Applications of computational algorithm tools to identify functional SNPs in cytokine genes

Understanding the functions of single nucleotide polymorphisms (SNPs) can greatly help to understand the genetics of the human phenotype variation and especially the genetic basis of human complex diseases. However, how to identify functional SNPs from a pool containing both functional and neutral SNPs is challenging. In this study, we analyzed the genetic variations that can alter the expression and function of a group of cytokine proteins using computational tools. As a result, we extracted 4552 SNPs from 45 cytokine proteins from SNPper database. Of particular interest, 828 SNPs were in the 5'UTR region, 961 SNPs were in the 3' UTR region, and 85 SNPs were non-synonymous SNPs (nsSNPs), which cause amino acid change. Evolutionary conservation analysis using the SIFT tool suggested that 8 nsSNPs may disrupt the protein function. Protein structure analysis using the PolyPhen tool suggested that 5 nsSNPs might alter protein structure. Binding motif analysis using the UTResource tool suggested that 27 SNPs in 5' or 3'UTR might change protein expression levels. Our study demonstrates the presence of naturally occurring genetic variations in the cytokine proteins that may affect their expressions and functions with possible roles in complex human disease, such as immune diseases.     

Identification and in silico analysis of functional SNPs of the BRCA1 gene

Single-nucleotide polymorphisms (SNPs) play a major role in the understanding of the genetic basis of many complex human diseases. Also, the genetics of human phenotype variation could be understood by knowing the functions of these SNPs. It is still a major challenge to identify the functional SNPs in a disease-related gene. In this work, we have analyzed the genetic variation that can alter the expression and the function of the BRCA1 gene using computational methods. Of the total 477 SNPs, 65 were found to be nonsynonymous (ns) SNPs. Among the 14 SNPs in the untranslated region, 4 were found in the 5' and 10 were found in the 3' untranslated region (UTR). It was found that 16.9% of the nsSNPs were damaging, by both the SIFT and the PolyPhen servers. The UTR Resource tool suggested that 2 of 4 SNPs in the 5' UTR and 3 of 10 SNPs in the 3' UTR might change the protein expression levels. We identified major mutations from proline to serine at positions 1776 and 1812 of the native protein of the BRCA1 gene. From a comparison of the stabilizing residues of the native and mutant proteins, we propose that an nsSNP (rs1800751) could be an important candidate for the breast cancer caused by the BRCA1 gene.     

Computational Analysis of Functional Single Nucleotide Polymorphisms Associated with the CYP11B2 Gene

Single nucleotide polymorphisms (SNPs) are the most common type of genetic variations in humans and play a major role in the genetics of human phenotype variation and the genetic basis of human complex diseases. Recently, there is considerable interest in understanding the possible role of the CYP11B2 gene with corticosterone methyl oxidase deficiency, primary aldosteronism, and cardio-cerebro-vascular diseases. Hence, the elucidation of the function and molecular dynamic behavior of CYP11B2 mutations is crucial in current genomics. In this study, we investigated the pathogenic effect of 51 nsSNPs and 26 UTR SNPs in the CYP11B2 gene through computational platforms. Using a combination of SIFT, PolyPhen, I-Mutant Suite, and ConSurf server, four nsSNPs (F487V, V129M, T498A, and V403E) were identified to potentially affect the structure, function, and activity of the CYP11B2 protein. Furthermore, molecular dynamics simulation and structure analyses also confirmed the impact of these nsSNPs on the stability and secondary properties of the CYP11B2 protein. Additionally, utilizing the UTRscan, MirSNP, PolymiRTS and miRNASNP, three SNPs in the 3′UTR region were predicted to exhibit a pattern change in the upstream open reading frames (uORF), and eight microRNA binding sites were found to be highly affected due to 3′UTR SNPs. This cataloguing of deleterious SNPs is essential for narrowing down the number of CYP11B2 mutations to be screened in genetic association studies and for a better understanding of the functional and structural aspects of the CYP11B2 protein.     

单核苷酸多态概述

单核苷酸多态ＳＮＰ是遍布于基因组中的一种ＤＮＡ序列变化类型,人类基因组中平均约每一千碱基中有一个。单核苷酸多态是一种双等位型多态,群体中出现的频率大于１％或２％者视为多态,低于１％或２％的则视为突变。由于其具有高信息量、高密度又便于自动化操作的特点,单核苷酸多态在遗传性疾病基因的克隆和药物的设计与开发方面具有广阔的应用前景。本文对单核苷酸的概念、特点、应用前景,及其研究应用的一些问题作一综述。     

A catalog of neutral and deleterious polymorphism in yeast

The abundance and identity of functional variation segregating in natural populations is paramount to dissecting the molecular basis of quantitative traits as well as human genetic diseases. Genome sequencing of multiple organisms of the same species provides an efficient means of cataloging rearrangements, insertion, or deletion polymorphisms (InDels) and single-nucleotide polymorphisms (SNPs). While inbreeding depression and heterosis imply that a substantial amount of polymorphism is deleterious, distinguishing deleterious from neutral polymorphism remains a significant challenge. To identify deleterious and neutral DNA sequence variation within Saccharomyces cerevisiae, we sequenced the genome of a vineyard and oak tree strain and compared them to a reference genome. Among these three strains, 6% of the genome is variable, mostly attributable to variation in genome content that results from large InDels. Out of the 88,000 polymorphisms identified, 93% are SNPs and a small but significant fraction can be attributed to recent interspecific introgression and ectopic gene conversion. In comparison to the reference genome, there is substantial evidence for functional variation in gene content and structure that results from large InDels, frame-shifts, and polymorphic start and stop codons. Comparison of polymorphism to divergence reveals scant evidence for positive selection but an abundance of evidence for deleterious SNPs. We estimate that 12% of coding and 7% of noncoding SNPs are deleterious. Based on divergence among 11 yeast species, we identified 1,666 nonsynonymous SNPs that disrupt conserved amino acids and 1,863 noncoding SNPs that disrupt conserved noncoding motifs. The deleterious coding SNPs include those known to affect quantitative traits, and a subset of the deleterious noncoding SNPs occurs in the promoters of genes that show allele-specific expression, implying that some cis-regulatory SNPs are deleterious. Our results show that the genome sequences of both closely and distantly related species provide a means of identifying deleterious polymorphisms that disrupt functionally conserved coding and noncoding sequences.     

FunctSNP: an R package to link SNPs to functional knowledge and dbAutoMaker: a suite of Perl scripts to build SNP databases

Background  

Whole genome association studies using highly dense single nucleotide polymorphisms (SNPs) are a set of methods to identify DNA markers associated with variation in a particular complex trait of interest. One of the main outcomes from these studies is a subset of statistically significant SNPs. Finding the potential biological functions of such SNPs can be an important step towards further use in human and agricultural populations (e.g., for identifying genes related to susceptibility to complex diseases or genes playing key roles in development or performance). The current challenge is that the information holding the clues to SNP functions is distributed across many different databases. Efficient bioinformatics tools are therefore needed to seamlessly integrate up-to-date functional information on SNPs. Many web services have arisen to meet the challenge but most work only within the framework of human medical research. Although we acknowledge the importance of human research, we identify there is a need for SNP annotation tools for other organisms.     

TILLING,high-resolution melting (HRM), and next-generation sequencing (NGS) techniques in plant mutation breeding

Induced mutations have been used effectively for plant improvement. Physical and chemical mutagens induce a high frequency of genome variation. Recently, developed screening methods have allowed the detection of single nucleotide polymorphisms (SNPs) and the identification of traits that are difficult to identify at the molecular level by conventional breeding. With the assistance of reverse genetic techniques, sequence variation information can be linked to traits to investigate gene function. Targeting induced local lesions in genomes (TILLING) is a high-throughput technique to identify single nucleotide mutations in a specific region of a gene of interest with a powerful detection method resulted from chemical-induced mutagenesis. The main advantage of TILLING as a reverse genetics strategy is that it can be applied to any species, regardless of genome size and ploidy level. However, TILLING requires laborious and time-consuming steps, and a lack of complete genome sequence information for many crop species has slowed the development of suitable TILLING targets. Another method, high-resolution melting (HRM), which has assisted TILLING in mutation detection, is faster, simpler and less expensive with non-enzymatic screening system. Currently, the sequencing of crop genomes has completely changed our vision and interpretation of genome organization and evolution. Impressive progress in next-generation sequencing (NGS) technologies has paved the way for the detection and exploitation of genetic variation in a given DNA or RNA molecule. This review discusses the applications of TILLING in combination with HRM and NGS technologies for screening of induced mutations and discovering SNPs in mutation breeding programs.     

Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms

Currently, single-nucleotide polymorphisms (SNPs) with minor allele frequency (MAF) of >5% are preferentially used in case-control association studies of common human diseases. Recent technological developments enable inexpensive and accurate genotyping of a large number of SNPs in thousands of cases and controls, which can provide adequate statistical power to analyze SNPs with MAF <5%. Our purpose was to determine whether evaluating rare SNPs in case-control association studies could help identify causal SNPs for common diseases. We suggest that slightly deleterious SNPs (sdSNPs) subjected to weak purifying selection are major players in genetic control of susceptibility to common diseases. We compared the distribution of MAFs of synonymous SNPs with that of nonsynonymous SNPs (1) predicted to be benign, (2) predicted to be possibly damaging, and (3) predicted to be probably damaging by PolyPhen. Our sources of data were the International HapMap Project, ENCODE, and the SeattleSNPs project. We found that the MAF distribution of possibly and probably damaging SNPs was shifted toward rare SNPs compared with the MAF distribution of benign and synonymous SNPs that are not likely to be functional. We also found an inverse relationship between MAF and the proportion of nsSNPs predicted to be protein disturbing. On the basis of this relationship, we estimated the joint probability that a SNP is functional and would be detected as significant in a case-control study. Our analysis suggests that including rare SNPs in genotyping platforms will advance identification of causal SNPs in case-control association studies, particularly as sample sizes increase.     

Using genetic variation to study human disease.

The generation of a draft sequence of the human genome has spawned a unique opportunity to investigate the role of genetic variation in human diseases. The difference between any two human genomes has been estimated to be less than 0.1% overall, but still, this means that there are at least several million nucleotide differences per individual. The study of single nucleotide polymorphisms (SNPs), the most common type of variant, is likely to contribute substantially to deciphering genetic determinants of common and rare diseases. The effort to identify SNPs has been accelerated by three developments: the availability of sequence data from the genome project, improved informatic tools for searching the former and high-throughput genotype platforms. With these new tools in hand, dissecting the genetics of disease will rapidly move forward, although a number of formidable challenges will have to be met to see its promise realized in clinical medicine.     

Current strategies in the search for low penetrance genes in cancer

The genetic etiology of most cancers remains largely unclear and it has been hypothesised that common genetic variants with modest effects on disease susceptibility cause the bulk of this unexplained risk. Case-control association studies are considered the most effective strategy to identify these low-penetrance genes. While traditionally, such studies have focused on putative functional single nucleotide polymorphisms (SNPs) in candidate genes, a more comprehensive approach can now be taken, as a result of a number of recent developments: the mapping of the human genome, including the identification of almost ten million SNPs; and the development of high-throughput genotyping technologies that enable hundreds of thousands of SNPs to be genotyped in a single reaction, in multiple subjects and at an affordable cost. All common genomic variation can be captured by genotyping SNPs in gene-, pathway- or genome-wide-based strategies and these are now being applied to many diseases, including cancer. We present an outline of each of these approaches, including recent published examples, and discuss a number of challenges that remain to be addressed.     

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.     

人类基因组SNPs的研究现状及应用前景

基因组DNA是生物体各种生理、病理性状的物质基础,人类DNA序列变异约90%表现为单核苷酸多态性(singlenucleotidepolymorphisms,SNPs),这是一种常见的遗传变异类型,在人类基因组中广泛存在,被认为是人类疾病易感性和药物反应的决定性因素。本文主要介绍了SNPs的分类及特点、人类基因组SNPs的研究现状、SNPs在实践中的应用,以及SNPs在遗传作图、医药、遗传易感性、个体化医疗等方面的研究前景,并探讨了当前SNPs研究中存在的问题。     

Development of high‐density SNP genotyping arrays for white spruce (Picea glauca) and transferability to subtropical and nordic congeners

High‐density SNP genotyping arrays can be designed for any species given sufficient sequence information of high quality. Two high‐density SNP arrays relying on the Infinium iSelect technology (Illumina) were designed for use in the conifer white spruce (Picea glauca). One array contained 7338 segregating SNPs representative of 2814 genes of various molecular functional classes for main uses in genetic association and population genetics studies. The other one contained 9559 segregating SNPs representative of 9543 genes for main uses in population genetics, linkage mapping of the genome and genomic prediction. The SNPs assayed were discovered from various sources of gene resequencing data. SNPs predicted from high‐quality sequences derived from genomic DNA reached a genotyping success rate of 64.7%. Nonsingleton in silico SNPs (i.e. a sequence polymorphism present in at least two reads) predicted from expressed sequenced tags obtained with the Roche 454 technology and Illumina GAII analyser resulted in a similar genotyping success rate of 71.6% when the deepest alignment was used and the most favourable SNP probe per gene was selected. A variable proportion of these SNPs was shared by other nordic and subtropical spruce species from North America and Europe. The number of shared SNPs was inversely proportional to phylogenetic divergence and standing genetic variation in the recipient species, but positively related to allele frequency in P. glauca natural populations. These validated SNP resources should open up new avenues for population genetics and comparative genetic mapping at a genomic scale in spruce species.     

The genome revolution and its role in understanding complex diseases

The completion of the human genome sequence in 2003 clearly marked the beginning of a new era for biomedical research. It spurred technological progress that was unprecedented in the life sciences, including the development of high-throughput technologies to detect genetic variation and gene expression. The study of genetics has become “big data science”. One of the current goals of genetic research is to use genomic information to further our understanding of common complex diseases. An essential first step made towards this goal was by the identification of thousands of single nucleotide polymorphisms showing robust association with hundreds of different traits and diseases. As insight into common genetic variation has expanded enormously and the technology to identify more rare variation has become available, we can utilize these advances to gain a better understanding of disease etiology. This will lead to developments in personalized medicine and P4 healthcare. Here, we review some of the historical events and perspectives before and after the completion of the human genome sequence. We also describe the success of large-scale genetic association studies and how these are expected to yield more insight into complex disorders. We show how we can now combine gene-oriented research and systems-based approaches to develop more complex models to help explain the etiology of common diseases. This article is part of a Special Issue entitled: From Genome to Function.     

The essence of SNPs.

Single nucleotide polymorphisms (SNPs) are an abundant form of genome variation, distinguished from rare variations by a requirement for the least abundant allele to have a frequency of 1% or more. A wide range of genetics disciplines stand to benefit greatly from the study and use of SNPs. The recent surge of interest in SNPs stems from, and continues to depend upon, the merging and coincident maturation of several research areas, i.e. (i) large-scale genome analysis and related technologies, (ii) bio-informatics and computing, (iii) genetic analysis of simple and complex disease states, and (iv) global human population genetics. These fields will now be propelled forward, often into uncharted territories, by ongoing discovery efforts that promise to yield hundreds of thousands of human SNPs in the next few years. Major questions are now being asked, experimentally, theoretically and ethically, about the most effective ways to unlock the full potential of the upcoming SNP revolution.     

Advanced technologies for genomic analysis in farm animals and its application for QTL mapping

Rapid progress in farm animal breeding has been made in the last few decades. Advanced technologies for genomic analysis in molecular genetics have led to the identification of genes or markers associated with genes that affect economic traits. Molecular markers, large-insert libraries and RH panels have been used to build the genetic linkage maps, physical maps and comparative maps in different farm animals. Moreover, EST sequencing, genome sequencing and SNPs maps are helping us to understand how genomes function in various organisms and further areas will be studied by DNA microarray technologies and proteomics methods. Because most economically important traits in farm animals are controlled by multiple genes and the environment, the main goal of genome research in farm animals is to map and characterize genes determining QTL. There are two main strategies to identify trait loci, candidate gene association tests and genome scan approaches. In recent years, some new concepts, such as RNAi, miRNA and eQTL, have been introduced into farm animal research, especially for QTL mapping and finding QTN. Several genes that influence important traits have already been identified or are close to being identified, and some of them have been applied in farm animal breeding programs by marker-assisted selection.     

A novel compression tool for efficient storage of genome resequencing data

With the advent of DNA sequencing technologies, more and more reference genome sequences are available for many organisms. Analyzing sequence variation and understanding its biological importance are becoming a major research aim. However, how to store and process the huge amount of eukaryotic genome data, such as those of the human, mouse and rice, has become a challenge to biologists. Currently available bioinformatics tools used to compress genome sequence data have some limitations, such as the requirement of the reference single nucleotide polymorphisms (SNPs) map and information on deletions and insertions. Here, we present a novel compression tool for storing and analyzing Genome ReSequencing data, named GRS. GRS is able to process the genome sequence data without the use of the reference SNPs and other sequence variation information and automatically rebuild the individual genome sequence data using the reference genome sequence. When its performance was tested on the first Korean personal genome sequence data set, GRS was able to achieve ～159-fold compression, reducing the size of the data from 2986.8 to 18.8 MB. While being tested against the sequencing data from rice and Arabidopsis thaliana, GRS compressed the 361.0 MB rice genome data to 4.4 MB, and the A. thaliana genome data from 115.1 MB to 6.5 KB. This de novo compression tool is available at http://gmdd.shgmo.org/Computational-Biology/GRS.