Application of pooled genotyping to scan candidate regions for association with HDL cholesterol levels
© Henry Stewart Publications 2004
Received: 10 August 2004
Accepted: 10 August 2004
Published: 1 November 2004
Association studies are used to identify genetic determinants of complex human traits of medical interest. With the large number of validated single nucleotide polymorphisms (SNPs) currently available, two limiting factors in association studies are genotyping capability and costs. Pooled DNA genotyping has been proposed as an efficient means of screening SNPs for allele frequency differences in case-control studies and for prioritising them for subsequent individual genotyping analysis. Here, we apply quantitative pooled genotyping followed by individual genotyping and replication to identify associations with human serum high-density lipoprotein (HDL) cholesterol levels. The DNA from individuals with low and high HDL cholesterol levels was pooled separately, each pool was amplified by polymerase chain reaction in triplicate and each amplified product was separately hybridised to a high-density oligonucleotide array. Allele frequency differences between case and control groups with low and high HDL cholesterol levels were estimated for 7,283 SNPs distributed across 71 candidate gene regions spanning a total of 17.1 megabases. A novel method was developed to take advantage of independently derived haplotype map information to improve the pooled estimates of allele frequency differences. A subset of SNPs with the largest estimated allele frequency differences between low and high HDL cholesterol groups was chosen for individual genotyping in the study population, as well as in a separate replication population. Four SNPs in a single haplotype block within the cholesteryl ester transfer protein (CETP) gene interval were significantly associated with HDL cholesterol levels in both populations. Our study is among the first to demonstrate the application of pooled genotyping followed by confirmation with individual genotyping to identify genetic determinants of a complex trait.
Keywordsassociation study HDL cholesterol CETP SNPs pooled genotyping haplotypes
Association studies are widely viewed as one of the most promising methods for identifying the genetic determinants of human phenotypic traits of medical interest, such as common diseases and individual responses to the drugs used to treat those diseases . Therefore, a considerable amount of research has been focused on developing methodologies that efficiently screen candidate gene regions or whole genomes for associations of complex phenotypes with genetic markers, such as single nucleotide polymorphisms (SNPs). The methodology relies on having a set of common genetic markers at a sufficiently dense coverage across the genome, such that either the causal variant itself or a marker in linkage disequilibrium with the causal variant will be tested in the association study. Thus, to be comprehensive and reproducible, a whole genome scan study requires the assay of hundreds of thousands of densely spaced SNP markers in a large number of samples. There is a considerable body of experimental [2–6] and theoretical [7–9] work that suggests genotyping of pools consisting of DNA from many individuals is a viable alternative to individual genotyping. Pooled assays replace many measurements of individual samples with a few measurements of a pooled sample -- with a corresponding reduction in cost, time and labour. Here, we describe one of the first large-scale SNP association studies in which this methodology has been applied and validated.
Human population studies have shown that serum high density lipoprotein (HDL) cholesterol concentrations are inversely correlated with the development of premature coronary heart disease . In this report, we describe a two-stage study to identify genetic markers associated with HDL cholesterol levels. First, we use a pooled genotyping screen to identify SNPs likely to have large frequency differences between low and high HDL cholesterol groups. Starting from 7,283 SNPs distributed across 71 candidate regions, we use the pooled data to select about 300 SNPs with the strongest evidence for association. We then individually genotype these SNPs, to confirm their allele frequency differences in the low and high HDL cholesterol individuals in the study group. We confirm associations identified in the study population by individually genotyping SNPs with significant allele frequency differences in a replicate population.
We also describe a novel method for using independently derived haplotype map data to improve the power of an association study based on pooled genotyping. Using genotype data from a separate set of ethnically diverse individuals, we determine haplotype blocks and sets of common haplotype patterns that together account for most of the variation in a given genomic interval. From pooled genotype data, we estimate frequency differences for these common patterns between case and control groups. These pattern differences enable us to make more accurate estimates of the individual SNP frequency differences that exploit redundancies in the haplotype map, thereby reducing experimental error in the individual SNP measurements.
Materials and methods
SNP discovery and haplotype map construction
In an independent, previously described study, a genome-wide SNP collection was obtained using high-density oligonucleotide array-based resequencing . Briefly, we generated somatic cell hybrids by fusing lymphoblast cell lines from the Coriell Polymorphism Discovery Resource  with a hamster cell line to form between 20 and 50 haploid somatic cell hybrids for each human chromosome. DNA was isolated and amplified by long-range polymerase chain reaction (PCR), and the PCR products were fragmented, labelled and hybridised to a series of SNP discovery arrays. These arrays were designed such that each base of the reference sequence was queried by eight 25-mer probes. We identified SNPs from the resulting fluorescence intensity data using a pattern recognition algorithm.
We used a dynamic programming algorithm  to partition these haploid SNP discovery data into haplotype blocks. SNPs having minor allele frequencies of at least 10 per cent in the SNP discovery data were included in the map. We required all blocks to satisfy the condition that at least 80 per cent of the haploid samples could be assigned to common haplotype patterns having greater than 10 per cent frequency. For a block having N common haplotype patterns, we also required at least N -1 patterns to have tagging SNPs that distinguished each of those patterns from all of the others.
The study population was derived from a cohort of individuals (self-reported Caucasian) from the ACCESS study , which was made up of males, postmenopausal females and premenopausal females that either had, or were at risk for, cardiovascular disease. Whole blood from subjects participating in this study was obtained in accordance with the Declaration of Helsinki (2000) of the World Medical Association, in addition to appropriate informed consent documentation defining the study design and providing an assessment of the risks and benefits associated with study participation. Individuals with high and low HDL cholesterol levels were selected as the top and bottom 15 per cent of the continuous HDL distribution from each group, resulting in the following samples: 166 high HDL (≥ 54.9 mg/dl) and 182 low HDL (≤ 36.1 mg/dl) males; 140 high HDL (≥ 64.0 mg/dl) and 142 low HDL (≤ 47.3 mg/dl) postmenopausal females; and 17 high HDL (≥ 67.4 mg/dl) and 24 low HDL (≤ 42.2 mg/dl) premenopausal females. HDL cholesterol was measured in fasting samples from four preclinical visits, all DNA samples were collected at baseline (ie without drug treatment). In this population, the interaction between age and HDL did not warrant an adjustment for age in the selection of cases and controls.
The replicate population consisted of 83 low HDL and 78 high HDL samples from postmenopausal women (self-reported Caucasians). These samples represented the 25 per cent tails from both ends of the continuous HDL distribution of an independent cohort from an osteoporosis study (the cohort was not selected on the basis of their HDL cholesterol levels or other cardiovascular risk factors), with the high HDL cut-off at 62 mg/dl, the low HDL cut-off at 42 mg/dl and a mean age of 54.4 years.
Construction of DNA pools
We constructed four DNA pools for estimation of SNP allele frequency differences between the low and high HDL cholesterol groups. Five of the 671 samples of the study population were excluded from pooled genotyping due to insufficient amount of DNA or failed normalisation. After removal of these samples, there were 345 low HDL samples and 321 high HDL samples remaining. The low HDL cholesterol samples were randomly split into two subgroups and used to construct pool A (consisting of 173 individuals) and pool B (consisting of 172 individuals). Likewise, the high HDL cholesterol samples were randomly split into two subgroups and used to construct pool C (consisting of 161 individuals) and pool D (consisting of 160 individuals).
Genomic DNA was extracted from whole blood using the PureGene DNA isolation system (Gentra) per manufacturer's protocol. DNA samples were quantified using a PicoGreen assay kit (Molecular Probes) and SpectraFluor Plus Tecan plate reader according to the manufacturers' instructions, and then diluted to a standard concentration using a Packard Multi-Probe Robot. Equimolar aliquots of DNA were transferred into one of four pool tubes using a Packard MultiProbe robot. Each pool was then requantified by PicoGreen assay and the pools diluted to 20 ng/μl for use as a PCR template.
SNP selection for pooled genotyping
Seventy candidate genes analysed in the association study.
High-density oligonucleotide arrays
Determination of pooled allele frequency estimates
For pooled genotyping, 7.25 ng genomic DNA (pooled samples) was amplified using long-range PCR reactions, pooled, labelled, hybridised to high-density arrays, stained and detected as described . The four DNA pools (low HDL pools A and B and high HDL pools C and D) were each amplified by PCR using 1,222 long-range primer pairs in three replicates. The 12 sets of PCR products were hybridised to separate chips.
where the numeric subscripts denote positions in the list of six sorted intensities.
Two quality control metrics were used to assess the reliability of the intensities for a SNP in an array scan. The first metric, 'conformance', measured the presence of specific target DNA for that SNP. The second metric, signal to background ratio, measured the relative amounts of specific and non-specific binding. Cut-offs were applied to both metrics, and SNP feature sets that did not pass either metric were discarded from further analysis.
Conformance was computed independently for both reference and alternate allele feature sets, and a maximum taken of the two values. The conformance for a particular allele was defined as the fraction of feature sets for which the perfect-match feature was brighter than all three mismatch features. In the 80-feature SNP tiling, each allele had ten such sets of four features. SNP measurements having conformance < 0.9 were discarded from further evaluations.
The trimmed mean intensities for perfect-match and mismatch feature sets were obtained as described above. SNP measurements having signal/background < 1.5 were discarded from further evaluations.
Haplotype block fitting algorithm
The method uses linear regression to determine these underlying haplotype pattern frequency differences, given a set of estimated SNP allele frequency differences for a haplotype block. Our method for haplotype map construction guarantees that in every block, there are at least enough SNPs to determine the frequencies of the common haplotype patterns. Most SNPs are in blocks that contain additional redundant SNPs, so if measurement errors are uncorrelated, regression should yield estimates that are more accurate than the original SNP measurements. From the fitted pattern differences, more accurate estimates of the true allele frequency differences for individual SNPs can then be determined.
Solving these equations given and r ij is a linear regression problem. Standard regression statistics (R and the P value for an F test) can be used to judge the quality of the fit of the SNP data to the haplotype pattern information. Deviations from a perfect fit can arise both from experimental errors and inaccuracies in the haplotype model. In instances where the quality of the fit to the haplotype map is good, the fitted allele frequency differences should have lower variance than the raw data for individual SNPs because they incorporate information about the expected correlations between SNPs.
A similar method could be used to estimate haplotype pattern frequencies in each pooled sample, with a constraint that the frequencies of common patterns add up to 1. We chose to work in the space of allele frequency differences for several reasons. The frequency differences are the quantities we are ultimately interested in, and it seemed most parsimonious to evaluate a fit for these differences directly, rather than performing separate fits on frequencies in each pool and then combining these to obtain differences. Also, the quality of a fit on absolute frequencies would be sensitive to the presence of rare haplotypes not included in the model, even under the null hypothesis of no pool differences. Our constraint on frequency differences summing to 0 only implies that the proportion of rare haplotypes in case and control pools is similar. Finally, due to experimental differences in SNP hybridisation characteristics, we have more confidence in our ability to detect pool differences than to obtain unbiased estimates of absolute allele frequencies.
Determination of individual genotypes
For individual genotyping by high-density oligonucleotide arrays, samples were amplified by short-range multiplex PCR, labelled, hybridised to the arrays, stained and detected as described .
The individual genotypes for an SNP were determined by clustering measurements from multiple scans in the two-dimensional space defined by reference and alternate perfectmatch trimmed mean intensities. Trimmed mean intensities were computed as described above. We used a K-means algorithm to assign measurements to clusters representing distinct diploid genotypes. Instead of estimating the background intensity term Ĩ MM from a single scan, we determined an optimal value for each SNP that minimised the variance in within the assigned genotype clusters. The K-means and background optimisation steps were iterated until cluster membership and background estimates converged. To determine the appropriate number of genotype clusters, we repeated the analysis for one, two and three clusters and selected the most likely solution, considering likelihoods of the data and the cluster parameters. The data likelihood was determined using a normal mixture model for the distribution of around the cluster means. The model likelihood was calculated using a prior distribution of expected cluster positions (ie homozygous reference allele near , heterozygote near and homozygous alternate allele near ).
For individual genotyping by template-directed dyeterminator incorporation with fluorescence-polarisation detection (FP-TDI) , samples were amplified by PCR, primer extension was performed using AcycloPrime FP SNP detection kit (Perkin Elmer Life Sciences) and changes in fluorescence polarisation were measured using Analyst HT (LJL Biosystems) as described .
Population stratification analysis
In an association study, systematic differences in ancestry between case and control groups can produce large numbers of statistically significant but spurious associations [20, 21]. We examined the 348 individuals with low HDL levels and the 329 individuals with high HDL levels in the study population to ensure that they were adequately matched prior to constructing DNA pools. We individually genotyped the samples for 300 SNPs that are genetically unlinked and uniformly spaced across the genome, as described previously .
In χ2 tests for association with the HDL cholesterol phenotype, we observed a small excess of moderate p values. For 280 SNPs giving high-quality genotype data, 43 had p < 0.1 versus 28 expected. A sensitive global test for population structure based on the sum of χ2 statistics  was significant (p < 0.001); however, a permutation analysis of the genotype data indicated that the expected increase in variance of allele frequency measurements due to stratification of this magnitude was less than 1 per cent. We also analysed the genotype data for population structure using the structure program . The structure program uses a model-based clustering method for identifying subpopulations such that, within a cluster, all markers are in Hardy-Weinberg and linkage equilibrium. This analysis did not show convincing evidence for more than one subpopulation. In runs with between two and five assumed clusters, most samples were assigned similar admixture proportions in each predicted subpopulation; for two clusters, 75 per cent of samples were given admixture proportions between 0.4 and 0.6. Based on these results, and the limited accuracy of pooled genotyping assays, we judged that the low and high HDL cholesterol groups were adequately matched.
Pooled genotyping results
For each SNP, we estimated an allele frequency difference, , between the low HDL cholesterol and high HDL cholesterol pools. We then excluded a small proportion of the pooled data due to spurious experimental errors, such as saturated features or inconsistent hybridisation patterns. We also excluded SNPs where all three measurements for any one of the four pools failed and SNPs where the standard error of exceeded 0.025. Of the 7,283 SNPs tiled on the array, 6,611 (91 per cent) passed all of these data quality filters.
Haplotype block fitting analysis
Haplotype block-fitting results and analysis of variance.
SNPs passing quality filters
SNPs contributing to fits
Fitted degrees of freedom
Residual degrees of freedom
% degrees of freedom used
Total sum of squares
Fitted sum of squares
Residual sum of squares
% variance explained
Analysis of variance allows us to determine how much of the variation in SNP allele frequencies observed between the DNA pools is consistent with the haplotype map and how much is residual variation due to experimental errors in the measurements, the contribution of rare patterns not represented in the haplotype map and errors in the haplotype map. We can measure the effectiveness of the algorithm by the extent to which the fraction of variance explained by the fitted haplotype patterns exceeds the fraction of degrees of freedom used in the fits. In this analysis (Table 2), we found that about 77 per cent of all the variance in the data was consistent with the model based on common haplotypes. Based on the number of free parameters in the haplotype model, we would have expected only 42 per cent of the variance to be accounted for by chance. We repeated this analysis after permuting the individual measurements. Here, the haplotype map explained only 43 per cent of the variance and only 5 per cent of SNPs were in blocks having p < 0.05 in an F test. This analysis shows that the agreement between the haplotype model and the original data could not arise by chance.
Selection of SNPs for individual genotyping
Selecting the SNP markers that are the most likely to have large allele frequency differences based on the pooled array data is difficult. The set of SNPs having the largest absolute is dominated by a subset of measurements with very high experimental variance. A t-test is also inadequate, because it favours SNPs with low experimental variance, even if the is too small to be of biological interest and is probably due to sampling variation. The experimental variance is poorly determined from the limited number of data points available. Due to differences in SNP calibration in our genotyping assay, our ability to estimate absolute allele frequencies, and hence sampling variance, is similarly limited. Based on data from experiments with pools of known composition, we found that the strategy of excluding data for SNPs with very high standard errors, and then selecting SNPs with the largest , performed as well or better than tests based on variance estimates for each SNP (data not shown).
A total of 312 SNP markers were chosen for individual genotyping based on the capacity of a small high-density oligonucleotide array. Based on the pooled allele frequency data, we selected 284 SNPs expected to have large allele frequency differences. Half of the 284 SNPs were chosen to be 'haplotype conforming' -- belonging to informative haplotype blocks that had good fits with p < 0.05 -- while the other half were chosen to be 'non-conforming' SNPs selected from the remainder based only on pooled estimates of . We ranked 1,934 conforming SNPs by the smaller of their actual and fitted values, and selected the top 142 SNPs yielding a cut-off of . For 4,677 non-conforming SNPs, ranking by absolute and selecting the top 142 yielded a cutoff of . We selected a higher proportion of conforming SNPs for individual genotyping because their consistency with the haplotype map provided additional evidence for allele frequency differences at those positions. We did include non-conforming SNPs, however, so as not to overlook signals that were not in blocks, or for which the fit to the haplotype map was poor for other reasons. An additional 28 SNPs that did not meet these criteria were selected because they were either in candidate loci of interest or had been independently genotyped in the same population using fluorescence polarisation. They were used to assess the accuracy of our high-density array-based individual genotyping.
Individual genotyping data quality analysis
A total of 832 DNA samples in the study and replicate populations were individually genotyped for the 312 selected SNPS. Three quality-control filters were applied to the individual genotype data. We first required that SNPs have an unambiguous genotype call in at least 80 per cent of the 832 DNA samples assayed. Secondly, we required that both SNP alleles be segregating in the population (ie have at least two identifiable genotype clusters). Finally, we required that the SNP alleles be in Hardy-Weinberg equilibrium (p > 0.001). We found that large deviations from Hardy-Weinberg equilibrium were generally associated with systematic hybridisation artefacts. Of the 312 SNPs, 284 (91 per cent) passed all three data quality filters.
To estimate the quality of the individual genotypes called using the high-density oligonucleotide array platform, we compared our genotype calls with those obtained using FPTDI for 19 SNPs in three gene regions (CETP, endothelial lipase [LIPG] and liver receptor alpha [LXRα]). The call rate (the fraction of assigned genotypes out of potential genotypes) for the array platform is above 98 per cent, very similar to that generated using FP-TDI using the same DNA samples (supplementary Table 3). Of the genotypes called by both methods, the concordance (the fraction of SNPs assigned genotypes by both methods that were in agreement) between the oligonucleotide array and FP-TDI methodologies is greater than 99 per cent.
Evaluation of the pooled genotyping screen
Distribution of χ2 test scores in SNPs selected for individual genotyping.
Number meeting quality criteria
p < 0.04c
p < 0.01c
To assess the effectiveness of the haplotype fitting procedure, we looked at the numbers of 'haplotype conforming' and 'non-conforming' SNPs having small χ2 test p values in individual genotyping (Table 3). Of SNPs having p < 0.04, about 65 per cent came from the 'haplotype conforming' category, and this excess was quite significant (p ≈ 0.001). The same trend was seen among SNPs having p < 0.01; however, the numbers of observations were insufficient to reach statistical significance. Thus, SNPs selected based on corroborating haplotype data indeed seem to be more likely to show larger allele frequency differences in individual genotyping.
SNP associations with HDL levels in the study population
Our two-stage experimental design posed a tricky multiple testing problem. While we performed tests on just 312 individually genotyped SNPs, these were selected as likely to have large allele frequency differences from a total of 6,611 SNPs with good pooled genotyping data quality. If our pooled assay was perfect, then we were effectively testing all 6,611 SNPs; if the pooled estimates were uncorrelated with allele frequencies, then we are really only testing the 312 SNPs selected for individual genotyping. Based on our capture rates for SNPs with small p values, we consider that the effective number of tests we were performing was a substantial fraction of 6,611.
SNPs having significant association with HDL cholesterol in the study population.
1.1 × 10-11
7.2 × 10-9
2.4 × 10-8
9.1 × 10-8
9.7 × 10-8
1.2 × 10-7
SNP associations in the replicate population
Genotyping results for 14 CETP gene SNPs in the test and replicate samples.
1.2 × 10-2
2.8 × 10-2
1. 4 × 10-3
1.1 × 10-11
1.5 × 10-1
1. 8 × 10-11
2.4 × 10 -8
5.0 × 10 -4
5.9 × 10 -11
7.2 × 10 -9
3.2 × 10 -4
1.2 × 10 -11
9.0 × 10 -8
1.9 × 10 -3
7.1 × 10 -10
9.7 × 10 -8
5.6 × 10 -3
1.9 × 10 -9
1.2 × 10-7
8.9 × 10-1
1.6 × 10-6
1.6 × 10-2
3.9 × 10-1
1.1 × 10-2
5.0 × 10-3
8.7 × 10-3
2.4 × 10-4
7.5 × 10-1
5.8 × 10-2
2.5 × 10-1
4.4 × 10-3
8.0 × 10-1
7.7 × 10-3
1.1 × 10-3
2.9 × 10-1
4.2 × 10-3
2.0 × 10-1
2.9 × 10-1
5.0 × 10-1
1.2 × 10-1
4.6 × 10-2
3.0 × 10-1
Linkage disequilibrium across the CETPlocus
Previous studies have identified two major blocks of linkage disequilibrium across the CETP locus [4, 17, 24]. Of the 14 SNP markers in CETP that we examined for association with HDL cholesterol levels (supplementary Table 4), nine are members of our whole-genome haplotype map (Figure 5). Consistent with these other studies, in our map, these nine SNPs are divided into two haplotype blocks, each having three common haplotype patterns. We computed D' for all pairs of the 14 SNPs in CETP, using an expectation maximisation (EM) algorithm to determine haplotype frequencies [25, 26]. These results, again, show two blocks of very strong disequilibrium.
The goal of this study was to determine the effectiveness of a large-scale pooled genotyping screen to identify common variants associated with a complex trait. CETP, which transfers cholesteryl esters from the anti-atherogenic HDL to the proatherogenic very-low- and low-density lipoprotein fractions, plays an important role in HDL cholesterol metabolism and served as our positive control. Correlations between SNPs in the genomic interval encoding CETP and variations in the mass/activity of the CETP protein and corresponding HDL levels have been intensively studied [10, 16, 27] and consistently shown to be associated. CETP is estimated to account for ~5 per cent of the variability of HDL levels in the general population . Here, four SNPs in CETP had strong signals and were independently identified as being associated with HDL levels in the pooled screen. The fact that we identified CETP in this study as being associated with HDL levels confirms that pooled genotyping can be used in genetic association studies to identify genes underlying complex phenotypes.
While we find CETP to be replicable and convincingly associated with HDL cholesterol serum levels, none of the 70 candidate genes or 230 other genes in the 17.1Mb of DNA screened appear to play a major role in the genetic variability of HDL cholesterol levels in this population. Based on the strong association of CETP with HDL observed in our study, we are likely to have had sufficient power to identify similar effect sizes in the other candidate genes. Recent work suggests that there are likely to be several additional genes that contribute to HDL phenotypic variance and are as yet unidentified . We examined SNPs distributed across only about 0.5 per cent of the genome, and thus it is likely that these unidentified genes are located in genomic intervals that we did not examine.
In our candidate region study, we used a design incorporating stratification analysis, pooled genotyping, confirmation of promising candidate loci by individual genotyping and replication in an independent cohort. We have demonstrated that independently derived haplotype map information can be used to improve SNP selection from a pooled genotyping screen. High-density oligonucleotide arrays permit the scale and efficiency required for very large-scale association studies. These experimental methods and analysis strategies can be directly scaled up to whole-genome association studies.
We would like to thank Charles Shear and the Lipitor Team for assistance in the clinical trials; Pascual Starink, Ellen Jacobs and Eric Buljan for LIMs development and support; Geoff Nilsen, Michael Jen and Wade Barrett for designing the high-density arrays and assistance with data analysis; John Sheehan for quality analysis of the high-density array data; Albert Yee, Reed George, Julie Marschner, Joe Karbowski and Mike Kennemer for the DNA normalisation and pooling; Patrick Chu for the pooled genotyping; Clariza LaRosa, Matt Morenzoni, Pei-Hua Wang, Rajal Patel, Rhode Vergara, Robin Li, Thai Lai, Vincent Mendoza, Karel Konvicka and Renee Stokowski for the high-density array individual genotyping; Maruja Lira, Amy Mank-Seymour and Jodi Richmond for the fluorescence polarisation genotyping; Erica Beilharz for assistance with manuscript preparation; Paul Feeney and Michael Swietek for assistance in sample handling; and the patients for donating samples for research.
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