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• W2067693186 abstract "In population-based case-control association studies, the regular χ2 test is often used to investigate association between a candidate locus and disease. However, it is well known that this test may be biased in the presence of population stratification and/or genotyping error. Unlike some other biases, this bias will not go away with increasing sample size. On the contrary, the false-positive rate will be much larger when the sample size is increased. The usual family-based designs are robust against population stratification, but they are sensitive to genotype error. In this article, we propose a novel method of simultaneously correcting for the bias arising from population stratification and/or for the genotyping error in case-control studies. The appropriate corrections depend on sample odds ratios of the standard 2×3 tables of genotype by case and control from null loci. Therefore, the test is simple to apply. The corrected test is robust against misspecification of the genetic model. If the null hypothesis of no association is rejected, the corrections can be further used to estimate the effect of the genetic factor. We considered a simulation study to investigate the performance of the new method, using parameter values similar to those found in real-data examples. The results show that the corrected test approximately maintains the expected type I error rate under various simulation conditions. It also improves the power of the association test in the presence of population stratification and/or genotyping error. The discrepancy in power between the tests with correction and those without correction tends to be more extreme as the magnitude of the bias becomes larger. Therefore, the bias-correction method proposed in this article should be useful for the genetic analysis of complex traits. In population-based case-control association studies, the regular χ2 test is often used to investigate association between a candidate locus and disease. However, it is well known that this test may be biased in the presence of population stratification and/or genotyping error. Unlike some other biases, this bias will not go away with increasing sample size. On the contrary, the false-positive rate will be much larger when the sample size is increased. The usual family-based designs are robust against population stratification, but they are sensitive to genotype error. In this article, we propose a novel method of simultaneously correcting for the bias arising from population stratification and/or for the genotyping error in case-control studies. The appropriate corrections depend on sample odds ratios of the standard 2×3 tables of genotype by case and control from null loci. Therefore, the test is simple to apply. The corrected test is robust against misspecification of the genetic model. If the null hypothesis of no association is rejected, the corrections can be further used to estimate the effect of the genetic factor. We considered a simulation study to investigate the performance of the new method, using parameter values similar to those found in real-data examples. The results show that the corrected test approximately maintains the expected type I error rate under various simulation conditions. It also improves the power of the association test in the presence of population stratification and/or genotyping error. The discrepancy in power between the tests with correction and those without correction tends to be more extreme as the magnitude of the bias becomes larger. Therefore, the bias-correction method proposed in this article should be useful for the genetic analysis of complex traits. Population-based case-control studies provide a powerful approach to identify the multiple variants of small effect that modulate susceptibility to common, complex diseases. However, the major shortcoming of these studies arises from the presence of population stratification (PS). When cases and controls have different allele frequencies attributable to diversity in background population, unrelated to the disease being studied, the study is said to have PS. PS is probably the most-often-cited reason for nonreplication of genetic association studies, since undetected PS can mimic the signal of association and lead to more false-positive findings or miss real effects.1Knowler WC Williams RC Pettitt DJ Steinberg AG Gm3;5,13,14 and type 2 diabetes mellitus: an association in American Indians with genetic mixture.Am J Hum Genet. 1988; 43: 520-526PubMed Google Scholar, 2Lander ES Schrok NJ Genetic dissection of complex traits.Science. 1994; 265: 2037-2048Crossref PubMed Scopus (2678) Google Scholar Besides these factors, often mentioned in the literature, an often-overlooked factor influencing the performance of case-control design is the presence of genotyping error (GE). Such error is important because without some method of correction, the power to detect association and thus to map genes may be significantly decreased.3Gordon D Matise TC Heath SC Ott J Power loss for multiallelic transmission/disequilibrium test when errors introduced: GAW11 simulated data.Genet Epidemiol. 1999; 17: S587-S592Crossref PubMed Scopus (25) Google Scholar, 4Gordon D Finch SJ Nothnagel M Ott J Power and sample size calculations for case-control genetic association tests when errors are present: application to single nucleotide polymorphisms.Hum Hered. 2002; 54: 22-33Crossref PubMed Scopus (237) Google Scholar, 5Morris RW Kaplan NL Testing for association with a case-parents design in the presence of genotyping errors.Genet Epidemiol. 2004; 26: 142-154Crossref PubMed Scopus (24) Google Scholar, 6Moskvina V Graddock N Hlmans P Owen MJ O’Donovan MC Effects of differential genotyping error rate on the type I error probability of case-control studies.Hum Hered. 2006; 61: 55-64Crossref PubMed Scopus (72) Google Scholar Family-based designs are robust against PS. However, under the assumption of no or small PS, case-control studies have been shown to be more powerful than family-based designs.7Morton NE Collins A Tests and estimates of allelic association in complex inheritance.Proc Natl Acad Sci USA. 1998; 95: 11389-11393Crossref PubMed Scopus (227) Google Scholar, 8Risch N Teng J The relative power of family-based and case-control designs for linkage disequilibrium studies of complex human disease. I. DNA pooling.Genome Res. 1998; 8: 1273-1288PubMed Google Scholar Unfortunately, it is rarely clear when PS can be ignored. The existence of PS, in general, weights against the use of case-control designs. Using population-based data, Devlin and Roeder9Devlin B Roeder K Genomic control for association studies.Biometrics. 1999; 55: 997-1004Crossref PubMed Scopus (2155) Google Scholar proposed an association method, termed “genomic control” (GC), to automatically correct for the effects caused by PS and cryptic relatedness. Another computationally more extensive approach for correcting the effects of PS is the structured association (SA) method.10Pritchard JK Stephens M Rosenberg NA Donnelly P Association mapping in structured populations.Am J Hum Genet. 2000; 67: 170-181Abstract Full Text Full Text PDF PubMed Scopus (1403) Google Scholar Both GC and SA methods require genotyping at additional null loci to perform the tests. Bacanu et al.11Bacanu SA Devlin B Roeder K The power of genomic control.Am J Hum Genet. 2000; 66: 1933-1944Abstract Full Text Full Text PDF PubMed Scopus (272) Google Scholar claimed that the transmission/disequilibrium test (TDT) is more powerful when population substructure is substantial and that the GC is more powerful otherwise. However, a recent study by Campbell et al.12Campbell CD Ogburn EL Lunetta KL Lyon HN Freedman ML Groop LC Altshuler D Ardlie KG Hirschhorn JN Demonstrating stratification in a European American population.Nat Genet. 2005; 37: 868-872Crossref PubMed Scopus (344) Google Scholar showed that both standard GC and SA methods failed to correct for the confounding effects of PS. The original TDT, GC, and SA methods are not intended to correct the bias due to GE. Recently, extensions of TDT methods to correct for nondifferential genotype error have been proposed.5Morris RW Kaplan NL Testing for association with a case-parents design in the presence of genotyping errors.Genet Epidemiol. 2004; 26: 142-154Crossref PubMed Scopus (24) Google Scholar, 13Gordon D Heath SC Liu X Ott J A transmission/disequilibrium test that allows for genotyping errors in the analysis of single-nucleotide polymorphism data.Am J Hum Genet. 2001; 69: 371-380Abstract Full Text Full Text PDF PubMed Scopus (123) Google Scholar, 14Bernardinelli L Berzuini C Seaman S Holmans P Bayesian trio models for association in the presence of genotyping errors.Genet Epidemiol. 2004; 26: 70-80Crossref PubMed Scopus (17) Google Scholar, 15Gordon D Haynes C Johnnidis C Patel SB Bowcock AM Ott J A transmission disequilibrium test for general pedigrees that is robust to the presence of random genotyping errors and any number of untyped parents.Eur J Hum Genet. 2004; 12: 752-761Crossref PubMed Scopus (50) Google Scholar Clayton et al.16Clayton DG Walker NM Smyth DJ Pask R Cooper JD Maier LM Smink LJ Lam AC Ovington NR Stevens HE et al.Population structure, differential bias and genomic control in a large scale, case-control association study.Nat Genet. 2005; 37: 1243-1246Crossref PubMed Scopus (442) Google Scholar also suggested that the idea of GC can be generalized to correct for the effects of differential errors in measurement of genotype. In their application, the variance inflation factor is not constant but depends on extra measures of genotyping accuracy, such as the half-call rate and the absolute difference in call rates between cases and controls. The GC method is based on the assumption that variance inflation factor is approximately constant across the genome for all null loci. However, many results17Chen HS Zhu X Zhao H Zhang S Qualitative semiparametric test for genetic associations in case-control designs under structured populations.Ann Hum Genet. 2003; 67: 250-264Crossref PubMed Scopus (57) Google Scholar, 18Shmulewitz D Zhang J Greenberg DA Case-control association studies in mixed populations: correcting using genomic control.Hum Hered. 2004; 58: 145-153Crossref PubMed Scopus (18) Google Scholar, 19Gorroochurn D Heiman GA Hodge SE Greenberg DA Centralizing the noncentral chi-square: a new method to correct for population stratification in genetic case-control association studies.Genet Epidemiol. 2006; 30: 277-289Crossref PubMed Scopus (32) Google Scholar showed that the regular χ2 statistic for testing independence follows a noncentral χ2 distribution asymptotically under stratified populations, even when there is no true association. They also showed that the noncentrality parameter can be large even when Wright’s20Wright S The genetic structure of populations.Ann Eugen. 1951; 15: 323-354Crossref PubMed Google ScholarFst is small. Here, Fst measures a sort of inbreeding coefficient, or heterozygote deficit, that is due to population subdivision. A new correction for PS was recently suggested by Epstein et al.21Epstein MP Allen AS Satten GA A simple and improved correction for population stratification in case-control studies.Am J Hum Genet. 2007; 80: 921-930Abstract Full Text Full Text PDF PubMed Scopus (121) Google Scholar using substructure-informative loci, instead of the usual null loci. This method was shown to have improved performance but was not designed to protect against the confounding effects of GE. It is well known that, for a single SNP locus, if the GE is random nondifferential with respect to affected status, then there is no effect on the expected type I error rate. However, its effect on the power is well recognized. Clayton et al.16Clayton DG Walker NM Smyth DJ Pask R Cooper JD Maier LM Smink LJ Lam AC Ovington NR Stevens HE et al.Population structure, differential bias and genomic control in a large scale, case-control association study.Nat Genet. 2005; 37: 1243-1246Crossref PubMed Scopus (442) Google Scholar pointed out that there might exist different error rates in genotype scoring between case and control samples. Under this circumstance, Moskvina et al.6Moskvina V Graddock N Hlmans P Owen MJ O’Donovan MC Effects of differential genotyping error rate on the type I error probability of case-control studies.Hum Hered. 2006; 61: 55-64Crossref PubMed Scopus (72) Google Scholar used simulations showing that, even with very low error rates, differential error rates can result in false-positive rates much greater than 0.05. The effect was maximal for loci with small minor-allele frequency.4Gordon D Finch SJ Nothnagel M Ott J Power and sample size calculations for case-control genetic association tests when errors are present: application to single nucleotide polymorphisms.Hum Hered. 2002; 54: 22-33Crossref PubMed Scopus (237) Google Scholar, 22Mote VL Anderson RL An investigation of the effect of misclassification on the properties of chi-square-tests in the analysis of categorical data.Biometrika. 1965; 52: 95-109PubMed Google Scholar The bias caused by PS and/or GE can be substantial, and it will not go away with increasing sample size. In fact, the false-positive rate will be much larger when the sample size is increased. The purpose of this article is to suggest a novel method that can automatically correct for the effects arising from PS and/or GE in case-control studies, only at the price of genotyping a panel of null loci. We remark that the usual approach for correcting the bias caused by GE is to assume an error model and often requires repeated genotyping or validation data. In contrast, the method proposed here does not depend on either an error model or validation data. To the best of our knowledge, this is the first article that gives a systematic study of the joint effect of PS and GE and provides a workable solution for correcting the related bias in case-control association studies. In this article, we point out that, under the null hypothesis of no association, the confounding effect caused by PS depends on the sampling proportions and genotype frequencies of the subpopulations. In the special case of simple random sampling, it also depends on the disease risks in subpopulations. We show how to use information from null loci to estimate the PS effect efficiently and, on that basis, suggest a genotype-based χ2 test (with 2 df) (hereafter called the “CS” test) for testing the existence of association. This method is very simple to apply and can be easily extended to provide a point (and interval) estimation of the genetic effect if the null hypothesis of no association is rejected. When there are genotyping errors, the CS test also can correct the bias caused by GE. This is because the likelihood functions for testing the null hypothesis of no association under GE and PS have the same form. We will give reasons showing why this is true. In fact, even in situations where error rates are not constant within or between case and control samples, the CS test can still be applied to test no association. When there is no PS and GE, the CS test automatically reduces to the regular χ2 test if the sizes of the case and control samples are large. This means that the CS test is a natural extension of the regular χ2 test for correcting the effects of PS and/or GE. In this article, we also present simulation results, to illustrate the performance of the CS test. Under various simulation parameter values—which were very similar to those found in real-data examples—for PS and/or GE, the CS test was shown to approximately maintain the expected false-positive rate. In contrast, the regular χ2 test tended to have inflated type I error rates. In most simulated instances, the CS test also showed improved power performance. Often, the increases in power were very significant. We report simulation results for the CS test on the basis of data from the candidate locus and 50 randomly selected null loci. Evidence from the simulation study also indicates that no advantage can be found by using a greater number of null loci in the analysis. In case-control studies, the data for each locus are given in a standard 2×3 table of genotype by case and control. Let D=1 denote that the individual has the disease and D=0 otherwise. Let G (equal to 0, 1, or 2) denote the number of copies of the high-risk candidate allele carried by the individual. The primary interest is to test whether there exists association between the genetic risk factor G and disease D. We assume that the general population comprises K subpopulations, and covariable S is used to indicate the subpopulation to which a person belongs. We postulate the risk model23Satten GA Flanders WD Yang Q Accounting for unmeasured population substructure in case-control studies of genetic association using novel latent-class model.Am J Hum Genet. 2001; 68: 466-477Abstract Full Text Full Text PDF PubMed Scopus (203) Google Scholarlog[⪻(D=1|G=g,S=s)⪻(D=0|G=g,S=s)]=μ+μs+βg .(1) For identifiability, we define μ1 and β0 to be zero, so that s=1 and g=0 represent the referent subpopulation and genotype, respectively. Model (1) assumes S to be a confounder, not an effect modifier. In the presence of PS, one can show (appendix A) that even when there exists no association between G and D, the ratio of the case and control genotype frequencies can be expressed as⪻(G=g|D=1)⪻(G=g|D=0)=exp(α*+βg*) ,(2) where β*0=0 but parameters β*1 and β*2 depend on the genotype frequency Pr(G=g|S=s) and the sampling proportions P*(S=s|D=1) and P*(S=s|D=0) of the diseased and nondiseased individuals, respectively, from subpopulation S (see eqs. (A2) and (A3) in appendix A for the exact definition of β*g). Note that the values of P*(S=s|D=1), P*(S=s|D=0), and Pr(G=g|S=s) are assumed to be unknown, but they are not required to be estimated in the analysis. According to model (2), β*1 and β*2 are log odds ratios of the 2×3 table under no association. Thus, β*=(β*1,β*2) can be used to measure the level of PS. Model (2) implies that, even when there is no true association between G and D, the case and control genotype frequencies cannot be identical if PS exists (i.e., β* does not equal zero). This makes the regular χ2 statistic for testing independence in a 2×3 table produce spurious association. In view of the definition of β*g in appendix A, if one can identify genetically distinct subpopulations and uses a design so that the sampling proportions are identical in the cases and controls (i.e., P*(S=s|D=1)=P*(S=s|D=0)), then the false-positive rate of the regular χ2 test will not be elevated, since no PS effect (β*=0) exists in this case. Otherwise, the effect of PS might be severe because of the different sampling proportions used in the cases and controls. Note that, in the special case of simple random sampling, the level of PS also depends on the disease risks in subpopulations (see eq. (A4) in appendix A). The level of PS is locus dependent. We use β*(l) to denote the level of PS corresponding to the lth null locus, l=1,…,L. The idea of the CS test is to first combine estimatesβˆ* of the PS levels at the null loci, to define a reasonable estimateβ˜* of β*, the PS level at the candidate locus. Next, using model (2), we define the CS test statistic (denoted by “X2(β˜*)”) to be the regular likelihood-ratio test statistic for testing H*0:β*=β˜* based on genotype data at the candidate locus. Define N0(g) and N1(g) to be the numbers of individuals in the control and case samples, respectively, with genotype G=g at the candidate locus. Under model (2), the retrospective likelihood function24Prentice RL Pyke R Logistic disease incidence models and case-control studies.Biometrika. 1979; 66: 403-441Crossref Scopus (731) Google Scholar isL=Πg=02[11+exp(α*+βg*)]N0(g)+N1(g)×[exp(α*+βg*)]N1(g) .LetLˆ(H*0) be the maximum likelihood under constraint H*0 andLˆ be the maximum likelihood under no constraint. The CS test statistic is defined as X2(β˜*)=2log[Lˆ/Lˆ(H*0). By use of existing software packages, the CS test statistic can be computed easily. The corresponding P value of the test is P=Pr[χ22>X2(β˜*), where χ22 has a χ2 distribution with 2 df. In this article, we define estimateβ˜*(l) to be the usual log of the sample odds ratio (maximum-likelihood estimate), using 2×3 genotype data at the lth null locus. Conceptually, if subpopulation genotype frequencies at the candidate locus approximately match those at the null loci, then the usual mean or median ofβ˜*(l) can be a good estimate of β*. However, it is difficult to verify this condition in real applications. Instead, we assume β* to be unknown but a smooth function of the genotype frequencies in the controls (at least approximately) and suggest using a nonparametric regression technique25Simonoff J Smoothing methods in statistics. Springer Verlag, New York1996Crossref Google Scholar to estimate β*. We let the sample genotype frequencies of the candidate locus and lth null locus in the controls be denoted by P0(g) and Pl(g), respectively, and define the difference of the two frequencies as dl(g)=P0(g)-Pl(g). A nonparametric regression estimate of β* is defined asβ˜*=Σl=1Lβ˜*(l)Wl. This is a weighted average ofβ˜*(l), with weights defined asWl=K[dl(0)/bn]K[dl(1)/bn]∑l′=1LK[dl′(0)/bn]K[dl′(1)/bn] .The weights are determined by “window size” bn>0 and the “quadratic kernel” K(t)=3(1-t2)I(|t|≤1)/4. It is well known that the performance of the nonparametric regression estimate is insensitive to the use of kernel function. However, it depends on the window size. We suggest that an optimal bn be selected so that the proposed CS test applied to each null locus can approximately maintain the correct type I error rate. To this end, for the lth null locus, we let Pl=Pr{χ22>X2[β˜*(l)]} denote the P value, where the nonparametric regression estimateβ˜*(l) is computed from the genotype data at the remaining L-1 null loci, with bn fixed. Next, for a prespecified level of significance α, we propose choosing an optimal bn (α dependent) from (0,1) so that |L−1Σl=1LI(Pl<α)-α| is minimized. A free software (CS test software) for computing optimal window size bn, an estimate of β*, and the final P value of the CS test is available at Cheng’s software Web site. In this section, we show that the CS test also can be applied to correct the bias caused by GE in case-control studies. For simplicity, we assume that case and control samples have differential genotype error rates, but it is understood that our approach can be applied under more-general error modeling. For example, our approach still works even when there are differential error rates within the case (or control) sample. Let Go (equal to 0, 1, 2) be the observed genotype, subject to genotyping error. We assume that the error rates are Pr(Go=go|G=g, D=1)=ϕ1(go;g) in the case sample and Pr(Go=go|G=g, D=0)=ϕ0(go;g) in the control sample. Thus, if one defines W1(go,g)=ϕ1(go;g)Pr(G=g|D=1) and W0(go,g)=ϕ0(go;g)Pr(G=g|D=0), then, under no true association, one can show that the ratio of the case and control genotype frequencies is⪻(Go=go|D=1)⪻(Go=go|D=0)=log(δ+γgo) ,(3) where parameters γ1 and γ2 depend on W0(go,g) and W1(go,g) (appendix B), and their values may be nonzero if error rates ϕ1(go;g) and ϕ0(go;g) are not identical. Thus, even under the null case, there may exist nonzero log odds ratios in the 2×3 table. In this case, there exists bias because of GE. On the other hand, if error rates ϕ1(go;g) and ϕ0(go;g) are identical, then there is no effect on the expected type I error rate, since γ1=γ2=0. Suppose that, using the same genotyping technique, we also have genotype data from the null loci. For the lth null locus, let the corresponding bias be denoted by γ(l)=[γ1(l),γ2(l)]. This bias also can be estimated by use of the log of the sample odds-ratios from the lth null loci (denoted byγˆ(l)). Next, using the same principle as above, we also define estimateγˆ of the bias (γ1,γ2) to be a weighted average ofγˆ(l). Thus, on the basis of the observed 2×3 table at the candidate locus, the regular likelihood-ratio test for testing H*0:(γ1,γ2)=(γˆ1,γˆ2) under model (3) is exactly identical to the CS test defined above. It is important to note that errors may not be distributed evenly across all loci—that is, the error rates are also locus dependent. Some loci may show error rates that are many times higher than those shown by other loci.25Simonoff J Smoothing methods in statistics. Springer Verlag, New York1996Crossref Google Scholar However, the validity of the CS test does not require error rates to be identical across candidate and null loci. We conclude that, in an association analysis, the CS test can be applied to correct for PS and GE simultaneously. We conducted several simulations to investigate the performance of the CS test and the regular χ2 test without adjustment (hereafter called the “CS*” test) under PS and/or GE. We included the CS* test in the study so that the empirical level of the bias caused by PS and/or GE could be measured. There are three factors affecting the level of PS (appendix A): (i) the sampling proportions for each subpopulation among cases and controls, (ii) the allele frequency at the candidate locus in each subpopulation (under the assumption that the Hardy-Weinberg condition holds in each subpopulation), and (iii) the penetrances of the candidate locus in each subpopulation. In our simulation study, the general population was assumed to comprise two subpopulations, and the case data were sampled from the first and second subpopulations with probabilities q=P*(S=1|D=1) and 1-q=P*(S=2|D=1), respectively, and the control data were sampled from the first and second subpopulations with probabilities 1-q=P*(S=1|D=0) and q=P*(S=2|D=0), respectively. Three q values were used: 0.5, 0.7, and 1.0. q=0.5 corresponds to the case of no PS effect, since the level of PS is zero. q=1.0 corresponds to the case with the most severe PS effect. In this situation, case and control samples were drawn from two different subpopulations. Zheng et al.27Zheng G Freidlin B Gastwirth JL Robust genomic control for association studies.Am J Hum Genet. 2006; 78: 350-356Abstract Full Text Full Text PDF PubMed Scopus (59) Google Scholar also considered this extreme case in their simulation study. The allele frequency at the candidate locus was chosen to be p1=0.30 for the first subpopulation and p2=0.30+t for the second subpopulation. A large difference, t, between the allele frequencies in the two subpopulations means that a large bias due to PS occurs in the study. In the simulations, t=0.03, 0.05, and 0.10 were considered, representing the range from weak PS to strong PS. Note that, on the basis of the International Project on Genetic Susceptibility to Environmental Carcinogenes database, Garte et al.28Garte S The role of ethnicity in cancer susceptibility gene polymorphisms: the example of CYP1A1.Carcinogenesis. 1998; 19: 1329-1332Crossref PubMed Scopus (146) Google Scholar, 29Garte S Gaspari L Alexandrie AK Ambrosone C Autrup H Autrup JL Baranova H Bathum L Benhamou S Boffetta P et al.Metabolic gene polymorphism frequencies in control populations.Cancer Epidemiol Biomarkers Prev. 2001; 10: 1239-1248PubMed Google Scholar pointed out the differences in allele frequencies within white populations from different countries are much smaller (e.g., t≤0.05) but more significant among whites, Asians, and African Americans. For example, the allele frequency of the CYP3A4-V gene, which is thought to be related to prostate cancer, was highest among Nigerians (87%), lowest among European Americans (10%), and intermediate among African Americans (66%).30Kittles RA Chen W Panguluri RK Ahaghotu C Jackson A Adebamowo CA Griffin R Williams T Ukoli F Adams-Campbell U et al.CYP3A4-V and prostate cancer in African Americans: causal or confounding association because of population stratification?.Hum Genet. 2002; 110: 553-560Crossref PubMed Scopus (145) Google Scholar Therefore, our choices of frequency differences are consistent with real-data examples. Finally, in the null and power simulations, the same penetrances were used for the two subpopulations. Under null simulations, identical penetrances f0=f1=f2=0.10 were used. Under power simulations, penetrances f0=0.01 and f1=f2=0.25 were used for the dominant genetic model, f0=f1=0.10 and f2=0.30 were used for the recessive genetic model, and f0=0.10, f1=0.20, and f2=0.30 were used for the additive genetic model. Note that the penetrances are defined as fg=Pr(D=1|G=g), and similar values were also considered in the simulation study by Zheng et al.27Zheng G Freidlin B Gastwirth JL Robust genomic control for association studies.Am J Hum Genet. 2006; 78: 350-356Abstract Full Text Full Text PDF PubMed Scopus (59) Google Scholar Next, according to the definition of the bias caused by GE (eq. (3)), there are three factors affecting the bias level: (i) the genotype frequencies of the cases and controls, (ii) error models, and (iii) error rates. The genotype frequencies were defined above. Two error models were considered in the simulations. The first model is the symmetric allele-dropout error model,5Morris RW Kaplan NL Testing for association with a case-parents design in the presence of genotyping errors.Genet Epidemiol. 2004; 26: 142-154Crossref PubMed Scopus (24) Google Scholar determined by one error rate, ɛ. This model assumes that one misclassifies homozygotes twice as frequently as heterozygotes. The second model is the allele-based error model,12Campbell CD Ogburn EL Lunetta KL Lyon HN Freedman ML Groop LC Altshuler D Ardlie KG Hirschhorn JN Demonstrating stratification in a European American population.Nat Genet. 2005; 37: 868-872Crossref PubMed Scopus (344) Google Scholar determined by two erro" @default.
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• W2067693186 date "2007-10-01" @default.
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• W2067693186 title "Simultaneously Correcting for Population Stratification and for Genotyping Error in Case-Control Association Studies" @default.
• W2067693186 cites W1605869384 @default.
• W2067693186 cites W1608693905 @default.
• W2067693186 cites W1963936426 @default.
• W2067693186 cites W1980519002 @default.
• W2067693186 cites W1990452333 @default.
• W2067693186 cites W2000769576 @default.
• W2067693186 cites W2002212600 @default.
• W2067693186 cites W2008047653 @default.
• W2067693186 cites W2009766041 @default.
• W2067693186 cites W2027748690 @default.
• W2067693186 cites W2028926885 @default.
• W2067693186 cites W2039608769 @default.
• W2067693186 cites W2040497305 @default.
• W2067693186 cites W2046387635 @default.
• W2067693186 cites W2047923046 @default.
• W2067693186 cites W2056866499 @default.
• W2067693186 cites W2057124144 @default.
• W2067693186 cites W2058828922 @default.
• W2067693186 cites W2066915988 @default.
• W2067693186 cites W2069420421 @default.
• W2067693186 cites W2075923653 @default.
• W2067693186 cites W2080289043 @default.
• W2067693186 cites W2090307617 @default.
• W2067693186 cites W2091341519 @default.
• W2067693186 cites W2096051150 @default.
• W2067693186 cites W2110871641 @default.
• W2067693186 cites W2123466824 @default.
• W2067693186 cites W2127431556 @default.
• W2067693186 cites W2134070988 @default.
• W2067693186 cites W2140597941 @default.
• W2067693186 cites W2153622221 @default.
• W2067693186 cites W2168416261 @default.
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