Allelic variation in gene expression identified through computational analysis of the dbEST database

Differential expression between the two alleles of an individual and between people with different genotypes has been commonly observed. Quantitative differences in gene expression between people may provide the genetic basis for the phenotypic difference between individuals and may be the primary cause of complex diseases. In this paper, we developed a computational method to identify genes that displayed allelic variation in gene expression in human EST libraries. To model allele-specific gene expression, we first identified EST libraries in which both A and B alleles were expressed and then identified allelic variation in gene expression based on the EST counts for each allele using a binomial test. Among 1107 SNPs that had a sufficient number of ESTs for the analysis, 524 (47%) displayed allelic variation in at least one cDNA library. We verified experimentally the allelic variation in gene expression for 6 of these SNPs. The frequency of allelic variation observed in EST libraries was similar to the previous studies using the SNP chip and primer extension method. We found that genes that displayed allelic variation were distributed throughout the human genome and were enriched in certain chromosome regions. The SNPs and genes identified in this study will provide a rich source for evaluating the effects of those SNPs and associated haplotypes in human health and diseases.     

Haplotype reconstruction from genotype data using Imperfect Phylogeny

Critical to the understanding of the genetic basis for complex diseases is the modeling of human variation. Most of this variation can be characterized by single nucleotide polymorphisms (SNPs) which are mutations at a single nucleotide position. To characterize the genetic variation between different people, we must determine an individual's haplotype or which nucleotide base occurs at each position of these common SNPs for each chromosome. In this paper, we present results for a highly accurate method for haplotype resolution from genotype data. Our method leverages a new insight into the underlying structure of haplotypes that shows that SNPs are organized in highly correlated 'blocks'. In a few recent studies, considerable parts of the human genome were partitioned into blocks, such that the majority of the sequenced genotypes have one of about four common haplotypes in each block. Our method partitions the SNPs into blocks, and for each block, we predict the common haplotypes and each individual's haplotype. We evaluate our method over biological data. Our method predicts the common haplotypes perfectly and has a very low error rate (<2% over the data) when taking into account the predictions for the uncommon haplotypes. Our method is extremely efficient compared with previous methods such as PHASE and HAPLOTYPER. Its efficiency allows us to find the block partition of the haplotypes, to cope with missing data and to work with large datasets. AVAILABILITY: The algorithm is available via a Web server at http://www.calit2.net/compbio/hap/     

Perspectives on human genetic variation from the HapMap Project

The completion of the International HapMap Project marks the start of a new phase in human genetics. The aim of the project was to provide a resource that facilitates the design of efficient genome-wide association studies, through characterising patterns of genetic variation and linkage disequilibrium in a sample of 270 individuals across four geographical populations. In total, over one million SNPs have been typed across these genomes, providing an unprecedented view of human genetic diversity. In this review we focus on what the HapMap project has taught us about the structure of human genetic variation and the fundamental molecular and evolutionary processes that shape it.     

与疾病相关的非同义单核苷酸多态性预测的研究进展

单核苷酸多态性(single nucleotide polymorphism,SNPs),即在基因组水平上由单个核苷酸的变异而引起的DNA序列多态性变化,具体是指在DNA序列中的单个碱基的变异,其是人类基因组变异种最常见的一种。SNP研究最主要的目的就是对人类表型变异遗传学的理解,尤其是关于人类遗传疾病的研究。而非同义单核苷酸多态性(nsSNPs)是SNPs中的一种,主要是指处于编码区会引起翻译后对应氨基酸序列变化的单核苷酸突变。因为nsSNPs可能会对蛋白质的功能造成影响,被认为是造成人类遗传病的主要原因。因此将与疾病相关的nsSNPs从中性的nsSNPs中区分出来是很重要的。本文根据国内外与疾病相关nsSNPs预测的研究,分析了预测中所涉及到的特征属性,总结了对这些特征进行优化的特征选择方法,并概述了在预测过程中使用的各种分类器。     

Alteration of Antiviral Signalling by Single Nucleotide Polymorphisms (SNPs) of Mitochondrial Antiviral Signalling Protein (MAVS)

Genetic variation is associated with diseases. As a type of genetic variation occurring with certain regularity and frequency, the single nucleotide polymorphism (SNP) is attracting more and more attention because of its great value for research and real-life application. Mitochondrial antiviral signalling protein (MAVS) acts as a common adaptor molecule for retinoic acid-inducible gene-I (RIG-I)-like receptors (RLRs), which can recognize foreign RNA, including viral RNA, leading to the induction of type I interferons (IFNs). Therefore, MAVS is thought to be a crucial molecule in antiviral innate immunity. We speculated that genetic variation of MAVS may result in susceptibility to infectious diseases. To assess the risk of viral infection based on MAVS variation, we tested the effects of twelve non-synonymous MAVS coding-region SNPs from the National Center for Biotechnology Information (NCBI) database that result in amino acid substitutions. We found that five of these SNPs exhibited functional alterations. Additionally, four resulted in an inhibitory immune response, and one had the opposite effect. In total, 1,032 human genomic samples obtained from a mass examination were genotyped at these five SNPs. However, no homozygous or heterozygous variation was detected. We hypothesized that these five SNPs are not present in the Japanese population and that such MAVS variations may result in serious immune diseases.     

Identification of a combination of SNPs associated with Graves’ disease using swarm intelligence

Graves’ disease, the production of thyroid-stimulating hormone receptor-stimulating antibodies leading to hyperthyroidism, is one of the most common forms of human autoimmune disease. It is widely agreed that complex diseases are not controlled simply by an individual gene or DNA variation but by their combination. Single nucleotide polymorphisms (SNPs), which are the most common form of DNA variation, have great potential as a medical diagnostic tool. In this paper, the P-value is used as a SNP pre-selection criterion, and a wrapper algorithm with binary particle swarm optimization is used to find the rule for discriminating between affected and control subjects. We analyzed the association between combinations of SNPs and Graves’ disease by investigating 108 SNPs in 384 cases and 652 controls. We evaluated our method by differentiating between cases and controls in a five-fold cross validation test, and it achieved a 72.9% prediction accuracy with a combination of 17 SNPs. The experimental results showed that SNPs, even those with a high P-value, have a greater effect on Graves’ disease when acting in a combination.     

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.     

Identification of the ancestral killer immunoglobulin-like receptor gene in primates

Background

The recent advancement in human genome sequencing and genotyping has revealed millions of single nucleotide polymorphisms (SNP) which determine the variation among human beings. One of the particular important projects is The International HapMap Project which provides the catalogue of human genetic variation for disease association studies. In this paper, we analyzed the genotype data in HapMap project by using National Institute of Environmental Health Sciences Environmental Genome Project (NIEHS EGP) SNPs. We first determine whether the HapMap data are transferable to the NIEHS data. Then, we study how well the HapMap SNPs capture the untyped SNPs in the region. Finally, we provide general guidelines for determining whether the SNPs chosen from HapMap may be able to capture most of the untyped SNPs.

Results

Our analysis shows that HapMap data are not robust enough to capture the untyped variants for most of the human genes. The performance of SNPs for European and Asian samples are marginal in capturing the untyped variants, i.e. approximately 55%. Expectedly, the SNPs from HapMap YRI panel can only capture approximately 30% of the variants. Although the overall performance is low, however, the SNPs for some genes perform very well and are able to capture most of the variants along the gene. This is observed in the European and Asian panel, but not in African panel. Through observation, we concluded that in order to have a well covered SNPs reference panel, the SNPs density and the association among reference SNPs are important to estimate the robustness of the chosen SNPs.

Conclusion

We have analyzed the coverage of HapMap SNPs using NIEHS EGP data. The results show that HapMap SNPs are transferable to the NIEHS SNPs. However, HapMap SNPs cannot capture some of the untyped SNPs and therefore resequencing may be needed to uncover more SNPs in the missing region.     

SNPeffect v2.0: a new step in investigating the molecular phenotypic effects of human non-synonymous SNPs

Single nucleotide polymorphisms (SNPs) constitute the most fundamental type of genetic variation in human populations. About 75 000 of these reported variations cause an amino acid change in the translated protein. An important goal in genomic research is to understand how this variability affects protein function, and whether or not particular SNPs are associated to disease susceptibility. Accordingly, the SNPeffect database uses sequence- and structure-based bioinformatics tools to predict the effect of non-synonymous SNPs on the molecular phenotype of proteins. SNPeffect analyses the effect of SNPs on three categories of functional properties: (1) structural and thermodynamic properties affecting protein dynamics and stability (2) the integrity of functional and binding sites and (3) changes in posttranslational processing and cellular localization of proteins. The search interface of the database can be used to search specifically for polymorphisms that are predicted to cause a change in one of these properties. Now based on the Ensembl human databases, the SNPeffect database has been remodeled to better fit an automatically updatable structure. The current edition holds the molecular phenotype of 74 567 nsSNPs in 23 426 proteins. AVAILABILITY: SNPeffect can be accessed through http://snpeffect.vib.be.     

Linked genetic variants on chromosome 10 control ear morphology and body mass among dog breeds

Background

The domestic dog is a rich resource for mapping the genetic components of phenotypic variation due to its unique population history involving strong artificial selection. Genome-wide association studies have revealed a number of chromosomal regions where genetic variation associates with morphological characters that typify dog breeds. A region on chromosome 10 is among those with the highest levels of genetic differentiation between dog breeds and is associated with body mass and ear morphology, a common motif of animal domestication. We characterised variation in this region to uncover haplotype structure and identify candidate functional variants.

Results

We first identified SNPs that strongly associate with body mass and ear type by comparing sequence variation in a 3 Mb region between 19 breeds with a variety of phenotypes. We next genotyped a subset of 123 candidate SNPs in 288 samples from 46 breeds to identify the variants most highly associated with phenotype and infer haplotype structure. A cluster of SNPs that associate strongly with the drop ear phenotype is located within a narrow interval downstream of the gene MSRB3, which is involved in human hearing. These SNPs are in strong genetic linkage with another set of variants that correlate with body mass within the gene HMGA2, which affects human height. In addition we find evidence that this region has been under selection during dog domestication, and identify a cluster of SNPs within MSRB3 that are highly differentiated between dogs and wolves.

Conclusions

We characterise genetically linked variants that potentially influence ear type and body mass in dog breeds, both key traits that have been modified by selective breeding that may also be important for domestication. The finding that variants on long haplotypes have effects on more than one trait suggests that genetic linkage can be an important determinant of the phenotypic response to selection in domestic animals.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1702-2) contains supplementary material, which is available to authorized users.     

Worldwide population differentiation at disease-associated SNPs

Background

Recent genome-wide association (GWA) studies have provided compelling evidence of association between genetic variants and common complex diseases. These studies have made use of cases and controls almost exclusively from populations of European ancestry and little is known about the frequency of risk alleles in other populations. The present study addresses the transferability of disease associations across human populations by examining levels of population differentiation at disease-associated single nucleotide polymorphisms (SNPs).

Methods

We genotyped ~1000 individuals from 53 populations worldwide at 25 SNPs which show robust association with 6 complex human diseases (Crohn's disease, type 1 diabetes, type 2 diabetes, rheumatoid arthritis, coronary artery disease and obesity). Allele frequency differences between populations for these SNPs were measured using Fst. The Fst values for the disease-associated SNPs were compared to Fst values from 2750 random SNPs typed in the same set of individuals.

Results

On average, disease SNPs are not significantly more differentiated between populations than random SNPs in the genome. Risk allele frequencies, however, do show substantial variation across human populations and may contribute to differences in disease prevalence between populations. We demonstrate that, in some cases, risk allele frequency differences are unusually high compared to random SNPs and may be due to the action of local (i.e. geographically-restricted) positive natural selection. Moreover, some risk alleles were absent or fixed in a population, which implies that risk alleles identified in one population do not necessarily account for disease prevalence in all human populations.

Conclusion

Although differences in risk allele frequencies between human populations are not unusually large and are thus likely not due to positive local selection, there is substantial variation in risk allele frequencies between populations which may account for differences in disease prevalence between human populations.     

Improved power by use of a weighted score test for linkage disequilibrium mapping

Association studies offer an exciting approach to finding underlying genetic variants of complex human diseases. However, identification of genetic variants still includes difficult challenges, and it is important to develop powerful new statistical methods. Currently, association methods may depend on single-locus analysis--that is, analysis of the association of one locus, which is typically a single-nucleotide polymorphism (SNP), at a time--or on multilocus analysis, in which multiple SNPs are used to allow extraction of maximum information about linkage disequilibrium (LD). It has been shown that single-locus analysis may have low power because a single SNP often has limited LD information. Multilocus analysis, which is more informative, can be performed on the basis of either haplotypes or genotypes. It may lose power because of the often large number of degrees of freedom involved. The ideal method must make full use of important information from multiple loci but avoid increasing the degrees of freedom. Therefore, we propose a method to capture information from multiple SNPs but with the use of fewer degrees of freedom. When a set of SNPs in a block are correlated because of LD, we might expect that the genotype variation among the different phenotypic groups would extend across all the SNPs, and this information could be compressed into the low-frequency components of a Fourier transform. Therefore, we develop a test based on weighted Fourier transformation coefficients, with more weight given to the low-frequency components. Our simulation results demonstrate the validity and substantially higher power of the proposed method compared with other common methods. This method provides an additional tool to existing methods for identification of causative genetic variants underlying complex diseases.     

Characterization of OCA2 cDNA in different porcine breeds and analysis of its potential effect on skin pigmentation in a red Iberian strain

Although the function of the OCA2 gene product has not been totally clarified, variation in OCA2 has been associated with skin and hair pigmentation in human and mouse. However, its contribution to skin colour in domestic species has not been reported. In this study, cDNA and intron 9 sequences of the porcine OCA2 gene have been characterized in several pig populations. The cDNA sequence alignment of 20 animals from eight porcine populations allowed the identification of 10 single nucleotide polymorphisms (SNPs); five of the 10 SNPs were non-synonymous. The intron 9 sequence alignment of 12 animals belonging to four pig populations revealed four additional SNPs. Skin colour variation was analysed in a red strain of Iberian pigs with segregation of three SNPs forming two OCA2 intragenic haplotypes. Results from this study provide evidence of a suggestive dominant effect of haplotypes on colour intensity and indicate an important contribution of additive polygenic effects (h2 = 0.56 +/- 0.21) to the variance of this trait.     

High resolution melting analysis of almond SNPs derived from ESTs

High resolution melting curve (HRM) is a recent advance for the detection of SNPs. The technique measures temperature induced strand separation of short PCR amplicons, and is able to detect variation as small as one base difference between samples. It has been applied to the analysis and scan of mutations in the genes causing human diseases. In plant species, the use of this approach is limited. We applied HRM analysis to almond SNP discovery and genotyping based on the predicted SNP information derived from the almond and peach EST database. Putative SNPs were screened from almond and peach EST contigs by HRM analysis against 25 almond cultivars. All 4 classes of SNPs, INDELs and microsatellites were discriminated, and the HRM profiles of 17 amplicons were established. The PCR amplicons containing single, double and multiple SNPs produced distinctive HRM profiles. Additionally, different genotypes of INDEL and microsatellite variations were also characterised by HRM analysis. By sequencing the PCR products, 100 SNPs were validated/revealed in the HRM amplicons and their flanking regions. The results showed that the average frequency of SNPs was 1:114 bp in the genic regions, and transition to transversion ratio was 1.16:1. Rare allele frequencies of the SNPs varied from 0.02 to 0.5, and the polymorphic information contents of the SNPs were from 0.04 to 0.53 at an average of 0.31. HRM has been demonstrated to be a fast, low cost, and efficient approach for SNP discovery and genotyping, in particular, for species without much genomic information such as almond.     

Cryptic Variation in the Human Mutation Rate

The mutation rate is known to vary between adjacent sites within the human genome as a consequence of context, the most well-studied example being the influence of CpG dinucelotides. We investigated whether there is additional variation by testing whether there is an excess of sites at which both humans and chimpanzees have a single-nucleotide polymorphism (SNP). We found a highly significant excess of such sites, and we demonstrated that this excess is not due to neighbouring nucleotide effects, ancestral polymorphism, or natural selection. We therefore infer that there is cryptic variation in the mutation rate. However, although this variation in the mutation rate is not associated with the adjacent nucleotides, we show that there are highly nonrandom patterns of nucleotides that extend ~80 base pairs on either side of sites with coincident SNPs, suggesting that there are extensive and complex context effects. Finally, we estimate the level of variation needed to produce the excess of coincident SNPs and show that there is a similar, or higher, level of variation in the mutation rate associated with this cryptic process than there is associated with adjacent nucleotides, including the CpG effect. We conclude that there is substantial variation in the mutation that has, until now, been hidden from view.     

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.