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Theoretical and Applied Genetics (2019) 132:767–784 https://doi.org/10.1007/s00122-019-03286-4

REVIEW ARTICLE

From markers to genome‑based breeding in wheat

Awais Rasheed1,2,3 · Xianchun Xia1

Received: 31 July 2018 / Accepted: 16 January 2019 / Published online: 23 January 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019

AbstractKey message Recent technological advances in wheat genomics provide new opportunities to uncover genetic varia-tion in traits of breeding interest and enable genome-based breeding to deliver wheat cultivars for the projected food requirements for 2050.Abstract There has been tremendous progress in development of whole-genome sequencing resources in wheat and its progenitor species during the last 5 years. High-throughput genotyping is now possible in wheat not only for routine gene introgression but also for high-density genome-wide genotyping. This is a major transition phase to enable genome-based breeding to achieve progressive genetic gains to parallel to projected wheat production demands. These advances have intrigued wheat researchers to practice less pursued analytical approaches which were not practiced due to the short his-tory of genome sequence availability. Such approaches have been successful in gene discovery and breeding applications in other crops and animals for which genome sequences have been available for much longer. These strategies include, (i) environmental genome-wide association studies in wheat genetic resources stored in genbanks to identify genes for local adaptation by using agroclimatic traits as phenotypes, (ii) haplotype-based analyses to improve the statistical power and resolution of genomic selection and gene mapping experiments, (iii) new breeding strategies for genome-based prediction of heterosis patterns in wheat, and (iv) ultimate use of genomics information to develop more efficient and robust genome-wide genotyping platforms to precisely predict higher yield potential and stability with greater precision. Genome-based breeding has potential to achieve the ultimate objective of ensuring sustainable wheat production through developing high yielding, climate-resilient wheat cultivars with high nutritional quality.

Introduction

Wheat (Triticum aestivum L.) is unique among recently domesticated species; as after originating in the Fertile Cres-cent, it spread to all parts of the world except Antarctica. The wide adaptation from 67°N in Scandinavia to 45°S in Argentina, including elevated regions in the tropics and sub-tropics, is attributed to high genome plasticity (Dubcovsky

and Dvorak 2007). This rapid geographic spread and expo-sure of wheat to diverse environments had a profound effect on species heterogeneity and created distinct gene pools of cultivated wheat based on stature, vernalization require-ment, photoperiod response, grain quality, and yield stabil-ity (Dubcovsky and Dvorak 2007; Moose and Mumm 2008; Worland et al. 1998). Currently, wheat is among three most important food crops that also include rice and maize. Wheat cultivated on ~ 200 million hectares worldwide, providing one fifth of the total caloric input to the global population (FAO 2017).

Estimates of global population are 9–10 billion by 2050 (FAO 2017), mostly living in the current developing coun-tries (Africa and South Asia) where wheat products are the most consumed staple food components. However, slow progress in yield improvement ranging from 0.8 to 1.0% annually will make it impossible to fulfill wheat production requirements at that time. The other key challenges in wheat production are; (i) elevating yield potential with stability, (ii) reducing the cost of increased productivity, by reducing

Communicated by Rajeev K. Varshney.

* Xianchun Xia xiaxianchun@caas.cn

1 Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing 100081, China

2 International Maize and Wheat Improvement Center (CIMMYT), c/o CAAS, 12 Zhongguancun South Street, Beijing 100081, China

3 Department of Plant Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan

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the requirements for water, fertilizers and other inputs, (iii) increasing the ability of wheat to grow on marginal lands, (iv) reduced greenhouse emissions, and (v) continuous pro-tection of wheat from emerging threats of climate change and continuously evolving races of pathogens and pests.

Traditional crop improvement programs by hybridiza-tion, and to a much lesser extent by mutation breeding, have played a significant role in wheat improvement during past decades. Wheat breeders have exploited ‘transgressive segregation’ in hybridization programs by selecting supe-rior traits that increase yield in target environments. This traditional wheat improvement was dependent on selection of traits without knowing molecular mechanisms of inher-itance. It sometimes led to tremendous improvements in yield exemplified by the ‘Green Revolution.’ The basis of this progress was integration of ‘slow but successful’ pre-breeding efforts that provided new genetic variation from Triticeae species to protect wheat from various biotic and abiotic stresses (Mujeeb-Kazi et al. 2013), and improved yield and nutritional quality (Rasheed et al. 2018a; Tabbita et al. 2017). Despite the slow progress of wheat genome sequencing and development of other genomic resources, these traditional wheat genetic improvement efforts undoubt-edly fulfilled the food requirements of masses. The genetic basis of the ‘Green Revolution’ and yield improvement in modern wheat cultivars are continuously being revealed and led to the conclusion that favorable alleles of major genes like vernalization requirements (Chen et al. 2013a), photo-period response (Ppd-1) and grain size (such as TaGS3-A1, TaTGW6 and TaSus1) were positively selected during tra-ditional plant breeding, and there is little room for further improvement by manipulating more alleles of these genes (Hanif et al. 2015; Hou et al. 2014; Ma et al. 2016; Wür-schum et al. 2018b). These studies also provided convincing evidence that wheat functional and comparative genomics will be key to identify and understand the genetic basis of yield in target environments and that genomics-enabled breeding could be the solution to all major challenges to wheat production (Li et al. 2018; Uauy 2017).

Reference genome sequences were recently released for bread wheat (https ://urgi.versa illes .inra.fr) and its pro-genitors, T. turgidum spp. dicoccoides (Avni et al. 2017), Aegilops tauschii (Luo et al. 2017; Zhao et al. 2017) and T. urartu (Ling et al. 2018). New tools and approaches for high-throughput, high-density genotyping in wheat have emerged during last few years as a result of technological advances in genomics and allied disciplines (Li et al. 2018; Rasheed et al. 2017). For example, SNP arrays for high-density genotyping in wheat (Allen et al. 2017; Cui et al. 2017; Wang et al. 2014; Wen et al. 2017) and related spe-cies (Winfield et al. 2016), and characterization and deploy-ment of functional genes are now possible in wheat using high-throughput PCR-based KASP markers (Rasheed et al.

2016). This is enabling ‘genomic selection’ to be a routine procedure in wheat breeding programs to predict superior traits based on DNA markers and is reflected from the recent plethora of genomic selection publications on all aspects of wheat breeding. Nevertheless, other breeding technologies like use of RNA-guided endonucleases such as CRISPR-Cas9 and its variations will play a crucial role to fine-tune genomics-assisted breeding efforts (Gao 2018).

Knowledge of the complete genome will play a pivotal role in gene discovery and wheat improvement. Both these components are necessary because breeding new cultivars will require the discovery and introduction of new genetic variation to add to, or replace, loci that have led to the cur-rent yield plateau and vulnerability to biotic and abiotic stresses. Therefore, we will review the advances in wheat genomics that will aid discovery of further variation in traits of breeding interest and the markers that will permit marker-assisted selection (MAS). We will describe how con-temporary technologies and strategies can be applied to the practice of genome-based breeding in wheat.

History of molecular markers in wheat

The common challenges in all areas of wheat genetics from linkage mapping to gene discovery and application of genome sequencing are attributed to: (i) the polyploid nature, (ii) large numbers of highly repetitive DNA sequences, (iii) large genome size, and (iv) narrow genetic base due to the recent origin of hexaploid wheat. On the contrary, the key advantage of wheat over other cereal grasses is its flexibility for cytogenetic applications and availability of a wide range of aneuploid stocks. These stocks have played a pivotal role in gene discovery in wheat (Khlestkina 2014; Mujeeb-Kazi et al. 2013; Rasheed et al. 2018a), and it was due to these cytogenetics stocks that the number of functional genes dis-covered in wheat is more than in other domesticated grasses like rice and maize that have a longer history of available genome sequence information (Kage et al. 2016; Rasheed et al. 2018a). A brief timeline of all key advances in wheat genomics is shown in Fig. 1. We will provide a brief histori-cal overview of molecular markers used in wheat genetics and breeding in the following two topics.

Genome‑wide markers for gene mapping

Application of molecular markers dates back to early 1990s when restriction fragment length polymorphism (RFLP) markers were applied to wheat for gene mapping, varietal identification, characterization of wheat-rye recombinants and identification of homoeologous chromosome arms (Gupta et al. 1999). However, the level of polymorphism was extremely low and negligible for the D genome in bread

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wheat, until the International Triticeae Mapping Initiative (ITMI) population (W7984 × Opata) was used for linkage mapping (Deynze et al. 1995). The success of creating rela-tively high-density linkage groups in the ITMI population was due to the use of a synthetic hexaploid wheat (SHW, T. turgidum × Ae. tauschii; AABBDtDt), which broadened the AB-genome base due to the durum wheat and D genome base due to an accession of the wild species Ae. tauschii. The ITMI population was subsequently used to make link-age groups using almost every class of molecular markers and facilitated QTL mapping. Because of the broad interest of wheat researchers in the ITMI population, it was recon-structed to overcome anomalies that accumulated in different laboratories over time due to human errors and chromosomal instability (Sorrells et al. 2011).

Despite the earlier success in developing linkage groups in wheat using RFLP markers, they did not become the marker of choice due to low frequency in bread wheat, high cost, and the time-consuming and labor-intensive methodology. This led researchers to use PCR-based molecular markers; two broad categories of which include randomly amplified poly-morphic DNA (RAPD) and simple sequence repeats (SSR). These markers significantly reduced cost and time for genetic mapping, but RAPDs were not used extensively in wheat due to lack of reproducibility and absence of information on their locations in the genome (Devos and Gale 1992). There are just a few examples where RAPD markers were used to map important QTL and were converted to more authentic

sequence tagged sites (STS) or sequence characterized ampli-fied regions (SCAR) markers; examples include QTL for Rus-sian wheat aphid (Dn) (Myburg et al. 1998), Lr24 (Dedryver et al. 1996) and Lr28 (Naik et al. 1998).

Microsatellites or simple sequence repeats (SSR) on the other hand were the most extensively used PCR-based molecu-lar markers used in wheat because they were relatively abun-dant, highly polymorphic and genome-specific (Röder et al. 1995). The first microsatellite map in wheat (Röder et al. 1998) opened a new era in wheat genomics to map and discover new loci with better resolution for important traits. Despite the frequent use of SSRs for genetic mapping and tagging, there was limited potential for use in practical plant breeding for the following four reasons (Xu and Crouch 2008). Firstly, it was a challenge to obtain precise information in terms of multiple alleles per locus; secondly, it was difficult to integrate or compare SSR data from different platforms or populations; thirdly, the numbers of SSR motifs were finite in a genome and were not evenly distributed; and fourthly, gel-based SSR analysis is cost-ineffective as genotyping is laborious and time consuming. Therefore, it was of paramount importance to establish simple, accurate, high-throughput platforms for marker-assisted breeding.

Fig. 1 Timeline of advancements in wheat genomics

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Functional genomics in wheat for diagnostic marker development and application

Functional genomics is the key to the molecular breeding and is the basis to develop diagnostic markers for gene intro-gressions and marker-assisted selection during wheat breed-ing. Although the progress of cloning functional genes has been slow in wheat and very few genes were cloned using the traditional ‘positional cloning’ or ‘map-based cloning’ in wheat, e.g., VRN1 (Yan et al. 2003), Gpc-B1 (Uauy et al. 2006), Lr21 (Huang et al. 2003). Positional cloning required high-quality genome sequence information spanning the region of interest; therefore, reference sequence from only one cultivar is not sufficient to produce such information, because the target genes could be absent from reference cultivars. Moreover, the repeated rounds of BAC library screening and chromosome waking approaches require a lot of time and are tedious in crops like wheat which have large and repetitive genome. Ultimately, wheat functional genom-ics largely relied on comparative genomics approaches with other grasses due to the high collinearity and genetic organi-zation among grass genomes (Valluru et al. 2014). Several quality related genes like phytoene synthase gene Psy1 (He et al. 2009), and zeta-carotene desaturase gene Zds1 (Zhang et al. 2011) and grain size encoding genes like TaCwi-A1 (Ma et al. 2012), TaGS3 (Zhang et al. 2014) and TaGS5 (Ma et al. 2016) were cloned following comparative genomics approaches. Recently, the cloning of some specific genes with conserved motifs like disease resistance genes in wheat has been hastened (Thind et al. 2017; Steuernagel et al. 2016). A mapping independent three-step strategy termed as ‘MutRenSeq’ combines mutagenesis with exome capture and sequencing to rapidly clone R genes from wheat wild relatives (Steuernagel et al. 2016). The classical example of MutRenSeq is the cloning of Sr22 from T. monococcum and Sr45 from Ae. tauschii. This approach can be applied to most crops or their wild relatives, and will allow the cloning of R genes that could be used in multi-R gene pyramids, a strat-egy that promises more durable disease resistance in crops. Thind et al. (2017) reported another approach termed as ‘targeted chromosome-based cloning via long-read assem-bly (TACCA),’ which offers great flexibility with respect to gene validation and could be used for traits with partial phenotypes such as partial disease resistance or abiotic stress tolerance. They also compared various rapid gene cloning methods and concluded that TACCA holds significant prom-ise and is equivalent to positional cloning.

Such functional genomics studies provide the causal variations within functional genes which act as templates to develop functional markers. Functional markers are PCR-based molecular markers designed from sequence polymor-phisms within functional genes; hence allelic variants can be diagnosed using functional markers (Liu et al. 2012). This is

contrary to neutral markers because functional markers have strong associations with relevant phenotypes and are ideal molecular markers for MAS. Liu et al. (2012) documented 97 functional markers that detect 93 alleles at 30 loci in bread wheat. This number has increased during last 5 years due to rapid advancements in wheat genomics. Currently, there are 157 functional markers documented for more than 100 loci underpinning adaptability, grain yield, disease resistance, end-use quality and tolerance to abiotic stresses. Functional markers are preferred for gene pyramiding and gene introgression and their use in genomic selection also enhances selection accuracy.

Genome sequencing of bread wheat and its progenitors: Current status

Wheat has lagged behind other major cereals in genome sequencing due to the very large genome size with highly repetitive contents (80–90%) and polyploidy. The Interna-tional Wheat Genome Sequencing Consortium (IWGSC; www.wheat genom e.org/) established in 2005 currently has 2100 members from 64 countries. This international collabo-rative consortium initially focused on analysis of individual chromosomes using long-insert Bacterial Artificial Chromo-some (BAC) libraries. Good examples of this effort are the sequencing and assembly of chromosome 3B of Chinese Spring (Paux et al. 2008) and the Ae. tauschii genome (Luo et al. 2013) using 67,968 and 461,706 BAC clones, respec-tively. This approach completely changed with the advent of new shotgun and short-read sequencing technologies and two strategies were adopted for genome sequencing and assembly. In the first strategy, the whole-genome sequence of Chinese Spring was assembled using low-coverage, long-read 454 pyrosequencing; this identified ~ 94–96 K genes, of which two-thirds were assigned to specific homoeologues (Brenchley et al. 2012). In the second strategy, ditelosomic stocks of Chinese Spring were used to isolate, sequence and de novo assemble each chromosome arm except those of 3B (Mayer et al. 2014). Each chromosome arm, representing 1.3 to 3.3% of the genome, was purified by flow-cytometric sorting (Šafář et al. 2010), and sequenced to a depth of 30 × to 241 × using an Illumina HiSeq 2000 or Genome Ana-lyzer IIx (Mayer et al. 2014). While chromosome 3B was sequenced and assembled separately with high accuracy, an improved assembly was achieved compared to the ear-lier one (Choulet et al. 2014). These strategies significantly improved the quality of the reference genome sequence of bread wheat; but the assemblies still included millions of contigs and were highly fragmented.

During the past few years there have been innovations in sequence library preparation, assembly algorithms, and long-range scaffolding that have dramatically improved

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whole-genome assemblies constructed from short-read sequences (Yuan et al. 2017). These advances in assem-bly algorithms in the both the public and private domains have again revolutionized the field, and the genome of the reference hexaploid wheat line ‘Chinese Spring’ is now a ‘gold standard’ reference genome assembled into the 21 constituent chromosomes (IWGSC 2018). Parallel studies also sequenced or improved the existing Chinese Spring assembly with new advances. Clavijo et al. (2017) gener-ated a new whole-genome shotgun sequence assembly using a combination of optimized data types and improved assem-bly algorithms like w2rap-contigger and SOAPdenovo (Luo et al. 2012). As compared to illumina short-read sequencing approaches, Pacific Biosciences single molecule real-time (SMRT) long-read sequencing dramatically improved the contiguity and repeat representation of the Ae. tauschii (Luo et al. 2017; Zimin et al. 2017b) and bread wheat (Zimin et al. 2017a) genomes.

These examples clearly depict the challenge of sequenc-ing the reference genome of bread wheat through parallel efforts from various research groups and huge investments of time and resources to achieve this goal. Similarly, two of the three diploid progenitors were sequenced and draft genome sequences of the D genome Ae. tauschii (Jia et al. 2013) and A genome T. urartu (Ling et al. 2013) donors were assem-bled. These assemblies were recently improved to complete the respective reference genomes (Luo et al. 2017; Zhao et al. 2017; and Ling et al. 2018, respectively), along with reference sequence of T. turgidum ssp. dicoccoides acces-sion Zavitan (Avni et al. 2017). These assemblies not only provide references that lack the complications of polyploidy but also enable the provenance of genome segments in the polyploids to be established. Moreover, these resources are templates for analyzing parallel resequencing data from cul-tivated hexaploid sources to uncover signatures of selection, genetic rearrangement, homoeologous gene exchange, and chromosome structural variation (Rasheed et al. 2018b). Last but not least, the genome sequences facilitate the dis-covery of genes at an unprecedented level, a critical aspect in breeding. For example, Avni et al. (2017) used the refer-ence genome sequence and parallel genome diversity analy-sis in wild and domesticated tetraploid wheats to identify the causal mutation in the Brittle Rachis 1 (TtBtr1) genes controlling shattering, a key trait in wheat domestication. Ling et al. (2018) identified a powdery mildew resistance gene, TuWAK, by aligning the reference genome with diver-sity data of global T. urartu accessions. This gene was pre-sent within a selective sweep containing more than 239 high confidence genes with functions in diverse physiologi-cal and molecular processes. In conclusion, these reference genomes are a significant step change in building tools and resources to accelerate genome-based breeding of modern wheat varieties.

Genomics resources for wheat functional genetics and molecular breeding

The availability of genome sequence from one accession is not sufficient to capture the whole spectrum of diversity in a gene pool responsible for phenotypic variation, plastic-ity, and environmental adaption. Resequencing more mem-bers of a gene pool is limited in terms of capturing many types of polymorphism, and would be effective in captur-ing additional SNPs, SSRs and small insertions/deletions (InDels). Structural variations like presence/absence vari-ation (PAV) and copy-number variation (CNV) are known to be responsible for variation in major genes for adapta-tion and important agronomic traits, e.g., Ppd-D1, CBF and Vrn-A1 alleles (Beales et al. 2007; Wurschum et al. 2015; Zhu et al. 2014) and such variation can be identified only by de novo sequencing of diverse members within a species. Thus, the de novo construction of a pan-genome for a species is the mandatory next step after the refer-ence genome sequence. A pan-genome consists of the core genome (portion of pan-genome common to all individu-als in the species), dispensable genome (portion of the genome partially shared by some individuals in the spe-cies), and unique genome (portion of the genome unique to each individual). Pan-genome resources in crop plants have been successfully used to discover important genes in Glycine soja (Li et al. 2014), Brassica rapa and Bras-sica oleracea (Cheng et al. 2016), and Zea mays (Hirsch et al. 2014).

Only a few cultivars in wheat have undergone whole-genome shotgun sequencing; however, low sequencing depths limit their use for pan-genome analysis. These cul-tivars include both parents of the ITMI mapping popula-tion sequenced at 15 × (Opata) and 30 × (W7984) coverage (Chapman et al. 2015) and 18 wheat cultivars sequenced between 8.4 × and 20 × (Montenegro et al. 2017). Due to the limitation of these datasets for construction of a high-quality pan-genome, IWGSC initiated the development of first high-quality wheat pan-genome comprising de novo sequence assemblies of 10 + wheat cultivars from world-wide sources (http://www.10whe atgen omes.com/). The cultivars include CDC Landmark, CDC Stanley (Canada), Jagger (USA), Julius (Germany), Arina (Switzerland), Mace, Lancer (Australia), Norin 61 (Japan), Aikang 58, Kenong 9204 (China), and SY Mattis (France). This first-generation pan-genome will accelerate gene discovery for improvement in wheat production and quality, and will also be helpful in understanding gene-models and regula-tory motifs.

There is also progress on sequencing of additional accessions of the wheat progenitors because that will reveal loci that underwent domestication and selection

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during modern breeding. More importantly, this will be helpful to develop SNP genotyping platforms to deploy and track useful introgressions from the immediate pro-genitors to overcome the wheat improvement bottleneck. An international consortium was recently established and crowd-funded the sequencing of a diversity panel of 155 non-redundant accessions of Ae. tauschii ssp. strangulata at sequencing depths between 10× and 30× (http://www.openw ildwh eat.org/). These data are not published but are available under the Toronto Agreement. This resource is extremely important because the D genome of wheat has a very narrow genetic base due to the evolutionary bot-tleneck in forming hexaploid wheat, caused by the limited number of hybridization events between Ae. tauschii and T. turgidum. Genome sequencing of diverse Ae. tauschii accessions will have huge breeding implications, because SHW derived from Ae. tauschii ssp. strangulata is a widely exploited genetic resource providing new genetic variation for grain yield and adaptability for wheat breed-ing (Ogbonnaya et  al. 2013). The available molecular markers are inadequate to capture variation in SHWs or their advanced derivatives because very limited informa-tion on variation in the Ae. tauschii genome is available (Rasheed et al. 2018b). Therefore, new molecular mark-ers developed from polymorphisms in diverse Ae. tauschii accessions will be useful in tracking variation in synthetic-derived germplasm.

Other available wheat genomic resources and databases helpful for wheat genetics and breeding are listed in Table 1. For example, the GrainGenes database (https ://wheat .pw.usda.gov) is one of the earliest repositories for wheat maps and QTL and has been extremely helpful for wheat research. Currently, GrainGenes have 16 diploid, 27 tetraploid and 68 hexaploid wheat linkage maps (redundant) constructed with various types of molecular markers. Another wheat database, WheatIS (http://www.wheat is.org/), is an excellent wheat information system for data resources and bioinformatics tools. Similarly, wheat expression browser (http://www.wheat -expre ssion .com/) is powerful database for transcriptome data analysis and visualization in wheat (Borrill et al. 2016). These databases together with other sequencing and physical genomics tools provide a community resource to integrate, visualize and com-pare datasets across experiments, thus enabling meta-analyses approaches to deepen understanding of wheat genetic variation for breeding objectives.

High‑throughput genotyping platforms in wheat

Establishing simple, robust and high-throughput genotyp-ing platforms is paramount for genome-based breeding. It is also important because the huge investments of time

and capital in basic research and sequencing can be trans-lated into sophisticated tools for applied wheat breeding. Rasheed et al. (2017) provided a comprehensive overview on development and application of high-throughput geno-typing platforms across crop species. They concluded that data throughput (both number of data points and turn-around time) is no longer a problem, but cost (per data point or per sample) remains a major decisive factor in choosing appro-priate high-throughput platforms. The genotyping require-ments in breeding and gene discovery programs differ in objectives. We discuss these objectives in the following two broad categories, i.e., (i) genome-wide genotyping as in the case of gene mapping, diversity and genomic selection, and (ii) targeted genotyping at one or few loci as in gene intro-gression, quality control and hybrid testing.

Whole‑genome genotyping

SNPs are the most abundant type of molecular markers and are amenable to high-throughput, which make them the marker of choice for crop breeding. Genome-wide high-throughput genotyping has to be achieved at the cost of flex-ibility. Nevertheless, PCR-based markers such as SSRs pro-vide flexibility to freely choose for any number of genotypes. However, modern genotyping platforms are non-flexible.

Genotyping‑by‑sequencing

The exponentially decreasing cost of NGS, customization and easy library preparation, and de novo SNP discovery are the main factors in adopting sequencing as a genotyp-ing tool. NGS-based platforms are applicable to various crops regardless of prior genomics knowledge, genome size, organization, or ploidy. Currently there are more than 15 different genotyping-by-sequencing (GBS) techniques that have been used in crop plants; each has some distinc-tive features (Scheben et al. 2016). GBS simply makes use of restriction-enzyme digestion, followed by adapter liga-tion, PCR, and sequencing. While the original Elshire GBS protocol employed a single enzyme protocol (Elshire et al. 2011), a two-enzyme modification was employed in barley and wheat (Poland et al. 2012b). Skim sequencing consists of low-coverage (for example, 5–10 ×) Illumina reads and presents a cost-effective way of identifying genetic varia-tion and haplotypes in populations. Exome capture refers to the sequencing of pre-designed probes within gene-coding regions (Jordan et al. 2015), and traditional GBS typically involves the sequencing of about 100–150 bases from a randomly located restriction-enzyme cleavage site in the genome (Poland et al. 2012b).

Elshire GBS and DArTseq are the most widely used platforms in wheat research and breeding. For exam-ple, GBS was used for genomic selection in CIMMYT’s

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Table 1 Open-source genomics and database resources in wheat

Name URL Description Reference

GrainGenes https://wheat.pw.usda.gov A comprehensive resource for molecular and phenotypic infor-mation for Triticeae and Avena

Odell et al. (2017)

MASWheat http://maswheat.ucdavis.edu/ Marker-assisted selection database for wheat

KOMUGI https://shigen.nig.ac.jp/wheat/komugi/

Integrated database of wheat by National Bioresource Project, Japan

WISP http://www.wheat isp.org/ Website of wheatSeeD http://seeds ofdis cover y.org/ Official website for the data

resources from Seed of Discov-ery project

expVIP http://wheat -expre ssion .com/ Wheat transcriptome resources for expression analysis

Borrill et al. (2016)

WheatExp https://wheat.pw.usda.gov/Wheat-Exp/

Homoeologue-specific database of gene expression profiles for polyploid wheat.

Pearce et al. (2015)

Cerealsdb http://www.cerealsdb.uk.net/cereal-genomics/CerealsDB/indexNEW.php

Database for SNPs, genotyping arrays and sequences

WheatIS http://wheat is.org/ Wheat information system for wheat data, resources and bioin-formatics tools

OpenWildWheat http://www.openw ildwh eat.org/ Sequencing resources of Ae. tauschii accessions

IWGSC http://www.wheat genom e.org/ Official website of IWGSC10 + Wheat genomes http://www.10whe atgen omes.com/ Wheat pan-genome resourcesPolymarker http://polym arker .tgac.ac.uk/ SNP assay development tool Ramirez-Gonzalez et al. (2015)Wheat SNPs http://wheatgenomics.plantpath.

ksu.edu/snp/Wheat 9 K and 90 K SNP arrays

Triticeae tool box https ://triti ceaet oolbo x.org/wheat / Repository of wheat data from Wheat CAP

Blake et al. (2016)

Wheat Atlas http://wheatatlas.org/ Atlas of wheat germplasm and production statistics

Wheat Transcription factors http://itak.feilab.net/cgi-bin/itak/db_family.cgi?cat=transcription%20factor&plant=4565

Database of wheat transcription factors

TILLING http://www.wheat-tilling.com/ Sequencing resource of wheat TILLING population

WGIN http://www.wgin.org.uk/about .php Wheat genetic improvement network

Wheat Training http://www.wheat -train ing.com/ Training resources in wheat genomics

URGI http://wheat -urgi.versa illes .inra.fr/ INRA based resources for wheat sequence resources

CIMMYT wheat germplasm bank http://wgb.cimmy t.org/gring lobal /searc h.aspx

CIMMYT wheat germplasm

Gramene http://www.grame ne.org/ Open-source, integrated data resource for comparative func-tional genomics in crops and model plant species

KnetMiner http://knetm iner.rotha msted .ac.uk Open-source software tools for integrating and visualizing large biological datasets.

Hassani-Pak and Rawlings (2017)

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semi-arid wheat breeding program and showed prediction accuracy between 0.28 and 0.45 for grain yield, with an improvement of 0.1 to 0.2 over the previously established marker platform (Poland et al. 2012a). DArTseq has been extensively used for genetic diversity studies in durum (Baloch et al. 2017) and bread wheat (Riaz et al. 2017; Vikram et al. 2016) landraces in developing high-density linkage maps (Li et al. 2015), dissection of loci under-pinning drought and heat stress adaptability in synthetic-derived germplasm (Jafarzadeh et al. 2016), and genomic selection (Crossa et al. 2016). DArTseq has been used to genotype wheat genetic resources from CIMMYT under the Seeds of Discovery (SeeD) initiative (http://seeds ofdis cover y.org) and huge datasets are publicly available. Another GBS platform termed specific locus amplified fragment sequencing (SLAF-seq) was used in species of distant wheat relatives Agropyron (Zhang et al. 2015) and Thinopyrum (Chen et al. 2013b) for genome-specific SNP marker discovery.

Exome capture or exome sequencing is another effective tool for de novo SNP discovery in pre-designed probes in protein coding genes. This is in contrast to RNA-seq for non-selective sequencing of cDNA for SNP discovery. First-generation exome capture in wheat was developed in durum and comprised 3497 genes (Saintenac et al. 2011); bread wheat came later (Jordan et al. 2015), consisting of probes covering 124,201 high confidence genes. The latter was initially used to sequence 62 diverse wheat accessions to identify selective sweeps and construction of a haplo-type map (Jordan et al. 2015). Winfield et al. (2016) used an exome capture assay to target ~ 57 Mb of coding sequence in 43 bread wheat accessions and wild species to discover almost one million SNPs. Krasileva et al. (2017) developed an 84 Mb exome capture assay covering 82,511 transcripts and characterized 2735 mutant lines of bread (Cadenza) and durum wheat (Kronos). Another exome capture assay was developed based on probes from a 90 K SNP array and 248 accessions from the Watkins global landrace collection were sequenced and several algorithms were compared for SNP imputation (Shi et al. 2017).

All these examples clearly show that use of NGS for SNP discovery, genetic diversity, gene mapping and genomic selection is cost-effective and provides SNP information independent of genetic background; thus NGS can be used simultaneously for SNP discovery and genotyping in wheat genetic resources. However, the drawbacks include the complexity in allele calling due to the absence of a pan-genome sequence, labor-intensive library preparation, high level of missing data, and lack of efficient SNP imputa-tion tools (Scheben et al. 2016). Despite these limitations, GBS has become increasingly popular and new develop-ments in sequencing chemistry, reductions in cost of long-read sequencing platforms and availability of pan-genome

sequence of wheat will accelerate its adoption in wheat breeding programs.

Array‑based genotyping in wheat

SNP arrays provided a step change in cost and throughput for SNP genotyping and currently several SNP arrays are available for wheat. SNP arrays provide a range of multiplex levels to facilitate high-density scanning, and have robust allele calling and high call rates compared to NGS, and they are cost-effective in terms of per data point when genotyping large number of SNPs and samples (Rasheed et al. 2017). The main disadvantages are that arrays are non-flexible and, despite the reduced cost per data point, the overall cost to genotype one sample is quite high, making them still inac-cessible for most crop genetics and breeding programs.

Diversity Array Technology (DArT) was one of the ini-tial array-based technologies that used multiplex hybridi-zation-based approaches to identify random single-copy sequences in genomes under investigation, and ultimately hundreds of anonymous markers were generated in wheat (Akbari et al. 2006). Although there were several other multiplex platforms, Illumina GoldenGate and BeadXpress were used for germplasm characterization in wheat (Akhu-nov et al. 2009; Chao et al. 2010). These platforms incor-porated locus and allele-specific oligos for hybridization, followed by allele-specific extension and fluorescent scan-ning of 48–384 and 384–3072 SNPs per sample. Significant and rapid advancement in technology led to development of high-density infinium arrays in wheat. Firstly, Cavanagh et al. (2013) developed a 9 K infinium SNP array contain-ing ~ 9000 gene-associated SNPs and used it to genotype 2994 hexaploid wheat accessions. Later, a 90 K SNP array was developed and almost 3380 wheat accessions were char-acterized (Sun et al. 2017; Wang et al. 2014). SNPs identi-fied by the 9 K and 90 K arrays are biased in having more representation from cultivated wheat and are ineffective in detecting polymorphisms in other wheat genetic resources, thus limiting their use in the characterization of landraces, synthetic wheats and germplasm derived from the secondary and tertiary gene pools (Rasheed et al. 2018a). To overcome this limitation, Winfield et al. (2016) developed an 820 K Affymetrix Axiom SNP array from resequencing exomes of 43 bread wheat and wild species accessions representing the primary, secondary and tertiary gene pools. The 820 K SNP array was used to characterize 475 hexaploid wheat and wheat relative accessions. A subset of SNPs from the 820 K array were then used to design a breeder-oriented Axiom 35 K SNP (Allen et al. 2017), which is effective in charac-terizing SNPs in wild relatives of wheat in a cost-effective manner (King et al. 2016).

The main bias in SNP arrays is that they contain only gene-derived SNPs, and genes only account for 1–2% of

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the genome. So far, the intergenic fraction of the wheat genome, which account for 98–99% of the total, has been poorly exploited for SNP discovery, with 10–15% being low-copy sequences. To overcome this problem, Rimbert et al. (2018) used whole-genome resequencing data from eight wheat accessions and discovered more than 3 million genome-wide SNPs from genic and intergenic regions that were mined for single-copy loci to design a 280 K SNP array. Out of them, 68.5% was successfully converted from in silico putative SNPs to functional SNP assays with almost one-half producing diploid-like clusters, and a high-density genetic map comprising more than 83 K loci was produced. A 660 K SNP array developed at the Chinese Academy of Agricultural Sciences (CAAS) was used to identify QTL for bread-making quality and kernel number (Jin et al. 2016; Cui et al. 2017), and a high-density linkage map of Agropy-ron cristatum was constructed (Zhou et al. 2018). However, the features of this SNP array and criteria for selection of SNP markers were not revealed. More recently, we devel-oped Wheat 50 K (Triticum TraitBreed array) and 15 K SNP arrays based on the high-quality SNPs selected from the Wheat 35 K, 90 K, and 660 K SNP chips. Around 135 and 150 functional markers, and 700 and 1000 SNPs tightly linked with known QTL are included in the 50 K and 15 K SNP arrays, respectively.

Undoubtedly, high-throughput, high-density SNP data are the main attraction for using NGS and SNP arrays in wheat research, especially when the analyses can be outsourced at affordable prices. Outsourcing empowers wheat breeding programs to make best use of the technologies without huge capital investments in equipment, technology upgrading and training (Rossetto and Henry 2014). For these reasons and good genome-wide coverage, SNP arrays have been used for all types of academic research including genetic diver-sity, QTL mapping, GWAS, and applied genomic selection experiments to predict grain yield, end-use quality and het-erosis patterns (Rasheed et al. 2018a).

High‑throughput single‑plex genotyping in wheat

While NGS and SNP arrays are excellent choices for gene discovery and mapping and for identifying linked markers for important traits, such markers, in addition to functional markers, are ideal for gene tagging and gene introgression in breeding. Therefore, it is challenging to develop a high-throughput platform to use single markers in wheat breeding programs. Rasheed et al. (2017) highlighted six factors in developing such platforms; these included (i) number of data points that can be generated in a short time period, (ii) ease of use, (iii) data quality (sensitivity, reliability, reproduc-ibility, and accuracy), (iv) flexibility (genotyping few sam-ples with many SNPs or many samples with few SNPs), (v) assay development requirements, and (vi) genotyping cost

per sample or data point. Sufficient recent reports indicate that LGC’s KASP (Kompetitive Allele-Specific PCR) is an acceptable global benchmark technology for such genotyp-ing requirements in terms of both cost-effectiveness and high throughput (Semagn et al. 2014).

At a first step, several groups worked on converting gel-based functional markers to high-throughput KASP mark-ers (http://www.cerea lsdb.uk.net/cerea lgeno mics/Cerea lsDB/kasp_downl oad.php?URL=). The numbers were increased to 72 after validation in a bread wheat diversity panel (Rasheed et al. 2016, 2019). This effort has continued in our group and we currently have more than 150 KASP markers for almost 100 functional genes (Rasheed et al. unpublished data). Currently, most gene mapping studies (both QTL and GWAS) use SNP arrays or NGS; the mark-ers linked to QTL are SNPs that can be easily converted to KASP assays for further diagnosis or QTL introgression in breeding. Similarly, diagnostic KASP assay development is preferred due to the wide acceptance and usefulness of this technology during functional gene discovery. Several QTL linked to important traits and SNPs in functional genes have been converted to KASP markers; examples include Lr16 (Kassa et al. 2017), Lr23 (Chhetri et al. 2017), Lr67 (Moore et al. 2015), and Yr26 (Wu et al. 2018), as well as many functional markers, including pre-harvest sprouting gene Phs1 (Liu et al. 2013), earliness per se gene Eps1 (Zikhali et al. 2016), and NAM-A1 (Cormier et al. 2015).

KASP provides the throughput required in breeding programs for gene tagging and gene introgression without compromising flexibility. However, higher cost is still an issue because KASP mastermix is a commercial proprietary from LGC and there are no other competitors. Due to this limitation, several groups tried to develop other open-source uniplex SNP genotyping techniques like semi-thermal asym-metric reverse PCR (STARP) (Long et al. 2016) and Amplif-luor (Jatayev et al. 2017) which can be used with any com-mercial mastermix, significantly reducing the per data point cost. More recently, other commercial alternatives of KASP assays were introduced, including PACE® mastermix from 3CR Biosciences (www.3crbi o.com) and rhAmp from Inte-grated DNA Technologies® (https ://www.idtdn a.com/pages /produ cts/qpcr-and-pcr/genot yping /rhamp -snp-genot yping ).

Their acceptance in wheat breeding programs is yet to be seen.

Genome‑based breeding strategies in wheat

There is an unprecedented increase in the number of QTL and GWAS studies due to advances in genomics and geno-typing methodologies. The genetic basis of quantitative traits underpinning yield potential, stress adaptability and end-use quality are continuously being revealed due to these studies.

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However, translating this research into improved wheat cul-tivars for farmers and consumers is still a huge challenge (Li et al. 2018). This eludes the earlier optimism from the concept that increased knowledge on genetic architecture of economic traits through gene mapping will increase our understanding of causal genes and their exploitation in breeding (Bernardo 2016). Therefore, innovative genome-based breeding strategies have to be used where breeders can simultaneously uncover genetic variation for the economic traits and deploy that variation in breeding pipelines.

Sources of new genetic variation for breeding

Considerable evidence suggests that like other crops, genetic diversity in wheat drastically declined during polyploid speciation and domestication as well as intensive selection during modern wheat breeding. Several studies have shown that favorable introgressions from wild relatives signifi-cantly improved grain yield, adaptability, end-use quality and disease resistance (Rasheed et al. 2018a). For exam-ple, unknown introgression from wild emmer is present in several northern European varieties, and cultivar Robigus is a direct progenitor of more than one-third of UK varie-ties (Gardner et al. 2016). Similarly, high frequencies of the 1BL.1RS translocation in wheat cultivars globally, Ae. ven-tricosa introgressions, and alleged Th. ponticum introgres-sions in wheat cultivars from China indicate the impact of wild relatives in farmers’ fields. The favorable phenotypic effects of such introgressions are very obvious, but other features like size, distribution, allelic diversity and linkage drag of these introgressions are unknown, thus limiting the possibility to recreate these introgressions with less linkage drag. Similarly, synthetic wheats are a prominent source of new genetic diversity from goatgrass and durum wheat for improving adaptation and yield potential in bread wheat. According to Börner et  al. (2015), more than 62 SHW derivatives had been released globally and that number has undoubtedly increased to the present time. However, it is still challenging to devise a genome-based strategy to deploy favorable introgressions from synthetic wheats to enhance breeding value. Nevertheless, it is expected that further stud-ies, including resequencing of both wild and domesticated species accessions will provide better tools to characterize population-level genetic variation and its association with relevant phenotypic traits (Rasheed et al. 2018a, b). We recently reviewed the impact of genomics advancements in gene discovery and gene deployment from wheat wild rela-tives (Rasheed et al. 2018a), hence we will describe these aspects only briefly.

Genbanks are important sources of diversity and it is estimated that half a million wheat accessions are stored in 23 genbanks worldwide. This diversity is vital for crop improvement because some extremely important loci have

been exhaustively selected during wheat domestication and modern breeding, and genbank accessions are important sources of alternative favorable alleles at those loci or may contain rare superior variant alleles (adaptability to extreme abiotic or biotic stresses) that remained unselected during domestication and modern breeding, and can be introduced into breeding programs.

Genotyping complete genbanks or entire collections of specific gene pools has become a major activity in programs such as the Seeds of Discovery initiative (Sehgal et al. 2015), WISP initiative (Moore 2015), and Vavilov wheat landrace collection (Riaz et al. 2017). The data, so-called ‘digital genbanks,’ from such huge genotyping efforts are impor-tant tools to unravel population structure, detect signatures of selection, develop core collections to avoid redundancy, and map QTL for important phenotypic traits. Two very important gene-based approaches that may have significant breeding implications are detailed below.

Divergent selections associated with phenotypes as new breeding targets

Comparative genome-wide scans coupled with GWAS in modern wheat cultivars and landraces (for example) can identify landraces with ‘divergent selective sweeps’ and strong associations with important phenotypes. This could provide superior alternative alleles for commercially impor-tant traits that can be utilized in wheat breeding, thus con-tributing to improved adaptation and yield potential. More than 200 regions identified in rice contained signatures of domestication or artificial selection covering more than 7.8% of the rice genome (Xie et al. 2015). These loci contained more than 4000 non-transposable element genes, and sev-eral functional genes associated with agronomic traits. The loci included GN1A, a causal gene for number of grains per panicle; SD1, a functional gene for plant height; AMT1;1, a gene regulating nitrogen uptake; XA4 and XA26, involved in disease reaction; and RF1, for restoration of male fertility in hybrid rice. Furthermore, grain yield was positively corre-lated with number of selection signatures, indicating that the signatures could be useful in predicting agronomic poten-tial and implicating the selected loci as potential targets for crop improvement (Xie et al. 2015). We recently identified 89 ‘divergent selections’ in synthetic-derived wheats when compared to modern bread wheats, and 30 selection loci were identified in the D genome (Afzal et al. unpublished). These key selections, especially those on chromosomes 4D, 5D and 7D, co-localized with important functional genes for adaptive traits, such as Elf3-D1 (1D) for earliness per se (Eps), TaCKX-D1 (3D), TaGS1a (6D) and TaGS-D1 (7D) for grain size, weight and morphology, TaCwi-D1 (5D) for drought tolerance, and Vrn-D3 (7D) for vernalization. How-ever, a major disadvantage was that the 90 K array used for

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the study was ineffective in tracking polymorphisms specific to Ae. tauschii. New SNP arrays with more representation from Ae. tauschii will likely overcome this disadvantage to harness more benefits for practical wheat breeding from this approach.

Discovering selective sweeps for local adaptation

Precise phenotypic characterization of a vast number of germplasm entries is a major limitation due to financial and physical constraints. Alternatively, georeferenced passport data for genbank accessions can be used to extract long-term climate data (e.g., rainfall, temperature, soil PH, frost risk, aridity index) from the Geographic Information System (GIS) for collection sites. These environmental data can be used as proxies for tolerance to biotic and abiotic stresses, and could lead to discovery of favorable alleles/haplotypes that are valuable for crop improvement. Lasky et al. (2015) used this approach in a study of 1943 sorghum georeferenced landraces and identified alleles associated with bioclimatic and soil gradients. Local adaptation in sorghum landraces was revealed due to the major effect of the Maturity 1 gene for photoperiod-sensitive flowering, the Tannin 1 gene for the presence of tannin in grain testa, and STAR1, an ABC (adenosine triphosphate-binding cassette) transporter can-didate gene conferring tolerance to aluminum toxicity. A similar approach was used to identify the genetic basis of adaptation to latitude and altitude in 4471 maize landraces (Navarro et al. 2017). However, an additional GWAS for flowering time revealed that 61.4% of the SNPs associated with latitude were also associated with flowering time. This demonstrated that loci underpinning local adaptation are positively correlated with phenotypic traits and this strat-egy has implications in applied breeding. In this way, the efficiency of pre-breeding and research could be improved, thus accelerating the deployment of climate-smart cultivars.

Although this approach has not been practiced in wheat, current advancements in genomics, allow prediction of agro-nomic potential in wheat genetic resources using genome-wide marker scans and selection of plants with specific genes for biotic and abiotic resistance without exposing them to the relevant stresses.

Genomic selection in wheat

As mentioned above, past selection processes in wheat breeding relied on phenotypic traits that historically led to a non-steady rate of genetic gain in wheat breeding. Genomic selection (also referred to genomic prediction) or genome-wide selection (GS) has emerged as a strategy extensively used in animal breeding to steadily achieve genetic gain. It has also shown significant outcomes in crop breeding (Ber-nardo 2016) in both pure line breeding and hybrid breeding

(Crossa et al. 2017). In GS, a test population representing the genetic diversity of a large breeding population is thor-oughly genotyped and phenotyped to predict phenotypic per-formance based on genomically estimated breeding values (GEBVs). The large breeding population is then genotyped and the GEBVs are used to predict the phenotypes of lines in the population. According to Hickey et al. (2017), GS directly addresses four factors that affect the rate of genetic gain: (i) the speed of GS should be faster than phenotypic selection and breeders can recycle genotypes more quickly, (ii) selection intensity is greater than phenotypic selection and more individuals can be selected based on GEBVs, (iii) GEBVs are more accurate than estimated breeding values based on phenotype and pedigree alone, and (iv) GS can more efficiently integrate wide crossing and pre-breeding.

GS has emerged as a valuable tool for improving complex traits controlled by QTL with small effects. Various simula-tion models for predicting selection accuracy depend largely on marker density, marker type, size of training populations, and trait heritability. Due to its promise, GS has been prac-ticed extensively in wheat breeding (Table 2). GS has not only been applied to bread wheat cultivars to predict grain yield (Belamkar et al. 2018), disease resistance (Juliana et al. 2017), and end-use quality (Hayes et al. 2017), but also in wheat genetic resources to predict breeding value. A detailed GS experiment on 97 synthetic-derived introgression popu-lations indicated several candidates with higher GEBVs than the respective recurrent bread wheat parents, and hence with a clear potential of synthetic parents in improving grain yield in heat-stressed environments (Jafarzadeh et al. 2016). Opti-mization of GS in wheat genetic resources is now becoming routine in many breeding programs focused on harnessing new diversity from alien species, and has been explored for domesticating new crops such as Th. intermedium (Zhang et al. 2016), and landraces for rust resistance (Daetwyler et al. 2014; Pasam et al. 2017), mineral contents (Manick-avelu et al. 2017), and heat and drought stress adaptation (Crossa et al. 2016). Compared with QTL mapping and GWAS, GS has more promise in harnessing genetic gains from genetic resources for quantitative traits and is seen as a more reliable and useful approach (Bernardo 2016). However, the key challenges in successful practice of GS depend on cost-effectiveness and less biased approaches for genotyping, software for handling, quality control and joint analysis of genotypic, phenotypic and environment data, and a streamlined work flow for using GS within the overall breeding pipeline.

Haplotype‑based genome profiling: the next epoch in wheat genomics

In wheat, SNPs are used as explanatory variables in genomic selection and GWAS studies. However, the academic and

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applied wheat research has to be transformed beyond bi-allelic SNP variation, and make use of the benefits of haplo-types. In human genetic studies, haplotype blocks combining two or more SNPs in strong LD are more informative than single bi-allelic SNPs (Stephens et al. 2001). Lorenz et al. (2010) defined haplotypes by several analytical procedures in barley and confirmed that the distribution of QTL alleles in nature was unlike the distribution of marker variants, and hence utilizing haplotype information could capture associa-tions that would elude detection by single SNPs. Haplotype GWAS-based analyses are still rare in wheat with few excep-tions (Hao et al. 2017), but have shown promise in Brassica (Wu et al. 2016), barley (Lorenz et al. 2010) and other crop species (Qian et al. 2017). Bevan et al. (2017) argued that haplotypes in progenitors and wild relatives of crops contain

a broad range of genetic variation, and identification of hap-lotypes with fewer deleterious alleles and improved pheno-types could accelerate genetic gain in crop improvement. Moreover, it was hypothesized that haplotypes can capture epistatic interactions between SNPs. Therefore, haplotype-based approaches should boost prediction accuracies. Jiang et al. (2018) proved that haplotype-based genomic predic-tions using HBLUP simulations in rice, maize and mouse were superior to SNP-based models when there is abun-dant local epistasis. The fact that haplotype-based models exploit local epistasis among markers is especially relevant for applied breeding as the local additive-by-additive epi-static effects can last for generations, like additive effects. Thus, haplotype-based models have the potential to increase the accuracy of genomic selection. Similarly, we recently

Table 2 Genome-based prediction studies in wheat

Traits Germplasm Marker Accuracy range References

Bread-making quality 5520 CIMMYT advanced lines GBS 0.32–0.62 Battenfield et al. (2016)Iron and zinc 330 diverse wheat lines 90 K SNP array 0.33–0.69 for iron;

0.32–0.73 for zinc

Velu et al. (2016)

Bread-making quality Two bi-parental soft winter wheat populations

DArT and SSRs 0.42–0.66 Heffner et al. (2011b)

Bread-making quality 840 winter wheats DArTseq 0.38–0.63 Michel et al. (2018)End-use quality 398 wheat lines 90 K SNP array 0–0.69 Hayes et al. (2017)Grain yield and protein 189 durum wheats DArTseq More than 0.3 Rapp et al. (2018)Yield 861 winter wheats DArTseq 0.39 Michel et al. (2017)Grain yield 254 CIMMYT lines DArT and GBS 0.28–0.45 Poland et al. (2012b)Grain yield 622 CIMMYT lines GBS 0.39–0.50 Dawson et al. (2013)13 agronomic traits 374 winter wheats DArT markers 0.17–0.83 Heffner et al. (2011a)Yield stability 273 soft winter wheats 90 K 0.33–0.67 Huang et al. (2016)Agronomic traits 10,375 lines 18 K Affy SNP array 0.31–0.82 Norman et al. (2017)Leaf, stem and stripe rust 206 accessions from Watkin’s lan-

draces collection90 K SNP array 0.27–0.40 Daetwyler et al. (2014)

Stem and stripe rust Five bi-parental populations DArTseq 0.41–0.82 Ornella et al. (2012)FHB 273 soft red winter wheat GBS 0.4–0.9 Arruda et al. (2016)FHB and STB 2325 Central European winter

wheats15 K SNP array 0.5–0.6 Mirdita et al. (2015)

Leaf, stem and stripe rust 646 CIMMYT lines GBS 0.31–0.78 Juliana et al. (2017)FHB 322 lines from USA DArT and SSRs 0.30–0.43 Rutkoski et al. (2011PHS 1118 hard winter wheat GBS 0.49–0.62 Moore et al. (2017)Adult plant Sr resistance 365 CIMMYT advanced lines GBS 0.46–0.59 Rutkoski et al. (2014)Heterosis prediction 90 hybrids 9 K 0.58–0.63 Zhao et al. (2013)FHB 372 European winter wheats 90 K 0.65–0.83 Jiang et al. (2017b)Grain yield and quality 170 varieties and mapping popula-

tion90 K SNP array 0–0.8 Haile et al. (2018)

Stripe rust and days to heading 282 DH population GBS 0.33–0.70 Song et al. (2017)Test weight, grain yield and heading

timeTwo DH populations DArT markers 0.11–0.4 Charmet et al. (2014)

FHB 170 spring wheat cultivars 90 K SNP array Dong et al. (2018)End-use quality 635 winter wheat lines 15 K Illumina array 0.50–0.79 Kristensen et al. (2018)

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used both SNP-GWAS and haplotype GWAS in a syn-thetic-derived wheat diversity panel using the 90 K SNP array and identified 20 genomic regions associated with adaptation to drought that did not overlap with SNP-GWAS (Afzal et al. unpublished). This proved that haplotype-based analysis improved QTL identification and can complement genetic mapping and genomic prediction studies.

Genome‑based strategies for heterosis breeding in wheat

Grain yield can be boosted by exploiting heterosis which is defined as superior performance of hybrids compared with the parents. Currently, hybrid wheats are cultivated on < 1% of the total worldwide wheat area, but genomics has helped significantly in overcoming several technical barriers in exploiting heterosis in wheat. Wheat is a highly self-pollinated crop; therefore, cost-effective hybrid seed production is a huge challenge due to poor understanding and exploitation of male-sterility systems in the species. Male sterility is a very important component of hybrid wheat development. The male-sterile 2 (ms2) mutant has been known for 40 years, but the causal gene for sterility was unknown until recently. Map-based cloning studies from two different groups identified TRIM element in the promoter of the ms2 allele that was responsible for activation of the anther-specific Ms2 allele conferring male sterility (Ni et al. 2017; Xia et al. 2017). The male sterility 1 (Ms1) gene was also cloned by a map-based cloning approach (Tucker et al. 2017; Wang et al. 2017). The evolutionary analysis sug-gested that MS1 is a newly evolved gene in the Poaceae, and MS1 protein is localized to the plastids and mitochondrial membranes, where it exhibits phospholipid-binding activ-ity to induce male sterility. Kempe et al. (2014) described a split gene system for male sterility that relies on the expres-sion of phytotoxic barnase controlled by two loci linked in repulsion. This system allows for growth and maintenance of male-sterile female crossing partners, whereas the hybrids are fertile. The technology does not require fertility restor-ers and is based solely on the genetic modification of the female partner.

Another strategy that shows promise for hybrid breed-ing is genomic prediction of heterotic patterns in exten-sively characterized large populations of hybrids and their parents. Superiority among 1604 hybrids was assessed for agronomic traits and disease resistance, and 69 hybrids outyielded the best commercial inbred line variety by 7.2 to 10.7% (Longin et al. 2013). Zhao et al. (2015) described a three-way genome-based strategy to predict heterotic pat-terns in wheat in the 1604 hybrids and 135 parents. This strategy included the compilation of the full hybrid perfor-mance based on genomic prediction, and a search for high yielding heterotic patterns based on a simulated annealing

algorithm. The long-term success of identified heterotic patterns was assessed by estimating the usefulness, selec-tion limit, and representativeness of the heterotic pattern with respect to a defined base population. Exploitation of some major genes like reduced height (Rht) can assist in selecting parents for hybrid breeding due to their effect on plant height and male floral architecture such as anther extrusion and pollen mass (Würschum et al. 2018a). Plant height was positively associated with anther extrusion, and both the Rht-B1b and Rht-D1b appeared to be alleles for poor anther extrusion. No cultivar carrying Rht-B1b or Rht-D1b proved to be good male parent due to low anther extrusion (Boeven et al. 2016; Lu et al. 2013). However, the heights of parental lines must be restricted to prevent lodging under high yielding conditions. Therefore, alterna-tive dwarfing genes such as Rht24, which is independent of anther extrusion or any effect on male floral characteristics must be used (Würschum et al. 2018b). These studies not only show the promise of a quantitative genetic framework to predict heterotic patterns in wheat (Jiang et al. 2017a), but also assist to deploy some major gene to fine-tune the expression of plant stature and male floral traits for producing hybrid seed. These advances suggest that the genomics-enabled discovery of these genes and genome-based prediction could facilitate the development of com-mercial hybrids and it is now projected that economically feasible hybrid wheat production will occur in the next few years (Fischer et al. 2014). However, this would further depend on issues like availability of pure hybrid seeds and cost of required seeds especially in countries like China where higher than optimal seed rate is used.

Conclusion

The technological surge in wheat genomics has undoubt-edly enhanced progress in wheat genetics and will assist wheat breeding if strategically integrated with advances in phenomics, gene editing and analytical procedures. Not only has the discovery of new genes and their functional characterization become much faster, but introgression of genes into breeding pipelines to obtain novel homozygous advanced lines can be greatly hastened with the speed-breeding (Watson et al. 2018) and high-throughput geno-typing platforms. Further improvement in wheat sequenc-ing resources will improve the efficiency of existing genomic tools to enable genome-based hybrid and inbred line breeding.

Author contribution statement Awais Rasheed and Xian-chun Xia wrote the review article, and approved this manu-script for publication in TAAG.

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Acknowledgements The authors are grateful to Prof. R. A. McIntosh, Plant Breeding Institute, University of Sydney, for critical review of this manuscript. This work was funded by the National Natural Sci-ence Foundation of China (31461143021, 31550110212), and CAAS Science and Technology Innovation Program.

Compliance with ethical standards

Conflict of interest We declare no conflict of interest

Ethical standard We declare that these works complied with the ethical standards in China.

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