scholarly journals Comparative QTL analysis and candidate genes identification of seed size, shape and weight in soybean (Glycine max L.)

2021 ◽  
Author(s):  
Mahmoud A Elattar ◽  
Benjamin Karikari ◽  
Shuguang Li ◽  
Shiyu Song ◽  
Yongce Cao ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Seed Size ◽  
Composite Interval Mapping ◽  
Mixed Model ◽  
Interval Mapping ◽  
Genetic Maps ◽  
Potential Candidate ◽  
Go Enrichment ◽  
Glycine Max L ◽  
Genomic Regions

Abstract Dissecting the genetic mechanism underlying seed size, shape and weight is essential to these traits for enhancing soybean cultivars. High-density genetic maps of two recombinant inbred line populations, LM6 and ZM6, evaluated in multiple environments to identify candidate genes behind seed-related traits major and stable QTLs. A total of 239 and 43 M-QTL were mapped by composite interval mapping and mixed-model based composite interval mapping approaches, respectively, from which 22 common QTLs including four major and novel QTLs. CIM and MCIM approaches identified 180 and 18 novel M-QTLs, respectively. Moreover, 18 QTLs showed significant AE effects, and 40 pairwise of the identified QTLs exhibited digenic epistatic effects. Seed flatness index QTLs (34 QTLs) were identified and reported for the first time. Seven QTL clusters underlying the inheritance of seed size, shape and weight on genomic regions of chromosomes 3, 4, 5, 7, 9, 17 and 19 were identified. Gene annotations, gene ontology (GO) enrichment and RNA-seq analyses identified 47 candidate genes for seed-related traits within the genomic regions of those 7 QTL clusters. These genes are highly expressed in seed-related tissues and nodules, that might be deemed as potential candidate genes regulating the above traits in soybean. This study provides detailed information for the genetic bases of the studied traits and candidate genes that could be efficiently implemented by soybean breeders for fine mapping and gene cloning as well as for MAS targeted at improving these traits individually or concurrently.

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

2021 ◽  
Vol 12 ◽  
Author(s):  
Mahmoud A. Elattar ◽  
Benjamin Karikari ◽  
Shuguang Li ◽  
Shiyu Song ◽  
Yongce Cao ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Seed Size ◽  
Composite Interval Mapping ◽  
Soybean Seed ◽  
Interval Mapping ◽  
Genetic Maps ◽  
Potential Candidate ◽  
Major Qtls ◽  
Multiple Environments ◽  
Genomic Regions

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

scholarly journals High-Resolution Mapping in Two RIL Populations Refines Major “QTL Hotspot” Regions for Seed Size and Shape in Soybean (Glycine max L.)

2020 ◽  
Vol 21 (3) ◽  
pp. 1040 ◽  
Author(s):  
Aiman Hina ◽  
Yongce Cao ◽  
Shiyu Song ◽  
Shuguang Li ◽  
Ripa Akter Sharmin ◽  
...  
Keyword(s):  
Seed Size ◽  
Composite Interval Mapping ◽  
Mixed Model ◽  
Gene Annotation ◽  
Soybean Seed ◽  
Interval Mapping ◽  
Epistatic Effect ◽  
Size And Shape ◽  
Network Analyses ◽  
Major Qtl

Seed size and shape are important traits determining yield and quality in soybean. However, the genetic mechanism and genes underlying these traits remain largely unexplored. In this regard, this study used two related recombinant inbred line (RIL) populations (ZY and K3N) evaluated in multiple environments to identify main and epistatic-effect quantitative trait loci (QTLs) for six seed size and shape traits in soybean. A total of 88 and 48 QTLs were detected through composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively, and 15 QTLs were common among both methods; two of them were major (R2 > 10%) and novel QTLs (viz., qSW-1-1ZN and qSLT-20-1K3N). Additionally, 51 and 27 QTLs were identified for the first time through CIM and MCIM methods, respectively. Colocalization of QTLs occurred in four major QTL hotspots/clusters, viz., “QTL Hotspot A”, “QTL Hotspot B”, “QTL Hotspot C”, and “QTL Hotspot D” located on Chr06, Chr10, Chr13, and Chr20, respectively. Based on gene annotation, gene ontology (GO) enrichment, and RNA-Seq analysis, 23 genes within four “QTL Hotspots” were predicted as possible candidates, regulating soybean seed size and shape. Network analyses demonstrated that 15 QTLs showed significant additive x environment (AE) effects, and 16 pairs of QTLs showing epistatic effects were also detected. However, except three epistatic QTLs, viz., qSL-13-3ZY, qSL-13-4ZY, and qSW-13-4ZY, all the remaining QTLs depicted no main effects. Hence, the present study is a detailed and comprehensive investigation uncovering the genetic basis of seed size and shape in soybeans. The use of a high-density map identified new genomic regions providing valuable information and could be the primary target for further fine mapping, candidate gene identification, and marker-assisted breeding (MAB).

scholarly journals Development and Validation of Markers for the Fertility Restorer Gene Rf1 in Sunflower

2019 ◽  
Vol 20 (6) ◽  
pp. 1260 ◽  
Author(s):  
Renate Horn ◽  
Aleksandra Radanovic ◽  
Lena Fuhrmann ◽  
Yves Sprycha ◽  
Sonia Hamrit ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Fertility Restoration ◽  
Association Studies ◽  
Restorer Gene ◽  
Hybrid Breeding ◽  
P Value ◽  
Potential Candidate ◽  
Association Panel ◽  
Dominant Markers ◽  
Genomic Regions

Hybrid breeding in sunflowers based on CMS PET1 requires development of restorer lines carrying, in most cases, the restorer gene Rf1. Markers for marker-assisted selection have been developed, but there is still need for closer, more versatile, and co-dominant markers linked to Rf1. Homology searches against the reference sunflower genome using sequences of cloned markers, as well as Bacterial Artificial Chromosome (BAC)-end sequences of clones hybridizing to them, allowed the identification of two genomic regions of 30 and 3.9 Mb, respectively, as possible physical locations of the restorer gene Rf1 on linkage group 13. Nine potential candidate genes, encoding six pentatricopeptide repeat proteins, one tetratricopeptide-like helical domain, a probable aldehyde dehydrogenase 22A1, and a probable poly(A) polymerase 3 (PAPS3), were identified in these two genomic regions. Amplicon targeted next generation sequencing of these nine candidate genes for Rf1 was performed in an association panel consisting of 27 maintainer and 32 restorer lines and revealed the presence of 210 Single Nucleotide Polymorphisms (SNPs) and 67 Insertions/Deletions (INDELs). Association studies showed significant associations of 10 SNPs with fertility restoration (p-value < 10−4), narrowing Rf1 down to three candidate genes. Three new markers, one co-dominant marker 67N04_P and two dominant markers, PPR621.5R for restorer, and PPR621.5M for maintainer lines were developed and verified in the association panel of 59 sunflower lines. The versatility of the three newly developed markers, as well as of three existing markers for the restorer gene Rf1 (HRG01 and HRG02, Cleaved Amplified Polymorphic Sequence (CAPS)-marker H13), was analyzed in a large association panel consisting of 557 accessions.

scholarly journals Genetic architecture and major genes for backfat thickness in pig lines of diverse genetic backgrounds

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Miguel Gozalo-Marcilla ◽  
Jaap Buntjer ◽  
Martin Johnsson ◽  
Lorena Batista ◽  
Federico Diez ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Genetic Variance ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Insulin Signalling ◽  
Additive Genetic Variance ◽  
Genomic Region ◽  
Carcass Composition ◽  
Backfat Thickness ◽  
Genomic Regions

Abstract Background Backfat thickness is an important carcass composition trait for pork production and is commonly included in swine breeding programmes. In this paper, we report the results of a large genome-wide association study for backfat thickness using data from eight lines of diverse genetic backgrounds. Methods Data comprised 275,590 pigs from eight lines with diverse genetic backgrounds (breeds included Large White, Landrace, Pietrain, Hampshire, Duroc, and synthetic lines) genotyped and imputed for 71,324 single-nucleotide polymorphisms (SNPs). For each line, we estimated SNP associations using a univariate linear mixed model that accounted for genomic relationships. SNPs with significant associations were identified using a threshold of p < 10–6 and used to define genomic regions of interest. The proportion of genetic variance explained by a genomic region was estimated using a ridge regression model. Results We found significant associations with backfat thickness for 264 SNPs across 27 genomic regions. Six genomic regions were detected in three or more lines. The average estimate of the SNP-based heritability was 0.48, with estimates by line ranging from 0.30 to 0.58. The genomic regions jointly explained from 3.2 to 19.5% of the additive genetic variance of backfat thickness within a line. Individual genomic regions explained up to 8.0% of the additive genetic variance of backfat thickness within a line. Some of these 27 genomic regions also explained up to 1.6% of the additive genetic variance in lines for which the genomic region was not statistically significant. We identified 64 candidate genes with annotated functions that can be related to fat metabolism, including well-studied genes such as MC4R, IGF2, and LEPR, and more novel candidate genes such as DHCR7, FGF23, MEDAG, DGKI, and PTN. Conclusions Our results confirm the polygenic architecture of backfat thickness and the role of genes involved in energy homeostasis, adipogenesis, fatty acid metabolism, and insulin signalling pathways for fat deposition in pigs. The results also suggest that several less well-understood metabolic pathways contribute to backfat development, such as those of phosphate, calcium, and vitamin D homeostasis.

scholarly journals Genome-wide association study (GWAS) for morphological and yield-related traits in an oil palm hybrid (Elaeis oleifera x Elaeis guineensis) population

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Jaime A. Osorio-Guarín ◽  
Gina A. Garzón-Martínez ◽  
Paola Delgadillo-Duran ◽  
Silvio Bastidas ◽  
Leidy P. Moreno ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Oil Palm ◽  
Elaeis Guineensis ◽  
Genome Wide Association ◽  
Hybrid Population ◽  
Potential Candidate ◽  
Elaeis Oleifera ◽  
Genome Wide ◽  
A Genome ◽  
Genomic Regions

Abstract Background The genus Elaeis has two species of economic importance for the oil palm agroindustry: Elaeis oleifera (O), native to the Americas, and Elaeis guineensis (G), native to Africa. This work provides to our knowledge, the first association mapping study in an interspecific OxG oil palm population, which shows tolerance to pests and diseases, high oil quality, and acceptable fruit bunch production. Results Using genotyping-by-sequencing (GBS), we identified a total of 3776 single nucleotide polymorphisms (SNPs) that were used to perform a genome-wide association analysis (GWAS) in 378 OxG hybrid population for 10 agronomic traits. Twelve genomic regions (SNPs) were located near candidate genes implicated in multiple functional categories, such as tissue growth, cellular trafficking, and physiological processes. Conclusions We provide new insights on genomic regions that mapped on candidate genes involved in plant architecture and yield. These potential candidate genes need to be confirmed for future targeted functional analyses. Associated markers to the traits of interest may be valuable resources for the development of marker-assisted selection in oil palm breeding.

scholarly journals Mapping Quantitative Trait Loci onto Chromosome-Scale Pseudomolecules in Flax

2020 ◽  
Vol 3 (2) ◽  
pp. 28 ◽  
Author(s):  
Frank M. You ◽  
Sylvie Cloutier
Keyword(s):  
Quantitative Trait Loci ◽  
Candidate Genes ◽  
Quantitative Trait ◽  
Quantitative Traits ◽  
Snp Markers ◽  
Genetic Maps ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Trait Loci ◽  
Genomic Regions

Quantitative trait loci (QTL) are genomic regions associated with phenotype variation of quantitative traits. To date, a total of 313 QTL for 31 quantitative traits have been reported in 14 studies on flax. Of these, 200 QTL from 12 studies were identified based on genetic maps, the scaffold sequences, or the pre-released chromosome-scale pseudomolecules. Molecular markers for QTL identification differed across studies but the most used ones were simple sequence repeats (SSRs) or single nucleotide polymorphisms (SNPs). To uniquely map the SSR and SNP markers from different references onto the recently released chromosome-scale pseudomolecules, methods with several scripts and database files were developed to locate PCR- and SNP-based markers onto the same reference, co-locate QTL, and scan genome-wide candidate genes. Using these methods, 195 out of 200 QTL were successfully sorted onto the 15 flax chromosomes and grouped into 133 co-located QTL clusters; the candidate genes that co-located with these QTL clusters were also predicted. The methods and tools presented in this article facilitate marker re-mapping to a new reference, genome-wide QTL analysis, candidate gene scanning, and breeding applications in flax and other crops.

scholarly journals Detection of candidate gene networks involved in resistance to Sclerotinia sclerotiorum in soybean

2021 ◽  
Author(s):  
Yu Zhang ◽  
Yuexing Wang ◽  
Wanying Zhou ◽  
Shimao Zheng ◽  
Runzhou Ye
Keyword(s):  
Candidate Genes ◽  
Sclerotinia Sclerotiorum ◽  
Mapk Signaling ◽  
Chromosome 17 ◽  
Potential Candidate ◽  
Nucleotide Polymorphism ◽  
Data Set ◽  
Trait Association ◽  
Trait Analysis ◽  
Genomic Regions

AbstractQuantitative trait locus (QTL) mapping often yields associations with dissimilar loci/genes as a consequence of diverse factors. One trait for which very limited agreement between mapping studies has been observed is resistance to white mold in soybean. To explore whether different approaches applied to a single data set could lead to more consistent results, haplotype-trait association and epistasis interaction effects were explored as a complement to a more conventional marker-trait analysis. At least 10 genomic regions were significantly associated with Sclerotinia sclerotiorum resistance in soybean, which have not been previously reported. At a significance level of α = 0.05, haplotype-trait association showed that the most prominent signal originated from a haplotype with 4-SNP (single nucleotide polymorphism) on chromosome 17, and single SNP-trait analysis located a nucleotide polymorphism at position rs34387780 on chromosome 3. All of the peak-SNPs (p-value < 0.05) of each chromosome also appeared in their respective haplotypes. Samples with extreme phenotypes were singled-out for association studies, 25–30% from each end of the phenotypic spectrum appeared in the present investigation to be the most appropriate sample size. Some key genes were identified by epistasis interaction analysis. By combining information on the nearest positional genes indicated that most loci have not been previously reported. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses suggest potential candidate genes underlying callose deposition in the cell wall and mitogen-activated protein kinase (MAPK) signaling pathway-plant, as well as plant-pathogen interaction pathway, were activated. Integration of multi-method genome-wide association study (GWAS) revealed novel genomic regions and promising candidate genes in novel regions, which include Glyma.01g048500, Glyma.03g129100, Glyma.17g072200, and the Dishevelled (Dvl) family of proteins on chromosomes 1, 3, 17, and 20, respectively.

scholarly journals Genetic dissection of photosynthetic performances in maize under drought-stressed and well-watered environments

2020 ◽  
Author(s):  
Xiaoqiang Zhao ◽  
Yuan Zhong ◽  
Wenli Li ◽  
Dan Zhang
Keyword(s):  
Drought Stress ◽  
Candidate Genes ◽  
Composite Interval Mapping ◽  
Interval Mapping ◽  
Mixed Linear Model ◽  
Joint Analysis ◽  
Carboxylase Activity ◽  
Critical Function ◽  
Photosynthetic Performances ◽  
Two Populations

Abstract BackgroundMaintaining photosynthetic capacity is a critical function that allows maize (Zea mays L.) to adapt to drought stress. The elucidation of genetic controls of photosynthetic performances, and tightly linked molecular markers under water stress are thus of great importance in marker-assisted selection (MAS) breeding. Meanwhile, little is known regarding their genetic controls under drought stress. Two F4 populations were developed to identify quantitative trait loci (QTLs) and dissect the genetic variation underlying six photosynthetic-related traits, namely, net photosynthetic rate (Pn), stomatal conductance (Gs), intercellular CO2 concentration (Ci), transpiration rate (Tr), ribulose 1,5-biphosphate carboxylase activity (RuBP), and water use efficiency (WUE) under drought-stressed and well-watered environments.ResultsFor two populations, we detected 54 QTLs under drought-stressed and well-watered environments by single-environment mapping with composite interval mapping (CIM), approximately 81.8~100 % QTLs displayed non-additive effects, and 43 of the 54 QTLs were identified under drought-stressed environment. We also dissected 54 QTLs via joint analysis of all environments with mixed-linear-model-based composite interval mapping (MCIM), 24 QTLs involved in QTL × environment interactions (QEIs), approximately 87.5 % QEIs were identified under drought-stressed environments, as well as 14 pair epistasis exhibited dominance-by-additive/dominance (DA/DD) effects under constracting environments. We further identified 8 constitutive QTLs (cQTLs) across two populations by CIM/MCIM under multiple environments. Remarkably, bin 1.07_1.10 (cQTL2), bin 6.05 (cQTL5), bin 7.02_7.04 (cQTL6), bin 8.03 (cQTL7), and bin 10.03 (cQTL8) exhibited 5 pleiotropic cQTLs that were consistent with phenotypic correlations among all photosynthetic-related traits. Additionally, 17 candidate genes were validated in above cQTLs.ConclusionsPhotosynthetic performances in maize were predominantly controlled by non-additive and QEIs effects, where more QEIs effects occurred in drought stress. 8 cQTLs affecting six photosynthetic-related traits could be useful for genetic improvement of these traits via QTL pyramiding, corresponding 5 QTLs clusters indicated tight linkage or pleiotropy in the inheritance of these traits, and 17 candidate genes involved in leaf morphology and development, photosynthesis, and stress reponse coincided with above corresponding cQTLs.

scholarly journals Mapping quantitative trait loci for yield-related traits and predicting candidate genes for grain weight in maize

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Yanming Zhao ◽  
Chengfu Su
Keyword(s):  
Quantitative Trait Loci ◽  
Candidate Genes ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Developmental Stages ◽  
Interval Mapping ◽  
Kernel Weight ◽  
Grain Weight ◽  
Trait Loci ◽  
Stable Qtls

Abstract Quantitative trait loci (QTLs) mapped in different genetic populations are of great significance for marker-assisted breeding. In this study, an F2:3 population were developed from the crossing of two maize inbred lines SG-5 and SG-7 and applied to QTL mapping for seven yield-related traits. The seven traits included 100-kernel weight, ear length, ear diameter, cob diameter, kernel row number, ear weight, and grain weight per plant. Based on an ultra-high density linkage map, a total of thirty-three QTLs were detected for the seven studied traits with composite interval mapping (CIM) method, and fifty-four QTLs were indentified with genome-wide composite interval mapping (GCIM) methods. For these QTLs, Fourteen were both detected by CIM and GCIM methods. Besides, eight of the thirty QTLs detected by CIM were identical to those previously mapped using a F2 population (generating from the same cross as the mapping population in this study), and fifteen were identical to the reported QTLs in other recent studies. For the fifty-four QTLs detected by GCIM, five of them were consistent with the QTLs mapped in the F2 population of SG-5 × SG-7, and twenty one had been reported in other recent studies. The stable QTLs associated with grain weight were located on maize chromosomes 2, 5, 7, and 9. In addition, differentially expressed genes (DEGs) between SG-5 and SG-7 were obtained from the transcriptomic profiling of grain at different developmental stages and overlaid onto the stable QTLs intervals to predict candidate genes for grain weight in maize. In the physical intervals of confirmed QTLs qKW-7, qEW-9, qEW-10, qGWP-6, qGWP-8, qGWP-10, qGWP-11 and qGWP-12, there were 213 DEGs in total. Finally, eight genes were predicted as candidate genes for grain size/weight. In summary, the stable QTLs would be reliable and the candidate genes predicted would be benefit for maker assisted breeding or cloning.

scholarly journals A targeted genotyping approach to enhance the identification of variants for lactation persistency in dairy cows

2019 ◽  
Vol 97 (10) ◽  
pp. 4066-4075
Author(s):  
Duy Ngoc Do ◽  
Nathalie Bissonnette ◽  
Pierre Lacasse ◽  
Filippo Miglior ◽  
Xin Zhao ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Mixed Model ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Genetic Selection ◽  
Phenotypic Variance ◽  
Potential Candidate ◽  
Nucleotide Polymorphisms ◽  
Functional Variants ◽  
Selection For

Abstract Lactation persistency (LP), defined as the ability of a cow to maintain milk production at a high level after milk peak, is an important phenotype for the dairy industry. In this study, we used a targeted genotyping approach to scan for potentially functional single nucleotide polymorphisms (SNPs) within 57 potential candidate genes derived from our previous genome wide association study on LP and from the literature. A total of 175,490 SNPs were annotated within 10-kb flanking regions of the selected candidate genes. After applying several filtering steps, a total of 105 SNPs were retained for genotyping using target genotyping arrays. SNP association analyses were performed in 1,231 Holstein cows with 69 polymorphic SNPs using the univariate liner mixed model with polygenic effects using DMU package. Six SNPs including rs43770847, rs208794152, and rs208332214 in ADRM1; rs209443540 in C5orf34; rs378943586 in DDX11; and rs385640152 in GHR were suggestively significantly associated with LP based on additive effects and associations with 4 of them (rs43770847, rs208794152, rs208332214, and rs209443540) were based on dominance effects at P < 0.05. However, none of the associations remained significant at false discovery rate adjusted P (FDR) < 0.05. The additive variances explained by each suggestively significantly associated SNP ranged from 0.15% (rs43770847 in ADRM1) to 5.69% (rs209443540 in C5orf34), suggesting that these SNPs might be used in genetic selection for enhanced LP. The percentage of phenotypic variance explained by dominance effect ranged from 0.24% to 1.35% which suggests that genetic selection for enhanced LP might be more efficient by inclusion of dominance effects. Overall, this study identified several potentially functional variants that might be useful for selection programs for higher LP. Finally, a combination of identification of potentially functional variants followed by targeted genotyping and association analysis is a cost-effective approach for increasing the power of genetic association studies.

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