Sugarcane genetics: Underlying theory and practical application

Abstract

Sugarcane is recognized as the fifth largest crop globally, supplying 80% of sugar and 40% of bioenergy production. However, sugarcane genetic research has significantly lagged behind other crops due to its complex genetic background, high ploidy (8-13 x), aneuploidy, limited flowering, and a long growth cycle (more than one year). Cross breeding began in 1887 following the discovery that sugarcane seeds could germinate. Both self- and cross-pollination and selection were conducted by sugarcane breeders, but new cultivars were often eliminated due to disease susceptibility. Within the Saccharum genus, different species possess variable numbers of chromosomes. Wild sugarcane species intercrossed with each other, leading to development of the 'Nobilization' breeding strategy, which significantly improved yield, sucrose, fiber content, and disease resistance, and accelerated genetic improvement of cultivars. In recent years, scientific achievements have also been made in sugarcane genome sequencing, molecular marker development, genetic linkage map construction, localization of quantitative trait locus (QTL), and traitassociated gene identification. This review focuses on the progress in sugarcane genetic research, analyzes the technical difficulties faced, presents opportunities and challenges, and provides guidance and references for future sugarcane genetics research and cultivar breeding. Finally, it offers directions for future on sugarcane genetics.

Headnote

ABSTRACT

Sugarcane is recognized as the fifth largest crop globally, supplying 80% of sugar and 40% of bioenergy production. However, sugarcane genetic research has significantly lagged behind other crops due to its complex genetic background, high ploidy (8-13 x), aneuploidy, limited flowering, and a long growth cycle (more than one year). Cross breeding began in 1887 following the discovery that sugarcane seeds could germinate. Both self- and cross-pollination and selection were conducted by sugarcane breeders, but new cultivars were often eliminated due to disease susceptibility. Within the Saccharum genus, different species possess variable numbers of chromosomes. Wild sugarcane species intercrossed with each other, leading to development of the 'Nobilization' breeding strategy, which significantly improved yield, sucrose, fiber content, and disease resistance, and accelerated genetic improvement of cultivars. In recent years, scientific achievements have also been made in sugarcane genome sequencing, molecular marker development, genetic linkage map construction, localization of quantitative trait locus (QTL), and traitassociated gene identification. This review focuses on the progress in sugarcane genetic research, analyzes the technical difficulties faced, presents opportunities and challenges, and provides guidance and references for future sugarcane genetics research and cultivar breeding. Finally, it offers directions for future on sugarcane genetics.

Keywords:

Interspecific hybridization Nobilization breeding Non-Mendelian inheritance Sugarcane breeding

1. Introduction

Sugarcane (Saccharum spp. hybrids), a low-carbon crop that fixes a large amount of CO,, has woide adaptability, high biomass, high photosynthetic efficiency, and ability to grow as a perennial crop. Modern sugarcane cultivars account for 80% of sugar production and 40% of bioenergy ethanol production worldwide [1]. The Saccharum genus, a member of the Poaceae family, is one of 10 genera in Saccharinae. Together with Narenga, Sclerostachya, Erianthus, and Miscanthus genera, it constitutes the 'Saccharum Complex' [2]. 5. robustum and S. spontaneum are the ancestral species of the Saccharum genus. Saccharum robustum is resistant to wind and insect pests, shows perennial characteristics, has low sucrose and high fiber content and pith stems, and is susceptible to diseases. Saccharum spontaneum has a wide distribution range from 10°S to 40°N and is the most genetically diverse species in the Saccharum genus [3]. It has high fiber content, hollow and pith stems, low sugar content, good perennial characteristics, strong stress resistance, good disease resistance, early maturation, and easy flowering, which makes it an important parent for hybridization. Saccharum officinarum, known as the Noble species, shares the same center of origin (New Guinea) and the same chromosome number x = 10 as 5. robustum, implying its domestication from 5. robustum [4]. S. officinarum is characterized by high sugar and low fiber content, thick stems, soft rind, and high yield. Saccharum sinense and S. barberi are natural interspecific hybrid descendants of wild species within the Saccharum genus [5]. These species, widely cultivated in ancient China and India, were prized for their slender stems, prolific tillering, perennial traits, and robust disease resistance.

The polyploid nature of Saccharum species, along with natural inter- and intra-species hybridization and non-Mendelian inheritance, causes the highly complex genetic background of sugarcane, hindering deciphering of its genomes, molecular marker development, genetic linkage map construction, and identification of quantitative trait loci (QTL) associated with agronomic traits. Here, we summarize the historical and contemporary aspects of sugarcane breeding and the advancements and challenges associated with classical genetics in sugarcane. Our objective is to provide valuable insights for molecular breeding research aimed at achieving high yield, high sugar content, and stress resistance in sugarcane and other polyploid crops.

2. A historical review of sugarcane genetic breeding

Before 1887, several S. officinarum clones, including Bamboo Cane, Creole, Lahaina, Cristalina, and Black Cheribon, were native to the South Pacific Islands. These clones originated from natural hybridization and domestication and were characterized by thick stems, high sugar content, and low fiber. However, after being widely cultivated, various issues emerged, such as wind damage, rodent infestation, pest outbreaks, and diseases. The emergence of sugarcane diseases such as smut, red rot, mosaic disease, and root rot, combined with lack of diversity due to very few cultivars such as Creole, Bourbon, and Cheribon, led to the rise of sugar beet production surpassing sugarcane sugar in Europe and its colonial countries in 1900 [6]. In 1887 Soltwe-del F discovered that scattered sugarcane seeds germinated on the ground in Javanese experimental sugarcane fields (10°S) [6]. Later, J.B. Harrison and J.R. Bovell in the Barbados (West Indies) Botanical Garden (10°N) also observed that sugarcane seeds could sprout into seedlings [6,7,8]. These observations opened a new era of genetic research and breeding by sexual hybridization. Sexual hybridization and Noble breeding methods have been gradually explored and refined by sugarcane breeders. In 1889, sugarcane breeding was initiated by natural self-pollination or cross hybridization of flower spikes in the field. Within a decade, cultivars such as EK 2, EK 28, and POJ 100 were selected, signifying the transition of sugarcane breeding into the era of sexual hybridization. For example, D 74 was bred from natural flower spikes of Cristallina, H 109 was bred from flower spikes in Lahaina cane field, and POJ 100 was bred from seeds of a Loethers x Black Cheribon cross, and EK 28 was bred from seeds of POJ 100 crossed with another noble species [7]. These cultivars were all derived from S. officinarum x S. officinarum hybrids and were susceptible to diseases. In 1893, Kobus [9] discovered the sugarcane cultivar Chunnee in India which had slender stems and was resistant to root and smut diseases. It was speculated then that Chunnee might be a natural interspecies hybrid. Most hybrids of Chunnee and Black Cheribon were resistant to smut disease and had thin stems. РО] 63 and РО) 213 were selected from these crosses. Inspired by Kobus, his successor, Jeswiet [10], found a wild sugarcane 'Kassoer' in Java that was resistant to mosaic and root diseases but had low sugar content. It was suspected that Kassoer was a naturally occurring hybrid of Black Cheribon and a local thin-stemmed, wild genotype of S. spontaneum [10]. Artificial crosses of Black Cheribon and S. spontaneum clone, "Glagah", exhibited the same phenotype as the wild 'Kassoer', but with lower plant height. Concurrently, Wibrink, a colleague of Jeswiet, established a hybrid population from a РО] 100 x Kassoer cross, from which Jeswiet selected РО] 2364 that was resistant to mosaic, root rot, and red rot diseases. Cultivar POJ 2364 had thin stems and was male sterile. From a cross of POJ 2364 (female parent) and EK 28 (Fig. 1A), POJ 2878, the 'King of Sugarcane', was selected from the progeny population of 2266 lines based on its robust stems, high sugar content, high yield, and resistance to wilt disease [11]. The cultivar РО] 2878 was popularized in Java from 1927 to 1930, leading to a 21% increase in cane yield from 6.88 to 8.32 tonnes per hectare and an increase in sugar content from 11.02% to 11.50% [3]. Currently, 80%-90% of sugarcane cultivars worldwide have kinship to POJ 2878. This pioneering method of sugarcane breeding involved interspecies hybridization between S. officinarum. cv. Black Cheribon (2n = 80, x = 10) and S. spontaneum cv. Glagah (2n = 40-128, x = 8), and backcrossing the Е, 'Kassoer' (2n = 136), with 5. officinarum РО] 100 (2п = 80) and EK 28 (2n = 80) for sucrose yield restoration [12], known as the 'Noble' breeding method. Interestingly, the offspring of the first two interspecific crosses showed a gametic transmission mode of 2n + n (Fig. 1A, B). This hybridization method led the globally important cultivars РО] 2878, РО] 2714, РО] 2725, and РО] 2883. Given the implications of these successful interspecific crosses, sugarcane breeders continued to incorporate wild species in developing more adapted cultivars. In India, РО] 213, a descendant of Black Cheribon x Chunnee (S. barberi), was crossed to a S. spontaneum clone (2n = 64) and cv. Co 281 and Co 290 were selected from the progeny. A Co 290 x РО] 2878 cross led to Co 419 and several other hybrids with typical traits of S. officinarum, S. barberi, and S. spontaneum [13]. Sugarcane breeders in Hawaii, utilized POJ 2878 and Co 213 to breed H 32-8560, which in turn, was crossed to H 34-1874, a S. robustum offspring, to breed H 37-1933, which was later crossed to a S. sinense clone H 44-3340 to produce H 49-5 and several other superior cultivars with growth cycles exceeding 24 months [3]. In China, sugarcane breeders crossed NCo 310 with PT 43-52, a progeny of S. robustum, and developed cultivars F 146 and F 151 [14].

For crossing of modern sugarcane cultivars. flowering is induced by controlling temperature, humidity, and light conditions. To prevent self-pollination, the stamens are killed by immersing the inflorescence in water at (45 °C) for 10 min [15], followed by bagging for hybridization with selected male inflorescences (Fig. 1C, F). This procedure led to the development of modern sugarcane cultivars with 2n = 100-130 that are hybrids resulting from interspecies hybridization between S. officinarum as female and 5. spontaneum. In cultivars, 70%-80% of the chromosomes originate from S. officinarum, 10%-20% originate from 5. spontaneum, and 5%-17% of the chromosomes are recombinant between the two founder species [16] (Fig. 1G). Following the interspecific hybridization there are multiple rounds of backcrossing with elite cultivars, and given the long generation breeding cycles are not highly efficient. In recent years, sugarcane hybrid breeding has primarily employed parent lines such as POJ 2878, РО] 213, Co 281, Co 290, and various leading national cultivars. Although excellent cultivars have been bred, the narrow genetic base has resulted in limited stress and disease resistance. Consequently, there is an urgent need to perform more interspecific hybridization to incorporate new lineages and resistance genes.

3. Cytogenetics of the Saccharum genus

Chromosomes are the main carriers of genetic components and determining their number and origin is crucial for understanding the taxonomy and evolution of different species within the Saccharum genus. Modern sugarcane cultivars are derived from interspecific crossing and backcrossing among six different Saccharum species. During the process, some chromosomes from different species recombine, and both Mendelian and non-Mendelian inheritance lead to variable numbers (2n = 100-130) and size (1-6 ит) in the hybrids or cultivars [17,18]. Substantial genetic diversity has been attributed to chromosome fragmentation, translocation, loss, and recombination. In addition, the difficulty in discerning monovalent, bivalent, and multivalent chromosomes, and infrequent or no flowering render cytogenetic investigations in sugarcane highly challenging [19]. Price [20] discovered two fundamental karyotypes in S. robustum, namely, 2n = 60 and 2n = 80 on the islands of New Guinea, Borneo, and Celebes, and chromosome numbers exceeding these two types (2n = 63-205) were attributed either to intraspecific crosses or outcrossing with the 'Noble' species. A Noble (S. officinarum) clone also has 2n = 80. Any Noble clone with a chromosome number deviating from 2n = 80 is regarded as a hybrid descendant. For instance, the clone Loether has 2п = 98-99, Naz Reunion has 2n = 109-110, Uba has 2n = 113, and Creole has 2n = 81. Clones of S. spontaneum have diverse chromosome numbers ranging from 2n = 40-128, with the majority having 2n = 80, 64, or 96. Meng et al. [21] identified six ploidy types, namely, 2n = 6x, 8x, 10x, 11x, 12x, and 13x, among 20 S. spontaneum clones by fluorescence in situ hybridization. The odd ploidy numbers of these clones originated from interspecific hybridization events rather than autopolyploidization [21]. S. sinense has 2n = 116-120, whereas 5. barberi has members in four chromosome types, all 2n = 81-124 [22].

During interspecific and more distant hybridizations, the transmission of chromosomes from parents to offsprings deviates from classical Mendelian genetics presenting various transmission types, such as n + n, 2n + n, n + 2n, 2n + 2n (Fig. 1A) [12,23,24]. This phenomenon, characterized by gametes with varying numbers of chromosomes, is termed 'unbalanced inheritance'. Bremer [25] demonstrated that in backcrossing F; hybrids involving Noble genotypes and S. spontaneum, the pattern of chromosome transmission was 2n + n, indicating that the Noble genotypes transferred its total chromosome set to the Fı and BC; generations (Fig. 1A). When a Noble genotype is hybridized with S. sinense or modern sugarcane cultivars, the transmission pattern is also 2n + п [23]. Kandasami [26] found that the majority of F; offspring from crosses between S. spontaneum (maternal parent) and Noble species (paternal parent), had an n + n constitution, but a minority were 2n + n. Price [20] reported that when Noble genotypes were intercrossed or crossed as female with S. robustum, the majority of offsprings were п + п, with a minority 2n + 2n. When $. robustum with different chromosome types (2n = 64 or 2n = 80) was crossed with S. spontaneum, the 2n = 64 type progeny were n + n and most of the 2n = 80 progeny were 2n + n, with a few being n + n [27]. This atypical cytogenetic phenomenon was also observed in hybridizations between sugarcane and closely related genera, such as sugarcane x Tripidium arundinaceum [17] and sugarcane x Erianthus [28]. Within the Saccharum genus, hybridization, chromosomal recombination, and non-Mendelian inheritance have contributed to the exceptional complexity of the genetic background of modern sugarcane cultivars. This complexity has significantly hindered the decoding of hybrid genomes and indirectly impacted the use molecular breeding tools, as well as genetic mapping and functional gene identification.

4. Genomic characteristics of Saccharum genus

Modern sugarcane cultivars are predominantly hybrids of S. officinarum and S. spontaneum. Sugarcane cultivars have 100-130 chromosomes with genomic chromosome sets ranging from 8x12x and are often aneuploid. The genome size is large (approximately 10 Gb) (Fig. 1G) [29,30], thereby presenting challenges in genome sequencing and assembly. Since 2003, several major sugarcane-growing countries, including Australia, Brazil, China, Colombia, France, India, South Africa, Thailand, and the United States, have addressed the task of decoding the genomes of sugarcane and related species. Based on sequence data analysis, three primary outcomes have emerged: (1) Genome sequencing of modern sugarcane cultivars - In 2003, Brazil was the first to publish a partial genome sequence from 26 cDNA libraries generated from different tissues of eight cultivars with 33,620 putative genes, albeit not assembled at the chromosomal level [31]. In 2018, France published a haploid genome sequence (382 Mb) of cv. R570 using Sorghum bicolor as the reference genome. The R570 haploid genome consisted of 4660 BAC library fragments and was annotated with 25,316 protein-coding genes [30]. The French team, in collaboration with groups in the U.S., Australia, and Czech Republic, published a whole genome sequence (5.04 Gb) of R570 in 2024 [29]and annotated 194,593 genes, the majority of which was assigned to chromosomes originating from S. officinarum (73%) and the remainder from S. spontaneum. Between 2019 and 2022, Brazil [32], Colombia [33], and Thailand [34] also assembled whole genome sequences of their respective representative cultivars. China recently published a whole genome sequence (10.4 Gb) of sugarcane hybrid 'ZZ1'; it was assembled into 114 chromosomes and with defined alleles of 68,509 genes [1]. (2) Genome sequencing of Saccharum wild species - Chinese scientists published a whole genome sequence (2.9 Gb) of S. spontaneum clone AP85-441 (2п = 4х = 32, х =8)in 2018 [35]. Its whole sequence was assembled into 32 chromosomes and annotated with 35,525 genes. In 2022, the same team deciphered the whole genome sequence (2.76 Gb) of another wild S. spontaneum clone Np-x (2n = 4x = 40, x = 10) [36]. That sequence was assembled into 40 chromosomes and annotated with 45,014 genes. (3) Genome sequencing of a related species Erianthus rufipilus. In 2023, two Chinese research teams published the genome sequences of two diploid E. rufipilus clones, one unknown and the other named Yunnan 2009-3 [37,38]. The whole genome sequence (902 Mb) of the unknown E. rufipilus clone consisted of 10 chromosomes with 35,065 genes annotated. The whole genome sequence (856.4 Mb) of Yunnan 2009-3 also had 10 chromosomes with 32,770 genes annotated. The Yunnan 2009-3 genome is the first telomere-to-telomere-free genome sequence from a Saccharum complex clone [38]. Taken together, substantial advancements have been made in decoding the genomes of sugarcane and related species. However, the genomes of six Saccharum species remain unfinished.

5. Development and application of DNA molecular markers in sugarcane

Modern sugarcane cultivars are derived from interspecific hybridization, resulting in derivatives that have a rich level of genetic diversity accumulated through chromosomal exchange and recombination (Fig. 2A). This is particularly notable in terms of stem shapes and colors, internode length and fertility. The types of molecular markers used in the construction of sugarcane genetic linkage maps include restriction fragment length polymorphism (RFLP), random amplified polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP), simple sequence repeats (SSRs), single nucleotide polymorphisms (SNPs), and array-based genotype by-sequencing (GBS). In 1992, Burnquist et al. [39] was the first to create a sugarcane random genome library; they selected low-copy probes to analyze the genetic diversity of 59 Saccharum and closely related species using RFLP markers. The main advantage of RFLP markers was that probes can come from different related species. Ming et al. [40,41] used RFLP probes from cDNA and genomic clones from sorghum, maize, rice, barley, and oat to construct the sugarcane genetic linkage maps. A further application of RFLP markers was determination of chromosome pairing affinity in sugarcane meiosis [42]. The transferability of RAPD markers between different sugarcane populations or laboratories is challenging due to their limited reproducibility. Despite their rapid generation, these markers were utilized to assess the genetic diversity among sugarcane cultivars and Saccharum species [43]. AFLP markers depend on the digestion of genomic DNA with two restriction enzymes (usually a 6-base and a 4-base). The most prevalent combinations are EcoR I and Mse 1. This is followed by primer ligation and preamplification using a single selective base, followed by three more selective bases [44]. AFLP markers were initially developed using polyacrylamide gels and demanded specific skills to produce clear banding patterns. The primary applications of the above markers were in constructing genetic linkage maps and analyzing the genetic relationships of diverse cultivars [45-48]. SSRs and SNPs are the other two major DNA markers with numerous reports for sugarcane [49-61]. However, due to its genome complexity, the number of useful polymorphic markers in sugarcane is still limited, consequently limiting their applicability. SSRs, commonly referred to as microsatellites, consist of 1-6 base pairs of perfect tandem and compound repeats [62] and are randomly and widely distributed across whole genomes (Fig. 2B) [63]. SSR markers have been used in sugarcane to evaluate genetic diversity, analyze segregation among single pollens, identify varieties, and construct genetic linkage maps [18,46,47,64-68]. The development and validation of sugarcane SSR markers primarily centers on genomic sequences and expressed sequence tags (ESTs), and the process costly, time-consuming, and requires high skill levels. SNP markers are highly abundant, highly efficient and high throughput, and have been extensively applied in assessment of species-relatedness, genetic map construction, and whole genome association analysis [69]. Li et al. [70] sequenced the genomes of 219 elite sugarcane accessions from worldwide sources and identified approximately 6 million high-quality SNPs across the entire genome. Yang et al. [71] conducted deep sequencing on 307 sugarcane accessions and obtained five million variant sequences. The sequencing depth was able to distinguish sequence variations among different alleles. The results revealed significant differences among S. barberi, S. sinense, and sugarcane cultivars in genome composition, hybridization pattern, and levels of genetic contribution rate from different ancestral species [71]. Nevertheless, SNP genotyping also bears some drawbacks, including high cost in identification and technical difficulties in utilization. The high ploidy levels, aneuploidy, and varying chromosome numbers greatly limit the application of SNP genotyping in sugarcane (Fig. 2C) [23,61].

6. Construction of genetic linkage maps in sugarcane

Genetic linkage maps are required to locate key candidate genes and investigate the genetic basis of important traits. Based on pop- ulation genetics and map-based cloning, numerous genes have been identified in several diploid crops, including corn, rice, and foxtail millet. Due to the large genome size (approximately 10 Gb), high chromosome number (100-130), polyploidy, and heterozygosity [18], many molecular markers are needed to cover the entire sugarcane genome. The initial estimates of sugarcane genome size were around 17,000-18,000 cM [46,72]. Therefore, to construct a saturated sugarcane genetic linkage map with an average distance of 0.5 cM between any two markers, 34,00036,000 single- and [ог double-dose markers are needed, many more than 3000 markers in rice or 5000 markers in corn [73]. From the early 1990s to 2023, 25 sugarcane genetic linkage maps were reported (Table 1); however, all were incomplete with limited chromosomal coverage and wide intervals between genetic mark ers. Consequently, accurate localization of important traitcontrolling genes is challenging, and development of more molecular markers and screening for candidate genes remains ongoing.

7. QTL and genome-wide association studies (GWAS) related to important traits in sugarcane

Most agronomic traits in sugarcane are controlled by multiple genes and are difficult to analyze due to the complex genome makeup. The phenotypes are easily influenced by environmental factors and can only be analyzed by statistical methods (Fig. 2D) [87]. Sugarcane, being a heterozygous polyploid crop that is asexually propagated, presents many challenges in sugarcane genetics research, such as self-incompatibility, difficulties with flowering or hybridization [88]. These factors contribute to the scarcity of markers for genetic mapping, and a likelihood that some of QTL would not be located. Currently, there has been limited research on sugarcane QTL localization, especially QTL that are associated with sucrose content, plant height, fiber content, stem diameter, and disease resistance (Fig. 2E). Table 2 summarizes sugarcane QTL work conducted from 1996 to 2023. The numbers of self- or F, progenies involved in the studies are relatively small, ranging from 83 to 305, which is not sufficient for map-based cloning. Simultaneously, quantitative traits like sucrose content, plant height, fiber content, and stem diameter [41,72,89], are significantly influenced by environmental factors, with relatively minor contributions from phenotype. These traits are mainly affected of multiple minor genes. On the other hand, there is more disease resistance-related QTL research in sugarcane [28,90,91], mostly due to the reason that disease resistance is often controlled by few genes with larger effects and is such cases molecular markers are easier to apply in breeding. The predominant markers utilized in sugarcane breeding thus far are only those closely associated with the Brul locus for brown rust resistance and the G1 region for orange rust resistance [84,92].

Agronomic traits of sugarcane are regulated by QTL, which typically display small and variable effects and environmentally sensitive. Genetic drift makes conventional methods of QTL identification time-consuming and inefficient [93]. In recent years, genomic selection (GS) has been widely adopted breeding values of individual plants [94]. Based on the yield and sugar content of 432 sugarcane clones, Islam et al. [95] applied GWAS and GS methods to identify several candidate genes and SNP loci with moderate genomic prediction (GP) accuracies ranging from 0.21 to 0.36. The same populations were further assessed for resistance to brown rust and orange rust with 8825 SNP markers to verify the accuracy of GP values for two GS models. These were 0.28 to 0.43 for brown rust and 0.13 for 0.29 for orange rust [96]. In another study, genetic values were predicted on 10 traits among 167 sugarcane clones based on 1499 diversity array technology (DAIT) markers [97]. Four prediction models applied for crossvalidation gave equivalent accuracies for each trait, but there were significant differences among traits [97]. Various prediction models have been developed, including genomic best linear unbiased prediction (G-BLUP), ridge regression best linear unbiased tion and Bayesian methods [93,98]. Full-sibling progenies were sequenced for genotyping and associating genomic regions for brown rust resistance [99]. A total of 14,540 SNPs were utilized for phenotypic prediction via machine learning and different models were applied to obtain an average prediction accuracy of 50%. In addition, eight selection techniques also were tested to achieve an accuracy of up to 95% [99]. Using a 26K SNP chip to to genotype 3984 sugarcane clones, Hayes et al. [100] showed that the genomic single step (GenomicSS) of three models achieved the most accurate prediction ranging from 0.30 to 0.44. Using stem biomass and sugar content data of 297 clones from 87 sugarcane families and 33,149 SNP markers genotyping data, Inamori et al. [101] made a genomic relationship matrix with 10-fold repeats and leave-one-family cross-validation, and obtained G-BLUP model with accuracies ranging from 0.36 to 0.74 and 0.15 to 0.63 respectively. Additionally, machine learning generally surpassed G-BLUP in prediction accuracy [101]. These reports suggested that the application of GS technology in sugarcane breeding is constrained by several factors, including the type and density of molecular markers, the effects of candidate genes, population size, and shortcomings of the statistical prediction models.

Being an asexually reproductive crop, the agronomic traits of natural sugarcane populations should be phenotypically evaluated over several years. Trait-associated QTL can be discovered by integrating this information with comparative genomics and candidate genes can be identified. Fifteen stable markers were developed from a marker-trait association study involving 108 Indian sugarcane germplasms accounted for 57% of variation in millable stalks, 34% in stem width, 27% in plant height, 20% in sugar content, and 19% in internode number [102]. Forty-seven SNPs were linked to sucrose content across 36 QTL, 138 SNPs were associated with single stem weight, millable stalks, stalk height, and internode number spanning 104 QTL, and 12 QTL associated with yield traits encompassed 35 candidate genes [103]. Utilizing the MLM approach, 2198 SNPs were significantly linked with 48 QTL encompassing six categories of agronomic traits distributed across 32 chromosomes. Notably, half of these SNPs were specifically linked to sucrose content and ratoon properties. Subsequently, 1500 potential genes were identified from these loci [70]. Genotyping via target enrichment sequencing was performed on 308 accessions from a world collection of sugarcane and related grasses, and nine yield traits were associated with the populations. This analysis revealed 217 non-redundant markers and 225 candidate genes that were significantly associated with those traits [104]. Based on previous GWAS studies on sugarcane, it was observed that current association analyses of natural populations primarily aim to identify quantitative traits such as cane yield, sucrose content, sucrose accumulation, stalk height, stalk diameter, tillering, fiber content, and fiber composition [70,103-107]. However, the candidate gene loci identified were widely distributed with low repeatability, and there were difficulties in developing molecular markers. Additionally, genetic and cytogenetic variations among different populations used to develop markers make it difficult to validate them in other populations. Comparative mapping of sugarcane with related crops such as sorghum for homology-based cloning is a rapid and highly effective method, which is commonly used for the fine mapping of genes and the identification of candidate key genes [40,108]. Genetic maps for S. officinarum and 5. robustum were constructed using single-dose RFLP probes [109]. A significant genome collinearity between sorghum and Saccharum was identified, and an investigation into transmission genetics revealed incomplete polysomy in these species; however multiple-dose markers could not be mapped due to the absence of a genetic model for their segregation [109]. Surprisingly, when sugarcane genetic maps were constructed using DArT, SNP, and EST-SSR markers, and compared with the sorghum genome, some sugarcane chromosomes exhibited a higher level of homology than others with sorghum. Furthermore, four major chromosomal rearrangements were detected between the remaining four sugarcane homologous groups and sorghum [110]. It is assumed that current of sugarcane genetic linkage maps have relatively low saturation rates, and sugarcane population genetic studies for QTL detection remain at a preliminary mapping stage.

8. Challenges, opportunities, and prospects of sugarcane genetic research

According to statistical data of the International Society of Sugar Cane Technologists (ISSCT), the technological contribution rate of sugarcane genetic improvement can reach as high as 60% [14]. Therefore, it is crucial to identify the biological functions of major candidate genes and unravel the genetic mechanisms underlying the formation of key traits in sugarcane (Fig. 2F). Current sugarcane germplasm consists primarily of clones of wild and domesticated Saccharum species or interspecific Saccharum spp. hybrids. Due to the technical challenges in flower induction, self-pollination, and inbreeding depression in sugarcane, it is impossible or extremely difficult to develop inbred lines or homozygous parental clones as in other Poaceae crops. In addition, to make things more complicated different combinations of multiple homozygous or heterozygous chromosomes during meiosis result in multiple allelic and gametic separation patterns. Moreover, the heterozygosity and high ploidy of sugarcane parents also cause rampant separation in pseudo-F, populations [122]. Most sugarcane breeding populations consist of three types of progenies, namely, interspecific hybrids of S. officinarum and S. spontaneum or other species, selfprogenies of cultivars, and hybrid between different cultivars. It is recommended that major sugarcane production countries establish and phenotype large-scale (over 800) self-progeny populations of their major cultivars, such as R570 (France), LCP85-384 (USA), ROC22 (China), CoS96268 (India) and share these self-progeny populations along with multi-year, multi-site phenotypic data for collaborative research on genetic linkage mapping. Only through such joint efforts can scientific breakthroughs in sugarcane population genetic research and localization of QTL of key traits become possible.

As one of the largest genomes among Gramineae species, the complex genetic background of sugarcane presents challenges in developing DNA molecular markers [16]. The number of polymorphic SSR markers developed in sugarcane genetics is still inadequate to meet the needs for constructing high-density genetic linkage maps even if enough SSR markers have been developed. Furthermore, the limited number of generations (~10 or less) in sugarcane cross breeding imposes constraints on the polymorphism of SSR markers [44]. Hence, it is necessary for multiple institutions involved in sugarcane breeding to collaborate in obtaining the genomes of sugarcane cultivars and in identifying many polymorphic SSR markers uniformly distributed across different linkage groups. These SSR markers should be single-dose markers that segregate at single loci 1:1 or 3:1 ratio. Traditional nondenaturing polyacrylamide gel electrophoresis has a limited resolution, resulting in a low number of polymorphic markers. Optimization of PCR amplification conditions with fluorescently labeled primers and further improvement of electrophoresis conditions, such as using denaturing polyacrylamide gel or capillary gel electrophoresis, to enhance the resolution should be considered to address these problems. Given the complex genetic background, high ploidy, and expensive sequencing costs, it may be possible to develop sugarcane SNP chips with single- and low-dose SNPs from publicly available transcriptomic and genomic data from different sugarcane hybrid combinations. One good example is the high-throughput, cost-effective, and automated genotyping technology developed by You et al. [123].

Modern sugarcane cultivars have allopolyploid genomes and aneuploidy, producing different segregation ratios [124]. The Join- Map 4.0 software is predominantly utilized to analyze the polymorphic markers of parents using the Cross Pollinators model designed for diploid species [125]. There is currently no suitable software available for constructing genetic maps in polyploid species. Therefore, only single-dose markers with segregation ratios of 1:1 or 3:1 have been used to estimate the linkage distances in sugarcane [124]. The primary drawback of current software is that it involves a single configuration, meaning that the frequency of chromosome exchanges cannot meet the requirements for constructing high-density genetic linkage maps [28]. Hence, it is necessary to use larger genetic populations to compensate for this limitation. It is also crucial to develop software suitable for constructing linkage maps for sugarcane based on complex segregation patterns.

The key agronomic traits in sugarcane mainly comprise sucrose content, brix of juice, plant height, stem diameter, stem number per hectare, fiber content, and disease resistance. QTL for these traits can offer entry points for cloning key genes and unraveling molecular genetic mechanisms. However, apart from the closely linked markers for brown rust resistance genes Brul [92] and orange rust resistance marker G1 [84], most QTL for various traits in sugarcane are still at a preliminary mapping stage (Table 2). GS has been widely applied in maize, rice, and sorghum [126-128]. However, its implementation in sugarcane remains nascent, with only 12 reports [93,95-97,99]. GS prediction accuracies for quantitative traits are generally low to moderate. Consequently, there is a pressing need to incorporate known conditions and parameters to enhance prediction accuracy, thereby opening new avenues for genetically improving the complex traits in sugarcane. Interspecific sugarcane hybrids originated from a limited number of parental accessions, including S. officinarum accessions Black Cheribon, EK 28, and РО] 100, and wild 5. spontaneum lines, such as Glagah from Java and an unknown clone from India. Fortunately, sugarcane parents and their progenies can propagate asexually to allow multiyear, multi-site phenotypic assessments and data collection. By leveraging these advantages, screening and identifying candidate genes is feasible in sugarcane by referencing to the genomes of S. spontaneum clone AP85-441 [35], Np-x [36], or cultivars R570 [29], and ZZ1 [1] that are already aligned to each other.

With the increasing global human population, decreasing available arable land, and multiple overlapping biotic and abiotic stresses, there is an urgent need to expedite genetic research in sugarcane [129]. To develop new germplasm or cultivars with superior agronomic traits, it is crucial to investigate the molecular mechanisms underpinning major sugarcane traits, identify key genes that regulate these traits, and select markers that are associated with these traits. It is thus recommended that researchers persist in exploring the genetic mechanisms of vital agronomic traits in sugarcane, such as yield, sucrose accumulation, and robust disease resistance. In addition, emphasis should be placed on integrating big genomic data, bioinformatics algorithms, artificial intelligence (AI) models, and high-throughput, accurate phenomics. For example, bioinformatics methods can be employed to analyze haplotypes, structural and epigenetic variations, insertions and deletions, and transposable elements inside candidate genes or regions from various sugarcane germplasm already sequenced. This will enhance our understanding of polymorphism at the population level and establish a foundation for the cloning and functional validation of candidate genes. Phenotypes are influenced by environmental factors such as light, temperature, humidity, and soil conditions. The challenges associated with phenotyping in sugarcane are further exacerbated by its large genome, tall plant structure, long growth cycle, and complex agronomic traits [130]. Consequently, researchers must develop suitable phenotyping technologies, along with improved data management and analysis tools [131,132]. These efforts are anticipated to advance sugarcane genetic research and breeding to achieve high yield, elevated sugar content, enhanced disease resistance, and reaching better ratooning ability.

CRediT authorship contribution statement

Hengbo Wang: Writing - review & editing, Writing - original draft, Funding acquisition. Yong-Bao Pan: Writing - review & editing. Mingxing Wu: Visualization. Junhong Liu: Visualization, Conceptualization. Shiwei Yang: Investigation. Qibin Wu: Writing - review & editing, Resources. Youxiong Que: Writing - review & editing, Supervision, Project administration, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The authors are thankful to Professors Paul White and James Todd from USDA-ARS for excellent comments in reviewing the draft manuscript. The authors also acknowledge Professor Liping Xu, Ms. Chunyan Feng, and Mr. Guogiang, Huang for providing pictures. Furthermore, the historical and cytological investigations of sugarcane genetic breeding in the manuscript is based on the foundational works of predecessors Junsu Luo and Shaoguang Peng, to whom we express our gratitude and respect. This work was supported by the National Key Research and Development Program of China (2022YFD2301100), National Natural Science Foundation of China (32272156), Natural Science Foundation of Fujian Province, China (2022J01160), Central Public-interest Scientific Institution Basal Research Fund (1630052024003, 1630052024020), Chinese Academy of Tropical Agricultural Sciences for Science and Technology Innovation Team of National Tropical Agricultural Science Center (CATASCXTD202402), and China Agriculture Research System of MOF and MARA (CARS-17).

Sidebar

© 2024 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NCND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

ARTICLE INFO

Article history:

Received 10 August 2024

Revised 5 November 2024

Accepted 14 November 2024

Available online 14 December 2024

References

References

[1] Y. Bao, О. Zhang, J. Huang, S. Zhang, W. Yao, 7. Yu, Z. Deng, J. Yu, W. Kong, X. Yu, S. Lu, Y. Wang, R. Li, Y. Song, С. Zou, Y. Xu, Z. Liu, Е. Yu, J. Song, У. Huang, J. Zhang, H. Wang, B. Chen, X. Zhang, M. Zhang, A chromosomal-scale genome assembly of modern cultivated hybrid sugarcane provides insights into origination and evolution, Nat. Commun. 15 (2024) 3041.

[2] VA. Amalraj, N. Balasundaram, On the taxonomy of the members of 'Saccharum complex', Genet. Resour. Crop Evol. 53 (2006) 35-41.

[3] S.G. Peng, Sugarcane Genetic Breeding, China Agriculture Press, Beijing, China, 1990, pp. 101-121.

[4] A. D'Hont, D. Ison, К. Alix, С. Roux, J.C. Glaszmann, Determination of basic chromosome numbers in the genus Saccharum by physical mapping of ribosomal RNA genes, Genome 41 (1998) 221-225.

[5] J.E. Irvine, Saccharum species as horticultural classes, Theor. Appl. Genet. 298 (1999) 186-194.

[6] J.S. Luo, Sugarcane Science, China Light Industry Press, Beijing, China, 1992, pp. 162-170.

[7] J. Harrison, Seedlings of sugar cane at Barbados. (Saccharum officinarum L.), Bull. Miscellaneous Inf. (Royal Botanic Gardens, Kew) (1888) 294-296.

[8] P.D. Griggs, Global Industry, Local Innovation: the History of Cane Sugar Production in Australia, 1820-1995, Peter Lang, Lausanne, Switzerland, 2011.

[9] J. Kobus, Historisch overzicht ober het zaaien van suikerriet, Arch. JavaSuikerind (1893) 14-20.

[10] J. Jeswiet, The development of selection and breeding of sugarcane in Java, Int. Soc. Sugar Cane Technol. (1930) 44-57.

[11] J. Jeswiet, The history of sugar cane selection work in Java, Int. Soc. Sugar Cane Technol. (1927) 115-122.

[12] S.R. Hermann, K.S. Aitken, Р.А. Jackson, A.W. George, № Piperidis, X. Wei, A. Kilian, F. Detering, Evidence for second division restitution as the basis for 2n + п maternal chromosome transmission in a sugarcane cross, Euphytica 187 (2012) 359-368.

[13] T.S. Venkatraman, Sugarcane-improved varieties for Punjab, Agric. J. India 22 (1927) 293-297.

[14] К. Chen, L. Xu, Y. Lin, Z. Deng, М. Zhang, J. Luo, Modern Sugarcane Genetic Breeding, China Agriculture Press, Beijing, China, 2011, pp. 2-23.

[15] Y.B. Pan, D.M. Burner, Q. Wei, С.М. Cordeiro, B.L. Legendre, RJ. Henry, New Saccharum hybrids in S. spontaneum cytoplasm developed through a combination of conventional and molecular breeding approaches, Plant Genet. Res. 2 (2004) 131-139.

[16] A. D'Hont, L. Grivet, P. Feldmann, J.C. Glaszmann, S. Rao, М. Berding, Characterisation of the double genome structure of modern sugarcane cultivars (Saccharum spp.) by molecular cytogenetics, Mol. Gen. Genet. 250 (1996) 405-413.

[17] N. Piperidis, J.W. Chen, Н.Н. Deng, L.P. Wang, P. Jackson, С. Piperidis, GISH characterization of Erianthus arundinaceus chromosomes in three generations of sugarcane intergeneric hybrids, Genome 53 (2010) 331-336.

[18] Y.B. Pan, P. Liu, Y. Que, Independently segregating simple sequence repeats (SSR) alleles in polyploid sugarcane, Sugar Tech 17 (2015) 235-242.

[19] A. D'Hont, Unraveling the genome structure of polyploids using FISH and GISH: examples of sugarcane and banana, Cytogenet. Genome Res. 109 (2005) 27-33.

[20] S. Price, Cytological studies in Saccharum and allied genera Ш. Chromosome numbers in interspecific hybrids, Bot. Gazette 118 (1957) 146-159.

[21] Z. Meng, J. Han, Y. Lin, Y. Zhao, О, Lin, X. Ma, J. Wang, М. Zhang, L. Zhang, О. Yang, K. Wang, Characterization of a Saccharum spontaneum with a basic chromosome number of x=10 provides new insights on genome evolution in genus Saccharum, Theor. Appl. Genet. 133 (2020) 187-199.

[22] A. D'Hont, Е. Paulet, J.C. Glaszmann, Oligoclonal interspecific origin of 'North Indian' and 'Chinese' sugarcanes, Chromosome Res. 10 (2002) 253-262.

[23] P. George, P. Nathalie, D.H. Angélique, Molecular cytogenetic investigation of chromosome composition and transmission in sugarcane, Mol. Genet. Genomics 284 (2010) 65-73.

[24] J. Wu, Y. Huang, Y. Lin, C. Fu, S. Liu, Z. Deng, Q. Li, Z. Huang, R. Chen, M. Zhang, Unexpected inheritance pattern of Erianthus arundinaceus chromosomes in the intergeneric progeny between Saccharum spp. and Erianthus arundinaceus, PLoS ONE 9 (2014) e110390.

[25] G. Bremer, Problems in breeding and cytology of sugar cane, Euphytica 2 (1961) 59-78.

[26] P.A. Kandasami, Economic characters of triploids of Saccharum spontaneum Linn, and their utilization in sugarcane breeding, Sci. Culture 25 (1960) 491492.

[27] Y. Wen, Chromosomal transmission and genetic effects of interspecific and intravarietal sugarcane hybridization, Sugarcane 112 (1997) 29-36.

[28] Z. Wang, G. Lu, Q. Wu, A. Li, Y. Que, L. Xu, Isolating QTL controlling sugarcane leaf blight resistance using a two-way pseudo-testcross strategy, Crop J. 10 (2022) 1131-1140.

[29] A.L. Healey, О. Garsmeur, J.T. Lovell, S. Shengquiang, A. Sreedasyam, J. Jenkins, С.В. Plott, N. Piperidis, N. Pompidor, У. Llaca, CJ. Metcalfe, J. DoleZel, P. Capal, JW. Carlson, J.Y. Hoarau, С. Hervouet, С. Zini, A. Dievart, A. Lipzen, M. Williams, L.B. Boston, J. Webber, К. Keymanesh, S. Tejomurthula, 5. Rajasekar, К. Suchecki, A. Furtado, С. May, P. Parakkal, B.A. Simmons, К. Barry, R.J. Henry, J. Grimwood, K.S. Aitken, J. Schmutz, A. D'Hont, The complex polyploid genome architecture of sugarcane, Nature 628 (2024) 804-810.

30O. Garsmeur, С. Droc, К. Antonise, J. Grimwood, В. Potier, К. Aitken, J. Jenkins, G. Martin, C. Charron, C. Hervouet, L. Costet, N. Yahiaoui, A. Healey, D. Sims, Y. Cherukuri, A. Sreedasyam, A. Kilian, A. Chan, M.A. van Sluys, K. Swaminathan, С. Town, H. Berges, В. Simmons, J.C. Glaszmann, E. van der Vossen, Е. Henry, J. Schmutz, A. D'Hont, A mosaic monoploid reference sequence for the highly complex genome of sugarcane, Nat. Commun. 9 (2018) 2638.

31A.L. Vettore, Е.К. da Silva, E.L. Kemper, С.М. Souza, A.M. da Silva, M.L.T. Ferro, Е. Henrique-Silva, E.A. Giglioti, M.V. Lemos, LL. Coutinho, Analysis and functional annotation of an expressed sequence tag collection for tropical crop sugarcane, Genome Res. 13 (2003) 2725-2735.

32G.M. Souza, M.A. Van Sluys, C.G. Lembke, H. Lee, G.R.A. Margarido, C.T. Hotta, J.W. Gaiarsa, A.L. Diniz, М.М. Oliveira, S.S. Ferreira, M.Y. Nishiyama, Е. TenCaten, С.Т. Ragagnin, P.M. Andrade, К.Е. de Souza, С.С. Nicastro, К. Pandya, С. Kim, H. Guo, А.М. Durham, M.S. Carneiro, J. Zhang, X. Zhang, О. Zhang, К. Ming, М.С. Schatz, В. Davidson, А.Н. Paterson, D. Heckerman, Assembly of the 373k gene space of the polyploid sugarcane genome reveals reservoirs of functional diversity in the world's leading biomass crop, GigaScience 8 (2019) giz129.

[33] J.H. Trujillo-Montenegro, М.). Rodríguez Cubillos, C.D. Loaiza, M. Quintero, H. F. Espitia-Navarro, F.A. Salazar Villareal, C.A. Viveros Valens, A.F. González Barrios, J. de Vega, J. Duitama, J.J. Riascos, Unraveling the genome of a high yielding Colombian sugarcane hybrid, Front. Plant Sci. 12 (2021) 694859.

[34] JR. Shearman, W. Pootakham, С. Sonthirod, С. Naktang, T. Yoocha, D. Sangsrakru, N. Jomchai, S. Tongsima, J. Piriyapongsa, C. Ngamphiw, N. Wanasen, K. Ukoskit, P. Punpee, P. Klomsa-Ard, K. Sriroth, J. Zhang, X. Zhang, R. Ming, S. Tragoonrung, S. Tangphatsornruang, A draft chromosomescale genome assembly of a commercial sugarcane, Sci. Rep. 12 (2022) 20474.

[35] J. Zhang, X. Zhang, H. Tang, О. Zhang, X. Hua, X. Ма, Е. Zhu, T. Jones, X. Zhu, J. Bowers, C.M. Wai, C. Zheng, Y. Shi, S. Chen, X. Xu, J. Yue, D.R. Nelson, L. Huang, Z. Li, H. Xu, D. Zhou, Y. Wang, W. Hu, J. Lin, Y. Deng, N. Pandey, M. Mancini, D. Zerpa, J.K. Nguyen, L. Wang, L. Yu, Y. Xin, L. Ge, J. Arro, J.O. Han, S. Chakrabarty, M. Pushko, W. Zhang, Y. Ma, P. Ma, M. Lv, F. Chen, G. Zheng, J. Xu, Z. Yang, F. Deng, X. Chen, Z. Liao, X. Zhang, Z. Lin, H. Lin, H. Yan, Z. Kuang, W. Zhong, P. Liang, G. Wang, Y. Yuan, J. Shi, J. Hou, J. Lin, J. Jin, P. Cao, Q. Shen, Q. Jiang, P. Zhou, Y. Ma, X. Zhang, R. Xu, J. Liu, Y. Zhou, H. Jia, Q. Ma, R. Qi, Z. Zhang, J. Fang, H. Fang, J. Song, M. Wang, G. Dong, G. Wang, Z. Chen, T. Ma, H. Liu, S.R. Dhungana, S.E. Huss, X. Yang, A. Sharma, J.H. Trujillo, M.C. Martinez, M. Hudson, J.J. Riascos, M. Schuler, L.Q. Chen, D.M. Braun, L. Li, О. Yu, J. Wang, К. Wang, M.C. Schatz, D. Heckerman, M.-A. Van Sluys, G.M. Souza, P.H. Moore, D. Sankoff, R. VanBuren, A.H. Paterson, C. Nagai, R. Ming, Allele-defined genome of the autopolyploid sugarcane Saccharum spontaneum L., Nat. Genet. 50 (2018) 1754.

[36] Q. Zhang, Y. Qi, H. Pan, H. Tang, G. Wang, X. Hua, Y. Wang, L. Lin, Z. Li, Y. Li, F. Yu, Z. Yu, Y. Huang, T. Wang, P. Ma, M. Dou, Z. Sun, Y. Wang, H. Wang, X. Zhang, W. Yao, Y. Wang, X. Liu, M. Wang, J. Wang, Z. Deng, J. Xu, О. Yang, 7. Liu, В. Chen, M. Zhang, К. Ming, J. Zhang, Genomic insights into the recent chromosome reduction of autopolyploid sugarcane Saccharum spontaneum, Nat. Genet. 54 (2022) 885-896.

33L. Kui, A. Majeed, X. Wang, Z. Yang, J. Chen, L. He, Y. Di, X. Li, Z. Qian, Y. Jiao, G. Wang, L. Liu, R. Xu, S. Gu, Q. Yang, S. Chen, H. Lou, Y. Meng, L. Xie, F. Xu, Q. Shen, A. Singh, K. Gruber, Y. Pan, T. Hao, Y. Dong, F. Li, A chromosome-level genome assembly for Erianthus fulvus provides insights into its biofuel potential and facilitates breeding for improvement of sugarcane, Plant Commun. 4 (2023) 100562.

[38] T. Wang, B. Wang, X. Hua, H. Tang, Z. Zhang, R. Gao, Y. Qi, Q. Zhang, G. Wang, Z. Yu, Y. Huang, Z. Zhang, J. Mei, Y. Wang, Y. Zhang, Y. Li, X. Meng, Y. Wang, H. Pan, S. Chen, Z. Li, H. Shi, X. Liu, Z. Deng, B. Chen, M. Zhang, L. Gu, J. Wang, R. Ming, W. Yao, J. Zhang, A complete gap-free diploid genome in Saccharum complex and the genomic footprints of evolution in the highly polyploid Saccharum genus, Nat. Plants 9 (2023) 554-571.

[39] W.L. Burnquist, M.E. Sorrelles, D. Tanksley, Characterization of genetic variability in Saccharum germplasm by means of restriction fragment length polymorphism (RFLP) analysis, Proc. Int. Soc. Sugar Cane Technol. 21 (1992) 355-365.

[40] К. Ming, S.C. Liu, Y.R. Lin, J. Silva, W. Wilson, D. Braga, A. Deynze, T. Wenslaff, K. Wu, P. Moore, W. Burnquist, M. Sorrells, J. Irvine, A. Paterson, Detailed alignment of Saccharum and sorghum chromosomes: comparative organization of closely related diploid and polyploid genomes, Genetics 150 (1999) 1663-1682.

[41] К. Ming, S.C. Liu, Р.Н. Moore, J.E. Irvine, А.Н. Paterson, QTL analysis in a complex autopolyploid: genetic control of sugar content in sugarcane, Genome Res. 11 (2001) 2075-2084.

[42] N. Jannoo, L. Grivet, J. David, A. D'Hont, J.C. Glaszmann, Differential chromosome pairing affinities at meiosis in polyploid sugarcane revealed by molecular markers, Heredity 93 (2004) 460-467.

[43] N. Nair, S. Nair, T. Sreenivasan, M. Mohan, Analysis of genetic diversity and phylogeny in Saccharum and related genera using RAPD markers, Genet. Resour. Crop. Evol. 46 (1999) 73-79.

[44] K. Aitken, History and development of molecular markers for sugarcane breeding, Sugar Tech 24 (2022) 341-353.

[45] J.Y. Hoarau, В. Offmann, A. D'Hont, A.M. Risterucci, D. Roques, J. Glaszmann, L. Grivet, Genetic dissection of a modern sugarcane cultivar (Saccharum spp.). 1. Genome mapping with AFLP markers, Theor. Appl. Genet. 103 (2001) 84-97.

[46] K.S. Aitken, Р.А. Jackson, C.L. McIntyre, A combination of AFLP and SSR markers provides extensive map coverage and identification of homo(eo) logous linkage groups in a sugarcane cultivar, Theor. Appl. Genet. 110 (2005) 789-801.

[47] 5. Alwala, С. Kimbeng, J. Veremis, К. Gravois, Linkage mapping and genome analysis in a Saccharum interspecific cross using AFLP, SRAP and TRAP markers, Euphytica 164 (2008) 37-51.

[48] XL. Liu, J. Mao, X. Lu, 1. Ma, KS. Aitken, Р.А. Jackson, O. Cai, Y.H. Fan, Construction of molecular genetic linkage map of sugarcane based on SSR and AFLP markers, Acta Agron. Sin. 36 (2010) 177-183.

[49] L. Liu, Н.В. Wang, Y.H. Li, S.Q. Chen, М.Х. Wu, MJ. Dou, Y.Y. Qi, J.P. Fang, J.S. Zhang, Genome-wide development of interspecific microsatellite markers for Saccharum officinarum and Saccharum spontaneum, J. Integr. Agric. 21 (2022) 3230-3244.

[50] К.В. Singh, M.D. Mahenderakar, AK. Jugran, RK. Singh, RK. Srivastava, Assessing genetic diversity and population structure of sugarcane cultivars, progenitor species and genera using microsatellite (SSR) markers, Gene 753 (2020) 144800.

[51] S. Parthiban, P. Govindaraj, S. Senthilkumar, Comparison of relative efficiency of genomic SSR and EST-SSR markers in estimating genetic diversity in sugarcane, 3 Biotech 3 (2018) 144.

[52] RK. Singh, S. Jena, M.S. Khan, S. Yadav, N. Banerjee, S. Raghuvanshi, V. Bhardwaj, S. Duttamajumder, R. Kapur, S. Soloman, M. Swapna, S. Srivastava, A. Tyagi, Development, cross-species/genera transferability of novel EST-SSR markers and their utility in revealing population structure and genetic diversity in sugarcane, Gene 524 (2013) 309-329.

[53] М.Е. Perera, М.Е. Arias, D. Costilla, A.C. Luque, М.В. Garcia, C.D. Romero, J. Racedo, S. Ostengo, М.Р. Filippone, M.I. Cuenya, А.Р. Castagnaro, Genetic diversity assessment and genotype identification in sugarcane based on DNA markers and morphological traits, Euphytica 185 (2012) 491-510.

[54] T.G. Marconi, E.A. Costa, H.R. Miranda, M.C. Mancini, C.B. Cardoso-Silva, K.M. Oliveira, L.R. Pinto, M. Mollinari, A.A. Garcia, A.P. Souza, Functional markers for gene mapping and genetic diversity studies in sugarcane, BMC Res. Notes 4 (2011) 264.

[55] K.M. Oliveira, L.R. Pinto, T.G. Marconi, M. Mollinari, E.C. Ulian, S.M. Chabregas, M.C. Falco, W. Burnquist, A.A. Garcia, A.P. Souza, Characterization of new polymorphic functional markers for sugarcane, Genome 52 (2009) 191-209.

[56] G.M. Cordeiro, Y.B. Pan, RJ. Henry, Sugarcane microsatellites for the assessment of genetic diversity in sugarcane germplasm, Plant Sci. 165 (2003) 181-189.

[57] Q. You, Y.B. Pan, L.P. Xu, S.W. Сао, О.М. Wang, Y.C. Su, Y.Q. Yang, О.В. Wu, D.G. Zhou, Y.X. Que, Genetic diversity analysis of sugarcane germplasm based on fluorescence-labeled simple sequence repeat markers and a capillary electrophoresis-based genotyping platform, Sugar Tech 18 (2016) 380-390.

[58] L.S.C. Duarte Filho, Р.Р. Silva, J.M. Santos, G.V.S. Barbosa, С.Е. Ramalho-Neto, L. Soares, J.C.F. Andrade, С. Almeida, Genetic similarity among genotypes of sugarcane estimated by SSR and coefficient of parentage, Sugar Tech 12 (2010) 145-149.

[59] L. Pinto, K. Oliveira, T. Marconi, A. Garcia, E. Ulian, A. de Souza, Characterization of novel sugarcane expressed sequence tag microsatellites and their comparison with genomic SSRs, Plant Breed. 125 (2006) 378-384.

[60] С.М. Cordeiro, К. Casu, C.L. McIntyre, J.M. Manners, RJ. Henry, Microsatellite markers from sugarcane (Saccharum spp.) ESTs cross transferable to Erianthus and sorghum, Plant Sci. 160 (2001) 1115-1123.

[61] ©. You, X. Yang, 7. Peng, M.S. Islam, S. Sood, Z. Luo, J. Comstock, L. Xu, J. Wang, Development of an Axiom Sugarcane100K SNP array for genetic map construction and QTL identification, Theor. Appl. Genet. 132 (2019) 2829-2845.

[62] C. Romero, A. Pedryc, V. Muñoz, G. Llácer, M.L. Badenes, Genetic diversity of different apricot geographical groups determined by SSR markers, Genome 46 (2003) 244-252.

[63] G. Levinson, G.A. Gutman, Slipped-strand mispairing: a major mechanism for DNA sequence evolution, Mol. Biol. Evol. 4 (1987) 203-221.

[64] P. Liu, A. Chandra, Y. Que, P.H. Chen, M. Grisham, W. White, C. Dalley, T. Tew, Y.B. Pan, Identification of quantitative trait loci controlling sucrose content based on an enriched genetic linkage map of sugarcane (Saccharum spp. hybrids) cultivar 'LCP 85-384', Euphytica 207 (2016) 527-549.

[65] S. Ul Haq, P. Kumar, RK. Singh, K.S. Verma, R. Bhatt, M. Sharma, S. Kachhwaha, S.L. Kothari, Assessment of functional EST-SSR markers (sugarcane) in crossspecies transferability, genetic diversity among Poaceae plants, and bulk segregation analysis, Genet. Res. Int. 216 (2016) 7052323.

[66] K. Ukoskit, G. Posudsavang, N. Pongsiripat, P. Chatwachirawong, P. Klomsaard, P. Poomipant, S. Tragoonrung, Detection and validation of EST-SSR markers associated with sugar-related traits in sugarcane using linkage and association mapping, Genomics 111 (2019) 1-9.

[67] N. Xiao, H. Wang, W. Yao, M. Zhang, R. Ming, J. Zhang, Development and evaluation of SSR markers based on large scale full-length transcriptome sequencing in sugarcane, Trop. Plant Biol. 13 (2020) 343-352.

[68] X. Lu, H. Zhou, Y. Pan, C. Chen, J. Zhu, P. Chen, Y. Li, Q. Cai, R. Chen, Segregation analysis of microsatellite (SSR) markers in sugarcane polyploids, Genet. Mol. Res. 14 (2015) 18384-18395.

[69] K.S. Aitken, M.D. McNeil, S. Hermann, Р.С. Bundock, A. Kilian, К. HellerUszynska, RJ. Henry, J. Li, A comprehensive genetic map of sugarcane that provides enhanced map coverage and integrates high-throughput Diversity Array Technology (DArT) markers, BMC Genomics 15 (2014) 152.

[70] X. Li, X. Chen, J. Fang, X. Feng, X. Zhang, H. Lin, МУ. Chen, М. Zhang, H. He, Z. Huang, X. Xue, Y. Li, L. Fan, К. Lai, Z. Huo, M. Cui, С. Deng, С. Zaid, Y. Su, J. Zhang, W. Cai, Y. Qi, Whole-genome sequencing of a worldwide collection of sugarcane cultivars (Saccharum spp.) reveals the genetic basis of cultivar improvement, Plant J. 119 (2024) 2151-2167.

[71]X. Yang, J. Song, J. Todd, Z. Peng, D. Paudel, Z. Luo, X. Ma, Q. You, E. Hanson, Z. Zhao, Y. Zhao, J. Zhang, К. Ming, J. Wang, Target enrichment sequencing of 307 germplasm accessions identified ancestry of ancient and modern hybrids and signatures of adaptation and selection in sugarcane (Saccharum spp.), a 'sweet' crop with 'bitter' genomes, Plant Biotechnol. J. 17 (2019) 488-498.

[72] J.Y. Hoarau, L. Grivet, В. Offmann, LM. Raboin, J.P. Diorflar, J. Payet, М. Hellmann, A. D'Hont, J.C. Glaszmann, Genetic dissection of a modern sugarcane cultivar (Saccharum spp.) II. Detection of QTLs for yield components, Theor. Appl. Genet. 105 (2002) 1027-1037.

[73] X. Fang, W. Wu, J. Tang, Crop DNA Marker-Assisted Breeding, China Science Press, Beijing, China, 2002, pp. 22-28.

[74] J. Da Silva, М. Sorrells, W. Burnquist, S. Tanksley, RFLP linkage map and genome analysis of Saccharum spontaneum, Genome 36 (1993) 782-791.

[75] S.M. Al-Janabi, RJ. Honeycutt, M. McClelland, B.W. Sobral, A genetic linkage map of Saccharum spontaneum L. 'SES 208', Genetics 134 (1993) 1249-1260.

[76] J. Da Silva, R. Honeycutt, W. Burnquist, S. Al-Janabi, M. Sorrells, S. Tanksley, В. Sobral, Saccharum spontaneum L. 'SES 208' genetic linkage map combining RFLP and PCR-based markers, Mol. Breed. 1 (1995) 165-179.

[77] L. Grivet, A. D'Hont, D. Roques, P. Feldmann, С. Lanaud, J. Glaszmann, RFLP mapping in cultivated sugarcane (Saccharum spp.): genome organization in a highly polyploid and aneuploid interspecific hybrid, Genetics 142 (1996) 987-1000.

[78] J. Mudge, W. Andersen, К. Kehrer, D. Fairbanks, A RAPD Genetic Map of Saccharum officinarum, Crop Sci. 36 (1996) 1362.

[79] A. Garcia, Е. Kido, A. Meza, H. Marques-Souza, L. Pinto, М.М. Pastina, С. Leite, J. Da Silva, E. Ulian, A. Figueira, A. de Souza, Development of an integrated genetic map of a sugarcane (Saccharum spp.) commercial cross, based on a maximum-likelihood approach for estimation of linkage and linkage phases, Theor. Appl. Genet. 112 (2006) 298-314.

[80] L.M. Raboin, К. Oliveira, L. Lecunff, H. Telismart, D. Roques, М. Butterfield, J.Y. Hoarau, A. D'Hont, Genetic mapping in sugarcane, a high polyploid, using biparental progeny: Identification of a gene controlling stalk colour and a new rust resistance gene, Theor. Appl. Genet. 112 (2006) 1382-1391.

[81] S.M. Aljanabi, Y. Parmessur, H. Kross, S. Dhayan, S. Saumtally, К. Ramdoyal, LJ. C. Autrey, A. Dookun-Saumtally, Identification of a major quantitative trait locus (QTL) for yellow spot (Mycovellosiella koepkei) disease resistance in sugarcane, Mol. Breed. 19 (2007) 1-14.

[82] A. Suman, С. Kimbeng, S. Edmé, J. Veremis, Sequence related amplified polymorphism (SRAP) markers for assessing genetic relationship and diversity in sugarcane germplasm collection, Plant Genet. Resour. 6 (2008) 222-231.

[83] J.W. Chen, F.Y. Lao, X.W. Chen, H.H. Deng, R. Liu, H.Y. He, C. Fu, Y.S. Chen, F.Y. Liu, Q.W. Li, P. Jackson, K. Aitken, DNA marker transmission and linkage analysis in populations derived from a sugarcane (Saccharum spp.) x Erianthus arundinaceus hybrid, PLoS ONE 10 (2015) e0128865.

[84] X. Yang, M.S. Islam, S. Sood, S. Maya, E.A. Hanson, J. Comstock, J. Wang, Identifying quantitative trait loci (QTLs) and developing diagnostic markers linked to orange rust resistance in sugarcane (Saccharum spp.), Front. Plant. Sci. 9 (2018) 350.

[85] Q. You, S. Sood, Z. Luo, H. Liu, M. Sariful Islam, M. Zhang, J. Wang, Identifying genomic regions controlling ratoon stunting disease resistance in sugarcane (Saccharum spp.) clonal F, population, Crop J. 9 (2021) 1070-1078.

[86] N. Banerjee, M.S. Khan, M. Swapna, S. Yadav, G. Tiwari, S. Jena, J. Patel, R. Manimekalai, S. Kumar, D. Sk, R. Kapur, J. Clement Koebernick, R.K. Singh, QTL mapping and identification of candidate genes linked to red rot resistance in sugarcane, 3 Biotech 13 (2023) 82.

[87] L. Zhang, B. Zhang, Mapping and cloning of quantitative trait genes in plants, Chin. Bul. Bot. 24 (2007) 553-560.

[88] M.L.G. Melloni, M.N.G. Melloni, M.S. Scarpari, J.C. Garcia, M.G.A. Landell, LR. Pinto, Flowering of sugarcane genotypes under different artificial photoperiod conditions, Am. J. Plant Sci. 06 (2015) 456-463.

[89] K. Singh, S. Singh, D. Tiwari, S. Srivastava, S. Singh, M. Sharma, R. Singh, T. Mohapatra, N. Singh, Genetic mapping and QTL analysis for sugar yieldrelated traits in sugarcane, Euphytica 191 (2012) 333-353.

[90] G. Lu, Z. Wang, Y.B. Pan, Q. Wu, W. Cheng, F. Xu, S. Dai, B. Li, Y. Que, L. Xu, Identification of QTLs and critical genes related to sugarcane mosaic disease resistance, Front. Plant Sci. 14 (2023) 1107314.

[91] T. Wang, F. Xu, Z. Wang, Q. Wu, W. Cheng, Y. Que, L. Xu, Mapping of QTLs and screening candidate genes associated with the ability of sugarcane tillering and ratooning, Int. J. Mol. Sci. 24 (2023) 2793.

[92] L. Costet, L. Le Cunff, S. Royaert, LM. Raboin, C. Hervouet, L. Toubi, H. Telismart, О. Garsmeur, У. Rousselle, J. Pauquet, 5. Nibouche, J.C. Glaszmann, J. Y. Hoarau, A. D'Hont, Haplotype structure around Brul reveals a narrow genetic basis for brown rust resistance in modern sugarcane cultivars, Theor. Appl. Genet. 125 (2012) 825-836.

[93] M.R. Meena, C. Appunu, R. Arun Kumar, R. Manimekalai, S. Vasantha, G. Krishnappa, R. Kumar, S.K. Pandey, G. Hemaprabha, Recent advances in sugarcane genomics, physiology, and phenomics for superior agronomic traits, Front. Genet. 13 (2022) 854936.

[94] J. Crossa, P. Pérez-Rodriguez, J. Cuevas, O. Montesinos-López, D. Jarquín, С. de Los Campos, J. Burgueño, J.M. González-Camacho, S. Pérez-Elizalde, Y. Beyene, S. Dreisigacker, R. Singh, X. Zhang, М. Gowda, М. Roorkiwal, J. Rutkoski, К.К. Varshney, Genomic selection in plant breeding: methods, models, and perspectives, Trends Plant Sci. 22 (2017) 961-975.

[95] M.S. Islam, K. Corak, P. McCord, A.M. Hulse-Kemp, A.E. Lipka, A first look at the ability to use genomic prediction for improving the ratooning ability of sugarcane, Front. Plant Sci. 14 (2023) 1205999.

[96] M.S. Islam, Р.Н. McCord, М.О. Olatoye, L. Qin, S. Sood, А.Е. Lipka, J.R. Todd, Experimental evaluation of genomic selection prediction for rust resistance in sugarcane, Plant Genome 14 (2021) e20148.

[97] M. Gouy, Y. Rousselle, D. Bastianelli, P. Lecomte, L. Bonnal, D. Roques, J.C. Efile, S. Rocher, J. Daugrois, 1. Toubi, S. Nabeneza, С. Hervouet, H. Telismart, М. Denis, A. Thong-Chane, J.C. Glaszmann, J.Y. Hoarau, S. Nibouche, L. Costet, Experimental assessment of the accuracy of genomic selection in sugarcane, Theor. Appl. Genet. 126 (2013) 2575-2586.

[98] X. Liu, H. Wang, H. Wang, Z. Guo, X. Xu, J. Liu, S. Wang, W.X. Li, C. Zou, B.M. Prasanna, M.S. Olsen, C. Huang, Y. Xu, Factors affecting genomic selection revealed by empirical evidence in maize, Crop J. 6 (2018) 341-352.

[99] А.Н. Aono, Е.А. Costa, H.V.S. Rody, J.S. Nagai, R.J.G. Pimenta, М.С. Mancini, Е.К. С. dos Santos, LR. Pinto, M.G.d.A. Landell, А.Р. de Souza, RM. Kuroshu, Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance, Sci. Rep. 10 (2020) 20057.

[100] B.J. Hayes, X. Wei, P. Joyce, Е. Atkin, Е. Deomano, J. Yue, L. Nguyen, Е.М. Ross, T. Cavallaro, K.S. Aitken, K.P. Voss-Fels, Accuracy of genomic prediction of complex traits in sugarcane, Theor. Appl. Genet. 134 (2021) 1455-1462.

[101] M. Inamori, T. Kimura, M. Mori, Y. Tarumoto, T. Hattori, M. Hayano, M. Umeda, H. Iwata, Machine learning for genomic and pedigree prediction in sugarcane, Plant Genome 26 (2024) e20486.

[102] N. Banerjee, A. Siraree, S. Yadav, S. Kumar, J. Singh, S. Kumar, D.K. Pandey, RK. Singh, Marker-trait association study for sucrose and yield contributing traits in sugarcane (Saccharum spp. Hybrid), Euphytica 205 (2015) 185-201.

B. Zhang, Y. Huang, L. Zhang, Z. Zhou, S. Zhou, W. Duan, C. Yang, Y. Gao, S. Li, M. Chen, Y. Li, X. Yang, G. Zhang, D. Huang, Genome-wide association study unravels quantitative trait loci and genes associated with yield-related traits in sugarcane, J. Agric. Food Chem. 71 (2023) 16815-16826.

[104] X. Yang, Z. Luo, J. Todd, S. Sood, J. Wang, Genome-wide association study of multiple yield traits in a diversity panel of polyploid sugarcane (Saccharum spp.), Plant Genome 13 (2020) e20006.

[105] X. Yang, J. Todd, К. Arundale, J.B. Binder, Z. Luo, M.S. Islam, S. Sood, J. Wang, Identifying loci controlling fiber composition in polyploid sugarcane (Saccharum spp.) through genome-wide association study, Ind. Crop Prod. 130 (2019) 598-605.

106F.Z. Barreto, J.R.B.F. Rosa, T.W.A. Balsalobre, М.М. Pastina, RR. Silva, H.P. Hoffmann, А.Р. de Souza, A.A.F. Garcia, M.S. Carneiro, A genome-wide association study identified loci for yield component traits in sugarcane (Saccharum spp.), PLoS ONE 14 (2019) e0219843.

107С. Saavedra-Díaz, J.H. Trujillo-Montenegro, Н.А. Jaimes, A. Londoño, F.A.S. Villareal, L.O. López, C.A.V. Valens, J. López-Gerena, J.J. Riascos, Y.M. Quevedo, F.S. Aguilar, Genetic association analysis in sugarcane (Saccharum spp.) for sucrose accumulation in humid environments in Colombia, BMC Plant Biol. 24 (2024) 570.

108L le Cunff, O. Garsmeur, LM. Raboin, J. Pauquet, H. Telismart, A. Selvi, L. Grivet, R. Philippe, D. Begum, M. Deu, L. Costet, R. Wing, J.C. Glaszmann, A. D'Hont, Diploid/Polyploid syntenic shuttle mapping and haplotype-specific chromosome walking toward a rust resistance gene (Brul) in highly polyploid sugarcane (2n~12x~115), Genetics 180 (2008) 649-660.

[109] CT. Guimarães, С.К. Sills, BW. Sobral, Comparative mapping of Andropogoneae: Saccharum L. (sugarcane) and its relation to sorghum and maize, Proc. Natl. Acad. Sci. U. S. A. 94 (1997) 14261-14266.

[110] K.S. Aitken, M.D. McNeil, P.J. Berkman, S. Hermann, A. Kilian, Р.С. Bundock, J. Li, Comparative mapping in the Poaceae family reveals translocations in the complex polyploid genome of sugarcane, BMC Plant Biol. 14 (2014) 190.

[111] J.H. Daugrois, L. Grivet, D. Roques, J.Y. Hoarau, H. Lombard, J. Glaszmann, A. D'Hont, A putative major gene for rust resistance linked with a RFLP marker in sugarcane cultivar 'R570', Theor. Appl. Genet. 92 (1996) 1059-1064.

[112] L. Costet, L.M. Raboin, М. Payet, A. D'Hont, S. Nibouche, A major quantitative trait allele for resistance to the Sugarcane yellow leaf virus (Luteoviridae), Plant Breed. 131 (2012) 637-640.

[113] X. Yang, S. Sood, № Glynn, M.S. Islam, J. Comstock, J. Wang, Constructing high-density genetic maps for polyploid sugarcane (Saccharum spp.) and identifying quantitative trait loci controlling brown rust resistance, Mol. Breed. 37 (2017) 116.

[114] M.S. Islam, X. Yang, 5. Sood, J.C. Comstock, Е. Zan, J. Wang, Molecular dissection of sugar related traits and it's attributes in Saccharum spp. hybrids, Euphytica 214 (2018) 170.

[115] M.S. Islam, X. Yang, $. Sood, J.C. Comstock, J. Wang, Molecular characterization of genetic basis of Sugarcane yellow leaf virus (SCYLV) resistance in Saccharum spp. hybrid, Plant Breed. 137 (2018) 598-604.

[116] T. Willian, T. Balsalobre, С. Da, С. Da Silva Pereira, С. Rodrigues, С. Margarido, R. Gazaffi, F. Barreto, C. Anoni, B. Claudio, S. Cardoso, E. Costa, M. Mancini, H. Hoffmann, A. de Souza, A. Garcia, F. Garcia, M. Carneiro, GBS-based single dosage markers for linkage and QTL mapping allow gene mining for yieldrelated traits in sugarcane, BMC Genomics 18 (2017) 72.

[117] M. Islam, Y.B. Pan, L. Lomax, M. Grisham, Identification of quantitative trait loci (QTL) controlling fibre content of sugarcane (Saccharum hybrids spp.), Plant Breed. 140 (2021) 360-366.

[118] M. Umeda, T. Sakaigaichi, M. Tanaka, Y. Tarumoto, K. Adachi, T. Hattori, M. Hayano, H. Takahashi, Y. Tamura, T. Kimura, M. Mori, Detection of a major QTL related to smut disease resistance inherited from a Japanese wild sugarcane using GRAS-Di technology, Breed. Sci. 71 (2021) 365-374.

[119] Z. Wang, H. Ren, C. Pang, G. Lu, F. Xu, W. Cheng, Y. Que, L. Xu, An autopolyploid-suitable polyBSA-seq strategy for screening candidate genetic markers linked to leaf blight resistance in sugarcane, Theor. Appl. Genet. 135 (2022) 623-636.

[120] H. Xiong, Y. Chen, Y.B. Pan, A. Shi, A Genome-wide association study and genomic prediction for fiber and sucrose contents in a mapping population of LCP 85-384 sugarcane, Proc. Am. Soc. Hortic. Sci. 12 (2023) 1041.

[121] J. Dijoux, S. Rio, C. Hervouet, O. Garsmeur, L. Barau, T. Dumont, P. Rott, A. D'Hont, J.-Y. Hoarau, Unveiling the predominance of Saccharum spontaneum alleles for resistance to orange rust in sugarcane using genome-wide association, Theor. Appl. Genet. 137 (2024) 81.

[122] Z. Xu, R. Kong, D. An, X. Zhang, Q. Li, H. Nie, Y. Liu, J. Su, Evaluation of a sugarcane (Saccharum spp.) hybrid F1 population phenotypic diversity and construction of a rapid sucrose yield estimation model for breeding, Plants 12 (2023) 647.

[123] ©. You, X. Yang, 7. Peng, L. Xu, ]. Wang, Development and applications of a high throughput genotyping tool for polyploid crops: single nucleotide polymorphism (SNP) array, Front. Plant. Sci. 9 (2018) 104.

[124] Е. Wu, CX. Ma, I. Painter, Z.B. Zeng, Simultaneous maximum likelihood estimation of linkage and linkage phases in outcrossing species, Theor. Popul. Biol. 61 (2002) 349-363.

[125] M. Mollinari, G. Margarido, R. Vencovsky, A. Garcia, Evaluation of algorithms used to order markers on genetic maps, Heredity 103 (2009) 494-502.

[126] J. Gaffney, J. Schussler, С. Löffler, W. Cai, S. Paszkiewicz, С. Messina, J. Groeteke, J. Keaschall, М. Cooper, Industry-Scale Evaluation of maize hybrids selected for increased yield in drought-stress conditions of the US corn belt, Crop Sci. 55 (2015) 1608-1618.

[127] J. Spindel, H. Begum, D. Akdemir, P. Virk, В. Collard, Е. Redoña, С. Atlin, J.L. Jannink, S.R. McCouch, Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines, PLoS Genet. 11 (2015) е1004982.

[128] S.B. Fernandes, К.О.С. Dias, D.F. Ferreira, P.J. Brown, Efficiency of multi-trait, indirect, and trait-assisted genomic selection for improvement of biomass sorghum, Theor. Appl. Genet. 131 (2018) 747-755.

[129] G. Lu, P. Liu, Q. Wu, S. Zhang, P. Zhao, Y. Zhang, Y. Que, Sugarcane breeding: a fantastic past and promising future driven by technology and methods, Front. Plant Sci. 15 (2024) 1375934.

[130] R. Ming, P. Moore, K.K. Wu, Sugarcane improvement through breeding and biotechnology, Plant Breed. Rev. 27 (2006) 15-118.

[131] Y. Xu, X. Zhang, H. Li, H. Zheng, J. Zhang, M.S. Olsen, R.K. Varshney, B.M. Prasanna, Q. Qian, Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction, Mol. Plant 15 (2022) 16641695.

[132] X. Wang, Q. Wu, H. Zeng, X. Yang, X. Yang, X. Yi, I. Khalil, Y. Que, Digital evolution and twin miracle of sugarcane breeding, Field Crops Res. 318 (2024) 109588.