Identifying the physical map-based locus of PFRU controlling flowering in cultivated octoploid strawberry by NGS-based genome wide association study.

doi:10.21203/rs.3.rs-1724573/v1

Download PDF

Research Article

Identifying the physical map-based locus of PFRU controlling flowering in cultivated octoploid strawberry by NGS-based genome wide association study.

https://doi.org/10.21203/rs.3.rs-1724573/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Controlling the flowering habit of strawberry (Fragaria × ananassa) is important for extending its period of production and increasing yields. In strawberry, the single dominant Perpetual Flowering Runnering (PFRU) locus controls the perpetual flowering (PF) trait, as revealed using a traditional linkage analysis with DNA markers. Additional DNA markers for PFRU have been developed using the linkage map; however, they have only been applied to the limited cultivars showing polymorphisms. Furthermore, distinguishing between genotypes homozygous and heterozygous for PFRU is difficult because only the dominant markers were previously available. Here, we identified the PFRU locus by performing a genome-wide association study with whole-genome sequencing data and a publicly available reference genome and mapped the previously reported DNA marker onto a physical map of F. × ananassa. This accurate location for PFRU may facilitate the development of DNA markers that can be applied to various cultivars/lines, accelerating the breeding of PF-type strawberry cultivars and facilitating the isolation of the PFRU causal gene by further narrowing down the candidate region.

Fragaria × ananassa

GWAS

PFRU

Strawberry (Fragaria × ananassa) is an allooctoploid (2n = 8x = 56) derived from a cross between F. virginiana and F. chiloensis (Hirakawa et al. 2014). Many strawberry cultivars have been bred to meet the various demands of growers and consumers, and they can be classified into seasonal-flowering (SF) and perpetual-flowering (PF) types based on their flowering habit (Hytönen and Kurokura, 2020). SF-type strawberries develop flower buds in response to low temperatures and short daylengths; therefore, they are difficult to harvest in the summer and fall in Japan. On the other hand, PF-type strawberries can flower even under hot and long-day conditions, contributing to stable year-round strawberry production, but the PF type has fewer runners, which makes propagation difficult (De Camacaro et al. 2002). Today, Japan has a high demand for tastier and higher-yielding PF-type cultivars.

Traditionally, strawberry breeders confirmed the flowering habit of new lines by cultivating the plants under long-day conditions, which was time-consuming and laborious work (Ahmadi et al. 1990); however, more recently, researchers and breeders have been working to develop DNA markers for the efficient selection of individuals showing a PF phenotype. To date, a linkage analysis using simple short repeat (SSR)-based DNA markers in strawberry has led to the identification of a single dominant locus, Perpetual Flowering Runnering (PFRU), involved in both flowering habit and the number of stolons (runners) produced (Gaston et al. 2013, Perrotte et al. 2016, Honjo et al. 2016). Salinas et al. (2017) confirmed that two SSR markers, Bx083 and Bx215, could be applied to select individuals with the dominant PFRU allele with high accuracy; however, these SSR markers can only be applied to the limited cultivars showing polymorphisms at this locus. Moreover, because the published SSR markers for PFRU are dominant, it is difficult to distinguish between the homozygous and heterozygous individuals at this locus. To breed PF-type cultivars, the dose effect of PFRU should be considered because individuals heterozygous for PFRU are reported to grow faster than individuals homozygous for this locus (Perrotte et al. 2016).

Recently, a 90K Affymetrix Axiom array named Istraw90 was made available for the high-throughput genotyping of F. × ananassa (Bassil et al. 2015, Verma et al. 2017). A recent genome-wide association study (GWAS) used the Istraw90 markers to identify the genomic region for PFRU in a 2.85-Mb region on chromosome 4-A in all subgenomes belonging to chromosome 4 (Cockerton et al. 2022). However, further narrowing down the candidate region for PFRU was difficult due to the lack of available markers for Istraw90 in this region. The markers in Istraw90 were designed at single-nucleotide polymorphisms (SNPs) and indels detected by resequencing cultivars of F. × ananassa using short reads from next-generation sequencing (NGS), with the diploid F. vesca strawberry genome used as the reference (Edger et al. 2017). The SNPs and indels located in the genomic regions harboring large structural variations between F. × ananassa and F. vesca would not be detectable in this simple resequencing using the F. vesca genome, which explains the marker gap region around PFRU in the Istraw90 array.

The chromosome-level assembly of the F. × ananassa genome has been achieved for the SF-type cultivars ‘Camarosa’ and ‘Reiko’ (Edger et al. 2019, Shirasawa et al. 2021). Additionally, increasing numbers of NGS reads from various cultivars are being made available in public databases, and more observations about the flowering habits of each cultivar are being made (Hardigan et al. 2018). Here, we performed a GWAS with a huge number of SNP positions detected across the entire reference genome of F. × ananassa to more accurately identify the candidate region for PFRU. Although the GWAS detected some false-positive regions due to highly homologous regions among the four subgenomes in the F. × ananassa genome, the correct genomic region for PFRU was determined based on the frequency of the candidate SNPs and a phylogenetic analysis of the candidate regions. Moreover, a co-dominant marker for PFRU was developed to distinguish between homozygous and heterozygous genotypes.

Plant materials

The F. × ananassa cultivars ‘Marton Princess’, ‘Ooishi shikinari 2’, ‘Morioka 33’, and ‘Morioka 34’ were obtained from the National Agriculture and Food Research Organization (NARO) Genebank of Japan (https://www.gene.affrc.go.jp/index_en.php), while the other 12 SF- and 12 PF-type cultivars used were purchased from a general gardening store in Japan. The NARO Genebank accession number for each cultivar/landrace is provided in Supplementary Table 1.

Collection of publicly available NGS data from the National Center for Biotechnology Information (NCBI)

NGS reads from 49 cultivars were obtained from the NCBI Sequence Read Archive (SRA) using SRA-tools (https://github.com/ncbi/sra-tools.git). The accession numbers of the NGS reads and the flowering habit for each cultivar/line described by Hardigan et al. (2018) are summarized in Supplementary Table 2.

GWAS

The NGS reads were aligned using BWA-mem2 version 2.2.1 (Vasimuddin et al. 2019), after which the SAM/BAM file conversion was performed using SAMtools version 1.9 (Li et al. 2009). In the resequencing analysis, the FASTA file “Camarosa genome; F_ana_Camarosa_6-28-17_hardmasked.fasta” from the SF-type cultivar ‘Camarosa’ was used as the reference genome (Edger et al. 2019). The SNP index values (Abe et al. 2012), representing SNP allele frequency, were calculated at SNP positions with a mapping quality ≥ 40 and a base quality ≥ 18 using Bcftools version 1.9 (Li 2011), with custom scripts to utilize the DP and AD tag data from the VCF file. In the application of the VCF file to the GWAS and the phylogenetic analysis, the genotype at each position with the tag ‘0/1’ and a depth ≥ 10 was redefined. The positions showing an SNP index value within the 95% confidence interval from the simulation of SNP index in F₁ individual resequencing retained the tag ‘0/1’ (Itoh et al. 2019). By contrast, the positions with a SNP index > 0.9 and < 0.1 were redefined as homozygous variant genotypes and reference alleles, respectively, in the VCF file. Of the positions that were not redefined, those with a tag ‘0/1’ and a depth < 10 or > 300 were excluded from the analysis.

The selected SNP positions were subjected to a GWAS, performed using rrBLUP version 4.6.1 (Endelman et al. 2011). And LD block was visualized by LDBlockShow ver. 1.40 (Dong et al. 2021) using all default parameters. Furthermore, the P-values for the different ratios in genotype between the SF- and PF-type cultivars at all SNP positions were calculated using Fisher’s exact test. For example, if the total numbers of ‘variant homozygous genotype’/‘heterozygous genotype’ at the same genomic position in SF- and PF-type cultivars are 20/10 and 18/15, respectively, the P-value of Fisher’s exact test is calculated based on a 2×2 table described by 20/10×18/15. The frequency of the SNP positions with a P-value < 0.01 was counted for each genomic region using a sliding window analysis with a 2-Mb window range and a 50-kbp step size (Supplementary Fig. 1). The significant peaks were defined as the regions showing a higher frequency than the 99% confidence interval in the 100 simulations using randomly selected SF- and PF-type cultivars, with the same number of individuals used in each analysis.

Phylogenetic analysis

A phylogenetic tree was constructed using the maximum likelihood (ML) method in IQ-tree version 2.0.3 (Nguyen et al. 2015). The sequences used in the phylogenetic analyses were prepared into FASTA-formatted files following the concatenation of the genotype for each cultivar at the SNP position used in the GWAS. The bootstrap simulation was conducted 1,000 times.

PCR amplification of the developed DNA marker

In this study, all the developed DNA markers were Amplification-Refractory Mutation System (ARMS) markers, in which the 3′ end of the primer targets the SNP and 2 bp upstream of the 3′ end contains a mismatched nucleotide (Ye et al. 2001). To increase the specificity of the PCR reaction, rTaq (TOYOBO) was used as the DNA polymerase, and a touchdown PCR was performed with a 60°C annealing temperature, which was reduced by 0.5°C every 32 cycles until it reached 55°C. The primers were mixed for use as a co-dominant marker to enable the identification of the genotype in one PCR reaction. The primer sequences and mixed ratio of primers are summarized in Supplementary Table 3.

Identifying the PFRU candidate region using GWAS

To perform a GWAS to identify the genomic region of PFRU, NGS short reads from 25 SF-type cultivars and 24 PF-type cultivars were obtained from the NCBI SRA (Supplementary Table 2). The NGS short reads of each cultivar were independently aligned to the publicly available strawberry genome “Camarosa genome”, which was generated using the SF-type cultivar ‘Camarosa’. A total of 17,536,173 positions revealed that at least one cultivar showed SNP. These SNP positions were applied to GWAS, which was performed using rrBLUP. The resulting GWAS led to the detection of PFRU-associated SNPs in 15 subgenomes across all chromosomes except chromosome Fvb5 (Supplementary Fig. 2). Because these results are inconsistent with previous reports that single loci of PFRU are located on chromosome Fvb4, we believe that the low accuracy of the genotyping at the SNP positions used for the GWAS caused a false-positive association. To exclude false-positive associations, we strictly redefined the genotype based on the SNP index values in the VCF file using rrBLUP and then used the VCF file to perform the GWAS again. Although the genotypes of 129,175,469 positions in each cultivar were redefined, the association was still detected in all subgenomes of chromosome Fvb4, as well as in Fvb3-1, Fvb7-1, and Fvb7-4 (Fig. 1a).

The candidate region for PFRU was therefore further narrowed using the supporting information of the frequency of the candidate SNP positions. In a frequency analysis for candidate SNPs with a P < 0.01 (Fisher’s exact test), the Fvb4-4 region 2.1–4.1 Mb was found to contain 5,640 candidate SNPs, which were detected as the highest significant peak (Fig. 1c). By contrast, Fvb4-1, -2, and − 3, the homoeologous chromosomes for Fvb4-4, only had 59, 64, and 82 candidate SNPs at their peak regions, respectively. Taken together, we concluded that PFRU is located on chromosome Fvb4-4 of the “Camarosa genome”. The region 0.07–4.06 Mb on Fvb4-4 contained the associated SNPs detected in the GWAS and was therefore defined as the candidate region. Moreover, to identify the tightly linked genomic interval within the 0.07–4.06 Mb on Fvb4-4, a linkage disequilibrium (LD) analysis was performed using the SNP positions with significant P-values in the GWAS (Fig. 2). As the result, two LD blocks divided at 3.29 Mb were detected.

Fvb3-1, Fvb7-1, and Fvb7-4 also had candidate SNPs in the GWAS, with sufficient numbers confirmed in the frequency analysis to imply that these regions may also be involved in flowering habit alongside the PFRU locus.

Mapping the genomic region for PFRU in the “Camarosa genome”

The location of the published SSR markers used for the linkage analysis for PFRU were confirmed using a BLASTn short analysis for the “Camarosa genome”. Here, we analyzed eight primers for four SSR markers: Bx083 and Bx215 mapped PFRU into a 7.3-cM interval (Perrotte et al. 2016), and FxaACA0218C and s2430859 mapped PFRU into a 1.1-cM interval (Honjo et al. 2016, 2020). These markers were independently developed to narrow the interval for PFRU using different segregating progenies from a crossing between cultivars, which explains why they mapped different intervals. The BLAST analysis showed that all primer sequences showed a high homology not only to Fvb4-4, but also to other chromosomes (Supplementary Table 4). Focusing on Fvb4-4, sequences homologous to the primers were only detected within the candidate PFRU region (0.07–4.06 Mb); however, three of the eight primer sequences showed no homology with Fvb4-4, possibly because the corresponding sequence in Fvb4-4 may not be assembled. The position for each of the markers revealed that the 2.75 − 3.71 Mb region of Fvb4-4, between Bx083 and Bx215, was the narrowest candidate region for PFRU (Fig. 2).

Phylogenetic relationships among the PFRU loci of strawberry cultivars

To understand the phylogenetic relationship between the PFRU genotypes of each strawberry cultivar, an unrooted phylogenetic tree was constructed for 50 cultivars using the ML method with the sequence for each genotype concatenated at the 14,698 SNP positions detected in any cultivar in the PFRU region (Fvb4-4: 2.75 − 3.71 Mb). In the phylogenetic tree, 18 of the 26 SF-type cultivars and 15 of the 24 PF-type cultivars were classified into two clades with short branches, named the SF and PF clades, respectively (Fig. 3). The remaining cultivars were located in independent branches.

We also constructed a phylogenetic tree using the SNPs detected within the corresponding PFRU-like regions in Fvb4-1, -2, and − 3 (Supplementary Fig. 3). Unlike the tree for the PFRU locus on Fvb4-4, the other three trees did not construct SF- and PF-specific clades. These phylogenetic trees provide further evidence that PFRU is located on Fvb4-4 rather than the other subgenomes.

Developing a co-dominant DNA marker for PFRU

Because the previously reported markers for PFRU, Bx083 and Bx215, were dominant, we developed a co-dominant marker that allowed us to confirm a heterozygous genotype in PFRU and for application during the selection process in breeding. Here, ARMS markers, which determine the genotype at an SNP position based on the annealing ability of a primer, were designed as DNA markers.

First, the PF-type-specific hetero SNP positions closely localized in PFRU were selected by a visual observation in Integrative Genomics Viewer (IGV) (Robinson et al. 2011). Here, the cultivars ‘Earliglow’, ‘Grenada’, and ‘Petaluma’ were used as the representative SF-type cultivars and ‘Albion’, ‘Diamante’, and ‘Monterey’ were the representative PF-type cultivars used for identifying the target SNP positions because they had sufficient amounts of short reads and were classified into the type-specific clades in the abovementioned phylogenetic tree. Next, two primer pairs were designed for SF- and PF-specific alleles at PF-specific hetero SNP positions. To avoid the amplification of the non-targeted subgenome, the primer specificity was confirmed by aligning the homologous region from each subgenome belonging to chromosome Fvb4 (Supplementary Fig. 4). Finally, the ARMS markers designed for the SF- and PF-specific alleles were mixed in a 1:4 volume, respectively, and used for genotyping as a co-dominant marker.

The developed marker was applied to genotype 13 SF-type and 15 PF-type cultivars obtained in Japan. All SF-type cultivars showed a single band, indicating a homozygous genotype for the SF allele. On the other hand, 15 PF-type cultivars contained the PF allele, of which 2 and 11 cultivars were homozygous and heterozygous genotypes, respectively (Fig. 4). We also applied this ARMS marker to the progenies from selfing of two PF-type cultivars, ‘Ooishi shikinari 2’ and ‘Yotsuboshi’. The genotype of 16 individuals obtained from selfing of ‘Ooishi shikinari 2’ was segregated to 1:2:1 by applying this ARMS marker, confirming that only one loci was targeted and amplification from other sub-genomes were circumvent (Supplementary Fig. 5a). Moreover, the16 individuals obtained from selfing of each seedling of ‘Yotsuboshi’, corresponding to F3 generation, showed the association between flowering habit and genotype except one individual, suggesting that this marker can be applied to practical breeding (Supplementary Fig. 5b).

Before the reference genome was made publicly available, linkage analyses of F. × ananassa genotypes depended on SSR markers designed with sequence information from the diploid strawberry (F. vesca) genome (Perrotte et al. 2016, Honjo et al. 2016). This type of analysis was time consuming and labor intensive, especially when developing DNA markers capable of distinguishing between the four subgenomes of F. × ananassa. Here, we described the GWAS of the F. × ananassa genome, which successfully eliminates the need for laborious SSR marker–dependent linkage analyses of the PFRU locus, accelerating the genetic analysis of strawberry cultivars. Despite this, the GWAS performed here detected not only the truly PFRU-associated SNPs on chromosome Fvb4-4, but also false-positive SNPs in other subgenomes of Fvb4. This occurred because the sequence homology and gene synteny among subgenomes of F. × ananassa are very high (Edger et al. 2019), making a partial misalignment of the genome very difficult to circumvent using simple resequencing with short reads from NGS. GWAS analyses of the F. × ananassa genome will therefore require the use of additional supports, such as the frequency of the candidate SNPs described in this study.

Developing DNA markers in F. × ananassa also requires consideration of the high homologous regions of subgenomes. Markers such as ARMS and Kompetitive Allele Specific PCR (KASP), which use primers specific to the allele in the target subgenome, should be suitable for use with F. × ananassa because they prevent the amplification of PCR products from non-targeted subgenomes. On the other hand, cleavage amplified polymorphic sequence (CAPS) and SSR markers using primers designed at flanking regions of the polymorphisms risk the amplification of products from the non-targeted subgenomes, making heterozygous genotypes indistinguishable from the homozygous genotype of the target allele. In the case of the PFRU locus, CAPS markers showed the same band patterns for both heterozygous and homozygous genotypes of the PF allele (data not shown), suggesting that this marker could be used only for a dominant allele.

In previous studies, the homolog gene of flowering locus T (FT), FaFT2, was considered to be a strong candidate for the causal gene underlying the effect of PFRU (Nakano et al. 2015, Perrotte et al. 2016); however, a BLAST analysis of Fvb4-4 using FaFT2 (KR139760) as the query revealed that only a partial homologous region was detected at Fvb4-4 3,395,810 − 3,395,893 bp on the PFRU locus, and no candidate mutations, such as amino acid substitutions, were identified in the FaFT2 by resequencing SF and PF cultivars. This indicates that FaFT2 is likely not the candidate for PFRU. The sequences and the expression levels of other putative candidate genes on PFRU should be compared between the SF and PF cultivars.

It is also possible that the “Camarosa genome” lacks the PFRU causal gene sequence. The PF-type allele is expected to be derived from an allele of a PF-type mutant of F. virginiana ssp. glauca collected from the Wassatch mountains in Utah (Bringhurst et al. 1980). If the genomic region containing the PF allele has a large structural variation compared with the SF-type allele in the “Camarosa genome”, the causal gene for PFRU could not be detectable by resequencing alone. The accurate genome sequence of the PFRU locus from PF-type cultivars is therefore essential for the isolation of its causal gene. So far, the de novo assembly of PFRU was challenging because most PF-type cultivars are heterozygous at this locus, causing genomic complexity. The co-dominant marker developed here can distinguish heterozygous and homozygous genotypes in PFRU, enabling the selection of individuals with a homozygous PF-type allele, which should also facilitate the de novo assembly of the PFRU genomic sequence. Moreover, the recent development of a local de novo assembly technique using long reads from Oxford nanopore sequencer has enabled the reconstruction of accurate sequences in a targeted region less than 2 Mb (Segawa et al. 2021). The long reads–based local de novo assembly of the genomes of individuals with a homozygous PF-type allele would enable the assembly of the complete genomic sequence for PFRU.

Our phylogenetic analysis using SNPs in PFRU revealed that most PF-type cultivars classified into the PF clade developed in California, USA (Supplementary Table 2), have a very similar allele pattern at this locus, implying that the diversity of the allele pattern in PFRU is lower than other corresponding regions of the subgenomes. Introducing genetic resources, such as distantly related cultivars, will increase the diversity of PF-type cultivars in the breeding program. Furthermore, the precise mapping of PFRU enabled the development of DNA markers for the selection of recombinant PFRU sequences between the SF- and PF-type alleles; therefore, novel alleles arising from recombination at PFRU could be utilized in the near future. Moreover, recombinant alleles in PFRU will also elucidate the reason behind the high ratio of heterozygous genotypes at this locus in the PF-type cultivars; in this study, 86.6% of PF-type cultivars had a heterozygous PFRU genotype, implying that breeders have selected individuals with a heterozygous genotype during the breeding process. Perrotte et al. (2016) confirmed that genotypes heterozygous for PFRU had greater plants heights and diameters than homozygous individuals. It is possible that the causal gene has a dose-dependent effect or that an undesirable recessive gene is located near the causal gene. Studying recombinants at the PFRU locus will clarify which of these hypotheses is correct.

In this study, we fine-mapped the PFRU locus in a physical map of F. × ananassa. The development of DNA markers in this genomic region will facilitate the identification of desirable PFRU alleles in new lines, accelerating the breeding of PF-type strawberry cultivars. Future studies should further narrow the PFRU candidate region to enable the isolation of the causal gene.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

Sorachi Saiga performed GWAS and blast analysis, Maiko Tada designed DNA markers, Tenta Segawa performed GWAS, Minami Nishikawa performed phylogenetic analysis, Natsu Makita performed genotyping, Minami Sakamoto confirmed the phenotype of flowering habit, Kanako Tanaka confirmed the phenotype of flowering habit, Takuya Wada conceived and supervised the entire study, Hiroki Takagi designed the research and wrote the paper. All authors contributed to the manuscript.

Data Availability

The accession numbers for the sequence data are listed in Supplementary Table 1. All data can be accessed in the National Center for Biotechnology Information (NCBI).

Abe A, Kosugi S, Yoshida K et al (2012) Genome sequencing reveals agronomically important loci in rice using MutMap. Nat Biotechnol 30:174–178. https://doi.org/10.1038/nbt.2095
Ahmadi H, Bringhurst RS, Voth V (1990) Modes of inheritance of photoperiodism in Fragaria. J Am Soc Hortic Sci 115:146–152. https://doi.org/10.21273/JASHS.115.1.146
Bassil NV, Davis TM, Zhang H et al (2015) Development and preliminary evaluation of a 90 K Axiom® SNP array for the allo-octoploid cultivated strawberry Fragaria × ananassa. BMC Genomics 16:1–30. https://doi.org/10.1186/s12864-015-1310-1
Bringhurst R, Voth V (1980) Six new strawberry varieties released. Calif Agric 34:12–15
Cockerton H, Nellist C, Hytönen T et al (2022) Epistatic Modifiers Influence the Expression of Continual Flowering in Strawberry. https://doi.org/10.1101/2022.03.15.484381. bioRxiv
de Camacaro MP, Camacaro GJ, Hadley P et al (2002) Pattern of growth and development of the strawberry cultivars Elsanta, Bolero, and Everest. J Am Soc Hortic Sci 127:901–907. https://doi.org/10.21273/JASHS.127.6.901
Dong SS, He WM, Ji JJ, Zhang C et al (2021) LDBlockShow: a fast and convenient tool for visualizing linkage disequilibrium and haplotype blocks based on variant call format files. Brief Bioinform 22:bbaa227. https://doi.org/10.1093/bib/bbaa227
Edger PP, Poorten TJ, VanBuren R et al (2019) Origin and evolution of the octoploid strawberry genome. Nat Genet 51:541–547. https://doi.org/10.5061/dryad.b2c58pc
Endelman JB (2011) Ridge regression and other kernels for genomic selection with R package rrBLUP. https://doi.org/10.3835/plantgenome2011.08.0024. Plant Genome 4
Gaston A, Perrotte J, Lerceteau-Köhler E et al (2013) PFRU, a single dominant locus regulates the balance between sexual and asexual plant reproduction in cultivated strawberry. J Exp Bot 64:1837–1848. https://doi.org/10.1093/jxb/ert047
Hardigan MA, Poorten TJ, Acharya CB et al (2018) Domestication of Temperate and Coastal Hybrids with Distinct Ancestral Gene Selection in Octoploid Strawberry. https://doi.org/10.3835/plantgenome2018.07.0049. Plant Genome 11
Hirakawa H, Shirasawa K, Kosugi S et al (2014) Dissection of the octoploid strawberry genome by deep sequencing of the genomes of Fragaria species. DNA Res 21:169–181. https://doi.org/10.1093/dnares/dst049
Honjo M, Koishihara H, Tsukazaki H et al (2020) DNA Marker Linked to Everbearing Flowering Gene in Cultivated Strawberry, with High Applicability to Various Breeding Populations. Hort J 89:161–166. https://doi.org/10.2503/hortj.UTD-034
Honjo M, Nunome T, Kataoka S et al (2016) Simple sequence repeat markers linked to the everbearing flowering gene in long-day and day-neutral cultivars of the octoploid cultivated strawberry Fragaria× ananassa. Euphytica 209:291–303. https://doi.org/10.1007/s10681-015-1626-6
Hytönen T, Kurokura T (2020) Control of flowering and runnering in strawberry. Hort J 89:96–107
Itoh N, Segawa T, Tamiru M et al (2019) Next- generation sequencing-based bulked segregant analysis for QTL mapping in the heterozygous species Brassica rapa. Theor Appl Genet 132:2913–2925. https://doi.org/10.1007/s00122-019-03396-z
Li H (2011) A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27:2987–2993. https://doi.org/10.1093/bioinformatics/btr509
Li H, Handsaker B, Wysoker A et al (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25:2078–2079. https://doi.org/10.1093/bioinformatics/btp352
Nakano Y, Higuchi Y, Yoshida Y et al (2015) Environmental responses of the FT/TFL1 gene family and their involvement in flower induction in Fragaria × ananassa. J Plant Physiol 177:60–66. https://doi.org/10.1016/j.jplph.2015.01.007
Nguyen LT, Schmidt HA, Haeseler AV et al (2014) IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol 32:268–274. https://doi.org/10.1093/molbev/msu300
Perrotte J, Gaston A, Potier A et al (2016) Narrowing down the single homoeologous FaPFRU locus controlling flowering in cultivated octoploid strawberry using a selective mapping strategy. Plant Biotechnol J 14:2176–2189. https://doi.org/10.1111/pbi.12574
Robinson JT, Thorvaldsdóttir H, Winckler W et al (2011) Integrative genomics viewer. Nat Biotechnol 29:24–26. https://doi.org/10.1038/nbt.1754
Salinas NR, Zurn JD, Mathey M et al (2017) Validation of molecular markers associated with perpetual flowering in octoploid Fragaria germplasm. Mol Breed 37:1–12. https://doi.org/10.1007/s11032-017-0672-2
Segawa T, Nishiyama C, Tamiru-Oli M et al (2021) Sat-BSA: an NGS-based method using local de novoassembly of long reads for rapid identification of genomic structural variations associated with agronomic traits. Breed Sci 71:299–312. https://doi.org/10.1270/jsbbs.20148
Shirasawa K, Hirakawa H, Nakayama S et al (2021) A chromosome-scale strawberry genome assembly of a Japanese variety. Reikou bioRxiv. https://doi.org/10.1101/2021.04.23.441065
Vasimuddin M, Misra S, Li H et al (2019) Efficient architecture-aware acceleration of BWA-MEM for multicore systems. https://doi.org/10.1109/IPDPS.2019.00041. IPDPS
Verma S, Zurn JD, Salinas N et al (2017) Clarifying sub-genomic positions of QTLs for flowering habit and fruit quality in U.S. strawberry (Fragaria×ananassa) breeding populations using pedigree-based QTL analysis. Hortic Res 4:17062. https://doi.org/10.1038/hortres.2017.62
Ye S, Dhillon S, Ke X et al (2001) An efficient procedure for genotyping single nucleotide polymorphisms. Nucleic Acids Res 29:E88–E88. https://doi.org/10.1093/nar/29.17.e88

Download PDF

Editorial decision: Major revisions
31 Jul, 2022
Reviewers invited by journal
13 Jul, 2022
Reviewers agreed at journal
21 Jun, 2022
Editor assigned by journal
04 Jun, 2022
First submitted to journal
03 Jun, 2022

You are reading this latest preprint version

Identifying the physical map-based locus of PFRU controlling flowering in cultivated octoploid strawberry by NGS-based genome wide association study.

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Results

Discussion

Declarations

References

Supplementary Files

Status:

Version 1