Combining DNA pooling with selective recombinant genotyping for increased efficiency in fine mapping

One of the key steps in positional cloning and marker-aided selection is to identify marker(s) tightly linked to the target gene (i.e., fine mapping). Selective genotyping such as selective recombinant genotyping (SRG) is commonly used in fine mapping for cost-saving. To further decrease genotyping effort and rapidly screen for tightly linked markers, we propose here a combined DNA pooling and SRG strategy. A two-stage pooled genotyping can be used for identifying recombinants between a pair of flanking markers more efficiently, and a joint use of bulked DNA analysis and two-stage pooling can also save cost for genotyping recombinants. The combined DNA pooling and SRG strategy can further be extended to fine mapping for polygenic traits. The numerical results based on hypothetical scenarios and an illustrative application to fine mapping of a mutant gene, called xl(t), in rice suggest that the proposed strategy can remarkably reduce genotyping amount compared with the conventional SRG.     

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.     

The efficacy of detecting variants with small effects on the Affymetrix 6.0 platform using pooled DNA

Genome-wide genotyping of a cohort using pools rather than individual samples has long been proposed as a cost-saving alternative for performing genome-wide association (GWA) studies. However, successful disease gene mapping using pooled genotyping has thus far been limited to detecting common variants with large effect sizes, which tend not to exist for many complex common diseases or traits. Therefore, for DNA pooling to be a viable strategy for conducting GWA studies, it is important to determine whether commonly used genome-wide SNP array platforms such as the Affymetrix 6.0 array can reliably detect common variants of small effect sizes using pooled DNA. Taking obesity and age at menarche as examples of human complex traits, we assessed the feasibility of genome-wide genotyping of pooled DNA as a single-stage design for phenotype association. By individually genotyping the top associations identified by pooling, we obtained a 14- to 16-fold enrichment of SNPs nominally associated with the phenotype, but we likely missed the top true associations. In addition, we assessed whether genotyping pooled DNA can serve as an inexpensive screen as the second stage of a multi-stage design with a large number of samples by comparing the most cost-effective 3-stage designs with 80% power to detect common variants with genotypic relative risk of 1.1, with and without pooling. Given the current state of the specific technology we employed and the associated genotyping costs, we showed through simulation that a design involving pooling would be 1.07 times more expensive than a design without pooling. Thus, while a significant amount of information exists within the data from pooled DNA, our analysis does not support genotyping pooled DNA as a means to efficiently identify common variants contributing small effects to phenotypes of interest. While our conclusions were based on the specific technology and study design we employed, the approach presented here will be useful for evaluating the utility of other or future genome-wide genotyping platforms in pooled DNA studies.     

A Permutation Test for Oligoset DNA Pooling Studies

Case-control association studies often suffer from population stratification bias. A previous triple combination strategy of stratum matching, genomic controlling, and multiple DNA pooling can correct the bias and save genotyping cost. However the method requires researchers to prepare a multitude of DNA pools—more than 30 case-control pooling sets in total (polyset). In this paper, the authors propose a permutation test for oligoset DNA pooling studies. Monte-Carlo simulations show that the proposed test has a type I error rate under control and a power comparable to that of individual genotyping. For a researcher on a tight budget, oligoset DNA pooling is a viable option.     

Selective DNA Pooling for Determination of Linkage between a Molecular Marker and a Quantitative Trait Locus

Selective genotyping is a method to reduce costs in marker-quantitative trait locus (QTL) linkage determination by genotyping only those individuals with extreme, and hence most informative, quantitative trait values. The DNA pooling strategy (termed: ``selective DNA pooling') takes this one step further by pooling DNA from the selected individuals at each of the two phenotypic extremes, and basing the test for linkage on marker allele frequencies as estimated from the pooled samples only. This can reduce genotyping costs of marker-QTL linkage determination by up to two orders of magnitude. Theoretical analysis of selective DNA pooling shows that for experiments involving backcross, F(2) and half-sib designs, the power of selective DNA pooling for detecting genes with large effect, can be the same as that obtained by individual selective genotyping. Power for detecting genes with small effect, however, was found to decrease strongly with increase in the technical error of estimating allele frequencies in the pooled samples. The effect of technical error, however, can be markedly reduced by replication of technical procedures. It is also shown that a proportion selected of 0.1 at each tail will be appropriate for a wide range of experimental conditions.     

Progressive fine mapping in experimental populations: an improved strategy toward positional cloning

Genetic mapping is one of the key steps in positional cloning. The traditional mapping strategies typically require to genotype a set of markers that are nearly evenly or randomly distributed across the genome or a region of interest. Such “grid” strategies work with low efficiency. We propose an improved mapping strategy by integrating the principle of one-dimensional optimization and information on physical map into the standard mapping procedure used in experimental populations. Computer simulations based on a set of empirical data suggest that our new procedure can reduce the number of markers required for genotyping to less than one-fourth of that of the standard procedure. An illustrative application also demonstrates a pronounced reduction of the burden in genotyping. The proposed strategy offers a quick and cost-effective access to the target gene for positional cloning without any extra expense except for making use of genomic sequence data. A Microsoft Excel spreadsheet, for performing easy calculations described in this article, is available on request from the authors.     

Identification of susceptibility genes for complex diseases using pooling-based genome-wide association scans

The success of genome-wide association studies (GWAS) to identify risk loci of complex diseases is now well-established. One persistent major hurdle is the cost of those studies, which make them beyond the reach of most research groups. Performing GWAS on pools of DNA samples may be an effective strategy to reduce the costs of these studies. In this study, we performed pooling-based GWAS with more than 550,000 SNPs in two case-control cohorts consisting of patients with Type II diabetes (T2DM) and with chronic rhinosinusitis (CRS). In the T2DM study, the results of the pooling experiment were compared to individual genotypes obtained from a previously published GWAS. TCF7L2 and HHEX SNPs associated with T2DM by the traditional GWAS were among the top ranked SNPs in the pooling experiment. This dataset was also used to refine the best strategy to correctly identify SNPs that will remain significant based on individual genotyping. In the CRS study, the top hits from the pooling-based GWAS located within ten kilobases of known genes were validated by individual genotyping of 1,536 SNPs. Forty-one percent (598 out of the 1,457 SNPs that passed quality control) were associated with CRS at a nominal P value of 0.05, confirming the potential of pooling-based GWAS to identify SNPs that differ in allele frequencies between two groups of subjects. Overall, our results demonstrate that a pooling experiment on high-density genotyping arrays can accurately determine the minor allelic frequency as compared to individual genotyping and produce a list of top ranked SNPs that captures genuine allelic differences between a group of cases and controls. The low cost associated with a pooling-based GWAS clearly justifies its use in screening for genetic determinants of complex diseases. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Bayesian method for gene detection and mapping, using a case and control design and DNA pooling

Association mapping studies aim to determine the genetic basis of a trait. A common experimental design uses a sample of unrelated individuals classified into 2 groups, for example cases and controls. If the trait has a complex genetic basis, consisting of many quantitative trait loci (QTLs), each group needs to be large. Each group must be genotyped at marker loci covering the region of interest; for dense coverage of a large candidate region, or a whole-genome scan, the number of markers will be very large. The total amount of genotyping required for such a study is formidable. A laboratory effort efficient technique called DNA pooling could reduce the amount of genotyping required, but the data generated are less informative and require novel methods for efficient analysis. In this paper, a Bayesian statistical analysis of the classic model of McPeek and Strahs is proposed. In contrast to previous work on this model, I assume that data are collected using DNA pooling, so individual genotypes are not directly observed, and also account for experimental errors. A complete analysis can be performed using analytical integration, a propagation algorithm for a hidden Markov model, and quadrature. The method developed here is both statistically and computationally efficient. It allows simultaneous detection and mapping of a QTL, in a large-scale association mapping study, using data from pooled DNA. The method is shown to perform well on data sets simulated under a realistic coalescent-with-recombination model, and is shown to outperform classical single-point methods. The method is illustrated on data consisting of 27 markers in an 880-kb region around the CYP2D6 gene.     

Empirical evaluation of selective DNA pooling to map QTL in dairy cattle using a half-sib design by comparison to individual genotyping and interval mapping

This study represents the first attempt at an empirical evaluation of the DNA pooling methodology by comparing it to individual genotyping and interval mapping to detect QTL in a dairy half-sib design. The findings indicated that the use of peak heights from the pool electropherograms without correction for stutter (shadow) product and preferential amplification performed as well as corrected estimates of frequencies. However, errors were found to decrease the power of the experiment at every stage of the pooling and analysis. The main sources of errors include technical errors from DNA quantification, pool construction, inconsistent differential amplification, and from the prevalence of sire alleles in the dams. Additionally, interval mapping using individual genotyping gains information from phenotypic differences between individuals in the same pool and from neighbouring markers, which is lost in a DNA pooling design. These errors cause some differences between the markers detected as significant by pooling and those found significant by interval mapping based on individual selective genotyping. Therefore, it is recommended that pooled genotyping only be used as part of an initial screen with significant results to be confirmed by individual genotyping. Strategies for improving the efficiency of the DNA pooling design are also presented.     

Quantitative trait locus mapping in dairy cattle by means of selective milk DNA pooling using dinucleotide microsatellite markers: analysis of milk protein percentage.

"Selective DNA pooling" accomplishes quantitative trait locus (QTL) mapping through densitometric estimates of marker allele frequencies in pooled DNA samples of phenotypically extreme individuals. With poly(TG) microsatellites, such estimates are confounded by "shadow" ("stutter") bands. A correction procedure was developed on the basis of an observed linear regression between shadow band intensity and allele TG repeat number. Using this procedure, a selective DNA pooling study with respect to milk protein percentage was implemented in Israel-Holstein dairy cattle. Pools were prepared from milk samples of high and low daughters of each of seven sires and genotyped with respect to 11 markers. Highly significant associations with milk protein percentage were found for 5 of the markers; 4 of these markers confirmed previous reports. Selective DNA pooling accessed 80.6 and 48.3%, respectively, of the information that would have been available through individual selective genotyping or total population genotyping. In effect, the statistical power of 45,600 individual genotypings was obtained from 328 pool genotypings. This methodology can make genome-wide mapping of QTL accessible to moderately sized breeding organizations.     

Fractioned DNA pooling: a new cost-effective strategy for fine mapping of quantitative trait loci

Selective DNA pooling (SDP) is a cost-effective means for an initial scan for linkage between marker and quantitative trait loci (QTL) in suitable populations. The method is based on scoring marker allele frequencies in DNA pools from the tails of the population trait distribution. Various analytical approaches have been proposed for QTL detection using data on multiple families with SDP analysis. This article presents a new experimental procedure, fractioned-pool design (FPD), aimed to increase the reliability of SDP mapping results, by "fractioning" the tails of the population distribution into independent subpools. FPD is a conceptual and structural modification of SDP that allows for the first time the use of permutation tests for QTL detection rather than relying on presumed asymptotic distributions of the test statistics. For situations of family and cross mapping design we propose a spectrum of new tools for QTL mapping in FPD that were previously possible only with individual genotyping. These include: joint analysis of multiple families and multiple markers across a chromosome, even when the marker loci are only partly shared among families; detection of families segregating (heterozygous) for the QTL; estimation of confidence intervals for the QTL position; and analysis of multiple-linked QTL. These new advantages are of special importance for pooling analysis with SNP chips. Combining SNP microarray analysis with DNA pooling can dramatically reduce the cost of screening large numbers of SNPs on large samples, making chip technology readily applicable for genomewide association mapping in humans and farm animals. This extension, however, will require additional, nontrivial, development of FPD analytical tools.     

DNA Pooling: a tool for large-scale association studies

DNA pooling is a practical way to reduce the cost of large-scale association studies to identify susceptibility loci for common diseases. Pooling allows allele frequencies in groups of individuals to be measured using far fewer PCR reactions and genotyping assays than are used when genotyping individuals. Here, we discuss recent developments in quantitative genotyping assays and in the design and analysis of pooling studies. Sophisticated pooling designs are being developed that can take account of hidden population stratification, confounders and inter-loci interactions, and that allow the analysis of haplotypes.     

Highly cost-efficient genome-wide association studies using DNA pools and dense SNP arrays

Genome-wide association (GWA) studies to map genes for complex traits are powerful yet costly. DNA-pooling strategies have the potential to dramatically reduce the cost of GWA studies. Pooling using Affymetrix arrays has been proposed and used but the efficiency of these arrays has not been quantified. We compared and contrasted Affymetrix Genechip HindIII and Illumina HumanHap300 arrays on the same DNA pools and showed that the HumanHap300 arrays are substantially more efficient. In terms of effective sample size, HumanHap300-based pooling extracts >80% of the information available with individual genotyping (IG). In contrast, Genechip HindIII-based pooling only extracts ~30% of the available information. With HumanHap300 arrays concordance with IG data is excellent. Guidance is given on best study design and it is shown that even after taking into account pooling error, one stage scans can be performed for >100-fold reduced cost compared with IG. With appropriately designed two stage studies, IG can provide confirmation of pooling results whilst still providing ~20-fold reduction in total cost compared with IG-based alternatives. The large cost savings with Illumina HumanHap300-based pooling imply that future studies need only be limited by the availability of samples and not cost.     

混合DNA样品池扩增法及其应用

将个体 DNA提取出来后 ,按一定方式进行混合 ,构成混合 DNA样品池。这种混合 DNA样品可用于病因未明的遗传性及遗传易感性疾病的研究。在研究常染色体隐性遗传性耳聋致病基因时 ,发现与染色体 9q的 D9S92 2和 D9S30 1位点有相关性。此方法比通常的连锁分析法省时省力。在肿瘤相关基因或责任基因的研究、法医学的个体认定、基因突变的检测等方面均显示出实用性 ,值得推广     

PDA: Pooled DNA analyzer

Background  

Association mapping using abundant single nucleotide polymorphisms is a powerful tool for identifying disease susceptibility genes for complex traits and exploring possible genetic diversity. Genotyping large numbers of SNPs individually is performed routinely but is cost prohibitive for large-scale genetic studies. DNA pooling is a reliable and cost-saving alternative genotyping method. However, no software has been developed for complete pooled-DNA analyses, including data standardization, allele frequency estimation, and single/multipoint DNA pooling association tests. This motivated the development of the software, 'PDA' (Pooled DNA Analyzer), to analyze pooled DNA data.     

Sequential bulked typing: a rapid approach for detecting QTLs

A strategy of DNA pooling aimed at identifying markers linked to quantitative trait loci (QTLs), ‘Sequential Bulked Typing’ (SBT), is presented. The method proposed consists in pooling DNA from consecutive pairs of individuals ranked phenotypically, i.e., pools are formed with individuals ranked (1st, 2nd), (3rd, 4th),…, (N-1st, Nth). The N/2 pools are subsequently amplified using the polymerase chain reaction (PCR). If the whole population is typed the number of PCRs per marker is halved with respect to individual typing (IT). But if this strategy is combined with selective genotyping of extreme individuals savings can be further increased. Two extreme cases are considered: in the first one (SBT⁰), it is assumed that only presence or absence of a given allele can be ascertained in a pool; in the second one (SBT¹), it is further assumed that differences between allele band intensities can be distinguished. The theory to estimate by maximum likelihood the QTL effect and its position with respect to flanking markers is presented. The behaviour of IT and SBT was studied using stochastic computer simulation in backcross and F₂ populations. Three percentages of subpattern distinction (0, 50 and 100%) two population sizes (n=1200 and 600) and two QTL effects (a=0.1 and 0.25 standard deviations) were considered. SBT¹ had the same power as individual genotyping at half the genotyping costs in all situations studied. Accuracy of QTL location is not increased with a dense number of markers, as opposed to individual typing. As a result DNA pooling is not useful for accurate location of the QTL but rather to pick up genome regions containing QTLs of at least moderate effect. The theory developed provides the general theoretical framework to deal with any DNA pooling strategy aimed at detecting QTLs. Received: 15 September 1997 / Accepted: 6 October 1997     

A genome‐wide association study using selective DNA pooling identifies candidate markers for fertility in Holstein cattle

The decline in the reproductive efficiency of dairy cows, especially those with high producing potential, has become a challenging problem. In this study, a selective DNA pooling approach was applied to a cow population whose oocytes were fertilized and cultured to obtain phenotypic records of fertilization rate and blastocyst rate. Using a stringent 5% genome‐wide significance level, 22 and five single nucleotide polymorphisms (SNPs) were found to be associated with fertilization rate and blastocyst rate, respectively. SNPs that showed significant association in selective DNA pooling were further evaluated by individual genotyping. Interestingly, the majority of the SNP associations were confirmed by individual genotyping, testifying to the effectiveness of selective DNA pooling using a high‐density SNP genotyping array. This study is the first application of the selective DNA pooling approach using the BovineSNP50 array in cattle.     

Using DNA pools for genotyping trios

The genotyping of mother–father–child trios is a very useful tool in disease association studies, as trios eliminate population stratification effects and increase the accuracy of haplotype inference. Unfortunately, the use of trios for association studies may reduce power, since it requires the genotyping of three individuals where only four independent haplotypes are involved. We describe here a method for genotyping a trio using two DNA pools, thus reducing the cost of genotyping trios to that of genotyping two individuals. Furthermore, we present extensions to the method that exploit the linkage disequilibrium structure to compensate for missing data and genotyping errors. We evaluated our method on trios from CEPH pedigree 66 of the Coriell Institute. We demonstrate that the error rates in the genotype calls of the proposed protocol are comparable to those of standard genotyping techniques, although the cost is reduced considerably. The approach described is generic and it can be applied to any genotyping platform that achieves a reasonable precision of allele frequency estimates from pools of two individuals. Using this approach, future trio-based association studies may be able to increase the sample size by 50% for the same cost and thereby increase the power to detect associations.     

Asymptotic variances of QTL estimators with selective DNA pooling

Investigation on QTL-marker linkage usually requires a great number of observed recombinations, inferred from combined analysis of phenotypes and genotypes. To avoid costly individual genotyping, inferences on QTL position and effects can instead make use of marker allele frequencies. DNA pooling of selected samples makes allele frequency estimation feasible for studies involving large sample sizes. Linkage studies in outbred populations have traditionally exploited half-sib family designs; within the animal production context, half-sibships provide large families that are highly suitable for DNA pooling. Estimators for QTL position and effect have been proposed that make use of information from flanking markers. We present formulas derived by the delta method for the asymptotic variance of these estimators.