High-throughput Single Nucleotide Polymorphism Research Articles

HaploMAGIC: Accurate phasing and detection of recombination in multiparental populations despite genotyping errors.

Recombination is a key mechanism in breeding for promoting genetic variability. Multiparental populations constitute an excellent platform for precise genotype phasing, identification of genome-wide crossovers, estimation of recombination frequencies and construction of recombination maps. Here, we introduce haploMAGIC, a pipeline to detect crossovers in multiparental populations with single-nucleotide polymorphism (SNP) data by exploiting the pedigree relationships for accurate genotype phasing and inference of grandparental haplotypes. haploMAGIC applies filtering to prevent false positive crossovers due to genotyping errors, a common problem in high-throughput SNP analysis of complex plant genomes. Hence it discards haploblocks not reaching a specified minimum number of informative alleles. A performance analysis using populations simulated with AlphaSimR revealed that haploMAGIC improves upon existing methods of crossover detection in terms of recall and precision, most notably when genotyping error rates are high. Furthermore, we constructed recombination maps using haploMAGIC with high-resolution genotype data from two large multi-parental populations of winter rapeseed (Brassica napus). The results demonstrate the applicability of the pipeline in real-world scenarios and showed good correlations in recombination frequency compared with alternative software. Therefore, we propose haploMAGIC as an accurate tool at crossover detection with multiparental populations that shows robustness against genotyping errors.

Sweet corn association panel and genome-wide association analysis reveal loci for chilling-tolerant germination

Sweet corn is highly susceptible to the deleterious effects of low temperatures during the initial stages of growth and development. Employing a 56K chip, high-throughput single-nucleotide polymorphism (SNP) sequencing was conducted on 100 sweet corn inbred lines. Subsequently, six germination indicators—germination rate, germination index, germination time, relative germination rate, relative germination index, and relative germination time—were utilized for genome-wide association analysis. Candidate genes were identified via comparative analysis of homologous genes in Arabidopsis and rice, and their functions were validated using quantitative real-time polymerase chain reaction (qRT-PCR). The results revealed 35,430 high-quality SNPs, 16 of which were significantly correlated. Within 50 kb upstream and downstream of the identified SNPs, 46 associated genes were identified, of which six were confirmed as candidate genes. Their expression patterns indicated that Zm11ΒHSDL5 and Zm2OGO likely play negative and positive regulatory roles, respectively, in the low-temperature germination of sweet corn. Thus, we determined that these two genes are responsible for regulating the low-temperature germination of sweet corn. This study contributes valuable theoretical support for improving sweet corn breeding and may aid in the creation of specific germplasm resources geared toward enhancing low-temperature tolerance in sweet corn.

Genetic Diversity and Population Structure in Ethiopian Mustard (Brassica carinata A. Braun) as Revealed by Single Nucleotide Polymorphism Markers.

Ethiopian mustard (Brassica carinata A. Braun) is currently one of the potential oilseeds dedicated to the production for biofuel and other bio-industrial applications. The crop is assumed to be native to Ethiopia where a number of diversified B. carinata germplasms are found and conserved ex situ. However, there is very limited information on the genetic diversity and population structure of the species. This study aimed to investigate the genetic diversity and population structure of B. carinata genotypes of different origins using high-throughput single nucleotide polymorphism (SNP) markers. We used Brassica 90K Illumina InfiniumTM SNP array for genotyping 90 B. carinata genotypes, and a total of 11,499 informative SNP markers were used for investigating the population structure and genetic diversity. The structure analysis, principal coordinate analysis (PcoA) and neighbor-joining tree analysis clustered the 90 B. carinata genotypes into two distinct subpopulations (Pop1 and Pop2). The majority of accessions (65%) were clustered in Pop1, mainly obtained from Oromia and South West Ethiopian People (SWEP) regions. Pop2 constituted dominantly of breeding lines and varieties, implying target selection contributed to the formation of distinct populations. Analysis of molecular variance (AMOVA) revealed a higher genetic variation (93%) within populations than between populations (7%), with low genetic differentiation (PhiPT = 0.07) and poor correlation between genetic and geographical distance (R = 0.02). This implies the presence of gene flow (Nm > 1) and weak geographical structure of accessions. Genetic diversity indices showed the presence of moderate genetic diversity in B. carinata populations with an average genetic diversity value (HE = 0.31) and polymorphism information content (PIC = 0.26). The findings of this study provide important and relevant information for future breeding and conservation efforts of B. carinata.

High‐throughput genotyping in estimating genetic resources and detecting pathogens in aquaculture

High‐throughput genotyping in estimating genetic resources and detecting pathogens in aquaculture

Accurate detection and frequency of abnormal ploidy in the human blastocyst

To validate the detection of abnormal ploidy in preimplantation embryos and evaluate its frequency in transferrable blastocysts. A high-throughput genome-wide single nucleotide polymorphism microarray-based preimplantation genetic testing (PGT) platform was validated using multiple positive controls, including cell lines of known haploid and triploid karyotypes and rebiopsies of embryos with initial abnormal ploidy results. This platform was then tested on all trophectoderm biopsies in a single PGT laboratory to calculate the frequency of abnormal ploidy and the parental and cell division origins of error. Preimplantation genetic testing laboratory. The embryos from invitro fertilization patients who elected for PGT were evaluated. Any patients who provided saliva samples were further analyzed for the parental and cell division origins of abnormal ploidy. None. Evaluable positive controls showed 100% concordance with original karyotypes. The overall frequency of abnormal ploidy within a single PGT laboratory cohort was 1.43%. All cell lines showed 100% concordance with the expected karyotype. Additionally, all evaluable rebiopsies showed 100% concordance with the original abnormal ploidy karyotype. The frequency of abnormal ploidy was 1.43%, with 29% of those being haploid or uniparental isodiploid, 2.5% uniparental heterodiploid, 68% triploid, and 0.4% tetraploid. Twelve haploid embryos contained maternal deoxyribonucleic acid, and 3 contained paternal deoxyribonucleic acid. Thirty-four triploid embryos were of maternal origin, and 2 were of paternal origin. Thirty-five triploid embryos had a meiotic origin of error, and 1 was of mitotic error. Of those 35 embryos, 5 originated from meiosis I, 22 originated from meiosis II, and 8 were deemed inconclusive. On the basis of specific abnormal ploidy karyotypes, 41.2% of embryos would be falsely classified as euploid, and 22.7% would be false-positive mosaics with the use of the conventional next-generation sequencing-based PGT methods. This study demonstrates the validity of a high-throughput genome-wide single nucleotide polymorphism microarray-based PGT platform to accurately detect abnormal ploidy karyotypes and predict the parental and cell division origins of error of evaluable embryos. This unique method improves the sensitivity of detection for abnormal karyotypes, which can reduce the chances of adverse pregnancy outcomes.

QTL Mapping for Fiber Quality Based on Introgression Lines Population from G. hirsutum × G. tomentosum

As one of the most widely cultivated cotton species in China, upland cotton has moderate fiber quality and wide applicability, but its genetic basis is relatively narrow. To expand genetic diversity and improve fiber quality, in this study an introgression population (BC5S5) containing 107 lines was constructed by using G. hirsutum acc. 4105 as the recurrent parent and G. tomentosum as the donor parent. Using the specific-locus amplified fragment sequencing (SLAF-seq) strategy, 3157 high-throughput single nucleotide polymorphism (SNP) markers were obtained. Linkage analysis showed that a total of ninety-one QTLs related to fiber quality traits were detected in three environments, and the phenotypic variance explained (PVE) rates were 4.53–20.92%. Forty-six QTL (50.55%) synergistic genes were derived from G. tomentosum. Among them, qFS-A02-1 and qSCI-A02-1 were stably detected with a PVE of 9.8–16.71% and 14.78–20.92%, respectively. Within the candidate interval, Ghir_A02G012730, Ghir_A02G012790 and Ghir_A02G012830 were found to be possibly involved in cellulose and cell wall biosynthesis, with a relatively high expression during fiber development, 20 DPA and 25 DPA, which suggested that these three genes may be involved in the regulation of fiber strength traits, but their functions need further validation to determine the regulatory mechanism. Our research lays the foundation of fiber quality related to basic genetic research and breeding in cotton.

KASP genotyping and semi-quantitation of G275E mutation in the α6 subunit of Thrips palmi nAChR gene conferring spinetoram resistance.

Pesticide resistance is a long-standing and growing problem in the chemical control of invertebrate pests. Molecular diagnostic methods can facilitate pesticide resistance management by accurately and efficiently detecting resistant mutations and their frequency. In this study, the kompetitive allele specific PCR (KASP) approach, a technology for high-throughput single nucleotide polymorphism (SNP) genotyping, is validated as a useful method for characterizing genotypes at a pesticide-resistance locus for the first time. We focus on the spinetoram resistance mutation of G275E in the nicotinic acetylcholine receptor alpha 6 (nAChR α6) subunit gene of Thrips palmi. Of the 341 individuals of Thrips palmi tested, 98.24% were successfully genotyped, with 100% concordance with Sanger sequencing results. We then quantitatively mixed genomic DNA of known genotypes to establish 21 DNA mixtures with a resistant allele frequency ranging from 0 to 100% at steps of 5%. The linear discriminant analysis (LDA) showed that 75.8% of original grouped cases were correctly classified; six groups had no overlap in membership (resistant allele frequency: 0%, 5%, 10-75%, 80-85%, 90-95%, and 100%). When we chose 11 pooled samples with 10% steps for LDA, 84.4% of original grouped cases were correctly classified; seven groups had no overlap in membership (0%, 10%, 20-30%, 40-70%, 80%, 90%, 100%). The results indicated that KASP applied to pooled samples may provide a semi-quantitative estimate of resistance. Our study points to the suitability of KASP for high-throughput genotyping of genotypes affecting pesticide resistance and semi-quantitative assessments of resistance allele frequencies in populations. © 2023 Society of Chemical Industry.

Genomewide Association Study Identifies Copy Number Variants Associated With Warfarin Dose Response and Risk of Venous Thromboembolism in African Americans.

The anticoagulant warfarin is commonly used to control and prevent thrombotic disorders, such as venous thromboembolism (VTE), which disproportionately afflicts African Americans. Despite the importance of copy number variants (CNVs), few studies have focused on characterizing and understanding their role in drug response and disease risk among African Americans. In this study, we conduct the first genome-wide analysis of CNVs to more comprehensively account for the contribution of genetic variation in warfarin dose requirement and VTE risk among African Americans. We used hidden Markov models to detect CNVs from high-throughput single-nucleotide polymorphism arrays for 340 African American participants in the International Warfarin Pharmacogenetics Consortium. We identified 11,570 CNVs resulting in 2,038 copy number variable regions (CNVRs) and found 3 CNVRs associated with warfarin dose requirement and 3 CNVRs associated with VTE risk in African Americans. CNVRs 1q31.2del and 6q14.1del were associated with increased warfarin dose requirement (β=11.18 and 4.94, respectively; Pemp =< 0.002); CNVR 19p13.31del was associated with decreased warfarin dose requirement (β=-1.41, Pemp =0.0004); CNVRs (2p22.1del and 5q35.1-q35.2del) were found to be associated with increased risk of VTE (odds ratios (ORs)=1.88 and 14.9, respectively; Pemp ≤0.02); and CNVR 10q26.12del was associated with a decreased risk of VTE (OR=0.6; Pemp =0.05). Modeling of the 10q26.12del in HepG2 cells revealed that this deletion results in decreased fibrinogen gene expression, decreased fibrinogen and WDR11 protein levels, and decreased secretion of fibrinogen into the extracellular matrix. We found robust evidence that CNVRs could contribute to warfarin dose requirement and risk of VTE in African Americans and for 10q26.3del describe a possible pathogenic mechanism.

Breeding and genomics approaches for improving phosphorus-use efficiency in grain legumes

Phosphorus (P) is an essential plant macronutrient, but P sources for plant growth are non-renewable, causing great concern for future sustainable agriculture and global food security. Thus, enhancing plant P-use efficiency (PUE) by improving P-acquisition and P-utilization efficiencies is urgently needed in various crops, including grain legumes, for intensive cropping systems. This review discusses how to harness the genetic variability in PUE traits across grain legume gene pools to improve PUE using various conventional breeding approaches and emerging breeding tools. The genetic architecture of PUE traits is complex, being quantitatively inherited and highly influenced by the environment. Thus, we discuss how the biparental QTL mapping approach has been used to dissect the genetic architecture of PUE traits. Unprecedented advances in legume genomics resources, especially high-throughput single nucleotide polymorphisms, have facilitated uncovering genomic regions related to PUE across the whole genome using a genome-wide association mapping approach. Likewise, the availability of complete genome sequence information, pangenome sequences, and the whole-genome resequencing approach have provided novel insights into structural variation, including presence/absence and copy number variations, underpinning PUE. Simultaneously, progress in functional genomics, including transcriptomics and computational biology, has facilitated the discovery of various underlying transcription factors and the corresponding downstream P signal perception genes and candidate gene(s) controlling PUE and conferring low-P tolerance in various legumes with putative function. We also discuss updated metabolomics and proteomics approaches that have improved our understanding of various gene networks, P-starvation signaling pathways, and P acquisition and utilization of molecular mechanisms important for PUE. Finally, we summarize how novel breeding schemes, including genomic selection, speed breeding, and emerging CRISPR/Cas9-based genome editing tools, will assist in designing P-use-efficient cultivars and adapting grain legumes to low-P environments. Thus, enhancing PUE by integrating various ‘omics’ approaches could improve P-acquisition and P-utilization efficiencies in various modern grain legume cultivars grown in intensive cropping systems to restrict P-fertilizer overuse and preserve the declining non-renewable global rock phosphate reservoir for securing an economical and sustainable future agriculture. • Phosphorus (P) is one of the most essential nutrients required for plant growth and development. • P fertilizers being non-renewable, judicious utilization of P fertilizer is urgently needed for sustaining legume yield for ensuring global food security. • Plants have evolved several molecular, physiological and biochemical mechanisms to sense, acquire and utilize soil P and adapt under P impoverished environments. • ‘Omics’ approach could greatly assist in unraveling the underlying genomic regions with their function contributing to improving phosphorus use efficiency in legume crops.

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.

Genetic diversity and population structure analyses of South African Bambara groundnut (Vigna subterranea [L]. Verdc.) collections using SNP markers

• Bambara groundnut productivity in South Africa is low (0.62 t/ha) compared to the attainable yield of the crop reaching up to 3 t/ha. • The low productivity is attributable to a lack of improved and high yielding cultivars that are locally bred and adapted in the country. • This paper presents baseline information on Bambara groundnut genetic profile of South African collections for effective breeding and conservation. • The magnitude of the genetic diversity and population structure of Bambara groundnut collections of South Africa using high throughput single nucleotide polymorphisms (SNP) markers are highlighted to guide current and future Bambara groundnut research and development by selection genetically unique genotypes for breeding. Bambara groundnut ( Vigna subterranea [L.] Verdc) is a highly nutritious grain legume with a significant potential to develop various commercial food and feed products. It is a resilient crop able to grow under harsh climates and poor soil conditions. Bambara groundnut productivity in South Africa is low (0.62 t/ha) compared to the attainable yield of the crop reaching up to 3 t/ha. The low productivity is attributable to a lack of improved and high yielding cultivars that are locally bred and adapted in the country. Genetically diverse and locally adapted Bambara groundnut landrace varieties and exotic collections are present in South Africa for strategic breeding to improve yield and yield-related traits and nutritional quality. The objective of this study was to determine the magnitude of the genetic diversity and population structure of Bambara groundnut collections of South Africa using high throughput single nucleotide polymorphisms (SNP) markers. Ninety-three genotypes were assessed using genotyping with 2286 SNP markers and some unique complementary morpho-agronomic traits of the crop. The mean genetic diversity value was 0.32, revealing moderate genetic differences among the assessed genotypes. Cluster and structure analyses grouped the test genotypes into two distinct categories. Further, the analysis of molecular variance partitioned the total genetic variation into among genotypes (90%), within genotypes (8%) and among populations (2%). The results revealed two heterotic groups for hybridization and selection programs. The following unique genotypes were selected: ARC Bamb 37 (with spreading growth type), ARC Bamb 49 (bunch type), ARC Bamb 61 (semi-bunch) and ARC Bamb 83 (spreading) using the SNP markers and desirable agronomic traits. The study provided new insight on Bambara groundnut genetic profiles of South African collections which will assist in conservation strategy and management of the crop for effective breeding.

Comparative Analysis of Molecular RFLP and SNP Markers in Assessing and Understanding the Genetic Diversity of Various Chicken Breeds.

Monitoring the genetic diversity of small populations is important with respect to conserving rare and valuable chicken breeds, as well as discovery and innovation in germplasm research and application. Restriction fragment length polymorphisms (RFLPs), the molecular markers that underlie multilocus DNA fingerprinting (MLDF), have historically been employed for this purpose, but over the past two decades, there has been an irreversible shift toward high-throughput single-nucleotide polymorphisms (SNPs). In this study, we conducted a comparative analysis of archived MLDF results and new data from whole-genome SNP genotyping (SNPg) among 18 divergently selected breeds representing a large sample of the world gene pool. As a result, we obtained data that fit the general concept of the phylogenetic distribution of the studied breeds and compared them with RFLP and SNP markers. RFLPs were found to be useful markers for retrospective assessment of changes in the genetic architecture and variability underlying the phenotypic variation in chicken populations, especially when samples from previous generations used for MLDF are unavailable for SNPg. These results can facilitate further research necessary to assess the possibility of extrapolating previous MLDF results to study the long-term dynamics of genetic diversity in various small chicken germplasm populations over time. In general, the whole-genome characterization of populations and breeds by multiple SNP loci will further form the basis for the development and implementation of genomic selection with the aim of effective use of the genetic potential of the domestic gene pool in the poultry industry.

Rapid varietal authentication of oolong tea products by microfluidic-based SNP genotyping

Oolong tea is one of the most popular Chinese teas, and its quality is significantly affected by the variety of tea plant. The growing demands lead to the adulteration of premium oolong tea products, e.g., Tieguanyin oolong tea. In this study, microfluidic technology and single-nucleotide polymorphism (SNP) biomarkers were used to authenticate the varieties of oolong tea products. Forty-eight pairs of primers were screened, and they can be used to authenticate Tieguanyin oolong tea via high-throughput microfluidic SNP chips. Through the combination of the NJ tree and PCoA plot methods, the study found that the most frequent adulterant of Tieguanyin oolong tea on the market is Benshan. For the first time, the commercial behavior of using Fuyun6 and Jinguanyin as adulterants or contamination in the production of Tieguanyin oolong tea was reported. This research has proposed rapid authentication technology for oolong tea to provide food quality supervision and promote consumer trust.

Progress in Marker-Assisted Selection to Genomics-Assisted Breeding in Tomato

Tomato is an important vegetable crop for fresh and processed products. In the past decades, conventional breeding cum marker-assisted selection (MAS) has been deployed widely to develop modern tomato cultivars with desirable agronomic traits, market classes, and consumer preferences. The rapid developments in sequencing technologies with the reduced costs per sample, high-throughput single nucleotide polymorphism (SNP) genotyping platforms, and bioinformatics tools have revolutionized crop improvement programs, and deciphered the tomato genome sequence in 2012. Since then thousands of cultivated, its close relatives, and wild species have been genome resequenced to analyze structural variants population structure, genetic diversity, high-density map construction so on. Further, tomato pan-genomes have been constructed to search genomics regions associated with agronomic traits to expedite the breeding process. Importantly, genomics-assisted research has begun in tomatoes with the identification of genes, and SNP markers associated with phenotypic variation by applying genome resequencing, genome-wide association studies (GWAS) using SNP array, and genotyping-by-sequencing techniques. Further, the genomic selection (GS) method is expected to increase breeding efficiency and genetic gain rapidly. This review provides the latest information on progress in MAS to genome resequencing, pan-genomes, SNP genotyping, GWAS, and GS for genomics-assisted breeding in tomatoes.

Identification of noble candidate gene associated with sensitivity to phytotoxicity of etofenprox in soybean

Phytotoxicity is caused by the interaction between plants and a chemical substance, which can cause critical damage to plants. Understanding the molecular mechanism underlying plant-chemical interactions is important for managing pests in crop fields and avoiding plant phytotoxicity by insecticides. The genomic region responsible for sensitivity to phytotoxicity of etofenprox (PE), controlled by a single dominant gene, was detected by constructing high density genetic map using recombination inbred lines (RILs) in soybean. The genomic region of ~ 80 kbp containing nine genes was identified on chromosome 16 using a high-throughput single nucleotide polymorphism (SNP) genotyping system using two different RIL populations. Through resequencing data of 31 genotypes, nonsynonymous SNPs were identified in Glyma.16g181900, Glyma.16g182200, and Glyma.16g182300. The genetic variation in Glyma.16g182200, encoding glycosylphosphatidylinositol-anchored protein (GPI-AP), caused a critical structure disruption on the active site of the protein. This structural variation of GPI-AP may change various properties of the ion channels which are the targets of pyrethroid insecticide including etofenprox. This is the first study that identifies the candidate gene and develops SNP markers associated with PE. This study would provide genomic information to understand the mechanism of phytotoxicity in soybean and functionally characterize the responsive gene.

High-throughput barcoding method for the genetic surveillance of insecticide resistance and species identification in Anopheles gambiae complex malaria vectors

Surveillance of malaria vector species and the monitoring of insecticide resistance are essential to inform malaria control strategies and support the reduction of infections and disease. Genetic barcoding of mosquitoes is a useful tool to assist the high-throughput surveillance of insecticide resistance, discriminate between sibling species and to detect the presence of Plasmodium infections. In this study, we combined multiplex PCR, custom designed dual indexing, and Illumina next generation sequencing for high throughput single nucleotide polymorphism (SNP)-profiling of four species from the Anopheles (An.) gambiae complex (An. gambiae sensu stricto, An. coluzzii, An. arabiensis and An. melas). By amplifying and sequencing only 14 genetic fragments (500 bp each), we were able to simultaneously detect Plasmodium infection; insecticide resistance-conferring SNPs in ace1, gste2, vgsc and rdl genes; the partial sequences of nuclear ribosomal internal transcribed spacers (ITS1 and ITS2) and intergenic spacers (IGS), Short INterspersed Elements (SINE), as well as mitochondrial genes (cox1 and nd4) for species identification and genetic diversity. Using this amplicon sequencing approach with the four selected An. gambiae complex species, we identified a total of 15 non-synonymous mutations in the insecticide target genes, including previously described mutations associated with resistance and two new mutations (F1525L in vgsc and D148E in gste2). Overall, we present a reliable and cost-effective high-throughput panel for surveillance of An. gambiae complex mosquitoes in malaria endemic regions.

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.

Genome-Wide Association Analysis and Gene Mining of Resistance to China Race 1 of Frogeye Leaf Spot in Soybean.

Soybean frogeye leaf spot (FLS) is a worldwide fungal disease. Its higher occurrence frequency and wider distribution range always led to severe yield losses of soybean, therefore, breeding new cultivars with FLS resistance has been an important breeding goal for soybean breeders. In this study, an association panel of 183 representative soybean accessions was used to evaluate their resistance to FLS race 1, and to identify quantitative trait nucleotides (QTNs) and candidate genes based on genome-wide association study (GWAS) and high-throughput single-nucleotide polymorphisms (SNPs). A total of 23,156 high-quality SNPs were developed using the specific locus-amplified fragment sequencing (SLAF-seq) approach. Finally, 13 novel association signals associated with FLS race 1 resistance were identified by the compressed mixed linear model (CMLM). In addition, 119 candidate genes were found within the 200-kb flanking genomic region of these 13 peak SNPs. Based on the gene-based association analysis, haplotype analysis, expression pattern analysis, and virus-induced gene silencing (VIGS) systems, four genes (Glyma.05G121100, Glyma.17G228300, Glyma.19G006900, and Glyma.19G008700) were preliminarily proved to play an important role in the soybean resistance to FLS race 1.

High-throughput SNP markers for authentication of Korean wheat cultivars based on seven complete plastomes and the nuclear genome.

The online version contains supplementary material available at 10.1007/s10068-022-01043-w.

Powerful Identification of Large Quantitative Trait Loci Using Genome-Wide R/glmnet-Based Regression.

R/glmnet has been successfully applied to jointly mapped multiple quantitative trait loci for linkage analysis, along with statistical inference for quantitative trait loci candidates with nonzero genetic effects using R/lm for normally distributed traits, R/glm for discrete traits, and R/coxph for survival times. In this study, we extended R/glmnet to a genome-wide association study by means of parallel computation. A multi-locus genome-wide association study for high-throughput single-nucleotide polymorphisms was implemented in the "Multi-Runking" software written within the R workspace. This software can better detect common and large quantitative trait nucleotides and more accurately estimate than genome-wide mixed model analysis for one single-nucleotide polymorphism at a time and linear mixed models-least absolute shrinkage and selection operator. Its applicability and utility were demonstrated by multi-locus genome-wide association studies for the simulated and real traits distributed normally, binary traits, and survival times.