Identifying and reducing AFLP genotyping error: an example of tradeoffs when comparing population structure in broadcast spawning versus brooding oysters

Phylogeographic inferences about gene flow are strengthened through comparison of co-distributed taxa, but also depend on adequate genomic sampling. Amplified fragment length polymorphisms (AFLPs) provide a rapid and inexpensive source of multilocus allele frequency data for making genomically robust inferences. Every AFLP study initially generates markers with a range of locus-specific genotyping error rates and applies criteria to select a subset for analysis. However, there has been very little empirical evaluation of the best tradeoff between culling all but the lowest-error loci to minimize overall genotyping error versus the potential for increasing population genetic signal by retaining more loci. Here, we used AFLPs to compare population structure in co-distributed broadcast spawning (Crassostrea virginica) and brooding (Ostrea equestris) oyster species. Using existing methods for almost entirely automated marker selection and scoring, genotyping error tradeoffs were evaluated by comparing results across a nested series of data sets with mean mismatch errors of 0, 1, 2, 3, 4 and >4%. Artifactual population structure was diagnosed in high-error data sets and we assessed the low-error point at which expected population substructure signal was lost. In both species, we identified substructure patterns deemed to be inaccurate at average mismatch error rates 2 and >4%. In the species comparison, the optimum data sets showed higher gene flow for the brooding oyster with more oceanic salinity tolerances. AFLP tradeoffs may differ among studies, but our results suggest that important signal may be lost in the pursuit of 'acceptable' error levels and our procedures provide a general method for empirically exploring these tradeoffs.     

Susceptibility of biallelic haplotype and genotype frequencies to genotyping error

With the availability of fast genotyping methods and genomic databases, the search for statistical association of single nucleotide polymorphisms with a complex trait has become an important methodology in medical genetics. However, even fairly rare errors occurring during the genotyping process can lead to spurious association results and decrease in statistical power. We develop a systematic approach to study how genotyping errors change the genotype distribution in a sample. The general M-marker case is reduced to that of a single-marker locus by recognizing the underlying tensor-product structure of the error matrix. Both method and general conclusions apply to the general error model; we give detailed results for allele-based errors of size depending both on the marker locus and the allele present. Multiple errors are treated in terms of the associated diffusion process on the space of genotype distributions. We find that certain genotype and haplotype distributions remain unchanged under genotyping errors, and that genotyping errors generally render the distribution more similar to the stable one. In case-control association studies, this will lead to loss of statistical power for nondifferential genotyping errors and increase in type I error for differential genotyping errors. Moreover, we show that allele-based genotyping errors do not disturb Hardy-Weinberg equilibrium in the genotype distribution. In this setting we also identify maximally affected distributions. As they correspond to situations with rare alleles and marker loci in high linkage disequilibrium, careful checking for genotyping errors is advisable when significant association based on such alleles/haplotypes is observed in association studies.     

An objective, rapid and reproducible method for scoring AFLP peak-height data that minimizes genotyping error

Amplified fragment length polymorphism (AFLP) fingerprint data are now commonly collected using DNA sequencers. AFLP genotypes are still often scored by eye from such data - a time-consuming, error-prone and subjective process. We present a semi-automated method of genotyping sequencer-collected AFLPs at predefined fragment locations (loci) within the fingerprint. Our method uses thresholds of AFLP-polymerase chain reaction-product fluorescence intensity (peak height) in order to: (i) exclude AFLP loci that are likely to contribute high rates of error to data sets, and (ii) determine the AFLP phenotype (fragment presence or absence) at the retained loci. Error rate analysis is an integral part of this process and is used to determine optimal thresholds that minimize genotyping error, while maximizing the numbers of retained loci. We show that application of this method to a large AFLP data set allows genotype calls that are rapid, objective and repeatable, facilitating the extraction of reliable genotype data for molecular ecological studies.     

A single‐nucleotide polymorphism‐based approach for rapid and cost‐effective genetic wolf monitoring in Europe based on noninvasively collected samples

Noninvasive genetics based on microsatellite markers has become an indispensable tool for wildlife monitoring and conservation research over the past decades. However, microsatellites have several drawbacks, such as the lack of standardisation between laboratories and high error rates. Here, we propose an alternative single‐nucleotide polymorphism (SNP)‐based marker system for noninvasively collected samples, which promises to solve these problems. Using nanofluidic SNP genotyping technology (Fluidigm), we genotyped 158 wolf samples (tissue, scats, hairs, urine) for 192 SNP loci selected from the Affymetrix v2 Canine SNP Array. We carefully selected an optimised final set of 96 SNPs (and discarded the worse half), based on assay performance and reliability. We found rates of missing data in this SNP set of <10% and genotyping error of ~1%, which improves genotyping accuracy by nearly an order of magnitude when compared to published data for other marker types. Our approach provides a tool for rapid and cost‐effective genotyping of noninvasively collected wildlife samples. The ability to standardise genotype scoring combined with low error rates promises to constitute a major technological advancement and could establish SNPs as a standard marker for future wildlife monitoring.     

Comparative analysis of AFLPs and SSRs efficiency in resolving population genetic structure of Mediterranean Solea vulgaris

The performance of different molecular markers in the assessment of population structure was tested using samples of Solea vulgaris collected in the Mediterranean within and outside the hypothetical dispersal ability of the species. A total of 172 individuals belonging to four population samples were analysed using 15 microsatellites [simple sequence repeats (SSRs)] and 153 amplified fragment length polymorphisms (AFLPs). Considering the global qualitative patterns, we found a correlation between SSRs and AFLPs in detecting genetic differentiation among samples. However, on a small geographical scale, AFLPs were able to discriminate individuals from neighbouring populations whereas SSRs were not, and the percentage of individuals correctly assigned to their population of origin was higher with AFLPs than with SSRs. The high number of loci analysed with the AFLP technique could increase the probability to include outlier loci in the analysis; however, the neutrality test performed on our data set did not show evidence of selection acting on the S. vulgaris samples. Even if the choice of the molecular marker depends mainly on the biological question to be addressed, the higher power of discrimination and the comparative technical ease of obtaining data from AFLPs with respect to SSRs suggest the use of AFLPs for many population genetics studies.     

Widespread amplification of amplified fragment length polymorphisms (AFLPs) in marine Antarctic animals

Although recent years have witnessed a rapid growth in the number of genetic studies of Antarctic organisms, relatively few studies have so far used nuclear markers, possibly due to the perceived cost and difficulty of isolating markers such as microsatellites. However, an often overlooked alternative is to use amplified fragment length polymorphisms (AFLPs), a versatile and low-cost method capable of generating large numbers of predominantly nuclear loci in virtually any organism. We conducted a literature review of population genetic studies of Antarctic organisms, finding that fewer than 10% used AFLPs. Moreover, a strong taxonomic bias was found, with studies employing mitochondrial DNA or microsatellites focussing predominantly on animals, while those using AFLPs were mostly of plants or lower organisms. Consequently, we explored the extent to which AFLPs amplify across a range of Antarctic marine animal taxa by genotyping eight individuals each of twelve different species, ranging from echinoderms through soft corals to pelagic fish, at four selective primer combinations. AFLPs readily amplified across all of the taxa, generating between 32 and 84 loci per species, with on average 56.5% of these being polymorphic. In general, levels of polymorphism bore little relationship with expectations based on larger populations of broadcast-spawning species being more variable, though we did find a tentative positive correlation between the number of AFLP bands amplified and a measure of effective population size. Our study lends further support for the utility and ease of use of AFLPs and their suitability for studies of Antarctic species across a wide range of taxa.     

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.     

鲤鱼微卫星突变速率和模式(英文)

利用150个微卫星分子标记在F1代家系的基因型分析过程中,共有27600个等位基因从亲本向子代传递,其中在5个微卫星座位上检测到6个突变的等位基因。对突变的等位基因数目进行统计分析后得出:鲤鱼平均每个世代每个微卫星座位的突变速率为2.53×10-4。在发现突变的5个位点中,经测序发现,突变序列中插入1个以上的重复单元就导致了突变的发生。这些突变表明,鲤鱼的微卫星突变没有遵循严格的渐变突变模型(stepwise mutation model,SMM)。该文关于鲤鱼微卫星突变速率和模式的研究将会对统计鲤鱼有效群体的统计提供有效参数。     

Microsatellites behaving badly: empirical evaluation of genotyping errors and subsequent impacts on population studies

Microsatellites are useful tools for ecological studies because they can be used to discern population structure, dispersal patterns and genetic relationships among individuals. However, they can also yield inaccurate genotypes that, in turn, bias results, promote biological misinterpretations, and create repercussions for population management and conservation programs. We used empirical data from a large-scale microsatellite DNA study of white-tailed deer (Odocoileus virginianus) to identify sources of genotyping error, evaluate corrective measures, and provide recommendations to prevent bias in population studies. We detected unreported mutations that led to erroneous genotypes in five of 13 previously evaluated microsatellites. Of the five problematic markers, two contained mutations that resulted in null alleles, and three contained mutations that resulted in imperfect repeats. These five microsatellites had error rates that were four times greater on average than those observed in the remaining eight. Methodological corrections, such as primer redesign, reduced errors up to 5-fold in two problematic loci, although analytical corrections (computational adjustment for errors) were unable to fully prevent bias and, consequently, measures of genetic differentiation and kinship were negatively impacted. Our results demonstrate the importance of error evaluation during all stages of population studies, and emphasize the need to standardize procedures for microsatellite analyses. This study facilitates the application of microsatellite technology in population studies by examining common sources of genotyping error, identifying unreported problems with microsatellites, and offering solutions to prevent error and bias in population studies.     

Estimation of genotyping error rate from repeat genotyping,unintentional recaptures and known parent–offspring comparisons in 16 microsatellite loci for brown rockfish (Sebastes auriculatus)

Genotyping errors are present in almost all genetic data and can affect biological conclusions of a study, particularly for studies based on individual identification and parentage. Many statistical approaches can incorporate genotyping errors, but usually need accurate estimates of error rates. Here, we used a new microsatellite data set developed for brown rockfish (Sebastes auriculatus) to estimate genotyping error using three approaches: (i) repeat genotyping 5% of samples, (ii) comparing unintentionally recaptured individuals and (iii) Mendelian inheritance error checking for known parent–offspring pairs. In each data set, we quantified genotyping error rate per allele due to allele drop‐out and false alleles. Genotyping error rate per locus revealed an average overall genotyping error rate by direct count of 0.3%, 1.5% and 1.7% (0.002, 0.007 and 0.008 per allele error rate) from replicate genotypes, known parent–offspring pairs and unintentionally recaptured individuals, respectively. By direct‐count error estimates, the recapture and known parent–offspring data sets revealed an error rate four times greater than estimated using repeat genotypes. There was no evidence of correlation between error rates and locus variability for all three data sets, and errors appeared to occur randomly over loci in the repeat genotypes, but not in recaptures and parent–offspring comparisons. Furthermore, there was no correlation in locus‐specific error rates between any two of the three data sets. Our data suggest that repeat genotyping may underestimate true error rates and may not estimate locus‐specific error rates accurately. We therefore suggest using methods for error estimation that correspond to the overall aim of the study (e.g. known parent–offspring comparisons in parentage studies).