SNP genotyping on pooled DNAs: comparison of genotyping technologies and a semi automated method for data storage and analysis

Clinical and cost-effectiveness of clopidogrel resistance genotype testing after ischaemic stroke or transient ischaemic attack: a systematic review and economic model

Background

Stroke or transient ischaemic attack patients are at increased risk of secondary vascular events. Antiplatelet medications, most commonly clopidogrel, are prescribed to reduce this risk. Factors including CYP2C19 genetic variants can hinder clopidogrel metabolism. Laboratory-based or point-of-care tests can detect these variants, enabling targeted treatment.

Objective

To assess the effectiveness of genetic testing to identify clopidogrel resistance in people with ischaemic stroke or transient ischaemic attack. Specific objectives: Do people tested for clopidogrel resistance, and treated accordingly, have a reduced risk of secondary vascular events? Do people with loss-of-function alleles associated with clopidogrel resistance have a reduced risk of secondary vascular events if treated with alternative interventions compared to clopidogrel? Do people with loss-of-function alleles associated with clopidogrel resistance have an increased risk of secondary vascular events when treated with clopidogrel? What is the accuracy of point-of-care tests for detecting variants associated with clopidogrel resistance? What is the technical performance and cost of CYP2C19 genetic tests? Is genetic testing for clopidogrel resistance cost-effective compared with no testing?

Design

Systematic review and economic model.

Results

Objective 1: Two studies assessed secondary vascular events in patients tested for loss-of-function alleles and treated accordingly. They found a reduced risk, but confidence intervals were wide (hazard ratio 0.50, 95% confidence interval 0.09 to 2.74 and hazard ratio 0.53, 95% confidence interval 0.24 to 1.18). Objective 2: Seven randomised controlled trials compared clopidogrel with alternative treatment in people with genetic variants. Ticagrelor was associated with a lower risk of secondary vascular events than clopidogrel (summary hazard ratio 0.76, 95% confidence interval 0.65 to 0.90; two studies). Objective 3: Twenty-five studies compared outcomes in people with and without genetic variants treated with clopidogrel. People with genetic variants were at an increased risk of secondary vascular events (hazard ratio 1.72, 95% confidence interval 1.43 to 2.08; 18 studies). There was no difference in bleeding risk (hazard ratio 0.98, 95% confidence interval 0.68 to 1.40; five studies). Objective 4: Eleven studies evaluated Genomadix Cube accuracy; no studies evaluated Genedrive. Summary sensitivity and specificity against laboratory reference standards were both 100% (95% confidence interval 94% to 100% and 99% to 100%). Objective 5: Seventeen studies evaluated technical performance of point-of-care tests. Test failure rate ranged from 0.4% to 19% for Genomadix Cube. A survey of 8/10 genomic laboratory hubs revealed variation in preferred technologies for testing, and cost per test ranging from £15 to £250. Most laboratories expected test failure rate to be < 1%. Additional resources could enhance testing capacity and expedite turnaround times. Objective 6: Laboratory and point-of-care CYP2C19 testing strategies were cost-saving and increase quality-adjusted life-years compared with no testing. Both strategies gave similar costs, quality-adjusted life-years and expected net monetary benefit.

Conclusions

Our results suggest that CYP2C19 testing followed by tailored treatment is likely to be effective and cost-effective in both populations.

Future work

Accuracy and technical performance of Genedrive. Test failure rate of Genomadix Cube in a National Health Service setting. Value of testing additional loss-of-function alleles. Appropriateness of treatment dichotomy based on loss-of-function alleles.

Limitations

Lack of data on Genedrive. No randomised 'test-and-treat' studies of dipyramidole plus aspirin.

Study registration

This study is registered as PROSPERO CRD42022357661.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135620) and is published in full in Health Technology Assessment; Vol. 28, No. 57. See the NIHR Funding and Awards website for further award information.

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.

Genome-Wide Association Study in Mexican Holstein Cattle Reveals Novel Quantitative Trait Loci Regions and Confirms Mapped Loci for Resistance to Bovine Tuberculosis

Bovine tuberculosis (bTB) is a disease of cattle that represents a risk to public health and causes severe economic losses to the livestock industry. Recently, genetic studies, like genome-wide association studies (GWAS) have greatly improved the investigation of complex diseases identifying thousands of disease-associated genomic variants. Here, we present evidence of genetic variants associated with resistance to TB in Mexican dairy cattle using a case-control approach with a selective DNA pooling experimental design. A total of 154 QTLRs (quantitative trait loci regions) at 10% PFP (proportion of false positives), 42 at 5% PFP and 5 at 1% PFP have been identified, which harbored 172 annotated genes. On BTA13, five new QTLRs were identified in the MACROD2 and KIF16B genes, supporting their involvement in resistance to bTB. Six QTLRs harbor seven annotated genes that have been previously reported as involved in immune response against Mycobacterium spp: BTA (Bos taurus autosome) 1 (CD80), BTA3 (CTSS), BTA 3 (FCGR1A), BTA 23 (HFE), BTA 25 (IL21R), and BTA 29 (ANO9 and SIGIRR). We identified novel QTLRs harboring genes involved in Mycobacterium spp. immune response. This is a first screening for resistance to TB infection on Mexican dairy cattle based on a dense SNP (Single Nucleotide Polymorphism) chip.

An evaluation of allele frequency estimation accuracy using pooled sequencing data

Next generation sequencing technology has matured, and with its current affordability, will replace the SNP chip as the genotyping tool of choice. Even with the current affordability of NGS, large scale studies will require careful study design to reduce cost. In this study, we designed an experiment to assess the accuracy of allele frequency estimated from pooled sequencing data. We compared the allele frequency estimated from sequencing data with the allele frequency estimated from individual SNP chip data and observed high correlations between them. However, by calculating error rate, we found that many SNPs had their allele frequency estimated from sequencing data significantly different from allele frequency estimated from SNP chip data. In conclusion, we found correlation is not an ideal measurement for comparing allele frequencies. And for the purpose of estimating allele frequency, we do not recommend using pooling with NGS as a cheaper alternative to genotype each sample individually.

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

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.

Pooling/bootstrap-based GWAS (pbGWAS) identifies new loci modifying the age of onset in PSEN1 p.Glu280Ala Alzheimer's disease

The literature on GWAS (genome-wide association studies) data suggests that very large sample sizes (for example, 50,000 cases and 50,000 controls) may be required to detect significant associations of genomic regions for complex disorders such as Alzheimer's disease (AD). Because of the challenges of obtaining such large cohorts, we describe here a novel sequential strategy that combines pooling of DNA and bootstrapping (pbGWAS) in order to significantly increase the statistical power and exponentially reduce expenses. We applied this method to a very homogeneous sample of patients belonging to a unique and clinically well-characterized multigenerational pedigree with one of the most severe forms of early onset AD, carrying the PSEN1 p.Glu280Ala mutation (often referred to as E280A mutation), which originated as a consequence of a founder effect. In this cohort, we identified novel loci genome-wide significantly associated as modifiers of the age of onset of AD (CD44, rs187116, P=1.29 × 10⁻¹²; NPHP1, rs10173717, P=1.74 × 10⁻¹²; CADPS2, rs3757536, P=1.54 × 10⁻¹⁰; GREM2, rs12129547, P=1.69 × 10⁻¹³, among others) as well as other loci known to be associated with AD. Regions identified by pbGWAS were confirmed by subsequent individual genotyping. The pbGWAS methodology and the genes it targeted could provide important insights in determining the genetic causes of AD and other complex conditions.

Association of the CCR5Δ32 polymorphism and its ligand RANTES-403G/A polymorphism with coronary artery disease: a meta-analysis

INTRODUCTION

To explore the relationship between polymorphisms in the RANTES and CCR5 genes and the risk of coronary artery disease (CAD).

MATERIALS AND METHODS

We conducted a meta-analysis on two genetic variants (RANTES-403G/A and CCR5Δ32). Publication bias was tested by the Egger's regression test and Begg's test. Sensitivity analysis and subgroup analyses were performed to explore the heterogeneity among studies.

RESULTS

No significant association of RANTES-403G/A polymorphism and CAD risk was observed (dominant model: RR=1.02, 95%CI=0.90-1.06; recessive model: RR=1.27, 95%CI=0.90-1.80). However, after excluding the study conducted by Yangsoo et al., the pooled relative ratio (RR) in the dominant model suggested that the RANTES-403G/A polymorphism was positively associated with CAD risk. The subgroup analyses found that a positive relationship between the polymorphism and CAD risk was restricted to the Caucasian population. A meta-analysis of studies on the CCR5Δ32 polymorphism showed no significant association with CAD risk both in dominant (RR=1.05, 95%CI=0.92-1.21) and recessive (RR=1.27, 95%CI=0.90-1.80) models. Moreover, no association was identified in the subgroup analyses.

CONCLUSIONS

The RANTES-403G/A polymorphism is not associated with CAD risk, but does most likely increase CAD risk in Caucasians. Moreover, no relationship between the CCR5∆32 polymorphism and risk of CAD was found.

Genome-wide association study for ovarian cancer susceptibility using pooled DNA

Recent Genome-Wide Association Studies (GWAS) have identified four low-penetrance ovarian cancer susceptibility loci. We hypothesized that further moderate- or low-penetrance variants exist among the subset of single-nucleotide polymorphisms (SNPs) not well tagged by the genotyping arrays used in the previous studies, which would account for some of the remaining risk. We therefore conducted a time- and cost-effective stage 1 GWAS on 342 invasive serous cases and 643 controls genotyped on pooled DNA using the high-density Illumina 1M-Duo array. We followed up 20 of the most significantly associated SNPs, which are not well tagged by the lower density arrays used by the published GWAS, and genotyping them on individual DNA. Most of the top 20 SNPs were clearly validated by individually genotyping the samples used in the pools. However, none of the 20 SNPs replicated when tested for association in a much larger stage 2 set of 4,651 cases and 6,966 controls from the Ovarian Cancer Association Consortium. Given that most of the top 20 SNPs from pooling were validated in the same samples by individual genotyping, the lack of replication is likely to be due to the relatively small sample size in our stage 1 GWAS rather than due to problems with the pooling approach. We conclude that there are unlikely to be any moderate or large effects on ovarian cancer risk untagged by less dense arrays. However, our study lacked power to make clear statements on the existence of hitherto untagged small-effect variants.

A Comparison of DNA Pools Constructed Following Whole Genome Amplification for Two-Stage SNP Genotyping Designs

Genotyping in DNA pools reduces the cost and the time required to complete large genotyping projects. The aim of the present study was to evaluate pooling as part of a strategy for fine mapping in regions of significant linkage. Thirty-nine single nucleotide polymorphisms (SNPs) were analyzed in two genomic DNA pools of 384 individuals each and results compared with data after typing all individuals used in the pools. There were no significant differences using data from either 2 or 8 heterozygous individuals to correct frequency estimates for unequal allelic amplification. After correction, the mean difference between estimates from the genomic pool and individual allele frequencies was .033. A major limitation of the use of DNA pools is the time and effort required to carefully adjust the concentration of each individual DNA sample before mixing aliquots. Pools were also constructed by combining DNA after Multiple Displacement Amplification (MDA). The MDA pools gave similar results to pools constructed after careful DNA quantitation (mean difference from individual genotyping .040) and MDA provides a rapid method to generate pools suitable for some applications. Pools provide a rapid and cost-effective screen to eliminate SNPs that are not polymorphic in a test population and can detect minor allele frequencies as low as 1% in the pooled samples. With current levels of accuracy, pooling is best suited to an initial screen in the SNP validation process that can provide high-throughput comparisons between cases and controls to priori- tize SNPs for subsequent individual genotyping.

Pooled versus individual genotyping in a breast cancer genome-wide association study

We examine the measurement properties of pooled DNA odds ratio estimates for 7,357 single nucleotide polymorphisms (SNPs) genotyped in a genome-wide association study of postmenopausal breast cancer. This study involved DNA pools formed from 125 cases or 125 matched controls. Individual genotyping for these SNPs subsequently came available for a substantial majority of women included in seven pool pairs, providing the opportunity for a comparison of pooled DNA and individual odds ratio estimates and their variances. We find that the "per minor allele" odds ratio estimates from the pooled DNA comparisons agree fairly well with those from individual genotyping. Furthermore, the log-odds ratio variance estimates support a pooled DNA measurement model that we previously described, although with somewhat greater extra-binomial variation than was hypothesized in project design. Implications for the role of pooled DNA comparisons in the future genetic epidemiology research agenda are discussed.

Use of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry for multiplex genotyping

After completion of the human genome project, the focus of geneticists has shifted to elucidation of gene function and genetic diversity to understand the mechanisms of complex diseases or variation of patient response in drug treatment. In the past decade, many different genotyping techniques have been described for the detection of single-nucleotide polymorphisms (SNPs) and other common polymorphic variants. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) is among the most powerful and widely used genotyping technologies. The method offers great flexibility in assay design and enables highly accurate genotyping at high sample throughput. Different strategies for allele discrimination and quantification have been combined with MALDI (hybridization, ligation, cleavage, and primer extension). Approaches based on primer extension have become the most popular applications. This combination enables rapid and reliable multiplexing of SNPs and other common variants, and makes MALDI-TOF-MS well suited for large-scale studies in fine-mapping and verification of genome-wide scans. In contrast to standard genotyping, more demanding approaches have enabled genotyping of DNA pools, molecular haplotyping or the detection of free circulating DNA for prenatal or cancer diagnostics. In addition, MALDI can also be used in novel applications as DNA methylation analysis, expression profiling, and resequencing. This review gives an introduction to multiplex genotyping by MALDI-MS and will focus on the latest developments of this technology.

A triple combination strategy corrects population stratification bias and saves genotyping cost

OBJECTIVE

The case-control association study has gained popularity for mapping disease-susceptibility gene(s) of complex human diseases. However, the study is prone to population stratification bias.

STUDY DESIGN AND SETTING

The authors proposed a triple combination strategy of stratum matching, genomic control, and multiple DNA pooling for population-based case-control association studies. Monte Carlo simulation showed that the type I error rates are well controlled using this strategy.

RESULTS

As for power, it increases under the following situations: (1) more accurate stratum matching, (2) more null markers for genomic control, (3) more DNA pooling sets, and (4) more accurate DNA pooling measurement.

CONCLUSION

The triple combination strategy corrects population stratification bias and saves genotyping cost.

Analysis of Schistosoma mansoni population structure using total fecal egg sampling

Many parasite populations are difficult to sample because they are not uniformly distributed between several host species and are often not easily collected from the living host, thereby limiting sample size and possibly distorting the representation of the population. For the parasite Schistosoma mansoni, we investigated the use of eggs, in aggregate, from the stools of infected individuals as a simple and representative sample. Previously, we demonstrated that microsatellite allele frequencies can be accurately estimated from pooled DNA of cloned S. mansoni adults. Here, we show that genotyping of parasite populations from reproductively isolated laboratory strains can be used to identify these specific populations based on characteristic patterns of allele frequencies, as observed by polyacrylamide gel electrophoresis and automated sequencer analysis of fluorescently labeled PCR products. Microsatellites used to genotype aggregates of eggs collected from stools of infected individuals produced results consistent with the geographic distribution of the samples. Preferential amplification of smaller alleles, and stutter PCR products, had negligible effect on measurement of genetic differentiation. Direct analysis of total stool eggs can be an important approach to questions of population genetics for this parasite by increasing the sample size to thousands per infected individual and by reducing bias.

Validation of pooled genotyping on the Affymetrix 500 k and SNP6.0 genotyping platforms using the polynomial-based probe-specific correction

BACKGROUND

The use of pooled DNA on SNP microarrays (SNP-MaP) has been shown to be a cost effective and rapid manner to perform whole-genome association evaluations. While the accuracy of SNP-MaP was extensively evaluated on the early Affymetrix 10 k and 100 k platforms, there have not been as many similarly comprehensive studies on more recent platforms. In the present study, we used the data generated from the full Affymetrix 500 k SNP set together with the polynomial-based probe-specific correction (PPC) to derive allele frequency estimates. These estimates were compared to genotyping results of the same individuals on the same platform, as the basis to evaluate the reliability and accuracy of pooled genotyping on these high-throughput platforms. We subsequently extended this comparison to the new SNP6.0 platform capable of genotyping 1.8 million genetic variants.

RESULTS

We showed that pooled genotyping on the 500 k platform performed as well as those previously shown on the relatively lower throughput 10 k and 100 k array sets, with high levels of accuracy (correlation coefficient: 0.988) and low median error (0.036) in allele frequency estimates. Similar results were also obtained from the SNP6.0 array set. A novel pooling strategy of overlapping sub-pools was attempted and comparison of estimated allele frequencies showed this strategy to be as reliable as replicate pools. The importance of an appropriate reference genotyping data set for the application of the PPC algorithm was also evaluated; reference samples with similar ethnic background to the pooled samples were found to improve estimation of allele frequencies.

CONCLUSION

We conclude that use of the PPC algorithm to estimate allele frequencies obtained from pooled genotyping on the high throughput 500 k and SNP6.0 platforms is highly accurate and reproducible especially when a suitable reference sample set is used to estimate the beta values for PPC.

Ligation dependent allele specific quantification (LASQ) of CFTR cDNA on the LightCycler using MLPA hybridization probes

BACKGROUND

As for Cystic Fibrosis (CF) and many other hereditary diseases there is still a lack in understanding the relationship between genetic (e.g. allelic) and phenotypic diversity. Therefore methods which allow fine quantification of allelic proportions of mRNA transcripts are of high importance.

METHODS

We used either genomic DNA (gDNA) or total RNA extracted from nasal cells as starting nucleic acid template for our assay. The subjects included in this study were 9 CF patients compound heterozygous for the F508del mutation and each one F508del homozygous and one wild type homozygous respectively. We established a novel ligation based quantification method which allows fine quantification of the allelic proportions of ss and ds CFTR cDNA. To verify reliability and accuracy of this novel assay we compared it with semiquantitative fluorescent PCR (SQF-PCR).

RESULTS

We established a novel assay for allele specific quantification of gene expression which combines the benefits of the specificity of the ligation reaction and the accuracy of quantitative real-time PCR. The comparison with SQF-PCR clearly demonstrates that LASQ allows fine quantification of allelic proportions.

CONCLUSION

This assay represents an alternative to other fine quantitative methods such as ARMS PCR and Pyrosequencing.

The SNPMaP package for R: a framework for genome-wide association using DNA pooling on microarrays

Summary: Large-scale genome-wide association (GWA) studies using thousands of high-density SNP microarrays are becoming an essential tool in the search for loci related to heritable variation in many phenotypes. However, the cost of GWA remains beyond the reach of many researchers. Fortunately, the majority of statistical power can still be obtained by estimating allele frequencies from DNA pools, reducing the cost to that of tens, rather than thousands of arrays. We present a set of software tools for processing SNPMaP (SNP microarrays and pooling) data from CEL files to Relative Allele Scores in the rich R statistical computing environment.

Availability: The SNPMaP package is available from http://cran.r-project.org/ under the GNU General Public License version 3 or later.

Contact:snpmap@iop.kcl.ac.uk

Supplementary information: Additional resources and test datasets are available at http://sgdp.iop.kcl.ac.uk/snpmap/

Optimal DNA pooling-based two-stage designs in case-control association studies

Study cost remains the major limiting factor for genome-wide association studies due to the necessity of genotyping a large number of SNPs for a large number of subjects. Both DNA pooling strategies and two-stage designs have been proposed to reduce genotyping costs. In this study, we propose a cost-effective, two-stage approach with a DNA pooling strategy. During stage I, all markers are evaluated on a subset of individuals using DNA pooling. The most promising set of markers is then evaluated with individual genotyping for all individuals during stage II. The goal is to determine the optimal parameters (pi(p)(sample ), the proportion of samples used during stage I with DNA pooling; and pi(p)(marker ), the proportion of markers evaluated during stage II with individual genotyping) that minimize the cost of a two-stage DNA pooling design while maintaining a desired overall significance level and achieving a level of power similar to that of a one-stage individual genotyping design. We considered the effects of three factors on optimal two-stage DNA pooling designs. Our results suggest that, under most scenarios considered, the optimal two-stage DNA pooling design may be much more cost-effective than the optimal two-stage individual genotyping design, which use individual genotyping during both stages.

Impact and quantification of the sources of error in DNA pooling designs

The analysis of genome wide variation offers the possibility of unravelling the genes involved in the pathogenesis of disease. Genome wide association studies are also particularly useful for identifying and validating targets for therapeutic intervention as well as for detecting markers for drug efficacy and side effects. The cost of such large-scale genetic association studies may be reduced substantially by the analysis of pooled DNA from multiple individuals. However, experimental errors inherent in pooling studies lead to a potential increase in the false positive rate and a loss in power compared to individual genotyping. Here we quantify various sources of experimental error using empirical data from typical pooling experiments and corresponding individual genotyping counts using two statistical methods. We provide analytical formulas for calculating these different errors in the absence of complete information, such as replicate pool formation, and for adjusting for the errors in the statistical analysis. We demonstrate that DNA pooling has the potential of estimating allele frequencies accurately, and adjusting the pooled allele frequency estimates for differential allelic amplification considerably improves accuracy. Estimates of the components of error show that differential allelic amplification is the most important contributor to the error variance in absolute allele frequency estimation, followed by allele frequency measurement and pool formation errors. Our results emphasise the importance of minimising experimental errors and obtaining correct error estimates in genetic association studies.

Applying polynomial standard curve method to correct bias encountered in estimating allele frequencies using DNA pooling strategy

DNA pooling approach is a cost-saving strategy which is crucial for multiple-SNP association study and particularly for laboratories with limited budget. However, the biased allele frequency estimates cannot be completely abolished by kappa correction. Using the SNaPshottrade mark, we systematically examined the relations between actual minor allele frequencies (AMiAFs) levels and estimates obtained from the pooling process for all six types of SNPs. We applied principle of polynomial standard curves method (PSCM) to produce allele frequency estimates in pooled DNA samples and compared it with the kappa method. The results showed that estimates derived from the PSCM were in general closer to AMiAFs than those from the kappa method, particularly for C/G and G/T polymorphisms at the range of AMiAF between 20-40%. We demonstrated that applying PSCM in the SNaPshottrade mark platform is suitable for multiple-SNP association study using pooling strategy, due to its cost effectiveness and estimation accuracy.

Multimarker analysis and imputation of multiple platform pooling-based genome-wide association studies

For many genome-wide association (GWA) studies individually genotyping one million or more SNPs provides a marginal increase in coverage at a substantial cost. Much of the information gained is redundant due to the correlation structure inherent in the human genome. Pooling-based GWA studies could benefit significantly by utilizing this redundancy to reduce noise, improve the accuracy of the observations and increase genomic coverage. We introduce a measure of correlation between individual genotyping and pooling, under the same framework that r(2) provides a measure of linkage disequilibrium (LD) between pairs of SNPs. We then report a new non-haplotype multimarker multi-loci method that leverages the correlation structure between SNPs in the human genome to increase the efficacy of pooling-based GWA studies. We first give a theoretical framework and derivation of our multimarker method. Next, we evaluate simulations using this multimarker approach in comparison to single marker analysis. Finally, we experimentally evaluate our method using different pools of HapMap individuals on the Illumina 450S Duo, Illumina 550K and Affymetrix 5.0 platforms for a combined total of 1 333 631 SNPs. Our results show that use of multimarker analysis reduces noise specific to pooling-based studies, allows for efficient integration of multiple microarray platforms and provides more accurate measures of significance than single marker analysis. Additionally, this approach can be extended to allow for imputing the association significance for SNPs not directly observed using neighboring SNPs in LD. This multimarker method can now be used to cost-effectively complete pooling-based GWA studies with multiple platforms across over one million SNPs and to impute neighboring SNPs weighted for the loss of information due to pooling.

An efficient method for purification of PCR products for sequencing

A high-throughput DNA sequencing method that generated high quality data was developed. A frame fashioned from a standard agarose gel combined with 0.1%-0.2% low-melting point (LMP) agarose gel was used to isolate the PCR product of interest. Collected PCR products were centrifuged without any reagents and the supernatants were directly used for a sequencing reaction. This method is simple and labor efficient, provides high quality sequences at a low cost, and bypasses problems with impure PCR products. This technique has been used for single nucleotide polymorphism (SNP) discovery in Populus angustifolia trees.

A simple and accurate SNP scoring strategy based on typeIIS restriction endonuclease cleavage and matrix-assisted laser desorption/ionization mass spectrometry

BACKGROUND

We describe the development of a novel matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF)-based single nucleotide polymorphism (SNP) scoring strategy, termed Restriction Fragment Mass Polymorphism (RFMP) that is suitable for genotyping variations in a simple, accurate, and high-throughput manner. The assay is based on polymerase chain reaction (PCR) amplification and mass measurement of oligonucleotides containing a polymorphic base, to which a typeIIS restriction endonuclease recognition was introduced by PCR amplification. Enzymatic cleavage of the products leads to excision of oligonucleotide fragments representing base variation of the polymorphic site whose masses were determined by MALDI-TOF MS.

RESULTS

The assay represents an improvement over previous methods because it relies on the direct mass determination of PCR products rather than on an indirect analysis, where a base-extended or fluorescent report tag is interpreted. The RFMP strategy is simple and straightforward, requiring one restriction digestion reaction following target amplification in a single vessel. With this technology, genotypes are generated with a high call rate (99.6%) and high accuracy (99.8%) as determined by independent sequencing.

CONCLUSION

The simplicity, accuracy and amenability to high-throughput screening analysis should make the RFMP assay suitable for large-scale genotype association study as well as clinical genotyping in laboratories.

Systematic association studies of mitochondrial DNA variations in schizophrenia: focus on the ND5 gene

Postmortem studies, as well as genetic association studies, have implicated mitochondrial dysfunction in schizophrenia (SZ). We conducted multistaged analysis to assess the involvement of mitochondrial DNA (mtDNA) variations in SZ. Initially, the entire mtDNA genome was sequenced in pools of DNA from SZ cases and controls (n = 180 in each group, set 1). Two polymorphisms localized to the NADH dehydrogenase subunit 5 (ND5) gene demonstrated suggestive case control allele frequency differences (mtDNA 13368 G/A, p = .019 and mtDNA 13708G/A, p = .043). Hence, the ND5 gene was sequenced in individual samples from the initial panel of cases and controls. Additional subjects from another independent set of cases and controls (set 2, cases, n = 244, controls n = 508) were also sequenced individually. No significant differences in allele frequencies for mtDNA 13368 G/A, and mtDNA 13708G/A were observed. However, we identified 216 other rare variants, 53 of which were reported earlier in association studies of other mitochondrial disorders. We compared the distribution of polymorphisms in both sets of cases and controls. No significant case-control differences were observed in the smaller, first set. In the second set, cases had more variants overall (p = 0.014), as well as synonymous variants (p = 0.02), but the difference for nonsynonymous variants was not significant (p = 0.19). Screening available first-degree relatives (n = 10) revealed 10 maternally inherited variations, suggesting that not all the variants are somatic mutations. Further investigations are warranted.

Association mapping using pooled DNA

The genetic dissection of complex disorders via genetic marker data has gained popularity in the postgenome era. Methods for typing genetic markers on human chromosomes continue to improve. Compared with the popular individual genotyping experiment, a pooled-DNA experiment (alleotyping experiment) is more cost effective when carrying out genetic typing. This chapter provides an overview of association mapping using pooled DNA and describes a five-stage study design including the preliminary calibration of peak intensities, estimation of allele frequency, single-locus association mapping, multilocus association mapping, and a confirmation study. Software and an analysis of authentic data are presented. The strengths and weaknesses of pooled-DNA analyses, as well as possible future applications for this method, are discussed.

SNP genotyping: technologies and biomedical applications

Single nucleotide polymorphisms (SNPs) are the most frequently occurring genetic variation in the human genome, with the total number of SNPs reported in public SNP databases currently exceeding 9 million. SNPs are important markers in many studies that link sequence variations to phenotypic changes; such studies are expected to advance the understanding of human physiology and elucidate the molecular bases of diseases. For this reason, over the past several years a great deal of effort has been devoted to developing accurate, rapid, and cost-effective technologies for SNP analysis, yielding a large number of distinct approaches. This article presents a review of SNP genotyping techniques and examines their principles of genotype determination in terms of allele differentiation strategies and detection methods. Further, several current biomedical applications of SNP genotyping are discussed.

HaploPool: improving haplotype frequency estimation through DNA pools and phylogenetic modeling

MOTIVATION

The search for genetic variants that are linked to complex diseases such as cancer, Parkinson's;, or Alzheimer's; disease, may lead to better treatments. Since haplotypes can serve as proxies for hidden variants, one method of finding the linked variants is to look for case-control associations between the haplotypes and disease. Finding these associations requires a high-quality estimation of the haplotype frequencies in the population. To this end, we present, HaploPool, a method of estimating haplotype frequencies from blocks of consecutive SNPs.

RESULTS

HaploPool leverages the efficiency of DNA pools and estimates the population haplotype frequencies from pools of disjoint sets, each containing two or three unrelated individuals. We study the trade-off between pooling efficiency and accuracy of haplotype frequency estimates. For a fixed genotyping budget, HaploPool performs favorably on pools of two individuals as compared with a state-of-the-art non-pooled phasing method, PHASE. Of independent interest, HaploPool can be used to phase non-pooled genotype data with an accuracy approaching that of PHASE. We compared our algorithm to three programs that estimate haplotype frequencies from pooled data. HaploPool is an order of magnitude more efficient (at least six times faster), and considerably more accurate than previous methods. In contrast to previous methods, HaploPool performs well with missing data, genotyping errors and long haplotype blocks (of between 5 and 25 SNPs).

Incorporation of genetic model parameters for cost-effective designs of genetic association studies using DNA pooling

BACKGROUND

Studies of association methods using DNA pooling of single nucleotide polymorphisms (SNPs) have focused primarily on the effects of "machine-error", number of replicates, and the size of the pool. We use the non-centrality parameter (NCP) for the analysis of variance test to compute the approximate power for genetic association tests with DNA pooling data on cases and controls. We incorporate genetic model parameters into the computation of the NCP. Parameters involved in the power calculation are disease allele frequency, frequency of the marker SNP allele in coupling with the disease locus, disease prevalence, genotype relative risk, sample size, genetic model, number of pools, number of replicates of each pool, and the proportion of variance of the pooled frequency estimate due to machine variability. We compute power for different settings of number of replicates and total number of genotypings when the genetic model parameters are fixed. Several significance levels are considered, including stringent significance levels (due to the increasing popularity of 100 K and 500 K SNP "chip" data). We use a factorial design with two to four settings of each parameter and multiple regression analysis to assess which parameters most significantly affect power.

RESULTS

The power can increase substantially as the genotyping number increases. For a fixed number of genotypings, the power is a function of the number of replicates of each pool such that there is a setting with maximum power. The four most significant parameters affecting power for association are: (1) genotype relative risk, (2) genetic model, (3) sample size, and (4) the interaction term between disease and SNP marker allele probabilities.

CONCLUSION

For a fixed number of genotypings, there is an optimal number of replicates of each pool that increases as the number of genotypings increases. Power is not substantially reduced when the number of replicates is close to but not equal to the optimal setting.

Applicability of DNA pools on 500 K SNP microarrays for cost-effective initial screens in genomewide association studies

Background

Genetic influences underpinning complex traits are thought to involve multiple quantitative trait loci (QTLs) of small effect size. Detection of such QTL associations requires systematic screening of large numbers of DNA markers within large sample populations. Using pooled DNA on SNP microarrays to screen for allelic frequency differences between groups such as cases and controls (called SNP Microarray and Pooling, or SNP-MaP) has been validated as an efficient solution on both 10 k and 100 k platforms. We demonstrate that this approach can be effectively applied to the truly genomewide Affymetrix GeneChip^® Mapping 500 K Array.

Results

In comparisons between five independent DNA pools (N ~200 per pool) on separate Affymetrix GeneChip^® Mapping 500 K Array sets, we show that, for SNPs with minor allele frequencies > 0.05, the reliability of the rank order of estimated allele frequencies, assessed as the average correlation between allele frequency estimates across the DNA pools, was 0.948 (average mean difference across the five pools = 0.069). Similarly, validity of the SNP-MaP approach was demonstrated by a rank-order correlation of 0.937 (average mean difference = 0.095) between the average DNA pool allele frequency estimates and the allele frequencies of an independent (CEPH) sample of 60 unrelated individually genotyped subjects.

Conclusion

We conclude that SNP-MaP can be extended for use on the Affymetrix GeneChip^® Mapping 500 K Array, providing a cost-effective, reliable and valid initial screen of 500 K SNP microarrays in genomewide association scans.

Multiplex genotyping of cytochrome p450 single-nucleotide polymorphisms by use of MALDI-TOF mass spectrometry

BACKGROUND

Polymorphisms in cytochrome P450 (CYP450) genes contribute to interindividual differences in the metabolism of xenobiotic chemicals, including the vast majority of drugs, and may lead to toxicity and adverse drug reactions. Studies on these polymorphisms in research and diagnostic settings typically involve large-scale genotyping and hence require high-throughput assays.

METHODS

We used the previously developed solid-phase capture-single-base extension (SPC-SBE) approach for concurrent analysis of 40 single-nucleotide polymorphisms (SNPs) of CYP2C9 and 50 SNPs of CYP2A13, both genes belonging to the CYP450 family. Desired SNP-containing regions for each gene were amplified in a single-step multiplex PCR. We designed a library of primers to anneal immediately upstream of the selected SNPs and extended it with biotinylated terminators using PCR products as templates. Biotinylated extension products were isolated by affinity purification and analyzed with MALDI-TOF mass spectrometry to determine SNP genotypes.

RESULTS

We analyzed 11 samples for CYP2C9 and 14 samples for CYP2A13 with unambiguous detection of SNPs in all samples. Many samples showed a high occurrence of heterozygotes for both genes, with as many as 10 of 50 SNPs appearing as heterozygotes in 1 sample genotyped for CYP2A13.

CONCLUSIONS

The SPC-SBE method provides an efficient means for genotyping SNPs from the CYP450 family. This approach is suitable for automation and can be extended to other genotyping applications.

Design and analysis of association studies using pooled DNA from large twin samples

Evidence is mounting that multiple genes are involved in complex traits and that these each account for very small proportions of the overall phenotypic variance. Association studies of many markers in 1000s of individuals will be required to identify such genes. A number of large twin cohorts have already been collected and provide a valuable resource for carrying out studies that are robust to the effect of population stratification. Technologies based on microarrays will soon allow 1.000,000 SNPs to be typed at one time, however financial considerations prevent most researchers from using these approaches to genotype all individuals. Recently, microarrays have been shown to give accurate allele frequency measurements in pooled DNA samples and provide a simple way to select the best markers for individual genotyping. This drastically reduces the cost and workload of large scale association studies. One limitation of this methodology relates to the analytical procedures which have only been developed to allow comparison of two pools e.g. case/control pools. In this paper we use meta-regression to analyze pooled DNA data allowing the allele frequency in each pool to be related to the average quantitative phenotypic measure of the individuals whose DNA were used to construct the pools. Alongside this we describe a technique that can be used to determine the power for such studies. We present results from some preliminary investigations of different pooling strategies that can be applied to large twin samples and demonstrate that the method retains a large proportion of the power available from individual genotyping.

Single nucleotide polymorphism (SNP) allele frequency estimation in DNA pools using Pyrosequencing™

Identifying the genetic variation underlying complex disease requires analysis of many single nucleotide polymorphisms (SNPs) in a large number of samples. Several high-throughput SNP genotyping techniques are available; however, their cost promotes the use of association screening with pooled DNA. This protocol describes the estimation of SNP allele frequencies in pools of DNA using the quantitative sequencing method Pyrosequencing (PSQ). PSQ is a relatively recently described high-throughput method for genotyping, allele frequency estimation and DNA methylation analysis based on the detection of real-time pyrophosphate release during synthesis of the complementary strand to a PCR product. The protocol involves the following steps: (i) quantity and quality assessment of individual DNA samples; (ii) DNA pooling, which may be undertaken at the pre- or post-PCR stage; (iii) PCR amplification of PSQ template containing the variable sequence region of interest; and (iv) PSQ to determine the frequency of alleles at a particular SNP site. Once the quantity and quality of individual DNA samples has been assessed, the protocol usually requires a few days for setting up pre-PCR pools, depending on sample number. After PCR amplification, preparation and analysis of PCR amplicon by PSQ takes 1 h per plate.

Identification of the genetic basis for complex disorders by use of pooling-based genomewide single-nucleotide-polymorphism association studies

We report the development and validation of experimental methods, study designs, and analysis software for pooling-based genomewide association (GWA) studies that use high-throughput single-nucleotide-polymorphism (SNP) genotyping microarrays. We first describe a theoretical framework for establishing the effectiveness of pooling genomic DNA as a low-cost alternative to individually genotyping thousands of samples on high-density SNP microarrays. Next, we describe software called "GenePool," which directly analyzes SNP microarray probe intensity data and ranks SNPs by increased likelihood of being genetically associated with a trait or disorder. Finally, we apply these methods to experimental case-control data and demonstrate successful identification of published genetic susceptibility loci for a rare monogenic disease (sudden infant death with dysgenesis of the testes syndrome), a rare complex disease (progressive supranuclear palsy), and a common complex disease (Alzheimer disease) across multiple SNP genotyping platforms. On the basis of these theoretical calculations and their experimental validation, our results suggest that pooling-based GWA studies are a logical first step for determining whether major genetic associations exist in diseases with high heritability.

QSNPlite, a software system for quantitative analysis of SNPs based on capillary array SSCP analysis

We present a newly developed software called "QSNPlite" that comprehensively interprets the data of SSCP and sequencing analyses obtained from capillary array electrophoresis systems used in the quantitative characterization of SNPs. QSNPlite assists in the genotyping of individuals with SNPs and in estimating the allele frequencies of SNPs using pooled DNA. We show that this estimation is accurate (mean absolute error, 1.4%) by comparing the results of the pooled analysis using QSNPlite with the true frequencies based on the allele counting after performing individual genotypings. The QSNPlite program runs on Windows XP and can be used to determine the allele frequencies of SNPs among a large number of individuals, such as in association studies of disease-responsible genes using the candidate gene approach.

A genome-wide study of preferential amplification/hybridization in microarray-based pooled DNA experiments

Microarray-based pooled DNA methods overcome the cost bottleneck of simultaneously genotyping more than 100 000 markers for numerous study individuals. The success of such methods relies on the proper adjustment of preferential amplification/hybridization to ensure accurate and reliable allele frequency estimation. We performed a hybridization-based genome-wide single nucleotide polymorphisms (SNPs) genotyping analysis to dissect preferential amplification/hybridization. The majority of SNPs had less than 2-fold signal amplification or suppression, and the lognormal distributions adequately modeled preferential amplification/hybridization across the human genome. Comparative analyses suggested that the distributions of preferential amplification/hybridization differed among genotypes and the GC content. Patterns among different ethnic populations were similar; nevertheless, there were striking differences for a small proportion of SNPs, and a slight ethnic heterogeneity was observed. To fulfill appropriate and gratuitous adjustments, databases of preferential amplification/hybridization for African Americans, Caucasians and Asians were constructed based on the Affymetrix GeneChip Human Mapping 100 K Set. The robustness of allele frequency estimation using this database was validated by a pooled DNA experiment. This study provides a genome-wide investigation of preferential amplification/hybridization and suggests guidance for the reliable use of the database. Our results constitute an objective foundation for theoretical development of preferential amplification/hybridization and provide important information for future pooled DNA analyses.

DNA analysis by mass spectrometry-past, present and future

The analysis of deoxyribose nucleic acid (DNA) by mass spectrometry (MS) has evolved to where it can be used to analyze most known types of DNA and ribose nucleic acid (RNA) situations. It can efficiently deal with the analysis of DNA polymorphisms, sequences, haplotypes, human leukocyte antigen (HLA) typing, DNA methylation and RNA expression. Implementations of MS for these forms of DNA analyses are reviewed. The use of DNA analysis by MS is compared with competing technologies. Finally, an overview is given of worthwhile applications where the know-how gained so far could be used for future developments.

DNA pooling: a comprehensive, multi-stage association analysis of ACSL6 and SIRT5 polymorphisms in schizophrenia

Many candidate gene association studies have evaluated incomplete, unrepresentative sets of single nucleotide polymorphisms (SNPs), producing non-significant results that are difficult to interpret. Using a rapid, efficient strategy designed to investigate all common SNPs, we tested associations between schizophrenia and two positional candidate genes: ACSL6 (Acyl-Coenzyme A synthetase long-chain family member 6) and SIRT5 (silent mating type information regulation 2 homologue 5). We initially evaluated the utility of DNA sequencing traces to estimate SNP allele frequencies in pooled DNA samples. The mean variances for the DNA sequencing estimates were acceptable and were comparable to other published methods (mean variance: 0.0008, range 0-0.0119). Using pooled DNA samples from cases with schizophrenia/schizoaffective disorder (Diagnostic and Statistical Manual of Mental Disorders edition IV criteria) and controls (n=200, each group), we next sequenced all exons, introns and flanking upstream/downstream sequences for ACSL6 and SIRT5. Among 69 identified SNPs, case-control allele frequency comparisons revealed nine suggestive associations (P<0.2). Each of these SNPs was next genotyped in the individual samples composing the pools. A suggestive association with rs 11743803 at ACSL6 remained (allele-wise P=0.02), with diminished evidence in an extended sample (448 cases, 554 controls, P=0.062). In conclusion, we propose a multi-stage method for comprehensive, rapid, efficient and economical genetic association analysis that enables simultaneous SNP detection and allele frequency estimation in large samples. This strategy may be particularly useful for research groups lacking access to high throughput genotyping facilities. Our analyses did not yield convincing evidence for associations of schizophrenia with ACSL6 or SIRT5.

Quantify single nucleotide polymorphism (SNP) ratio in pooled DNA based on normalized fluorescence real-time PCR

Background

Conventional real-time PCR to quantify the allele ratio in pooled DNA mainly depends on PCR amplification efficiency determination and Ct value, which is defined as the PCR cycle number at which the fluorescence emission exceeds the fixed threshold. Because of the nature of exponential calculation, slight errors are multiplied and the variations of the results seem too large. We have developed a new PCR data point analysis strategy for allele ratio quantification based on normalized fluorescence ratio.

Results

In our method, initial reaction background fluorescence was determined based upon fitting of raw fluorescence data to four-parametric sigmoid function. After that, each fluorescence data point was first subtracted by respective background fluorescence and then each subtracted fluorescence data point was divided by the specific background fluorescence to get normalized fluorescence. By relating the normalized fluorescence ratio to the premixed known allele ratio of two alleles in standard samples, standard linear regression equation was generated, from which unknown specimens allele ratios were extrapolated using the measured normalized fluorescence ratio. In this article, we have compared the results of the proposed method with those of baseline subtracted fluorescence ratio method and conventional Ct method.

Conclusion

Results demonstrated that the proposed method could improve the reliability, precision, and repeatability for quantifying allele ratios. At the same time, it has the potential of fully automatic allelic ratio quantification.

Analysis of pooled DNA samples on high density arrays without prior knowledge of differential hybridization rates

Array based DNA pooling techniques facilitate genome-wide scale genotyping of large samples. We describe a structured analysis method for pooled data using internal replication information in large scale genotyping sets. The method takes advantage of information from single nucleotide polymorphisms (SNPs) typed in parallel on a high density array to construct a test statistic with desirable statistical properties. We utilize a general linear model to appropriately account for the structured multiple measurements available with array data. The method does not require the use of additional arrays for the estimation of unequal hybridization rates and hence scales readily to accommodate arrays with several hundred thousand SNPs. Tests for differences between cases and controls can be conducted with very few arrays. We demonstrate the method on 384 endometriosis cases and controls, typed using Affymetrix Genechip© HindIII 50 K arrays. For a subset of this data there were accurate measures of hybridization rates available. Assuming equal hybridization rates is shown to have a negligible effect upon the results. With a total of only six arrays, the method extracted one-third of the information (in terms of equivalent sample size) available with individual genotyping (requiring 768 arrays). With 20 arrays (10 for cases, 10 for controls), over half of the information could be extracted from this sample.

Two-stage designs in case-control association analysis

DNA pooling is a cost-effective approach for collecting information on marker allele frequency in genetic studies. It is often suggested as a screening tool to identify a subset of candidate markers from a very large number of markers to be followed up by more accurate and informative individual genotyping. In this article, we investigate several statistical properties and design issues related to this two-stage design, including the selection of the candidate markers for second-stage analysis, statistical power of this design, and the probability that truly disease-associated markers are ranked among the top after second-stage analysis. We have derived analytical results on the proportion of markers to be selected for second-stage analysis. For example, to detect disease-associated markers with an allele frequency difference of 0.05 between the cases and controls through an initial sample of 1000 cases and 1000 controls, our results suggest that when the measurement errors are small (0.005), approximately 3% of the markers should be selected. For the statistical power to identify disease-associated markers, we find that the measurement errors associated with DNA pooling have little effect on its power. This is in contrast to the one-stage pooling scheme where measurement errors may have large effect on statistical power. As for the probability that the disease-associated markers are ranked among the top in the second stage, we show that there is a high probability that at least one disease-associated marker is ranked among the top when the allele frequency differences between the cases and controls are not <0.05 for reasonably large sample sizes, even though the errors associated with DNA pooling in the first stage are not small. Therefore, the two-stage design with DNA pooling as a screening tool offers an efficient strategy in genomewide association studies, even when the measurement errors associated with DNA pooling are nonnegligible. For any disease model, we find that all the statistical results essentially depend on the population allele frequency and the allele frequency differences between the cases and controls at the disease-associated markers. The general conclusions hold whether the second stage uses an entirely independent sample or includes both the samples used in the first stage and an independent set of samples.

Family-based association tests for different family structures using pooled DNA

DNA pooling is a cost-effective strategy for genomewise association studies to identify disease genes. In the context of family-based association studies, Risch & Teng (1998) mainly considered families of identical structures to detect associations between genetic markers and disease, and suggested possible approaches to incorporating different family types without a thorough study of their properties. However, families collected in real genetic studies often have different structures and, more importantly, the informativeness of each family structure depends on the disease model which is generally unknown. So there is a need to develop and investigate statistical methods to combine information from diverse family types. In this article, we propose a general strategy to incorporate different family types by assigning each family an "optimal" weight in association tests. In addition, we consider measurement errors in our analysis. When we evaluate our approach under different disease models and measurement errors, we find that our weighting scheme may lead to a substantial reduction in sample size required over the approach suggested by Risch & Teng (1998), and measurement errors may have significant impact on the required sample size when the error rates are not negligible.

Statistical issues arising in the Women's Health Initiative

A brief overview of the design of the Women's Health Initiative (WHI) clinical trial and observational study is provided along with a summary of results from the postmenopausal hormone therapy clinical trial components. Since its inception in 1992, the WHI has encountered a number of statistical issues where further methodology developments are needed. These include measurement error modeling and analysis procedures for dietary and physical activity assessment; clinical trial monitoring methods when treatments may affect multiple clinical outcomes, either beneficially or adversely; study design and analysis procedures for high-dimensional genomic and proteomic data; and failure time data analysis procedures when treatment group hazard ratios are time dependent. This final topic seems important in resolving the discrepancy between WHI clinical trial and observational study results on postmenopausal hormone therapy and cardiovascular disease.

Pooled DNA genotyping on Affymetrix SNP genotyping arrays

BACKGROUND

Genotyping technology has advanced such that genome-wide association studies of complex diseases based upon dense marker maps are now technically feasible. However, the cost of such projects remains high. Pooled DNA genotyping offers the possibility of applying the same technologies at a fraction of the cost, and there is some evidence that certain ultra-high throughput platforms also perform with an acceptable accuracy. However, thus far, this conclusion is based upon published data concerning only a small number of SNPs.

RESULTS

In the current study we prepared DNA pools from the parents and from the offspring of 30 parent-child trios that have been extensively genotyped by the HapMap project. We analysed the two pools with Affymetrix 10 K Xba 142 2.0 Arrays. The availability of the HapMap data allowed us to validate the performance of 6843 SNPs for which we had both complete individual and pooled genotyping data. Pooled analyses averaged over 5-6 microarrays resulted in highly reproducible results. Moreover, the accuracy of estimating differences in allele frequency between pools using this ultra-high throughput system was comparable with previous reports of pooling based upon lower throughput platforms, with an average error for the predicted allelic frequencies differences between the two pools of 1.37% and with 95% of SNPs showing an error of < 3.2%.

CONCLUSION

Genotyping thousands of SNPs with DNA pooling using Affymetrix microarrays produces highly accurate results and can be used for genome-wide association studies.

Genotyping pooled DNA using 100K SNP microarrays: a step towards genomewide association scans

The identification of quantitative trait loci (QTLs) of small effect size that underlie complex traits poses a particular challenge for geneticists due to the large sample sizes and large numbers of genetic markers required for genomewide association scans. An efficient solution for screening purposes is to combine single nucleotide polymorphism (SNP) microarrays and DNA pooling (SNP-MaP), an approach that has been shown to be valid, reliable and accurate in deriving relative allele frequency estimates from pooled DNA for groups such as cases and controls for 10K SNP microarrays. However, in order to conduct a genomewide association study many more SNP markers are needed. To this end, we assessed the validity and reliability of the SNP-MaP method using Affymetrix GeneChip Mapping 100K Array set. Interpretable results emerged for 95% of the SNPs (nearly 110,000 SNPs). We found that SNP-MaP allele frequency estimates correlated 0.939 with allele frequencies for 97 605 SNPs that were genotyped individually in an independent population; the correlation was 0.971 for 26 SNPs that were genotyped individually for the 1028 individuals used to construct the DNA pools. We conclude that extending the SNP-MaP method to the Affymetrix GeneChip Mapping 100K Array set provides a useful screen of >100,000 SNP markers for QTL association scans.

Aspects of the design and analysis of high-dimensional SNP studies for disease risk estimation

The state of readiness for high-dimensional single nucleotide polymorphism (SNP) epidemiologic association studies is described, as background for a discussion of statistical aspects of case-control study design and analysis. Specifically, the important role that multistage designs can play in the elimination of false-positive associations and in the control of study costs will be noted. Also, the trade-offs associated with using pooled DNA at early design stages for additional important cost reductions will be discussed in some detail. An odds ratio approach to relating SNP alleles to disease risk using pooled DNA will be proposed, in conjunction with a simple empirical variance estimator, based on comparisons among log-odds ratio estimators from distinct pairs of case and control pools. Simulation studies will be presented to evaluate the moderate sample size properties of such multistage designs and estimation procedures. The design of an ongoing three-stage study in the Women's Health Initiative to relate 250,000 SNPs to the risk of coronary heart disease, stroke, and breast cancer will provide illustration, and will be used to motivate the choice of simulation configurations.

Determination of allele frequency in pooled DNA: comparison of three PCR-based methods

Determination of allele frequency in pooled DNA samples is a powerful and efficient tool for large-scale association studies. In this study, we tested and compared three PCR-based methods for accuracy, reproducibility, cost, and convenience. The methods compared were: (i) real-time PCR with allele-specific primers, (ii) real-time PCR with allele-specific TaqMan® probes, and (iii) quantitative sequencing. Allele frequencies of three single nucleotide polymorphisms in three different genes were estimated from pooled DNA. The pools were made of genomic DNA samples from 96 cases with basal cell carcinoma of the skin and 96 healthy controls with known genotypes. In this study, the allele frequency estimation made by real-time PCR with allele-specific primers had the smallest median deviation (MD) from the real allele frequency with 1.12% (absolute percentage points) and was also the cheapest method. However, this method required the most time for optimization and showed the highest variation between replicates (SD = 6.47%). Quantitative sequencing, the simplest method, was found to have intermediate accuracies (MD = 1.44%, SD = 4.2%). Real-time PCR with TaqMan probes, a convenient but very expensive method, had an MD of 1.47% and the lowest variation between replicates (SD = 3.18%).

Microsatellite analysis of pooled Schistosoma mansoni DNA: an approach for studies of parasite populations

Human parasites are often distributed in metapopulations, which makes random sampling for genetic epidemiology difficult. The typical approach to sampling Schistosoma mansoni involves laboratory passage to obtain individual worms with small sample size and selection bias as a consequence. By contrast, the naturally pooled samples from egg output in stool or urine directly represent the genetic composition of current populations. To test whether pooled samples could be used to estimate population allele frequencies, DNA from individual cloned parasites was pooled and amplified by PCR for 7 microsatellites. By polyacrylamide gel analysis, the relative band intensities of the products from the major alleles in the pooled samples differed by 0-6% from the summed intensities of the individual clones (mean = 2.1%+/-2.1% S.D.). The number of PCR cycles (25-40) did not influence the accuracy of the estimate. Varying the frequency of 1 allele in pooled samples from 32 to 69% likewise did not affect accuracy. Allele frequency estimates from aggregate samples such as eggs will be a better foundation for studies of parasite population dynamics as well as the basis for large-scale association studies of host and parasite characteristics.