TagSNP transferability and relative loss of variability prediction from HapMap to an admixed population

<p>Abstract</p> <p>Background</p> <p>The application of a subset of single nucleotide polymorphisms, the tagSNPs, can be useful in capturing untyped SNPs information in a genomic region. TagSNP transferability from the HapMap dataset to admixed populations is of uncertain value due population structure, admixture, drift and recombination effects. In this work an empirical dataset from a Brazilian admixed sample was evaluated against the HapMap population to measure tagSNP transferability and the relative loss of variability prediction.</p> <p>Methods</p> <p>The transferability study was carried out using SNPs dispersed over four genomic regions: the PTPN22, HMGCR, VDR and CETP genes. Variability coverage and the prediction accuracy for tagSNPs in the selected genomic regions of HapMap phase II were computed using a prediction accuracy algorithm. Transferability of tagSNPs and relative loss of prediction were evaluated according to the difference between the Brazilian sample and the pooled and single HapMap population estimates.</p> <p>Results</p> <p>Each population presented different levels of prediction per gene. On average, the Brazilian (BRA) sample displayed a lower power of prediction when compared to HapMap and the pooled sample. There was a relative loss of prediction for BRA when using single HapMap populations, but a pooled HapMap dataset generated minor loss of variability prediction and lower standard deviations, except at the VDR locus at which loss was minor using CEU tagSNPs.</p> <p>Conclusion</p> <p>Studies that involve tagSNP selection for an admixed population should not be generally correlated with any specific HapMap population and can be better represented with a pooled dataset in most cases.</p

SNP selection for genes of iron metabolism in a study of genetic modifiers of hemochromatosis

<p>Abstract</p> <p>Background</p> <p>We report our experience of selecting tag SNPs in 35 genes involved in iron metabolism in a cohort study seeking to discover genetic modifiers of hereditary hemochromatosis.</p> <p>Methods</p> <p>We combined our own and publicly available resequencing data with HapMap to maximise our coverage to select 384 SNPs in candidate genes suitable for typing on the Illumina platform.</p> <p>Results</p> <p>Validation/design scores above 0.6 were not strongly correlated with SNP performance as estimated by Gentrain score. We contrasted results from two tag SNP selection algorithms, LDselect and Tagger. Varying r²from 0.5 to 1.0 produced a near linear correlation with the number of tag SNPs required. We examined the pattern of linkage disequilibrium of three levels of resequencing coverage for the transferrin gene and found HapMap phase 1 tag SNPs capture 45% of the ≥ 3% MAF SNPs found in SeattleSNPs where there is nearly complete resequencing. Resequencing can reveal adjacent SNPs (within 60 bp) which may affect assay performance. We report the number of SNPs present within the region of six of our larger candidate genes, for different versions of stock genotyping assays.</p> <p>Conclusion</p> <p>A candidate gene approach should seek to maximise coverage, and this can be improved by adding to HapMap data any available sequencing data. Tag SNP software must be fast and flexible to data changes, since tag SNP selection involves iteration as investigators seek to satisfy the competing demands of coverage within and between populations, and typability on the technology platform chosen.</p

Evaluating the performance of commercial whole-genome marker sets for capturing common genetic variation

<p>Abstract</p> <p>Background</p> <p>New technologies have enabled genome-wide association studies to be conducted with hundreds of thousands of genotyped SNPs. Several different first-generation genome-wide panels of SNPs have been commercialized. The total amount of common genetic variation is still unknown; however, the coverage of commercial panels can be evaluated against reference population samples genotyped by the International HapMap project. Less information is available about coverage in samples from other populations.</p> <p>Results</p> <p>In this study we compare four commercial panels: the HumanHap 300 and HumanHap 550 Array Sets from the Illumina Infinium series and the Mapping 100 K and Mapping 500 K Array Sets from the Affymetrix GeneChip series. Tagging performance is compared among HapMap CEPH (CEU), Asian (JPT, CHB) and Yoruba (YRI) population samples. It is also evaluated in an Estonian population sample with more than 1000 individuals genotyped in two 500-kbp ENCODE regions of chromosome 2: ENr112 on 2p16.3 and ENr131 on 2p37.1.</p> <p>Conclusion</p> <p>We found that in a non-reference Caucasian population, commercial SNP panels provide levels of coverage similar to those in the HapMap CEPH population sample. We present the proportions of universal and population-specific SNPs in all the commercial platforms studied.</p

Calibrating the Performance of SNP Arrays for Whole-Genome Association Studies

To facilitate whole-genome association studies (WGAS), several high-density SNP genotyping arrays have been developed. Genetic coverage and statistical power are the primary benchmark metrics in evaluating the performance of SNP arrays. Ideally, such evaluations would be done on a SNP set and a cohort of individuals that are both independently sampled from the original SNPs and individuals used in developing the arrays. Without utilization of an independent test set, previous estimates of genetic coverage and statistical power may be subject to an overfitting bias. Additionally, the SNP arrays' statistical power in WGAS has not been systematically assessed on real traits. One robust setting for doing so is to evaluate statistical power on thousands of traits measured from a single set of individuals. In this study, 359 newly sampled Americans of European descent were genotyped using both Affymetrix 500K (Affx500K) and Illumina 650Y (Ilmn650K) SNP arrays. From these data, we were able to obtain estimates of genetic coverage, which are robust to overfitting, by constructing an independent test set from among these genotypes and individuals. Furthermore, we collected liver tissue RNA from the participants and profiled these samples on a comprehensive gene expression microarray. The RNA levels were used as a large-scale set of quantitative traits to calibrate the relative statistical power of the commercial arrays. Our genetic coverage estimates are lower than previous reports, providing evidence that previous estimates may be inflated due to overfitting. The Ilmn650K platform showed reasonable power (50% or greater) to detect SNPs associated with quantitative traits when the signal-to-noise ratio (SNR) is greater than or equal to 0.5 and the causal SNP's minor allele frequency (MAF) is greater than or equal to 20% (N = 359). In testing each of the more than 40,000 gene expression traits for association to each of the SNPs on the Ilmn650K and Affx500K arrays, we found that the Ilmn650K yielded 15% times more discoveries than the Affx500K at the same false discovery rate (FDR) level

Global similarity with local differences in linkage disequilibrium between the Dutch and HapMap–CEU populations

The HapMap project has facilitated the selection of tagging single nucleotide polymorphisms (tagSNPs) for genome-wide association studies (GWAS) under the assumption that linkage disequilibrium (LD) in the HapMap populations is similar to the populations under investigation. Earlier reports support this assumption, although in most of these studies only a few loci were evaluated. We compared pair-wise LD and LD block structure across autosomes between the Dutch population and the CEU-HapMap reference panel. The impact of sampling distribution on the estimation of LD blocks was studied by bootstrapping. A high Pearson correlation (genome-wide; 0.93) between pair-wise

Using Population Mixtures to Optimize the Utility of Genomic Databases: Linkage Disequilibrium and Association Study Design in India

When performing association studies in populations that have not been the focus of large-scale investigations of haplotype variation, it is often helpful to rely on genomic databases in other populations for study design and analysis – such as in the selection of tag SNPs and in the imputation of missing genotypes. One way of improving the use of these databases is to rely on a mixture of database samples that is similar to the population of interest, rather than using the single most similar database sample. We demonstrate the effectiveness of the mixture approach in the application of African, European, and East Asian HapMap samples for tag SNP selection in populations from India, a genetically intermediate region underrepresented in genomic studies of haplotype variation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65949/1/j.1469-1809.2008.00457.x.pd

An evaluation of the performance of HapMap SNP data in a Shanghai Chinese population: Analyses of allele frequency, linkage disequilibrium pattern and tagging SNPs transferability on chromosome 1q21-q25

<p>Abstract</p> <p>Background</p> <p>The HapMap project aimed to catalog millions of common single nucleotide polymorphisms (SNPs) in the human genome in four major populations, in order to facilitate association studies of complex diseases. To examine the transferability of Han Chinese in Beijing HapMap data to the Southern Han Chinese in Shanghai, we performed comparative analyses between genotypes from over 4,500 SNPs in a 21 Mb region on chromosome 1q21-q25 in 80 unrelated Shanghai Chinese and 45 HapMap Chinese data.</p> <p>Results</p> <p>Three thousand and forty-two SNPs were analyzed after removal of SNPs that failed quality control and those not in the HapMap panel. We compared the allele frequency distributions, linkage disequilibrium patterns, haplotype frequency distributions and tagging SNP sets transferability between the HapMap population and Shanghai Chinese population. Among the four HapMap populations, Beijing Chinese showed the best correlation with Shanghai population on allele frequencies, linkage disequilibrium and haplotype frequencies. Tagging SNP sets selected from four HapMap populations at different thresholds were evaluated in the Shanghai sample. Under the threshold of r²equal to 0.8 or 0.5, both HapMap Chinese and Japanese data showed better coverage and tagging efficiency than Caucasian and African data.</p> <p>Conclusion</p> <p>Our study supported the applicability of HapMap Beijing Chinese SNP data to the study of complex diseases among southern Chinese population.</p

iHAP – integrated haplotype analysis pipeline for characterizing the haplotype structure of genes

BACKGROUND: The advent of genotype data from large-scale efforts that catalog the genetic variants of different populations have given rise to new avenues for multifactorial disease association studies. Recent work shows that genotype data from the International HapMap Project have a high degree of transferability to the wider population. This implies that the design of genotyping studies on local populations may be facilitated through inferences drawn from information contained in HapMap populations. RESULTS: To facilitate analysis of HapMap data for characterizing the haplotype structure of genes or any chromosomal regions, we have developed an integrated web-based resource, iHAP. In addition to incorporating genotype and haplotype data from the International HapMap Project and gene information from the UCSC Genome Browser Database, iHAP also provides capabilities for inferring haplotype blocks and selecting tag SNPs that are representative of haplotype patterns. These include block partitioning algorithms, block definitions, tag SNP definitions, as well as SNPs to be "force included" as tags. Based on the parameters defined at the input stage, iHAP performs on-the-fly analysis and displays the result graphically as a webpage. To facilitate analysis, intermediate and final result files can be downloaded. CONCLUSION: The iHAP resource, available at , provides a convenient yet flexible approach for the user community to analyze HapMap data and identify candidate targets for genotyping studies

Evaluating the transferability of Hapmap SNPs to a Singapore Chinese population

<p>Abstract</p> <p>Background</p> <p>The International Hapmap project serves as a valuable resource for human genome variation data, however its applicability to other populations has yet to be exhaustively investigated. In this paper, we use high density genotyping chips and resequencing strategies to compare the Singapore Chinese population with the Hapmap populations. First we compared 1028 and 114 unrelated Singapore Chinese samples genotyped using the Illumina Human Hapmap 550 k chip and Affymetrix 500 k array respectively against the 270 samples from Hapmap. Secondly, data from 20 candidate genes on 5q31-33 resequenced for an asthma candidate gene based study was also used for the analysis.</p> <p>Results</p> <p>A total of 237 SNPs were identified through resequencing of which only 95 SNPs (40%) were in Hapmap; however an additional 56 SNPs (24%) were not genotyped directly but had a proxy SNP in the Hapmap. At the genome-wide level, Singapore Chinese were highly correlated with Hapmap Han Chinese with correlation of 0.954 and 0.947 for the Illumina and Affymetrix platforms respectively with deviant SNPs randomly distributed within and across all chromosomes.</p> <p>Conclusions</p> <p>The high correlation between our population and Hapmap Han Chinese reaffirms the applicability of Hapmap based genome-wide chips for GWA studies. There is a clear population signature for the Singapore Chinese samples and they predominantly resemble the southern Han Chinese population; however when new migrants particularly those with northern Han Chinese background were included, population stratification issues may arise. Future studies needs to address population stratification within the sample collection while designing and interpreting GWAS in the Chinese population.</p

An evaluation of the performance of HapMap SNP data in a Shanghai Chinese population: Analyses of allele frequency, linkage disequilibrium pattern and tagging SNPs transferability on chromosome Iq2I-q25

Background: The HapMap project aimed to catalog millions of common single nucleotide polymorphisms ( SNPs) in the human genome in four major populations, in order to facilitate association studies of complex diseases. To examine the transferability of Han Chinese in Beijing HapMap data to the Southern Han Chinese in Shanghai, we performed comparative analyses between genotypes from over 4,500 SNPs in a 21 Mb region on chromosome Iq2I-q25 in 80 unrelated Shanghai Chinese and 45 HapMap Chinese data. Results: Three thousand and forty-two SNPs were analyzed after removal of SNPs that failed quality control and those not in the HapMap panel. We compared the allele frequency distributions, linkage disequilibrium patterns, haplotype frequency distributions and tagging SNP sets transferability between the HapMap population and Shanghai Chinese population. Among the four HapMap populations, Beijing Chinese showed the best correlation with Shanghai population on allele frequencies, linkage disequilibrium and haplotype frequencies. Tagging SNP sets selected from four HapMap populations at different thresholds were evaluated in the Shanghai sample. Under the threshold of r(2) equal to 0.8 or 0.5, both HapMap Chinese and Japanese data showed better coverage and tagging efficiency than Caucasian and African data. Conclusion: Our study supported the applicability of HapMap Beijing Chinese SNP data to the study of complex diseases among southern Chinese population