Variations on a theme: cataloging human DNA sequence variation

Biobanks and personalized medicine

We provide a mini-review of how biobanks can support clinical genetics in the era of personalized medicine. We discuss types of biobanks, including disease specific and general biobanks not focused on one disease. We present considerations in setting up a biobank, including consenting and governance, biospecimens, risk factor and related data, informatics, and linkage to electronic health records for phenotyping. We also discuss the uses of biobanks and ongoing considerations, including genotype-driven recruitment, investigations of gene-environment associations, and the re-use of data generated from studies. Finally, we present a brief discussion of some of the unresolved issues, such as return of research results and sustaining biobanks over time. In summary, carefully designed biobanks can provide critical research and infrastructure support for clinical genetics in the era of personalized medicine.

Predicting the impact of single-nucleotide polymorphisms in CDK2-flavopiridol complex by molecular dynamics analysis

Cyclic-dependent kinase 2 (CDK2) is one of the primary protein kinases involved in the regulation of cell cycle progression. Flavopiridol is a flavonoid derived from an indigenous plant act as a potent antitumor drug showing increased inhibitory activity toward CDK2. The presence of deleterious variations in CDK2 may produce different effects in drug-binding adaptability. Studies on nsSNPs of CDK2 gene will provide information on the most likely variants associated with the disease. Furthermore, investigating the relationship between deleterious variants and its ripple effect in the inhibitory action with drug will provide fundamental information for the development of personalized therapies. In this study, we predicted four variants Y15S, V18L, P45L, and V69A of CDK2 as highly deleterious. Occurrence of these variations seriously affected the normal binding capacity of flavopiridol with CDK2. Analysis of 10-ns molecular dynamics (MD) simulation trajectories indicated that the predicted deleterious variants altered the CDK2 stability, flexibility, and surface area. Notably, we noticed the decrease in number of hydrogen bonds between CDK2 and flavopiridol mutant complexes in the whole dynamic period. Overall, this study explores the possible relationship between the CDK2 deleterious variants and the drug-binding ability with the help of molecular docking and MD approaches.

Implications of genome-wide association studies in novel therapeutics in primary biliary cirrhosis

Genome-wide association studies (GWAS) have revolutionized the search for genetic influences on complex disorders, such as primary biliary cirrhosis (PBC). Recent GWAS have identified many disease-associated genetic variants. These, overall, highlighted the remarkable contribution of key immunological pathways in PBC that may be involved in the initial mechanisms of loss of tolerance and the subsequent inflammatory response and chronic bile duct damage. Results from GWAS have the potential to be translated in biological knowledge and, hopefully, clinical application. There are a number of immune pathways highlighted in GWAS that may have therapeutic implications in PBC and in other autoimmune diseases, such as the anti-interleukin-12/interleukin-23, nuclear factor-kb, tumor necrosis factor, phosphatidylinositol signaling and hedgehog signaling pathways. Further areas in which GWAS findings are leading to clinical applications either in PBC or in other autoimmune conditions, include disease classification, risk prediction and drug development. In this review we outline the possible next steps that may help accelerate progress from genetic studies to the biological knowledge that would guide the development of predictive, preventive, or therapeutic measures in PBC.

Non-synonymous variations in cancer and their effects on the human proteome: workflow for NGS data biocuration and proteome-wide analysis of TCGA data

BACKGROUND

Next-generation sequencing (NGS) technologies have resulted in petabytes of scattered data, decentralized in archives, databases and sometimes in isolated hard-disks which are inaccessible for browsing and analysis. It is expected that curated secondary databases will help organize some of this Big Data thereby allowing users better navigate, search and compute on it.

RESULTS

To address the above challenge, we have implemented a NGS biocuration workflow and are analyzing short read sequences and associated metadata from cancer patients to better understand the human variome. Curation of variation and other related information from control (normal tissue) and case (tumor) samples will provide comprehensive background information that can be used in genomic medicine research and application studies. Our approach includes a CloudBioLinux Virtual Machine which is used upstream of an integrated High-performance Integrated Virtual Environment (HIVE) that encapsulates Curated Short Read archive (CSR) and a proteome-wide variation effect analysis tool (SNVDis). As a proof-of-concept, we have curated and analyzed control and case breast cancer datasets from the NCI cancer genomics program - The Cancer Genome Atlas (TCGA). Our efforts include reviewing and recording in CSR available clinical information on patients, mapping of the reads to the reference followed by identification of non-synonymous Single Nucleotide Variations (nsSNVs) and integrating the data with tools that allow analysis of effect nsSNVs on the human proteome. Furthermore, we have also developed a novel phylogenetic analysis algorithm that uses SNV positions and can be used to classify the patient population. The workflow described here lays the foundation for analysis of short read sequence data to identify rare and novel SNVs that are not present in dbSNP and therefore provides a more comprehensive understanding of the human variome. Variation results for single genes as well as the entire study are available from the CSR website (http://hive.biochemistry.gwu.edu/dna.cgi?cmd=csr).

CONCLUSIONS

Availability of thousands of sequenced samples from patients provides a rich repository of sequence information that can be utilized to identify individual level SNVs and their effect on the human proteome beyond what the dbSNP database provides.

PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations

Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.

Challenges of SNP genotyping and genetic variation: its future role in diagnosis and treatment of cancer

Thorough annotation of common germline genetic variation in the human genome has generated a foundation for the investigation of the contribution of genetics to the etiology and pathogenesis of cancer. For many malignancies, it has become increasingly apparent that numerous alleles, with small-to-moderate effects, additively contribute to cancer susceptibility. The most common genetic variant in the genome, the single nucleotide polymorphism, is of special interest for the study of susceptibility to and protection from cancer. Similarly, intense effort has focused on genetic variants that can predict either response or toxicity to therapeutic interventions. This review discusses the challenges and prospects of genetic association studies in cancer research. On the basis of recent changes in genomics and high-throughput genotyping platforms, future genetic findings of association studies could impact clinical care and public health screening.

Enhancing systems medicine beyond genotype data by dynamic patient signatures: having information and using it too

In order to establish systems medicine, based on the results and insights from basic biological research applicable for a medical and a clinical patient care, it is essential to measure patient-based data that represent the molecular and cellular state of the patient's pathology. In this paper, we discuss potential limitations of the sole usage of static genotype data, e.g., from next-generation sequencing, for translational research. The hypothesis advocated in this paper is that dynOmics data, i.e., high-throughput data that are capable of capturing dynamic aspects of the activity of samples from patients, are important for enabling personalized medicine by complementing genotype data.

Windfalls and pitfalls: Applications of population genetics to the search for disease genes

Association mapping can be viewed as an application of population genetics and evolutionary biology to the problem of identifying genes causally connected to phenotypes. However, some population-genetic principles important to the design and analysis of association studies have not been widely understood or have even been generally misunderstood. Some of these principles underlie techniques that can aid in the discovery of genetic variants that influence phenotypes (‘windfalls’), whereas others can interfere with study design or interpretation of results (‘pitfalls’). Here, considering examples involving genetic variant discovery, linkage disequilibrium, power to detect associations, population stratification and genotype imputation, we address misunderstandings in the application of population genetics to association studies, and we illuminate how some surprising results in association contexts can be easily explained when considered from evolutionary and population-genetic perspectives. Through our examples, we argue that population-genetic thinking—which takes a theoretical view of the evolutionary forces that guide the emergence and propagation of genetic variants—substantially informs the design and interpretation of genetic association studies. In particular, population-genetic thinking sheds light on genetic confounding, on the relationships between association signals of typed markers and causal variants, and on the advantages and disadvantages of particular strategies for measuring genetic variation in association studies.

Molecular docking and molecular dynamics study on the effect of ERCC1 deleterious polymorphisms in ERCC1-XPF heterodimer

Excision repair cross complementation group 1 (ERCC1) is an important protein in the nucleotide excision repair (NER) pathway, which is responsible for removing DNA adducts induced by platinum based compounds. The heterodimer ERCC1-XPF is one of two endonucleases required for NER. Genetic variations or polymorphisms in ERCC1 gene alter DNA repair capacity. Reduced DNA repair (NER) capacity may result in tumors and enhances cisplatin chemotherapy in cancer patients, which functions by causing DNA damage. Therefore, ERCC1 variants have the potential to be used as a strong candidate biomarker in cancer treatments. In this study we identified five variants V116M, R156Q, A199T, S267P, and R322C of ERCC1 gene as highly deleterious. Further structural and functional analysis has been conducted for ERCC1 protein in the presence of three variants V116M, R156Q, and A199T. Occurrence of theses variations adversely affected the regular interaction between ERCC1 and XPF protein. Analysis of 20 ns molecular dynamics simulation trajectories reveals that the predicted deleterious variants altered the ERCC1-XPF complex stability, flexibility, and surface area. Notably, the number of hydrogen bonds in ERCC1-XPF mutant complexes decreased in the molecular dynamic simulation periods. Overall, this study explores the link between the ERCC1 deleterious variants and cisplatin chemotherapy for various cancers with the help of molecular docking and molecular dynamic approaches.

Genomics and anterior segment dysgenesis: a review

Anterior segment dysgenesis refers to a spectrum of disorders affecting structures in the anterior segment of the eye including the iris, cornea and trabecular meshwork. Approximately 50% of patients with anterior segment dysgenesis develop glaucoma. Traditional genetic methods using linkage analysis and family-based studies have identified numerous disease-causing genes such as PAX6, FOXC1 and PITX2. Despite these advances, phenotypic and genotypic heterogeneity pose continuing challenges to understand the mechanisms underlying the complexity of anterior segment dysgenesis disorders. Genomic methods, such as genome-wide association studies, are potentially an effective tool to understand anterior segment dysgenesis and the individual susceptibility to the development of glaucoma. In this review, we provide the rationale, as well as the challenges, to utilizing genomic methods to examine anterior segment dysgenesis disorders.

Molecular mechanisms of disease-causing missense mutations

Genetic variations resulting in a change of amino acid sequence can have a dramatic effect on stability, hydrogen bond network, conformational dynamics, activity and many other physiologically important properties of proteins. The substitutions of only one residue in a protein sequence, so-called missense mutations, can be related to many pathological conditions and may influence susceptibility to disease and drug treatment. The plausible effects of missense mutations range from affecting the macromolecular stability to perturbing macromolecular interactions and cellular localization. Here we review the individual cases and genome-wide studies that illustrate the association between missense mutations and diseases. In addition, we emphasize that the molecular mechanisms of effects of mutations should be revealed in order to understand the disease origin. Finally, we report the current state-of-the-art methodologies that predict the effects of mutations on protein stability, the hydrogen bond network, pH dependence, conformational dynamics and protein function.

Candidate gene association studies: a comprehensive guide to useful in silico tools

The candidate gene approach has been a pioneer in the field of genetic epidemiology, identifying risk alleles and their association with clinical traits. With the advent of rapidly changing technology, there has been an explosion of in silico tools available to researchers, giving them fast, efficient resources and reliable strategies important to find casual gene variants for candidate or genome wide association studies (GWAS). In this review, following a description of candidate gene prioritisation, we summarise the approaches to single nucleotide polymorphism (SNP) prioritisation and discuss the tools available to assess functional relevance of the risk variant with consideration to its genomic location. The strategy and the tools discussed are applicable to any study investigating genetic risk factors associated with a particular disease. Some of the tools are also applicable for the functional validation of variants relevant to the era of GWAS and next generation sequencing (NGS).

GWAS in autoimmune thyroid disease: redefining our understanding of pathogenesis

The ability of the immune system to protect the body from attack by foreign antigens is essential for human survival. The immune system can, however, start to attack the body's own organs. An autoimmune response against components of the thyroid gland affects 2-5% of the general population. Considerable familial clustering is also observed in autoimmune thyroid disease (AITD). Teasing out the genetic contribution to AITD over the past 40 years has helped unravel how immune disruption leads to disease onset. Breakthroughs in genome-wide association studies (GWAS) in the past decade have facilitated screening of a greater proportion of the genome, leading to the identification of a before unimaginable number of AITD susceptibility loci. This Review will focus on the new susceptibility loci identified by GWAS, what insights these loci provide about the pathogenesis of AITD and how genetic susceptibility loci shared between different autoimmune diseases could help explain disease co-clustering within individuals and families. This Review also discusses where future efforts should be focused to translate this step forward in our understanding of the genetic contribution to AITD into a better understanding of disease presentation and progression, and improved therapeutic options.

The evidence for association of ATP2B2 polymorphisms with autism in Chinese Han population

Background

Autism is a neurodevelopmental disorder with a high estimated heritability. ATP2B2, located on human chromosome 3p25.3, encodes the plasma membrane calcium-transporting ATPase 2 which extrudes Ca²⁺ from cytosol into extracellular space. Recent studies reported association between ATP2B2 and autism in samples from Autism Genetic Resource Exchange (AGRE) and Italy. In this study, we investigated whether ATP2B2 polymorphisms were associated with autism in Chinese Han population.

Methods

We performed a family based association study between five SNPs (rs35678 in exon, rs241509, rs3774180, rs3774179, and rs2278556 in introns) in ATP2B2 and autism in 427 autism trios of Han Chinese descent. All SNPs were genotyped using the Sequenom genotyping platform. The family-based association test (FBAT) program was used to perform association test for SNPs and haplotype analyses.

Results

This study demonstrated a preferential transmission of T allele of rs3774179 to affected offsprings under an additive model (T>C, Z = 2.482, p = 0.013). While C allele of rs3774179 showed an undertransmission from parents to affected children under an additive and a dominant model, respectively (Z = −2.482, p = 0.013; Z = −2.591, p = 0.0096). Haplotype analyses revealed that three haplotypes were significantly associated with autism. The haplotype C-C (rs3774180–rs3774179) showed a significant undertransmission from parents to affected offsprings both in specific and global haplotype FBAT (Z = −2.037, p = 0.042; Global p = 0.03). As for the haplotype constructed by rs3774179 and rs2278556, C-A might be a protective haplotype (Z = −2.206, p = 0.027; Global p = 0.04), while T-A demonstrated an excess transmission from parents to affected offsprings (Z = 2.143, p = 0.032). These results were still significant after using the permutation method to obtain empirical p values.

Conclusions

Our research suggested that ATP2B2 might play a role in the etiology of autism in Chinese Han population.

Identification of rare variants from exome sequence in a large pedigree with autism

We carried out analyses with the goal of identifying rare variants in exome sequence data that contribute to disease risk for a complex trait. We analyzed a large, 47-member, multigenerational pedigree with 11 cases of autism spectrum disorder, using genotypes from 3 technologies representing increasing resolution: a multiallelic linkage marker panel, a dense diallelic marker panel, and variants from exome sequencing. Genome-scan marker genotypes were available on most subjects, and exome sequence data was available on 5 subjects. We used genome-scan linkage analysis to identify and prioritize the chromosome 22 region of interest, and to select subjects for exome sequencing. Inheritance vectors (IVs) generated by Markov chain Monte Carlo analysis of multilocus marker data were the foundation of most analyses. Genotype imputation used IVs to determine which sequence variants reside on the haplotype that co-segregates with the autism diagnosis. Together with a rare-allele frequency filter, we identified only one rare variant on the risk haplotype, illustrating the potential of this approach to prioritize variants. The associated gene, MYH9, is biologically unlikely, and we speculate that for this complex trait, the key variants may lie outside the exome.

Efficient techniques for genotype-phenotype correlational analysis

Background

Single Nucleotide Polymorphisms (SNPs) are sequence variations found in individuals at some specific points in the genomic sequence. As SNPs are highly conserved throughout evolution and within a population, the map of SNPs serves as an excellent genotypic marker. Conventional SNPs analysis mechanisms suffer from large run times, inefficient memory usage, and frequent overestimation. In this paper, we propose efficient, scalable, and reliable algorithms to select a small subset of SNPs from a large set of SNPs which can together be employed to perform phenotypic classification.

Methods

Our algorithms exploit the techniques of gene selection and random projections to identify a meaningful subset of SNPs. To the best of our knowledge, these techniques have not been employed before in the context of genotype‐phenotype correlations. Random projections are used to project the input data into a lower dimensional space (closely preserving distances). Gene selection is then applied on the projected data to identify a subset of the most relevant SNPs.

Results

We have compared the performance of our algorithms with one of the currently known best algorithms called Multifactor Dimensionality Reduction (MDR), and Principal Component Analysis (PCA) technique. Experimental results demonstrate that our algorithms are superior in terms of accuracy as well as run time.

Conclusions

In our proposed techniques, random projection is used to map data from a high dimensional space to a lower dimensional space, and thus overcomes the curse of dimensionality problem. From this space of reduced dimension, we select the best subset of attributes. It is a unique mechanism in the domain of SNPs analysis, and to the best of our knowledge it is not employed before. As revealed by our experimental results, our proposed techniques offer the potential of high accuracies while keeping the run times low.

GIGI: an approach to effective imputation of dense genotypes on large pedigrees

Recent emergence of the common-disease-rare-variant hypothesis has renewed interest in the use of large pedigrees for identifying rare causal variants. Genotyping with modern sequencing platforms is increasingly common in the search for such variants but remains expensive and often is limited to only a few subjects per pedigree. In population-based samples, genotype imputation is widely used so that additional genotyping is not needed. We now introduce an analogous approach that enables computationally efficient imputation in large pedigrees. Our approach samples inheritance vectors (IVs) from a Markov Chain Monte Carlo sampler by conditioning on genotypes from a sparse set of framework markers. Missing genotypes are probabilistically inferred from these IVs along with observed dense genotypes that are available on a subset of subjects. We implemented our approach in the Genotype Imputation Given Inheritance (GIGI) program and evaluated the approach on both simulated and real large pedigrees. With a real pedigree, we also compared imputed results obtained from this approach with those from the population-based imputation program BEAGLE. We demonstrated that our pedigree-based approach imputes many alleles with high accuracy. It is much more accurate for calling rare alleles than is population-based imputation and does not require an outside reference sample. We also evaluated the effect of varying other parameters, including the marker type and density of the framework panel, threshold for calling genotypes, and population allele frequencies. By leveraging information from existing genotypes already assayed on large pedigrees, our approach can facilitate cost-effective use of sequence data in the pursuit of rare causal variants.

Update on cystic fibrosis-related diabetes

Diabetes mellitus has emerged as a common comorbidity in cystic fibrosis and is considered a clinical entity (cystic fibrosis-related diabetes, CFRD) distinct from that of type 1 diabetes (T1DM) and type 2 diabetes (T2DM). The relevance of this diagnosis extends not only from its imposition of additional medical burden but its association with worse health outcomes in individuals with CF. This paper will review the 2010 U.S. and other international guidelines for screening and treating CFRD. It will highlight newer data regarding early glucose and insulin secretion defects, mechanisms linking CFRD to worse outcomes, and recent advances in T2DM that may provide insights for CFRD; insulin secretion will be reviewed as background for these recent developments.

PASE: a novel method for functional prediction of amino acid substitutions based on physicochemical properties

Background: Non-synonymous single-nucleotide polymorphisms (nsSNPs) within the coding regions of genes causing amino acid substitutions (AASs) may have a large impact on protein function. The possibilities to identify nsSNPs across genomes have increased notably with the advent of next-generation sequencing technologies. Thus, there is a strong need for efficient bioinformatics tools to predict the functional effect of AASs. Such tools can be used to identify the most promising candidate mutations for further experimental validation.

Results: Here we present prediction of AAS effects (PASE), a novel method that predicts the effect of an AASs based on physicochemical property changes. Evaluation of PASE, using a few AASs of known phenotypic effects and 3338 human AASs, for which functional effects have previously been scored with the widely used SIFT and PolyPhen tools, show that PASE is a useful method for functional prediction of AASs. We also show that the predictions can be further improved by combining PASE with information about evolutionary conservation.

Conclusion: PASE is a novel algorithm for predicting functional effects of AASs, which can be used for pinpointing the most interesting candidate mutations. PASE predictions are based on changes in seven physicochemical properties and can improve predictions from many other available tools, which are based on evolutionary conservation. Using available experimental data and predictions from the already existing tools, we demonstrate that PASE is a useful method for predicting functional effects of AASs, even when a limited number of query sequence homologs/orthologs are available.

Proteome-wide analysis of amino acid variations that influence protein lysine acetylation

Next-generation sequencing (NGS) technologies are yielding ever higher volumes of genetic variation data. Given this large amount of data, it has become both a possibility and a priority to determine what the functional implication of genetic variations is. Considering the essential roles of acetylation in protein functions, it is highly likely that acetylation related genetic variations change protein functions. In this work, we performed a proteome-wide analysis of amino acid variations that could potentially influence protein lysine acetylation characteristics in human variant proteins. Here, we defined the AcetylAAVs as acetylation related amino acid variations that affect acetylation sites or their interacting acetyltransferases, and categorized three types of AcetylAAVs. Using the developed prediction system, named KAcePred, we detected that 50.87% of amino acid variations are potential AcetylAAVs and 12.32% of disease mutations could result in AcetylAAVs. More interestingly, from the statistical analysis, we found that the amino acid variations that directly create new potential lysine acetylation sites have more chance to cause diseases. It can be anticipated that the analysis of AcetylAAVs might be useful to screen important polymorphisms and help to identify the mechanism of genetic diseases. A user-friendly web interface for analysis of AcetylAAVs is now freely available at http://bioinfo.ncu.edu.cn/AcetylAAVs_Home.aspx .

Could 8-oxoguanine DNA glycosylase 1 Ser326Cys polymorphism be a biomarker of susceptibility in cancer?

Biomarkers are key molecular or cellular events that give an indication whether there is a threat for disease, whether a disease already exists, or how such disease may develop in an individual case. The discovery of polymorphisms in genes that function in the metabolism of chemicals and in DNA repair has demonstrated the importance of understanding the phenomenon of genetic susceptibility in a population. Polymorphisms in DNA repair genes as an important component of the individual susceptibility to the development of cancer and various hereditary diseases have been commonly studied, since these genes have critical roles in maintaining genome integrity. Furthermore, the evaluation of cancer risk depends on the level of exposure to carcinogenic factors as well as on the genetic codes of the individual. This approach is supported by studies that present positive association between these polymorphic genes and cancers. Although 8-oxoguanine DNA glycosylase 1 (OGG1) is one of the promising biomarker candidates of cancer susceptibility, there are also some controversial results. Epidemiological studies show that the OGG1 might be a biomarker of susceptibility for various cancers; however, the small sample size and difference in the eligibility criteria for inclusion of subjects and sources might limit the studies to demonstrate the association between the OGG1 Ser326Cys polymorphism and the risk of cancer. Thus, meta-analyses may provide more valuable and reliable data to demonstrate the potential of OGG1 Ser326Cys DNA repair enzyme polymorphisms that could be the biomarkers of susceptibility of cancer. Our aim in this review is to compile published studies, including some controversial results on the association between the OGG1 Ser326Cys polymorphism and the risk of cancer.

A Novel SNPs Detection Method Based on Gold Magnetic Nanoparticles Array and Single Base Extension

To fulfill the increasing need for large-scale genetic research, a high-throughput and automated SNPs genotyping method based on gold magnetic nanoparticles (GMNPs) array and dual-color single base extension has been designed. After amplification of DNA templates, biotinylated extension primers were captured by streptavidin coated gold magnetic nanoparticle (SA-GMNPs). Next a solid-phase, dual-color single base extension (SBE) reaction with the specific biotinylated primer was performed directly on the surface of the GMNPs. Finally, a “bead array” was fabricated by spotting GMNPs with fluorophore on a clean glass slide, and the genotype of each sample was discriminated by scanning the “bead array”. MTHFR gene C677T polymorphism of 320 individual samples were interrogated using this method, the signal/noise ratio for homozygous samples were over 12.33, while the signal/noise ratio for heterozygous samples was near 1. Compared with other dual-color hybridization based genotyping methods, the method described here gives a higher signal/noise ratio and SNP loci can be identified with a high level of confidence. This assay has the advantage of eliminating the need for background subtraction and direct analysis of the fluorescence values of the GMNPs to determine their genotypes without the necessary procedures for purification and complex reduction of PCR products. The application of this strategy to large-scale SNP studies simplifies the process, and reduces the labor required to produce highly sensitive results while improving the potential for automation.

Polymorphism in exons CpG rich regions of the cyp17-II gene affecting its mRNA expression and reproductive endocrine levels in female Japanese flounder (Paralichthys olivaceus)

Cytochrome P450c17-II (cyp17-II) gene is an important factor affecting the growth, gonad differentiation and development, and other reproductive traits of fish. There are three CpG rich regions in the coding region of cyp17-II gene in Japanese flounder (Paralichthys olivaceus). The aim of this study was to understand whether mutations in exons of the cyp17-II gene occured at CpG sites, and mutations and methylation status of those CpG sites were involved in regulation of the expression level of cyp17-II gene and the reproductive endocrine of Japanese flounder. The results showed that three single nucleotide polymorphisms (SNPs) were identified. SNP1 [(c. G594A (p.Gly 188Arg)] located in exon 4 of L1 locus, and SNP2 (c.A939G) and SNP3 (c.C975T) of L2 locus located in CpG rich region of the exon 6 of cyp17-II gene. Furthermore, the A to G transition at 939bp position added a new methylation site to the cyp17-II coding region. According to multiple-comparison analysis, two loci (L1 and L2) were significantly associated with serum testosterone (T) level (P<0.05) and the expression of cyp17-II in ovary (P<0.01). Intriguingly, individuals with GG genotype of L2 locus containing eight CpG methylation sites had significantly lower serum testosterone level and cyp17-II mRNA expression than those with AA genotype containing seven CpG methylation sites. Moreover, the CpG site was highly methylated (≥77.8%) at 938 bp position of individuals with GG genotype of L2 locus. These implied that the mutation and methylation status of the coding region of cyp17-II could influence the gene expression and the reproductive endocrine levels in female Japanese flounder and L2 locus could be regarded as a candidate genetic or epigenetic marker for Japanese flounder breeding programs.

The role of large pedigrees in an era of high-throughput sequencing

Rare variation is the current frontier in human genetics. The large pedigree design is practical, efficient, and well-suited for investigating rare variation. In large pedigrees, specific rare variants that co-segregate with a trait will occur in sufficient numbers so that effects can be measured, and evidence for association can be evaluated, by making use of methods that fully use the pedigree information. Evidence from linkage analysis can focus investigation, both reducing the multiple testing burden and expanding the variants that can be evaluated and followed up, as recent studies have shown. The large pedigree design requires only a small fraction of the sample size needed to identify rare variants of interest in population-based designs, and many highly suitable, well-understood, and available statistical and computational tools already exist. Samples consisting of large pedigrees with existing rich phenotype and genome scan data should be prime candidates for high-throughput sequencing in the search of the determinants of complex traits.

Bioinformatics analysis reveals potential candidate drugs for different subtypes of glioma

Gliomas are the most common primary brain tumors of the central nervous system. However, current approaches for treating glioma have limited success, with a low 5-year survival rate. Besides, gliomas can be classified based on various criteria and the exact method of grading changes over time, it is hard for the surgeons to choose the suitable treatment strategies for glioma patients. In present study, we sought to explore the commonalities between different subtypes of glioma, and then identify biologically active small molecules capable of targeting all subtypes of glioma using a computational bioinformatics analysis of gene expression. Results showed that there were common differentially expressed genes between different subtypes of glioma. Pathways related to tumorigenesis and signaling transduction were dysfunctional in the progression of glioma. Further, we identified a group of small molecules. Candidate agents identified by our approach may provide the groundwork for a combination therapy approach for glioma. However, further evaluation for their potential use in the treatment of glioma is still needed.

Impact of DNA polymorphisms in key DNA base excision repair proteins on cancer risk

Genetic variation in DNA repair genes can modulate DNA repair capacity and may be related to cancer risk. However, study findings have been inconsistent. Inheritance of variant DNA repair genes is believed to influence individual susceptibility to the development of environmental cancer. Reliable knowledge on which the base excision repair (BER) sequence variants are associated with cancer risk would help elucidate the mechanism of cancer. Given that most of the previous studies had inadequate statistical power, we have conducted a systematic review on sequence variants in three important BER proteins. Here, we review published studies on the association between polymorphism in candidate BER genes and cancer risk. We focused on three key BER genes: 8-oxoguanine DNA glycosylase (OGG1), apurinic/apyrimidinic endonuclease (APE1/APEX1) and x-ray repair cross-complementing group 1 (XRCC1). These specific DNA repair genes were selected because of their critical role in maintaining genome integrity and, based on previous studies, suggesting that single-nucleotide polymorphisms (SNPs) in these genes have protective or deleterious effects on cancer risk. A total of 136 articles in the December 13, 2010 MEDLINE database (National Center for Biotechnology Information, http://www.ncbi.nlm.nih.gov/pubmed/) reporting polymorphism in OGG1, XRCC1 or APE1 genes were analyzed. Many of the reported SNPs had diverse association with specific human cancers. For example, there was a positive association between the OGG1 Ser326Cys variant and gastric and lung cancer, while the XRCC1 Arg399Gln variant was associated with reduced cancer risk. Gene-environment interactions have been noted and may be important for colorectal and lung cancer risk and possibly other human cancers.

Future possibilities in migraine genetics

Migraine with and without aura (MA and MO, respectively) have a strong genetic basis. Different approaches using linkage-, candidate gene- and genome-wide association studies have been explored, yielding limited results. This may indicate that the genetic component in migraine is due to rare variants; capturing these will require more detailed sequencing in order to be discovered. Next-generation sequencing (NGS) techniques such as whole exome and whole genome sequencing have been successful in finding genes in especially monogenic disorders. As the molecular genetics research progresses, the technology will follow, rendering these approaches more applicable in the search for causative migraine genes in MO and MA. To date, no studies using NGS in migraine genetics have been published. In order to gain insight into the future possibilities of migraine genetics, we have looked at NGS studies in other diseases and have interviewed three experts in the field of genetics and complex traits. The experts’ ideas suggest that the preferred NGS approach depends on the expected effect size and the frequency of the variants of interest. Family-specific variants can be found by sequencing a small number of individuals, while a large number of unrelated cases are needed to find common and rare variants. NGS is currently hampered by high cost and technical problems concurrent with analyzing large amounts of data generated, especially by whole genome sequencing. As genome-wide association chips, exome sequencing and whole genome sequencing gradually become more affordable, these approaches will be used on a larger scale. This may reveal new risk variants in migraine which may offer previously unsuspected biological insights.

Association of eleven common, low-penetrance colorectal cancer susceptibility genetic variants at six risk loci with clinical outcome

BACKGROUND

Low-penetrance genetic variants have been increasingly recognized to influence the risk of tumor development. Risk variants for colorectal cancer (CRC) have been mapped to chromosome positions 8q23.3, 8q24, 9p24.1, 10p14, 11q23, 14q22.2, 15q13, 16q22.1, 18q21, 19q13.1 and 20p12.3. In particular, the 8q24 single nucleotide polymorphism (SNP), rs6983267, has reproducibly been associated with the risk of developing CRC. As the CRC risk SNPs may also influence disease outcome, thus in this study, we evaluated whether they influence patient survival.

METHODOLOGY/PRINCIPAL FINDINGS

DNA samples from 583 CRC patients enrolled in the prospective, North Carolina Cancer Care Outcomes Research and Surveillance Consortium Study (NC CanCORS) were genotyped for 11 CRC susceptibility SNPs at 6 CRC risk loci. Relationships between genotypes and patient survival were examined using Cox regression analysis. In multivariate analysis, patients homozygous for the CRC risk allele of rs7013278 or rs7014346 (both at 8 q24) were only nominally significant for poorer overall survival compared to patients homozygous for the protective allele (hazard ratio = 2.20 and 1.96, respectively; P<0.05). None of these associations, however, remained statistically significant after correction for multiple testing. The other nine susceptibility SNPs tested were not significantly associated with survival.

CONCLUSIONS/SIGNIFICANCE

We did not find evidence of association of CRC risk variants with patient survival.

Variation in the autism candidate gene GABRB3 modulates tactile sensitivity in typically developing children

Background

Autism spectrum conditions have a strong genetic component. Atypical sensory sensitivities are one of the core but neglected features of autism spectrum conditions. GABRB3 is a well-characterised candidate gene for autism spectrum conditions. In mice, heterozygous Gabrb3 deletion is associated with increased tactile sensitivity. However, no study has examined if tactile sensitivity is associated with GABRB3 genetic variation in humans. To test this, we conducted two pilot genetic association studies in the general population, analysing two phenotypic measures of tactile sensitivity (a parent-report and a behavioural measure) for association with 43 SNPs in GABRB3.

Findings

Across both tactile sensitivity measures, three SNPs (rs11636966, rs8023959 and rs2162241) were nominally associated with both phenotypes, providing a measure of internal validation. Parent-report scores were nominally associated with six SNPs (P <0.05). Behaviourally measured tactile sensitivity was nominally associated with 10 SNPs (three after Bonferroni correction).

Conclusions

This is the first human study to show an association between GABRB3 variation and tactile sensitivity. This provides support for the evidence from animal models implicating the role of GABRB3 variation in the atypical sensory sensitivity in autism spectrum conditions. Future research is underway to directly test this association in cases of autism spectrum conditions.

Influence of age on the association of GIRK4 with metabolic syndrome

BACKGROUND

G protein-coupled inward rectifier K+ channel 4 (GIRK4) gene expressions have been implicated in the development of obesity, a key feature of metabolic syndrome (MetS). We investigated whether sequence variants of GIRK4 may represent metabolic risk factors for the Uygur Chinese population.

METHODS

The entire GIRK4 gene, including all exons, the promoter and untranslated regions from 48 MetS individuals, was studied in order to identify genetic variations associated with the disorder. Targeted genotyping of four common single nucleotide polymorphisms (SNPs: rs11221497, rs6590357, rs4937391 and rs2604204) and one novel missense mutation (M210I) was performed using the TaqMan polymerase chain reaction method for 443 MetS and 786 non-MetS subjects.

RESULTS

When all MetS cases were compared against all non-MetS controls, no significant association was found between the three SNPs (rs2604204, rs4937391 and rs6590357) and MetS status or metabolic traits. After adjustment, rs11221497 was associated with MetS (odds ratio (OR) [95% CI]=0.731 [0.551-0.968], P=0.029). Interestingly, when the MetS group was stratified into subclasses by age, an association was found for the three SNPs (rs2604204, rs4937391 and rs6590357) having estimated false discovery rates<0.001 and age of <50 y. After adjustment, the SNPs rs2604204, rs4937391 and rs6590357 were also associated with MetS in younger subjects: ORs [95% CI]: 1.678 [1.149-2.450], 1.839 [1.204-2.809] and 0.602 [0.379-0.958], respectively. All of the four SNPs showed a trend towards lower or higher metabolic traits (P<0.05) in younger subjects. In addition, a newly identified missense mutation (M210I) was not specifically related with MetS.

CONCLUSIONS

GIRK4 sequence variants appear to associate with MetS in the Uygurian population, and this association may be influenced by age.

Community engagement about genetic variation research

The aim of this article is to describe the methods and effectiveness of the Public Engagement in Genetic Variation and Haplotype Mapping Issues (PEGV) Project, which engaged a community in policy discussion about genetic variation research. The project implemented a 6-stage community engagement model in New Rochelle, New York. First, researchers recruited community partners. Second, the project team created community oversight. Third, focus groups discussed concerns generated by genetic variation research. Fourth, community dialogue sessions addressed focus group findings and developed policy recommendations. Fifth, a conference was held to present these policy recommendations and to provide a forum for HapMap (haplotype mapping) researchers to dialogue directly with residents. Finally, findings were disseminated via presentations and papers to the participants and to the wider community beyond. The project generated a list of proposed guidelines for genetic variation research that addressed the concerns of New Rochelle residents. Project team members expressed satisfaction with the engagement model overall but expressed concerns about how well community groups were utilized and what segment of the community actually engaged in the project. The PEGV Project represents a model for researchers to engage the general public in policy development about genetic research. There are benefits of such a process beyond the desired genetic research.

Modeling haplotype-haplotype interactions in case-control genetic association studies

Haplotype analysis has been increasingly used to study the genetic basis of human diseases, but models for characterizing genetic interactions between haplotypes from different chromosomal regions have not been well developed in the current literature. In this article, we describe a statistical model for testing haplotype-haplotype interactions for human diseases with a case-control genetic association design. The model is formulated on a contingency table in which cases and controls are typed for the same set of molecular markers. By integrating well-established quantitative genetic principles, the model is equipped with a capacity to characterize physiologically meaningful epistasis arising from interactions between haplotypes from different chromosomal regions. The model allows the partition of epistasis into different components due to additive × additive, additive × dominance, dominance × additive, and dominance × dominance interactions. We derive the EM algorithm to estimate and test the effects of each of these components on differences in the pattern of genetic variation between cases and controls and, therefore, examine their role in the pathogenesis of human diseases. The method was further extended to investigate gene-environment interactions expressed at the haplotype level. The statistical properties of the models were investigated through simulation studies and its usefulness and utilization validated by analyzing the genetic association of sarcoidosis from a human genetics project.

Understanding the genetics of coronary artery disease through the lens of noninvasive imaging

Coronary artery disease is a common condition with a known heritable component that has spurred interest in genetic research for decades, resulting in a handful of candidate genes and an appreciation for the complexity of its genetic contributions. Recent advances in sequencing technologies have resulted in large-scale association studies, possibly adding to our current understanding of the genetics of coronary artery disease. Sifting through the statistical noise, however, requires the selection of effective phenotypic markers. New imaging technologies have improved our ability to detect subclinical atherosclerosis in a safe and reproducible manner in large numbers of patients. In this article, we propose that advances in imaging technology have generated improved phenotypic markers for genetic association studies of coronary artery disease.

Role of tumour necrosis factor (TNF)-α and TNFRSF1A R92Q mutation in the pathogenesis of TNF receptor-associated periodic syndrome and multiple sclerosis

It has long been known that tumour necrosis factor (TNF)/TNFRSF1A signalling is involved in the pathophysiology of multiple sclerosis (MS). Different genetic and clinical findings over the last few years have generated renewed interest in this relationship. This paper provides an update on these recent findings. Genome-wide association studies have identified the R92Q mutation in the TNFRSF1A gene as a genetic risk factor for MS (odds ratio 1·6). This allele, which is also common in the general population and in other inflammatory conditions, therefore only implies a modest risk for MS and provides yet another piece of the puzzle that defines the multiple genetic risk factors for this disease. TNFRSF1A mutations have been associated with an autoinflammatory disease known as TNF receptor-associated periodic syndrome (TRAPS). Clinical observations have identified a group of MS patients carrying the R92Q mutation who have additional TRAPS symptoms. Hypothetically, the co-existence of MS and TRAPS or a co-morbidity relationship between the two could be mediated by this mutation. The TNFRSF1A R92Q mutation behaves as a genetic risk factor for MS and other inflammatory diseases, including TRAPS. Nevertheless, this mutation does not appear to be a severity marker of the disease, neither modifying the clinical progression of MS nor its therapeutic response. An alteration in TNF/TNFRS1A signalling may increase proinflammatory signals; the final clinical phenotype may possibly be determined by other genetic or environmental modifying factors that have not yet been identified.

Genetic polymorphism studies in periodontitis and Fcγ receptors

Periodontitis is a complex chronic subgingival plaque-induced inflammatory disease influenced by multiple factors, including genetics, behavior and the environment. Many genetic association studies have been conducted in periodontology. One of the most extensively investigated gene families is the Fcγ receptor gene family, which plays a key role in regulating host immune responses to bacteria. Unlike other genetic polymorphisms reported in periodontology, most Fcγ receptor polymorphisms reported not only have established biological functions but are reported to associate with other autoimmune diseases, such as rheumatoid arthritis and systemic lupus erythematosus. There are, however, few recent reviews summarizing the association of this gene family with periodontitis. This article critically reviews the current understanding of genetic polymorphism studies in periodontitis, then summarizes the research status of Fcγ receptor polymorphisms and periodontitis and also of other genes involved in the regulatory network of Fcγ receptors, with special reference to their anticipated biological roles. Moreover, some possible future research directions in the related area are discussed.

Single-molecule analysis using DNA origami

During the last two decades, scientists have developed various methods that allow the detection and manipulation of single molecules, which have also been called "in singulo" approaches. Fundamental understanding of biochemical reactions, folding of biomolecules, and the screening of drugs were achieved by using these methods. Single-molecule analysis was also performed in the field of DNA nanotechnology, mainly by using atomic force microscopy. However, until recently, the approaches used commonly in nanotechnology adopted structures with a dimension of 10-20 nm, which is not suitable for many applications. The recent development of scaffolded DNA origami by Rothemund made it possible for the construction of larger defined assemblies. One of the most salient features of the origami method is the precise addressability of the structures formed: Each staple can serve as an attachment point for different kinds of nanoobjects. Thus, the method is suitable for the precise positioning of various functionalities and for the single-molecule analysis of many chemical and biochemical processes. Here we summarize recent progress in the area of single-molecule analysis using DNA origami and discuss the future directions of this research.

SNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants

Single nucleotide variants (SNVs) are, together with copy number variation, the primary source of variation in the human genome and are associated with phenotypic variation such as altered response to drug treatment and susceptibility to disease. Linking structural effects of non-synonymous SNVs to functional outcomes is a major issue in structural bioinformatics. The SNPeffect database (http://snpeffect.switchlab.org) uses sequence- and structure-based bioinformatics tools to predict the effect of protein-coding SNVs on the structural phenotype of proteins. It integrates aggregation prediction (TANGO), amyloid prediction (WALTZ), chaperone-binding prediction (LIMBO) and protein stability analysis (FoldX) for structural phenotyping. Additionally, SNPeffect holds information on affected catalytic sites and a number of post-translational modifications. The database contains all known human protein variants from UniProt, but users can now also submit custom protein variants for a SNPeffect analysis, including automated structure modeling. The new meta-analysis application allows plotting correlations between phenotypic features for a user-selected set of variants.

Recent methods for polygenic analysis of genome-wide data implicate an important effect of common variants on cardiovascular disease risk

BACKGROUND

Traditional genome-wide association studies are generally limited in their ability explain a large portion of genetic risk for most common diseases. We sought to use both traditional GWAS methods, as well as more recently developed polygenic genome-wide analysis techniques to identify subsets of single-nucleotide polymorphisms (SNPs) that may be involved in risk of cardiovascular disease, as well as estimate the heritability explained by common SNPs.

METHODS

Using data from the Framingham SNP Health Association Resource (SHARe), three complimentary methods were applied to examine the genetic factors associated with the Framingham Risk Score, a widely accepted indicator of underlying cardiovascular disease risk. The first method adopted a traditional GWAS approach - independently testing each SNP for association with the Framingham Risk Score. The second two approaches involved polygenic methods with the intention of providing estimates of aggregate genetic risk and heritability.

RESULTS

While no SNPs were independently associated with the Framingham Risk Score based on the results of the traditional GWAS analysis, we were able to identify cardiovascular disease-related SNPs as reported by previous studies. A predictive polygenic analysis was only able to explain approximately 1% of the genetic variance when predicting the 10-year risk of general cardiovascular disease. However, 20% to 30% of the variation in the Framingham Risk Score was explained using a recently developed method that considers the joint effect of all SNPs simultaneously.

CONCLUSION

The results of this study imply that common SNPs explain a large amount of the variation in the Framingham Risk Score and suggest that future, better-powered genome-wide association studies, possibly informed by knowledge of gene-pathways, will uncover more risk variants that will help to elucidate the genetic architecture of cardiovascular disease.

Gold nanoparticles for high-throughput genotyping of long-range haplotypes

Completion of the Human Genome Project and the HapMap Project has led to increasing demands for mapping complex traits in humans to understand the aetiology of diseases. Identifying variations in the DNA sequence, which affect how we develop disease and respond to pathogens and drugs, is important for this purpose, but it is difficult to identify these variations in large sample sets. Here we show that through a combination of capillary sequencing and polymerase chain reaction assisted by gold nanoparticles, it is possible to identify several DNA variations that are associated with age-related macular degeneration and psoriasis on significant regions of human genomic DNA. Our method is accurate and promising for large-scale and high-throughput genetic analysis of susceptibility towards disease and drug resistance.

New experiments for an undivided genetics

There used to be a broad split within the experimental genetics research community between those who did mechanistic research using homozygous laboratory strains and those who studied patterns of genetic variation in wild populations. The former benefited from the advantage of reproducible experiments, but faced difficulties of interpretation given possible genomic and evolutionary complexities. The latter research approach featured readily interpreted evolutionary and genomic contexts, particularly phylogeny, but was poor at determining functional significance. Such burgeoning experimental strategies as genome-wide analysis of quantitative trait loci, genotype-phenotype associations, and the products of experimental evolution are now fostering a unification of experimental genetic research that strengthens its scientific power.

New type 2 diabetes risk genes provide new insights in insulin secretion mechanisms

Type 2 diabetes results from the inability of beta cells to increase insulin secretion sufficiently to compensate for insulin resistance. Insulin resistance is thought to result mainly from environmental factors, such as obesity. However, there is compelling evidence that the decline of both insulin sensitivity and insulin secretion have also a genetic component. Recent genome-wide association studies identified several novel risk genes for type 2 diabetes. The vast majority of these genes affect beta cell function by molecular mechanisms that remain unknown in detail. Nevertheless, we and others could show that a group of genes affect glucose-stimulated insulin secretion, a group incretin-stimulated insulin secretion (incretin sensitivity or secretion) and a group proinsulin-to-insulin conversion. The most important so far type 2 diabetes risk gene, TCF7L2, interferes with all three mechanisms. In addition to advancing knowledge in the pathophysiology of type 2 diabetes, the discovery of novel genetic determinants of diabetes susceptibility may help understanding of gene-environment, gene-therapy and gene-gene interactions. It was also hoped that it could make determination of the individual risk for type 2 diabetes feasible. However, the allelic relative risks of most genetic variants discovered so far are relatively low. Thus, at present, clinical criteria assess the risk for type 2 diabetes with greater sensitivity and specificity than the combination of all known genetic variants.

Germline mutations in MSR1, ASCC1, and CTHRC1 in patients with Barrett esophagus and esophageal adenocarcinoma

CONTEXT

Barrett esophagus (BE) occurs in 1% to 10% of the general population and is believed to be the precursor of esophageal adenocarcinoma (EAC). The incidence of EAC has increased 350% in the last 3 decades without clear etiology. Finding predisposition genes may improve premorbid risk assessment, genetic counseling, and management. Genome-wide multiplatform approaches may lead to the identification of genes important in BE/EAC development.

OBJECTIVE

To identify risk alleles or mutated genes associated with BE/EAC.

DESIGN, SETTING, AND PATIENTS

Model-free linkage analyses of 21 concordant-affected sibling pairs with BE/EAC and 11 discordant sibling pairs (2005-2006). Significant germline genomic regions in independent prospectively accrued series of 176 white patients with BE/EAC and 200 ancestry-matched controls (2007-2010) were validated and fine mapped. Integrating data from these significant genomic regions with somatic gene expression data from 19 BE/EAC tissues yielded 12 "priority" candidate genes for mutation analysis (2010). Genes that showed mutations in cases but not in controls were further screened in an independent prospectively accrued validation series of 58 cases (2010).

MAIN OUTCOME MEASURES

Identification of germline mutations in genes associated with BE/EAC cases. Functional interrogation of the most commonly mutated gene.

RESULTS

Three major genes, MSR1, ASCC1, and CTHRC1 were associated with BE/EAC (all P < .001). In addition, 13 patients (11.2%) with BE/EAC carried germline mutations in MSR1, ASCC1, or CTHRC1. MSR1 was the most frequently mutated, with 8 of 116 (proportion, 0.069; 95% confidence interval [CI], 0.030-0.130; P < .001) cases with c.877C>T (p.R293X). An independent validation series confirmed germline MSR1 mutations in 2 of 58 cases (proportion, 0.035; 95% CI, 0.004-0.120; P = .09). MSR1 mutation resulted in CCND1 up-regulation in peripheral-protein lysate. Immunohistochemistry of BE tissues in MSR1-mutation carriers showed increased nuclear expression of CCND1.

CONCLUSION

MSR1 was significantly associated with the presence of BE/EAC in derivation and validation samples, although it was only present in a small percentage of the cases.

Genetics of pelvic organ prolapse: crossing the bridge between bench and bedside in urogynecologic research

An increasing number of scientists have studied the molecular and biochemical basis of pelvic organ prolapse (POP). The extracellular matrix content of the pelvic floor is the major focus of those investigations and pointed for potential molecular markers of the dysfunction. The identification of women predisposed to develop POP would help in the patients' management and care. This article includes a critical analysis of the literature up to now; discusses implications for future research and the role of the genetics in POP.

In silico SNP analysis and bioinformatics tools: a review of the state of the art to aid drug discovery

SNPs can alter protein function and phenotype, leading to altered pharmacogenomic drug profiles. The exponential number of SNPs makes it impossible to perform wet laboratory experiments to determine the biological significance of each one. However, bioinformatics tools can be used to screen for potentially deleterious SNPs that might affect important drug targets before further investigation by wet laboratory techniques. As there are numerous web-based bioinformatics tools, much time and effort is needed to select the most appropriate tool to use. Here, we review state-of-the-art bioinformatics tools to help researchers analyze and select the most promising SNPs for drug discovery in the shortest time possible.