Interaction between dietary fat intake and the cholesterol ester transfer protein TaqIB polymorphism in relation to HDL-cholesterol concentrations among US diabetic men

BACKGROUND: A low plasma HDL-cholesterol concentration is a major characteristic of diabetic dyslipidemia. HDL concentrations are determined by both environmental factors and genetic factors. Cholesterol ester transfer protein (CETP) plays an important role in the regulation of HDL metabolism, and the TaqIB polymorphism of the CETP gene has been associated with elevated HDL concentrations. OBJECTIVE: We examined the association between the CETP TaqIB polymorphism and plasma HDL concentrations and evaluated whether this association was modified by dietary fat intake. DESIGN: We followed 780 diabetic men aged 40-75 y who participated in the Health Professionals Follow-Up Study since its initiation in 1986. The participants had confirmed type 2 diabetes and were free of cardiovascular disease at the time blood was drawn. RESULTS: After adjustment for age, smoking, alcohol consumption, fasting status, hemoglobin A(1c), physical activity, total energy intake, and body mass index, HDL concentrations were significantly higher in men with the B2B2 or B1B2 genotype than in those with the B1B1 genotype (adjusted x +/- SE: 37.9 +/- 0.02, 40.3 +/- 0.01, and 42.6 +/- 0.02 mg/dL for B1B1, B1B2, and B2B2, respectively; P for trend = 0.0004). This inverse association of the B1 allele with plasma HDL concentrations existed for those with a high consumption of animal fat (P for interaction = 0.02), saturated fat (P for interaction = 0.02), and monounsaturated fat (P for interaction = 0.04). CONCLUSION: These data confirmed a significant effect of the CETP Taq1 gene on HDL concentrations and suggested a potential interaction between the CETP TaqIB polymorphism and intake of dietary fat on plasma HDL concentration.     

Extensive association analysis between the CETP gene and coronary heart disease phenotypes reveals several putative functional polymorphisms and gene-environment interaction

An extensive association analysis of a candidate gene for coronary heart disease, Cholesteryl Ester Transfer Protein (CETP) gene, was performed. Ten polymorphisms, out of which three were newly identified in regulatory regions, were investigated for association with myocardial infarction (MI) and 2 MI endophenotypes (CETP mass and HDL-cholesterol level) in 568 MI patients and 668 controls. The polymorphisms affecting codon 405 (Ile(405)Val) and the nucleotide 524 downstream from the stop codon (G(+524)T) were almost completely concordant and associated with plasma CETP mass (P < 0.001). The polymorphisms -629 (located in promoter), intron1 (Taq1B) and intron7 were almost completely concordant and associated with plasma CETP mass (P < 0.0001) and HDL-cholesterol levels (P < 0.0001). This latter association was not found in teetotalers and increased with the quantity of alcohol consumed. Heavy drinkers (>75g/day) homozygous for the (-628)A allele had a reduced risk of MI (OR = 0. 33, P < 0.02). Subjects both homozygous for (451)Arg and heterozygous for (373)Pro had decreased plasma HDL-cholesterol levels and this effect increased with alcohol consumption. The results illustrate the complexity of polymorphism-phenotype associations. They suggest that the CETP gene may carry several functional polymorphisms. Observed interactions between alcohol consumption and polymorphisms associated with HDL-cholesterol level constitute concrete examples of gene-environment interactions. Furthermore, the pattern of association between HDL-cholesterol levels and the polymorphisms at codons 373 and 451 illustrated how two polymorphisms may be confounders (in the usual epidemiological sense) one for the other: their marginal effects are neutralized because of linkage disequilibrium and thus are not detectable by standard univariate association analysis.     

Effect of postprandial lipaemia and Taq 1B polymorphism of the cholesteryl ester transfer protein (CETP) gene on CETP mass, activity, associated lipoproteins and plasma lipids

A large number of studies in recent years have investigated the effects of hyperlipidaemias and diabetes on cholesteryl ester transfer protein (CETP) on neutral lipid transfer activity and plasma lipids. There has been an ongoing debate as to whether CETP is pro- or anti-atherogenic as it provides a mechanism for the transfer of cholesterol from the cardioprotective HDL subfraction to the potentially atherogenic LDL subfraction. This study was designed to investigate whether there was significant variability of CETP mass and activity in a large normolipidaemic population and whether there is an association between CETP and plasma lipoprotein composition. The presence of a known polymorphism of CETP gene (Taq 1B) was investigated to see if there was any association between this polymorphism and CETP mass and activity, and plasma lipids. There was significant (P < 0.0001) increase in CETP mass and activity in plasma postprandially at 6 h. Using multiple stepwise regression analysis there was significant association with fasting CETP mass and activity (beta = 0.055; P = 0.002) and triacylglycerol-rich lipoprotein (beta = 0.013; P = 0.005) and postprandial CETP mass (beta = 0.254; P = 0.007). Repeated-measures analysis showed a strong association between the absence of Taq 1B polymorphism and low CETP mass and elevated HDL- and HDL2-cholesterol and HDL-phospholipid concentrations than did those who were homozygous or heterozygous for the presence of the restriction site.     

Identifying SNPs predictive of phenotype using random forests

There has been a great interest and a few successes in the identification of complex disease susceptibility genes in recent years. Association studies, where a large number of single-nucleotide polymorphisms (SNPs) are typed in a sample of cases and controls to determine which genes are associated with a specific disease, provide a powerful approach for complex disease gene mapping. Genes of interest in those studies may contain large numbers of SNPs that classical statistical methods cannot handle simultaneously without requiring prohibitively large sample sizes. By contrast, high-dimensional nonparametric methods thrive on large numbers of predictors. This work explores the application of one such method, random forests, to the problem of identifying SNPs predictive of the phenotype in the case-control study design. A random forest is a collection of classification trees grown on bootstrap samples of observations, using a random subset of predictors to define the best split at each node. The observations left out of the bootstrap samples are used to estimate prediction error. The importance of a predictor is quantified by the increase in misclassification occurring when the values of the predictor are randomly permuted. We extend the concept of importance to pairs of predictors, to capture joint effects, and we explore the behavior of importance measures over a range of two-locus disease models in the presence of a varying number of SNPs unassociated with the phenotype. We illustrate the application of random forests with a data set of asthma cases and unaffected controls genotyped at 42 SNPs in ADAM33, a previously identified asthma susceptibility gene. SNPs and SNP pairs highly associated with asthma tend to have the highest importance index value, but predictive importance and association do not always coincide.     

Data mining, neural nets, trees--problems 2 and 3 of Genetic Analysis Workshop 15

Genome-wide association studies using thousands to hundreds of thousands of single nucleotide polymorphism (SNP) markers and region-wide association studies using a dense panel of SNPs are already in use to identify disease susceptibility genes and to predict disease risk in individuals. Because these tasks become increasingly important, three different data sets were provided for the Genetic Analysis Workshop 15, thus allowing examination of various novel and existing data mining methods for both classification and identification of disease susceptibility genes, gene by gene or gene by environment interaction. The approach most often applied in this presentation group was random forests because of its simplicity, elegance, and robustness. It was used for prediction and for screening for interesting SNPs in a first step. The logistic tree with unbiased selection approach appeared to be an interesting alternative to efficiently select interesting SNPs. Machine learning, specifically ensemble methods, might be useful as pre-screening tools for large-scale association studies because they can be less prone to overfitting, can be less computer processor time intensive, can easily include pair-wise and higher-order interactions compared with standard statistical approaches and can also have a high capability for classification. However, improved implementations that are able to deal with hundreds of thousands of SNPs at a time are required.     

The levels of plasma low density lipoprotein are independent of cholesterol ester transfer protein in fish-oil fed F1B hamsters

BACKGROUND: Cholesterol ester transfer protein (CETP) plays a major role in regulating the levels of LDL- and HDL-cholesterol. We previously observed a fish-oil-induced elevation of low-density lipoprotein (LDL)-and very-low-density lipoprotein (VLDL)-cholesterol concentrations and a decrease in high-density lipoprotein (HDL)-cholesterol concentration in F1B hamsters. The molecular mechanism/s by which fish oil induces hyperlipidaemic effect was investigated in this study. We examined whether the effects of dietary fish oil on plasma lipoprotein concentrations are due to fish-oil-induced alterations in plasma CETP activity. MIX diet, a diet supplemented with a mixture of lard and safflower oil, was used as the control diet. RESULTS: We found that fish oil feeding in hamsters reduced CETP mass as well as CETP activity. Increasing the dietary fat level of fish-oil from 5% to 20% (w/w) led to a further decrease in CETP mass. Supplementation with dietary cholesterol increased both CETP mass and CETP activity in fish-oil and MIX-diet fed hamsters. However, there was no correlation between CETP mass as well as CETP activity and LDL-cholesterol concentrations. CONCLUSION: These findings suggest that cholesterol ester transfer between HDL and LDL is not likely to play a major role in determining fish-oil-induced changes in LDL- and HDL-cholesterol concentrations in F1B hamsters. A possible role of reduced clearance of LDL-particles as well as dietary fat level and dietary cholesterol dependent changes in LDL-lipid composition have been discussed.     

Ciprofibrate increases cholesteryl ester transfer protein gene expression and the indirect reverse cholesterol transport to the liver

Background  

CETP is a plasma protein that modulates atherosclerosis risk through its HDL-cholesterol reducing action. The aim of this work was to examine the effect of the PPARα agonist, ciprofibrate, on the CETP gene expression, in the presence and absence of apolipoprotein (apo) CIII induced hypertriglyceridemia, and its impact on the HDL metabolism.     

The human cholesteryl ester transfer protein I405V polymorphism is associated with plasma cholesterol concentration and its reduction by dietary phytosterol esters

We examined the relationships of I405V cholesteryl ester transfer protein (CETP), Taq1B CETP and apolipoprotein (apo)E polymorphisms with the pattern of response to dietary plant sterol ester (PSE) by plasma lipids and CETP concentrations as well as lecithin-cholesterol acyltransferase (LCAT) activity. Subjects with moderate primary hypercholesterolemia (20-60 y old; 50 women; 10 men) consumed margarine (20 g/d) without (placebo) or with PSE (2.8 g/d = 1.68 g/d phytosterols) for 4 wk each period, in a crossover, double-blind study. Plasma CETP concentration was measured by ELISA; endogenous LCAT activity was expressed as the percentage of esterification (30 min incubation) of the subjects' (14)C-unesterified cholesterol HDL. PSE reduced concentrations of plasma total cholesterol (TC) (10%) and LDL cholesterol (LDL-C) (12%). In relation to the I405V CETP polymorphism, the percentage reductions in TC with consumption of PSE for the II, IV and VV phenotypes were 7.2, 4.2 and not significant, respectively, whereas LDL-C significant reductions occurred only for II (9.5%). However, the CETP concentration diminished only in the II phenotype.     

Role of cholesteryl ester transfer protein activity and genetic polymorphism in ischemic heart disease in type 2 diabetic patients

Cholesteryl Ester Transfer Protein (CETP) facilates the exchange of triglycerides (TG) and cholesteryl ester between lipoproteins particles. Diabetic subjects have been reported to have higher TG levels and lower high density lipoprotein-cholesterol (HDL-C) levels which contribute to the increased cardiovascular risk observed in some of these patients. The CETP activity was shown to be more important in a group of 93 non insulino-dependant diabetics with coronary artery disease than in a group of 92 healthy subjects (p = 0.033). Several polymorphisms have been reported in the CETP gene. The common Taq IB polymorphism is associated with decreased CETP activity and increased HDL-C. We have observed a frequency of 0.31 for B2 allele in deference to those reported in subjects from Caucasian population. An association between the presence of the B2B2 genotype, decreased CETP activity and increased of plasma HDL-C was observed in healthy subjects but not in diabetics with coronary artery disease.     

胆固醇酯转运蛋白基因多态性与肥胖及对膳食干预的影响

目的探讨胆固醇酯转运蛋白(CETP)基因多态性与肥胖的关系及其对肥胖膳食干预的影响。方法从血凝块中提取DNA,用聚合酶链反应和限制性片段长度多态性方法(PCR-RFLP)检测上海340名成年人的CETP基因TaqIB位点多态性;对研究对象进行体格检查并测定血脂;对其中的肥胖人群进行膳食干预,分析CETP基因多态性对干预效果的影响。结果(1)B1B1、B1B2和B2B2三种基因型的频率分别为35·6%、47·9%和16·5%,符合Hardy-Weinberg定律;B1等位基因为优势等位基因;肥胖组人群和正常组的基因型构成差异无显著性,控制相关影响因素后结果相同。(2)三种基因型的高密度脂蛋白(HDL)水平差异有显著性,B2B2型的HDL水平最高。控制环境影响因素后这种相关性仍然存在。(3)B1B2型膳食干预后HDL水平明显升高,这与其他基因型显著不同;控制基线HDL水平和性别因素后基因型对HDL水平改变没有影响。结论CETP-TaqIB位点B2B2基因型具有较高的血清HDL水平,成年肥胖人中此位点多态性无特殊性;基线HDL水平影响不同基因型的HDL对膳食干预的反应。     

Testing association between disease and multiple SNPs in a candidate gene

Current technology allows investigators to obtain genotypes at multiple single nucleotide polymorphism (SNPs) within a candidate locus. Many approaches have been developed for using such data in a test of association with disease, ranging from genotype-based to haplotype-based tests. We develop a new approach that involves two basic steps. In the first step, we use principal components (PCs) analysis to compute combinations of SNPs that capture the underlying correlation structure within the locus. The second step uses the PCs directly in a test of disease association. The PC approach captures linkage-disequilibrium information within a candidate region, but does not require the difficult computing implicit in a haplotype analysis. We demonstrate by simulation that the PC approach is typically as or more powerful than both genotype- and haplotype-based approaches. We also analyze association between respiratory symptoms in children and four SNPs in the Glutathione-S-Transferase P1 locus, based on data from the Children's Health Study. We observe stronger evidence of an association using the PC approach (p = 0.044) than using either a genotype-based (p = 0.13) or haplotype-based (p = 0.052) approach.     

Detecting genetic interactions for quantitative traits with U-statistics

The genetic etiology of complex human diseases has been commonly viewed as a process that involves multiple genetic variants, environmental factors, as well as their interactions. Statistical approaches, such as the multifactor dimensionality reduction (MDR) and generalized MDR (GMDR), have recently been proposed to test the joint association of multiple genetic variants with either dichotomous or continuous traits. In this study, we propose a novel Forward U-Test to evaluate the combined effect of multiple loci on quantitative traits with consideration of gene-gene/gene-environment interactions. In this new approach, a U-Statistic-based forward algorithm is first used to select potential disease-susceptibility loci and then a weighted U-statistic is used to test the joint association of the selected loci with the disease. Through a simulation study, we found the Forward U-Test outperformed GMDR in terms of greater power. Aside from that, our approach is less computationally intensive, making it feasible for high-dimensional gene-gene/gene-environment research. We illustrate our method with a real data application to nicotine dependence (ND), using three independent datasets from the Study of Addiction: Genetics and Environment. Our gene-gene interaction analysis of 155 SNPs in 67 candidate genes identified two SNPs, rs16969968 within gene CHRNA5 and rs1122530 within gene NTRK2, jointly associated with the level of ND (P-value = 5.31e-7). The association, which involves essential interaction, is replicated in two independent datasets with P-values of 1.08e-5 and 0.02, respectively. Our finding suggests that joint action may exist between the two gene products.     

Leveraging cell type specific regulatory regions to detect SNPs associated with tissue factor pathway inhibitor plasma levels

Tissue factor pathway inhibitor (TFPI) regulates the formation of intravascular blood clots, which manifest clinically as ischemic heart disease, ischemic stroke, and venous thromboembolism (VTE). TFPI plasma levels are heritable, but the genetics underlying TFPI plasma level variability are poorly understood. Herein we report the first genome‐wide association scan (GWAS) of TFPI plasma levels, conducted in 251 individuals from five extended French‐Canadian Families ascertained on VTE. To improve discovery, we also applied a hypothesis‐driven (HD) GWAS approach that prioritized single nucleotide polymorphisms (SNPs) in (1) hemostasis pathway genes, and (2) vascular endothelial cell (EC) regulatory regions, which are among the highest expressers of TFPI . Our GWAS identified 131 SNPs with suggestive evidence of association (P‐value < 5 × 10^?8), but no SNPs reached the genome‐wide threshold for statistical significance. Hemostasis pathway genes were not enriched for TFPI plasma level associated SNPs (global hypothesis test P‐value = 0.147), but EC regulatory regions contained more TFPI plasma level associated SNPs than expected by chance (global hypothesis test P‐value = 0.046). We therefore stratified our genome‐wide SNPs, prioritizing those in EC regulatory regions via stratified false discovery rate (sFDR) control, and reranked the SNPs by q‐value. The minimum q‐value was 0.27, and the top‐ranked SNPs did not show association evidence in the MARTHA replication sample of 1,033 unrelated VTE cases. Although this study did not result in new loci for TFPI, our work lays out a strategy to utilize epigenomic data in prioritization schemes for future GWAS studies.     

Association of gene polymorphism at cholesterol ester transfer protein locus with obesity and response to dietary intervention in obesity]

OBJECTIVE: To study the association of gene polymorphism at cholesterol ester transfer protein (CETP) locus with obesity and response to dietary intervention in obesity. METHODS: The PCR-PFLP method was used to detect the polymorphism of CETP gene of 340 adults in Shanghai. The levels of serum lipid profile, including TG, TC, HDL and LDL were analyzed. Obesity was selected to dietary intervention. RESULTS: (1)The genotype frequencies of CETP-TaqIB B1 B1, B1 B2 and B2 B2 were 35.6%, 47.9% and 16.5% respectively, which were in agreement with Hardy-Weinberg equilibrium. There was no significant difference in the distribution of genotypes between the obesity group and control group. The result was same after several influence factors controlled. (2) The levels of HDL were significantly different among genotype groups. Subjects for the B2 B2 genotype had the highest HDL levels. The relationship was steady after adjusting several influence factors. (3)Subjects for the B1 B2 genotype had higher HDL level after intervention, which was significantly different to other genotype groups. After adjusting baseline HDL level and gender, genotype didn't effect the change in HDL. CONCLUSION: CETP-TaqIB gene polymorphism influenced serum HDL level. But this gene polymorphism at CETP locus wasn't especial in adult obesity. Baseline HDL level influenced the change in HDL response to dietary intervention in three genotype groups.     

A joint association test for multiple SNPs in genetic case-control studies

For a dense set of genetic markers such as single nucleotide polymorphisms (SNPs) on high linkage disequilibrium within a small candidate region, a haplotype-based approach for testing association between a disease phenotype and the set of markers is attractive in reducing the data complexity and increasing the statistical power. However, due to unknown status of the underlying disease variant, a comprehensive association test may require consideration of various combinations of the SNPs, which often leads to severe multiple testing problems. In this paper, we propose a latent variable approach to test for association of multiple tightly linked SNPs in case-control studies. First, we introduce a latent variable into the penetrance model to characterize a putative disease susceptible locus (DSL) that may consist of a marker allele, a haplotype from a subset of the markers, or an allele at a putative locus between the markers. Next, through using of a retrospective likelihood to adjust for the case-control sampling ascertainment and appropriately handle the Hardy-Weinberg equilibrium constraint, we develop an expectation-maximization (EM)-based algorithm to fit the penetrance model and estimate the joint haplotype frequencies of the DSL and markers simultaneously. With the latent variable to describe a flexible role of the DSL, the likelihood ratio statistic can then provide a joint association test for the set of markers without requiring an adjustment for testing of multiple haplotypes. Our simulation results also reveal that the latent variable approach may have improved power under certain scenarios comparing with classical haplotype association methods.     

SNPs, haplotypes, and model selection in a candidate gene region: the SIMPle analysis for multilocus data

Modern molecular techniques make discovery of numerous single nucleotide polymorphims (SNPs) in candidate gene regions feasible. Conventional analysis relies on either independent tests with each variant or the use of haplotypes in association analysis. The first technique ignores the dependencies between SNPs. The second, though it may increase power, often introduces uncertainty by estimating haplotypes from population data. Additionally, as the number of loci expands for a haplotype, ambiguity in interpretation increases for determining the underlying genetic components driving a detected association. Here, we present a genotype-level analysis to jointly model the SNPs via a SNP interaction model with phase information (SIMPle) to capture the underlying haplotype structure. This analysis estimates both the risk associated with each variant and the importance of phase between pairwise combinations of SNPs. Thus, rather than selecting between genotype- or haplotype-level approaches, the SIMPle method frames the analysis of multilocus data in a model selection paradigm, the aim to determine which SNPs, phase terms, and linear combinations best describe the relation between genetic variation and a trait of interest. To avoid unstable estimation due to sparse data and to incorporate both the dependencies among terms and the uncertainty in model selection, we propose a Bayes model averaging procedure. This highlights key SNPs and phase terms and yields a set of best representative models. Using simulations, we demonstrate the utility of the SIMPle model to identify crucial SNPs and underlying haplotype structures across a variety of causal models and genetic architectures.     

eQuIPS: eQTL Analysis Using Informed Partitioning of SNPs – A Fully Bayesian Approach

We develop a Bayesian multi‐SNP Markov chain Monte Carlo approach that allows published functional significance scores to objectively inform single nucleotide polymorphism (SNP) prior effect sizes in expression quantitative trait locus (eQTL) studies. We developed the Normal Gamma prior to allow the inclusion of functional information. We partition SNPs into predefined functional groups and select prior distributions that fit the group‐specific observed functional significance scores. We test our method on two simulated datasets and previously analysed human eQTL data containing validated causal SNPs. In our simulations the modified Normal Gamma always performs at least as well, and generally outperforms, the other methods considered. When analysing the human eQTL data, we placed all SNPs into their actual functional group. The ranks of the four validated causal SNPs analysed using the modified Normal Gamma increase dramatically compared to those of the other methods considered. Using our new method, three of the four validated SNPs are ranked in the top 1% of SNPs and the other is in the top 2%. For the standard Normal Gamma, the best of the other methods, the four validated SNPs had ranks in the top 1%, 4%, 20% and 59%. Crucially these substantive improvements in the ranks make it highly likely that most, if not all, of these validated SNPs would have been flagged for follow‐up using our new method, whereas at least two of them would certainly not have been using the current approaches.     

Decreasing dietary fat saturation lowers HDL-cholesterol and increases hepatic HDL binding in hamsters

In order to study the mechanism by which increasing unsaturation of dietary fat lowers HDL-cholesterol levels, we studied various measures of HDL metabolism in hamsters fed with fats with different degrees of saturation. Hamsters were fed on a cholesterol-enriched (1 g/kg) semipurified diet containing 200 g/kg of maize oil, olive oil, or palm oil for 9 weeks. Increasing saturation of dietary fat resulted in increasing concentrations of total plasma cholesterol (4.29 (SD 0.51), 5.30 (SD 0.67) and 5.58 (SD 0.76) mmol/l respectively, n 12) and HDL-cholesterol (3.31 (SD 0.50), 3.91 (SD 0.12) and 3.97 (SD 0.43) mmol/l) and these concentrations were significantly higher (P < 0.05) in the palm-oil and olive-oil-fed hamsters compared with the maize-oil group. Total plasma triacylglycerol levels also increased with increasing fat saturation (1.01 (SD 0.59), 1.56 (SD 0.65) and 2.75 (SD 1.03) mmol/l) and were significantly higher (P < 0.05) in the palm-oil group compared with the olive-oil and maize-oil-fed hamsters. The three diets did not have differential effects on plasma activity levels of lecithin: cholesterol acyltransferase (LCAT) and cholesteryl ester transfer protein (CETP). Levels of phospholipid transfer protein (PLTP) tended to be higher with increasing fat saturation but this effect was not significant. The capacity of liver membranes to bind human HDL3 was significantly higher (P < 0.05) in the hamsters fed with maize oil (810 (SD 100) ng HDL3 protein/mg membrane protein, n 4) compared with those fed on palm oil (655 (SD 56) ng/mg), whereas the olive-oil group had intermediate values (674 (SD 26) ng/mg). The affinity of HDL3 for the binding sites was not affected by the type of dietary fat. Hepatic lipase (EC 3.1.1.3) activity, measured in liver homogenates, increased with increasing fat saturation. We conclude that dietary maize oil, when compared with either olive oil or palm-oil, may lower HDL-cholesterol concentrations by enhancing HDL binding to liver membranes.     

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].     

Meta-analysis of genetic association studies and adjustment for multiple testing of correlated SNPs and traits

Meta-analysis has become a key component of well-designed genetic association studies due to the boost in statistical power achieved by combining results across multiple samples of individuals and the need to validate observed associations in independent studies. Meta-analyses of genetic association studies based on multiple SNPs and traits are subject to the same multiple testing issues as single-sample studies, but it is often difficult to adjust accurately for the multiple tests. Procedures such as Bonferroni may control the type-I error rate but will generally provide an overly harsh correction if SNPs or traits are correlated. Depending on study design, availability of individual-level data, and computational requirements, permutation testing may not be feasible in a meta-analysis framework. In this article, we present methods for adjusting for multiple correlated tests under several study designs commonly employed in meta-analyses of genetic association tests. Our methods are applicable to both prospective meta-analyses in which several samples of individuals are analyzed with the intent to combine results, and retrospective meta-analyses, in which results from published studies are combined, including situations in which (1) individual-level data are unavailable, and (2) different sets of SNPs are genotyped in different studies due to random missingness or two-stage design. We show through simulation that our methods accurately control the rate of type I error and achieve improved power over multiple testing adjustments that do not account for correlation between SNPs or traits.