To popularize this method, we developed an R package. This program with two versions (Graphical User Interface: QTL.gCIMapping.GUI v2.0 and code: QTL.gCIMapping v3.2) can be used to identify QTLs for quantitative traits in recombinant inbred lines, doubled haploid lines, backcross and F₂ populations. To save running time, fread function was used to read the dataset, parallel operation was used in parameter estimation, and conditional probability calculation was implemented by C++. Once one input file with *.csv or *.txt formats is uploaded into the package, one or two output files and one figure can be obtained. The input file with the ICIM and win QTL cartographer formats is available as well. Real data analysis for 1000-grain weight in rice showed that the GCIM detects the maximum previously reported QTLs and genes, and has the minimum AIC value in the stepwise regression of all the identified QTLs for this trait; using stepwise regression and empirical Bayesian analyses, there are some false QTLs around the previously reported QTLs and genes from the CIM method. The above software packages on Windows, Mac and Linux can be downloaded from https://cran.r-project.org/web/packages/ or https://bigd.big.ac.cn/biocode/tools/7078/releases/27 in order to identify all kinds of omics QTLs.

InDel Marker Based Estimation of Multi-Gene Allele Contribution and Genetic Variations for Grain Size and Weight in Rice (Oryza sativa L.)

The market success of any rice cultivar is exceedingly dependent on its grain appearance, as well as its grain yield, which define its demand by consumers as well as growers. The present study was undertaken to explore the contribution of nine major genes, qPE9~1, GW2, SLG7, GW5, GS3, GS7, GW8, GS5, and GS2, in regulating four size and weight related traits, i.e., grain length (GL), grain width (GW), grain thickness (GT), and thousand grain weight (TGW) in 204 diverse rice germplasms using Insertion/Deletion (InDel) markers. The studied germplasm displayed wide-ranging variability in the four studied traits. Except for three genes, all six genes showed considerable association with these traits with varying strengths. Whole germplasm of 204 genotypes could be categorized into three major clusters with different grain sizes and weights that could be utilized in rice breeding programs where grain appearance and weight are under consideration. The study revealed that TGW was 24.9% influenced by GL, 37.4% influenced by GW, and 49.1% influenced by GT. Hence, assuming the trend of trait selection, i.e., GT > GW > GL, for improving TGW in the rice yield enhancement programs. The InDel markers successfully identified a total of 38 alleles, out of which 27 alleles were major and were found in more than 20 genotypes. GL was associated with four genes (GS3, GS7, GW8, and GS2). GT was also found to be regulated by four different genes (GS3, GS7, GW8, and GS2) out of the nine studied genes. GW was found to be under the control of three studied genes (GW5, GW8, and GS2), whereas TGW was found to be under the influence of four genes (SLG7, GW5, GW8, and GS5) in the germplasm under study. The Unweighted Pair Group Method with Arithmetic means (UPGMA) tree based on the studied InDel marker loci segregated the whole germplasm into three distinct clusters with dissimilar grain sizes and weights. A two-dimensional scatter plot constructed using Principal Coordinate Analysis (PCoA) based on InDel markers further separated the 204 rice germplasms into four sub-populations with prominent demarcations of extra-long, long, medium, and short grain type germplasms that can be utilized in breeding programs accordingly. The present study could help rice breeders to select a suitable InDel marker and in formulation of breeding strategies for improving grain appearance, as well as weight, to develop rice varieties to compete international market demands with higher yield returns. This study also confirms the efficient application of InDel markers in studying diverse types of rice germplasm, allelic frequencies, multiple-gene allele contributions, marker-trait associations, and genetic variations that can be explored further.

Marker-assisted selection in rice breeding programs in Hokkaido

Rice breeding programs in Hokkaido over the past 100 years have dramatically increased productivity and improved the eating quality of rice. Commercial varieties with high yield and good eating quality, such as Kirara 397, Hoshinoyume, and Nanatsuboshi, have been continuously registered since 1990. Furthermore, varieties with better eating quality using Wx1-1, which reduces amylose content to improve the taste of sticky rice, such as Oborozuki and Yumepirika, were registered in 2006 and 2008, respectively. However, to the best of our knowledge the genomic changes associated with these improvements have not been determined. Better understanding of the relationships between DNA sequences and agricultural traits could facilitate rice breeding programs in Hokkaido. Marker-assisted selection (MAS), which can select the plants with chromosomal regions tagged with DNA markers for desirable traits, is an advanced technology to manage genetic improvements. Here, we summarize the current states of MAS in rice breeding programs in Hokkaido before huge data sets of genome sequences using next-generation sequencing technology come into practical use in rice breeding programs.

Mapping quantitative trait loci using binned genotypes

Precise mapping of quantitative trait loci (QTLs) is critical for assessing genetic effects and identifying candidate genes for quantitative traits. Interval and composite interval mappings have been the methods of choice for several decades, which have provided tools for identifying genomic regions harboring causal genes for quantitative traits. Historically, the concept was developed on the basis of sparse marker maps where genotypes of loci within intervals could not be observed. Currently, genomes of many organisms have been saturated with markers due to the new sequencing technologies. Genotyping by sequencing usually generates hundreds of thousands of single nucleotide polymorphisms (SNPs), which often include the causal polymorphisms. The concept of interval no longer exists, prompting the necessity of a norm change in QTL mapping technology to make use of the high-volume genomic data. Here we developed a statistical method and a software package to map QTLs by binning markers into haplotype blocks, called bins. The new method detects associations of bins with quantitative traits. It borrows the mixed model methodology with a polygenic control from genome-wide association studies (GWAS) and can handle all kinds of experimental populations under the linear mixed model (LMM) framework. We tested the method using both simulated data and data from populations of rice. The results showed that this method has higher power than the current methods. An R package named binQTL is available from GitHub.

Deciphering the Genetics of Major End-Use Quality Traits in Wheat

Improving the end-use quality traits is one of the primary objectives in wheat breeding programs. In the current study, a population of 127 recombinant inbred lines (RILs) derived from a cross between Glenn (PI-639273) and Traverse (PI-642780) was developed and used to identify quantitative trait loci (QTL) for 16 end-use quality traits in wheat. The phenotyping of these 16 traits was performed in nine environments in North Dakota, USA. The genotyping for the RIL population was conducted using the wheat Illumina iSelect 90K SNP assay. A high-density genetic linkage map consisting of 7,963 SNP markers identified a total of 76 additive QTL (A-QTL) and 73 digenic epistatic QTL (DE-QTL) associated with these traits. Overall, 12 stable major A-QTL and three stable DE-QTL were identified for these traits, suggesting that both A-QTL and DE-QTL played an important role in controlling end-use quality traits in wheat. The most significant A-QTL (AQ.MMLPT.ndsu.1B) was detected on chromosome 1B for mixograph middle line peak time. The AQ.MMLPT.ndsu.1B A-QTL was located very close to the position of the Glu-B1 gene encoding for a subunit of high molecular weight glutenin and explained up to 24.43% of phenotypic variation for mixograph MID line peak time. A total of 23 co-localized QTL loci were detected, suggesting the possibility of the simultaneous improvement of the end-use quality traits through selection procedures in wheat breeding programs. Overall, the information provided in this study could be used in marker-assisted selection to increase selection efficiency and to improve the end-use quality in wheat.

Identification of optimal prediction models using multi-omic data for selecting hybrid rice

Genomic prediction benefits hybrid rice breeding by increasing selection intensity and accelerating breeding cycles. With the rapid advancement of technology, other omic data, such as metabolomic data and transcriptomic data, are readily available for predicting breeding values for agronomically important traits. In this study, the best prediction strategies were determined for yield, 1000 grain weight, number of grains per panicle, and number of tillers per plant of hybrid rice (derived from recombinant inbred lines) by comprehensively evaluating all possible combinations of omic datasets with different prediction methods. It was demonstrated that, in rice, the predictions using a combination of genomic and metabolomic data generally produce better results than single-omics predictions or predictions based on other combined omic data. Best linear unbiased prediction (BLUP) appears to be the most efficient prediction method compared to the other commonly used approaches, including least absolute shrinkage and selection operator (LASSO), stochastic search variable selection (SSVS), support vector machines with radial basis function and epsilon regression (SVM-R(EPS)), support vector machines with radial basis function and nu regression (SVM-R(NU)), support vector machines with polynomial kernel and epsilon regression (SVM-P(EPS)), support vector machines with polynomial kernel and nu regression (SVM-P(NU)) and partial least squares regression (PLS). This study has provided guidelines for selection of hybrid rice in terms of which types of omic datasets and which method should be used to achieve higher trait predictability. The answer to these questions will benefit academic research and will also greatly reduce the operative cost for the industry which specializes in breeding and selection.

Statistical power in genome-wide association studies and quantitative trait locus mapping

Power calculation prior to a genetic experiment can help investigators choose the optimal sample size to detect a quantitative trait locus (QTL). Without the guidance of power analysis, an experiment may be underpowered or overpowered. Either way will result in wasted resource. QTL mapping and genome-wide association studies (GWAS) are often conducted using a linear mixed model (LMM) with controls of population structure and polygenic background using markers of the whole genome. Power analysis for such a mixed model is often conducted via Monte Carlo simulations. In this study, we derived a non-centrality parameter for the Wald test statistic for association, which allows analytical power analysis. We show that large samples are not necessary to detect a biologically meaningful QTL, say explaining 5% of the phenotypic variance. Several R functions are provided so that users can perform power analysis to determine the minimum sample size required to detect a given QTL with a certain statistical power or calculate the statistical power with given sample size and known values of other population parameters.

Marker Assisted Breeding to Develop Multiple Stress Tolerant Varieties for Flood and Drought Prone Areas

BACKGROUND

Climate extremes such as drought and flood have become major constraints to the sustainable rice crop productivity in rainfed environments. Availability of suitable climate-resilient varieties could help farmers to reduce the grain yield losses resulting from the climatic extremities. The present study was undertaken with an aim to develop high-yielding drought and submergence tolerant rice varieties using marker assisted introgression of qDTY1.1, qDTY2.1, qDTY3.1 and Sub1. Performance of near isogenic lines (NILs) developed in the background of Swarna was evaluated across 60 multi-locations trials (MLTs). The selected promising lines from MLTs were nominated and evaluated in national trials across 18 locations in India and 6 locations in Nepal.

RESULTS

Grain yield advantage of the NILs with qDTY1.1 + qDTY2.1 + qDTY3.1 + Sub1 and qDTY2.1 + qDTY3.1 + Sub1 ranged from 76 to 2479 kg ha- 1 and 396 to 2376 kg ha- 1 under non-stress (NS) respectively and 292 to 1118 kg ha- 1 and 284 to 2086 kg ha- 1 under reproductive drought stress (RS), respectively. The NIL, IR96322-34-223-B-1-1-1-1 having qDTY1.1 + qDTY2.1 + qDTY3.1 + Sub1 has been released as variety CR dhan 801 in India. IR 96321-1447-651-B-1-1-2 having qDTY1.1 + qDTY3.1 + Sub 1 and IR 94391-131-358-19-B-1-1-1 having qDTY3.1 + Sub1 have been released as varieties Bahuguni dhan-1' and 'Bahuguni dhan-2' respectively in Nepal. Background recovery of 94%, 93% and 98% was observed for IR 96322-34-223-B-1-1-1-1, IR 96321-1447-651-B-1-1-2 and IR 94391-131-358-19-B-1-1-1 respectively on 6 K SNP Infinium chip.

CONCLUSION

The drought and submergence tolerant rice varieties with pyramided multiple QTLs can ensure 0.2 to 1.7 t ha- 1 under reproductive stage drought stress and 0.1 to 1.0 t ha- 1 under submergence conditions with no yield penalty under non-stress to farmers irrespective of occurrence of drought and/or flood in the same or different seasons.

Modulation of ACD6 dependent hyperimmunity by natural alleles of an Arabidopsis thaliana NLR resistance gene

Plants defend themselves against pathogens by activating an array of immune responses. Unfortunately, immunity programs may also cause unintended collateral damage to the plant itself. The quantitative disease resistance gene ACCELERATED CELL DEATH 6 (ACD6) serves to balance growth and pathogen resistance in natural populations of Arabidopsis thaliana. An autoimmune allele, ACD6-Est, which strongly reduces growth under specific laboratory conditions, is found in over 10% of wild strains. There is, however, extensive variation in the strength of the autoimmune phenotype expressed by strains with an ACD6-Est allele, indicative of genetic modifiers. Quantitative genetic analysis suggests that ACD6 activity can be modulated in diverse ways, with different strains often carrying different large-effect modifiers. One modifier is SUPPRESSOR OF NPR1-1, CONSTITUTIVE 1 (SNC1), located in a highly polymorphic cluster of nucleotide-binding domain and leucine-rich repeat (NLR) immune receptor genes, which are prototypes for qualitative disease resistance genes. Allelic variation at SNC1 correlates with ACD6-Est activity in multiple accessions, and a common structural variant affecting the NL linker sequence can explain differences in SNC1 activity. Taken together, we find that an NLR gene can mask the activity of an ACD6 autoimmune allele in natural A. thaliana populations, thereby linking different arms of the plant immune system.

Plants defend themselves against pathogens by activating immune responses. Unfortunately, these can cause unintended collateral damage to the plant itself. Nevertheless, some wild plants have genetic variants that confer a low threshold for the activation of immunity. While these enable a plant to respond particularly quickly to pathogen attack, such variants might be potentially dangerous. We are investigating one such variant of the immune gene ACCELERATED CELL DEATH 6 (ACD6) in the plant Arabidopsis thaliana. We discovered that there are variants at other genetic loci that can mask the effects of an overly active ACD6 gene. One of these genes, SUPPRESSOR OF NPR1-1, CONSTITUTIVE 1 (SNC1), codes for a known immune receptor. The SNC1 variant that attenuates ACD6 activity is rather common in A. thaliana populations, suggesting that new combinations of the hyperactive ACD6 variant and this antagonistic SNC1 variant will often arise by natural crosses. Similarly, because the two genes are unlinked, outcrossing will often lead to the hyperactive ACD6 variants being unmasked again. We propose that allelic diversity at SNC1 contributes to the maintenance of the hyperactive ACD6 variant in natural A. thaliana populations.

Identification of Major QTLs Associated With First Pod Height and Candidate Gene Mining in Soybean

First pod height (FPH) is a quantitative trait in soybean [Glycine max (L.) Merr.] that affects mechanized harvesting. A compatible combination of the FPH and the mechanized harvester is required to ensure that the soybean is efficiently harvested. In this study, 147 recombinant inbred lines, which were derived from a cross between 'Dongnong594' and 'Charleston' over 8 years, were used to identify the major quantitative trait loci (QTLs) associated with FPH. Using a composite interval mapping method with WinQTLCart (version 2.5), 11 major QTLs were identified. They were distributed on five soybean chromosomes, and 90 pairs of QTLs showed significant epistatic associates with FPH. Of these, 3 were main QTL × main QTL interactions, and 12 were main QTL × non-main QTL interactions. A KEGG gene annotation of the 11 major QTL intervals revealed 8 candidate genes related to plant growth, appearing in the pathways K14486 (auxin response factor 9), K14498 (serine/threonine-protein kinase), and K13946 (transmembrane amino acid transporter family protein), and 7 candidate genes had high expression levels in the soybean stems. These results will aid in building a foundation for the fine mapping of the QTLs related to FPH and marker-assisted selection for breeding in soybean.

Adaptive selection of founder segments and epistatic control of plant height in the MAGIC winter wheat population WM-800

BACKGROUND

Multi-parent advanced generation intercross (MAGIC) populations are a newly established tool to dissect quantitative traits. We developed the high resolution MAGIC wheat population WM-800, consisting of 910 F4:6 lines derived from intercrossing eight recently released European winter wheat cultivars.

RESULTS

Genotyping WM-800 with 7849 SNPs revealed a low mean genetic similarity of 59.7% between MAGIC lines. WM-800 harbours distinct genomic regions exposed to segregation distortion. These are mainly located on chromosomes 2 to 6 of the wheat B genome where founder specific DNA segments were positively or negatively selected. This suggests adaptive selection of individual founder alleles during population development. The application of a genome-wide association study identified 14 quantitative trait loci (QTL) controlling plant height in WM-800, including the known semi-dwarf genes Rht-B1 and Rht-D1 and a potentially novel QTL on chromosome 5A. Additionally, epistatic effects controlled plant height. For example, two loci on chromosomes 2B and 7B gave rise to an additive epistatic effect of 13.7 cm.

CONCLUSION

The present study demonstrates that plant height in the MAGIC-WHEAT population WM-800 is mainly determined by large-effect QTL and di-genic epistatic interactions. As a proof of concept, our study confirms that WM-800 is a valuable tool to dissect the genetic architecture of important agronomic traits.

Identifying a large number of high-yield genes in rice by pedigree analysis, whole-genome sequencing, and CRISPR-Cas9 gene knockout

Repeated artificial selection of a complex trait facilitates the identification of genes underlying the trait, especially if multiple selected descendant lines are available. Here we developed a pedigree-based approach to identify genes underlying the Green Revolution (GR) phenotype. From a pedigree analysis, we selected 30 cultivars including the "miracle rice" IR8, a GR landmark, its ancestors and descendants, and also other related cultivars for identifying high-yield genes. Through sequencing of these genomes, we identified 28 ancestral chromosomal blocks that were maintained in all the high-yield cultivars under study. In these blocks, we identified six genes of known function, including the GR gene sd1, and 123 loci with genes of unknown function. We randomly selected 57 genes from the 123 loci for knockout or knockdown studies and found that a high proportion of these genes are essential or have phenotypic effects related to rice production. Notably, knockout lines have significant changes in plant height (P < 0.003), a key GR trait, compared with wild-type lines. Some gene knockouts or knockdowns were especially interesting. For example, knockout of Os10g0555100, a putative glucosyltransferase gene, showed both reduced growth and altered panicle architecture. In addition, we found that in some retained chromosome blocks several GR-related genes were clustered, although they have unrelated sequences, suggesting clustering of genes with similar functions. In conclusion, we have identified many high-yield genes in rice. Our method provides a powerful means to identify genes associated with a specific trait.

Genomic selection using principal component regression

Many statistical methods are available for genomic selection (GS) through which genetic values of quantitative traits are predicted for plants and animals using whole-genome SNP data. A large number of predictors with much fewer subjects become a major computational challenge in GS. Principal components regression (PCR) and its derivative, i.e., partial least squares regression (PLSR), provide a solution through dimensionality reduction. In this study, we show that PCR can perform better than PLSR in cross validation. PCR often requires extracting more components to achieve the maximum predictive ability than PLSR and thus may be associated with a higher computational cost. However, application of the HAT method (a strategy of describing the relationship between the fitted and observed response variables with a hat matrix) to PCR circumvents conventional cross validation in testing predictive ability, resulting in substantially improved computational efficiency over PLSR where cross validation is mandatory. Advantages of PCR over PLSR are illustrated with a simulated trait of a hypothetical population and four agronomical traits of a rice population. The benefit of using PCR in genomic selection is further demonstrated in an effort to predict 1000 metabolomic traits and 24,973 transcriptomic traits in the same rice population.

Shanyou 63: an elite mega rice hybrid in China

Hybrid rice has been successfully used for commercial rice production for 40 years in China. Shanyou 63, a mega rice hybrid, derived from the parents Zhenshan 97A and Minghui 63, was a milestone for China's hybrid rice development and production because of its high yield and wide adaptability. It was planted in 16 provinces of the country on 17% of the national hybrid rice area annually during the 29 years from 1984 to 2012. The hybrid and its parents have also been widely used for basic and agronomic studies related to rice heterosis, stress tolerance, molecular markers and genomics. We review the development of the hybrid and its parents and their major characteristics for the purpose of learning from the history and guiding future hybrid rice development. The history and development experience show that a successful hybrid rice variety should have multiple traits, including high yield, wide adaptability, resistances to major diseases, and high rice quality that meets the demands of consumers. From the breeding aspect, hybrid rice provides the advantage of combining elite traits or genes from different types of parents, such as those from subspecies of indica and japonica, into a single variety. Farmers prefer not only a variety with high yield potential, but also stable yields and local adaptability.

Genome-Wide Analysis of Grain Yield Stability and Environmental Interactions in a Multiparental Soybean Population

Genetic improvement toward optimized and stable agronomic performance of soybean genotypes is desirable for food security. Understanding how genotypes perform in different environmental conditions helps breeders develop sustainable cultivars adapted to target regions. Complex traits of importance are known to be controlled by a large number of genomic regions with small effects whose magnitude and direction are modulated by environmental factors. Knowledge of the constraints and undesirable effects resulting from genotype by environmental interactions is a key objective in improving selection procedures in soybean breeding programs. In this study, the genetic basis of soybean grain yield responsiveness to environmental factors was examined in a large soybean nested association population. For this, a genome-wide association to performance stability estimates generated from a Finlay-Wilkinson analysis and the inclusion of the interaction between marker genotypes and environmental factors was implemented. Genomic footprints were investigated by analysis and meta-analysis using a recently published multiparent model. Results indicated that specific soybean genomic regions were associated with stability, and that multiplicative interactions were present between environments and genetic background. Seven genomic regions in six chromosomes were identified as being associated with genotype-by-environment interactions. This study provides insight into genomic assisted breeding aimed at achieving a more stable agronomic performance of soybean, and documented opportunities to exploit genomic regions that were specifically associated with interactions involving environments and subpopulations.

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.

Intragenic recombination between two non-functional semi-dwarf 1 alleles produced a functional SD1 allele in a tall recombinant inbred line in rice

Intragenic recombination is one of the most important sources of genetic variability. In our previous study, RI92 a tall line (160 cm of plant height) was observed in the cross progeny between two semi-dwarf indica cultivars Zhenshan 97 and Minghui 63. Genome-wide genotyping and sequencing indicated that the genome constitution of RI92 was completely from both parents. Bulk segregant analysis in a BC₃F₂ population revealed that “green revolution gene” semi-dwarf 1 (sd1) was most likely the gene controlling the tall plant height in RI92. Sequencing analysis of SD1 revealed that an intragenic recombination occurred between two parental non-functional sd1 alleles and generated a functional SD1 in RI92. Four-fold high recombination rate in SD1 located bins to the genome-wide average was observed in two RIL populations, indicating recombination hotspot in the SD1 region. Intragenic recombination creates new alleles in the progeny distinct from parental alleles and diversifies natural variation.

Genetic dissection of main and epistatic effects of QTL based on augmented triple test cross design

The use of heterosis has considerably increased the productivity of many crops; however, the biological mechanism underpinning the technique remains elusive. The North Carolina design III (NCIII) and the triple test cross (TTC) are powerful and popular genetic mating design that can be used to decipher the genetic basis of heterosis. However, when using the NCIII design with the present quantitative trait locus (QTL) mapping method, if epistasis exists, the estimated additive or dominant effects are confounded with epistatic effects. Here, we propose a two-step approach to dissect all genetic effects of QTL and digenic interactions on a whole genome without sacrificing statistical power based on an augmented TTC (aTTC) design. Because the aTTC design has more transformation combinations than do the NCIII and TTC designs, it greatly enriches the QTL mapping for studying heterosis. When the basic population comprises recombinant inbred lines (RIL), we can use the same materials in the NCIII design for aTTC-design QTL mapping with transformation combination Z₁, Z₂, and Z₄ to obtain genetic effect of QTL and digenic interactions. Compared with RIL-based TTC design, RIL-based aTTC design saves time, money, and labor for basic population crossed with F₁. Several Monte Carlo simulation studies were carried out to confirm the proposed approach; the present genetic parameters could be identified with high statistical power, precision, and calculation speed, even at small sample size or low heritability. Additionally, two elite rice hybrid datasets for nine agronomic traits were estimated for real data analysis. We dissected the genetic effects and calculated the dominance degree of each QTL and digenic interaction. Real mapping results suggested that the dominance degree in Z₂ that mainly characterize heterosis showed overdominance and dominance for QTL and digenic interactions. Dominance and overdominance were the major genetic foundations of heterosis in rice.

Identification and molecular mapping of indica high-tillering dwarf mutant htd4, a mild phenotype allelic mutant of D14 in rice (Oryza sativa L.)

Metabolism of strigolactones (SLs) can improve the efficiency of nutrient use by regulating the development of roots and shoots in crops, making them an important research focus for molecular breeding. However, as a very important plant hormone, the molecular mechanism of SL signal transduction still remains largely unknown. In this study, we isolated an indica high-tillering dwarf mutant 4 (htd4), a spontaneous mutant of rice, from the restorer line Gui99. Mapping and sequencing analysis showed that htd4 was a novel allelic mutant of D14, in which a single base substitution forms a premature termination codon. Quantitative RT-PCR analyses revealed that expression levels of the genes D10, D17, D27, D3 and D14 increased significantly, while expression of D53 decreased in htd4, compared with the wild type. A subcellular localisation assay showed that the mutant of D14 in htd4 did not disturb the normal localisation of D14 proteins. However, a BiFC assay suggested that the mutant-type D14 could not interact with D3. Additionally, compared with other D14 allelic mutants, htd4 was the first mutant of D14 discovered in indica, and the differences in many yield traits such as plant height, seed-setting rate and grain sizes between htd4 and the wild type were less than those between other D14 allelic mutants and the wild type. Therefore, htd4 is considered a mild phenotype allelic mutant of D14. We conclude that the absence of functional D14 caused the high-tillering dwarf phenotype of htd4. Our results may provide vital information for research on D14 function and the application of htd4 in molecular breeding.

Both maternally and paternally imprinted genes regulate seed development in rice

Genetic imprinting refers to the unequal expression of paternal and maternal alleles of a gene in sexually reproducing organisms, including mammals and flowering plants. Although many imprinted genes have been identified in plants, the functions of these imprinted genes have remained largely uninvestigated. We report genome-wide analysis of gene expression, DNA methylation and small RNAs in the rice endosperm and functional tests of five imprinted genes during seed development using Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated gene9 (CRISPR/Cas9) gene editing technology. In the rice endosperm, we identified 162 maternally expressed genes (MEGs) and 95 paternally expressed genes (PEGs), which were associated with miniature inverted-repeat transposable elements, imprinted differentially methylated loci and some 21-22 small interfering RNAs (siRNAs) and long noncoding RNAs (lncRNAs). Remarkably, one-third of MEGs and nearly one-half of PEGs were associated with grain yield quantitative trait loci. Most MEGs and some PEGs were expressed specifically in the endosperm. Disruption of two MEGs increased the amount of small starch granules and reduced grain and embryo size, whereas mutation of three PEGs reduced starch content and seed fertility. Our data indicate that both MEGs and PEGs in rice regulate nutrient metabolism and endosperm development, which optimize seed development and offspring fitness to facilitate parental-offspring coadaptation. These imprinted genes and mechanisms could be used to improve the grain yield of rice and other cereal crops.

Genetic mapping of the qSBN7 locus, a QTL controlling secondary branch number per panicle in rice

Secondary branch number (SBN) is an important component affecting spikelet number per panicle (SPP) and yield in rice. During recurrent backcross breeding, four BC₂F₄ populations derived from the high-yield donor parent IR65598-112-2 and the recurrent parent Tainan 13 (a local japonica cultivar) showed discontinuous variations of SPP and SBN within populations. Genetic analysis of 92 BC₂F₄ individuals suggested that both SPP and SBN are controlled by a single recessive allele. Two parents and 37 BC₂F₄ individuals showing high- and low-SBN type phenotypes were analyzed by restriction-site associated DNA sequencing (RAD-seq). Based on 2,522 reliable SNPs, the qSBN7 was mapped to a distal region of the long arm of chromosome 7. Trait-marker association analysis with an additional 166 high-SBN type BC₂F₄ individuals and 8 newly developed cleaved amplified polymorphic sequence markers further delimited the qSBN7 locus to a 601.4-kb region between the markers SNP2788 and SNP2849. Phenotype evaluation of two BC₂F₅ backcross inbred lines revealed that qSBN7 increased SPP by 83.2% and SBN by 61.0%. The qSBN7 of IR65598-112-2 could be used for improving reproductive sink capacity in rice.

Limitation of Unloading in the Developing Grains Is a Possible Cause Responsible for Low Stem Non-structural Carbohydrate Translocation and Poor Grain Yield Formation in Rice through Verification of Recombinant Inbred Lines

Remobilisation of non-structural carbohydrates (NSC) from leaves and stems and unloading into developing grains are essential for yield formation of rice. In present study, three recombinant inbred lines of rice, R91, R156 and R201 have been tested for source-flow-sink related attributes determining the nature of NSC accumulation and translocation at two nitrogen levels in the field. Compared to R91 and R156, R201 had lower grain filling percentage, harvest index, and grain yield. Meanwhile, R201 had significantly lower stem NSC translocation during grain filling stage. Grain filling percentage, harvest index, and grain yield showed the consistent trend with stem NSC translocation among the three lines. In comparison with R91 and R156, R201 had similarity in leaf area index, specific leaf weight, stem NSC concentration at heading, biomass, panicles m-2, spikelets per panicle, remobilization capability of assimilation in stems, sink capacity, sink activity, number and cross sectional area of small vascular bundles, greater number and cross sectional area of large vascular bundles, and higher SPAD, suggesting that source, flow, and sink were not the limiting factors for low stem NSC translocation and grain filling percentage of R201. However, R201 had significant higher stem and rachis NSC concentrations at maturity, which implied that unloading in the developing grains might result in low NSC translocation in R201. The results indicate that stem NSC translocation could be beneficial for enhancement of grain yield potential, and poor unloading into caryopsis may be the possible cause of low stem NSC translocation, poor grain filling and yield formation in R201.

Quantitative trait loci from identification to exploitation for crop improvement

Advancement in the field of genetics and genomics after the discovery of Mendel's laws of inheritance has led to map the genes controlling qualitative and quantitative traits in crop plant species. Mapping of genomic regions controlling the variation of quantitatively inherited traits has become routine after the advent of different types of molecular markers. Recently, the next generation sequencing methods have accelerated the research on QTL analysis. These efforts have led to the identification of more closely linked molecular markers with gene/QTLs and also identified markers even within gene/QTL controlling the trait of interest. Efforts have also been made towards cloning gene/QTLs or identification of potential candidate genes responsible for a trait. Further new concepts like crop QTLome and QTL prioritization have accelerated precise application of QTLs for genetic improvement of complex traits. In the past years, efforts have also been made in exploitation of a number of QTL for improving grain yield or other agronomic traits in various crops through markers assisted selection leading to cultivation of these improved varieties at farmers' field. In present article, we reviewed QTLs from their identification to exploitation in plant breeding programs and also reviewed that how improved cultivars developed through introgression of QTLs have improved the yield productivity in many crops.

Epistasis × environment interactions among Arabidopsis thaliana glucosinolate genes impact complex traits and fitness in the field

Despite the growing number of studies showing that genotype × environment and epistatic interactions control fitness, the influences of epistasis × environment interactions on adaptive trait evolution remain largely uncharacterized. Across three field trials, we quantified aliphatic glucosinolate (GSL) defense chemistry, leaf damage, and relative fitness using mutant lines of Arabidopsis thaliana varying at pairs of causal aliphatic GSL defense genes to test the impact of epistatic and epistasis × environment interactions on adaptive trait variation. We found that aliphatic GSL accumulation was primarily influenced by additive and epistatic genetic variation, leaf damage was primarily influenced by environmental variation and relative fitness was primarily influenced by epistasis and epistasis × environment interactions. Epistasis × environment interactions accounted for up to 48% of the relative fitness variation in the field. At a single field site, the impact of epistasis on relative fitness varied significantly over 2 yr, showing that epistasis × environment interactions within a location can be temporally dynamic. These results suggest that the environmental dependency of epistasis can profoundly influence the response to selection, shaping the adaptive trajectories of natural populations in complex ways, and deserves further consideration in future evolutionary studies.

Comparative analysis of genetic architectures for nine developmental traits of rye

Genetic architectures of plant height, stem thickness, spike length, awn length, heading date, thousand-kernel weight, kernel length, leaf area and chlorophyll content were aligned on the DArT-based high-density map of the 541 × Ot1–3 RILs population of rye using the genes interaction assorting by divergent selection (GIABDS) method. Complex sets of QTL for particular traits contained 1–5 loci of the epistatic D class and 10–28 loci of the hypostatic, mostly R and E classes controlling traits variation through D–E or D–R types of two-loci interactions. QTL were distributed on each of the seven rye chromosomes in unique positions or as a coinciding loci for 2–8 traits. Detection of considerable numbers of the reversed (D′, E′ and R′) classes of QTL might be attributed to the transgression effects observed for most of the studied traits. First examples of E* and F QTL classes, defined in the model, are reported for awn length, leaf area, thousand-kernel weight and kernel length. The results of this study extend experimental data to 11 quantitative traits (together with pre-harvest sprouting and alpha-amylase activity) for which genetic architectures fit the model of mechanism underlying alleles distribution within tails of bi-parental populations. They are also a valuable starting point for map-based search of genes underlying detected QTL and for planning advanced marker-assisted multi-trait breeding strategies.

Identification of QTL Associated with Nitrogen Uptake and Nitrogen Use Efficiency Using High Throughput Genotyped CSSLs in Rice (Oryza sativa L.)

Nitrogen (N) availability is a major factor limiting crop growth and development. Identification of quantitative trait loci (QTL) for N uptake (NUP) and N use efficiency (NUE) can provide useful information regarding the genetic basis of these traits and their associated effects on yield production. In this study, a set of high throughput genotyped chromosome segment substitution lines (CSSLs) derived from a cross between recipient 9311 and donor Nipponbare were used to identify QTL for rice NUP and NUE. Using high throughput sequencing, each CSSL were genotyped and an ultra-high-quality physical map was constructed. A total of 13 QTL, seven for NUP and six for NUE, were identified in plants under hydroponic culture with all nutrients supplied in sufficient quantities. The proportion of phenotypic variation explained by these QTL for NUP and NUE ranged from 3.16-13.99% and 3.76-12.34%, respectively. We also identified several QTL for biomass yield (BY) and grain yield (GY), which were responsible for 3.21-45.54% and 6.28-7.31%, respectively, of observed phenotypic variation. GY were significantly positively correlated with NUP and NUE, with NUP more closely correlated than NUE. Our results contribute information to NUP and NUE improvement in rice.

Mining Centuries Old In situ Conserved Turkish Wheat Landraces for Grain Yield and Stripe Rust Resistance Genes

Wheat landraces in Turkey are an important genetic resource for wheat improvement. An exhaustive 5-year (2009-2014) effort made by the International Winter Wheat Improvement Programme (IWWIP), a cooperative program between the Ministry of Food, Agriculture and Livestock of Turkey, the International Center for Maize and Wheat Improvement (CIMMYT) and the International Center for Agricultural Research in the Dry Areas (ICARDA), led to the collection and documentation of around 2000 landrace populations from 55 provinces throughout Turkey. This study reports the genetic characterization of a subset of bread wheat landraces collected in 2010 from 11 diverse provinces using genotyping-by-sequencing (GBS) technology. The potential of this collection to identify loci determining grain yield and stripe rust resistance via genome-wide association (GWA) analysis was explored. A high genetic diversity (diversity index = 0.260) and a moderate population structure based on highly inherited spike traits was revealed in the panel. The linkage disequilibrium decayed at 10 cM across the whole genome and was slower as compared to other landrace collections. In addition to previously reported QTL, GWA analysis also identified new candidate genomic regions for stripe rust resistance, grain yield, and spike productivity components. New candidate genomic regions reflect the potential of this landrace collection to further increase genetic diversity in elite germplasm.

Genetic analysis of Upland cotton dynamic heterosis for boll number per plant at multiple developmental stages

Yield is an important breeding target. As important yield components, boll number per plant (BNP) shows dynamic character and strong heterosis in Upland cotton. However, the genetic basis underlying the dynamic heterosis is poorly understood. In this study, we conducted dynamic quantitative trait loci (QTL) analysis for BNP and heterosis at multiple developmental stages and environments using two recombinant inbred lines (RILs) and two corresponding backcross populations. By the single-locus analysis, 23 QTLs were identified at final maturity, while 99 QTLs were identified across other three developmental stages. A total of 48 conditional QTLs for BNP were identified for the adjacent stages. QTLs detected at later stage mainly existed in the partial dominance to dominance range and QTLs identified at early stage mostly showed effects with the dominance to overdominance range during plant development. By two-locus analysis, we observe that epistasis played an important role not only in the variation of the performance of the RIL population but also in the expression of heterosis in backcross population. Taken together, the present study reveals that the genetic basis of heterosis is dynamic and complicated, and it is involved in dynamic dominance effect, epistasis and QTL by environmental interactions.

Main Effect QTL with Dominance Determines Heterosis for Dynamic Plant Height in Upland Cotton

Plant height, which shows dynamic development and heterosis, is a major trait affecting plant architecture and has an indirect influence on economic yield related to biological yield in cotton. In the present study, we carried out dynamic analysis for plant height and its heterosis by quantitative trait loci (QTL) mapping at multiple developmental stages using two recombinant inbred lines (RILs) and their backcross progeny. At the single-locus level, 47 QTL were identified at five developmental stages in two hybrids. In backcross populations, QTL identified at an early stage mainly showed partial effects and QTL detected at a later stage mostly displayed overdominance effects. At the two-locus level, we found that main effect QTL played a more important role than epistatic QTL in the expression of heterosis in backcross populations. Therefore, this study implies that the genetic basis of plant height heterosis shows dynamic character and main effect QTL with dominance determines heterosis for plant height in Upland cotton.