New softwares for automated microsatellite marker development

Microsatellites are repeated small sequence motifs that are highly polymorphic and abundant in the genomes of eukaryotes. Often they are the molecular markers of choice. To aid the development of microsatellite markers we have developed a module that integrates a program for the detection of microsatellites (TROLL), with the sequence assembly and analysis software, the Staden Package. The module has easily adjustable parameters for microsatellite lengths and base pair quality control. Starting with large datasets of unassembled sequence data in the form of chromatograms and/or text data, it enables the creation of a compact database consisting of the processed and assembled microsatellite containing sequences. For the final phase of primer design, we developed a program that accepts the multi-sequence ‘experiment file’ format as input and produces a list of primer pairs for amplification of microsatellite markers. The program can take into account the quality values of consensus bases, improving success rate of primer pairs in PCR. The software is freely available and simple to install in both Windows and Unix-based operating systems. Here we demonstrate the software by developing primer pairs for 427 new candidate markers for peanut.     

Comparing single-nucleotide polymorphism marker-based and microsatellite marker-based linkage analyses

We compared linkage analysis results for an alcoholism trait, ALDX1 (DSM-III-R and Feigner criteria) using a nonparametric linkage analysis method, which takes into account allele sharing among several affected persons, for both microsatellite and single-nucleotide polymorphism (SNP) markers (Affymetrix and Illumina) in the Collaborative Study on the Genetics of Alcoholism (COGA) dataset provided to participants at the Genetic Analysis Workshop 14 (GAW14). The two sets of linkage results from the dense Affymetrix SNP markers and less densely spaced Illumina SNP markers are very similar. The linkage analysis results from microsatellite and SNP markers are generally similar, but the match is not perfect. Strong linkage peaks were found on chromosome 7 in three sets of linkage analyses using both SNP and microsatellite marker data. We also observed that for SNP markers, using the given genetic map and using the map by converting 1 megabase pair (1 Mb) to 1 centimorgan (cM), did not change the linkage results. We recommend the use of the 1 Mb-to-1 cM converted map in a first round of linkage analysis with SNP markers in which map integration is an issue.     

Lessons learned from microsatellite development for nonmodel organisms using 454 pyrosequencing

Microsatellites, also known as simple sequence repeats (SSRs), are among the most commonly used marker types in evolutionary and ecological studies. Next Generation Sequencing techniques such as 454 pyrosequencing allow the rapid development of microsatellite markers in nonmodel organisms. 454 pyrosequencing is a straightforward approach to develop a high number of microsatellite markers. Therefore, developing microsatellites using 454 pyrosequencing has become the method of choice for marker development. Here, we describe a user friendly way of microsatellite development from 454 pyrosequencing data and analyse data sets of 17 nonmodel species (plants, fungi, invertebrates, birds and a mammal) for microsatellite repeats and flanking regions suitable for primer development. We then compare the numbers of successfully lab‐tested microsatellite markers for the various species and furthermore describe diverse challenges that might arise in different study species, for example, large genome size or nonpure extraction of genomic DNA. Successful primer identification was feasible for all species. We found that in species for which large repeat numbers are uncommon, such as fungi, polymorphic markers can nevertheless be developed from 454 pyrosequencing reads containing small repeat numbers (five to six repeats). Furthermore, the development of microsatellite markers for species with large genomes was also with Next Generation Sequencing techniques more cost and time‐consuming than for species with smaller genomes. In this study, we showed that depending on the species, a different amount of 454 pyrosequencing data might be required for successful identification of a sufficient number of microsatellite markers for ecological genetic studies.     

atetra, a new software program to analyse tetraploid microsatellite data: comparison with tetra and tetrasat

Despite the importance of tetraploid species, most population genetic studies deal with diploid ones because of difficulties in analysing codominant microsatellite data in tetraploid species. We developed a new software program-atetra-which combines both the rigorous method of enumeration for small data sets and Monte Carlo simulations for large ones. We discuss the added value of atetra by comparing its precision, stability and calculation time for different population sizes with those obtained from previous software programs tetrasat and tetra. The influence of the number of simulations on the calculation stability is also investigated. atetra and tetrasat proved to be more precise when compared with tetra, which, however, remains faster. atetra has the same precision than tetrasat, but is much faster, can handle an infinite number of partial heterozygotes and calculates more genetic variables. The more user-friendly interface of atetra reduces possible mistakes.     

Combination of multiple microsatellite data sets to investigate genetic diversity and admixture of domestic cattle

Microsatellite markers are commonly used for population genetic analyses of livestock. However, up to now, combinations of microsatellite data sets or comparison of population genetic parameters from different studies and breeds has proven difficult. Often different genotyping methods have been employed, preventing standardization of microsatellite allele calling. In other cases different sets of markers have been genotyped, providing differing estimates of population genetic parameters. Here, we address these issues and illustrate a general two-step regression approach in cattle using three different sets of microsatellite data, to combine population genetics estimates of diversity and admixture. This regression-based method is independent of the loci genotyped but requires common breeds in the data sets. We show that combining microsatellite data sets can provide new insights on the origin and geographical distribution of genetic diversity and admixture in cattle, which will facilitate global management of this livestock species.     

Genome-wide linkage analysis for alcohol dependence: a comparison between single-nucleotide polymorphism and microsatellite marker assays

Both theoretical and applied studies have proven that the utility of single nucleotide polymorphism (SNP) markers in linkage analysis is more powerful and cost-effective than current microsatellite marker assays. Here we performed a whole-genome scan on 115 White, non-Hispanic families segregating for alcohol dependence, using one 10.3-cM microsatellite marker set and two SNP data sets (0.33-cM, 0.78-cM spacing). Two definitions of alcohol dependence (ALDX1 and ALDX2) were used. Our multipoint nonparametric linkage analysis found alcoholism was nominal linked to 12 genomic regions. The linkage peaks obtained by using the microsatellite marker set and the two SNP sets had a high degree of correspondence in general, but the microsatellite marker set was insufficient to detect some nominal linkage peaks. The presence of linkage disequilibrium between markers did not significantly affect the results. Across the entire genome, SNP datasets had a much higher average linkage information content (0.33 cM: 0.93, 0.78 cM: 0.91) than did microsatellite marker set (0.57). The linkage peaks obtained through two SNP datasets were very similar with some minor differences. We conclude that genome-wide linkage analysis by using approximately 5,000 SNP markers evenly distributed across the human genome is sufficient and might be more powerful than current 10-cM microsatellite marker assays.     

Merging microsatellite data.

Genotype calling procedures vary from laboratory to laboratory for many microsatellite markers. Even within the same laboratory, application of different experimental protocols often leads to ambiguities. The impact of these ambiguities ranges from irksome to devastating. Resolving the ambiguities can increase effective sample size and preserve evidence in favor of disease-marker associations. Because different data sets may contain different numbers of alleles, merging is unfortunately not a simple process of matching alleles one to one. Merging data sets manually is difficult, time-consuming, and error-prone due to differences in genotyping hardware, binning methods, molecular weight standards, and curve fitting algorithms. Merging is particularly difficult if few or no samples occur in common, or if samples are drawn from ethnic groups with widely varying allele frequencies. It is dangerous to align alleles simply by adding a constant number of base pairs to the alleles of one of the data sets. To address these issues, we have developed a Bayesian model and a Markov chain Monte Carlo (MCMC) algorithm for sampling the posterior distribution under the model. Our computer program, MicroMerge, implements the algorithm and almost always accurately and efficiently finds the most likely correct alignment. Common allele frequencies across laboratories in the same ethnic group are the single most important cue in the model. MicroMerge computes the allelic alignments with the greatest posterior probabilities under several merging options. It also reports when data sets cannot be confidently merged. These features are emphasized in our analysis of simulated and real data.     

Automated fluorescent detection of microsatellite instability

Microsatellites are highly polymorphic repetitive DNA segments dispersed throughout the genome and have been widely used for genetic linkage analysis and allele loss. Instability of microsatellites sequences has been linked to deficiencies in DNA mismatch repair, and is observed in a number of different tumor types. Analysis of microsatellite instability is thought to be a useful clinical tool for cancer diagnosis. Fluorescent detection of microsatellite instability using an automated DNA sequencer holds several distinct advantages over traditional radioactive analysis and electrophoresis, allowing simultaneous analysis of a number of different markers for a large number of samples, high resolution, sensitivity, and clear interpretation of data. In this article we present an established protocol, which has been used successfully to detect microsatellite instability in DNA samples from human tumors and circulating tumor DNA in serum/plasma.     

Estimation and adjustment of microsatellite null alleles in nonequilibrium populations

Nonamplified (null) alleles are a common feature of microsatellite genotyping and can bias estimates of allele and genotype frequencies, thereby hindering population genetic analyses. The frequency of microsatellite null alleles in diploid populations can be estimated for populations that are in Hardy–Weinberg equilibrium. However, many microsatellite data sets are from nonequilibrium populations, often with known inbreeding coefficients (F) or fixation indices (F_IS or F_ST). Here, we propose a novel null allele estimator that can be used to estimate the null allele frequency and adjust visible allele frequencies in populations for which independent estimates of F, F_IS or F_ST are available. The algorithm is currently available as an Excel macro that can be downloaded at no cost from http://www.microchecker.hull.ac.uk/ and will be incorporated into the software micro ‐checker .     

Joint inference of microsatellite mutation models, population history and genealogies using transdimensional Markov Chain Monte Carlo

We provide a framework for Bayesian coalescent inference from microsatellite data that enables inference of population history parameters averaged over microsatellite mutation models. To achieve this we first implemented a rich family of microsatellite mutation models and related components in the software package BEAST. BEAST is a powerful tool that performs Bayesian MCMC analysis on molecular data to make coalescent and evolutionary inferences. Our implementation permits the application of existing nonparametric methods to microsatellite data. The implemented microsatellite models are based on the replication slippage mechanism and focus on three properties of microsatellite mutation: length dependency of mutation rate, mutational bias toward expansion or contraction, and number of repeat units changed in a single mutation event. We develop a new model that facilitates microsatellite model averaging and Bayesian model selection by transdimensional MCMC. With Bayesian model averaging, the posterior distributions of population history parameters are integrated across a set of microsatellite models and thus account for model uncertainty. Simulated data are used to evaluate our method in terms of accuracy and precision of estimation and also identification of the true mutation model. Finally we apply our method to a red colobus monkey data set as an example.     

PSMD: An extensive database for pan‐species microsatellite investigation and marker development

Microsatellites are widely distributed throughout nearly all genomes which have been extensively exploited as powerful genetic markers for diverse applications due to their high polymorphisms. Their length variations are involved in gene regulation and implicated in numerous genetic diseases even in cancers. Although much effort has been devoted in microsatellite database construction, the existing microsatellite databases still had some drawbacks, such as limited number of species, unfriendly export format, missing marker development, lack of compound microsatellites and absence of gene annotation, which seriously restricted researchers to perform downstream analysis. In order to overcome the above limitations, we developed PSMD (Pan‐Species Microsatellite Database, http://big.cdu.edu.cn/psmd/ ) as a web‐based database to facilitate researchers to easily identify microsatellites, exploit reliable molecular markers and compare microsatellite distribution pattern on genome‐wide scale. In current release, PSMD comprises 678,106,741 perfect microsatellites and 43,848,943 compound microsatellites from 18,408 organisms, which covered almost all species with available genomic data. In addition to interactive browse interface, PSMD also offers a flexible filter function for users to quickly gain desired microsatellites from large data sets. PSMD allows users to export GFF3 formatted file and CSV formatted statistical file for downstream analysis. We also implemented an online tool for analysing occurrence of microsatellites with user‐defined parameters. Furthermore, Primer3 was embedded to help users to design high‐quality primers with customizable settings. To our knowledge, PSMD is the most extensive resource which is likely to be adopted by scientists engaged in biological, medical, environmental and agricultural research.     

megasat: automated inference of microsatellite genotypes from sequence data

megasat is software that enables genotyping of microsatellite loci using next‐generation sequencing data. Microsatellites are amplified in large multiplexes, and then sequenced in pooled amplicons. megasat reads sequence files and automatically scores microsatellite genotypes. It uses fuzzy matches to allow for sequencing errors and applies decision rules to account for amplification artefacts, including nontarget amplification products, replication slippage during PCR (amplification stutter) and differential amplification of alleles. An important feature of megasat is the generation of histograms of the length–frequency distributions of amplification products for each locus and each individual. These histograms, analogous to electropherograms traditionally used to score microsatellite genotypes, enable rapid evaluation and editing of automatically scored genotypes. megasat is written in Perl, runs on Windows, Mac OS X and Linux systems, and includes a simple graphical user interface. We demonstrate megasat using data from guppy, Poecilia reticulata. We genotype 1024 guppies at 43 microsatellites per run on an Illumina MiSeq sequencer. We evaluated the accuracy of automatically called genotypes using two methods, based on pedigree and repeat genotyping data, and obtained estimates of mean genotyping error rates of 0.021 and 0.012. In both estimates, three loci accounted for a disproportionate fraction of genotyping errors; conversely, 26 loci were scored with 0–1 detected error (error rate ≤0.007). Our results show that with appropriate selection of loci, automated genotyping of microsatellite loci can be achieved with very high throughput, low genotyping error and very low genotyping costs.     

A novel method for automatic genotyping of microsatellite markers based on parametric pattern recognition

Genetic mapping of loci affecting complex phenotypes in human and other organisms is presently being conducted on a very large scale, using either microsatellite or single nucleotide polymorphism (SNP) markers and by partly automated methods. A critical step in this process is the conversion of the instrument output into genotypes, both a time-consuming and error prone procedure. Errors made during this calling of genotypes will dramatically reduce the ability to map the location of loci underlying a phenotype. Accurate methods for automatic genotype calling are therefore important. Here, we describe novel algorithms for automatic calling of microsatellite genotypes using parametric pattern recognition. The analysis of microsatellite data is complicated both by the occurrence of stutter bands, which arise from Taq polymerase misreading the number of repeats, and additional bands derived form the non-template dependent addition of a nucleotide to the 3 end of the PCR products. These problems, together with the fact that the lengths of two alleles in a heterozygous individual may differ by only two nucleotides, complicate the development of an automated process. The novel algorithms markedly reduce the need for manual editing and the frequency of miscalls, and compares very favourably with commercially available software for automatic microsatellite genotyping.     

Maximum-likelihood estimation of allelic dropout and false allele error rates from microsatellite genotypes in the absence of reference data

The importance of quantifying and accounting for stochastic genotyping errors when analyzing microsatellite data is increasingly being recognized. This awareness is motivating the development of data analysis methods that not only take errors into consideration but also recognize the difference between two distinct classes of error, allelic dropout and false alleles. Currently methods to estimate rates of allelic dropout and false alleles depend upon the availability of error-free reference genotypes or reliable pedigree data, which are often not available. We have developed a maximum-likelihood-based method for estimating these error rates from a single replication of a sample of genotypes. Simulations show it to be both accurate and robust to modest violations of its underlying assumptions. We have applied the method to estimating error rates in two microsatellite data sets. It is implemented in a computer program, Pedant, which estimates allelic dropout and false allele error rates with 95% confidence regions from microsatellite genotype data and performs power analysis. Pedant is freely available at http://www.stats.gla.ac.uk/ approximately paulj/pedant.html.     

Nonradioactive detection of microsatellite polymorphisms

Summary Simple and accurate detection of microsatellite polymorphisms became an important tool in linkage analysis, gene mapping and DNA typing. Fluorescent labeling of PCR products enabled fast and accurate typing of a large number of individuals using an automated laser fluorescence DNA sequencer. An other simple possibility for the detection of microsatellite polymorphisms is rapid silver staining of non labeled PCR products separated in native PAA gels.     

Bayesian estimation of range for microsatellite loci

Microsatellite loci have become important in population genetics because of their high level of polymorphism in natural populations, very frequent occurrence throughout the genome, and apparently high mutation rate. Observed repeat numbers (alleles size) in natural populations and expectations based on computer simulations suggest that the range of repeat numbers at a microsatellite locus is restricted. This range is a key parameter that should be properly estimated in order to proceed with calculations of divergence times in phylogenetic studies and to better investigate the within- and between-population variability. The 'plug-in' estimate of range based on the minimum and maximum value observed in a sample is not satisfactory because of the relatively large number of alleles in comparison with typical sample sizes. In this paper, a set of data from 30 dinucleotide microsatellite loci is analysed under the assumption of independence among loci. Bayesian inference on range for one locus is obtained by assuming that constraints on range values exist as sharp bounds. Closed-form calculations and robustness revealed by our analysis suggest that the proposed Bayesian approach might be routinely used by researchers to classify microsatellite loci according to the estimated value of their allelic range.     

POLYSAT: an R package for polyploid microsatellite analysis

We present an R package to help remedy the lack of software for manipulating and analysing autopolyploid and allopolyploid microsatellite data. POLYSAT can handle genotype data of any ploidy, including populations of mixed ploidy, and assumes that allele copy number is always ambiguous in partial heterozygotes. It can import and export genotype data in eight different formats, calculate pairwise distances between individuals using a stepwise mutation and infinite alleles model, estimate ploidy based on allele counts and estimate allele frequencies and pairwise F(ST) values. This software is freely available through the Comprehensive R Archive Network (http://cran.r-project.org/) and includes a thorough tutorial.     

New polymorphic microsatellite loci,cross‐species amplification and PCR multiplexing in the black aphid,Aphis fabae Scopoli

Aphis fabae includes four morphological cryptic subspecies, which are mostly identified by their partially distinct secondary host range. To determine the extent of gene flow and isolation between these four taxa, we isolated and characterized 12 microsatellite loci from Aphis fabae fabae and tested cross‐species amplification of eight loci from the closely related species Aphis gossypii. Using eight previously described microsatellite loci, we have developed the polymerase chain reaction (PCR) multiplexing of 24 loci, which were separated in tree sets and five PCRs. These sets of microsatellite loci provide high throughput capacity for large‐scale population genetic studies at a minimum cost.