Genome-wide association study and genomic prediction using parental and breeding populations of Japanese pear (Pyrus pyrifolia Nakai)

Minamikawa, Mai F.; Takada, Norio; Terakami, Shingo; Saito, Toshihiro; Onogi, Akio; Kajiya‐Kanegae, Hiromi; Hayashi, Takeshi; Yamamoto, Takatsugu; Iwata, Hiroyoshi

doi:10.1038/s41598-018-30154-w

Cited by 56 publications

(84 citation statements)

References 71 publications

(105 reference statements)

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“…The current study used 4,113 SNP markers imputed with high accuracy, though analysis of LD found that LD declined rapidly over short distances (35). The number of markers in the current study is comparable with other studies in fruit trees (13,(15)(16)(17); however, the fragmented nature of the macadamia genome scaffolds means the distribution of markers across the whole genome is still unknown. Genetic linkage maps have been used to anchor scaffolds to chromosomes (Langdon et al…”

Section: Genetic Datasupporting

confidence: 63%

Genome-wide association studies for yield component traits in a macadamia breeding population

O’Connor

Hayes

Hardner

et al. 2019

Preprint

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Background: Breeding for new macadamia cultivars with high nut yield is expensive in terms of time, labour and cost. Most trees set nuts after four to five years, and candidate varieties for breeding are evaluated for at least eight years for various traits. Genome-wide association studies (GWAS) are promising methods to reduce evaluation and selection cycles by identifying genetic markers linked with key traits, potentially enabling early selection through marker-assisted selection. This study used 295 progeny from 32 full-sib families and 29 parents (18 phenotyped) which were planted across four sites, with each tree genotyped for 4,113 SNPs. ASReml-R was used to perform association analyses with linear mixed models including a genomic relationship matrix to account for population structure. Traits investigated were: nut weight (NW), kernel weight (KW), kernel recovery (KR), percentage of whole kernels (WK), tree trunk circumference (TC), percentage of racemes that survived from flowering through to nut set, and number of nuts per raceme. Results: Seven SNPs were significantly associated with NW (at a genome-wide false discovery rate of <0.05), and four with WK. Multiple regression, as well as mapping of markers to genome assembly scaffolds suggested that some SNPs were detecting the same QTL. There were 44 significant SNPs identified for TC although multiple regression suggested detection of 16 separate QTLs. Conclusions: These findings have important implications for macadamia breeding, and highlight the difficulties of heterozygous populations with rapid LD decay. By coupling validated marker-trait associations detected through GWAS with MAS, genetic gain could be increased by reducing the selection time for economically important nut characteristics. Genomic selection may be a more appropriate method to predict complex traits like tree size and yield.

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Section: Genetic Datasupporting

confidence: 63%

Genome-wide association studies for yield component traits in a macadamia breeding population

O’Connor

Hayes

Hardner

et al. 2019

Preprint

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“…Furthermore, prediction accuracies obtained from all Bayesian and Machine Learning methods were trait-dependent, as found by [60,88]. However, all the Machine Learning methods provided better prediction accuracies for all focal traits of S. platyclados than the Bayesian methods.…”

Section: Genomic Prediction Accuracies Of Bayesian and Machine Learnimentioning

confidence: 68%

“…Thus, population structure should be properly accounted for in marker-trait association analysis [62,63]. We found no distinct spatial clusters in PCA analysis, indicating that the S. platyclados alleles were distributed without strong structure, which is highly beneficial for GWAS resolution and genomic prediction accuracy [60,64].…”

Section: Population Structurementioning

confidence: 84%

“…A Mixed Linear Model was applied to reduce spurious associations or false positive (type I errors) by computing a kinship matrix (K) and account for population genetic structure (Q). The kinship matrix was computed by the "A.mat" function of the rrBLUP package in R, while the "n.PC" option was used to specify the number of principal components (PCs) as fixed-effect covariates to account for population structure [33,60]. PCs were estimated by Probabilistic Principal Component Analysis (PPCA).…”

Section: Four-fold Cross-validation Of Gwas-based Genomic Predictionmentioning

confidence: 99%

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Potential of Genome-Wide Association Studies and Genomic Selection to Improve Productivity and Quality of Commercial Timber Species in Tropical Rainforest, a Case Study of Shorea platyclados

Sawitri

Tani

Na’iem

et al. 2020

Forests

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Shorea platyclados (Dark Red Meranti) is a commercially important timber tree species in Southeast Asia. However, its stocks have dramatically declined due, inter alia, to excessive logging, insufficient natural regeneration and a slow recovery rate. Thus, there is a need to promote enrichment planting and develop effective technique to support its rehabilitation and improve timber production through implementation of Genome-Wide Association Studies (GWAS) and Genomic Selection (GS). To assist such efforts, plant materials were collected from a half-sib progeny population in Sari Bumi Kusuma forest concession, Kalimantan, Indonesia. Using 5900 markers in sequences obtained from 356 individuals, we detected high linkage disequilibrium (LD) extending up to >145 kb, suggesting that associations between phenotypic traits and markers in LD can be more easily and feasibly detected with GWAS than with analysis of quantitative trait loci (QTLs). However, the detection power of GWAS seems low, since few single nucleotide polymorphisms linked to any focal traits were detected with a stringent false discovery rate, indicating that the species’ phenotypic traits are mostly under polygenic quantitative control. Furthermore, Machine Learning provided higher prediction accuracies than Bayesian methods. We also found that stem diameter, branch diameter ratio and wood density were more predictable than height, clear bole, branch angle and wood stiffness traits. Our study suggests that GS has potential for improving the productivity and quality of S. platyclados, and our genomic heritability estimates may improve the selection of traits to target in future breeding of this species.

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“…GS would be a more efficient strategy for background selection, especially for complex traits, as favourable alleles from the donor and recipient lines can be targeted across the whole genome. Accuracy of GS studies in apple and pear cultivar breeding were encouraging 13,[31][32][33] , but the current applications of GS in perennial fruit crops are generally aimed at skipping Stage 1 seedling evaluation so that candidates for Stage 2 evaluation can be identified based on GEBV-hence reducing the cultivar development timeline by at least five years 13 . Although, the candidates selected for Stage 2 testing could also be used as parents of the next generation, it is suspected that their worthiness as parents could be compromised mainly because of recombination events.…”

Section: Discussionmentioning

confidence: 99%

Turbocharging introgression breeding of perennial fruit crops: a case study on apple

et al. 2020

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The allelic diversity of primitive germplasm of fruit crops provides a useful resource for introgressing novel genes to meet consumer preferences and environmental challenges. Pre-breeding facilitates the identification of novel genetic variation in the primitive germplasm and expedite its utilisation in cultivar breeding programmes. Several generations of pre-breeding could be required to minimise linkage drag from the donor parent and to maximise the genomic content of the recipient parent. In this study we investigated the potential of genomic selection (GS) as a tool for rapid background selection of parents for the successive generation. A diverse set of 274 accessions was genotyped using random-tag genotyping-by-sequencing, and phenotyped for eight fruit quality traits. The relationship between 'own phenotypes' of 274 accessions and their general combining ability (GCA) was also examined. Trait heritability influenced the strength of correspondence between own phenotype and the GCA. The average (across eight traits) accuracy of predicting own phenotype was 0.70, and the correlations between genomic-predicted own phenotype and GCA were similar to the observed correlations. Our results suggest that genome-assisted parental selection (GAPS) is a credible alternative to phenotypic parental selection, so could help reduce the generation interval to allow faster accumulation of favourable alleles from donor and recipient parents.

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Genome-wide association study and genomic prediction using parental and breeding populations of Japanese pear (Pyrus pyrifolia Nakai)

Cited by 56 publications

References 71 publications

Genome-wide association studies for yield component traits in a macadamia breeding population

Genome-wide association studies for yield component traits in a macadamia breeding population

Potential of Genome-Wide Association Studies and Genomic Selection to Improve Productivity and Quality of Commercial Timber Species in Tropical Rainforest, a Case Study of Shorea platyclados

Turbocharging introgression breeding of perennial fruit crops: a case study on apple

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