Genome-wide association improves genomic selection for ammonia tolerance in the orange-spotted grouper (Epinephelus coioides)

Abstract Background Understanding mechanisms of sugar accumulation and composition is essential to determining fruit quality and maintaining a desirable balance of sugars in plant storage organs. The major sugars in mature Rosaceae fruits are sucrose, fructose, glucose, and sorbitol. Among these, sucrose and fructose have high sweetness, whereas glucose and sorbitol have low sweetness. Japanese pear has extensive variation in individual sugar contents in mature fruit. Increasing total sugar content and that of individual high-sweetness sugars is a major target of breeding programs. The objective of this study was to identify quantitative trait loci (QTLs) associated with fruit traits including individual sugar accumulation, to infer the candidate genes underlying the QTLs, and to assess the potential of genomic selection for breeding pear fruit traits. Results We evaluated 10 fruit traits and conducted genome-wide association studies (GWAS) for 106 cultivars and 17 breeding populations (1112 F1 individuals) using 3484 tag single-nucleotide polymorphisms (SNPs). By implementing a mixed linear model and a Bayesian multiple-QTL model in GWAS, 56 SNPs associated with fruit traits were identified. In particular, a SNP located close to acid invertase gene PPAIV3 on chromosome 7 and a newly identified SNP on chromosome 11 had quite large effects on accumulation of sucrose and glucose, respectively. We used ‘Golden Delicious’ doubled haploid 13 (GDDH13), an apple reference genome, to infer the candidate genes for the identified SNPs. In the region flanking the SNP on chromosome 11, there is a tandem repeat of early responsive to dehydration (ERD6)-like sugar transporter genes that might play a role in the phenotypes observed. Conclusions SNPs associated with individual sugar accumulation were newly identified at several loci, and candidate genes underlying QTLs were inferred using advanced apple genome information. The candidate genes for the QTLs are conserved across Pyrinae genomes, which will be useful for further fruit quality studies in Rosaceae. The accuracies of genomic selection for sucrose, fructose, and glucose with genomic best linear unbiased prediction (GBLUP) were relatively high (0.67–0.75), suggesting that it would be possible to select individuals having high-sweetness fruit with high sucrose and fructose contents and low glucose content.

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Genomic selection using a subset of SNPs identified by genome-wide association analysis for disease resistance traits in aquaculture species

Aquaculture ◽

10.1016/j.aquaculture.2021.736620 ◽

2021 ◽

Vol 539 ◽

pp. 736620

Author(s):

Zheng Luo ◽

Yang Yu ◽

Jianhai Xiang ◽

Fuhua Li

Keyword(s):

Disease Resistance ◽

Genomic Selection ◽

Association Analysis ◽

Genome Wide Association ◽

Genome Wide Association Analysis ◽

Genome Wide ◽

Resistance Traits

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Genome-Wide Association and Prediction of Male and Female Floral Hybrid Potential Traits in Elite Spring Bread Wheat Genotypes

Plants ◽

10.3390/plants10050895 ◽

2021 ◽

Vol 10 (5) ◽

pp. 895

Author(s):

Samira El Hanafi ◽

Souad Cherkaoui ◽

Zakaria Kehel ◽

Ayed Al-Abdallat ◽

Wuletaw Tadesse

Keyword(s):

Genomic Selection ◽

Bread Wheat ◽

Genome Wide Association ◽

Hybrid Breeding ◽

Floral Traits ◽

Phenotypic Variance ◽

Spring Bread Wheat ◽

Genome Wide ◽

Anther Length ◽

Anther Extrusion

Hybrid wheat breeding is one of the most promising technologies for further sustainable yield increases. However, the cleistogamous nature of wheat displays a major bottleneck for a successful hybrid breeding program. Thus, an optimized breeding strategy by developing appropriate parental lines with favorable floral trait combinations is the best way to enhance the outcrossing ability. This study, therefore, aimed to dissect the genetic basis of various floral traits using genome-wide association study (GWAS) and to assess the potential of genome-wide prediction (GP) for anther extrusion (AE), visual anther extrusion (VAE), pollen mass (PM), pollen shedding (PSH), pollen viability (PV), anther length (AL), openness of the flower (OPF), duration of floret opening (DFO) and stigma length. To this end, we employed 196 ICARDA spring bread wheat lines evaluated for three years and genotyped with 10,477 polymorphic SNP. In total, 70 significant markers were identified associated to the various assessed traits at FDR ≤ 0.05 contributing a minor to large proportion of the phenotypic variance (8–26.9%), affecting the traits either positively or negatively. GWAS revealed multi-marker-based associations among AE, VAE, PM, OPF and DFO, most likely linked markers, suggesting a potential genomic region controlling the genetic association of these complex traits. Of these markers, Kukri_rep_c103359_233 and wsnp_Ex_rep_c107911_91350930 deserve particular attention. The consistently significant markers with large effect could be useful for marker-assisted selection. Genomic selection revealed medium to high prediction accuracy ranging between 52% and 92% for the assessed traits with the least and maximum value observed for stigma length and visual anther extrusion, respectively. This indicates the feasibility to implement genomic selection to predict the performance of hybrid floral traits with high reliability.

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Genome-wide association study and genomic selection for plant height, maturity, seed weight, and yield in soybean

10.21203/rs.2.17481/v1 ◽

2019 ◽

Author(s):

Waltram Ravelombola ◽

Jun Qin ◽

Ainong Shi ◽

Fengmin Wang ◽

Yan Feng ◽

...

Keyword(s):

Association Study ◽

Genomic Selection ◽

Candidate Genes ◽

Plant Height ◽

Seed Weight ◽

Genome Wide Association Study ◽

Agronomic Traits ◽

Snp Markers ◽

Genome Wide Association ◽

Genome Wide

Abstract Background Soybean [ Glycine max (L.) Merr.] is a legume of great interest worldwide. Enhancing genetic gain for agronomic traits via molecular approaches has been long considered as the main task for soybean breeders and geneticists. The objectives of this study were to evaluate maturity, plant height, seed weight, and yield in a diverse soybean accession panel, to conduct a genome-wide association study (GWAS) for these traits and identify SNP markers associated with the four traits, and to assess genomic selection (GS) accuracy. Results A total of 250 soybean accessions were evaluated for maturity, plant height, seed weight, and yield over three years. This panel was genotyped with a total of 10,259 high quality SNPs postulated from genotyping by sequencing (GBS). GWAS was performed using a Bayesian Information and Linkage Disequilibrium Iteratively Nested Keyway (BLINK) model, and GS was evaluated using a ridge regression best linear unbiased predictor (rrBLUP) model. The results revealed that a total of 20, 31, 37, 31, and 23 SNPs were significantly associated with the average 3-year data for maturity, plant height, seed weight, and yield, respectively; some significant SNPs were mapped into previously described loci ( E2 , E4 , and Dt1 ) affecting maturity and plant height in soybean and a new locus mapped on chromosome 20 was significantly associated with plant height; Glyma.10g228900 , Glyma.19g200800 , Glyma.09g196700 , and Glyma.09g038300 were candidate genes found in the vicinity of the top or the second best SNP for maturity, plant height, seed weight, and yield, respectively; a 11.5-Mb region of chromosome 10 was associated with both seed weight and yield; and GS accuracy was trait-, year-, and population structure-dependent. Conclusions The SNP markers identified from this study for plant height, maturity, seed weight and yield can be used to improve the four agronomic traits through marker-assisted selection (MAS) and GS in soybean breeding programs. After validation, the candidate genes can be transferred to new cultivars using SNP markers through MAS. The high GS accuracy has confirmed that the four agronomic traits can be selected in molecular breeding through GS.

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Genetic characterization of carrot root shape and size using genome-wide association analysis and genomic-estimated breeding values

Theoretical and Applied Genetics ◽

10.1007/s00122-021-03988-8 ◽

2021 ◽

Author(s):

Scott H. Brainard ◽

Shelby L. Ellison ◽

Philipp W. Simon ◽

Julie C. Dawson ◽

Irwin L. Goldman

Keyword(s):

Image Analysis ◽

Genetic Control ◽

Digital Image Analysis ◽

Genome Wide Association ◽

Carrot Root ◽

Size And Shape ◽

Genome Wide ◽

Market Class ◽

Selection For ◽

Estimated Breeding Values

Abstract Key message The principal phenotypic determinants of market class in carrot—the size and shape of the root—are under primarily additive, but also highly polygenic, genetic control. Abstract The size and shape of carrot roots are the primary determinants not only of yield, but also market class. These quantitative phenotypes have historically been challenging to objectively evaluate, and thus subjective visual assessment of market class remains the primary method by which selection for these traits is performed. However, advancements in digital image analysis have recently made possible the high-throughput quantification of size and shape attributes. It is therefore now feasible to utilize modern methods of genetic analysis to investigate the genetic control of root morphology. To this end, this study utilized both genome wide association analysis (GWAS) and genomic-estimated breeding values (GEBVs) and demonstrated that the components of market class are highly polygenic traits, likely under the influence of many small effect QTL. Relatively large proportions of additive genetic variance for many of the component phenotypes support high predictive ability of GEBVs; average prediction ability across underlying market class traits was 0.67. GWAS identified multiple QTL for four of the phenotypes which compose market class: length, aspect ratio, maximum width, and root fill, a previously uncharacterized trait which represents the size-independent portion of carrot root shape. By combining digital image analysis with GWAS and GEBVs, this study represents a novel advance in our understanding of the genetic control of market class in carrot. The immediate practical utility and viability of genomic selection for carrot market class is also described, and concrete guidelines for the design of training populations are provided.

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