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      Fine-mapping and validation of the genomic region underpinning pear red skin colour

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          Abstract

          Red skin colour is an important target trait in various pear breeding programmes. In this study, the genetic control of red skin colour was investigated in an interspecific population derived using the descendants of the red sport European pear cultivar ‘Max Red Bartlett’ (MRB) and the red-blushed Chinese pear cultivar ‘Huobali’. Approximately 550 seedlings from nine families were phenotyped for red skin over-colour coverage (Ocolcov) and the intensity of red over-colour (Ocolint) on a 0–9 scale, and genotyped using genotyping-by-sequencing. Genome-wide association analyses were conducted using 7500 high-quality single nucleotide polymorphisms (SNPs). Genomic regions on linkage groups (LG) 4 and 5 were found to be associated, and the best SNP (S578_25116) on LG4 accounted for ~15% of phenotypic variation in Ocolcov and Ocolint. The association of S578_25116 with Ocolcov and Ocolint was successfully validated in a sample of ~200 European and Asian pear accessions. The association with red skin at locus S578_25116 was not present in Asian pear accessions, suggesting its close proximity to the MRB’s Cardinal gene. Several putative candidate genes, including MYB transcription factors ( PCP027962 and PCP027967), were identified in the quantitative trait locus region on LG4 and await functional validation.

          Crop genetics: What makes a pear red?

          Researchers in New Zealand have produced a map of genetic variants linked with red skin color in pears, opening the door to identifying the genes responsible. Satish Kumar and others at the New Zealand Institute for Plant and Food Research Limited measured the skin color of 550 hybrid pear seedlings and sequenced their genomes. Combining these data produced a map of 7,500 variants throughout the genome and identified those associated with red skin color. The most significant variant accounted for about 15% of the color variation. Further analysis of that genomic region revealed several genes which might be related to red skin color. The genomic map produced by this study will improve breeding efficiency by making it possible to screen seedlings for fruit color, but further research is necessary to characterize the candidate genes.

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          Most cited references28

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          The genome of the pear ( Pyrus bretschneideri Rehd.)

          The draft genome of the pear ( Pyrus bretschneideri ) using a combination of BAC-by-BAC and next-generation sequencing is reported. A 512.0-Mb sequence corresponding to 97.1% of the estimated genome size of this highly heterozygous species is assembled with 194× coverage. High-density genetic maps comprising 2005 SNP markers anchored 75.5% of the sequence to all 17 chromosomes. The pear genome encodes 42,812 protein-coding genes, and of these, ∼28.5% encode multiple isoforms. Repetitive sequences of 271.9 Mb in length, accounting for 53.1% of the pear genome, are identified. Simulation of eudicots to the ancestor of Rosaceae has reconstructed nine ancestral chromosomes. Pear and apple diverged from each other ∼5.4–21.5 million years ago, and a recent whole-genome duplication (WGD) event must have occurred 30–45 MYA prior to their divergence, but following divergence from strawberry. When compared with the apple genome sequence, size differences between the apple and pear genomes are confirmed mainly due to the presence of repetitive sequences predominantly contributed by transposable elements (TEs), while genic regions are similar in both species. Genes critical for self-incompatibility, lignified stone cells (a unique feature of pear fruit), sorbitol metabolism, and volatile compounds of fruit have also been identified. Multiple candidate SFB genes appear as tandem repeats in the S -locus region of pear; while lignin synthesis-related gene family expansion and highly expressed gene families of HCT , C3′H , and CCOMT contribute to high accumulation of both G-lignin and S-lignin. Moreover, alpha-linolenic acid metabolism is a key pathway for aroma in pear fruit.
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            An R2R3 MYB transcription factor associated with regulation of the anthocyanin biosynthetic pathway in Rosaceae

            Background The control of plant anthocyanin accumulation is via transcriptional regulation of the genes encoding the biosynthetic enzymes. A key activator appears to be an R2R3 MYB transcription factor. In apple fruit, skin anthocyanin levels are controlled by a gene called MYBA or MYB1, while the gene determining fruit flesh and foliage anthocyanin has been termed MYB10. In order to further understand tissue-specific anthocyanin regulation we have isolated orthologous MYB genes from all the commercially important rosaceous species. Results We use gene specific primers to show that the three MYB activators of apple anthocyanin (MYB10/MYB1/MYBA) are likely alleles of each other. MYB transcription factors, with high sequence identity to the apple gene were isolated from across the rosaceous family (e.g. apples, pears, plums, cherries, peaches, raspberries, rose, strawberry). Key identifying amino acid residues were found in both the DNA-binding and C-terminal domains of these MYBs. The expression of these MYB10 genes correlates with fruit and flower anthocyanin levels. Their function was tested in tobacco and strawberry. In tobacco, these MYBs were shown to induce the anthocyanin pathway when co-expressed with bHLHs, while over-expression of strawberry and apple genes in the crop of origin elevates anthocyanins. Conclusions This family-wide study of rosaceous R2R3 MYBs provides insight into the evolution of this plant trait. It has implications for the development of new coloured fruit and flowers, as well as aiding the understanding of temporal-spatial colour change.
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              Increased accuracy of artificial selection by using the realized relationship matrix.

              Dense marker genotypes allow the construction of the realized relationship matrix between individuals, with elements the realized proportion of the genome that is identical by descent (IBD) between pairs of individuals. In this paper, we demonstrate that by replacing the average relationship matrix derived from pedigree with the realized relationship matrix in best linear unbiased prediction (BLUP) of breeding values, the accuracy of the breeding values can be substantially increased, especially for individuals with no phenotype of their own. We further demonstrate that this method of predicting breeding values is exactly equivalent to the genomic selection methodology where the effects of quantitative trait loci (QTLs) contributing to variation in the trait are assumed to be normally distributed. The accuracy of breeding values predicted using the realized relationship matrix in the BLUP equations can be deterministically predicted for known family relationships, for example half sibs. The deterministic method uses the effective number of independently segregating loci controlling the phenotype that depends on the type of family relationship and the length of the genome. The accuracy of predicted breeding values depends on this number of effective loci, the family relationship and the number of phenotypic records. The deterministic prediction demonstrates that the accuracy of breeding values can approach unity if enough relatives are genotyped and phenotyped. For example, when 1000 full sibs per family were genotyped and phenotyped, and the heritability of the trait was 0.5, the reliability of predicted genomic breeding values (GEBVs) for individuals in the same full sib family without phenotypes was 0.82. These results were verified by simulation. A deterministic prediction was also derived for random mating populations, where the effective population size is the key parameter determining the effective number of independently segregating loci. If the effective population size is large, a very large number of individuals must be genotyped and phenotyped in order to accurately predict breeding values for unphenotyped individuals from the same population. If the heritability of the trait is 0.3, and N(e)=100, approximately 12474 individuals with genotypes and phenotypes are required in order to predict GEBVs of un-phenotyped individuals in the same population with an accuracy of 0.7 [corrected].
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                Author and article information

                Contributors
                +64 69758893 , satish.kumar@plantandfood.co.nz
                Journal
                Hortic Res
                Hortic Res
                Horticulture Research
                Nature Publishing Group UK (London )
                2052-7276
                14 January 2019
                14 January 2019
                2019
                : 6
                : 29
                Affiliations
                [1 ]The New Zealand Institute for Plant and Food Research Limited, Hawkes Bay Research Centre, Havelock North, New Zealand
                [2 ]The New Zealand Institute for Plant and Food Research Limited, Palmerston North Research Centre, Palmerston North, New Zealand
                [3 ]The New Zealand Institute for Plant and Food Research Limited, Mount Albert Research Centre, Auckland, New Zealand
                [4 ]ISNI 0000 0000 9750 7019, GRID grid.27871.3b, Centre of Pear Engineering Technology Research, , Nanjing Agricultural University, ; 210095 Nanjing, China
                [5 ]The New Zealand Institute for Plant and Food Research Limited, Motueka Research Centre, Motueka, New Zealand
                Author information
                http://orcid.org/0000-0002-2954-762X
                Article
                112
                10.1038/s41438-018-0112-4
                6331550
                301697dd-965e-4a86-a903-2ec1d6b72233
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 July 2018
                : 8 November 2018
                : 15 November 2018
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