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Background To date, genome-scale analyses in the domestic horse have been limited by suboptimal single nucleotide polymorphism (SNP) density and uneven genomic coverage of the current SNP genotyping arrays. The recent availability of whole genome sequences has created the opportunity to develop a next generation, high-density equine SNP array. Results Using whole genome sequence from 153 individuals representing 24 distinct breeds collated by the equine genomics community, we cataloged over 23 million de novo discovered genetic variants. Leveraging genotype data from individuals with both whole genome sequence, and genotypes from lower-density, legacy SNP arrays, a subset of ~5 million high-quality, high-density array candidate SNPs were selected based on breed representation and uniform spacing across the genome. Considering probe design recommendations from a commercial vendor (Affymetrix, now Thermo Fisher Scientific) a set of ~2 million SNPs were selected for a next-generation high-density SNP chip (MNEc2M). Genotype data were generated using the MNEc2M array from a cohort of 332 horses from 20 breeds and a lower-density array, consisting of ~670 thousand SNPs (MNEc670k), was designed for genotype imputation. Conclusions Here, we document the steps taken to design both the MNEc2M and MNEc670k arrays, report genomic and technical properties of these genotyping platforms, and demonstrate the imputation capabilities of these tools for the domestic horse.

Background Black point is a serious threat to wheat production and can be managed by host resistance. Marker-assisted selection (MAS) has the potential to accelerate genetic improvement of black point resistance in wheat breeding. We performed a genome-wide association study (GWAS) using the high-density wheat 90 K and 660 K single nucleotide polymorphism (SNP) assays to better understand the genetic basis of black point resistance and identify associated molecular markers. Results Black point reactions were evaluated in 166 elite wheat cultivars in five environments. Twenty-five unique loci were identified on chromosomes 2A, 2B, 3A, 3B (2), 3D, 4B (2), 5A (3), 5B (3), 6A, 6B, 6D, 7A (5), 7B and 7D (2), respectively, explaining phenotypic variation ranging from 7.9 to 18.0%. The highest number of loci was detected in the A genome (11), followed by the B (10) and D (4) genomes. Among these, 13 were identified in two or more environments. Seven loci coincided with known genes or quantitative trait locus (QTL), whereas the other 18 were potentially novel loci. Linear regression showed a clear dependence of black point scores on the number of favorable alleles, suggesting that QTL pyramiding will be an effective approach to increase resistance. In silico analysis of sequences of resistance-associated SNPs identified 6 genes possibly involved in oxidase, signal transduction and stress resistance as candidate genes involved in black point reaction. Conclusion SNP markers significantly associated with black point resistance and accessions with a larger number of resistance alleles can be used to further enhance black point resistance in breeding. This study provides new insights into the genetic architecture of black point reaction.

Background Comparing mutation patterns and population-specific genetic signatures, such as single-nucleotide polymorphisms (SNPs), is of fundamental importance in genetic and genomic studies today. The problem is challenging because it falls into the category of NP-hard problems, meaning that the computational time increases exponentially, or even faster, as the problem size grows. Additionally, existing approaches often fails to account for the exact number of SNPs per gene (gene-SNP abundance) nor their distribution across populations, hindering the detection of genes that are uniquely or richly mutated in specific groups. Results To bridge this gap, by harnessing gene-SNP abundance and distribution information as well as their heterogeneity across individuals and populations, we develop the SnpC (SNP comparison) method (framework) for detecting genes with unique or enriched SNPs, referred to as unique genes (UGs) and enriched genes (EGs) for specific population. The power of SnpC lies in two novel metrics: Gene-SNP Heterogeneity (GSH), which identifies genes with unique or enriched number of SNPs in one population compared to others, and Population Gene-SNP Diversity (PGSD), which measures the population-level diversity of gene mutations including SNP. These metrics are coupled with robust permutation tests to provide statistical significance. Conclusions We demonstrate SnpC on data from the 1000 Genomes Project, where it successfully catalogs population-specific (unique/enriched) genes and quantifies their inter-population mutation diversity pattern. SnpC delivers a unique, actionable output for gene prioritization in association studies and comparative population genomic analysis. It is easily extended to other variation types, and the R code is provided in the article's supplements.

We used whole genome resequencing of pooled individuals to develop a high‐density single‐nucleotide polymorphism (SNP) chip for Eucalyptus. Genomes of 240 trees of 12 species were sequenced at 3.5× each, and 46 997 586 raw SNP variants were subject to multivariable filtering metrics toward a multispecies, genome‐wide distributed chip content. Of the 60 904 SNPs on the chip, 59 222 were genotyped and 51 204 were polymorphic across 14 Eucalyptus species, providing a 96% genome‐wide coverage with 1 SNP/12–20 kb, and 47 069 SNPs at ≤ 10 kb from 30 444 of the 33 917 genes in the Eucalyptus genome. Given the EUChip60K multi‐species genotyping flexibility, we show that both the sample size and taxonomic composition of cluster files impact heterozygous call specificity and sensitivity by benchmarking against ‘gold standard’ genotypes derived from deeply sequenced individual tree genomes. Thousands of SNPs were shared across species, likely representing ancient variants arisen before the split of these taxa, hinting to a recent eucalypt radiation. We show that the variable SNP filtering constraints allowed coverage of the entire site frequency spectrum, mitigating SNP ascertainment bias. The EUChip60K represents an outstanding tool with which to address population genomics questions in Eucalyptus and to empower genomic selection, GWAS and the broader study of complex trait variation in eucalypts.

The rapid development and application of molecular marker assays have facilitated genomic selection and genome‐wide linkage and association studies in wheat breeding. Although PCR‐based markers (e.g. simple sequence repeats and functional markers) and genotyping by sequencing have contributed greatly to gene discovery and marker‐assisted selection, the release of a more accurate and complete bread wheat reference genome has resulted in the design of single‐nucleotide polymorphism (SNP) arrays based on different densities or application targets. Here, we evaluated seven types of wheat SNP arrays in terms of their SNP number, distribution, density, associated genes, heterozygosity and application. The results suggested that the Wheat 660K SNP array contained the highest percentage (99.05%) of genome‐specific SNPs with reliable physical positions. SNP density analysis indicated that the SNPs were almost evenly distributed across the whole genome. In addition, 229 266 SNPs in the Wheat 660K SNP array were located in 66 834 annotated gene or promoter intervals. The annotated genes revealed by the Wheat 660K SNP array almost covered all genes revealed by the Wheat 35K (97.44%), 55K (99.73%), 90K (86.9%) and 820K (85.3%) SNP arrays. Therefore, the Wheat 660K SNP array could act as a substitute for other 6 arrays and shows promise for a wide range of possible applications. In summary, the Wheat 660K SNP array is reliable and cost‐effective and may be the best choice for targeted genotyping and marker‐assisted selection in wheat genetic improvement.

Marker-assisted selection (MAS) refers to the use of molecular markers to assist phenotypic selections in crop improvement. Several types of molecular markers, such as single nucleotide polymorphism (SNP), have been identified and effectively used in plant breeding. The application of next-generation sequencing (NGS) technologies has led to remarkable advances in whole genome sequencing, which provides ultra-throughput sequences to revolutionize plant genotyping and breeding. To further broaden NGS usages to large crop genomes such as maize and wheat, genotyping-by-sequencing (GBS) has been developed and applied in sequencing multiplexed samples that combine molecular marker discovery and genotyping. GBS is a novel application of NGS protocols for discovering and genotyping SNPs in crop genomes and populations. The GBS approach includes the digestion of genomic DNA with restriction enzymes followed by the ligation of barcode adapter, PCR amplification and sequencing of the amplified DNA pool on a single lane of flow cells. Bioinformatic pipelines are needed to analyze and interpret GBS datasets. As an ultimate MAS tool and a cost-effective technique, GBS has been successfully used in implementing genome-wide association study (GWAS), genomic diversity study, genetic linkage analysis, molecular marker discovery and genomic selection under a large scale of plant breeding programs.

Homeobox (HOX) transcription factors, encoded by a subset of homeodomain superfamily genes, play pivotal roles in many aspects of cellular physiology, embryonic development, and tissue homeostasis. Findings over the past decade have revealed that mutations in HOX genes can lead to increased cancer predisposition, and HOX genes might mediate the effect of many other cancer susceptibility factors by recognizing or executing altered genetic information. Remarkably, several lines of evidence highlight the interplays between HOX transcription factors and cancer risk loci discovered by genome-wide association studies, thereby gaining molecular and biological insight into cancer etiology. In addition, deregulated HOX gene expression impacts various aspects of cancer progression, including tumor angiogenesis, cell autophagy, proliferation, apoptosis, tumor cell migration, and metabolism. In this review, we will discuss the fundamental roles of HOX genes in cancer susceptibility and progression, highlighting multiple molecular mechanisms of HOX involved gene misregulation, as well as their potential implications in clinical practice.

Polypoid species play significant roles in agriculture and food production. Many crop species are polyploid, such as potato, wheat, strawberry, and sugarcane. Genotyping has been a daunting task for genetic studies of polyploid crops, which lags far behind the diploid crop species. Single nucleotide polymorphism (SNP) array is considered to be one of, high-throughput, relatively cost-efficient and automated genotyping approaches. However, there are significant challenges for SNP identification in complex, polyploid genomes, which has seriously slowed SNP discovery and array development in polyploid species. Ploidy is a significant factor impacting SNP qualities and validation rates of SNP markers in SNP arrays, which has been proven to be a very important tool for genetic studies and molecular breeding. In this review, we (1) discussed the pros and cons of SNP array in general for high throughput genotyping, (2) presented the challenges of and solutions to SNP calling in polyploid species, (3) summarized the SNP selection criteria and considerations of SNP array design for polyploid species, (4) illustrated SNP array applications in several different polyploid crop species, then (5) discussed challenges, available software, and their accuracy comparisons for genotype calling based on SNP array data in polyploids, and finally (6) provided a series of SNP array design and genotype calling recommendations. This review presents a complete overview of SNP array development and applications in polypoid crops, which will benefit the research in molecular breeding and genetics of crops with complex genomes.

Terminal drought is a major constraint to chickpea productivity. Two component traits responsible for reduction in yield under drought stress include reduction in seeds size and root length/root density. QTL-seq approach, therefore, was used to identify candidate genomic regions for 100-seed weight (100SDW) and total dry root weight to total plant dry weight ratio (RTR) under rainfed conditions. Genomewide SNP profiling of extreme phenotypic bulks from the ICC 4958 × ICC 1882 population identified two significant genomic regions, one on CaLG01 (1.08 Mb) and another on CaLG04 (2.7 Mb) linkage groups for 100SDW. Similarly, one significant genomic region on CaLG04 (1.10 Mb) was identified for RTR. Comprehensive analysis revealed four and five putative candidate genes associated with 100SDW and RTR, respectively. Subsequently, two genes (Ca_04364 and Ca_04607) for 100SDW and one gene (Ca_04586) for RTR were validated using CAPS/dCAPS markers. Identified candidate genomic regions and genes may be useful for molecular breeding for chickpea improvement.