Comparison of genotyping using pooled DNA samples (allelotyping) and individual genotyping using the affymetrix genome-wide human SNP array 6.0

Derived Polymorphic Amplified Cleaved Sequence (dPACS) Assay

The derived polymorphic amplified cleaved sequence (dPACS) assay is a simple polymerase chain reaction/restriction fragment length polymorphism (PCR-RFLP)-based procedure for detecting known single-nucleotide polymorphisms (SNPs) and deletion-insertion polymorphisms (DIPs). It is relatively straightforward to carry out using basic and commonly available molecular biology kits. The method differs from other PCR-RFLP assays in that it employs 35-55 bp primer pairs that encompass the entire targeted DNA region except for a few diagnostic nucleotides being examined. In so doing, it allows for the introduction of nucleotide mismatches in one or both primers for differentiating wild from mutant sequences following polymerase chain reaction, restriction digestion and MetaPhor gel electrophoresis. Primer design and the selection of discriminating enzymes are achieved with the help of the dPACS 1.0 program. The method is exemplified here with the positive detection of serine 264-psbA, a key determinant for the effective binding of some photosystem II inhibitors to their target. A serine-to-glycine mutation at codon 264 of psbA causes resistance to serine-binding photosystem II herbicides in several grasses and broad-leaf weeds, including Amaranthus retroflexus, which is employed in this study.

Deciphering the Genetic Diversity of Landraces With High-Throughput SNP Genotyping of DNA Bulks: Methodology and Application to the Maize 50k Array

Genebanks harbor original landraces carrying many original favorable alleles for mitigating biotic and abiotic stresses. Their genetic diversity remains, however, poorly characterized due to their large within genetic diversity. We developed a high-throughput, cheap and labor saving DNA bulk approach based on single-nucleotide polymorphism (SNP) Illumina Infinium HD array to genotype landraces. Samples were gathered for each landrace by mixing equal weights from young leaves, from which DNA was extracted. We then estimated allelic frequencies in each DNA bulk based on fluorescent intensity ratio (FIR) between two alleles at each SNP using a two step-approach. We first tested either whether the DNA bulk was monomorphic or polymorphic according to the two FIR distributions of individuals homozygous for allele A or B, respectively. If the DNA bulk was polymorphic, we estimated its allelic frequency by using a predictive equation calibrated on FIR from DNA bulks with known allelic frequencies. Our approach: (i) gives accurate allelic frequency estimations that are highly reproducible across laboratories, (ii) protects against false detection of allele fixation within landraces. We estimated allelic frequencies of 23,412 SNPs in 156 landraces representing American and European maize diversity. Modified Roger's genetic Distance between 156 landraces estimated from 23,412 SNPs and 17 simple sequence repeats using the same DNA bulks were highly correlated, suggesting that the ascertainment bias is low. Our approach is affordable, easy to implement and does not require specific bioinformatics support and laboratory equipment, and therefore should be highly relevant for large-scale characterization of genebanks for a wide range of species.

Derived Polymorphic Amplified Cleaved Sequence (dPACS): A Novel PCR-RFLP Procedure for Detecting Known Single Nucleotide and Deletion-Insertion Polymorphisms

Most methods developed for detecting known single nucleotide polymorphisms (SNP) and deletion-insertion polymorphisms (DIP) are dependent on sequence conservation around the SNP/DIP and are therefore not suitable for application to heterogeneous organisms. Here we describe a novel, versatile and simple PCR-RFLP procedure baptised 'derived Polymorphic Amplified Cleaved Sequence' (dPACS) for genotyping individual samples. The notable advantage of the method is that it employs a pair of primers that cover the entire fragment to be amplified except for one or few diagnostic bases around the SNP/DIP being investigated. As such, it provides greater opportunities to introduce mismatches in one or both of the 35-55 bp primers for creating a restriction site that unambiguously differentiates wild from mutant sequences following PCR-RFLP and horizontal MetaPhorTM gel electrophoresis. Selection of effective restriction enzymes and primers is aided by the newly developed dPACS 1.0 software. The highly transferable dPACS procedure is exemplified here with the positive detection (in up to 24 grass and broadleaf species tested) of wild type proline106 of 5-enolpyruvylshikimate-3-phosphate synthase and its serine, threonine and alanine variants that confer resistance to glyphosate, and serine264 and isoleucine2041 which are key target-site determinants for weed sensitivities to some photosystem II and acetyl-CoA carboxylase inhibiting herbicides, respectively.

Screening of Genetic Factor in the Interaction Between Periodontitis and Metabolic Traits Using Candidate Gene Association Study (CGAS)

Periodontitis has been reported to relate to metabolic syndrome traits such as obesity, blood pressure, and so on. However, the relation between periodontitis and metabolic syndrome remains unclear. The present study aimed to confirm common genetic factors between periodontitis and metabolic traits using Candidate gene association study (CGAS) in the Korean population. Based on the analysis of CGAS, this study performed linear regression analyses to examine the single-nucleotide polymorphisms (SNPs) between periodontitis and metabolic syndrome traits. Among the analyzed SNPs, 2649 SNPs in five genes (TENM2, LDLRAD4, SLC9C2, MFSD1, and A2BP1) showed a statistical significance at p < 0.05. Interestingly, A2BP1 and TENM2 were related to obesity. Also, elevated levels of LDLRAD4, SLC9C2, and MFSD1 were observed in the patients with high blood pressure. Taken together, the present study suggests that some of the SNPs are related to periodontitis. Therefore, if any of TENM2, A2BP1, LDLRAD4, SLC9C2, and MFSD1 is detected in the patients with periodontitis, obesity and blood pressure have to be treated simultaneously.

Pooled genome wide association detects association upstream of FCRL3 with Graves' disease

Background

Graves’ disease is an autoimmune thyroid disease of complex inheritance. Multiple genetic susceptibility loci are thought to be involved in Graves’ disease and it is therefore likely that these can be identified by genome wide association studies. This study aimed to determine if a genome wide association study, using a pooling methodology, could detect genomic loci associated with Graves’ disease.

Results

Nineteen of the top ranking single nucleotide polymorphisms including HLA-DQA1 and C6orf10, were clustered within the Major Histo-compatibility Complex region on chromosome 6p21, with rs1613056 reaching genome wide significance (p = 5 × 10⁻⁸). Technical validation of top ranking non-Major Histo-compatablity complex single nucleotide polymorphisms with individual genotyping in the discovery cohort revealed four single nucleotide polymorphisms with p ≤ 10⁻⁴. Rs17676303 on chromosome 1q23.1, located upstream of FCRL3, showed evidence of association with Graves’ disease across the discovery, replication and combined cohorts. A second single nucleotide polymorphism rs9644119 downstream of DPYSL2 showed some evidence of association supported by finding in the replication cohort that warrants further study.

Conclusions

Pooled genome wide association study identified a genetic variant upstream of FCRL3 as a susceptibility locus for Graves’ disease in addition to those identified in the Major Histo-compatibility Complex. A second locus downstream of DPYSL2 is potentially a novel genetic variant in Graves’ disease that requires further confirmation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3276-z) contains supplementary material, which is available to authorized users.

DNA methylation levels are highly correlated between pooled samples and averaged values when analysed using the Infinium HumanMethylation450 BeadChip array

BACKGROUND

DNA methylation is a heritable and stable epigenetic mark implicated in complex human traits. Epigenome-wide association studies (EWAS) using array-based technology are becoming widely used to identify differentially methylated sites associated with complex diseases. EWAS studies require large sample sizes to detect small effects, which increases project costs. In the present study we propose to pool DNA samples in methylation array studies as an affordable and accurate alternative to individual samples studies, in order to reduce economic costs or when low amounts of DNA are available. For this study, 20 individual DNA samples and 4 pooled DNA samples were analysed using the Illumina Infinium HumanMethylation450 BeadChip array to evaluate the efficiency of the pooling approach in EWAS studies. Statistical power calculations were also performed to discover the minimum sample size needed for the pooling strategy in EWAS.

RESULTS

A total of 485,577 CpG sites across the whole genome were assessed. Comparison of methylation levels of all CpG sites between individual samples and their related pooled samples revealed highly significant correlations (rho > 0.99, p-val < 10(-16)). These results remained similar when assessing the 101 most differentially methylated CpG sites (rho > 0.98, p-val < 10(-16)). Also, it was calculated that n = 43 is the minimum sample size required to achieve a 95 % statistical power and a 10(-06) significance level in EWAS, when using a DNA pool strategy.

CONCLUSIONS

DNA pooling strategies seems to accurately provide estimations of averaged DNA methylation state using array based EWAS studies. This type of approach can be applied to the assessment of disease phenotypes, reducing the amount of DNA required and the cost of large-scale epigenetic analyses.

Numerical analysis of intensity signals resulting from genotyping pooled DNA samples in beef cattle and broiler chicken

Pooled genomic DNA has been proposed as a cost-effective approach in genomewide association studies (GWAS). However, algorithms for genotype calling of biallelic SNP are not adequate with pooled DNA samples because they assume the presence of 2 fluorescent signals, 1 for each allele, and operate under the expectation that at most 2 copies of the variant allele can be found for any given SNP and DNA sample. We adapt analytical methodology from 2-channel gene expression microarray technology to SNP genotyping of pooled DNA samples. Using 5 datasets from beef cattle and broiler chicken of varying degrees of complexity in terms of design and phenotype, continuous and dichotomous, we show that both differential hybridization (M = green minus red intensity signal) and abundance (A = average of red and green intensities) provide useful information in the prediction of SNP allele frequencies. This is predominantly true when making inference about extreme SNP that are either nearly fixed or highly polymorphic. We propose the use of model-based clustering via mixtures of bivariate normal distributions as an optimal framework to capture the relationship between hybridization intensity and allele frequency from pooled DNA samples. The range of M and A values observed here are in agreement with those reported within the context of gene expression microarray and also with those from SNP array data within the context of analytical methodology for the identification of copy number variants. In particular, we confirm that highly polymorphic SNP yield a strong signal from both channels (red and green) while lowly or nonpolymorphic SNP yield a strong signal from 1 channel only. We further confirm that when the SNP allele frequencies are known, either because the individuals in the pools or from a closely related population are themselves genotyped, a multiple regression model with linear and quadratic components can be developed with high prediction accuracy. We conclude that when these approaches are applied to the estimation of allele frequencies, the resulting estimates allow for the development of cost-effective and reliable GWAS.