To protect from extinction, plant genetic resources are stored and regenerated in > 1750 gene banks storing > 7,000,000 accessions (FAO 2010; Levin 2013) where major research areas include genetic integrity, diversity and seed longevity (Börner et al. 2012). A systematic evaluation of survival of the plant material at gene bank repositories is always underway (Rehman-Arif and Börner 2019). Since seeds are the prime storage material, research on seed longevity is of particular importance (Börner et al. 2012).

Genetic mapping of seed longevity was initiated at the beginning of last decade with the arrival of molecular markers (Rehman-Arif et al. 2012). In wheat, in fact, albeit few likely genes, dissimilar loci linked to germination after long-term cold storage [~ 35 years at 0 ± 1 °C at 10 ± 1% relative humidity (RH)] (Rehman-Arif et al. 2017) or medium-term cold storage (12–14 years at 10 °C and 50% RH) (Agacka-Mołdoch et al. 2016) in comparison with artificial ageing protocols were identified which implied that the induced ageing protocols do not reveal predictive behaviour of the deteriorative effect of long-term storage (Schwember and Bradford 2010).

Previously, we have shown that re-analysis of two association mapping panels from Rehman-Arif et al. (2012) and Rehman-Arif et al. (2017) using newly created SNP marker data resulted in the detection of 13 potential novel quantitative trait loci (QTLs) linked to seed longevity (Rehman-Arif and Börner 2020). Based on comparisons from Zuo et al. (2019) and Zuo et al. (2018), stem rust resistance protein Rpg1, NBS-LRR resistance-like protein and FAR1-related sequence 6-like protein were identified as probable candidate genes for seed longevity in wheat.

In this report, we turn up to the re-analysis of a biparental mapping population known as “International Triticeae Mapping Initiative” (ITMI) which has been investigated regarding seed longevity after artificial ageing and dormancy by Rehman-Arif et al. (2012) as well as seed longevity after natural ageing by Agacka-Mołdoch et al. (2016). The concerned plant material is recently reported to be mapped with 7584 high-quality single-nucleotide polymorphism (SNP) markers that have yielded many novel loci for many diverse traits (Rehman-Arif et al. 2021). Hence, the objective of this study was to map new loci linked to seed longevity and dormancy based on the newly generated genotypic data reported in Rehman-Arif et al. (2021). In addition, epistasis QTLs and the connection between longevity and dormancy were explored. Finally, a possible alliance between longevity and dormancy was sought.

Materials and methods


A subset of 92 RILs from an “International Triticeae Mapping Initiative” (ITMI) population reported in Rehman Arif et al. (2021) was used in this study.


Phenotypic data published in Rehman-Arif et al. (2012) and Agacka-Mołdoch et al. (2016) were used. Data were gathered from different experiments performed with seeds from five different production years produced at two different locations (IPK-Gatersleben, Germany [years 2003 and 2009 referred to as set (i)] and Tulelake, CA, USA [years 2000, 2002 and 2013 referred to as set (ii)]. Seed longevity in set (i) was assessed by subjecting seeds to accelerated ageing (AA) and/or controlled deterioration (CD) tests [for details see Rehman-Arif et al. (2012)] and dormancy assessed by germinating seeds at 10 and 20 °C from which dormancy index (DI) was calculated. Whereas set (i) experiments were performed in 2009, set (ii) seeds were assessed in 2014 through standard germination tests of fresh seeds produced in 2013 (IG_13) and seeds produced in 2000 and 2002 (GNA_00 and GNA_02, respectively). The number of seeds, replicates, storage conditions and abbreviations are provided in Table 1.

Table 1 Production year and location, storage conditions, year of experiments, traits measured, number of seeds and replicates and abbreviations of the set of grains analysed

Genetic mapping

Additive QTLs were detected using inclusive composite interval mapping (ICIM-ADD) command implemented in IciMapping 4.1 (, where the walking speed was kept 1.0 cM. On the lines of Rehman-Arif and Börner (2019) and Rehman-Arif et al. (2012), all QTLs > 1.5 LOD and explaining ≥ 5% phenotypic variation (PVE) were reported. Digenic epistasis QTLs were identified using the ICIM-EPI command where LOD was kept 5.0 cM. Only, the epistasis QTLs with LOD ≥ 5 and explaining ≥ 5% PVE were reported. All QTLs were assigned names according the rules set out in the Catalog of Gene Symbols (McIntosh et al. 2008). All additive and epistasis QTLs were visualized using “circlize” package in R (Gu et al. 2014).


Phenotypic variation

Phenotypic distributions of all the traits are provided in Fig. 1a–e. IG_03 was higher (83.95 ± 7.86) than IG_09 (75.86 ± 16.43) in spite of the fact that 2003 seeds were stored for six years at 0 ± 1 °C and 10 ± 1% RH. On the same pattern, AA_03 (62.36 ± 13.07) and RAA_03 (74.07 ± 12.94) were higher than corresponding AA_09 (32.75 ± 22.34) and RAA_09 (40.61 ± 24.37) values. Mean values of CD_09 and RCD_09 were 26.79 ± 25.20 and 33.25 ± 28.40, respectively. There were more seeds dormant at 20 °C (76.10 ± 26.88) than at 10 °C (9.58 ± 10.44) where the value of DI was 31.77 ± 12.79. IG_13 (96.94 ± 1.85) was also higher than GNA_00 (70.68 ± 15.97) and GNA_02 (67.37 ± 13.30).

Fig. 1
figure 1

Frequency distribution of a IG_03, GAA_03 and RAA_03, b IG_09, GAA_09 and RAA_09, c IG_09, GCD_09 and RCD_09, d D10, D20 and DI and e IG_13, GNA_00 and GNA_02 in various years. The x-axis shows the germination % in a, b, c and e and dormancy % in d and the y-axis shows the number of RILs (counts). The thick vertical lines indicate mean values of respective traits in each experiment. Tables below the graph indicate range and mean ± SD of the traits

QTL mapping

QTL analysis of the 15 traits recorded across two years from five production years (Table 1) revealed a total of 46 additive QTLs where there were only six QTLs (linked to four traits viz. D20, DI, IG_13 and GNA_02) with LOD > 3 (Table S1) which explained up to 32% variation (D20) (Fig. 2). For IG_03, there were three QTLs on chromosomes 4A, 5A and 5B. The five QTLs of AA_03 were distributed on chromosomes 2B, 4A, 5B, 6B and 7B, whereas the two QTLs linked to RAA_03 were located on chromosomes 2B and 7A.

Fig. 2
figure 2

Distribution of additive (unconnected blue lines in the inner circle) and epistatic (connected blue lines in inner circle) QTLs. Green lines in the outer track indicate the SNP positions on each chromosome; red bars in the second circle indicate the LOD values of QTLs. The blue lines under the track circle indicate the confidence interval of QTLs with small vertical lines point to the peak position of QTL. The coloured lines linked different biallelic epistasic QTLs (yellow = IG_03; pink = AA_03; sky blue = RAA_03; navy blue = AA_09, aqua = RCD_09; green = D20; grey = DI; black = GNA_00; deep pink = GNA_02 and red = IG_13) (for reference, Tables S1 and S2). Coloured figure online

For IG_09, we detected five QTLs on chromosomes 2B, 4A, 5D, 6D and 7A. The three QTLs of AA_09 colocated with the three QTLs of RAA_09 on chromosomes 5A (2 QTLs) and 5D. The lone QTL of CD_09 was located on chromosome 5D, whereas the four QTLs of RCD_09 were distributed on chromosome 1A, 5B, 5D and 7B. Six QTLs each for D10 (on chromosomes 1A, 4A (4 QTLs) and 6D) and D20 (on chromosomes 2D, 4A, 4B, 5A, 7A and 7D) were detected, whereas DI was represented by two QTLs on chromosomes 4A and 5A. Two QTLs each were discovered for IG_13 (4A and 4B), GNA_00 (2B and 5B) and GNA_02 (3D and 4A).

Exploration of the QTLs with epistatic effect and QTL × environment interactions using QTL IciMapping resulted in the detection of 29 (58 QTLs in total) pairs of epistatic QTLs (Fig. 2, Table S2).

The two pairs of epistatic loci linked to IG_03 were detected on chromosomes 1B-7D and 6B-7D. Likewise, the two pairs of AA_03 epistatic QTLs were located on chromosomes 2D-5B and 4D-6A. Another three pairs linked to RAA_03 were located on chromosomes 2D-5B, 4D-6A and 4D-7B. Here the first two pairs were identical to AA_03 epistasis QTLs.

Of the seeds in 2009 production year, two pairs were linked to AA_09 on chromosomes 1B-3B and 1D-2B. Likewise, the first two pairs of RAA_09 epistasis QTLs overlapped exactly with AA_09 epistasis QTLs in addition to a pair on chromosomes 2A-7A. The maximum numbers of epistasis QTL pairs were detected for RCD_09 (eight pairs). Six of them involved one single locus on chromosome 4B that interacted with six other loci on chromosomes 2A, 2D, 5B, 5D, 6A and 7A. Furthermore, two other pairs were located on chromosomes 3A-5A and 5A-6B. Two pairs of epistasis QTLs linked with D20 interacted on chromosomes 1A-5A and 4B-7A. Likewise, two pairs linked with DI interacted on chromosomes 1D-2B and 4A-4D.

With regard to IG_13, the two epistasis QTL pairs were located on chromosomes 2D-6D and 4B-5A. Another two pairs linked to GNA_00 were located on chromosomes 1D-2D and 2B-7D. Finally, the lone pair linked to GNA_02 was located on chromosomes 4B-7A.


It has been shown that proper genome coverage is required to fully uncover the genetic makeup of any given character in a mapping population or germplasm collection (Rehman-Arif et al. 2021). An example with regard to seed longevity is the discovery of 13 potentially novel QTLs in two associations mapping panels (Rehman-Arif and Börner 2020). To add to it, most QTL mapping studies on seed longevity were focused on exploration of the additive effects of the genetic components in wheat (Agacka-Mołdoch et al. 2016, Rehman-Arif et al. 2012), barley (Nagel et al. 2009), rapeseed (Schatzki et al. 2013) and tobacco (Agacka-Mołdoch et al. 2015), whereas epistatic effects of the genetic components have not been addressed yet.

This study reports QTLs for series (i) seeds produced in 2003 on chromosomes 4A, 5A and 5B (IG_03), 2B, 4A, 5B, 6B and 7B (AA_03) and 2B and 7A (RAA_03). Common chromosomes to Rehman-Arif et al. (2012) and Agacka-Mołdoch et al. (2016) were 6B and 2B. Likewise, this study highlighted QTLs for series (i) seeds produced in 2009 on chromosomes 2B, 4A, 5D, 6D and 7A (IG_09), 5A and 5D (AA_09 and RAA_09), 5D (CD_09) and 1A, 5B, 5D and 7B (RCD_09). Here, the common chromosomes to Rehman-Arif et al. (2012) and Agacka-Mołdoch et al. (2016) include 7A and 7B. D10 and D20 QTLs alongside the DI QTL on chromosome 5A remained unaddressed before. Thus, several new loci in particular on chromosomes 4A and group 5 chromosomes (16 QTLs) linked to either seed longevity or dormancy were detected. It is pertinent to mention that Rehman-Arif et al. (2012) did not detect any QTLs on group 5 chromosomes, whereas only one QTL on chromosome 5D was detected by Agacka-Mołdoch et al. (2016) for AA_03. This could be attributed to the 1147 SNPs mapped to group 5 chromosomes which was otherwise scarcely covered hitherto (Rehman-Arif et al. 2021). Chromosomes 5A and 5B have been reported to be an important site for genes linked to plant survival (Rehman-Arif et al. 2020, Rehman-Arif and Börner 2019, Peleg et al. 2009). Furthermore, chromosome 5D has been reported to harbour several longevity loci according to Landjeva et al. (2010). In rice, chromosome 9 carried consistent seed longevity QTLs and synchronicity between group 5 chromosomes of wheat and chromosome 9 of rice with respect to longevity is already reported Rehman-Arif et al. (2017). The same conclusion is drawn in a barley study by Nagel et al. (2009).

Of the 32 sequences of SNPs involved in QTLs on group 4 and 5 chromosomes, 11 provided blast hits connected to various probable candidate genes involved in longevity and dormancy in bread wheat. For example, for a DI QTL on chromosome 4A, noroxomaritidine synthase (also known as CYP96T1) 2-like was identified. CYP96T1 is known to be involved in galanthamine metabolic pathway (Li et al. 2020). However, its association with dormancy remained unknown. Another gene involved with DI was protein kinase G11A. In addition, aspargine synthetase was identified as candidate gene for DI, AA and RGAA on chromosome 5A. The bread wheat asparagine synthetase gene family is composed of five genes, viz. TaASN1, TaASN2, TaASN3.1, TaASN3.2 and TaASN4. Among them, TaASN1s are located on group 5 chromosomes and expressed during mid-development of grains (Raffan and Halford 2021).

For standard germination, on chromosome 4A, ankyrin (ANK)-3-like isoform X1 and 60S ribosomal protein L10a and on chromosome 5A pentatricopeptide repeat-containing protein were identified. ANK proteins in plants play important roles in plant immunity, development and growth, and they are typically involved in protein–protein interactions (Kolodziej et al. 2021), whereas members of the pentatricopeptide repeat (PPR) protein family are sequence specific RNA binding proteins that play crucial roles in organelle RNA metabolism (Yan et al. 2019).

Likewise, for germination after natural ageing, geraniol 8-hydroxylase-like provided a blast hit on chromosome 4B. Geraniol 8-hydroxylase-like protein is known to be associated with MAP kinase signalling pathways (Figueiredo et al. 2018). In the end, the candidate genes associated with experimental ageing (AA/CD) included ETHYLENE-INSENSITIVE 2-like (EIN2) and RDM16-like isoform X2 on chromosome 5A and carotenoid 9,10(9′,10′)-cleavage dioxygenase-like isoform X1 and aspartyl protease family protein 2-like on chromosome 5B. EIN2 controls miRNA164 expression that remains unchanged during ageing (Reinbothe et al. 2009). Similarly, carotenoid cleavage deoxygenases cleaved the carotenoids and apocarotenoids are produced by their action which are known to play various roles in the growth and development of plants (Ohmiya 2009) and members of aspartyl protease family are involved in regulating plant defence responses (Saibi et al. 2016).

We further conclude that in the ITMI/MP, seed longevity and dormancy are controlled differently just like in Arabidopsis (Nguyen et al. 2012), in spite of the fact that there were some common markers shared between dormancy and longevity, particularly on chromosome 5A where the possible candidate genes involved were protein kinase G11A and aspargine synthetase whose exact role in these two phenomena remain elusive so far. This complexity could also be attributed to the hexaploid nature of bread wheat. Hence, much work remains to shed more light on the molecular mechanisms involved in dormancy and longevity in wheat.

We determined that in addition to additive loci, seed longevity is also controlled by epistatic loci. For example, we detected an additional 24.6, 20.9 and 44.3% variation in IG_03, AA_03 and RAA_03, respectively, in set (i) seeds of 2003 contributed by digenic epistatic loci (Table S3 and Fig. 2). On the same grounds, 37.3, 40.9 and 41% additional variations were uncovered for AA_09, CD_09 and RCD_09, respectively, in set (i) seeds of 2009. Additionally, 53.6 and 38.8% variation could be explained for D20 and DI. For set (ii) seeds, 16.8, 33.4 and 21.4 variations could be explained, respectively, for IG_13, GNA_00 and GNA_02. Previous studies using biparental populations and/or association mapping panels have reported additive QTLs only (Rehman-Arif and Börner 2020, 2019, Nagel et al. 2015, Agacka-Mołdoch et al. 2015, Schatzki et al. 2013, Rehman-Arif et al. 2012), but information on epistatic QTLs is just starting to be addressed in wheat (Shi et al. 2020) and tobacco (Agacka-Mołdoch et al. 2021). This report is only the second after Shi et al. (2020) to identify epistatic QTLs for seed longevity in bread wheat. A similar level of interactions has recently been detected for Karnal bunt in wheat in an association mapping panel by Singh et al. (2020). Full understanding of the impact of epistasis on quantitative traits, nevertheless, remains challenging (Le Rouzic and Álvarez-Castro 2008). Yet, on the basis of conclusion of the increased selection gain via marker-assisted breeding towards wheat yield (Reif et al. 2011) loci linked with seed longevity should be searched keeping in view the epistasis that might explain hidden variations of quantitative traits.