Single nucleotide polymorphisms for pig identification and parentage exclusion

Single nucleotide polymorphisms (SNPs) have become an important type of marker for commercial diagnostic and parentage genotyping applications as automated genotyping systems have been developed that yield accurate genotypes. Unfortunately, allele frequencies for public SNP markers in commercial pig populations have not been available. To fulfil this need, SNP markers previously mapped in the USMARC swine reference population were tested in a panel of 155 boars that were representative of US purebred Duroc, Hampshire, Landrace and Yorkshire populations. Multiplex assay groups of 5-7 SNP assays/group were designed and genotypes were determined using Sequenom's massarray system. Of 80 SNPs that were evaluated, 60 SNPs with minor allele frequencies >0.15 were selected for the final panel of markers. Overall identity power across breeds was 4.6 x 10(-23), but within-breed values ranged from 4.3 x 10(-14) (Hampshire) to 2.6 x 10(-22) (Yorkshire). Parentage exclusion probability with only one sampled parent was 0.9974 (all data) and ranged from 0.9594 (Hampshire) to 0.9963 (Yorkshire) within breeds. Sire exclusion probability when the dam's genotype was known was 0.99998 (all data) and ranged from 0.99868 (Hampshire) to 0.99997 (Yorkshire) within breeds. Power of exclusion was compared between the 60 SNP and 10 microsatellite markers. The parental exclusion probabilities for SNP and microsatellite marker panels were similar, but the SNP panel was much more sensitive for individual identification. This panel of SNP markers is theoretically sufficient for individual identification of any pig in the world and is publicly available.     

Precision and accuracy of divergence time estimates from STR and SNPSTR variation

Inference of intraspecific population divergence patterns typically requires genetic data for molecular markers with relatively high mutation rates. Microsatellites, or short tandem repeat (STR) polymorphisms, have proven informative in many such investigations. These markers are characterized, however, by high levels of homoplasy and varying mutational properties, often leading to inaccurate inference of population divergence. A SNPSTR is a genetic system that consists of an STR polymorphism closely linked (typically < 500 bp) to one or more single-nucleotide polymorphisms (SNPs). SNPSTR systems are characterized by lower levels of homoplasy than are STR loci. Divergence time estimates based on STR variation (on the derived SNP allele background) should, therefore, be more accurate and precise. We use coalescent-based simulations in the context of several models of demographic history to compare divergence time estimates based on SNPSTR haplotype frequencies and STR allele frequencies. We demonstrate that estimates of divergence time based on STR variation on the background of a derived SNP allele are more accurate (3% to 7% bias for SNPSTR versus 11% to 20% bias for STR) and more precise than STR-based estimates, conditional on a recent SNP mutation. These results hold even for models involving complex demographic scenarios with gene flow, population expansion, and population bottlenecks. Varying the timing of the mutation event generating the SNP revealed that estimates of divergence time are sensitive to SNP age, with more recent SNPs giving more accurate and precise estimates of divergence time. However, varying both mutational properties of STR loci and SNP age demonstrated that multiple independent SNPSTR systems provide less biased estimates of divergence time. Furthermore, the combination of estimates based separately on STR and SNPSTR variation provides insight into the age of the derived SNP alleles. In light of our simulations, we interpret estimates from data for human populations.     

Finding Markers That Make a Difference: DNA Pooling and SNP-Arrays Identify Population Informative Markers for Genetic Stock Identification

Genetic stock identification (GSI) using molecular markers is an important tool for management of migratory species. Here, we tested a cost-effective alternative to individual genotyping, known as allelotyping, for identification of highly informative SNPs for accurate genetic stock identification. We estimated allele frequencies of 2880 SNPs from DNA pools of 23 Atlantic salmon populations using Illumina SNP-chip. We evaluated the performance of four common strategies (global F _ST, pairwise F _ST, Delta and outlier approach) for selection of the most informative set of SNPs and tested their effectiveness for GSI compared to random sets of SNP and microsatellite markers. For the majority of cases, SNPs selected using the outlier approach performed best followed by pairwise F _ST and Delta methods. Overall, the selection procedure reduced the number of SNPs required for accurate GSI by up to 53% compared with randomly chosen SNPs. However, GSI accuracy was more affected by populations in the ascertainment group rather than the ranking method itself. We demonstrated for the first time the compatibility of different large-scale SNP datasets by compiling the largest population genetic dataset for Atlantic salmon to date. Finally, we showed an excellent performance of our top SNPs on an independent set of populations covering the main European distribution range of Atlantic salmon. Taken together, we demonstrate how combination of DNA pooling and SNP arrays can be applied for conservation and management of salmonids as well as other species.     

Improved multiple displacement amplification with phi29 DNA polymerase for genotyping of single human cells

The ability to genotype multiple loci of single cells would be of significant benefit to investigations of cellular processes such as oncogenesis, meiosis, fertilization, and embryogenesis. We report a simple two-step, single-tube protocol for whole-genome amplification (WGA) from single human cells using components of the GenomiPhi V2 DNA Amplification kit. For the first time, we demonstrate reliable generation of 4-7 microg amplified DNA from a single human cell within 4 h with a minimum amount of artifactual DNA synthesis. DNA amplified from single cells was genotyped for 13 heterozygous short tandem repeats (STRs) and 7 heterozygous single nucleotide polymorphisms (SNPs), and the genotyping results were compared with purified genomic DNA. Accuracy of genotyping (percent of single-cell amplifications genotyped accurately for any particular STR or SNP) varied from 37% to 100% (with an average of 80%) for STRs and from 89% to 100% (averaging 94%) for SNPs. We suggest that the method described in this report is suitable for WGA from single cells, the product of which can be subsequently used for many applications, such as preimplantation genetic analysis (PGD).     

Allele frequency matching between SNPs reveals an excess of linkage disequilibrium in genic regions of the human genome

Significant interest has emerged in mapping genetic susceptibility for complex traits through whole-genome association studies. These studies rely on the extent of association, i.e., linkage disequilibrium (LD), between single nucleotide polymorphisms (SNPs) across the human genome. LD describes the nonrandom association between SNP pairs and can be used as a metric when designing maximally informative panels of SNPs for association studies in human populations. Using data from the 1.58 million SNPs genotyped by Perlegen, we explored the allele frequency dependence of the LD statistic r(2) both empirically and theoretically. We show that average r(2) values between SNPs unmatched for allele frequency are always limited to much less than 1 (theoretical approximately 0.46 to 0.57 for this dataset). Frequency matching of SNP pairs provides a more sensitive measure for assessing the average decay of LD and generates average r(2) values across nearly the entire informative range (from 0 to 0.89 through 0.95). Additionally, we analyzed the extent of perfect LD (r(2) = 1.0) using frequency-matched SNPs and found significant differences in the extent of LD in genic regions versus intergenic regions. The SNP pairs exhibiting perfect LD showed a significant bias for derived, nonancestral alleles, providing evidence for positive natural selection in the human genome.     

Common benzothiazole and benzoxazole fluorescent DNA intercalators for studying Alzheimer Aβ1-42 and prion amyloid peptides

DNA-based methods for human identification principally rely upon genotyping of short tandem repeat (STR) loci. Electrophoretic-based techniques for variable-length classification of STRs are universally utilized, but are limited in that they have relatively low throughput and do not yield nucleotide sequence information. High-throughput sequencing technology may provide a more powerful instrument for human identification, but is not currently validated for forensic casework. Here, we present a systematic method to perform high-throughput genotyping analysis of the Combined DNA Index System (CODIS) STR loci using short-read (150 bp) massively parallel sequencing technology. Open source reference alignment tools were optimized to evaluate PCR-amplified STR loci using a custom designed STR genome reference. Evaluation of this approach demonstrated that the 13 CODIS STR loci and amelogenin (AMEL) locus could be accurately called from individual and mixture samples. Sensitivity analysis showed that as few as 18,500 reads, aligned to an in silico referenced genome, were required to genotype an individual (>99% confidence) for the CODIS loci. The power of this technology was further demonstrated by identification of variant alleles containing single nucleotide polymorphisms (SNPs) and the development of quantitative measurements (reads) for resolving mixed samples.     

The impact of missing and erroneous genotypes on tagging SNP selection and power of subsequent association tests

OBJECTIVE: Single nucleotide polymorphisms (SNPs) serve as effective markers for localizing disease susceptibility genes, but current genotyping technologies are inadequate for genotyping all available SNP markers in a typical linkage/association study. Much attention has recently been paid to methods for selecting the minimal informative subset of SNPs in identifying haplotypes, but there has been little investigation of the effect of missing or erroneous genotypes on the performance of these SNP selection algorithms and subsequent association tests using the selected tagging SNPs. The purpose of this study is to explore the effect of missing genotype or genotyping error on tagging SNP selection and subsequent single marker and haplotype association tests using the selected tagging SNPs. METHODS: Through two sets of simulations, we evaluated the performance of three tagging SNP selection programs in the presence of missing or erroneous genotypes: Clayton's diversity based program htstep, Carlson's linkage disequilibrium (LD) based program ldSelect, and Stram's coefficient of determination based program tagsnp.exe. RESULTS: When randomly selected known loci were relabeled as 'missing', we found that the average number of tagging SNPs selected by all three algorithms changed very little and the power of subsequent single marker and haplotype association tests using the selected tagging SNPs remained close to the power of these tests in the absence of missing genotype. When random genotyping errors were introduced, we found that the average number of tagging SNPs selected by all three algorithms increased. In data sets simulated according to the haplotype frequecies in the CYP19 region, Stram's program had larger increase than Carlson's and Clayton's programs. In data sets simulated under the coalescent model, Carlson's program had the largest increase and Clayton's program had the smallest increase. In both sets of simulations, with the presence of genotyping errors, the power of the haplotype tests from all three programs decreased quickly, but there was not much reduction in power of the single marker tests. CONCLUSIONS: Missing genotypes do not seem to have much impact on tagging SNP selection and subsequent single marker and haplotype association tests. In contrast, genotyping errors could have severe impact on tagging SNP selection and haplotype tests, but not on single marker tests.     

Forensically relevant SNP classes

Forensic samples that contain too little template DNA or are too degraded require alternate genetic marker analyses or approaches to what is currently used for routine casework. Single nucleotide polymorphisms (SNPs) offer promise to support forensic DNA analyses because of an abundance of potential markers, amenability to automation, and potential reduction in required fragment length to only 60-80 bp. The SNP markers will serve an important role in analyzing challenging forensic samples, such as those that are very degraded, for augmenting the power of kinship analyses and family reconstructions for missing persons and unidentified human remains, as well as for providing investigative lead value in some cases without a suspect (and no genetic profile match in CODIS). The SNPs for forensic analyses can be divided into four categories: identity-testing SNPs; lineage informative SNPs; ancestry informative SNPs; and phenotype informative SNPs. In addition to discussing the applications of these different types of SNPs, this article provides some discussion on privacy issues so that society and policymakers can be more informed.     

Selection of 29 highly informative InDel markers for human identification and paternity analysis in Chinese Han population by the SNPlex genotyping system

The interest of forensic researchers in single nucleotide polymorphism (SNP) has been attracted because of its potential advantages, such as low mutation rates, amenable to high-throughput automated platform and the improved application in the analysis of degraded samples. In this paper, 29 highly informative insertion/deletion (InDel, a special kind of SNP) markers were selected from the dbSNP () according to the given criteria. 109 unrelated Chinese Han subjects were genotyped for the 29 InDels with SNPlex genotyping system. The allele frequency data revealed that the combined power of discrimination for the 29 InDel markers was 0.999999999990867 and the combined probability of paternity exclusion (PE) was 0.9930. Sensitivity studies were performed to evaluate the flexibility of the SNPlex genotyping system on the set of 29 InDels. Highly reproducible results could be obtained with 40–100 ng genomic DNA and the proportion of total allele drop-in was significantly increased when the amount of DNA added to PCR was lower than 35 ng. These results suggested that the set of 29 InDels was useful in paternity analysis or human identification in the future.     

MLR-tagging: informative SNP selection for unphased genotypes based on multiple linear regression

The search for the association between complex diseases and single nucleotide polymorphisms (SNPs) or haplotypes has recently received great attention. For these studies, it is essential to use a small subset of informative SNPs accurately representing the rest of the SNPs. Informative SNP selection can achieve (1) considerable budget savings by genotyping only a limited number of SNPs and computationally inferring all other SNPs or (2) necessary reduction of the huge SNP sets (obtained, e.g. from Affymetrix) for further fine haplotype analysis. A novel informative SNP selection method for unphased genotype data based on multiple linear regression (MLR) is implemented in the software package MLR-tagging. This software can be used for informative SNP (tag) selection and genotype prediction. The stepwise tag selection algorithm (STSA) selects positions of the given number of informative SNPs based on a genotype sample population. The MLR SNP prediction algorithm predicts a complete genotype based on the values of its informative SNPs, their positions among all SNPs, and a sample of complete genotypes. An extensive experimental study on various datasets including 10 regions from HapMap shows that the MLR prediction combined with stepwise tag selection uses fewer tags than the state-of-the-art method of Halperin et al. (2005). AVAILABILITY: MLR-Tagging software package is publicly available at http://alla.cs.gsu.edu/~software/tagging/tagging.html     

Precision-mapping and statistical validation of quantitative trait loci by machine learning

Background

DNA sequence diversity within the human genome may be more greatly affected by copy number variations (CNVs) than single nucleotide polymorphisms (SNPs). Although the importance of CNVs in genome wide association studies (GWAS) is becoming widely accepted, the optimal methods for identifying these variants are still under evaluation. We have previously reported a comprehensive view of CNVs in the HapMap DNA collection using high density 500 K EA (Early Access) SNP genotyping arrays which revealed greater than 1,000 CNVs ranging in size from 1 kb to over 3 Mb. Although the arrays used most commonly for GWAS predominantly interrogate SNPs, CNV identification and detection does not necessarily require the use of DNA probes centered on polymorphic nucleotides and may even be hindered by the dependence on a successful SNP genotyping assay.

Results

In this study, we have designed and evaluated a high density array predicated on the use of non-polymorphic oligonucleotide probes for CNV detection. This approach effectively uncouples copy number detection from SNP genotyping and thus has the potential to significantly improve probe coverage for genome-wide CNV identification. This array, in conjunction with PCR-based, complexity-reduced DNA target, queries over 1.3 M independent NspI restriction enzyme fragments in the 200 bp to 1100 bp size range, which is a several fold increase in marker density as compared to the 500 K EA array. In addition, a novel algorithm was developed and validated to extract CNV regions and boundaries.

Conclusion

Using a well-characterized pair of DNA samples, close to 200 CNVs were identified, of which nearly 50% appear novel yet were independently validated using quantitative PCR. The results indicate that non-polymorphic probes provide a robust approach for CNV identification, and the increasing precision of CNV boundary delineation should allow a more complete analysis of their genomic organization.     

High-throughput single nucleotide polymorphism genotyping for breeding applications in rice using the BeadXpress platform

Multiplexed single nucleotide polymorphism (SNP) markers have the potential to increase the speed and cost-effectiveness of genotyping, provided that an optimal SNP density is used for each application. To test the efficiency of multiplexed SNP genotyping for diversity, mapping and breeding applications in rice (Oryza sativa L.), we designed seven GoldenGate VeraCode oligo pool assay (OPA) sets for the Illumina BeadXpress Reader. Validated markers from existing 1536 Illumina SNPs and 44?K Affymetrix SNP chips developed at Cornell University were used to select subsets of informative SNPs for different germplasm groups with even distribution across the genome. A 96-plex OPA was developed for quality control purposes and for assigning a sample into one of the five O. sativa population subgroups. Six 384-plex OPAs were designed for genetic diversity analysis, DNA fingerprinting, and to have evenly-spaced polymorphic markers for quantitative trait locus (QTL) mapping and background selection for crosses between different germplasm pools in rice: Indica/Indica, Indica/Japonica, Japonica/Japonica, Indica/O. rufipogon, and Japonica/O. rufipogon. After testing on a diverse set of rice varieties, two of the SNP sets were re-designed by replacing poor-performing SNPs. Pilot studies were successfully performed for diversity analysis, QTL mapping, marker-assisted backcrossing, and developing specialized genetic stocks, demonstrating that 384-plex SNP genotyping on the BeadXpress platform is a robust and efficient method for marker genotyping in rice.     

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.     

High-throughput genotyping in citrus accessions using an SNP genotyping array

We developed a 384 multiplexed SNP array, named CitSGA-1, for the genotyping of Citrus cultivars, and evaluated the performance and reliability of the genotyping. SNPs were surveyed by direct sequence comparison of the sequence tagged site (STS) fragment amplified from genomic DNA of cultivars representing the genetic diversity of citrus breeding in Japan. Among 1497 SNPs candidates, 384 SNPs for a high-throughput genotyping array were selected based on physical parameters of Illumina’s bead array criteria. The assay using CitSGA-1 was applied to a hybrid population of 88 progeny and 103 citrus accessions for breeding in Japan, which resulted in 73,726 SNP calls. A total of 351 SNPs (91 %) could call different genotypes among the DNA samples, resulting in a success rate for the assay comparable to previously reported rates for other plant species. To confirm the reliability of SNP genotype calls, parentage analysis was applied, and it indicated that the number of reliable SNPs and corresponding STSs were 276 and 213, respectively. The multiplexed SNP genotyping array reported here will be useful for the efficient construction of linkage map, for the detection of markers for marker-assisted breeding, and for the identification of cultivars.     

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.     

Full flexibility genotyping of single nucleotide polymorphisms by the GOOD assay

Recently a facile method for genotyping single nucleotide polymorphisms (SNPs) using MALDI mass spectrometry, termed the GOOD assay, was developed. It does not require any purification and is performed with simple liquid handling, thermal incubation and cycling steps. Although this method is well suited to automation and high-throughput analysis of SNPs, it did not allow full flexibility due to lack of certain reagents. A complete set of β-cyanoethyl phosphoramidites is presented herein that give this SNP genotyping method full sequence and multiplex capabilities. Applications to SNP genotyping in the prion protein gene, the β-2-adrenergic receptor gene and the angiotensin converting enzyme gene using the GOOD assay are demonstrated. Because SNP genotyping technologies are generally very sensitive to varying DNA quality, the GOOD assay has been stabilised and optimised for low quality DNA. A template extraction method is introduced that allows genotyping from tissue that was taken while placing an ear tag on an animal. This dramatically facilitates the application of genotyping to animal agricultural applications, as it demonstrates that expensive and cumbersome DNA extraction procedures prior to genotyping can be avoided.     

Large-scale genotyping of complex DNA

Genetic studies aimed at understanding the molecular basis of complex human phenotypes require the genotyping of many thousands of single-nucleotide polymorphisms (SNPs) across large numbers of individuals. Public efforts have so far identified over two million common human SNPs; however, the scoring of these SNPs is labor-intensive and requires a substantial amount of automation. Here we describe a simple but effective approach, termed whole-genome sampling analysis (WGSA), for genotyping thousands of SNPs simultaneously in a complex DNA sample without locus-specific primers or automation. Our method amplifies highly reproducible fractions of the genome across multiple DNA samples and calls genotypes at >99% accuracy. We rapidly genotyped 14,548 SNPs in three different human populations and identified a subset of them with significant allele frequency differences between groups. We also determined the ancestral allele for 8,386 SNPs by genotyping chimpanzee and gorilla DNA. WGSA is highly scaleable and enables the creation of ultrahigh density SNP maps for use in genetic studies.     

Genotyping performance between saliva and blood-derived genomic DNAs on the DMET array: a comparison

The Affymetrix Drug Metabolism Enzymes and Transporters (DMET) microarray is the first assay to offer a large representation of SNPs conferring genetic diversity across known pharmacokinetic markers. As a convenient and painless alternative to blood, saliva samples have been reported to work well for genotyping on the high density SNP arrays, but no reports to date have examined this application for saliva-derived DNA on the DMET platform. Genomic DNA extractions from saliva samples produced an ample quantity of genomic DNA for DMET arrays, however when human amplifiable DNA was measured, it was determined that a large percentage of this DNA was from bacteria or fungi. A mean of 37.3% human amplifiable DNA was determined for saliva-derived DNAs, which results in a significant decrease in the genotyping call rate (88.8%) when compared with blood-derived DNAs (99.1%). More interestingly, the percentage of human amplifiable DNA correlated with a higher genotyping call rate, and almost all samples with more than 31.3% human DNA produced a genotyping call rate of at least 96%. SNP genotyping results for saliva derived DNA (n = 39) illustrated a 98.7% concordance when compared with blood DNA. In conclusion, when compared with blood DNA and tested on the DMET array, saliva-derived DNA provided adequate genotyping quality with a significant lower number of SNP calls. Saliva-derived DNA does perform very well if it contains greater than 31.3% human amplifiable DNA.