High-throughput Genotyping Platforms Research Articles

Rare copy number variant analysis in case-control studies using snp array data: a scalable and automated data analysis pipeline.

Rare copy number variants (CNVs) significantly influence the human genome and may contribute to disease susceptibility. High-throughput SNP genotyping platforms provide data that can be used for CNV detection, but it requires the complex pipelining of bioinformatic tools. Here, we propose a flexible bioinformatic pipeline for rare CNV analysis from human SNP array data. The pipeline consists of two major sub-pipelines: (1) Calling and quality control (QC) analysis, and (2) Rare CNV analysis. It is implemented in Snakemake following a rule-based structure that enables automation and scalability while maintaining flexibility. Our pipeline automates the detection and analysis of rare CNVs. It implements a rigorous CNV quality control, assesses the frequencies of these rare CNVs in patients versus controls, and evaluates the impact of CNVs on specific genes or pathways. We hence aim to provide an efficient yet flexible bioinformatic framework to investigate rare CNVs in biomedical research.

Development and application of a 1K functional liquid chip for lactation performance in Bactrian camels.

The advancement of high-throughput, high-quality, flexible, and cost-effective genotyping platforms is crucial for the progress of dairy breeding in Bactrian camels. This study focuses on developing and evaluating a 1K functional liquid single nucleotide polymorphism (SNP) array specifically designed for milk performance in Bactrian camels. We utilized RNA sequencing data from 125 lactating camels to identify and select 1,002 loci associated with milk production traits for inclusion in the SNP array. The array's performance was then assessed using 24 randomly selected camels. Additionally, the array was employed to genotype 398 individuals, which allowed for population validation to assess the polymorphism of SNP sites. The SNP array demonstrated high overall SNP call rates (> 99%) and a remarkable 100% consistency in genotyping. Population validation results indicate that camels from six breeding areas in Northwest China share a similar genetic background regarding lactation functionality. This study highlights the potential of the SNP array to accelerate the breeding process of lactating Bactrian camels and provides a robust technical foundation for improving lactation performance.

Genomic selection for target traits in the Australian lentil breeding program.

Genomic selection (GS) uses associations between markers and phenotypes to predict the breeding values of individuals. It can be applied early in the breeding cycle to reduce the cross-to-cross generation interval and thereby increase genetic gain per unit of time. The development of cost-effective, high-throughput genotyping platforms has revolutionized plant breeding programs by enabling the implementation of GS at the scale required to achieve impact. As a result, GS is becoming routine in plant breeding, even in minor crops such as pulses. Here we examined 2,081 breeding lines from Agriculture Victoria's national lentil breeding program for a range of target traits including grain yield, ascochyta blight resistance, botrytis grey mould resistance, salinity and boron stress tolerance, 100-grain weight, seed size index and protein content. A broad range of narrow-sense heritabilities was observed across these traits (0.24-0.66). Genomic prediction models were developed based on 64,781 genome-wide SNPs using Bayesian methodology and genomic estimated breeding values (GEBVs) were calculated. Forward cross-validation was applied to examine the prediction accuracy of GS for these targeted traits. The accuracy of GEBVs was consistently higher (0.34-0.83) than BLUP estimated breeding values (EBVs) (0.22-0.54), indicating a higher expected rate of genetic gain with GS. GS-led parental selection using early generation breeding materials also resulted in higher genetic gain compared to BLUP-based selection performed using later generation breeding lines. Our results show that implementing GS in lentil breeding will fast track the development of high-yielding cultivars with increased resistance to biotic and abiotic stresses, as well as improved seed quality traits.

Tomato: Breeding and Genomics

Tomato is an important vegetable of the human diet. In tomatoes, through conventional breeding methods, many cultivars with desirable traits have been developed. With the advancement in sequencing technologies combined with reducing cost per sample, high-throughput genotyping platforms and bioinformatics pipelines have revolutionized tomato improvement. After the tomato genome sequencing in 2012, thousands of cultivated and wild species have been sequenced with respect to studies on population structure, genetic diversity, high-density maps and structural variants analysis so on. Now, genomics-assisted research would aim to discover of QTLs/genes and associated single nucleotide polymorphisms (SNP) markers by genome-wide association studies (GWAS) using high-throughput genotyping (SNP array or genotyping-by-sequencing) for rapid breeding. Moreover, genome editing and genomics selection tools would increase breeding efficiency and higher genetic gain in tomatoes.

MCPtaggR: R package for accurate genotype calling in reduced representation sequencing data by eliminating error-prone markers based on genome comparison.

Reduced representation sequencing (RRS) offers cost-effective, high-throughput genotyping platforms such as genotyping-by-sequencing (GBS). RRS reads are typically mapped onto a reference genome. However, mapping reads harbouring mismatches against the reference can potentially result in mismapping and biased mapping, leading to the detection of error-prone markers that provide incorrect genotype information. We established a genotype-calling pipeline named mappable collinear polymorphic tag genotyping (MCPtagg) to achieve accurate genotyping by eliminating error-prone markers. MCPtagg was designed for the RRS-based genotyping of a population derived from a biparental cross. The MCPtagg pipeline filters out error-prone markers prior to genotype calling based on marker collinearity information obtained by comparing the genome sequences of the parents of a population to be genotyped. A performance evaluation on real GBS data from a rice F2 population confirmed its effectiveness. Furthermore, our performance test using a genome assembly that was obtained by genome sequence polishing on an available genome assembly suggests that our pipeline performs well with converted genomes, rather than necessitating de novo assembly. This demonstrates its flexibility and scalability. The R package, MCPtaggR, was developed to provide functions for the pipeline and is available at https://github.com/tomoyukif/MCPtaggR.

A multiplexed plant-animal SNP array for selective breeding and species conservation applications.

Reliable and high-throughput genotyping platforms are of immense importance for identifying and dissecting genomic regions controlling important phenotypes, supporting selection processes in breeding programs, and managing wild populations and germplasm collections. Amongst available genotyping tools, single nucleotide polymorphism arrays have been shown to be comparatively easy to use and generate highly accurate genotypic data. Single-species arrays are the most commonly used type so far; however, some multi-species arrays have been developed for closely related species that share single nucleotide polymorphism markers, exploiting inter-species cross-amplification. In this study, the suitability of a multiplexed plant-animal single nucleotide polymorphism array, including both closely and distantly related species, was explored. The performance of the single nucleotide polymorphism array across species for diverse applications, ranging from intra-species diversity assessments to parentage analysis, was assessed. Moreover, the value of genotyping pooled DNA of distantly related species on the single nucleotide polymorphism array as a technique to further reduce costs was evaluated. Single nucleotide polymorphism performance was generally high, and species-specific single nucleotide polymorphisms proved suitable for diverse applications. The multi-species single nucleotide polymorphism array approach reported here could be transferred to other species to achieve cost savings resulting from the increased throughput when several projects use the same array, and the pooling technique adds another highly promising advancement to additionally decrease genotyping costs by half.

Genomics for Yield and Yield Components in Durum Wheat.

In recent years, many efforts have been conducted to dissect the genetic basis of yield and yield components in durum wheat thanks to linkage mapping and genome-wide association studies. In this review, starting from the analysis of the genetic bases that regulate the expression of yield for developing new durum wheat varieties, we have highlighted how, currently, the reductionist approach, i.e., dissecting the yield into its individual components, does not seem capable of ensuring significant yield increases due to diminishing resources, land loss, and ongoing climate change. However, despite the identification of genes and/or chromosomal regions, controlling the grain yield in durum wheat is still a challenge, mainly due to the polyploidy level of this species. In the review, we underline that the next-generation sequencing (NGS) technologies coupled with improved wheat genome assembly and high-throughput genotyping platforms, as well as genome editing technology, will revolutionize plant breeding by providing a great opportunity to capture genetic variation that can be used in breeding programs. To date, genomic selection provides a valuable tool for modeling optimal allelic combinations across the whole genome that maximize the phenotypic potential of an individual under a given environment.

The potentialities of omics resources for millet improvement.

Millets are nutrient-rich (nutri-rich) cereals with climate resilience attributes. However, its full productive potential is not realized due to the lack of a focused yield improvement approach, as evidenced by the available literature. Also, the lack of well-characterized genomic resources significantly limits millet improvement. But the recent availability of genomic data and advancement in omics tools has shown its enormous potential to enhance the efficiency and precision faced by conventional breeding in millet improvement. The development of high throughput genotyping platforms based on next-generation sequencing (NGS) has provided a low-cost method for genomic information, specifically for neglected nutri-rich cereals with the availability of a limited number of reference genome sequences. NGS has created new avenues for millet biotechnological interventions such as mutation-based study, GWAS, GS, and other omics technologies. The simultaneous discovery of high-throughput markers and multiplexed genotyping platform has aggressively aided marker-assisted breeding for millet improvement. Therefore, omics technology offers excellent opportunities to explore and combine useful variations for targeted traits that could impart high nutritional value to high-yielding cultivars under changing climatic conditions. In millet improvement, an in-depth account of NGS, integrating genomics data with different biotechnology tools, is reviewed in this context.

Genotyping by Sequencing (GBS) for Genome-Wide SNP Identification in Plants.

Marker-assisted selection has played a pivotal role in developing several elite varieties in the past two decades. Molecular markers employed in plant breeding programs have recently shifted from microsatellites or simple sequence repeats (SSRs) to single nucleotide polymorphisms (SNPs) due to the ubiquity of SNP markers in the genome and the availability of various high-throughput SNP genotyping platforms. Rapid advances in sequencing technologies and the reduction in sequencing cost have facilitated SNP discovery in several plant species including non-model organisms with little or no genomic resources. Despite the lower cost of sequencing, genome complexity reduction approaches are still useful for SNP identification because many applications do not require every base of the genome to be sequenced. Genotyping-by-sequencing (GBS) is a quick and affordable reduced representation method that can simultaneously identify and genotype a large number of SNPs that has been successfully applied to a wide range of plant species. This chapter describes a robust two-enzyme GBS method for SNP discovery and genotyping that has been verified in non-model plant species.

Development of a 135K SNP genotyping array for Actinidia arguta and its applications for genetic mapping and QTL analysis in kiwifruit.

Kiwifruit (Actinidia spp) is a woody, perennial and deciduous vine. In this genus, there are multiple ploidy levels but the main cultivated cultivars are polyploid. Despite the availability of many genomic resources in kiwifruit, SNP genotyping is still a challenge given these different levels of polyploidy. Recent advances in SNP array technologies have offered a high-throughput genotyping platform for genome-wide DNA polymorphisms. In this study, we developed a high-density SNP genotyping array to facilitate genetic studies and breeding applications in kiwifruit. SNP discovery was performed by genome-wide DNA sequencing of 40 kiwifruit genotypes. The identified SNPs were stringently filtered for sequence quality, predicted conversion performance and distribution over the available Actinidia chinensis genome. A total of 134 729 unique SNPs were put on the array. The array was evaluated by genotyping 400 kiwifruit individuals. We performed a multidimensional scaling analysis to assess the diversity of kiwifruit germplasm, showing that the array was effective to distinguish kiwifruit accessions. Using a tetraploid F1 population, we constructed an integrated linkage map covering 3060.9cM across 29 linkage groups and performed QTL analysis for the sex locus that has been identified on Linkage Group 3 (LG3) in Actinidia arguta. Finally, our dataset presented evidence of tetrasomic inheritance with partial preferential pairing in A. arguta. In conclusion, we developed and evaluated a 135K SNP genotyping array for kiwifruit. It has the advantage of a comprehensive design that can be an effective tool in genetic studies and breeding applications in this high-value crop.

Development of a high-throughput SNP array for sea cucumber (Apostichopus japonicus) and its application in genomic selection with MCP regularized deep neural networks

High-throughput single nucleotide polymorphism (SNP) genotyping assays are powerful tools for genetic studies and genomic breeding applications for many species. Though large numbers of SNPs have been identified in sea cucumber (Apostichopus japonicus), but, as yet, no high-throughput genotyping platform is available for this species. In this study, we designed and developed a high-throughput 24 K SNP genotyping array named HaishenSNP24K for A. japonicus, based on the multi-objective-local optimization (MOLO) algorithm and HD-Marker genotyping method. The SNP array exhibited a relatively high genotyping call rate (> 96%), genotyping accuracy (>95%) and exhibited highly polymorphic in sea cucumber populations. In addition, we also assessed its application in genomic selection (GS). Deep neural networks (DNN) that can capture the complicated interactions of genes have been proposed as a promising tool in GS for SNP-based genomic prediction of complex traits in animal breeding. To overcome the problem of over-fitting when using the HaishenSNP24K array as high-dimensional DNN input, we developed minmax concave penalty (MCP) regularization for sparse deep neural networks (DNN-MCP) that finds an optimal sparse structure of a DNN by minimizing the square error subject to the non-convex penalty MCP on the parameters (weights and biases). Compared to two linear models, namely RR-GBLUP and Bayes B, and the nonlinear model DNN, DNN-MCP has greatly improved the genomic prediction ability for three quantitative traits (e.g., wet weight, dry weight and survival time) in the sea cucumber population. To the best of our knowledge, this is the first work to develop a high-throughput SNP array for A. japonicus and a new model DNN-MCP for genomic prediction of complex traits in GS. The present results provide evidence that supports the HaishenSNP24K array with DNN-MCP will be valuable for genetic studies and molecular breeding in A. japonicus.

Development and Application of a Target Capture Sequencing SNP-Genotyping Platform in Rice.

The development of efficient, robust, and high-throughput SNP genotyping platforms is pivotal for crop genetics and breeding. Recently, SNP genotyping platforms based on target capture sequencing, which is very flexible in terms of the number of SNP markers, have been developed for maize, cassava, and fava bean. We aimed to develop a target capture sequencing SNP genotyping platform for rice. A target capture sequencing panel containing 2565 SNPs, including 1225 SNPs informative for japonica and 1339 SNPs informative for indica, was developed. This platform was used in diversity analysis of 50 rice varieties. Of the 2565 SNP markers, 2341 (91.3%) produced useful polymorphic genotype data, enabling the production of a phylogenetic tree of the 50 varieties. The mean number of markers polymorphic between any two varieties was 854. The platform was used for QTL mapping of preharvest sprouting (PHS) resistance in an F8 recombinant inbred line population derived from the cross Odae × Joun. A genetic map comprising 475 markers was constructed, and two QTLs for PHS resistance were identified on chromosomes 4 and 11. This system is a powerful tool for rice genetics and breeding and will facilitate QTL studies and gene mapping, germplasm diversity analysis, and marker-assisted selection.

Genome-wide compound heterozygote analysis highlights DPY19L2 alleles in a non-consanguineous Spanish family with total globozoospermia

Research questionWould the use of genome-wide genotyping be an advantageous strategy to identify the molecular aetiology of two brothers from a non-consanguineous family, clinically diagnosed with total globozoospermia? DesignTwo related Spanish globozoospermic patients were studied. Eight first- and second-degree family members were also included in the study. The clinical procedure included anamnesis, physical examination and semen analyses. Acrosome visualization was performed by fluorescein isothiocyanate–Pisum sativum agglutinin labelling and ultrastructural electron microscope sperm analysis. Sperm DNA fragmentation was determined by TUNEL and SCD. Molecular analysis included: the detection of deletion of the DPY19L2 gene by a BPa (break point "a") gap-polymerase chain reaction, and genotyping by using a high-throughput genome-wide genotyping platform and a genotype imputation strategy. ResultsThe biological characteristics of the two globozoospermic siblings included round-headed spermatozoa without an acrosome; ultrastructural defects in spermatozoa; increased sperm fragmentation and aneuploidies, inability of spermatozoa to activate oocytes (correctable with artificial activation) and good developmental potential of embryos generated by IVF/intracytoplasmic sperm injection. This genetic study focused on a genome-wide compound heterozygote analysis that identified two deleterious rare coding variants in the DPY19L2 gene [rs771726551 (c.431T>A exon 3) and rs147579680 (c.869G>A exon 8)]. ConclusionA genome-wide compound heterozygote analysis strategy should be considered for molecular screening in globozoospermia and other rare congenital diseases, particularly in cases from non-consanguineous families.

Breeding Drought-Tolerant Pearl Millet Using Conventional and Genomic Approaches: Achievements and Prospects.

Pearl millet [Pennisetum glaucum (L.) R. Br.] is a C4 crop cultivated for its grain and stover in crop-livestock-based rain-fed farming systems of tropics and subtropics in the Indian subcontinent and sub-Saharan Africa. The intensity of drought is predicted to further exacerbate because of looming climate change, necessitating greater focus on pearl millet breeding for drought tolerance. The nature of drought in different target populations of pearl millet-growing environments (TPEs) is highly variable in its timing, intensity, and duration. Pearl millet response to drought in various growth stages has been studied comprehensively. Dissection of drought tolerance physiology and phenology has helped in understanding the yield formation process under drought conditions. The overall understanding of TPEs and differential sensitivity of various growth stages to water stress helped to identify target traits for manipulation through breeding for drought tolerance. Recent advancement in high-throughput phenotyping platforms has made it more realistic to screen large populations/germplasm for drought-adaptive traits. The role of adapted germplasm has been emphasized for drought breeding, as the measured performance under drought stress is largely an outcome of adaptation to stress environments. Hybridization of adapted landraces with selected elite genetic material has been stated to amalgamate adaptation and productivity. Substantial progress has been made in the development of genomic resources that have been used to explore genetic diversity, linkage mapping (QTLs), marker-trait association (MTA), and genomic selection (GS) in pearl millet. High-throughput genotyping (HTPG) platforms are now available at a low cost, offering enormous opportunities to apply markers assisted selection (MAS) in conventional breeding programs targeting drought tolerance. Next-generation sequencing (NGS) technology, micro-environmental modeling, and pearl millet whole genome re-sequence information covering circa 1,000 wild and cultivated accessions have helped to greater understand germplasm, genomes, candidate genes, and markers. Their application in molecular breeding would lead to the development of high-yielding and drought-tolerant pearl millet cultivars. This review examines how the strategic use of genetic resources, modern genomics, molecular biology, and shuttle breeding can further enhance the development and delivery of drought-tolerant cultivars.

Comparative analysis of inbreeding parameters and runs of homozygosity islands in 2 Italian autochthonous cattle breeds mainly raised in the Parmigiano-Reggiano cheese production region

Reggiana and Modenese are autochthonous cattle breeds, reared in the North of Italy, that can be mainly distinguished for their standard coat color (Reggiana is red, whereas Modenese is white with some pale gray shades). Almost all milk produced by these breeds is transformed into 2 mono-breed branded Parmigiano-Reggiano cheeses, from which farmers receive the economic incomes needed for the sustainable conservation of these animal genetic resources. After the setting up of their herd books in 1960s, these breeds experienced a strong reduction in the population size that was subsequently reverted starting in the 1990s (Reggiana) or more recently (Modenese) reaching at present a total of about 2,800 and 500 registered cows, respectively. Due to the small population size of these breeds, inbreeding is a very important cause of concern for their conservation programs. Inbreeding is traditionally estimated using pedigree data, which are summarized in an inbreeding coefficient calculated at the individual level (FPED). However, incompleteness of pedigree information and registration errors can affect the effectiveness of conservation strategies. High-throughput SNP genotyping platforms allow investigation of inbreeding using genome information that can overcome the limits of pedigree data. Several approaches have been proposed to estimate genomic inbreeding, with the use of runs of homozygosity (ROH) considered to be the more appropriate. In this study, several pedigree and genomic inbreeding parameters, calculated using the whole herd book populations or considering genotyping information (GeneSeek GGP Bovine 150K) from 1,684 Reggiana cattle and 323 Modenese cattle, were compared. Average inbreeding values per year were used to calculate effective population size. Reggiana breed had generally lower genomic inbreeding values than Modenese breed. The low correlation between pedigree-based and genomic-based parameters (ranging from 0.187 to 0.195 and 0.319 to 0.323 in the Reggiana and Modenese breeds, respectively) reflected the common problems of local populations in which pedigree records are not complete. The high proportion of short ROH over the total number of ROH indicates no major recent inbreeding events in both breeds. ROH islands spread over the genome of the 2 breeds (15 in Reggiana and 14 in Modenese) identified several signatures of selection. Some of these included genes affecting milk production traits, stature, body conformation traits (with a main ROH island in both breeds on BTA6 containing the ABCG2, NCAPG, and LCORL genes) and coat color (on BTA13 in Modenese containing the ASIP gene). In conclusion, this work provides an extensive comparative analysis of pedigree and genomic inbreeding parameters and relevant genomic information that will be useful in the conservation strategies of these 2 iconic local cattle breeds.

Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies.

Genome-wide association studies (GWAS) are effectively applied to detect the marker trait associations (MTAs) using whole genome-wide variants for complex quantitative traits in different crop species. GWAS has been applied in wheat for different quality, biotic and abiotic stresses, and agronomic and yield-related traits. Predictions for marker-trait associations are controlled with the development of better statistical models taking population structure and familial relatedness into account. In this review, we have provided a detailed overview of the importance of association mapping, population design, high-throughput genotyping and phenotyping platforms, advancements in statistical models and multiple threshold comparisons, and recent GWA studies conducted in wheat. The information about MTAs utilized for gene characterization and adopted in breeding programs is also provided. In the literature that we surveyed, as many as 86,122 wheat lines have been studied under various GWA studies reporting 46,940 loci. However, further utilization of these is largely limited. The future breakthroughs in area of genomic selection, multi-omics-based approaches, machine, and deep learning models in wheat breeding after exploring the complex genetic structure with the GWAS are also discussed. This is a most comprehensive study of a large number of reports on wheat GWAS and gives a comparison and timeline of technological developments in this area. This will be useful to new researchers or groups who wish to invest in GWAS.

Population-tailored mock genome enables genomic studies in species without a reference genome.

Based on molecular markers, genomic prediction enables us to speed up breeding schemes and increase the response to selection. There are several high-throughput genotyping platforms able to deliver thousands of molecular markers for genomic study purposes. However, even though its widely applied in plant breeding, species without a reference genome cannot fully benefit from genomic tools and modern breeding schemes. We used a method to assemble a population-tailored mock genome to call single-nucleotide polymorphism (SNP) markers without an available reference genome, and for the first time, we compared the results with standard genotyping platforms (array and genotyping-by-sequencing (GBS) using a reference genome) for performance in genomic prediction models. Our results indicate that using a population-tailored mock genome to call SNP delivers reliable estimates for the genomic relationship between genotypes. Furthermore, genomic prediction estimates were comparable to standard approaches, especially when considering only additive effects. However, mock genomes were slightly worse than arrays at predicting traits influenced by dominance effects, but still performed as well as standard GBS methods that use a reference genome. Nevertheless, the array-based SNP markers methods achieved the best predictive ability and reliability to estimate variance components. Overall, the mock genomes can be a worthy alternative for genomic selection studies, especially for those species where the reference genome is not available.

Accelerating the Development of Heat Tolerant Tomato Hybrids through a Multi-Traits Evaluation of Parental Lines Combining Phenotypic and Genotypic Analysis.

The constitution of heat tolerant F1 hybrids is a challenge to ensure high yield and good fruit quality in the global climate. In the present work, we evaluated 15 genotypes for yield-related traits highly affected by high temperatures (HT). This phenotypic analysis allowed to identify four parental genotypes showing promising yield performances under HT conditions. Two of these genotypes also exhibited good fruit quality traits. A molecular marker analysis was carried out for six resistance genes to pathogens mostly affecting tomatoes. This analysis evidenced the presence of a maximum of three resistant alleles in parental genotypes. Exploring single nucleotide polymorphisms (SNPs) revealed by two high-throughput genotyping platforms allowed identifying additional 12 genes potentially involved in resistance to biotic stress, to be further investigated. Following these considerations, 13 F1 hybrids were constituted combining the parental genotypes and then evaluated for multiple traits under HT conditions. By estimating a hybrid index based on yield performances, desirable quality and resistance gene, we identified seven hybrids showing the best performances. The promising results obtained in the present work should be confirmed by evaluating the best hybrids selected for additional years and environments before proposing them as novel commercial hybrids that could maintain high performances under HT conditions.

TU45. SYSTEMATIC ANNOTATION OF BIOLOGICAL NETWORKS ASSOCIATED WITH DEPRESSION AND OBESITY OF THE GENOMIC REGIONS COVERED IN A TARGETED SEQUENCING PANEL

Would the use of genome-wide genotyping be an advantageous strategy to identify the molecular aetiology of two brothers from a non-consanguineous family, clinically diagnosed with total globozoospermia?Two related Spanish globozoospermic patients were studied. Eight first- and second-degree family members were also included in the study. The clinical procedure included anamnesis, physical examination and semen analyses. Acrosome visualization was performed by fluorescein isothiocyanate–Pisum sativum agglutinin labelling and ultrastructural electron microscope sperm analysis. Sperm DNA fragmentation was determined by TUNEL and SCD. Molecular analysis included: the detection of deletion of the DPY19L2 gene by a BPa (break point "a") gap-polymerase chain reaction, and genotyping by using a high-throughput genome-wide genotyping platform and a genotype imputation strategy.The biological characteristics of the two globozoospermic siblings included round-headed spermatozoa without an acrosome; ultrastructural defects in spermatozoa; increased sperm fragmentation and aneuploidies, inability of spermatozoa to activate oocytes (correctable with artificial activation) and good developmental potential of embryos generated by IVF/intracytoplasmic sperm injection. This genetic study focused on a genome-wide compound heterozygote analysis that identified two deleterious rare coding variants in the DPY19L2 gene [rs771726551 (c.431T>A exon 3) and rs147579680 (c.869G>A exon 8)].A genome-wide compound heterozygote analysis strategy should be considered for molecular screening in globozoospermia and other rare congenital diseases, particularly in cases from non-consanguineous families.

Single Seed-Based High-Throughput Genotyping and Rapid Generation Advancement for Accelerated Groundnut Genetics and Breeding Research

The groundnut breeding program at International Crops Research Institute for the Semi-Arid Tropics routinely performs marker-based early generation selection (MEGS) in thousands of segregating populations. The existing MEGS includes planting of segregating populations in fields or glasshouses, label tagging, and sample collection using leaf-punch from 20–25 day old plants followed by genotyping with 10 single nucleotide polymorphisms based early generation selection marker panels in a high throughput genotyping (HTPG) platform. The entire process is laborious, time consuming, and costly. Therefore, in order to save the time of the breeder and to reduce the cost during MEGS, we optimized a single seed chipping (SSC) process based MEGS protocol and deployed on large scale by genotyping >3000 samples from ongoing groundnut breeding program. In SSC-based MEGS, we used a small portion of cotyledon by slicing-off the posterior end of the single seed and transferred to the 96-deep well plate for DNA isolation and genotyping at HTPG platform. The chipped seeds were placed in 96-well seed-box in the same order of 96-well DNA sampling plate to enable tracking back to the selected individual seed. A high germination rate of 95–99% from the chipped seeds indicated that slicing of seeds from posterior end does not significantly affect germination percentage. In addition, we could successfully advance 3.5 generations in a year using a low-cost rapid generation turnover glass-house facility as compared to routine practice of two generations in field conditions. The integration of SSC based genotyping and rapid generation advancement (RGA) could significantly reduce the operational requirement of person-hours and expenses, and save a period of 6–8 months in groundnut genetics and breeding research.