High throughput genotyping technologies.

A comprehensive genetic map containing several hundred microsatellite markers resulted from a large microsatellite mapping project. This was the first real study that introduced high throughput methods to the genetic community. This map and the concurrent technological advances, which will briefly be reviewed, led to further numerous mapping investigations of simple and complex diseases. The annotated draft sequence of approximately three billion base pairs (bp) of the human genome has been completed much sooner than many imagined, due to considerable technological advancements and the international enterprise that resulted. This was a major development for the genetics community, but is only the precursor to the next phase of studying and understanding the variation within the human genome. The awareness of the differences may help us understand the effects on the genetics of the variation between individuals and disease. It is these variations at the nucleotide level that determine the physiological differences, or phenotypes of each individual, including all biological functions at the cellular and body level. Single nucleotide polymorphisms (SNPs) will provide the next high density map, and be the genetic tool to study these genetic variations. There are many sources of SNPs and exhaustive numbers of methods of SNP detection to be considered. The focus in this paper will be on the merits of selected, varied SNP typing methodologies that are emerging to genotype many individuals with the required huge number of SNPs to make the study of complex diseases and pharmacogenomics a practical and economically viable option.     

Using single nucleotide polymorphisms as a means to understanding the pathophysiology of asthma

Asthma is the most common chronic childhood disease in the developed nations, and is a complex disease that has high social and economic costs. Studies of the genetic etiology of asthma offer a way of improving our understanding of its pathogenesis, with the goal of improving preventive strategies, diagnostic tools, and therapies. Considerable effort and expense have been expended in attempts to detect specific polymorphisms in genetic loci contributing to asthma susceptibility. Concomitantly, the technology for detecting single nucleotide polymorphisms (SNPs) has undergone rapid development, extensive catalogues of SNPs across the genome have been constructed, and SNPs have been increasingly used as a method of investigating the genetic etiology of complex human diseases. This paper reviews both current and potential future contributions of SNPs to our understanding of asthma pathophysiology.     

A Laboratory Exercise for Genotyping Two Human Single Nucleotide Polymorphisms

The dramatic decrease in the cost of sequencing a human genome is leading to an era in which a wide range of students will benefit from having an understanding of human genetic variation. Since over 90% of sequence variation between humans is in the form of single nucleotide polymorphisms (SNPs), a laboratory exercise has been devised in order to illustrate the importance of SNPs. Two separate SNPs are analysed, one of which has a significant effect on a person’s phenotype and one which does not. The genotyping protocol is relatively inexpensive and uses standard molecular biology reagents and equipment.     

SNP analysis to dissect human traits

The analysis of complex human diseases has been spurred by the number of published genomic sequence variants - many identified in the course of sequencing the human genome. But, to be useful for genetic analysis, variants have to be mapped accurately, their frequencies in various populations determined, and automated high-throughput assay techniques developed. Recently proposed methods address these issues: the use of 'reduced representation shotgun' methods for more efficient detection of single nucleotide polymorphisms (SNPs), the employment of high-throughput genotyping techniques, the development of SNP maps that incorporate information about linkage disequilibrium, and the use of SNPs in identifying susceptibility genes for common illnesses.     

SNP discovery in associating genetic variation with human disease phenotypes

With the completion of the human genome project, attention is now rapidly shifting towards the study of individual genetic variation. The most abundant source of genetic variation in the human genome is represented by single nucleotide polymorphisms (SNPs), which can account for heritable inter-individual differences in complex phenotypes. Identification of SNPs that contribute to susceptibility to common diseases will provide highly accurate diagnostic information that will facilitate early diagnosis, prevention, and treatment of human diseases. Over the past several years, the advancement of increasingly high-throughput and cost-effective methods to discover and measure SNPs has begun to open the door towards this endeavor. Genetic association studies are considered to be an effective approach towards the detection of SNPs with moderate effects, as in most common diseases with complex phenotypes. This requires careful study design, analysis and interpretation. In this review, we discuss genetic association studies and address the prospect for candidate gene association studies, comparing the strengths and weaknesses of indirect and direct study designs. Our focus is on the continuous need for SNP discovery methods and the use of currently available prescreening methods for large-scale genetic epidemiological research until more advanced sequencing methods currently under development will become available.     

Navigating the HapMap

With the availability of the HapMap--a resource which describes common patterns of linkage disequilibrium (LD) in four different human population samples, we now have a powerful tool to help dissect the role of genetic variation in the biology of the genome. HapMap is entirely complimentary to the human genome map and so it is particularly fitting that it should be viewed in a full genomic context. However, characterization of high resolution LD across the genome can be a challenging task, owing in part to the sheer volume of data and the inherent dimensionality that its analysis entails. However, a number of tools are now available to make this task easier for researchers. This review will examine tools for viewing and analysing haplotype and LD data, enabling a number of tasks; including identification of optimal sets of haplotype tagging single nucleotide polymorphisms (SNPs); drawing links between associated SNPs and putative causal alleles; or simply viewing LD and haplotypes across a gene or region of interest. The data generated by the HapMap also has other important applications, informing, for example, on the demographic history and evidence of selection in human populations and on previously undetected regulatory relationships and gene networks. All of these properties make the HapMap no less an important resource than the human genome sequence itself and so this makes it essential viewing for all in the field of human biology.     

The Cassava Genome: Current Progress, Future Directions

The starchy swollen roots of cassava provide an essential food source for nearly a billion people, as well as possibilities for bioenergy, yet improvements to nutritional content and resistance to threatening diseases are currently impeded. A 454-based whole genome shotgun sequence has been assembled, which covers 69% of the predicted genome size and 96% of protein-coding gene space, with genome finishing underway. The predicted 30,666 genes and 3,485 alternate splice forms are supported by 1.4 M expressed sequence tags (ESTs). Maps based on simple sequence repeat (SSR)-, and EST-derived single nucleotide polymorphisms (SNPs) already exist. Thanks to the genome sequence, a high-density linkage map is currently being developed from a cross between two diverse cassava cultivars: one susceptible to cassava brown streak disease; the other resistant. An efficient genotyping-by-sequencing (GBS) approach is being developed to catalog SNPs both within the mapping population and among diverse African farmer-preferred varieties of cassava. These resources will accelerate marker-assisted breeding programs, allowing improvements in disease-resistance and nutrition, and will help us understand the genetic basis for disease resistance.     

New generation pharmacogenomic tools: a SNP linkage disequilibrium Map,validated SNP assay resource,and high-throughput instrumentation system for large-scale genetic studies

Since public and private efforts announced the first draft of the human genome last year, researchers have reported great numbers of single nucleotide polymorphisms (SNPs). We believe that the availability of well-mapped, quality SNP markers constitutes the gateway to a revolution in genetics and personalized medicine that will lead to better diagnosis and treatment of common complex disorders. A new generation of tools and public SNP resources for pharmacogenomic and genetic studies--specifically for candidate-gene, candidate-region, and whole-genome association studies--will form part of the new scientific landscape. This will only be possible through the greater accessibility of SNP resources and superior high-throughput instrumentation-assay systems that enable affordable, highly productive large-scale genetic studies. We are contributing to this effort by developing a high-quality linkage disequilibrium SNP marker map and an accompanying set of ready-to-use, validated SNP assays across every gene in the human genome. This effort incorporates both the public sequence and SNP data sources, and Celera Genomics' human genome assembly and enormous resource ofphysically mapped SNPs (approximately 4,000,000 unique records). This article discusses our approach and methodology for designing the map, choosing quality SNPs, designing and validating these assays, and obtaining population frequency ofthe polymorphisms. We also discuss an advanced, high-performance SNP assay chemisty--a new generation of the TaqMan probe-based, 5' nuclease assay-and high-throughput instrumentation-software system for large-scale genotyping. We provide the new SNP map and validation information, validated SNP assays and reagents, and instrumentation systems as a novel resource for genetic discoveries.     

Bioinformatics for personal genome interpretation

An international consortium released the first draft sequence of the human genome 10 years ago. Although the analysis of this data has suggested the genetic underpinnings of many diseases, we have not yet been able to fully quantify the relationship between genotype and phenotype. Thus, a major current effort of the scientific community focuses on evaluating individual predispositions to specific phenotypic traits given their genetic backgrounds. Many resources aim to identify and annotate the specific genes responsible for the observed phenotypes. Some of these use intra-species genetic variability as a means for better understanding this relationship. In addition, several online resources are now dedicated to collecting single nucleotide variants and other types of variants, and annotating their functional effects and associations with phenotypic traits. This information has enabled researchers to develop bioinformatics tools to analyze the rapidly increasing amount of newly extracted variation data and to predict the effect of uncharacterized variants. In this work, we review the most important developments in the field--the databases and bioinformatics tools that will be of utmost importance in our concerted effort to interpret the human variome.     

A first high-density map of 981 biallelic markers on human chromosome 14

As the largest set of sequence variants, single-nucleotide polymorphisms (SNPs) constitute powerful assets for mapping genes and mutations related to common diseases and for pharmacogenetic studies. A major goal in human genetics is to establish a high-density map of the genome containing several hundred thousand SNPs. Here we assayed 3.7 Mb (154,397 bp in 24 alleles) of chromosome 14 expressed sequence tags (ESTs) and sequence-tagged sites, for sequence variation in DNA samples from 12 African individuals. We identified and mapped 480 biallelic markers (459 SNPs and 21 small insertions and deletions), equally distributed between EST and non-EST classes. Extensive research in public databases also yielded 604 chromosome 14 SNPs (dbSNPs), 520 of which could be mapped and 19 of which are common between CNG (i.e., identified at the Centre National de Génotypage) and dbSNP polymorphisms. We present a dense map of SNP variation of human chromosome 14 based on 981 nonredundant biallelic markers present among 1345 radiation hybrid mapped sequence objects. Next, bioinformatic tools allowed 945 significant sequence alignments to chromosome 14 contigs, giving the precise chromosome sequence position for 70% of the mapped sequences and SNPs. In addition, these tools also permitted the identification and mapping of 273 SNPs in 159 known genes. The availability of this SNP map will permit a wide range of genetic studies on a complete chromosome. The recognition of 45 genes with multiple SNPs, by allowing the construction of haplotypes, should facilitate pharmacogenetic studies in the corresponding regions.     

Haplotype Parsing

While the shared consensus genetic sequence of our species contains a great deal of information about our common biology, there is also much to be learned from the subtle genetic variations across our species. These variations are believed to be generally of little or no direct functional significance and predominantly reflect the chance accumulation of small genetic changes since our emergence as a species. Therefore, they carry little useful information when observed in a single individual. When tallied across a whole population though, these chance mutations can teach us a great deal about our evolutionary history and the patterns of inheritance in particular individuals. In particular, frequently observed patterns of single nucleotide polymorphisms (SNPs) in a population can identify segments of chromosome that have been passed down largely intact through long stretches of our evolution. Finding these frequently conserved chromosomal segments, or haplotypes, and developing methods to identify haplotype patterns in particular individuals, will in turn help us to identify those particular segments that carry genetic factors influencing risk for many common human diseases. To make the best use of this data, we will need to develop new models for the encoding of information in genome variations--the "language of genetic variation"--and new algorithms for fitting datasets to those models. This article surveys past work by the author and colleagues on this problem, utilising computational methods for locating frequent patterns in haploid sequence data, and "parsing" sequences so as to optimally explain them given the knowledge of the general population structure. The author's recent work in this area has been compiled into a set of computational tools available at http://www-2.cs.cmu.edu/~russells/software/hapmotif.html.     

Domain altering SNPs in the human proteome and their impact on signaling pathways

Single nucleotide polymorphisms (SNPs) constitute an important mode of genetic variations observed in the human genome. A small fraction of SNPs, about four thousand out of the ten million, has been associated with genetic disorders and complex diseases. The present study focuses on SNPs that fall on protein domains, 3D structures that facilitate connectivity of proteins in cell signaling and metabolic pathways. We scanned the human proteome using the PROSITE web tool and identified proteins with SNP containing domains. We showed that SNPs that fall on protein domains are highly statistically enriched among SNPs linked to hereditary disorders and complex diseases. Proteins whose domains are dramatically altered by the presence of an SNP are even more likely to be present among proteins linked to hereditary disorders. Proteins with domain-altering SNPs comprise highly connected nodes in cellular pathways such as the focal adhesion, the axon guidance pathway and the autoimmune disease pathways. Statistical enrichment of domain/motif signatures in interacting protein pairs indicates extensive loss of connectivity of cell signaling pathways due to domain-altering SNPs, potentially leading to hereditary disorders.     

SNPper: retrieval and analysis of human SNPs

MOTIVATION: Single Nucleotide Polymorphisms (SNPs) are an increasingly important tool for the study of the human genome. SNPs can be used as markers to create high-density genetic maps, as causal candidates for diseases, or to reconstruct the history of our genome. SNP-based studies rely on the availability of large numbers of validated, high-frequency SNPs whose position on the chromosomes is known with precision. Although large collections of SNPs exist in public databases, researchers need tools to effectively retrieve and manipulate them. RESULTS: We describe the implementation and usage of SNPper, a web-based application to automate the tasks of extracting SNPs from public databases, analyzing them and exporting them in formats suitable for subsequent use. Our application is oriented toward the needs of candidate-gene, whole-genome and fine-mapping studies, and provides several flexible ways to present and export the data. The application has been publicly available for over a year, and has received positive user feedback and high usage levels.     

Use of bioinformatics tools for the annotation of disease-associated mutations in animal models

Single-point mutations are one of the most frequent causes of genetic variability in both human and close species. The recent availability of different bioinformatics tools for annotating human single nucleotide polymorphisms (SNPs) has opened the possibility of using them to score SNPs from species with a biomedical interest, in particular from mice and other models of human disease. Also, this ability to predict pathogenicity of single point mutations in one species, based on data from another species, opens the possibility to predict the pathological character of single point mutations in humans using data from well-characterized model systems of human disease. This could provide a valuable alternative to the more traditional genetic population approaches. However, transferral of prediction tools may be limited by different factors, from a species bias in the training set, to a large sequence divergence between the proteomes of the training and the target species. Here we study the conditions under which prediction tools can be transferred among species, concentrating in the case of mice. We find that for the majority of the human-mouse homolog pairs, the sequence similarity is large enough to preserve the pathological character of mutations among species, in general. We then establish that prediction/annotation tools developed for one organism can be used to predict the neutral/pathological character of mutations/SNPs in the other organism.     

Sequence-based marker development in wheat: advances and applications to breeding

In the past two decades, the wheat community has made remarkable progress in developing molecular resources for breeding. A wide variety of molecular tools has been established to accelerate genetic and physical mapping for facilitating the efficient identification of molecular markers linked to genes and QTL of agronomic interest. Already, wheat breeders are benefiting from a wide range of techniques to follow the introgression of the most favorable alleles in elite material and develop improved varieties. Breeders soon will be able to take advantage of new technological developments based on Next Generation Sequencing. In this paper, we review the molecular toolbox available to wheat scientists and breeders for performing fundamental genomic studies and breeding. Special emphasis is given on the production and detection of single nucleotide polymorphisms (SNPs) that should enable a step change in saturating the wheat genome for more efficient genetic studies and for the development of new selection methods. The perspectives offered by the access to an ordered full genome sequence for further marker development and enhanced precision breeding is also discussed. Finally, we discuss the advantages and limitations of marker-assisted selection for supporting wheat improvement.     

QualitySNP: a pipeline for detecting single nucleotide polymorphisms and insertions/deletions in EST data from diploid and polyploid species

Background  

Single nucleotide polymorphisms (SNPs) are important tools in studying complex genetic traits and genome evolution. Computational strategies for SNP discovery make use of the large number of sequences present in public databases (in most cases as expressed sequence tags (ESTs)) and are considered to be faster and more cost-effective than experimental procedures. A major challenge in computational SNP discovery is distinguishing allelic variation from sequence variation between paralogous sequences, in addition to recognizing sequencing errors. For the majority of the public EST sequences, trace or quality files are lacking which makes detection of reliable SNPs even more difficult because it has to rely on sequence comparisons only.     

Bioinformatics approaches and resources for single nucleotide polymorphism functional analysis

Since the initial sequencing of the human genome, many projects are underway to understand the effects of genetic variation between individuals. Predicting and understanding the downstream effects of genetic variation using computational methods are becoming increasingly important for single nucleotide polymorphism (SNP) selection in genetics studies and understanding the molecular basis of disease. According to the NIH, there are now more than four million validated SNPs in the human genome. The volume of known genetic variations lends itself well to an informatics approach. Bioinformaticians have become very good at functional inference methods derived from functional and structural genomics. This review will present a broad overview of the tools and resources available to collect and understand functional variation from the perspective of structure, expression, evolution and phenotype. Additionally, public resources available for SNP identification and characterisation are summarised.     

Single nucleotide polymorphism discovery in barley using autoSNPdb

Molecular markers are used to provide the link between genotype and phenotype, for the production of molecular genetic maps and to assess genetic diversity within and between related species. Single nucleotide polymorphisms (SNPs) are the most abundant molecular genetic marker. SNPs can be identified in silico , but care must be taken to ensure that the identified SNPs reflect true genetic variation and are not a result of errors associated with DNA sequencing. The SNP detection method autoSNP has been developed to identify SNPs from sequence data for any species. Confidence in the predicted SNPs is based on sequence redundancy, and haplotype co-segregation scores are calculated for a further independent measure of confidence. We have extended the autoSNP method to produce autoSNPdb, which integrates SNP and gene annotation information with a graphical viewer. We have applied this software to public barley expressed sequences, and the resulting database is available over the Internet. SNPs can be viewed and searched by sequence, functional annotation or predicted synteny with a reference genome, in this case rice. The correlation between SNPs and barley cultivar, expressed tissue type and development stage has been collated for ease of exploration. An average of one SNP per 240 bp was identified, with SNPs more prevalent in the 5' regions and simple sequence repeat (SSR) flanking sequences. Overall, autoSNPdb can provide a wealth of genetic polymorphism information for any species for which sequence data are available.     

Future tools for association mapping in crop plants

Association mapping currently relies on the identification of genetic markers. Several technologies have been adopted for genetic marker analysis, with single nucleotide polymorphisms (SNPs) being the most popular where a reasonable quantity of genome sequence data are available. We describe several tools we have developed for the discovery, annotation, and visualization of molecular markers for association mapping. These include autoSNPdb for SNP discovery from assembled sequence data; TAGdb for the identification of gene specific paired read Illumina GAII data; CMap3D for the comparison of mapped genetic and physical markers; and BAC and Gene Annotator for the online annotation of genes and genomic sequences.     

A gentle introduction to SNP analysis: resources and tools

Bioinformatics is the use of informatics tools and techniques in the study of molecular biology, genetic, or clinical data. The field of bioinformatics has expanded tremendously to cope with the large expansion of information generated by the mouse and human genome projects, as newer generations of computers that are much more powerful have emerged in the commercial market. It is now possible to employ the computing hardware and software at hand to generate novel methodologies in order to link data across the different databanks generated by these international projects and derive clinical and biological relevance from all of the information gathered. The ultimate goal would be to develop a computer program that can provide information correlating genes, their single nucleotide polymorphisms (SNPs), and the possible structural and functional effects on the encoded proteins with relation to known information on complex diseases with great ease and speed. Here, the recent developments of available software methods to analyze SNPs in relation to complex diseases are reviewed with emphasis on the type of predictions on protein structure and functions that can be made. The need for further development of comprehensive bioinformatics tools that can cope with information generated by the genomics communities is emphasized.