A comprehensive benchmark of graph-based genetic variant genotyping algorithms on plant genomes for creating an accurate ensemble pipeline.

BACKGROUND: Although sequencing technologies have boosted the measurement of the genomic diversity of plant crops, it remains challenging to accurately genotype millions of genetic variants, especially structural variations, with only short reads. In recent years, many graph-based variation genotyping methods have been developed to address this issue and tested for human genomes. However, their performance in plant genomes remains largely elusive. Furthermore, pipelines integrating the advantages of current genotyping methods might be required, considering the different complexity of plant genomes. RESULTS: Here we comprehensively evaluate eight such genotypers in different scenarios in terms of variant type and size, sequencing parameters, genomic context, and complexity, as well as graph size, using both simulated and real data sets from representative plant genomes. Our evaluation reveals that there are still great challenges to applying existing methods to plants, such as excessive repeats and variants or high resource consumption. Therefore, we propose a pipeline called Ensemble Variant Genotyper (EVG) that can achieve better genotyping performance in almost all experimental scenarios and comparably higher genotyping recall and precision even using 5× reads. Furthermore, we demonstrate that EVG is more robust with an increasing number of graphed genomes, especially for insertions and deletions. CONCLUSIONS: Our study will provide new insights into the development and application of graph-based genotyping algorithms. We conclude that EVG provides an accurate, unbiased, and cost-effective way for genotyping both small and large variations and will be potentially used in population-scale genotyping for large, repetitive, and heterozygous plant genomes.

Construction of relatedness matrices in autopolyploid populations using low-depth high-throughput sequencing data.

KEY MESSAGE: An improved estimator of genomic relatedness using low-depth high-throughput sequencing data for autopolyploids is developed. Its outputs strongly correlate with SNP array-based estimates and are available in the package GUSrelate. High-throughput sequencing (HTS) methods have reduced sequencing costs and resources compared to array-based tools, facilitating the investigation of many non-model polyploid species. One important quantity that can be computed from HTS data is the genetic relatedness between all individuals in a population. However, HTS data are often messy, with multiple sources of errors (i.e. sequencing errors or missing parental alleles) which, if not accounted for, can lead to bias in genomic relatedness estimates. We derive a new estimator for constructing a genomic relationship matrix (GRM) from HTS data for autopolyploid species that accounts for errors associated with low sequencing depths, implemented in the R package GUSrelate. Simulations revealed that GUSrelate performed similarly to existing GRM methods at high depth but reduced bias in self-relatedness estimates when the sequencing depth was low. Using a panel consisting of 351 tetraploid potato genotypes, we found that GUSrelate produced GRMs from genotyping-by-sequencing (GBS) data that were highly correlated with a GRM computed from SNP array data, and less biased than existing methods when benchmarking against the array-based GRM estimates. GUSrelate provides researchers with a tool to reliably construct GRMs from low-depth HTS data.

Template-specific optimization of NGS genotyping pipelines reveals allele-specific variation in MHC gene expression.

Using high-throughput sequencing for precise genotyping of multi-locus gene families, such as the major histocompatibility complex (MHC), remains challenging, due to the complexity of the data and difficulties in distinguishing genuine from erroneous variants. Several dedicated genotyping pipelines for data from high-throughput sequencing, such as next-generation sequencing (NGS), have been developed to tackle the ensuing risk of artificially inflated diversity. Here, we thoroughly assess three such multi-locus genotyping pipelines for NGS data, the DOC method, AmpliSAS and ACACIA, using MHC class IIß data sets of three-spined stickleback gDNA, cDNA and "artificial" plasmid samples with known allelic diversity. We show that genotyping of gDNA and plasmid samples at optimal pipeline parameters was highly accurate and reproducible across methods. However, for cDNA data, the gDNA-optimal parameter configuration yielded decreased overall genotyping precision and consistency between pipelines. Further adjustments of key clustering parameters were required tο account for higher error rates and larger variation in sequencing depth per allele, highlighting the importance of template-specific pipeline optimization for reliable genotyping of multi-locus gene families. Through accurate paired gDNA-cDNA typing and MHC-II haplotype inference, we show that MHC-II allele-specific expression levels correlate negatively with allele number across haplotypes. Lastly, sibship-assisted cDNA-typing of MHC-I revealed novel variants linked in haplotype blocks, and a higher-than-previously-reported individual MHC-I allelic diversity. In conclusion, we provide novel genotyping protocols for the three-spined stickleback MHC-I and -II genes, and evaluate the performance of popular NGS-genotyping pipelines. We also show that fine-tuned genotyping of paired gDNA-cDNA samples facilitates amplification bias-corrected MHC allele expression analysis.

Seq2Sat and SatAnalyzer toolkit: Towards comprehensive microsatellite genotyping from sequencing data.

Accurate and efficient microsatellite loci genotyping is an essential process in population genetics that is also used in various demographic analyses. Protocols for next-generation sequencing of microsatellite loci enable high-throughput and cross-compatible allele scoring, common issues that are not addressed by conventional capillary-based approaches. To improve this process, we have developed an all-in-one software, called Seq2Sat (sequence to microsatellite), in C++ to support automated microsatellite genotyping. It directly takes raw reads of microsatellite amplicons and conducts read quality control before inferring genotypes based on depth-of-read, read ratio, sequence composition and length. We have also developed a module for sex identification based on sex chromosome-specific locus amplicons. To allow for greater user access and complement autoscoring, we developed SatAnalyzer (microsatellite analyzer), a user-friendly web-based platform that conducts reads-to-report analyses by calling Seq2Sat for genotype autoscoring and produces interactive genotype graphs for manual editing. SatAnalyzer also allows users to troubleshoot multiplex optimization by analysing read quality and distribution across loci and samples in support of high-quality library preparation. To evaluate its performance, we benchmarked our toolkit Seq2Sat/SatAnalyzer against a conventional capillary gel method and existing microsatellite genotyping software, MEGASAT, using two datasets. Results showed that SatAnalyzer can achieve >99.70% genotyping accuracy and Seq2Sat is ~5 times faster than MEGASAT despite many more informative tables and figures being generated. Seq2Sat and SatAnalyzer are freely available on github (https://github.com/ecogenomicscanada/Seq2Sat) and dockerhub (https://hub.docker.com/r/rocpengliu/satanalyzer).

Genotyping of SNPs in bread wheat at reduced cost from pooled experiments and imputation.

KEY MESSAGE: Pooling and imputation are computational methods that can be combined for achieving cost-effective and accurate high-density genotyping of both common and rare variants, as demonstrated in a MAGIC wheat population. The plant breeding industry has shown growing interest in using the genotype data of relevant markers for performing selection of new competitive varieties. The selection usually benefits from large amounts of marker data, and it is therefore crucial to dispose of data collection methods that are both cost-effective and reliable. Computational methods such as genotype imputation have been proposed earlier in several plant science studies for addressing the cost challenge. Genotype imputation methods have though been used more frequently and investigated more extensively in human genetics research. The various algorithms that exist have shown lower accuracy at inferring the genotype of genetic variants occurring at low frequency, while these rare variants can have great significance and impact in the genetic studies that underlie selection. In contrast, pooling is a technique that can efficiently identify low-frequency items in a population, and it has been successfully used for detecting the samples that carry rare variants in a population. In this study, we propose to combine pooling and imputation and demonstrate this by simulating a hypothetical microarray for genotyping a population of recombinant inbred lines in a cost-effective and accurate manner, even for rare variants. We show that with an adequate imputation model, it is feasible to accurately predict the individual genotypes at lower cost than sample-wise genotyping and time-effectively. Moreover, we provide code resources for reproducing the results presented in this study in the form of a containerized workflow.

A rapid and reference-free imputation method for low-cost genotyping platforms.

Most current genotype imputation methods are reference-based, which posed several challenges to users, such as high computational costs and reference panel inaccessibility. Thus, deep learning models are expected to create reference-free imputation methods performing with higher accuracy and shortening the running time. We proposed a imputation method using recurrent neural networks integrating with an additional discriminator network, namely GRUD. This method was applied to datasets from genotyping chips and Low-Pass Whole Genome Sequencing (LP-WGS) with the reference panels from The 1000 Genomes Project (1KGP) phase 3, the dataset of 4810 Singaporeans (SG10K), and The 1000 Vietnamese Genome Project (VN1K). Our model performed more accurately than other existing methods on multiple datasets, especially with common variants with large minor allele frequency, and shrank running time and memory usage. In summary, these results indicated that GRUD can be implemented in genomic analyses to improve the accuracy and running-time of genotype imputation.

Cost-effective and reliable genomic DNA extraction from plant seedlings for high-throughput genotyping in seed industries.

Genetic purity of seeds is one of the critical aspects in the seed industry. Molecular seed testing laboratories are utilizing PCR based diagnostic tools for genetic purity analysis. High quality DNA is an essential prerequisite for such analyses. Here, we demonstrate a robust and inexpensive DNA extraction method to isolate genomic DNA from variety of crops. Current method (M2) was compared with four commonly used DNA isolation methods for PCR-based genetic characterization and High Resolution Melt (HRM) based hybridity analysis of cotton, okra, tomato and maize using SSR markers. DNA extracted through current method showed excellent yield and quality as compared to other methods. High quality, PCR ready DNA was isolated within 30-50 min and displayed best results for genetic purity analysis using HRM. In contrast, several genomic DNA samples extracted using other methods were found unsuitable for HRM analysis. Our method can be a perfect choice in seed industry, where thousands of samples are processed every day. Notably, using our method single technician can extract DNA from 96 leaf samples within 30-50 min, at a cost of only $0.11/sample. Overall, current DNA extraction method is a reliable and cost-effective solution for large-scale genotyping experiments in the agricultural industry.

A Comparison of Non-Destructive Visceral Swab and Tissue Biopsy Sampling Methods for Genotyping-by-Sequencing in the Freshwater Mussel Fusconaia askewi.

Limiting harm to organisms caused by genetic sampling is an important consideration for rare species, and a number of non-destructive sampling techniques have been developed to address this issue in freshwater mussels. Two methods, visceral swabbing and tissue biopsies, have proven to be effective for DNA sampling, though it is unclear as to which method is preferable for genotyping-by-sequencing (GBS). Tissue biopsies may cause undue stress and damage to organisms, while visceral swabbing potentially reduces the chance of such harm. Our study compared the efficacy of these two DNA sampling methods for generating GBS data for the unionid freshwater mussel, the Texas pigtoe (Fusconaia askewi). Our results find both methods generate quality sequence data, though some considerations are in order. Tissue biopsies produced significantly higher DNA concentrations and larger numbers of reads when compared with swabs, though there was no significant association between starting DNA concentration and number of reads generated. Swabbing produced greater sequence depth (more reads per sequence), while tissue biopsies revealed greater coverage across the genome (at lower sequence depth). Patterns of genomic variation as characterized in principal component analyses were similar regardless of the sampling method, suggesting that the less invasive swabbing is a viable option for producing quality GBS data in these organisms.

Imputation of ancient human genomes.

Due to postmortem DNA degradation and microbial colonization, most ancient genomes have low depth of coverage, hindering genotype calling. Genotype imputation can improve genotyping accuracy for low-coverage genomes. However, it is unknown how accurate ancient DNA imputation is and whether imputation introduces bias to downstream analyses. Here we re-sequence an ancient trio (mother, father, son) and downsample and impute a total of 43 ancient genomes, including 42 high-coverage (above 10x) genomes. We assess imputation accuracy across ancestries, time, depth of coverage, and sequencing technology. We find that ancient and modern DNA imputation accuracies are comparable. When downsampled at 1x, 36 of the 42 genomes are imputed with low error rates (below 5%) while African genomes have higher error rates. We validate imputation and phasing results using the ancient trio data and an orthogonal approach based on Mendel's rules of inheritance. We further compare the downstream analysis results between imputed and high-coverage genomes, notably principal component analysis, genetic clustering, and runs of homozygosity, observing similar results starting from 0.5x coverage, except for the African genomes. These results suggest that, for most populations and depths of coverage as low as 0.5x, imputation is a reliable method that can improve ancient DNA studies.

Microsatellites as Molecular Markers with Applications in Exploitation and Conservation of Aquatic Animal Populations.

A large number of species and taxa have been studied for genetic polymorphism. Microsatellites have been known as hypervariable neutral molecular markers with the highest resolution power in comparison with any other markers. However, the discovery of a new type of molecular marker-single nucleotide polymorphism (SNP) has put the existing applications of microsatellites to the test. To ensure good resolution power in studies of populations and individuals, a number of microsatellite loci from 14 to 20 was often used, which corresponds to about 200 independent alleles. Recently, these numbers have tended to be increased by the application of genomic sequencing of expressed sequence tags (ESTs) and the choice of the most informative loci for genotyping depends on the aims of research. Examples of successful applications of microsatellite molecular markers in aquaculture, fisheries, and conservation genetics in comparison to SNPs are summarized in this review. Microsatellites can be considered superior markers in such topics as kinship and parentage analysis in cultured and natural populations, the assessment of gynogenesis, androgenesis and ploidization. Microsatellites can be coupled with SNPs for mapping QTL. Microsatellites will continue to be used in research of genetic diversity in cultured stocks, and also in natural populations as an economically advantageous genotyping technique.

Teste de genotipagem HLA-DQ2 e/ou DQ8 para o diagnóstico de doença celíaca em pacientes com fatores de risco / HLA-DQ2 and/or DQ8 genotyping test for the diagnosis of celiac disease in patients with risk factors

INTRODUÇÃO: A doença celíaca é uma doença autoimune de caráter inflamatório, causada pela sensibilidade ao glúten e proteínas associadas. É considerada um problema de saúde pública devido à sua prevalência, frequente associação a morbidade e surgimento de complicações graves. Apresenta-se na forma clássica e não clássica, e as manifestações clínicas variam desde diarreia crônica, vômitos, irritabilidade, anorexia, déficit de crescimento, distensão abdominal, diminuição do tecido celular subcutâneo, atrofia da musculatura glútea, podendo até gerar baixa estatura, anemia por deficiência de ferro, constipação intestinal, osteoporose, esterilidade, artralgia ou artrite e alguns tipos de epilepsia. A doença celíaca pode estar associada a fatores ambientais e genéticos, sendo que o último possui correlação com a presença dos alelos do Complexo Principal de Histocompatibilidade da Classe II que codificam os heterodímeros HLA-DQ2 e HLA-DQ8. Dentro do contexto de predisposição genética, destacam-se os parentes de primeiro grau, que possuem um risco entre 5% a 20% de desenvolver a doença. Ademais, outras doenças autoimunes podem ser listadas como fatores de risco de desenvolver, a saber: como diabetes mellitus tipo 1, tireoidite autoimune, deficiência seletiva de IgA, Síndrome de Sjögren, colestase autoimune e miocardite autoimune; síndrome de Down, síndrome de Turner e síndrome de Williams. Atualmente, o diagnóstico é feito com base nas manifestações clínicas, dosagem sorológica de anticorpos anti-transglutaminase IgA e exame anatomopatológico por biópsia. Na maioria dos casos, o diagnóstico é subnotificado devido a dificuldades inerentes aos testes disponíveis. TECNOLOGIA: : Teste de genotipagem HLA (Antígeno Leucocitário Humano - do inglês Human Leukocyte Antigen) DQ2 e HLA-DQ8. PERGUNTA DE PESQUISA: Qual a acurácia diagnóstica dos exames de genotipagem HLA-DQ2 e HLA-DQ8 para o diagnóstico de doença celíaca em pacientes com fatores de risco para esta condição? EVIDÊNCIAS CIENTÍFICAS: A revisão sistematizada recuperou 3.333 registros dos quais 108 foram selecionados por revisores independentes para leitura completa dos textos e cinco estudos foram incluídos. Dos estudos incluídos ao final da seleção, três apresentavam delineamento transversal e duas coortes prospectivas. A partir desses cinco estudos, foi possível avaliar os desfechos primários (sensibilidade e especificidade) e os desfechos secundários (valor preditivo positivo, valor preditivo negativo, acurácia, razão de verossimilhança positiva e razão de verossimilhança negativa) comparando a genotipagem de HLA-DQ2, HLA-DQ8 e/ou HLA-DQ2:DQ8 com biópsia e teste sorológico de anticorpos anti-transglutaminase IgA. O risco de viés individual de cada estudo foi avaliado utilizando a ferramenta QUADAS-2, sendo que os cincos estudos incluídos apresentaram alto risco de viés com relação ao critério de seleção e à interpretação do teste index. Foi conduzida a análise de acurácia diagnóstica comparando biópsia versus HLA-DQ2 e/ou DQ8 e IgA e o resultado da meta-análise genotipagem do HLA-DQ2/DQ8 versus IgA mostrou-se com alta sensibilidade e baixa especificidade. Por fim, foi mensurada a qualidade da evidência utilizando a metodologia GRADE que, no geral, mostrou uma qualidade baixa e muito baixa. AVALIAÇÃO ECONÔMICA (AE): A análise de custo-efetividade, para anos de vida ajustados pela qualidade, demonstrou que, ao comparar com o exame diagnóstico atualmente disponíveis no SUS (tTG-IgA associado a biopsia), o HLA DQ2/8 apresenta maior custo e menor efetividade. Desta forma, considerando o contexto do SUS, a adição do HLA DQ2/8 não irá promover melhora da qualidade de vida destes pacientes, assim como não será reduzido nenhum custo além do atualmente disponível. ANÁLISE DE IMPACTO ORÇAMENTÁRIO (AIO): Foi realizada análise para estimar o impacto orçamentário com a incorporação do teste de genotipagem HLA-DQ2 e DQ8, comparado a biópsia duodenal ou a testagem de tTG-IgA. Foi adotado o horizonte temporal de cinco anos (2023 a 2027), com estimativas de impacto orçamentário ano a ano. Considerando o cenário 1 biópsia + HLA, o impacto orçamentário incremental acumulado em cinco anos seria de cerca de 713 milhões de reais. Já no cenário 2 ttg-IgA + HLA, com market share de 30%, o impacto orçamentário acumulado em cinco anos seria de 818 milhões de reais. No cenário 3 ttg-IgA + HLA + Biópsia, com o market share de 30%, o impacto orçamentário em cinco anos seria de 677 milhões de reais. Por fim, com o cenário 4 ttg-IgA + HLA + Biópsia considerando a população que relata ter reações adversas ao glúten, o impacto orçamentário acumulado em cinco anos seria de sete bilhões de reais. A análise de sensibilidade revelou que o custo do teste HLA-DQ2/DQ8 é a variável com maior potencial de afetar a estimativa de impacto orçamentário. CONSIDERAÇÕES FINAIS: A qualidade da evidência da presente revisão sistemática foi considerada baixa e os estudos incluídos apresentaram alto risco de viés em vários domínios do QUADAS-2. As análises mostraram que a genotipagem do HLA-DQ2 e/ou DQ8 apresentou alta sensibilidade e baixa especificidade, sendo capaz de identificar os indivíduos verdadeiramente positivos, sugerindo a aplicabilidade do teste como complementar ao diagnóstico da doença celíaca. A razão de custo-efetividade incremental foi de ­R$ 286,86 para o cenário IgA versus HLA e de ­R$214,64 por ano de vida ajustado pela qualidade ganho no cenário Biópsia versus HLA. Na análise de impacto orçamentário, o custo incremental calculado para o SUS foi de R$ 120.060,76 para os cinco anos no cenário Biópsia versus HLA, com market share inicial de 30%. Ressalta-se que as limitações apresentadas em relação ao exame esofagogastroduodenoscopia podem apresentar resultados superestimados. Já o impacto incremental no cenário IgA versus HLA foi de R$ 144.358,27 ao final dos cinco anos. RECOMENDAÇÃO PRELIMINAR DA CONITEC: O tema foi avaliado na 113ª Reunião Ordinária da Conitec em 5 de outubro de 2022. A recomendação inicial foi desfavorável à incorporação do teste de genotipagem HLA-DQ2 e/ou DQ8, por não fornecer diagnóstico conclusivo e ter grande impacto orçamentário em cinco anos. CONSULTA PÚBLICA: Foram recebidas 46 contribuições, sendo 25 pelo formulário para contribuições técnico-científicas e 21 pelo formulário para contribuições sobre experiência ou opinião de pacientes, familiares, amigos ou cuidadores de pacientes, profissionais de saúde ou pessoas interessadas no tema. No total, 24 expressaram que o teste de genotipagem HLA DQ2/DQ8 deve ser incorporado ao SUS e 1 participante não tinha opinião formada. De maneira geral, as contribuições abordaram a acessibilidade restrita aos exames clínicos para o diagnóstico de doença celíaca tanto na rede privada quanto pública e a necessidade de o exame ser ofertado a familiares de 1º grau. RECOMENDAÇÃO FINAL DA CONITEC: As contribuições da consulta pública foram apresentadas à Conitec por ocasião da 117ª Reunião Ordinária, realizada em 29 de março de 2023. Os membros presentes do Comitê de Produtos e Procedimentos, deliberaram, por unanimidade, recomendar a não incorporação do teste de genotipagem HLA-DQ2 e/ou DQ8 para diagnóstico da doença celíaca em pacientes com fatores de risco. Por fim, foi assinado o Registro de Deliberação Nº 812 / 2023. DECISÃO: Não incorporar, no âmbito do Sistema Único de Saúde - SUS, o teste de genotipagem HLA-DQ2 e/ou DQ8 para o diagnóstico de doença celíaca em pacientes com fatores de risco, publicada no Diário Oficial da União nº 81, seção 1, página 111, em 28 de abril de 2023.

Intergenic region polymorphism analysis: a novel genotyping method for Klebsiella pneumoniae.

AIMS: The ability to distinguish between Klebsiella pneumoniae strains is critical for outbreak investigations. A new typing method, intergenic region polymorphism analysis (IRPA), was developed, validated, and the discriminatory power was determined by comparison with multiple-locus variable-number tandem repeat analysis (MLVA) in this study. METHODS AND RESULTS: This method is based on the idea that every IRPA locus (polymorphic fragment of intergenic regions present in one strain but not in other strains or different fragment sizes in other strains) could divide strains into different genotypes. A 9-loci IRPA scheme was designed to type 64 K. pneumoniae isolates. Five IRPA loci were identified that conferred the same level of discrimination as the 9-loci initially examined. Among these K. pneumoniae isolates, 7.81% (5/64), 6.25% (4/64), 4.96% (3/64), 9.38% (6/64), and 1.56% (1/64) were capsular serotypes K1, K2, K5, K20, and K54, respectively. The discriminatory power of the IRPA method was better than that of MLVA expressed in Simpson's index of diversity (SI) at 0.997 and 0.988, respectively. The congruent analysis of the IRPA method and MLVA showed moderate congruence between the two methods (AR = 0.378). The AW indicated that if IRPA data are availabl, one can accurately predict the MLVA cluster. CONCLUSION: The IRPA method was found to have higher discriminatory power than MLVA and allowed for simpler band profile interpretation. The IRPA method is a rapid, simple, and high-resolution technique for molecular typing of K. pneumoniae.

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.

Genotyping by Multiplexed Sequencing (GMS) Using SNP Markers.

SNP-based genotyping has become the most effective approach to generate target-specific data for use in genetic studies. In this chapter, we will describe a high-throughput genotyping method that multiplexes hundreds to thousands of SNP markers in a two-step PCR protocol that can be customized to fit the specific needs of a study.

qPCR Genotyping of Polyploid Species.

A simple and cost-effective method for genotyping polyploid plants using quantitative PCR (qPCR) is described in this chapter. There is no additional operation, only simultaneous amplification of alleles and reference sequences with constant copy number in the genome. The qPCR genotyping can detect the genotypes of important traits in polyploid plants without whole genome sequencing data.

Semi-Thermal Asymmetric Reverse PCR (STARP) Genotyping.

PCR-based individual Single nucleotide polymorphism (SNP) genotyping methods are preferred due to their flexibility, high-throughput, and improved accuracy. Semi-thermal asymmetric reverse PCR (STARP) is one of the SNP genotyping methods developed to reduce operational cost with improved platform compatibility. STARP is a unique method which can be used either as a gel-free SNP genotyping by detection of fluorescent signals or polyacrylamide gel-based size separation. SNP assay designing using sequence information and detection methods of STARP are described in detail.

High-Resolution Melting (HRM) Genotyping.

High-resolution melting (HRM) analysis is a simple, fast, and inexpensive real-time polymerase chain reaction (PCR)-based method used to identify genetic variation between populations and detect single-nucleotide polymorphisms (SNPs) in nucleic acid sequences. HRM is a powerful technique that detects the differences between SNP allele melting temperatures by using a fluorescent dye inserted into the duplex deoxyribonucleic acid (DNA) structure. Prior to performing HRM analysis, optimizing the primer design, PCR mixture, and software settings is essential to obtain accurate and reliable results. In this chapter, we describe a detailed SNP genotyping method that includes primer design and the analysis of the shapes and positions of the melt curve of the luminescence intensity of the fluorescent dye attached to amplified DNA using software of qPCR instruments. This protocol is applicable for genotyping germplasm, genetic mapping, and marker-assisted breeding in plants.

A New SNP Genotyping Technology by Target SNP-Seq.

In order to promote the widespread application of single-nucleotide polymorphism (SNP)-based genotyping, a new method was developed and called target SNP-seq which combined the advantages of multiplex polymerase chain reaction (PCR) amplification and high throughput sequencing on Illumina X Ten platform. Compared with kompetitive allele-specific PCR (KASP), microchips, and genotyping by sequencing (GBS), target SNP-seq uses perfect SNPs based on the analysis of variome (whole-genome sequence data of different accessions) and is flexible, cost-effective, and highly accurate for genotyping middle-scale SNPs. It could genotype hundreds of SNPs in massive DNA samples within 3 days at the cost of $7 for each DNA sample. The high efficiency and low cost of target SNP-seq make it more competitive than current SNP genotyping methods, and it has excellent potential for application in genetic research, as well as in promoting plant-breeding processes in the near future.

Multiplexed ISSR Genotyping by Sequencing (MIG-Seq).

Multiplexed inter-simple sequence repeat (ISSR) genotyping by sequencing (MIG-seq) is a simple, rapid, and inexpensive method for detecting single-nucleotide polymorphisms (SNPs) using next-generation sequencing (NGS). The advantages of MIG-seq include easy application to various species without prior genetic information. In addition, this method opens the door to genome-wide nucleotide sequence analyses of low-quality and trace-level deoxyribonucleic acid (DNA) samples, which have previously been difficult to analyze. Another advantage is that the procedure is simple, time-saving, and inexpensive. Recently, MIG-seq has been applied to wild and cultivated plants and has produced novel results. Using invisible DNA information, questions related to gene flow through pollination and seed dispersal, the genetic structure and diversity of populations, clonality, and the hybridization of wild and cultivated plants are being rapidly answered. In this chapter, I present the results of plant research based on MIG-seq and describe the procedure for this method as a user of MIG-seq.

Genetic diversity analysis and variety identification using SSR and SNP markers in melon.

Melon is an important horticultural crop with a pleasant aromatic flavor and abundance of health-promoting substances. Numerous melon varieties have been cultivated worldwide in recent years, but the high number of varieties and the high similarity between them poses a major challenge for variety evaluation, discrimination, as well as innovation in breeding. Recently, simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs), two robust molecular markers, have been utilized as a rapid and reliable method for variety identification. To elucidate the genetic structure and diversity of melon varieties, we screened out 136 perfect SSRs and 164 perfect SNPs from the resequencing data of 149 accessions, including the most representative lines worldwide. This study established the DNA fingerprint of 259 widely-cultivated melon varieties in China using Target-seq technology. All melon varieties were classified into five subgruops, including ssp. agrestis, ssp. melo, muskmelon and two subgroups of foreign individuals. Compared with ssp. melo, the ssp. agrestis varieties might be exposed to a high risk of genetic erosion due to their extremely narrow genetic background. Increasing the gene exchange between ssp. melo and ssp. agrestis is therefore necessary in the breeding procedure. In addition, analysis of the DNA fingerprints of the 259 melon varieties showed a good linear correlation (R2 = 0.9722) between the SSR genotyping and SNP genotyping methods in variety identification. The pedigree analysis based on the DNA fingerprint of 'Jingyu' and 'Jingmi' series melon varieties was consistent with their breeding history. Based on the SNP index analysis, ssp. agrestis had low gene exchange with ssp. melo in chromosome 4, 7, 10, 11and 12, two specific SNP loci were verified to distinguish ssp. agrestis and ssp. melon varieties. Finally, 23 SSRs and 40 SNPs were selected as the core sets of markers for application in variety identification, which could be efficiently applied to variety authentication, variety monitoring, as well as the protection of intellectual property rights in melon.