Untyped Single Nucleotide Polymorphisms Research Articles

A fast data-driven method for genotype imputation, phasing and local ancestry inference: MendelImpute.jl.

Current methods for genotype imputation and phasing exploit the volume of data in haplotype reference panels and rely on hidden Markov models (HMMs). Existing programs all have essentially the same imputation accuracy, are computationally intensive and generally require prephasing the typed markers. We introduce a novel data-mining method for genotype imputation and phasing that substitutes highly efficient linear algebra routines for HMM calculations. This strategy, embodied in our Julia program MendelImpute.jl, avoids explicit assumptions about recombination and population structure while delivering similar prediction accuracy, better memory usage and an order of magnitude or better run-times compared to the fastest competing method. MendelImpute operates on both dosage data and unphased genotype data and simultaneously imputes missing genotypes and phase at both the typed and untyped SNPs (single nucleotide polymorphisms). Finally, MendelImpute naturally extends to global and local ancestry estimation and lends itself to new strategies for data compression and hence faster data transport and sharing. Software, documentation and scripts to reproduce our results are available from https://github.com/OpenMendel/MendelImpute.jl. Supplementary data are available at Bioinformatics online.

Validation of genotype imputation in Southeast Asian populations and the effect of single nucleotide polymorphism annotation on imputation outcome

BackgroundImputation involves the inference of untyped single nucleotide polymorphisms (SNPs) in genome-wide association studies. The haplotypic reference of choice for imputation in Southeast Asian populations is unclear. Moreover, the influence of SNP annotation on imputation results has not been examined.MethodsThis study was divided into two parts. In the first part, we applied imputation to genotyped SNPs from Southeast Asian populations from the Pan-Asian SNP database. Five percent of the total SNPs were removed. The remaining SNPs were applied to imputation with IMPUTE2. The imputed outcomes were verified with the removed SNPs. We compared imputation references from Chinese and Japanese haplotypes from the HapMap phase II (HMII) and the complete set of haplotypes from the 1000 Genomes Project (1000G). The second part was imputation accuracy and yield in Thai patient dataset. Half of the autosomal SNPs was removed to create Set 1. Another dataset, Set 2, was then created where we switched which half of the SNPs were removed. Both Set 1 and Set 2 were imputed with HMII to create a complete imputed SNPs dataset. The dataset was used to validate association testing, SNPs annotation and imputation outcome.ResultsThe accuracy was highest for all populations when using the HMII reference, but at the cost of a lower yield. Thai genotypes showed the highest accuracy over other populations in both HMII and 1000G panels, although accuracy and yield varied across chromosomes. Imputation was tested in a clinical dataset to compare accuracy in gene-related regions, and coding regions were found to have a higher accuracy and yield.ConclusionsThis work provides the first evidence of imputation reference selection for Southeast Asian studies and highlights the effects of SNP locations respective to genes on imputation outcome. Researchers will need to consider the trade-off between accuracy and yield in future imputation studies.

Using imputed genotype data in the joint score tests for genetic association and gene-environment interactions in case-control studies.

Genome-wide association studies (GWAS) are now routinely imputed for untyped single nucleotide polymorphisms (SNPs) based on various powerful statistical algorithms for imputation trained on reference datasets. The use of predicted allele counts for imputed SNPs as the dosage variable is known to produce valid score test for genetic association. In this paper, we investigate how to best handle imputed SNPs in various modern complex tests for genetic associations incorporating gene-environment interactions. We focus on case-control association studies where inference for an underlying logistic regression model can be performed using alternative methods that rely on varying degree on an assumption of gene-environment independence in the underlying population. As increasingly large-scale GWAS are being performed through consortia effort where it is preferable to share only summary-level information across studies, we also describe simple mechanisms for implementing score tests based on standard meta-analysis of "one-step" maximum-likelihood estimates across studies. Applications of the methods in simulation studies and a dataset from GWAS of lung cancer illustrate ability of the proposed methods to maintain type-I error rates for the underlying testing procedures. For analysis of imputed SNPs, similar to typed SNPs, the retrospective methods can lead to considerable efficiency gain for modeling of gene-environment interactions under the assumption of gene-environment independence. Methods are made available for public use through CGEN R software package.

Polymorphisms in IL10 may alter CD4 T-cell counts in Indonesian HIV patients beginning antiretroviral therapy

Interleukin 10 (IL-10) is a potent anti-inflammatory cytokine influenced by single nucleotide polymorphisms (SNP) located in upstream regulatory regions. Here we address the effects of five SNP (rs1518111, rs3021094, rs3024491, rs1800872 and rs1800871) on CD4 T-cell counts in Indonesian HIV patients assessed before ART and over 12months on treatment. Heterozygosity at rs1518111 or rs1800872 associated with low CD4 T-cell counts at all time points. Both alleles were carried in two haplotypes. Haplotype 21122 (present in 30% of participants) associated with low CD4 T-cell counts, whereas 21222 (in 6% of participants) did not. Hence untyped SNP(s) tagged by 21122 may depress CD4 T-cell counts. The association with heterozygosity suggests synergy with an allele from a haplotype lacking rs1518111 and/or rs1800872.

Genotype Imputation for African Americans Using Data From HapMap Phase II Versus 1000 Genomes Projects

Genotype imputation provides imputation of untyped single nucleotide polymorphisms (SNPs) that are present on a reference panel such as those from the HapMap Project. It is popular for increasing statistical power and comparing results across studies using different platforms. Imputation for African American populations is challenging because their linkage disequilibrium blocks are shorter and also because no ideal reference panel is available due to admixture. In this paper, we evaluated three imputation strategies for African Americans. The intersection strategy used a combined panel consisting of SNPs polymorphic in both CEU and YRI. The union strategy used a panel consisting of SNPs polymorphic in either CEU or YRI. The merge strategy merged results from two separate imputations, one using CEU and the other using YRI. Because recent investigators are increasingly using the data from the 1000 Genomes (1KG) Project for genotype imputation, we evaluated both 1KG-based imputations and HapMap-based imputations. We used 23,707 SNPs from chromosomes 21 and 22 on Affymetrix SNP Array 6.0 genotyped for 1,075 HyperGEN African Americans. We found that 1KG-based imputations provided a substantially larger number of variants than HapMap-based imputations, about three times as many common variants and eight times as many rare and low-frequency variants. This higher yield is expected because the 1KG panel includes more SNPs. Accuracy rates using 1KG data were slightly lower than those using HapMap data before filtering, but slightly higher after filtering. The union strategy provided the highest imputation yield with next highest accuracy. The intersection strategy provided the lowest imputation yield but the highest accuracy. The merge strategy provided the lowest imputation accuracy. We observed that SNPs polymorphic only in CEU had much lower accuracy, reducing the accuracy of the union strategy. Our findings suggest that 1KG-based imputations can facilitate discovery of significant associations for SNPs across the whole MAF spectrum. Because the 1KG Project is still under way, we expect that later versions will provide better imputation performance.

Abstract LB-334: Functional polymorphisms in the stem-cell related pathway affect susceptibility to non-small cell lung cancer: a finding from Texas lung cancer genome-wide association study

Abstract Cancer stem cells (CSCs) are the putative origin of cancer developing from normal cells. Wnt, Notch, Hedgehog (Hh, including sonic Hedgehog pathway) are those signaling pathways that are shared by both normal stem cells and CSCs, and regulate and maintain the ability to self-renew, form differentiated progeny, develop resistance to therapy. Previous studies showed that chromosome 4q31 locus (Hedgehog-interacting protein (HHIP) /glycophorin A (GYPA)) in chronic obstructive pulmonary disease are associated with lung cancer, while HHIP is an important gene in the Hh pathway. We hypothesized that CSCs evolve into cells with metastatic potential through accumulated mutations in stem cells and that genetic variants in Wnt, Notch, and Hh pathways (stem cell-related pathways) may modulate phenotypes of stem cell, hence affecting susceptibility to cancer (including non-small cell lung cancer). Published genome-wide association studies (GWAS) have identified few variants in known biological pathways involved in lung cancer etiology. To mine the additional causal single nucleotide polymorphisms (SNPs), we explored tagSNPs in the stem cell-related pathways from our published GWAS dataset for 1154 lung cancer cases and 1137 cancer-free controls. In an initial association analysis of 2685 tagSNPs in 196 stem cell-related pathway genes, the p values for the most significant tagSNPs (rs1828591, rs720485, rs6845536) also located in chromosome 4q31 (HHIP/GYPA) were 8.21×10−5, 3.19×10−5, and 7.15×10−5, respectively. However, these tagSNPs are neither functional SNPs themselves nor tagged other predicted functional but untyped SNPs. Additional 16 tagSNPs that either are functional SNPs themselves or tagged other predicted functional but untyped SNPs associated with lung cancer risk only had a p value of <0.05. We imputed (NCBI35/hg17) 18072 tagSNPs in the same 196 genes, and identified an additional 39 tagSNPs associated with lung cancer risk with a p value <0.05, but the total number was reduced to 41 after taking into account LD among all SNPs with a p value <0.05 (ranging from 3.81×10−4 to 4.96×10−2). The most significant SNP rs2229263 in LRP2 had a p value of 3.81×10−4. The genes with top significant P values (<10−3) were located in Hh and Notch pathways. When we used Ingenuity Systems pathway analysis (IPA), we found that Hh and Notch signaling pathways were the most significant pathways (p-values were 1.31×10−10, 5.48×10−10, respectively), and the top transcription regulators in the signaling pathway were Gli, SMAD4, GLI3, Notch, and GLI2, with the p value 4.42×10−5, 7.43×10−4, 8.91E×10−4, 1.13×10−3, 3.34×10−3. Our findings suggest that functional SNPs in the Hh and Notch pathways genes may affect susceptibility to non-small cell lung cancer. Further replication and functional studies are warranted to confirm these findings. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-334. doi:1538-7445.AM2012-LB-334

Genome-wide association study of smoking behaviours in patients with COPD

BackgroundCigarette smoking is a major risk factor for chronic obstructive pulmonary disease (COPD) and COPD severity. Previous genome-wide association studies (GWAS) have identified numerous single nucleotide polymorphisms (SNPs) associated with...

Association study of Nogo‐related genes with schizophrenia in a Japanese case–control sample

Many studies have suggested that myelin dysfunction may be causally involved in the pathogenesis of schizophrenia. Nogo (RTN4), myelin-associated glycoprotein (MAG) and oligodendrocyte myelin glycoprotein (OMG) all bind to the common receptor, Nogo-66 receptor 1 (RTN4R). We examined 68 single nucleotide polymorphisms (SNPs) (51 with genotyping and 17 with imputation analysis) from these four genes for genetic association with schizophrenia, using a 2,120 case-control sample from the Japanese population. Allelic tests showed nominally significant association of two RTN4 SNPs (P = 0.047 and 0.037 for rs11894868 and rs2968804, respectively) and two MAG SNPs (P = 0.034 and 0.029 for rs7249617 and rs16970218, respectively) with schizophrenia. The MAG SNP rs7249617 also showed nominal significance in a genotypic test (P = 0.017). In haplotype analysis, the MAG haplotype block including rs7249617 and rs16970218 showed nominal significance (P = 0.008). These associations did not remain significant after correction for multiple testing, possibly due to their small genetic effect. In the imputation analysis of RTN4, the untyped SNP rs2972090 showed nominally significant association (P = 0.032) and several imputed SNPs showed marginal associations. Moreover, in silico analysis (PolyPhen) of a missense variant (rs11677099: Asp357Val), which is in strong linkage disequilibrium with rs11894868, predicted a deleterious effect on Nogo protein function. Despite a failure to detect robust associations in this Japanese cohort, our nominally positive signals, taken together with previously reported biological and genetic findings, add further support to the "disturbed myelin system theory of schizophrenia" across different populations.

Association of a novel functional promoter variant (rs2075533 C>T) in the apoptosis gene TNFSF 8 with risk of lung cancer—a finding from Texas lung cancer genome-wide association study

Published genome-wide association studies (GWASs) have identified few variants in the known biological pathways involved in lung cancer etiology. To mine the possibly hidden causal single nucleotide polymorphisms (SNPs), we explored all SNPs in the extrinsic apoptosis pathway from our published GWAS dataset for 1154 lung cancer cases and 1137 cancer-free controls. In an initial association analysis of 611 tagSNPs in 41 apoptosis-related genes, we identified only 10 tagSNPs associated with lung cancer risk with a P value<10(-2), including four tagSNPs in DAPK1 and three tagSNPs in TNFSF8. Unlike DAPK1 SNPs, TNFSF8 rs2181033 tagged other four predicted functional but untyped SNPs (rs776576, rs776577, rs31813148 and rs2075533) in the promoter region. Therefore, we further tested binding affinity of these four SNPs by performing the electrophoretic mobility shift assay. We found that only rs2075533T allele modified levels of nuclear proteins bound to DNA, leading to significantly decreased expression of luciferase reporter constructs by 5- to -10-fold in H1299, HeLa and HCT116 cell lines compared with the C allele. We also performed a replication study of the untyped rs2075533 in an independent Texas population but did not confirm the protective effect. We further performed a mini meta-analysis for SNPs of TNFSF8 obtained from other four published lung cancer GWASs with 12 214 cases and 47 721 controls, and we found that only rs3181366 (r2=0.69 with the untyped rs2075533) was associated to lung cancer risk (P=0.008). Our findings suggest a possible role of novel TNFSF8 variants in susceptibility to lung cancer.

Analysis of untyped SNPs: maximum likelihood and imputation methods

Analysis of untyped single nucleotide polymorphisms (SNPs) can facilitate the localization of disease-causing variants and permit meta-analysis of association studies with different genotyping platforms. We present two approaches for using the linkage disequilibrium structure of an external reference panel to infer the unknown value of an untyped SNP from the observed genotypes of typed SNPs. The maximum-likelihood approach integrates the prediction of untyped genotypes and estimation of association parameters into a single framework and yields consistent and efficient estimators of genetic effects and gene-environment interactions with proper variance estimators. The imputation approach is a two-stage strategy, which first imputes the untyped genotypes by either the most likely genotypes or the expected genotype counts and then uses the imputed values in a downstream association analysis. The latter approach has proper control of type I error in single-SNP tests with possible covariate adjustments even when the reference panel is misspecified; however, type I error may not be properly controlled in testing multiple-SNP effects or gene-environment interactions. In general, imputation yields biased estimators of genetic effects and gene-environment interactions, and the variances are underestimated. We conduct extensive simulation studies to compare the bias, type I error, power, and confidence interval coverage between the maximum likelihood and imputation approaches in the analysis of single-SNP effects, multiple-SNP effects, and gene-environment interactions under cross-sectional and case-control designs. In addition, we provide an illustration with genome-wide data from the Wellcome Trust Case-Control Consortium (WTCCC) [2007].

Fast and Robust Association Tests for Untyped SNPs in Case-Control Studies

Genome-wide association studies (GWASs) aim to genotype enough single nucleotide polymorphisms (SNPs) to effectively capture common genetic variants across the genome. Even though the number of SNPs genotyped in such studies can exceed a million, there is still interest in testing association with SNPs that were not genotyped in the study sample. Analyses of such untyped SNPs can assist in signal localization, permit cross-platform integration of samples from separate studies, and can improve power – especially for rarer SNPs. External information on a larger collection of SNPs from an appropriate reference panel, comprising both SNPs typed in the sample and the untyped SNPs we wish to test for association, is necessary for an untyped variant analysis to proceed. Linkage disequilibrium patterns observed in the reference panel are then used to infer the likely genotype at the untyped SNPs in the study sample. We propose here a novel statistical approach for testing untyped SNPs in case-control GWAS, based on an efficient score function derived from a prospective likelihood, that automatically accounts for the variability in the process of estimating the untyped variant. Computationally efficient methods of phasing can be used without affecting the validity of the test, and simple measures of haplotype sharing can be used to infer genotypes at the untyped SNPs, making our approach computationally much faster than existing approaches for untyped analysis. At the same time, we show, using simulated data, that our approach often has performance nearly equivalent to hidden Markov methods of untyped analysis. The software package ‘untyped’ is available to implement our approach.

Application of imputation methods to the analysis of rheumatoid arthritis data in genome-wide association studies

Most genetic association studies only genotype a small proportion of cataloged single-nucleotide polymorphisms (SNPs) in regions of interest. With the catalogs of high-density SNP data available (e.g., HapMap) to researchers today, it has become possible to impute genotypes at untyped SNPs. This in turn allows us to test those untyped SNPs, the motivation being to increase power in association studies. Several imputation methods and corresponding software packages have been developed for this purpose. The objective of our study is to apply three widely used imputation methods and corresponding software packages to a data from a genome-wide association study of rheumatoid arthritis from the North American Rheumatoid Arthritis Consortium in Genetic Analysis Workshop 16, to compare the performances of the three methods, to evaluate their strengths and weaknesses, and to identify additional susceptibility loci underlying rheumatoid arthritis. The software packages used in this paper included a program for Bayesian imputation-based association mapping (BIMBAM), a program for imputing unobserved genotypes in case-control association studies (IMPUTE), and a program for testing untyped alleles (TUNA). We found some untyped SNP that showed significant association with rheumatoid arthritis. Among them, a few of these were not located near any typed SNP that was found to be significant and thus may be worth further investigation.

Evaluation of single-nucleotide polymorphism imputation using random forests

Genome-wide association studies (GWAS) have helped to reveal genetic mechanisms of complex diseases. Although commonly used genotyping technology enables us to determine up to a million single-nucleotide polymorphisms (SNPs), causative variants are typically not genotyped directly. A favored approach to increase the power of genome-wide association studies is to impute the untyped SNPs using more complete genotype data of a reference population.Random forests (RF) provides an internal method for replacing missing genotypes. A forest of classification trees is used to determine similarities of probands regarding their genotypes. These proximities are then used to impute genotypes of untyped SNPs.We evaluated this approach using genotype data of the Framingham Heart Study provided as Problem 2 for Genetic Analysis Workshop 16 and the Caucasian HapMap samples as reference population. Our results indicate that RFs are faster but less accurate than alternative approaches for imputing untyped SNPs.

ATRIUM: Testing Untyped SNPs in Case-Control Association Studies with Related Individuals

In genome-wide association studies, only a subset of all genomic variants are typed by current, high-throughput, SNP-genotyping platforms. However, many of the untyped variants can be well predicted from typed variants, with linkage disequilibrium (LD) information among typed and untyped variants available from an external reference panel such as HapMap. Incorporation of such external information can allow one to perform tests of association between untyped variants and phenotype, thereby making more efficient use of the available genotype data. When related individuals are included in case-control samples, the dependence among their genotypes must be properly addressed for valid association testing. In the context of testing untyped variants, an additional analytical challenge is that the dependence, across related individuals, of the partial information on untyped-SNP genotypes must also be assessed and incorporated into the analysis for valid inference. We address this challenge with ATRIUM, a method for case-control association testing with untyped SNPs, based on genome screen data in samples in which some individuals are related. ATRIUM uses LD information from an external reference panel to specify a one-degree-of-freedom test of association with an untyped SNP. It properly accounts for dependence in the partial information on untyped-SNP genotypes across related individuals. We demonstrate that ATRIUM is robust in that it maintains the nominal type I error rate even when the external reference panel is not well matched to the case-control sample. We apply the method to detect association between type 2 diabetes and variants on chromosome 10 in the Framingham SHARe data.

Analysis of Case-Control Association Studies: SNPs, Imputation and Haplotypes

Although prospective logistic regression is the standard method of analysis for case-control data, it has been recently noted that in genetic epidemiologic studies one can use the “retrospective” likelihood to gain major power by incorporating various population genetics model assumptions such as Hardy–Weinberg-Equilibrium (HWE), gene–gene and gene–environment independence. In this article we review these modern methods and contrast them with the more classical approaches through two types of applications (i) association tests for typed and untyped single nucleotide polymorphisms (SNPs) and (ii) estimation of haplotype effects and haplotype–environment interactions in the presence of haplotype-phase ambiguity. We provide novel insights to existing methods by construction of various score-tests and pseudo-likelihoods. In addition, we describe a novel two-stage method for analysis of untyped SNPs that can use any flexible external algorithm for genotype imputation followed by a powerful association test based on the retrospective likelihood. We illustrate applications of the methods using simulated and real data.

Use of weighted reference panels based on empirical estimates of ancestry for capturing untyped variation

Many association methods use a subset of genotyped single nucleotide polymorphisms (SNPs) to capture or infer genotypes at other untyped SNPs. We and others previously showed that tag SNPs selected to capture common variation using data from The International HapMap Consortium (Nature 437:1299-1320, 2005), The International HapMap Consortium (Nature 449:851-861, 2007) could also capture variation in populations of similar ancestry to HapMap reference populations (de Bakker et al. in Nat Genet 38:1298-1303, 2006; González-Neira et al. in Genome Res 16:323-330, 2006; Montpetit et al. in PLoS Genet 2:282-290, 2006; Mueller et al. in Am J Hum Genet 76:387-398, 2005). To capture variation in admixed populations or populations less similar to HapMap panels, a "cosmopolitan approach," in which all samples from HapMap are used as a single reference panel, was proposed. Here we refine this suggestion and show that use of a "weighted reference panel," constructed based on empirical estimates of ancestry in the target population (relative to available reference panels), is more efficient than the cosmopolitan approach. Weighted reference panels capture, on average, only slightly fewer common variants (minor allele frequency > 5%) than the cosmopolitan approach (mean r (2) = 0.977 vs. 0.989, 94.5% variation captured vs. 96.8% at r (2) > 0.8), across the five populations of the Multiethnic Cohort, but entail approximately 25% fewer tag SNPs per panel (average 538 vs. 718). These results extend a recent study in two Indian populations (Pemberton et al. in Ann Hum Genet 72:535-546, 2008). Weighted reference panels are potentially useful for both the selection of tag SNPs in diverse populations and perhaps in the design of reference panels for imputation of untyped genotypes in genome-wide association studies in admixed populations.

Imputation of missing genotypes: an empirical evaluation of IMPUTE

BackgroundImputation of missing genotypes is becoming a very popular solution for synchronizing genotype data collected with different microarray platforms but the effect of ethnic background, subject ascertainment, and amount of missing data on the accuracy of imputation are not well understood.ResultsWe evaluated the accuracy of the program IMPUTE to generate the genotype data of partially or fully untyped single nucleotide polymorphisms (SNPs). The program uses a model-based approach to imputation that reconstructs the genotype distribution given a set of referent haplotypes and the observed data, and uses this distribution to compute the marginal probability of each missing genotype for each individual subject that is used to impute the missing data. We assembled genome-wide data from five different studies and three different ethnic groups comprising Caucasians, African Americans and Asians. We randomly removed genotype data and then compared the observed genotypes with those generated by IMPUTE. Our analysis shows 97% median accuracy in Caucasian subjects when less than 10% of the SNPs are untyped and missing genotypes are accepted regardless of their posterior probability. The median accuracy increases to 99% when we require 0.95 minimum posterior probability for an imputed genotype to be acceptable. The accuracy decreases to 86% or 94% when subjects are African Americans or Asians. We propose a strategy to improve the accuracy by leveraging the level of admixture in African Americans.ConclusionOur analysis suggests that IMPUTE is very accurate in samples of Caucasians origin, it is slightly less accurate in samples of Asians background, but substantially less accurate in samples of admixed background such as African Americans. Sample size and ascertainment do not seem to affect the accuracy of imputation.

How well do HapMap SNPs capture the untyped SNPs?

BackgroundThe recent advancement in human genome sequencing and genotyping has revealed millions of single nucleotide polymorphisms (SNP) which determine the variation among human beings. One of the particular important projects is The International HapMap Project which provides the catalogue of human genetic variation for disease association studies. In this paper, we analyzed the genotype data in HapMap project by using National Institute of Environmental Health Sciences Environmental Genome Project (NIEHS EGP) SNPs. We first determine whether the HapMap data are transferable to the NIEHS data. Then, we study how well the HapMap SNPs capture the untyped SNPs in the region. Finally, we provide general guidelines for determining whether the SNPs chosen from HapMap may be able to capture most of the untyped SNPs.ResultsOur analysis shows that HapMap data are not robust enough to capture the untyped variants for most of the human genes. The performance of SNPs for European and Asian samples are marginal in capturing the untyped variants, i.e. approximately 55%. Expectedly, the SNPs from HapMap YRI panel can only capture approximately 30% of the variants. Although the overall performance is low, however, the SNPs for some genes perform very well and are able to capture most of the variants along the gene. This is observed in the European and Asian panel, but not in African panel. Through observation, we concluded that in order to have a well covered SNPs reference panel, the SNPs density and the association among reference SNPs are important to estimate the robustness of the chosen SNPs.ConclusionWe have analyzed the coverage of HapMap SNPs using NIEHS EGP data. The results show that HapMap SNPs are transferable to the NIEHS SNPs. However, HapMap SNPs cannot capture some of the untyped SNPs and therefore resequencing may be needed to uncover more SNPs in the missing region.

Polymorphisms in DNA damage binding protein 2 (DDB2) and susceptibility of primary lung cancer in the Chinese: a case–control study

DNA damage binding protein 2 (DDB2) is one of the major DNA repair proteins involved in the nucleotide excision repair (NER) pathway. Mutations in the DDB2 gene can cause a repair-deficiency syndrome xeroderma pigmentosum group E. Because tobacco carcinogens can cause DNA damage that is repaired by NER and suboptimal NER capacity is reported to be associated with lung cancer risk, we hypothesized that common variants in the DDB2 gene are associated with lung cancer risk. To test this hypothesis, we conducted a case-control study of 1010 patients with incident lung cancer and 1011 cancer-free controls and genotyped two DDB2 single nucleotide polymorphisms (SNPs) (rs830083 and rs3781620) that are in linkage disequilibrium with other untyped SNPs. We found that compared with the rs830083CC, subjects carrying the heterozygous rs830083CG genotype had a significantly 1.31-fold increased risk of lung cancer [95% confidence interval (CI) 1.08-1.60] and those carrying the homozygous rs830083GG genotype had a non-significantly 1.22-fold elevated risk (95% CI 0.89-1.67). In addition, effects of the combined rs830083CG/GG variant genotypes were more evident in young subjects, heavy smokers and subjects with a positive family history of cancer. These findings indicate, for the first time, that the DDB2 rs830083 polymorphism may contribute to the etiology of lung cancer. Further functional studies on this SNP and/or related variants are warranted to elucidate the underlying molecular mechanisms of the association.

5423685 CPR training manikin: Adamson Ian A; Quercioli Leonard P Denver, CO, 80206, United States

Interleukin 10 (IL-10) is a potent anti-inflammatory cytokine influenced by single nucleotide polymorphisms (SNP) located in upstream regulatory regions. Here we address the effects of five SNP (rs1518111, rs3021094, rs3024491, rs1800872 and rs1800871) on CD4 T-cell counts in Indonesian HIV patients assessed before ART and over 12 months on treatment. Heterozygosity at rs1518111 or rs1800872 associated with low CD4 T-cell counts at all time points. Both alleles were carried in two haplotypes. Haplotype 21122 (present in 30% of participants) associated with low CD4 T-cell counts, whereas 21222 (in 6% of participants) did not. Hence untyped SNP(s) tagged by 21122 may depress CD4 T-cell counts. The association with heterozygosity suggests synergy with an allele from a haplotype lacking rs1518111 and/or rs1800872.