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Assessing SNP genotyping of noninvasively collected wildlife samples using microfluidic arrays.

ABSTRACT: Noninvasively collected samples are a common source of DNA in wildlife genetic studies. Currently, single nucleotide polymorphism (SNP) genotyping using microfluidic arrays is emerging as an easy-to-use and cost-effective methodology. Here we assessed the performance of microfluidic SNP arrays in genotyping noninvasive samples from grey wolves, European wildcats and brown bears, and we compared results with traditional microsatellite genotyping. We successfully SNP-genotyped 87%, 80% and 97% of the wolf, cat and bear samples, respectively. Genotype recovery was higher based on SNPs, while both marker types identified the same individuals and provided almost identical estimates of pairwise differentiation. We found that samples for which all SNP loci were scored had no disagreements across the three replicates (except one locus in a wolf sample). Thus, we argue that call rate (amplification success) can be used as a proxy for genotype quality, allowing the reduction of replication effort when call rate is high. Furthermore, we used cycle threshold values of real-time PCR to guide the choice of protocols for SNP amplification. Finally, we provide general guidelines for successful SNP genotyping of degraded DNA using microfluidic technology.

SUBMITTER: von Thaden A

PROVIDER: S-EPMC5589735 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Assessing SNP genotyping of noninvasively collected wildlife samples using microfluidic arrays.

von Thaden Alina A Cocchiararo Berardino B Jarausch Anne A Jüngling Hannah H Karamanlidis Alexandros A AA Tiesmeyer Annika A Nowak Carsten C Muñoz-Fuentes Violeta V

Scientific reports 20170907 1

Noninvasively collected samples are a common source of DNA in wildlife genetic studies. Currently, single nucleotide polymorphism (SNP) genotyping using microfluidic arrays is emerging as an easy-to-use and cost-effective methodology. Here we assessed the performance of microfluidic SNP arrays in genotyping noninvasive samples from grey wolves, European wildcats and brown bears, and we compared results with traditional microsatellite genotyping. We successfully SNP-genotyped 87%, 80% and 97% of ...[more]

PMID: 28883428

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Project description:BACKGROUND: Genome-wide association studies (GWAS) using array-based genotyping technology are widely used to identify genetic loci associated with complex diseases or other phenotypes. The costs of GWAS projects based on individual genotyping are still comparatively high and increase with the size of study populations. Genotyping using pooled DNA samples, as also being referred as to allelotyping approach, offers an alternative at affordable costs. In the present study, data from 100 DNA samples individually genotyped with the Affymetrix Genome-Wide Human SNP Array 6.0 were used to estimate the error of the pooling approach by comparing the results with those obtained using the same array type but DNA pools each composed of 50 of the same samples. Newly developed and established methods for signal intensity correction were applied. Furthermore, the relative allele intensity signals (RAS) obtained by allelotyping were compared to the corresponding values derived from individual genotyping. Similarly, differences in RAS values between pools were determined and compared. RESULTS: Regardless of the intensity correction method applied, the pooling-specific error of the pool intensity values was larger for single pools than for the comparison of the intensity values of two pools, which reflects the scenario of a case-control study. Using 50 pooled samples and analyzing 10,000 SNPs with a minor allele frequency of >1% and applying the best correction method for the corresponding type of comparison, the 90% quantile (median) of the pooling-specific absolute error of the RAS values for single sub-pools and the SNP-specific difference in allele frequency comparing two pools was 0.064 (0.026) and 0.056 (0.021), respectively. CONCLUSIONS: Correction of the RAS values reduced the error of the RAS values when analyzing single pool intensities. We developed a new correction method with high accuracy but low computational costs. Correction of RAS, however, only marginally reduced the error of true differences between two sample groups and those obtained by allelotyping. Exclusion of SNPs with a minor allele frequency of ? 1% notably reduced the pooling-specific error. Our findings allow for improving the estimation of the pooling-specific error and may help in designing allelotyping studies using the Affymetrix Genome-Wide Human SNP Array 6.0.

Project description:BackgroundGenotypes obtained with commercial SNP arrays have been extensively used in many large case-control or population-based cohorts for SNP-based genome-wide association studies for a multitude of traits. Yet, these genotypes capture only a small fraction of the variance of the studied traits. Genomic structural variants (GSV) such as Copy Number Variation (CNV) may account for part of the missing heritability, but their comprehensive detection requires either next-generation arrays or sequencing. Sophisticated algorithms that infer CNVs by combining the intensities from SNP-probes for the two alleles can already be used to extract a partial view of such GSV from existing data sets.ResultsHere we present several advances to facilitate the latter approach. First, we introduce a novel CNV detection method based on a Gaussian Mixture Model. Second, we propose a new algorithm, PCA merge, for combining copy-number profiles from many individuals into consensus regions. We applied both our new methods as well as existing ones to data from 5612 individuals from the CoLaus study who were genotyped on Affymetrix 500K arrays. We developed a number of procedures in order to evaluate the performance of the different methods. This includes comparison with previously published CNVs as well as using a replication sample of 239 individuals, genotyped with Illumina 550K arrays. We also established a new evaluation procedure that employs the fact that related individuals are expected to share their CNVs more frequently than randomly selected individuals. The ability to detect both rare and common CNVs provides a valuable resource that will facilitate association studies exploring potential phenotypic associations with CNVs.ConclusionOur new methodologies for CNV detection and their evaluation will help in extracting additional information from the large amount of SNP-genotyping data on various cohorts and use this to explore structural variants and their impact on complex traits.

Project description:BackgroundMultiple investigators have established the feasibility of using buccal brush samples to genotype single nucleotide polymorphisms (SNPs) with high-density genome-wide microarrays, but there is currently no consensus on the accuracy of copy number variants (CNVs) inferred from these data. Regardless of the source of DNA, it is more difficult to detect CNVs than to genotype SNPs using these microarrays, and it therefore remains an open question whether buccal brush samples provide enough high-quality DNA for this purpose.MethodsTo demonstrate the quality of CNV calls generated from DNA extracted from buccal samples, compared to calls generated from blood samples, we evaluated the concordance of calls from individuals who provided both sample types. The Illumina Human660W-Quad BeadChip was used to determine SNPs and CNVs of 39 Arkansas participants in the National Birth Defects Prevention Study (NBDPS), including 16 mother-infant dyads, who provided both whole blood and buccal brush DNA samples.ResultsWe observed a 99.9% concordance rate of SNP calls in the 39 blood-buccal pairs. From the same dataset, we performed a similar analysis of CNVs. Each of the 78 samples was independently segmented into regions of like copy number using the Optimal Segmentation algorithm of Golden Helix SNP & Variation Suite 7.Across 640,663 loci on 22 autosomal chromosomes, segment-mean log R ratios had an average correlation of 0.899 between blood-buccal pairs of samples from the same individual, while the average correlation between all possible blood-buccal pairs of samples from unrelated individuals was 0.318. An independent analysis using the QuantiSNP algorithm produced average correlations of 0.943 between blood-buccal pairs from the same individual versus 0.332 between samples from unrelated individuals.Segment-mean log R ratios had an average correlation of 0.539 between mother-offspring dyads of buccal samples, which was not statistically significantly different than the average correlation of 0.526 between mother-offspring dyads of blood samples (p=0.302).ConclusionsWe observed performance from the subject-collected mail-in buccal brush samples comparable to that of blood. These results show that such DNA samples can be used for genome-wide scans of both SNPs and CNVs, and that high rates of CNV concordance were achieved whether using a change-point-based algorithm or one based on a hidden Markov model (HMM).

Dataset Information

Assessing SNP genotyping of noninvasively collected wildlife samples using microfluidic arrays.

Publications

Assessing SNP genotyping of noninvasively collected wildlife samples using microfluidic arrays.

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