Multiple Displacement Amplification for Generating an Unlimited Source of DNA for Genotyping in Nonhuman Primate Species

We evaluated a whole genome amplification method—multiple displacement amplification (MDA)—as a means to conserve valuable nonhuman primate samples. We tested 148 samples from a variety of species and sample sources, including blood, tissue, cell-lines, plucked hair and noninvasively collected semen. To evaluate genotyping success and accuracy of MDA, we used routine genotyping methods, including short tandem repeat (STR) analysis, denaturing gradient gel electrophoresis (DGGE), Alu repeat analysis, direct sequencing, and nucleotide detection by tag-array minisequencing. We compared genotyping results from MDA products to genotypes generated from the original (non-MD amplified) DNA samples. All genotyping methods showed good results with the MDA products as a DNA template, and for some samples MDA improved genotyping success. We show that the MDA procedure has the potential to provide a long-lasting source of DNA for genetic studies, which would be highly valuable for the primate research field, in which genetic resources are limited and for other species in which similar sampling constraints apply.     

Low genotyping error rates in wild ungulate faeces sampled in winter

We show that Alpine ibex (Capra ibex) and Corsican mouflon (Ovis musimon) faeces yield useful DNA for microsatellite analysis, however, we detected higher genotyping error rates for spring faeces than for winter faeces. We quantified the genotyping error rate by repeatedly genotyping four microsatellites. Respectively, 99 and 95% of mouflon and ibex genotyping repetitions provided a correct genotype using winter samples, whereas spring samples provided only 52 and 59% correct genotypes. Thus, before starting a noninvasive study, we recommend that researchers conduct a pilot study to quantify genotyping error rates for each season, population and species to be studied.     

Effect of genotyping error in model-free linkage analysis using microsatellite or single-nucleotide polymorphism marker maps

Errors while genotyping are inevitable and can reduce the power to detect linkage. However, does genotyping error have the same impact on linkage results for single-nucleotide polymorphism (SNP) and microsatellite (MS) marker maps? To evaluate this question we detected genotyping errors that are consistent with Mendelian inheritance using large changes in multipoint identity-by-descent sharing in neighboring markers. Only a small fraction of Mendelian consistent errors were detectable (e.g., 18% of MS and 2.4% of SNP genotyping errors). More SNP genotyping errors are Mendelian consistent compared to MS genotyping errors, so genotyping error may have a greater impact on linkage results using SNP marker maps. We also evaluated the effect of genotyping error on the power and type I error rate using simulated nuclear families with missing parents under 0, 0.14, and 2.8% genotyping error rates. In the presence of genotyping error, we found that the power to detect a true linkage signal was greater for SNP (75%) than MS (67%) marker maps, although there were also slightly more false-positive signals using SNP marker maps (5 compared with 3 for MS). Finally, we evaluated the usefulness of accounting for genotyping error in the SNP data using a likelihood-based approach, which restores some of the power that is lost when genotyping error is introduced.     

SNP genotyping on a genome-wide amplified DOP-PCR template

With the increasing demand for higher throughput single nucleotide polymorphism (SNP) genotyping, the quantity of genomic DNA often falls short of the number of assays required. We investigated the use of degenerate oligonucleotide primed polymerase chain reaction (DOP-PCR) to generate a template for our SNP genotyping methodology of fluorescence polarization template-directed dye-terminator incorporation detection. DOP-PCR employs a degenerate primer (5′-CCGACTCGAGNNNNNNATGTGG-3′) to produce non-specific uniform amplification of DNA. This approach has been successfully applied to microsatellite genotyping. We compared genotyping of DOP-PCR-amplified genomic DNA to genomic DNA as a template. Results were analyzed with respect to feasibility, allele loss of alleles, genotyping accuracy and storage conditions in a high-throughput genotyping environment. DOP-PCR yielded overall satisfactory results, with a certain loss in accuracy and quality of the genotype assignments. Accuracy and quality of genotypes generated from the DOP-PCR template also depended on storage conditions. Adding carrier DNA to a final concentration of 10 ng/µl improved results. In conclusion, we have successfully used DOP-PCR to amplify our genomic DNA collection for subsequent SNP genotyping as a standard process.     

Genotyping‐by‐sequencing for estimating relatedness in nonmodel organisms: Avoiding the trap of precise bias

There has been remarkably little attention to using the high resolution provided by genotyping‐by‐sequencing (i.e., RADseq and similar methods) for assessing relatedness in wildlife populations. A major hurdle is the genotyping error, especially allelic dropout, often found in this type of data that could lead to downward‐biased, yet precise, estimates of relatedness. Here, we assess the applicability of genotyping‐by‐sequencing for relatedness inferences given its relatively high genotyping error rate. Individuals of known relatedness were simulated under genotyping error, allelic dropout and missing data scenarios based on an empirical ddRAD data set, and their true relatedness was compared to that estimated by seven relatedness estimators. We found that an estimator chosen through such analyses can circumvent the influence of genotyping error, with the estimator of Ritland (Genetics Research, 67, 175) shown to be unaffected by allelic dropout and to be the most accurate when there is genotyping error. We also found that the choice of estimator should not rely solely on the strength of correlation between estimated and true relatedness as a strong correlation does not necessarily mean estimates are close to true relatedness. We also demonstrated how even a large SNP data set with genotyping error (allelic dropout or otherwise) or missing data still performs better than a perfectly genotyped microsatellite data set of tens of markers. The simulation‐based approach used here can be easily implemented by others on their own genotyping‐by‐sequencing data sets to confirm the most appropriate and powerful estimator for their data.     

Evaluation of multiple displacement amplification in a 5 cM STR genome-wide scan

Multiple displacement amplification (MDA) has emerged as a promising new method of whole genome amplification (WGA) with the potential to generate virtually unlimited genome-equivalent DNA from only a small amount of seed DNA. To date, genome-wide high marker density assessments of MDA–DNA have focussed mainly upon suitability for single nucleotide polymorphism (SNP) genotyping applications. Suitability for short tandem repeat (STR) genotyping has not been investigated in great detail, despite their inherent instability during DNA replication, and the obvious challenge that this presents to WGA techniques. Here, we aimed to assess the applicability of MDA in STR genotyping by conducting a genome-wide scan of 768 STR markers for MDAs of 15 high quality genomic DNAs. We found that MDA genotyping call and accuracy rates were only marginally lower than for genomic DNA. Pooling of three replicate MDAs resulted in a small increase in both call rate and genotyping accuracy. We identified 34 STRs (4.4% of total markers) of which five essentially failed with MDA samples, and 29 of which showed elevated genotyping failures/discrepancies in the MDAs. We emphasise the importance of DNA and MDA quality checks, and the use of appropriate controls to identify problematic STR markers.     

Genotyping by Genome Reducing and Sequencing for Outbred Animals

Next-generation sequencing (NGS) approaches are widely used in genome-wide genetic marker discovery and genotyping. However, current NGS approaches are not easy to apply to general outbred populations (human and some major farm animals) for SNP identification because of the high level of heterogeneity and phase ambiguity in the haplotype. Here, we reported a new method for SNP genotyping, called genotyping by genome reducing and sequencing (GGRS) to genotype outbred species. Through an improved procedure for library preparation and a marker discovery and genotyping pipeline, the GGRS approach can genotype outbred species cost-effectively and high-reproducibly. We also evaluated the efficiency and accuracy of our approach for high-density SNP discovery and genotyping in a large genome pig species (2.8 Gb), for which more than 70,000 single nucleotide polymorphisms (SNPs) can be identified for an expenditure of only $80 (USD)/sample.     

SLAF-seq: An Efficient Method of Large-Scale De Novo SNP Discovery and Genotyping Using High-Throughput Sequencing

Large-scale genotyping plays an important role in genetic association studies. It has provided new opportunities for gene discovery, especially when combined with high-throughput sequencing technologies. Here, we report an efficient solution for large-scale genotyping. We call it specific-locus amplified fragment sequencing (SLAF-seq). SLAF-seq technology has several distinguishing characteristics: i) deep sequencing to ensure genotyping accuracy; ii) reduced representation strategy to reduce sequencing costs; iii) pre-designed reduced representation scheme to optimize marker efficiency; and iv) double barcode system for large populations. In this study, we tested the efficiency of SLAF-seq on rice and soybean data. Both sets of results showed strong consistency between predicted and practical SLAFs and considerable genotyping accuracy. We also report the highest density genetic map yet created for any organism without a reference genome sequence, common carp in this case, using SLAF-seq data. We detected 50,530 high-quality SLAFs with 13,291 SNPs genotyped in 211 individual carp. The genetic map contained 5,885 markers with 0.68 cM intervals on average. A comparative genomics study between common carp genetic map and zebrafish genome sequence map showed high-quality SLAF-seq genotyping results. SLAF-seq provides a high-resolution strategy for large-scale genotyping and can be generally applicable to various species and populations.     

Testing genotyping strategies for ultra‐deep sequencing of a co‐amplifying gene family: MHC class I in a passerine bird

Characterization of highly duplicated genes, such as genes of the major histocompatibility complex (MHC), where multiple loci often co‐amplify, has until recently been hindered by insufficient read depths per amplicon. Here, we used ultra‐deep Illumina sequencing to resolve genotypes at exon 3 of MHC class I genes in the sedge warbler (Acrocephalus schoenobaenus). We sequenced 24 individuals in two replicates and used this data, as well as a simulated data set, to test the effect of amplicon coverage (range: 500–20 000 reads per amplicon) on the repeatability of genotyping using four different genotyping approaches. A third replicate employed unique barcoding to assess the extent of tag jumping, that is swapping of individual tag identifiers, which may confound genotyping. The reliability of MHC genotyping increased with coverage and approached or exceeded 90% within‐method repeatability of allele calling at coverages of >5000 reads per amplicon. We found generally high agreement between genotyping methods, especially at high coverages. High reliability of the tested genotyping approaches was further supported by our analysis of the simulated data set, although the genotyping approach relying primarily on replication of variants in independent amplicons proved sensitive to repeatable errors. According to the most repeatable genotyping method, the number of co‐amplifying variants per individual ranged from 19 to 42. Tag jumping was detectable, but at such low frequencies that it did not affect the reliability of genotyping. We thus demonstrate that gene families with many co‐amplifying genes can be reliably genotyped using HTS, provided that there is sufficient per amplicon coverage.     

SNP genotyping using microsphere-linked PNA and flow cytometric detection.

BACKGROUND: Single nucleotide polymorphisms (SNPs) represent the most frequent form of genetic variations. Some of the most sensitive methods for SNP genotyping employ synthetic oligonucleotides, such as the peptide nucleic acid (PNA). We introduce a new method combining allele-specific hybridization, PNA technology, and flow cytometric detection. We tested the design by genotyping a Danish basal cell carcinoma cohort of 80 individuals for an A/C SNP in exon 6 of the XPD gene. METHODS: Genomic DNA was amplified by a two-step polymerase chain reaction (PCR) in the presence of fluorescein-dyed primers and fluorescein-12-dUTP. The allele-specific PNA molecules were covalently coupled to carboxylated microspheres with and without rhodamine. Allele-specific hybridization between PCR products and immobilized PNA was carried out at 60 degrees C followed by flow cytometric detection. RESULTS: We present a fully functional two-bead genotyping system based on PNA capture and flow cytometric detection used for the correct and fast regenotyping of a Danish basal cell carcinoma cohort. CONCLUSIONS: This new assay presents a simple, rapid, and robust method for SNP genotyping for laboratories equipped with a standard flow cytometer. Moreover, this system offers potential for multiplexing and will be operational for middle-scale genotyping.     

High-density genotyping and linkage disequilibrium in the human genome using chromosome 22 as a model

Technology and genetics have advanced to the point where genotyping thousands of individuals at thousands of marker locations around the whole human genome is possible. The whole-genome scan for detection of complex disease genes is a widely discussed topic. We review some of the recent high-density genotyping experiments and discuss related details, particularly the extent and variability of linkage disequilibrium. We also discuss the quality of single nucleotide polymorphisms (SNPs) in public databases and its consequences to the number of SNPs required for large-scale genotyping projects.     

Genotyping‐by‐sequencing approaches to characterize crop genomes: choosing the right tool for the right application

In the last decade, the revolution in sequencing technologies has deeply impacted crop genotyping practice. New methods allowing rapid, high‐throughput genotyping of entire crop populations have proliferated and opened the door to wider use of molecular tools in plant breeding. These new genotyping‐by‐sequencing (GBS) methods include over a dozen reduced‐representation sequencing (RRS) approaches and at least four whole‐genome resequencing (WGR) approaches. The diversity of methods available, each often producing different types of data at different cost, can make selection of the best‐suited method seem a daunting task. We review the most common genotyping methods used today and compare their suitability for linkage mapping, genomewide association studies (GWAS), marker‐assisted and genomic selection and genome assembly and improvement in crops with various genome sizes and complexity. Furthermore, we give an outline of bioinformatics tools for analysis of genotyping data. WGR is well suited to genotyping biparental cross populations with complex, small‐ to moderate‐sized genomes and provides the lowest cost per marker data point. RRS approaches differ in their suitability for various tasks, but demonstrate similar costs per marker data point. These approaches are generally better suited for de novo applications and more cost‐effective when genotyping populations with large genomes or high heterozygosity. We expect that although RRS approaches will remain the most cost‐effective for some time, WGR will become more widespread for crop genotyping as sequencing costs continue to decrease.     

Conversion of array‐based single nucleotide polymorphic markers for use in targeted genotyping by sequencing in hexaploid wheat (Triticum aestivum)

Wheat breeders and academics alike use single nucleotide polymorphisms (SNP s) as molecular markers to characterize regions of interest within the hexaploid wheat genome. A number of SNP ‐based genotyping platforms are available, and their utility depends upon factors such as the available technologies, number of data points required, budgets and the technical expertise required. Unfortunately, markers can rarely be exchanged between existing and newly developed platforms, meaning that previously generated data cannot be compared, or combined, with more recently generated data sets. We predict that genotyping by sequencing will become the predominant genotyping technology within the next 5–10 years. With this in mind, to ensure that data generated from current genotyping platforms continues to be of use, we have designed and utilized SNP ‐based capture probes from several thousand existing and publicly available probes from Axiom® and KASP ? genotyping platforms. We have validated our capture probes in a targeted genotyping by sequencing protocol using 31 previously genotyped UK elite hexaploid wheat accessions. Data comparisons between targeted genotyping by sequencing, Axiom® array genotyping and KASP ? genotyping assays, identified a set of 3256 probes which reliably bring together targeted genotyping by sequencing data with the previously available marker data set. As such, these probes are likely to be of considerable value to the wheat community. The probe details, full probe sequences and a custom built analysis pipeline may be freely downloaded from the CerealsDB website (http://www.cerealsdb.uk.net/cerealgenomics/CerealsDB /sequence_capture.php).     

Power and sample size calculations for case-control genetic association tests when errors are present: application to single nucleotide polymorphisms

The purpose of this work is to quantify the effects that errors in genotyping have on power and the sample size necessary to maintain constant asymptotic Type I and Type II error rates (SSN) for case-control genetic association studies between a disease phenotype and a di-allelic marker locus, for example a single nucleotide polymorphism (SNP) locus. We consider the effects of three published models of genotyping errors on the chi-square test for independence in the 2 x 3 table. After specifying genotype frequencies for the marker locus conditional on disease status and error model in both a genetic model-based and a genetic model-free framework, we compute the asymptotic power to detect association through specification of the test's non-centrality parameter. This parameter determines the functional dependence of SSN on the genotyping error rates. Additionally, we study the dependence of SSN on linkage disequilibrium (LD), marker allele frequencies, and genotyping error rates for a dominant disease model. Increased genotyping error rate requires a larger SSN. Every 1% increase in sum of genotyping error rates requires that both case and control SSN be increased by 2-8%, with the extent of increase dependent upon the error model. For the dominant disease model, SSN is a nonlinear function of LD and genotyping error rate, with greater SSN for lower LD and higher genotyping error rate. The combination of lower LD and higher genotyping error rates requires a larger SSN than the sum of the SSN for the lower LD and for the higher genotyping error rate.     

Power calculations for selective genotyping in QTL mapping in backcross mice

Selective genotyping concerns the genotyping of a portion of individuals chosen on the basis of their phenotypic values. Often individuals are selected for genotyping from the high and low extremes of the phenotypic distribution. This procedure yields savings in cost and time by decreasing the total number of individuals genotyped. Previous work by Darvasi et al. (1993) has shown that the power to detect a QTL by genotyping 40-50 % of a population is roughly equivalent to genotyping the entire sample. However, these power studies have not accounted for different strategies of analysing the data when phenotypes of individuals in the middle are excluded, nor have they investigated the genome-wide type I error rate under these different strategies or different selection percentages. Further, these simulation studies have not considered markers over the entire genome. In this paper, we present simulation studies of power for the maximum likelihood approach to QTL mapping by Lander & Botstein (1989) in the context of selective genotyping. We calculate the power of selectively genotyping the individuals from the middle of the phenotypic distribution when performing QTL mapping over the whole mouse genome.     

Characterization of cytochrome P450 2D6 alleles using the Invader system

The cytochrome p450 (CYP) superfamily comprises enzymes that play an essential role in the transformation of medically relevant compounds. Accurate genotyping of polymorphisms in members of this family is drawing increasing interest because certain allelic variants may result in either loss of efficacy or toxic accumulation of therapeutic agents. Debrisoquine 4-hydroxylase, or CYP2D6, is among the most widely studied of the CYPs. The complexity of the CYP2D6 genomic region, including pseudogenes, gene deletions, and gene duplications, has offered numerous challenges to developing a genotyping strategy. We describe a comprehensive CYP2D6 genotyping strategy that employs both a PCR/Invader genotyping assay system and an Invader genomic copy number assay The Invader system is a homogeneous, isothermal, highly specific, and robust signal amplification system. Resultsfrom II CYP2D6 assays in an alle frequency study compare well to published allele frequency values for Caucasians. Further, Invader assays provided unambiguous genotyping determinations for 100% of the 171 samples that yielded a visible PCR product on an agarose gel. A copy number assay yielded only one equivocal result in 205 samples. We identified 17 single-copy individuals and 17 three-copy (or more) individuals.     

Further development of multiplex single nucleotide polymorphism typing method, the DigiTag2 assay

A number of single nucleotide polymorphisms (SNPs) are considered to be candidate susceptibility or resistance genetic factors for multifactorial disease. Genome-wide searches for disease susceptibility regions followed by high-resolution mapping of primary genes require cost-effective and highly reliable technology. To accomplish successful and low-cost typing for candidate SNPs, new technologies must be developed. We previously reported a multiplex SNP typing method, designated the DigiTag assay, that has the potential to analyze nearly any SNP with high accuracy and reproducibility. However, the DigiTag assay requires multiple washing steps in manipulation and uses genotyping probes modified with biotin for each target SNP. Here we describe the next version of the assay, DigiTag2, which works with simple protocols and uses unmodified genotyping probes. We investigated the feasibility of the DigiTag2 assay by genotyping 96 target SNPs spanning a 610-kb region of human chromosome 5. The DigiTag2 assay is suitable for genotyping an intermediate number of SNPs (tens to hundreds of sites) with a high conversion rate (>90%), high accuracy, and low cost.     

A single nucleotide polymorphism genotyping method using phosphate-affinity polyacrylamide gel electrophoresis

To date, various methods have been developed to facilitate the genotyping of a single nucleotide polymorphism (SNP) for aiding in the diagnosis and treatment of inherited diseases. The most commonly used method for SNP genotyping is an allele-specific hybridization procedure using an expensive fluorochrome-labeled oligonucleotide probe and a specialized fluorescence analyzer. Here, we introduce a simple and reliable genotyping method using a 1:1 mixture of 5'-phosphate-labeled and nonlabeled allele-specific polymerase chain reaction (PCR) primers. The method is based on the difference in mobility of the phosphorylated and nonphosphorylated PCR products (in the same number of basepairs) on phosphate-affinity polyacrylamide gel electrophoresis. The phosphate-affinity site is a polyacrylamide-bound dinuclear zinc(II) complex, which preferentially captures the 5'-phosphate-labeled allele-specific product compared with the corresponding nonlabeled product. The obtained DNA migration bands can be visualized by ethidium bromide staining. We demonstrate the genotyping of a SNP reported in a human cardiac sodium channel gene, SCN5A, using this novel procedure.     

Quantification of the power of Hardy-Weinberg equilibrium testing to detect genotyping error

Deviation from Hardy-Weinberg equilibrium has become an accepted test for genotyping error. While it is generally considered that testing departures from Hardy-Weinberg equilibrium to detect genotyping error is not sensitive, little has been done to quantify this sensitivity. Therefore, we have examined various models of genotyping error, including error caused by neighboring SNPs that degrade the performance of genotyping assays. We then calculated the power of chi-square goodness-of-fit tests for deviation from Hardy-Weinberg equilibrium to detect such error. We have also examined the affects of neighboring SNPs on risk estimates in the setting of case-control association studies. We modeled the power of departure from Hardy-Weinberg equilibrium as a test to detect genotyping error and quantified the effect of genotyping error on disease risk estimates. Generally, genotyping error does not generate sufficient deviation from Hardy-Weinberg equilibrium to be detected. As expected, genotyping error due to neighboring SNPs attenuates risk estimates, often drastically. For the moment, the most widely accepted method of detecting genotyping error is to confirm genotypes by sequencing and/or genotyping via a separate method. While these methods are fairly reliable, they are also costly and time consuming.     

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.