scholarly journals Discovery of Single Nucleotide Polymorphisms and Mutations by Pyrosequencing

2002 ◽  
Vol 3 (1) ◽  
pp. 51-56 ◽  
Author(s):  
Mostafa Ronaghi ◽  
Elahe Elahi
Keyword(s):  
Comparative Genomics ◽  
Dna Sequencing ◽  
Large Scale ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Mutation Scanning ◽  
Large Scale Analysis ◽  
Polymorphism Discovery ◽  
Sequencing Method ◽  
The Cost

Comparative genomics, analyzing variation among individual genomes, is an area of intense investigation. DNA sequencing is usually employed to look for polymorphisms and mutations. Pyrosequencing, a real-time DNA sequencing method, is emerging as a popular platform for comparative genomics. Here we review the use of this technology for mutation scanning, polymorphism discovery and chemical haplotyping. We describe the methodology and accuracy of this technique and discuss how to reduce the cost for large-scale analysis.

scholarly journals Single Nucleotide Polymorphism Discovery and Genetic Differentiation Analysis of Geese Bred in Poland, Using Genotyping-by-Sequencing (GBS)

Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 1074
Author(s):  
Joanna Grzegorczyk ◽  
Artur Gurgul ◽  
Maria Oczkowicz ◽  
Tomasz Szmatoła ◽  
Agnieszka Fornal ◽  
...  
Keyword(s):  
Genotyping By Sequencing ◽  
Read Depth ◽  
Model Organisms ◽  
Single Nucleotide Polymorphism Discovery ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Polymorphism Discovery ◽  
Genome Wide ◽  
Plumage Development ◽  
Edar Gene

Poland is the largest European producer of goose, while goose breeding has become an essential and still increasing branch of the poultry industry. The most frequently bred goose is the White Kołuda® breed, constituting 95% of the country’s population, whereas geese of regional varieties are bred in smaller, conservation flocks. However, a goose’s genetic diversity is inaccurately explored, mainly because the advantages of the most commonly used tools are strongly limited in non-model organisms. One of the most accurate used markers for population genetics is single nucleotide polymorphisms (SNP). A highly efficient strategy for genome-wide SNP detection is genotyping-by-sequencing (GBS), which has been already widely applied in many organisms. This study attempts to use GBS in 12 conservative goose breeds and the White Kołuda® breed maintained in Poland. The GBS method allowed for the detection of 3833 common raw SNPs. Nevertheless, after filtering for read depth and alleles characters, we obtained the final markers panel used for a differentiation analysis that comprised 791 SNPs. These variants were located within 11 different genes, and one of the most diversified variants was associated with the EDAR gene, which is especially interesting as it participates in the plumage development, which plays a crucial role in goose breeding.

scholarly journals A NOTE ON PHASING LONG GENOMIC REGIONS USING LOCAL HAPLOTYPE PREDICTIONS

2006 ◽  
Vol 04 (03) ◽  
pp. 639-647 ◽  
Author(s):  
ELEAZAR ESKIN ◽  
RODED SHARAN ◽  
ERAN HALPERIN
Keyword(s):  
Large Scale ◽  
Computational Cost ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Novel Approach ◽  
Maximum Likelihood Criterion ◽  
The Common ◽  
Genomic Regions ◽  
High Computational Cost ◽  
Combining Information

The common approaches for haplotype inference from genotype data are targeted toward phasing short genomic regions. Longer regions are often tackled in a heuristic manner, due to the high computational cost. Here, we describe a novel approach for phasing genotypes over long regions, which is based on combining information from local predictions on short, overlapping regions. The phasing is done in a way, which maximizes a natural maximum likelihood criterion. Among other things, this criterion takes into account the physical length between neighboring single nucleotide polymorphisms. The approach is very efficient and is applied to several large scale datasets and is shown to be successful in two recent benchmarking studies (Zaitlen et al., in press; Marchini et al., in preparation). Our method is publicly available via a webserver at .

scholarly journals Investigation of Genetic Relationships within Miscanthus using SNP Markers Identified using SLAF-Seq

2021 ◽  
Author(s):  
ZHIYONG Chen ◽  
Yancen He ◽  
Yasir Iqbal ◽  
Yanlan Shi ◽  
Hongmei Huang ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Genetic Relationships ◽  
Female Parent ◽  
Snp Markers ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Breeding Programs ◽  
Sequencing Method ◽  
Specific Locus

Abstract Background: Miscanthus, which is a leading dedicated-energy grass in Europe and in parts of Asia, is expected to play a key role in the development of the future bioeconomy. However, due to its complex genetic background, it is difficult to investigate phylogenetic relationships and the evolution of gene function in this genus. Here, we investigated 50 Miscanthus germplasms: 1 female parent (M. lutarioriparius), 30 candidate male parents (M. lutarioriparius, M. sinensis, and M. sacchariflorus), and 19 offspring. We used high-throughput Specific-Locus Amplified Fragment sequencing (SLAF-seq) to identify informative single nucleotide polymorphisms (SNPs) in all germplasms.Results: We identified 800,081 SLAF tags, of which 160,368 were polymorphic. Each tag was 264–364 bp long. The obtained SNPs were used to investigate genetic relationships within Miscanthus. We constructed a phylogenetic tree of the 50 germplasms using the obtained SNPs, and found that the germplasms fell into two clades: one clade of M. sinensis only and one clade that included the offspring, M. lutarioriparius, and M. sacchariflorus. Genetic cluster analysis indicated that M. lutarioriparius germplasm C3 was the most likely male parent of the offspring.Conclusions: As a high-throughput sequencing method, SLAF-seq can be used to identify informative SNPs in Miscanthus germplasms and to rapidly characterize genetic relationships within this genus. Our results will support the development of breeding programs utilizing Miscanthus cultivars with elite biomass- or fiber-production potential.

scholarly journals DNA Melting Analysis for Detection of Single Nucleotide Polymorphisms

2001 ◽  
Vol 47 (4) ◽  
pp. 635-644 ◽  
Author(s):  
Robert H Lipsky ◽  
Chiara M Mazzanti ◽  
Joseph G Rudolph ◽  
Ke Xu ◽  
Gopal Vyas ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Large Scale ◽  
Denaturing Gradient Gel Electrophoresis ◽  
Dna Melting ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Efficient System ◽  
Gradient Gel Electrophoresis ◽  
Melting Analysis ◽  
Scale Detection

Abstract Background: Several methods for detection of single nucleotide polymorphisms (SNPs; e.g., denaturing gradient gel electrophoresis and denaturing HPLC) are indirectly based on the principle of differential melting of heteroduplex DNA. We present a method for detecting SNPs that is directly based on this principle. Methods: We used a double-stranded DNA-specific fluorescent dye, SYBR Green I (SYBR) in an efficient system (PE 7700 Sequence Detector) in which DNA melting was controlled and monitored in a 96-well plate format. We measured the decrease in fluorescence intensity that accompanied DNA duplex denaturation, evaluating the effects of fragment length, dye concentration, DNA concentration, and sequence context using four naturally occurring polymorphisms (three SNPs and a single-base deletion/insertion). Results: DNA melting analysis (DM) was used successfully for variant detection, and we also discovered two previously unknown SNPs by this approach. Concentrations of DNA amplicons were readily monitored by SYBR fluorescence, and DNA amplicon concentrations were highly reproducible, with a CV of 2.6%. We readily detected differences in the melting temperature between homoduplex and heteroduplex fragments 15–167 bp in length and differing by only a single nucleotide substitution. Conclusions: The efficiency and sensitivity of DMA make it highly suitable for the large-scale detection of sequence variants.

scholarly journals Validation of variants using cost effective highresolution melting (HRM) analysis predicted from target re-sequencing in Eucalyptus

2020 ◽  
Vol 79 (2) ◽  
pp. 105-113
Author(s):  
Abdul Bari Muneera Parveen ◽  
Divya Lakshmanan ◽  
Modhumita Ghosh Dasgupta
Keyword(s):  
Next Generation Sequencing ◽  
Large Scale ◽  
Sequence Data ◽  
Cost Effective ◽  
Nucleotide Polymorphisms ◽  
Next Generation ◽  
Time Saving ◽  
Hrm Analysis ◽  
The Cost ◽  
Generation Sequencing

The advent of next-generation sequencing has facilitated large-scale discovery and mapping of genomic variants for high-throughput genotyping. Several research groups working in tree species are presently employing next generation sequencing (NGS) platforms for marker discovery, since it is a cost effective and time saving strategy. However, most trees lack a chromosome level genome map and validation of variants for downstream application becomes obligatory. The cost associated with identifying potential variants from the enormous amount of sequence data is a major limitation. In the present study, high resolution melting (HRM) analysis was optimized for rapid validation of single nucleotide polymorphisms (SNPs), insertions or deletions (InDels) and simple sequence repeats (SSRs) predicted from exome sequencing of parents and hybrids of Eucalyptus tereticornis Sm. ? Eucalyptus grandis Hill ex Maiden generated from controlled hybridization. The cost per data point was less than 0.5 USD, providing great flexibility in terms of cost and sensitivity, when compared to other validation methods. The sensitivity of this technology in variant detection can be extended to other applications including Bar-HRM for species authentication and TILLING for detection of mutants.

scholarly journals TNF-αPolymorphisms in Juvenile Idiopathic Arthritis: Which Potential Clinical Implications?

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
A. Scardapane ◽  
L. Breda ◽  
M. Lucantoni ◽  
F. Chiarelli
Keyword(s):  
Juvenile Idiopathic Arthritis ◽  
Large Scale ◽  
Nucleotide Polymorphisms ◽  
Necrosis Factor Alpha ◽  
Single Nucleotide ◽  
Adult Rheumatoid Arthritis ◽  
Functional Consequences ◽  
Factor Alpha ◽  
Necrosis Factor ◽  
Important Cytokine

Whether tumor necrosis factor alpha (TNF-α) gene polymorphisms (SNPs) influence disease susceptibility and treatment of patients with juvenile idiopathic arthritis (JIA) is presently uncertain. TNF-αis one of the most important cytokine involved in JIA pathogenesis. Several single nucleotide polymorphisms (SNPs) have been identified within the region of the TNF-αgene but only a very small minority have proven functional consequences and have been associated with susceptibility to JIA. An association between some TNF-αSNPs and adult rheumatoid arthritis (RA) susceptibility, severity and clinical response to anti-TNF-αtreatment has been reported. The most frenquetly studied TNF-αSNP is located at −308 position, where a substitution of the G allele with the rare A allele has been found. The presence of the allele −308A is associated to JIA and to a poor prognosis. Besides, the −308G genotype has been associated with a better response to anti-TNF-αtherapy in JIA patients, confirming adult data. Psoriatic and oligoarticular arthritis are significantly associated to the −238 SNP only in some works. Studies considering other SNPs are conflicting and inconclusive. Large scale studies are required to define the contribution of TNF-αgene products to disease pathogenesis and anti-TNF-αtherapeutic efficacy in JIA.

scholarly journals A regression framework to uncover pleiotropy in large-scale electronic health record data

2019 ◽  
Vol 26 (10) ◽  
pp. 1083-1090 ◽  
Author(s):  
Ruowang Li ◽  
Rui Duan ◽  
Rachel L Kember ◽  
Daniel J Rader ◽  
Scott M Damrauer ◽  
...  
Keyword(s):  
Large Scale ◽  
Pleiotropic Effects ◽  
Nucleotide Polymorphisms ◽  
Reduced Rank Regression ◽  
Electronic Health Record Data ◽  
Single Nucleotide ◽  
Rank Regression ◽  
Reduced Rank ◽  
Multiple Phenotypes ◽  
Regression Framework

Abstract Objective Pleiotropy, where 1 genetic locus affects multiple phenotypes, can offer significant insights in understanding the complex genotype–phenotype relationship. Although individual genotype–phenotype associations have been thoroughly explored, seemingly unrelated phenotypes can be connected genetically through common pleiotropic loci or genes. However, current analyses of pleiotropy have been challenged by both methodologic limitations and a lack of available suitable data sources. Materials and Methods In this study, we propose to utilize a new regression framework, reduced rank regression, to simultaneously analyze multiple phenotypes and genotypes to detect pleiotropic effects. We used a large-scale biobank linked electronic health record data from the Penn Medicine BioBank to select 5 cardiovascular diseases (hypertension, cardiac dysrhythmias, ischemic heart disease, congestive heart failure, and heart valve disorders) and 5 mental disorders (mood disorders; anxiety, phobic and dissociative disorders; alcohol-related disorders; neurological disorders; and delirium dementia) to validate our framework. Results Compared with existing methods, reduced rank regression showed a higher power to distinguish known associated single-nucleotide polymorphisms from random single-nucleotide polymorphisms. In addition, genome-wide gene-based investigation of pleiotropy showed that reduced rank regression was able to identify candidate genetic variants with novel pleiotropic effects compared to existing methods. Conclusion The proposed regression framework offers a new approach to account for the phenotype and genotype correlations when identifying pleiotropic effects. By jointly modeling multiple phenotypes and genotypes together, the method has the potential to distinguish confounding from causal genotype and phenotype associations.

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