Inferring population parameters from single-feature polymorphism data

This article is concerned with a statistical modeling procedure to call single-feature polymorphisms from microarray experiments. We use this new type of polymorphism data to estimate the mutation and recombination parameters in a population. The mutation parameter can be estimated via the number of single-feature polymorphisms called in the sample. For the recombination parameter, a two-feature sampling distribution is derived in a way analogous to that for the two-locus sampling distribution with SNP data. The approximate-likelihood approach using the two-feature sampling distribution is examined and found to work well. A coalescent simulation study is used to investigate the accuracy and robustness of our method. Our approach allows the utilization of single-feature polymorphism data for inference in population genetics.     

SNPHunter: a bioinformatic software for single nucleotide polymorphism data acquisition and management

Background  

Single nucleotide polymorphisms (SNPs) provide an important tool in pinpointing susceptibility genes for complex diseases and in unveiling human molecular evolution. Selection and retrieval of an optimal SNP set from publicly available databases have emerged as the foremost bottlenecks in designing large-scale linkage disequilibrium studies, particularly in case-control settings.     

Estimation of population parameters and recombination rates from single nucleotide polymorphisms

Some general likelihood and Bayesian methods for analyzing single nucleotide polymorphisms (SNPs) are presented. First, an efficient method for estimating demographic parameters from SNPs in linkage equilibrium is derived. The method is applied in the estimation of growth rates of a human population based on 37 SNP loci. It is demonstrated how ascertainment biases, due to biased sampling of loci, can be avoided, at least in some cases, by appropriate conditioning when calculating the likelihood function. Second, a Markov chain Monte Carlo (MCMC) method for analyzing linked SNPs is developed. This method can be used for Bayesian and likelihood inference on linked SNPs. The utility of the method is illustrated by estimating recombination rates in a human data set containing 17 SNPs and 60 individuals. Both methods are based on assumptions of low mutation rates.     

A nonlinear method for estimating nucleotide polymorphism from restriction maps

Restriction mapping is used to estimate nucleotide sequence polymorphism when the regions to be studied are too long or too numerous to be sequenced. Restriction mapping is less costly than DNA sequencing, but it does not allow direct measurement of underlying nucleotide polymorphism. It is therefore useful to be able to estimate underlying nucleotide polymorphism from observations of polymorphism in restriction maps, as this offers some of the resolution afforded by DNA sequencing at a reduced cost. Previous estimators of underlying nucleotide polymorphism have assumed that each restriction-enzyme- binding site contains, at most, a single polymorphic nucleotide position (the low-polymorphism-frequency assumption), and this assumption has placed an upper limit on the level of polymorphism that can be resolved by these estimators. The present study documents an estimator which allows relaxation of this assumption. The new estimator more accurately estimates underlying nucleotide polymorphism when the polymorphism level is high enough to falsify the low-polymorphism- frequency assumption. The new estimator therefore yields good results for data sets that are too divergent for analysis by present methods.      

Evidence for a large-scale population structure of Arabidopsis thaliana from genome-wide single nucleotide polymorphism markers

Population-based methods for the genetic mapping of adaptive traits and the analysis of natural selection require that the population structure and demographic history of a species are taken into account. We characterized geographic patterns of genetic variation in the model plant Arabidopsis thaliana by genotyping 115 genome-wide single nucleotide polymorphism (SNP) markers in 351 accessions from the whole species range using a matrix-assisted laser desorption/ionization time-of-flight assay, and by sequencing of nine unlinked short genomic regions in a subset of 64 accessions. The observed frequency distribution of SNPs is not consistent with a constant-size neutral model of sequence polymorphism due to an excess of rare polymorphisms. There is evidence for a significant population structure as indicated by differences in genetic diversity between geographic regions. Accessions from Central Asia have a low level of polymorphism and an increased level of genome-wide linkage disequilibrium (LD) relative to accessions from the Iberian Peninsula and Central Europe. Cluster analysis with the structure program grouped Eurasian accessions into K=6 clusters. Accessions from the Iberian Peninsula and from Central Asia constitute distinct populations, whereas Central and Eastern European accessions represent admixed populations in which genomes were reshuffled by historical recombination events. These patterns likely result from a rapid postglacial recolonization of Eurasia from glacial refugial populations. Our analyses suggest that mapping populations for association or LD mapping should be chosen from regional rather than a species-wide sample or identified genetically as sets of individuals with similar average genetic distances. Electronic Supplementary Material Supplementary material is available for this article at and is accessible for authorized users.     

Quantitative heteroduplex analysis for single nucleotide polymorphism genotyping

High-resolution melting of polymerase chain reaction (PCR) products can detect heterozygous mutations and most homozygous mutations without electrophoretic or chromatographic separations. However, some homozygous single nucleotide polymorphism (SNPs) have melting curves identical to that of the wild-type, as predicted by nearest neighbor thermodynamic models. In these cases, if DNA of a known reference genotype is added to each unknown before PCR, quantitative heteroduplex analysis can differentiate heterozygous, homozygous, and wild-type genotypes if the fraction of reference DNA is chosen carefully. Theoretical calculations suggest that melting curve separation is proportional to heteroduplex content difference and that the addition of reference homozygous DNA at one seventh of total DNA results in the best discrimination between the three genotypes of biallelic SNPs. This theory was verified experimentally by quantitative analysis of both high-resolution melting and temperature-gradient capillary electrophoresis data. Reference genotype proportions other than one seventh of total DNA were suboptimal and failed to distinguish some genotypes. Optimal mixing before PCR followed by high-resolution melting analysis permits genotyping of all SNPs with a single closed-tube analysis.     

Web-based primer design for single nucleotide polymorphism analysis

The detection of single nucleotide polymorphisms by PCR is necessary for many types of genetic analysis, from mapping genomes to tracking specific mutations. This technique is most commonly used when polymorphisms alter restriction endonuclease recognition sites. Here we describe a web-based program, dCAPS Finder 2.0, that facilitates the design of mismatched PCR primers to create or remove a restriction endonuclease recognition site relative to the polymorphism being analyzed.     

A method for estimating nucleotide diversity from AFLP data

A method for estimating the nucleotide diversity from AFLP data is developed by using the relationship between the number of nucleotide changes and the proportion of shared bands. The estimation equation is based on the assumption that GC-content is 0.5. Computer simulations, however, show that this method gives a reasonably accurate estimate even when GC-content deviates from 0.5, as long as the number of nucleotide changes per site (nucleotide diversity) is small. As an example, the nucleotide diversity of the wild yam, Dioscorea tokoro, was estimated. The estimated nucleotide diversity is 0.0055, which is larger than estimations from nucleotide sequence data for Adh and Pgi.     

ITPKC single nucleotide polymorphism associated with the Kawasaki disease in a Taiwanese population

Kawasaki disease (KD) is characterized by systemic vasculitis with unknown etiology. Previous studies from Japan indicated that a gene polymorphism of ITPKC (rs28493229) is responsible for susceptibility to KD. We collected DNA samples from 1,531 Taiwanese subjects (341 KD patients and 1,190 controls) for genotyping ITPKC. In this study, no significant association was noted for the ITPKC polymorphism (rs28493229) between the controls and KD patients, although the CC genotype was overrepresented. We further combined our data with previously published case/control KD studies in the Taiwanese population and performed a meta-analysis. A significant association between rs28493229 and KD was found (Odds Ratio:1.36, 95% Confidence Interval 1.12–1.66). Importantly, a significant association was obtained between rs28493229 and KD patients with aneurysm formation (P = 0.001, under the recessive model). Taken together, our results indicated that C-allele of ITPKC SNP rs28493229 is associated with the susceptibility and aneurysm formation in KD patients in a Taiwanese population.     

SNPchiMp v.3: integrating and standardizing single nucleotide polymorphism data for livestock species

Background

In recent years, the use of genomic information in livestock species for genetic improvement, association studies and many other fields has become routine. In order to accommodate different market requirements in terms of genotyping cost, manufacturers of single nucleotide polymorphism (SNP) arrays, private companies and international consortia have developed a large number of arrays with different content and different SNP density. The number of currently available SNP arrays differs among species: ranging from one for goats to more than ten for cattle, and the number of arrays available is increasing rapidly. However, there is limited or no effort to standardize and integrate array- specific (e.g. SNP IDs, allele coding) and species-specific (i.e. past and current assemblies) SNP information.

Results

Here we present SNPchiMp v.3, a solution to these issues for the six major livestock species (cow, pig, horse, sheep, goat and chicken). Original data was collected directly from SNP array producers and specific international genome consortia, and stored in a MySQL database. The database was then linked to an open-access web tool and to public databases. SNPchiMp v.3 ensures fast access to the database (retrieving within/across SNP array data) and the possibility of annotating SNP array data in a user-friendly fashion.

Conclusions

This platform allows easy integration and standardization, and it is aimed at both industry and research. It also enables users to easily link the information available from the array producer with data in public databases, without the need of additional bioinformatics tools or pipelines. In recognition of the open-access use of Ensembl resources, SNPchiMp v.3 was officially credited as an Ensembl E!mpowered tool. Availability at http://bioinformatics.tecnoparco.org/SNPchimp.     

Flow cytometric platform for high-throughput single nucleotide polymorphism analysis

We have developed a rapid, cost-effective, high-throughput readout for single nucleotide polymorphism (SNP) genotyping using flow cytometric analysis performed on a Luminex 100 flow cytometer. This robust technique employs a PCR-derived target DNA containing the SNP, a synthetic SNP-complementary ZipCode-bearing capture probe, a fluorescent reporter molecule, and a thermophilic DNA polymerase. An array of fluorescent microspheres, covalently coupled with complementary ZipCode sequences (cZipCodes), was hybridized to the reaction products and sequestered them for flow cytometric analysis. The single base chain extension (SBCE) reaction was used to assay 20 multiplexed SNPs for 633 patients in 96-well format. Comparison of the microsphere-based SBCE assay results to gel-based oligonucleotide ligation assay (OLA) results showed 99.3% agreement in genotype assignments. Substitution of direct-labeled R6G dideoxynucleotide with indirect-labeled phycoerythrin dideoxynucleotide enhanced signal five- to tenfold while maintaining low noise levels. A new assay based on allele-specific primer extension (ASPE) was validated on a set of 15 multiplexed SNPs for 96 patients. ASPE offers both the advantage of streamlining the SNP analysis protocol and the ability to perform multiplex SNP analysis on any mixture of allelic variants.     

Characterization of 19 single nucleotide polymorphism markers for coho salmon

We report 39 single nucleotide polymorphisms (SNPs) observed in 23 nuclear DNA sequences in coho salmon Oncorhynchus kisutch. High‐throughput genotyping assays based on the 5′‐nuclease reaction were developed for 17 of these nuclear SNPs and for two previously published mitochondrial DNA SNPs. Minor allele frequency differences (Δq) among collections were between 5.2% and 51.2%, resulting in per locus F_ST estimates of 0.00–0.24 with an average of 0.09.     

Numerical methods for estimating iron requirements from population data

The estimation of iron requirements is crucial for nutrition and food policy. The traditional methods for estimating iron requirements are balance methods based on iron intakes and excretions and factorial methods based on estimated iron absorption rates and estimated iron losses from body compartments. As an alternative, numerical methods for estimating iron requirements from population data of iron status were developed. The iron status data reported by Satoh (1991) were used in the sixth edition of Recommended Dietary Allowances for Japanese. The menstrual iron losses in Japanese premenopausal women were estimated from the literature to calculate total iron losses as the sum of basal iron losses and menstrual iron losses. The use of this alternative method is illustrated by analyzing the same population data comprising the prevalence of iron deficiency and the distribution of iron intake. The estimated average requirements were affected by the form of distribution function, the relative standard deviation of requirements, and the correlation coefficient between iron intakes and requirements. We conclude that numerical methods can be very useful for estimating iron requirements and to elucidate dietary recommendations of iron. These methods may contribute to determining requirements of other nutrients as well as iron.     

A new method for detecting single nucleotide polymorphism using GFP-display

The single nucleotide polymorphism (SNP) of aldehyde dehydrogenase-2 (ALDH2) codon 487, GAA (Glu) or AAA (Lys), was examined using green fluorescent protein (GFP)-display, an electrophoretic detection method for single amino acid changes. Although no shift in migration between the GFP-ALDH (Glu487) and GFP-ALDH (Lys487) fusion proteins was observed on SDS/urea gel, the two migrated to different positions when tagged with Asp. The SNP analysis was performed with GFP-ALDH-Asp3, and GFP-ALDH-Asp3 constructed from donors having the codon GAA/GAA, GAA/AAA or AAA/AAA was detected as different patterns as expected. GFP-display is potentially a unique method in SNP analysis, which does not require any special equipment or chemicals.     

Bioinformatics approaches and resources for single nucleotide polymorphism functional analysis

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

Algorithmic strategies for the single nucleotide polymorphism haplotype assembly problem

With the consensus human genome sequenced and many other sequencing projects at varying stages of completion, greater attention is being paid to the genetic differences among individuals and the abilities of those differences to predict phenotypes. A significant obstacle to such work is the difficulty and expense of determining haplotypes--sets of variants genetically linked because of their proximity on the genome--for large numbers of individuals for use in association studies. This paper presents some algorithmic considerations in a new approach for haplotype determination: inferring haplotypes from localised polymorphism data gathered from short genome 'fragments.' Formalised models of the biological system under consideration are examined, given a variety of assumptions about the goal of the problem and the character of optimal solutions. Some theoretical results and algorithms for handling haplotype assembly given the different models are then sketched. The primary conclusion is that some important simplified variants of the problem yield tractable problems while more general variants tend to be intractable in the worst case.     

Genotyping of single nucleotide polymorphism using model-based clustering

MOTIVATION: Single nucleotide polymorphisms have been investigated as biological markers and the representative high-throughput genotyping method is a combination of the Invader assay and a statistical clustering method. A typical statistical clustering method is the k-means method, but it often fails because of the lack of flexibility. An alternative fast and reliable method is therefore desirable. RESULTS: This paper proposes a model-based clustering method using a normal mixture model and a well-conceived penalized likelihood. The proposed method can judge unclear genotypings to be re-examined and also work well even when the number of clusters is unknown. Some results are illustrated and then satisfactory genotypings are shown. Even when the conventional maximum likelihood method and the typical k-means clustering method failed, the proposed method succeeded.     

A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data

We describe a statistical method for the characterization of genomic aberrations in single nucleotide polymorphism microarray data acquired from cancer genomes. Our approach allows us to model the joint effect of polyploidy, normal DNA contamination and intra-tumour heterogeneity within a single unified Bayesian framework. We demonstrate the efficacy of our method on numerous datasets including laboratory generated mixtures of normal-cancer cell lines and real primary tumours.     

A method for estimating rates of nucleotide substitution using DNA sequence data

An estimate of the average number of evolutionarily acceptable substitutions per nucleotide since the most recent common ancestor of a pair of homologous sequences is found which uses nucleotide sequence data. The estimate is derived assuming a Poisson-like model for the evolutionary process. A method is also suggested for analyzing nucleotide sequence data in M homologous sequences (M 3). A simulation study is reported showing that the estimates are satisfactory providing there is sufficient homology between the sequences. To demonstrate the methods a numerical example using some β-globin data is presented.