High-density single nucleotide polymorphism (SNP) genotyping arrays are a powerful tool for studying genomic patterns of diversity, inferring ancestral relationships between individuals in populations and studying marker–trait associations in mapping experiments. We developed a genotyping array including about 90 000 gene-associated SNPs and used it to characterize genetic variation in allohexaploid and allotetraploid wheat populations. The array includes a significant fraction of common genome-wide distributed SNPs that are represented in populations of diverse geographical origin. We used density-based spatial clustering algorithms to enable high-throughput genotype calling in complex data sets obtained for polyploid wheat. We show that these model-free clustering algorithms provide accurate genotype calling in the presence of multiple clusters including clusters with low signal intensity resulting from significant sequence divergence at the target SNP site or gene deletions. Assays that detect low-intensity clusters can provide insight into the distribution of presence–absence variation (PAV) in wheat populations. A total of 46 977 SNPs from the wheat 90K array were genetically mapped using a combination of eight mapping populations. The developed array and cluster identification algorithms provide an opportunity to infer detailed haplotype structure in polyploid wheat and will serve as an invaluable resource for diversity studies and investigating the genetic basis of trait variation in wheat.

An annotated reference sequence representing the hexaploid bread wheat genome in 21 pseudomolecules has been analyzed to identify the distribution and genomic context of coding and noncoding elements across the A, B, and D subgenomes. With an estimated coverage of 94% of the genome and containing 107,891 high-confidence gene models, this assembly enabled the discovery of tissue- and developmental stage–related coexpression networks by providing a transcriptome atlas representing major stages of wheat development. Dynamics of complex gene families involved in environmental adaptation and end-use quality were revealed at subgenome resolution and contextualized to known agronomic single-gene or quantitative trait loci. This community resource establishes the foundation for accelerating wheat research and application through improved understanding of wheat biology and genomics-assisted breeding.

Recently, increasingly more microsatellites, or simple sequence repeats (SSRs) have been found and characterized within protein-coding genes and their untranslated regions (UTRs). These data provide useful information to study possible SSR functions. Here, we review SSR distributions within expressed sequence tags (ESTs) and genes including protein-coding, 3'-UTRs and 5'-UTRs, and introns; and discuss the consequences of SSR repeat-number changes in those regions of both prokaryotes and eukaryotes. Strong evidence shows that SSRs are nonrandomly distributed across protein-coding regions, UTRs, and introns. Substantial data indicates that SSR expansions and/or contractions in protein-coding regions can lead to a gain or loss of gene function via frameshift mutation or expanded toxic mRNA. SSR variations in 5'-UTRs could regulate gene expression by affecting transcription and translation. The SSR expansions in the 3'-UTRs cause transcription slippage and produce expanded mRNA, which can be accumulated as nuclear foci, and which can disrupt splicing and, possibly, disrupt other cellular function. Intronic SSRs can affect gene transcription, mRNA splicing, or export to cytoplasm. Triplet SSRs located in the UTRs or intron can also induce heterochromatin-mediated-like gene silencing. All these effects caused by SSR expansions or contractions within genes can eventually lead to phenotypic changes. SSRs within genes evolve through mutational processes similar to those for SSRs located in other genomic regions including replication slippage, point mutation, and recombination. These mutational processes generate DNA changes that should be connected by DNA mismatch repair (MMR) system. Mutation that has escaped from the MMR system correction would become new alleles at the SSR loci, and then regulate and/or change gene products, and eventually lead to phenotype changes. Therefore, SSRs within genes should be subjected to stronger selective pressure than other genomic regions because of their functional importance. These SSRs may provide a molecular basis for fast adaptation to environmental changes in both prokaryotes and eukaryotes.

Microsatellites, or tandem simple sequence repeats (SSR), are abundant across genomes and show high levels of polymorphism. SSR genetic and evolutionary mechanisms remain controversial. Here we attempt to summarize the available data related to SSR distribution in coding and noncoding regions of genomes and SSR functional importance. Numerous lines of evidence demonstrate that SSR genomic distribution is nonrandom. Random expansions or contractions appear to be selected against for at least part of SSR loci, presumably because of their effect on chromatin organization, regulation of gene activity, recombination, DNA replication, cell cycle, mismatch repair system, etc. This review also discusses the role of two putative mutational mechanisms, replication slippage and recombination, and their interaction in SSR variation.

Wheat (Triticum spp.) is one of the founder crops that likely drove the Neolithic transition to sedentary agrarian societies in the Fertile Crescent more than 10,000 years ago. Identifying genetic modifications underlying wheat’s domestication requires knowledge about the genome of its allo-tetraploid progenitor, wild emmer (T. turgidum ssp. dicoccoides). We report a 10.1-gigabase assembly of the 14 chromosomes of wild tetraploid wheat, as well as analyses of gene content, genome architecture, and genetic diversity. With this fully assembled polyploid wheat genome, we identified the causal mutations in Brittle Rachis 1 (TtBtr1) genes controlling shattering, a key domestication trait. A study of genomic diversity among wild and domesticated accessions revealed genomic regions bearing the signature of selection under domestication. This reference assembly will serve as a resource for accelerating the genome-assisted improvement of modern wheat varieties.

As the staple food for 35% of the world's population, wheat is one of the most important crop species. To date, sequence-based tools to accelerate wheat improvement are lacking. As part of the international effort to sequence the 17-billion-base-pair hexaploid bread wheat genome (2n = 6x = 42 chromosomes), we constructed a bacterial artificial chromosome (BAC)-based integrated physical map of the largest chromosome, 3B, that alone is 995 megabases. A chromosome-specific BAC library was used to assemble 82% of the chromosome into 1036 contigs that were anchored with 1443 molecular markers, providing a major resource for genetic and genomic studies. This physical map establishes a template for the remaining wheat chromosomes and demonstrates the feasibility of constructing physical maps in large, complex, polyploid genomes with a chromosome-based approach.

Summary Consensus linkage maps are important tools in crop genomics. We have assembled a high‐density tetraploid wheat consensus map by integrating 13 data sets from independent biparental populations involving durum wheat cultivars (Triticum turgidum ssp. durum), cultivated emmer (T. turgidum ssp. dicoccum) and their ancestor (wild emmer, T. turgidum ssp. dicoccoides). The consensus map harboured 30 144 markers (including 26 626 SNPs and 791 SSRs) half of which were present in at least two component maps. The final map spanned 2631 cM of all 14 durum wheat chromosomes and, differently from the individual component maps, all markers fell within the 14 linkage groups. Marker density per genetic distance unit peaked at centromeric regions, likely due to a combination of low recombination rate in the centromeric regions and even gene distribution along the chromosomes. Comparisons with bread wheat indicated fewer regions with recombination suppression, making this consensus map valuable for mapping in the A and B genomes of both durum and bread wheat. Sequence similarity analysis allowed us to relate mapped gene‐derived SNPs to chromosome‐specific transcripts. Dense patterns of homeologous relationships have been established between the A‐ and B‐genome maps and between nonsyntenic homeologous chromosome regions as well, the latter tracing to ancient translocation events. The gene‐based homeologous relationships are valuable to infer the map location of homeologs of target loci/QTLs. Because most SNP and SSR markers were previously mapped in bread wheat, this consensus map will facilitate a more effective integration and exploitation of genes and QTL for wheat breeding purposes.

In cereals, several mildew resistance genes occur as large allelic series; for example, in wheat (Triticum aestivum and Triticum turgidum), 17 functional Pm3 alleles confer agronomically important race-specific resistance to powdery mildew (Blumeria graminis). The molecular basis of race specificity has been characterized in wheat, but little is known about the corresponding avirulence genes in powdery mildew. Here, we dissected the genetics of avirulence for six Pm3 alleles and found that three major Avr loci affect avirulence, with a common locus_1 involved in all AvrPm3-Pm3 interactions. We cloned the effector gene AvrPm3 a2/f2 from locus_2, which is recognized by the Pm3a and Pm3f alleles. Induction of a Pm3 alleledependent hypersensitive response in transient assays in Nicotiana benthamiana and in wheat demonstrated specificity. Gene expression analysis of Bcg1 (encoded by locus_1) and AvrPm3 a2/f2 revealed significant differences between isolates, indicating that in addition to protein polymorphisms, expression levels play a role in avirulence. We propose a model for race specificity involving three components: an allele-specific avirulence effector, a resistance gene allele, and a pathogenencoded suppressor of avirulence. Thus, whereas a genetically simple allelic series controls specificity in the plant host, recognition on the pathogen side is more complex, allowing flexible evolutionary responses and adaptation to resistance genes.