329 Mendelian sampling, QTL similarity, and accuracy of genomic selection

Abstract Superiority of genomic selection (GS) is argued to be due to better modeling of the Mendelian sampling (MS) and tracking of QTL similarities between individuals. It is not clear that a better genome-wide modeling of MS contributes to the increased accuracy. In fact, it might be that modeling of MS outside areas of the genome under selection pressure is detrimental to the accuracy of GS. If true, this hypothesis will provide a better framework to understand the complex relationships between MS, QTL similarity and accuracy. Increases in marker density and the need for marker prioritization makes this hypothesis even more practically important. Answering this question could have a significant impact on accuracy and the computational costs of GS implementation. A 30-chromosome genome with 50K SNPs was simulated. 200 QTL were simulated on two chromosomes for a trait with heritability of 0.4. Genomic relationships were calculated based on all 50K SNPs (G30), 3,333 SNPs on the two chromosomes carrying QTL (G2), and 46,667 SNPs on chromosomes without QTL (G28). Table 1 shows accuracies after 3 and 10 generations of (G)EBV-based selection (M1) and random selection (M2). BLUP accuracies are consistently higher (11.5 to 43.8%) than G28, showing that expected relationships better model QTL similarities than a dense panel of markers that lie outside QTL regions. Inclusion of markers that lie outside QTL regions with markers inside QTL regions reduces accuracies, as shown by the inferior (20.2 to 22.8%) performance of G30 compared to G2. Coefficients of variation were higher for low than high additive relationships suggesting that errors made in estimating QTL similarities for lowly related animals may have the most detrimental impact. Furthermore, while G28 markers capture more variation than pedigree, the superiority of BLUP indicates that variation captured by G28 is not consistent with true variation in QTL inheritance.

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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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Ideas in Genomic Selection that Transformed Plant Molecular Breeding: A Review

10.20944/preprints202010.0460.v1 ◽

2020 ◽

Author(s):

Matthew McGowan ◽

Zhiwu Zhang ◽

Jiabo Wang ◽

Haixiao Dong ◽

Xiaolei Liu ◽

...

Keyword(s):

Genomic Selection ◽

Genetic Markers ◽

Random Effects ◽

Genome Wide Association Study ◽

Single Step ◽

Hybrid Breeding ◽

Multiple Trait ◽

Underlying Distribution ◽

Genome Wide ◽

Before And After

Estimation of breeding values through Best Linear Unbiased Prediction (BLUP) using pedigree-based kinship and Marker-Assisted Selection (MAS) are the two fundamental breeding methods used before and after the introduction of genetic markers, respectively. The emergence of high-density genome-wide markers has led to the development of two parallel series of approaches inspired by BLUP and MAS, which are collectively referred to as Genomic Selection (GS). The first series of GS methods alters pedigree-based BLUP by replacing pedigree-based kinship with marker-based kinship in a variety of ways, including weighting markers by their effects in genome-wide association study (GWAS), joining both pedigree and marker-based kinship together in a single-step BLUP, and substituting individuals with groups in a compressed BLUP. The second series of GS methods estimates the effects for all genetic markers simultaneously. For the second series methods, the marker effects are summed together regardless of their individual significance. Instead of fitting individuals as random effects like in the BLUP series, the second series fits markers as random effects. Differing assumptions regarding the underlying distribution of these marker effects have resulted in the development of many Bayesian-based GS methods. This review highlights critical concept developments for both of these series and explores ongoing GS developments in machine learning, multiple trait selection, and adaptation for hybrid breeding. Furthermore, considering the increasing use and variety of GS methods in plant breeding programs, this review addresses important concerns for future GS development and application, such as the use of GWAS-assisted GS, the long-term effectiveness of GS methods, and the valid assessment of prediction accuracy.

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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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Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.)

Cells ◽

10.3390/cells10123372 ◽

2021 ◽

Vol 10 (12) ◽

pp. 3372

Author(s):

Cesar A. Medina ◽

Harpreet Kaur ◽

Ian Ray ◽

Long-Xi Yu

Keyword(s):

Salt Stress ◽

Abiotic Stress ◽

Medicago Sativa ◽

Genomic Selection ◽

Complex Traits ◽

Prediction Accuracy ◽

Breeding Value ◽

Phenotypic Traits ◽

Genome Wide ◽

Medicago Sativa L

Agronomic traits such as biomass yield and abiotic stress tolerance are genetically complex and challenging to improve through conventional breeding approaches. Genomic selection (GS) is an alternative approach in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population. In alfalfa (Medicago sativa L.), previous results indicated that low to moderate prediction accuracy values (<70%) were obtained in complex traits, such as yield and abiotic stress resistance. There is a need to increase the prediction value in order to employ GS in breeding programs. In this paper we reviewed different statistic models and their applications in polyploid crops, such as alfalfa and potato. Specifically, we used empirical data affiliated with alfalfa yield under salt stress to investigate approaches that use DNA marker importance values derived from machine learning models, and genome-wide association studies (GWAS) of marker-trait association scores based on different GWASpoly models, in weighted GBLUP analyses. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress. Finally, we expended the weighted GBLUP approach to potato and analyzed 13 phenotypic traits and obtained similar results. This is the first report on alfalfa to use variable importance and GWAS-assisted approaches to increase the prediction accuracy of GS, thus helping to select superior alfalfa lines based on their GEBVs.

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Prospects and challenges for genome-wide association and genomic selection in oilseed Brassica speciesThis article is one of a selection of papers from the conference “Exploiting Genome-wide Association in Oilseed Brassicas: a model for genetic improvement of major OECD crops for sustainable farming”

Genome ◽

10.1139/g10-087 ◽

2010 ◽

Vol 53 (11) ◽

pp. 1024-1028 ◽

Cited By ~ 8

Author(s):

W.A. Cowling ◽

E. Balázs

Keyword(s):

Genomic Selection ◽

Genetic Improvement ◽

Genome Wide Association ◽

Sustainable Farming ◽

Genome Wide ◽

Oilseed Brassicas ◽

Selection Of

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Looking to the future: what next for the dingo?

Australian Zoologist ◽

10.7882/az.2021.040 ◽

2021 ◽

Vol 41 (3) ◽

pp. 643-653

Author(s):

Thomas Newsome

Keyword(s):

New South ◽

Agricultural Landscapes ◽

Future Research ◽

Cultural Significance ◽

Zoological Society ◽

South Wales ◽

Genome Wide ◽

Independent Panel ◽

State And Local ◽

Made In

Few animals in Australia evoke as much controversy as the dingo. There are debates about its cultural significance, what to call it, and its ecological and economic impacts. Resolving these debates requires consensus and agreement among researchers, land managers and other stakeholders. To aid this, I briefly summarise how far we have come in terms of increasing our knowledge of the ecology and behaviour of dingoes since the Royal Zoological Society of New South Wales held its first symposium on the dingo in 1999. I summarise the key debates that have arisen during this period, and then summarise some of the key recommendations made in papers that were written following the 2019 symposium. I finish with some suggestions for future dingo research, focusing on (1) how we can better understand and appropriately acknowledge the cultural significance of the dingo through research, broader consultations and appropriate representations on national, state and local pest planning committees, (2) produce taxonomic consensus through the appointment of an independent panel and future research using genome-wide DNA technology, and (3) resolving ecological and economic debates via reintroduction experiments in both conservation and managed agricultural landscapes. Without such efforts, I see a future for the dingo that continues to be steeped in controversy and debate.

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