Molecular markers are pivotal components in wheat breeding. Markers, as a sign or flag, are associated with a QTL/gene. Since they are applied for gene mapping and MAS with varying degrees of efficiency, various marker technologies have been developed [2,12,14].

STSs are short and unique sequences that identify the specific loci. A large quantity of sequencing data and functional genes can be used for the development of STS markers. For example, peroxidase (POD) activity is correlated with flour color, processing and product quality. Through sequence blast against the wheat expressed sequence tag (EST) database, six STS markers were developed to characterize POD genes (TaPod-A2, TaPod-A3, TaPod-B1 and TaPod-D1) [15]. Two complementary dominant functional markers were further designed to distinguish two haplotypes (TaPod-D1a and TaPod-D1b) with different POD activities [15].

In recent years, many STS markers from ESTs and sequencing datasets were developed in wild relatives, such as Dasypyrum villosum [16] and Agropyron cristatum [17]. A total of 507 STS markers of Thinopyrum ponticum were developed to discriminate chromosome 1J^s genetic stocks in wheat backgrounds by specific-locus amplified fragment sequencing (SLAF-seq) [18].

SSRs are DNA stretches containing a variable number of short and simple sequence repeats. They are ubiquitous in wheat genomes, generally codominant and highly polymorphic, which allows for the direct detection of heterozygosity and makes them the marker of choice for a diversity of purposes, such as gene mapping and tagging [19]. Despite the frequent application of SSRs, there is limited potential in practical crop breeding. SSRs are also finite and distributed unevenly in a certain genome. It is challenging to properly identify precise information in terms of multiple alleles per locus and integrate or compare SSR data from different platforms or populations.

By developing new SSR markers using comparative genomics analysis, powdery mildew resistance gene Pm52 was placed in a 0.21 cM genetic bin on chromosome arm 2BL in wheat [20]. The two Pm52-linked markers, Xicsl234 and Xicscl817, could expedite the mining of Pm52 in the Liangxing99-derived materials. A total of 192 SSR markers were selected to evaluate 123 wheat stocks for lipoxygenase (LOX) activity, and association analysis found 22 marker loci with a significant correlation with LOX [21]. A novel QTL QFhb.cau-7DL for FHB resistance was identified from the wheat line AQ24788-83 (AQ) by genotyping recombinant inbred lines (RILs) using SSR, DArT and SNP markers [22]. This QTL was closely linked to the SSR marker gwm428, which could be used in the selection for QFhb.cau-7DL when it has polymorphisms in a given population.

SNPs are the most common type of genetic variation, and are ideal markers for genetic discovery and molecular breeding. Their density varies from one per 370 bp to one per 540 bp in wheat [23]. SNPs are generally regarded as biallelic markers and classified based on nucleotide substitutions, genomic location or phenotype [14].

Next-generation sequencing (NGS) technology thoroughly promotes the development of SNP related platforms for genotyping in an ultra-high-throughput style, substantially improving QTL mapping and gene mining studies and genotyping large populations in a time-saving and cost-saving way, because these platforms can rapidly scan genomes at high-density with a range of multiplex levels and find robust allele calling with high call rates.

Advances in NGS have greatly reduced the costs of DNA sequencing, so that genotyping by sequencing (GBS) is feasible for discovering and genotyping SNPs in wheat [24]. GBS is a high-throughput, cost-effective and rapid molecular tool for rating wheat complex genomes whose complexity is reduced through multiple enzyme digestions. GBS-SNPs provide better genome coverage and can be applied to construct high-density linkage maps, compared with conventional marker systems (e.g., SSRs) [25]. In addition, GBS does not depend on prior genome information for constructing genetic linkage maps. There are at least 15 restriction enzymes available and multiple genotyping strategies for GBS in crop plants [25,26]. However, there are some shortcomings. GBS is easily affected by the errors resulting from the low reads coverage and the poor capacity to identify true homozygotes [27]. Furthermore, GBS performance is extremely affected by the quality of the reference genome. The large genome size (16Gb) of hexaploid wheat and three homoeologous genomes also increases the occurrence of genotyping errors [2].

In wheat, GBS has been widely exploited for genomic selection for various traits. Using GBS technology, Hessian fly resistance genes h4 [28] and H7 [29] were mapped to chromosomes 1A and 6A, which explained 60.4–70.5% and 60.7–78.3% of the phenotypic variation, respectively. The GBS sequence data (102,147 SNP markers) of 439 elite spring wheat breeding lines were used for genome-wide association study (GWAS), which identified a QTL for FHB resistance on chromosome arm 1AL, explaining 5.3% of the total phenotypic variation [30]. The GWAS using 14,063 polymorphic GBS-SNP markers found eight QTL related with spot blotch disease resistance [31].

NGS technologies provide a surplus of sequencing data, which further enhance the development of chip-based marker platforms for high-throughput genotyping. Compared with NGS and PCR-based markers, SNP arrays are flexible in the light of sample and data point number customization, which helps its high-density scanning and high and powerful call rates. In wheat, various SNP solid chips are now available, including 15K [32], 35K [33], 55K [34], 90K [35], 660k [36] and 820K [37] in different platforms. Among them, each SNP array has its own advantages and disadvantages. For example, Wheat 820K SNP array were mainly customized by markers from wheat and its relatives [38] and designed for various stocks, such as landraces, synthetic hexaploids and wheat relatives, so that it is improper for breeders to specifically assess hexaploid germplasms. However, most markers of Wheat 660K SNP array were based on hexaploid and tetraploid wheats, emmer wheat and Aegilops tauschii, so they have a wide range of possible applications on wheat evaluation due to the genome-specificity, high-density and high-efficiency [39].

GBS-SNPs and chip-based SNPs are usually used together to discover loci of interest traits. Using 90K array and GBS SNPs, eight QTL for stripe rust resistance were identified on chromosomes 1A, 2A, 2B, 4A, 4B, 6B and 7D, and two of them were novel [40]. Flanking markers closely linked to green bug resistance gene Gb7 and Hessian fly resistance gene H32 were located on chromosome arms 7DL and 3DL, respectively [41].

Given the advances in NGS techniques and reduced sequencing costs, it is now feasible to perform whole genome sequencing (WGS) to identify the genetic variants in a mapping population [42]. Nevertheless, WGS remains expensive in wheat due to its large and complex genome, making it necessary to have adequate coverage. To overcome this limitation, exome capture technology is an alternative solution to cover most gene coding regions [43]. On the other hand, the exome variants are the coding sequence information, which can facilitate the finding of the causative genes. On the contrary, using traditional techniques, forward mapping is a lengthy, multi-step process based on linked markers identification on the target trait in a mapping population and the candidate genes sequencing in a broad genetic region. Using this technology, some useful genes were identified, such as the leaf rust resistance genes (Lr1, Lr10, Lr21 and Lr34) and novel genes (on chromosomes 1A and 3D) [44], the stripe rust resistance locus QYr.ucw-1BL [45] and reduced plant height gene Rht-B1 [46]. Using bulked segregant exome capture sequencing (BSE-Seq) by combining the exome capture and sequencing of bulked segregant pools, a wheat yellow leaf mutant gene ygl1 was quickly mapped [47].

SNPs have shown many benefits, including high quantity, high density, high genetic stability and easy automatic detection. Thus, NGS technologies (GBS and exome capture) and SNP arrays have been rapidly replacing traditional markers and are now widely applied in genetic studies and molecular breeding. The linked SNP markers are usually converted to fluorescence-based or gel-based markers, including KASP, CAPS and STARP described below, before they are further verified in wheat populations and finely mapped using haplotype analysis and MAS.

Based on both PCR and fluorescence detection, Kompetitive Allele Specific PCR (KASP) detection technology meets both low- and high-throughput genotyping requirements. For KASP assays, the two forward primers have the 5′ tags carrying the standard FAM (5’-GAAGGTGACCAAGTTCATGCT-3’) or HEX (5’-GAAGGTCGGAGTCAACGGATT-3’) fluorescence when amplified in KASP master mix. A common genome-specific reverse primer is also needed, and the total amplicon length usually ranges from 50 to 125 bp. The PCR results are read with a microplate reader and visualized using an SNP allele calling software, such as KlusterCaller (LGC Genomics, Middlesex, UK). The genotypes in each cluster are analyzed to reveal their association with phenotypic data. Currently, the vast majority of studies choose KASP technologies to convert linked SNP markers [48].

Cleaved amplified polymorphic sequence (CAPS) technology combines PCR amplification and restriction enzyme treatment. A CAPS marker usually amplifies the same sized bands for the two alleles. If polymorphism exists in the restriction site near the SNP loci of one allele, the restriction enzyme digestion cleaves one amplified sequence and make different fragments in length, which can be easily analyzed by gel electrophoresis. However, in some cases, the SNP between alleles does not generate any polymorphic restriction site. This kind of SNP could be utilized for the development of ‘derived CAPS’ (dCAPS, [49]).

Semi-thermal asymmetric reverse PCR (STARP) is another innovative way to perform flexible SNP genotyping depending on a similar competitive PCR reaction without requiring the third-party KASP master mix [50]. In this method, genotyping assay is conducted under unique PCR conditions using two universal priming element-adjustable primers (PEA-primers) [also called asymmetrically modified allele-specific primers (AMAS-primers)] and their common reverse primer. The two PEA-primers each have one base substituted at the third or fourth base of their 3′ regions to greatly increase the amplification specificity of the two alleles. In addition, a 4-10 bp insertion tailed at the 5′ end of one AMAS primer comes into the final PCR products between two genotypes, allowing the discrimination of allelic variants in traditional gel electrophoresis [51]. Wu et al. [51] developed 56 gene-specific STARP markers for 46 genes for wheat quality, tolerance to biotic and abiotic stresses, grain yield and adaptation-related traits, which provided a powerful and reliable marker toolkit for wheat breeding programs.

Molecular detection technologies have been profoundly innovated, from gel-based and fluorescence-based to solid chip-based and now liquid chip-based, as well as possibly automated genotyping platforms in the future. Solid-based SNP arrays from different platforms have been playing a vital role in gene discovery for genotyping large populations in wheat, but some shortcomings of their applications in crop improvement exists, such as low customization efficiency, little flexibility, expensive equipment and high cost [52]. On the contrary, GBTS can compensate for their limits [53,54]. GBTS is mainly divided into multiplex PCR-based target sequencing (GenoPlexs) and probe-in-solution-based target sequencing (GenoBaits). GBTS can accurately capture sequences at random positions and lengths, except for the areas with highly repeated sequences. In addition, it is upgradable so that newly mapped important loci can be joined into an existing marker panel without resynthesis. Using these two techniques, a set of marker panels can be developed to meet almost all the requirements of marker applications in the fields. In wheat, a few studies have begun to apply GBTS in genetic studies [55,56]. GBTS will potentially be used in wheat improvement on a large scale in the future.

Many QTL/genes of interest and their tightly linked markers or functional markers (FMs) from linkage analysis, association analysis, gene cloning and sequencing have been reported in wheat [11,57,58]. More than 200 QTL underpinning yield-related traits have been documented in different wheat genotypes in last decade [59]. Around 150 FMs for important genes have been reported to select desirable characteristics in wheat [2]; 97 FMs that identify 93 alleles at 30 loci in wheat have been documented, including 56, 27 and 14 FMs for processing quality, agronomic and disease resistance traits, respectively [60]. These markers are of potential value in MAS for improving wheat agronomic traits, such as biotic stresses, yield-related traits and quality characters. Numerous loci for agronomic traits have been reported in recent years, and it is impossible to summarize all of them, so only some important loci of a few traits are detailed in this review (Table 1).

Wheat is exposed to many biotic threats, such as fungi, viruses and insects, which heavily affect its yield and the quality. On average, yield loss in wheat production is about 21.5% due to pathogens and pests [61]. Plant pathogens are ever-evolving at a high pace through mutations or recombinations. FHB, powdery mildew and rusts are the three main menaces; therefore, it is of vital significance to find novel genes against them.

FHB is a serious disease in vast wheat-growing areas of the globe where rainfall frequently occurs during flowering time and leads to heavy reductions in wheat yield worldwide. A total of 50 QTL, controlling different types of FHB resistance, have been reported with unique chromosome locations [62,63]. Among them, only seven QTL have been officially named, from Fhb1 on 3B [64] to Fhb7 on 7D [65], which have been described in detail [63]. Fhb1 from cultivar Sumai3 is the most frequent selection of FHB resistance in many breeding programs [66]. Two functional markers (TaHRC-GSM and TaHRC-KASP) for Fhb1 were developed based on the critical sequence deletion of the putative histidine-rich calcium-binding protein (TaHRC) and validated to be diagnostic in different types of populations [67]. Recently, a novel major QTL for FHB resistance (QFhb-2DL) was identified on chromosome 2D (Type II) in a Chinese wheat cultivar Ji5265, which can explain ~30% of the phenotypic variation for FHB resistance [68]. Two linked KASP markers (KASP10238 and KASP12056) were proved to be diagnostic in 2065 wheat accessions.

To date, more than 69 powdery mildew resistance loci (Pm1-Pm69) have been catalogued in wheat [69]. Among these named genes, some new alleles were found and more information is available for better utilization. For example, a previously uncategorized Pm60 allele was discovered from T. urartu and two molecular markers (M-Pm60-S1 and M-Pm60-S2) were developed to distinguish the functional Pm60a allele from the non-functional Pm60a′ allele, which were helpful for precisely identifying the Pm60 allele [70]. Furthermore, many new loci were identified, including MlIW39 [71], PmKN0816 [72] and Qpm-3BL [73]. A new adult plant resistance (APR) gene, QPm.caas-3BS, to powdery mildew pathogen was delimited from the RILs derived from the cross Zhou8425B/Chinese Spring. One linked SNP STARP marker was developed and validated on 103 wheat cultivars, showing that QPm.caas-3BS could reduce maximum disease severity by 5.3% [35].

Up to now, 82 leaf rust (Lr) resistance genes, 84 stripe rust (Yr) resistance genes and 63 stem rust (Sr) resistance genes have been permanently cataloged in wheat [69]. In addition to them, most genes can be chosen for improving wheat rust resistance, such as Lr22a [74], Lr80 [92], YrAS2388 [93] and Sr13 [77]. Some genes have not been extensively used in wheat cultivars, such as the broadly effective resistance gene Lr22a [74]. Three linked KASP markers (Kwh636, Kwh637 and Kwh638) were developed to reliably detect the presence or absence of Lr22a, which can facilitate Lr22a selection in MAS [74]. Some genes confer APR, such as QLr.cau-2BL [94] from wheat landrace Hongmazha, while QLr-2BS is a valuable all-stage resistance gene [76]. Their linked SSR or KASP markers were developed and verified in genetic populations and were potentially useful for introducing them into commercial wheat cultivars. A new APR to stripe rust loci, QYr.AYH-5BL from Chinese wheat landrace Anyuehong (AYH), was stably detected in all environments and could explain 13.6–21.4% of the phenotypic variation [76]. Its linked KASP markers have potential value for MAS to improve stripe rust resistance in breeding programs. One KASP and several STARP markers were developed to identify Sr13 haplotypes [77]. Both KASPSr13 and rwgsnp37 were robust markers for Sr13 and could be used by geneticists and breeders. STARP markers rwgsnp38, rwgsnp39 and rwgsnp40 could be ideally used to discriminate four haplotypes.

In addition to biotic threatens, wheat growth and development are also badly impacted by various abiotic stresses. About 50% crop losses are, on average, caused by abiotic factors, including drought (9%), heat (20%), cold (7%) and other forms of stresses [48].

Improving water absorption capacity is a good means to improve drought tolerance in crops. Roots are in charge of the uptake of water from soil; strong root architecture is beneficial for crops to absorb water stored in soil and avoid drought stress [95]. There are numerous QTL/genes related to root traits as well as their reported linked markers [36,95,96,97]. TaWRKY51 is a positive regulator contributing to the root system and grain yield (GY). Hap-2A-I is a favorable haplotype for large spike, and Hap-2B-II and allele-G are elite haplotypes/alleles for long root. Their functional markers have been developed for the utilization in the MAS [78]. In addition to root-related genes, other markers for drought tolerance genes have been reported. Sixteen functional KASP markers for 16 alleles related to drought tolerance were used to assess 153 Pakistani wheat cultivars released from 1953 to 2016 [98]. Favored haplotypes of five genes were unconsciously pyramided and selected during selection breeding, while six genes had lower frequencies in favorable haplotypes among those stocks.

Winter hardiness is also a crucial breeding goal, since it is vital for wheat to adapt to harsh winter conditions. Fr-A2 is a major QTL for frost resistance on chromosome 5A, and its polymorphisms contribute to the variation in winter hardiness [79]. Two KASP markers (S1862541 and S1298957) could differentiate two haplotypes of Fr-A2. Among 11 cold tolerance QTL found by genotyping a panel of 768 wheat germplasms using GBS, two significantly associated SNPs of qCT5A.3 were converted into KASP markers and were validated successfully in a F₂ population [80].

Lodging is also an important concern for reducing wheat yield and grain quality. Caffeic acid 3-O-methyltransferase (COMT) is a key enzyme involved in lignin biosynthesis contributing to lodging resistance and has two allelic variants [81]. A codominant Indel marker was developed to validate the association between allelic patterns and stem lignin content, showing that TaCOMT-3Ba was the excellent haplotype.

Additionally, wheat yield is seriously influenced by extremely hot weather, terminal heat stress in particular. It affects approximately 40% of the wheat-cultivating regions of the world. Sihag et al. [99] identified two microRNA (miRNA)-based SSR markers (miR159c and miR165b), which showed specific alleles and discriminated terminal heat-tolerant genotypes from the susceptible genotypes.

Agronomic traits such as plant height (PH), heading date (HD), spike length (SL) and thousand kernel weight (TKW) are critical factors affecting wheat yield. Many genes were cloned and their haplotypes were analyzed, such as vernalization genes Vrn1 [5], Vrn2/ZCCT1 [100] and Vrn3 [101]; and PH-related genes Rht-B1b, Rht-D1b [4], Rht8 and Rht24 [102]. These genes have been widely utilized in wheat breeding.

As a polygenic trait, TKW is the most stably inherited determinant of yield potential, exhibiting higher heritability values than overall yield and yield components. Many genes controlling grain weight as well as their haplotypes were analyzed, such as TaAGP [103], TaSus1 [8], TaSDIR1 [84], TaDA1 [104] and KAT-2B [105]. Fourteen distinct haplotypes of six TaCKX genes were explored by single-molecule real-time (SMRT) sequencing and seven functional markers were then developed (four Indel and three CAPS markers). Among them, six specific haplotypes were significantly correlated with high TKW and short plant height (PH) [106]. TaTAP46-5A (Type 2A-phosphatase-associated protein) has been proven to influence kernel size and TKW in transgenic wheat, which was rarely selected (less than 1%) in conventional breeding according to phenotype and experience [82]. A KASP functional marker could be used to select it to improve breeding efficiency. TaSDIR1-4A, a Salt- and Drought-Induced RING Finger 1 (SDIR1) member, was found to affect TKW [83]. A dCAPS marker can be used to differentiate good haplotypes from two genotypes.

In addition, a series of QTL for grain weight were finely mapped, including QTKW.caas-4BS [107], QTKW.caas-5DL [108], QTgw.caas-5B [109] and QYld.aww-1B.2 [110]. Duan et al. [84] dissected a major and stable QTL, QGW4B.4-17, for TKW with an increase of 2.19–3.06g and high phenotypic variation explained (PVE) of 22.5–36.3%. The corresponding CAPS marker was developed and verified in 205 wheat cultivars and showed a highly significant correlation with TKW.

Many QTL/genes have pleiotropic effects on multiple traits. Two QTL clusters (QSc/Sl.cib-5A and QSc/Sl.cib-6A) have pleiotropic effects on plant height, TKW and grain length; their related KASP markers might be potentially applicable in wheat breeding [85]. The FT-D1 gene controls spikelet number and heading date, and a robust STARP marker was reported to simplify and streamline MAS for this gene in wheat breeding [86]. A pleiotropic gene TaCol-B5 can not only increase the number of spikelet nodes per spike, but can also produce more tillers and spikes, thereby improving the yield of transgenic wheat under field conditions. It was found in only 33 of 1657 stocks from a global collection of wheat germplasms. A diagnostic marker identifying the SNP involving the Ser269/Gly269 substitution was developed to plausibly accelerate this rare allele in a variety of genetic backgrounds [88].

Wheat grain protein content (GPC) is a major end-use quality. Wheat quality can be improved by the manipulation of the main storage protein genes, many of which have been efficiently utilized for wheat quality improvement, such as GluD1 (5 + 10) [111] and GluB1 (17 + 18) [112]. A robust and reliable KASP marker toolkit was reported for HMW-GS at Glu-A1, Glu-B1 and Glu-D1 loci; polyphenol oxidase (PPO) activity genes (Ppo-A1 and Ppo-D1) [113], which can distantly facilitate the selection and stacking of excellent genes in wheat breeding programs. Jiang et al. [88] successfully developed three KASP markers (Kgpc-2B, Kgpc-2D and Kgpc-4A) associated with GPC, which were applied to screen 15 lines with high GPC from 164 F₆ breeding lines, indicating their high selective efficiency.

Breeding wheat with a weak and extensible gluten feature is another aspect aiming to improve biscuit making quality. Nap Hal is an Indian wheat landrace with weak gluten because of Glu-D1 double null [89]. The codominant marker (gwm642) tightly linked with Glu-D1 double null could be applied to transferring it into high yielding backgrounds.

Pre-harvest sprouting is another aspect to ultimately affect the end product quality. Three novel QTL for pre-harvest sprouting tolerance (PHST) were detected based on the phenotypes of 192 wheat varieties (lines) and the corresponding genotypes [91]. SNP markers from 90K SNP array were tightly linked with these major QTL (Qphs.ahau-1A, Qphs.ahau-3B and Qphs.ahau-6B) and further converted to two CAPS and one dCAPS markers. The CAPS marker EX06323 for Qphs.ahau-6B co-segregated with a novel major QTL underlying PHST in a RIL population raised from the cross Jing 411/Wanxianbaimaizi. In addition, the allele EX06323-G revealed a highly significant correlation with all PHST traits in 374 wheat varieties.

Grain micronutrient content is also an important trait for the nutritional quality improvement for achieving biofortification. QTL and related markers for improving Fe or Zn contents in wheat were identified [114]. Among the many QTL/genes for Fe, Zn and Se contents in another review, two key QTL deserve attention, QGZn.cimmyt-7B_1P2 and QGFe.cimmyt-4A_P2, which explained the largest PVE of 32.7% for Zn and 21.14% for Fe, respectively [115].