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1.  Comparison of univariate and multivariate linkage analysis of traits related to hypertension 
BMC Proceedings  2009;3(Suppl 7):S99.
Complex traits are often manifested by multiple correlated traits. One example of this is hypertension (HTN), which is measured on a continuous scale by systolic blood pressure (SBP). Predisposition to HTN is predicted by hyperlipidemia, characterized by elevated triglycerides (TG), low-density lipids (LDL), and high-density lipids (HDL). We hypothesized that the multivariate analysis of TG, LDL, and HDL would be more powerful for detecting HTN genes via linkage analysis compared with univariate analysis of SBP. We conducted linkage analysis of four chromosomal regions known to contain genes associated with HTN using SBP as a measure of HTN in univariate Haseman-Elston regression and using the correlated traits TG, LDL, and HDL in multivariate Haseman-Elston regression. All analyses were conducted using the Framingham Heart Study data. We found that multivariate linkage analysis was better able to detect chromosomal regions in which the angiotensinogen, angiotensin receptor, guanine nucleotide-binding protein 3, and prostaglandin I2 synthase genes reside. Univariate linkage analysis only detected the AGT gene. We conclude that multivariate analysis is appropriate for the analysis of multiple correlated phenotypes, and our findings suggest that it may yield new linkage signals undetected by univariate analysis.
PMCID: PMC2796003  PMID: 20018096
2.  A framework for analyzing both linkage and association: an analysis of Genetic Analysis Workshop 16 simulated data 
BMC Proceedings  2009;3(Suppl 7):S98.
We examine a Bayesian Markov-chain Monte Carlo framework for simultaneous segregation and linkage analysis in the simulated single-nucleotide polymorphism data provided for Genetic Analysis Workshop 16. We conducted linkage only, linkage and association, and association only tests under this framework. We also compared these results with variance-component linkage analysis and regression analyses. The results indicate that the method shows some promise, but finding genes that have very small (<0.1%) contributions to trait variance may require additional sources of information. All methods examined fared poorly for the smallest in the simulated "polygene" range (h2 of 0.0015 to 0.0002).
PMCID: PMC2796002  PMID: 20018095
3.  New score tests for age-at-onset linkage analysis in general pedigrees 
BMC Proceedings  2009;3(Suppl 7):S97.
Our aim is to develop methods for mapping genes related to age at onset in general pedigrees. We propose two score tests, one derived from a gamma frailty model with pairwise likelihood and one derived from a log-normal frailty model with approximated likelihood around the null random effect. The score statistics are weighted nonparametric linkage statistics, with weights depending on the age at onset. These tests are correct under the null hypothesis irrespective of the weight used. They are simple, robust, computationally fast, and can be applied to large, complex pedigrees. We apply these methods to simulated data and to the Genetic Analysis Workshop 16 Framingham Heart Study data set. We investigate the time to the first of three events: hard coronary heart disease, diabetes, or death from any cause. We use a two-step procedure. In the first step, we estimate the population parameters under the null hypothesis of no linkage. In the second step, we apply the score tests, using the population parameters estimated in the first step.
PMCID: PMC2796001  PMID: 20018094
4.  Comparing gene set analysis methods on single-nucleotide polymorphism data from Genetic Analysis Workshop 16 
BMC Proceedings  2009;3(Suppl 7):S96.
Recently, gene set analysis (GSA) has been extended from use on gene expression data to use on single-nucleotide polymorphism (SNP) data in genome-wide association studies. When GSA has been demonstrated on SNP data, two popular statistics from gene expression data analysis (gene set enrichment analysis [GSEA] and Fisher's exact test [FET]) have been used. However, GSEA and FET have shown a lack of power and robustness in the analysis of gene expression data. The purpose of this work is to investigate whether the same issues are also true for the analysis of SNP data. Ultimately, we conclude that GSEA and FET are not optimal for the analysis of SNP data when compared with the SUMSTAT method. In analysis of real SNP data from the Framingham Heart Study, we find that SUMSTAT finds many more gene sets to be significant when compared with other methods. In an analysis of simulated data, SUMSTAT demonstrates high power and better control of the type I error rate. GSA is a promising approach to the analysis of SNP data in GWAS and use of the SUMSTAT statistic instead of GSEA or FET may increase power and robustness.
PMCID: PMC2796000  PMID: 20018093
5.  Integration of a priori gene set information into genome-wide association studies 
BMC Proceedings  2009;3(Suppl 7):S95.
In genome-wide association studies (GWAS) genetic markers are often ranked to select genes for further pursuit. Especially for moderately associated and interrelated genes, information on genes and pathways may improve the selection. We applied and combined two main approaches for data integration to a GWAS for rheumatoid arthritis, gene set enrichment analysis (GSEA) and hierarchical Bayes prioritization (HBP). Many associated genes are located in the HLA region on 6p21. However, the ranking lists of genes and gene sets differ considerably depending on the chosen approach: HBP changes the ranking only slightly and primarily contains HLA genes in the top 100 gene lists. GSEA includes also many non-HLA genes.
PMCID: PMC2795999  PMID: 20018092
6.  Integration of gene ontology pathways with North American Rheumatoid Arthritis Consortium genome-wide association data via linear modeling 
BMC Proceedings  2009;3(Suppl 7):S94.
We describe an empirical Bayesian linear model for integration of functional gene annotation data with genome-wide association data. Using case-control study data from the North American Rheumatoid Arthritis Consortium and gene annotation data from the Gene Ontology, we illustrate how the method can be used to prioritize candidate genes for further investigation.
PMCID: PMC2795998  PMID: 20018091
7.  Comparative analysis of different approaches for dealing with candidate regions in the context of a genome-wide association study 
BMC Proceedings  2009;3(Suppl 7):S93.
Genome-wide association studies (GWAS) test hundreds of thousands of single-nucleotide polymorphisms (SNPs) for association to a trait, treating each marker equally and ignoring prior evidence of association to specific regions. Typically, promising regions are selected for further investigation based on p-values obtained from simple tests of association. However, loci that exert only a weak, low-penetrant role on the trait, producing modest evidence of association, are not detectable in the context of a GWAS. Implementing prior knowledge of association in GWAS could increase power, help distinguish between false and true positives, and identify better sets of SNPs for follow-up studies.
Here we performed a GWAS on rheumatoid arthritis (RA) patients and controls (Problem 1, Genetic Analysis Workshop 16). In order to include prior information in the analysis, we applied four methods that distinctively deal with markers in candidate genes in the context of GWAS. SNPs were divided into a random and a candidate subset, then we applied empirical correction by permutation, false-discovery rate, false-positive report probability, and posterior odds of association using different prior probabilities. We repeated the same analyses on two different sets of candidate markers defined on the basis of previously reported association to RA following two different approaches. The four methods showed similar relative behavior when applied to the two sets, with the proportion of candidate SNPs ranked among the top 2,000 varying from 0 to 100%. The use of different prior probabilities changed the stringency of the methods, but not their relative performance.
PMCID: PMC2795997  PMID: 20018090
8.  Toward the identification of causal genes in complex diseases: a gene-centric joint test of significance combining genomic and transcriptomic data 
BMC Proceedings  2009;3(Suppl 7):S92.
Gene identification using linkage, association, or genome-wide expression is often underpowered. We propose that formal combination of information from multiple gene-identification approaches may lead to the identification of novel loci that are missed when only one form of information is available.
Firstly, we analyze the Genetic Analysis Workshop 16 Framingham Heart Study Problem 2 genome-wide association data for HDL-cholesterol using a "gene-centric" approach. Then we formally combine the association test results with genome-wide transcriptional profiling data for high-density lipoprotein cholesterol (HDL-C), from the San Antonio Family Heart Study, using a Z-transform test (Stouffer's method).
We identified 39 genes by the joint test at a conservative 1% false-discovery rate, including 9 from the significant gene-based association test and 23 whose expression was significantly correlated with HDL-C. Seven genes identified as significant in the joint test were not independently identified by either the association or expression tests.
This combined approach has increased power and leads to the direct nomination of novel candidate genes likely to be involved in the determination of HDL-C levels. Such information can then be used as justification for a more exhaustive search for functional sequence variation within the nominated genes. We anticipate that this type of analysis will improve our speed of identification of regulatory genes causally involved in disease risk.
PMCID: PMC2795996  PMID: 20018089
9.  A pathway analysis applied to Genetic Analysis Workshop 16 genome-wide rheumatoid arthritis data 
BMC Proceedings  2009;3(Suppl 7):S91.
The identification of several hundred genomic regions affecting disease risk has proven the ability of genome-wide association studies have proven their ability to identify genetic contributors to disease. Currently, single-nucleotide polymorphism (SNP) association analysis is the most widely used method of genome-wide association data, but recent research shows that multi-marker tests of association may provide greater power, especially when more than one mutation is present within a gene and the mutations are in low linkage disequilibrium with each other. Here we use a multi-marker association test based on regression to SNPs located within known genes to obtain a gene-level score of association. We then perform pathway analysis using this score as a measure of gene importance. We use two tests of pathway enrichment - a binomial test and a random set method. By utilizing publicly available gene and pathway information, we identify B cell, cytokine and inflammation response, and antigen presentation pathways as being associated with rheumatoid arthritis. These results confirm known biological mechanisms for auto-immunity disorders, of which rheumatoid arthritis is one.
PMCID: PMC2795995  PMID: 20018088
10.  Assessment of sex-specific effects in a genome-wide association study of rheumatoid arthritis 
BMC Proceedings  2009;3(Suppl 7):S90.
Rheumatoid arthritis (RA) is three times more common in females than in males, suggesting that sex may play a role in modifying genetic associations with disease. We have addressed this hypothesis by performing sex-differentiated and sex-interaction analyses of a genome-wide association study of RA in a North American population. Our results identify a number of novel associations that demonstrate strong evidence of association in both sexes combined, with no evidence of heterogeneity in risk between males and females. However, our analyses also highlight a number of associations with RA in males or females only. These signals may represent true sex-specific effects, or may reflect a lack of power to detect association in the smaller sample of males, and thus warrant further investigation.
PMCID: PMC2795994  PMID: 20018087
11.  Application of Bayesian classification with singular value decomposition method in genome-wide association studies 
BMC Proceedings  2009;3(Suppl 7):S9.
To analyze multiple single-nucleotide polymorphisms simultaneously when the number of markers is much larger than the number of studied individuals, as is the situation we have in genome-wide association studies (GWAS), we developed the iterative Bayesian variable selection method and successfully applied it to the simulated rheumatoid arthritis data provided by the Genetic Analysis Workshop 15 (GAW15). One drawback for applying our iterative Bayesian variable selection method is the relatively long running time required for evaluation of GWAS data. To improve computing speed, we recently developed a Bayesian classification with singular value decomposition (BCSVD) method. We have applied the BCSVD method here to the rheumatoid arthritis data distributed by GAW16 Problem 1 and demonstrated that the BCSVD method works well for analyzing GWAS data.
PMCID: PMC2795993  PMID: 20018086
12.  Application of three-level linear mixed-effects model incorporating gene-age interactions for association analysis of longitudinal family data 
BMC Proceedings  2009;3(Suppl 7):S89.
Longitudinal studies that collect repeated measurements on the same subjects over time have long been considered as being more powerful and providing much better information on individual changes than cross-sectional data. We propose a three-level linear mixed-effects model for testing genetic main effects and gene-age interactions with longitudinal family data. The simulated Genetic Analysis Workshop 16 Problem 3 data sets were used to evaluate the method. Genome-wide association analyses were conducted based on cross-sectional data, i.e., each of the three single-visit data sets separately, and also on the longitudinal data, i.e., using data from all three visits simultaneously. Results from the analysis of coronary artery calcification phenotype showed that the longitudinal association tests were much more powerful than those based on single-visit data only. Gene-age interactions were evaluated under the same framework for detecting genetic effects that are modulated by age.
PMCID: PMC2795992  PMID: 20018085
13.  Detecting gene-by-smoking interactions in a genome-wide association study of early-onset coronary heart disease using random forests 
BMC Proceedings  2009;3(Suppl 7):S88.
Genome-wide association studies are often limited in their ability to attain their full potential due to the sheer volume of information created. We sought to use the random forest algorithm to identify single-nucleotide polymorphisms (SNPs) that may be involved in gene-by-smoking interactions related to the early-onset of coronary heart disease.
Using data from the Framingham Heart Study, our analysis used a case-only design in which the outcome of interest was age of onset of early coronary heart disease.
Smoking status was dichotomized as ever versus never. The single SNP with the highest importance score assigned by random forests was rs2011345. This SNP was not associated with age alone in the control subjects. Using generalized estimating equations to adjust for sex and account for familial correlation, there was evidence of an interaction between rs2011345 and smoking status.
The results of this analysis suggest that random forests may be a useful tool for identifying SNPs taking part in gene-by-environment interactions in genome-wide association studies.
PMCID: PMC2795991  PMID: 20018084
14.  Longitudinal age-dependent effect on systolic blood pressure 
BMC Proceedings  2009;3(Suppl 7):S87.
Age-dependent genetic effects on susceptibility to hypertension have been documented. We present a novel variance-component method for the estimation of age-dependent genetic effects on longitudinal systolic blood pressure using 57,827 Affymetrix single-nucleotide polymorphisms (SNPs) on chromosomes 17-22 genotyped in 2,475 members of the Offspring Cohort of the Framingham Heart Study. We used the likelihood-ratio test statistic to test the main genetic effect, genotype-by-age interaction, and simultaneously, main genetic effect and genotype-by-age interactions (2 degrees of freedom (df) test) for each SNP. Applying Bonferroni correction, three SNPs were significantly associated with longitudinal blood pressure in the analysis of main genetic effects or in combined 2-df analyses. For the associations detected using the simultaneous 2-df test, neither main effects nor genotype-by-age interaction p-values reached genome-wide statistical significance. The value of the 2-df test for screening genetic interaction effects could not be established in this study.
PMCID: PMC2795990  PMID: 20018083
15.  Genetic association analysis of coronary heart disease by profiling gene-environment interaction based on latent components in longitudinal endophenotypes 
BMC Proceedings  2009;3(Suppl 7):S86.
Studies of complex diseases collect panels of disease-related traits, also known as secondary phenotypes or endophenotypes. They reflect intermediate responses to environment exposures, and as such, are likely to contain hidden information of gene-environment (G × E) interactions. The information can be extracted and used in genetic association studies via latent-components analysis. We present such a method that extracts G × E information in longitudinal data of endophenotypes, and apply the method to repeated measures of multiple phenotypes related to coronary heart disease in Genetic Analysis Workshop 16 Problem 2. The new method identified many genes, including SCNN1B (sodium channel nonvoltage-gated 1 beta) and PKP2 (plakophilin 2), with potential time-dependent G × E interactions; and several others including a novel cardiac-specific kinase gene (TNNI3K), with potential G × E interactions independent of time and marginal effects.
PMCID: PMC2795989  PMID: 20018082
16.  Assessment of gene-covariate interactions by incorporating covariates into association mapping 
BMC Proceedings  2009;3(Suppl 7):S85.
The HLA region is considered to be the main genetic risk factor for rheumatoid arthritis. Previous research demonstrated that HLA-DRB1 alleles encoding the shared epitope are specific for disease that is characterized by antibodies to cyclic citrullinated peptides (anti-CCP). In the present study, we incorporated the shared epitope and either anti-CCP antibodies or rheumatoid factor into linkage disequilibrium mapping, to assess the association between the shared epitope or antibodies with the disease gene identified. Incorporating the covariates into the association mapping provides a mechanism 1) to evaluate gene-gene and gene-environment interactions and 2) to dissect the pathways underlying disease induction/progress in quantitative antibodies.
PMCID: PMC2795988  PMID: 20018081
17.  Effects of covariates and interactions on a genome-wide association analysis of rheumatoid arthritis 
BMC Proceedings  2009;3(Suppl 7):S84.
While genetic and environmental factors and their interactions influence susceptibility to rheumatoid arthritis (RA), causative genetic variants have not been identified. The purpose of the present study was to assess the effects of covariates and genotype × sex interactions on the genome-wide association analysis (GWAA) of RA using Genetic Analysis Workshop 16 Problem 1 data and a logistic regression approach as implemented in PLINK. After accounting for the effects of population stratification, effects of covariates and genotype × sex interactions on the GWAA of RA were assessed by conducting association and interaction analyses. We found significant allelic associations, covariate, and genotype × sex interaction effects on RA. Several top single-nucleotide polymorphisms (SNPs) (~22 SNPs) showed significant associations with strong p-values (p < 1 × 10-4 - p < 1 × 10-24). Only three SNPs on chromosomes 4, 13, and 20 were significant after Bonferroni correction, and none of these three SNPs showed significant genotype × sex interactions. Of the 30 top SNPs with significant (p < 1 × 10-4 - p < 1 × 10-6) interactions, ~23 SNPs showed additive interactions and ~5 SNPs showed only dominance interactions. Those SNPs showing significant associations in the regular logistic regression failed to show significant interactions. In contrast, the SNPs that showed significant interactions failed to show significant associations in models that did not incorporate interactions. It is important to consider interactions of genotype × sex in addition to associations in a GWAA of RA. Furthermore, the association between SNPs and RA susceptibility varies significantly between men and women.
PMCID: PMC2795987  PMID: 20018080
18.  Classification tree for detection of single-nucleotide polymorphism (SNP)-by-SNP interactions related to heart disease: Framingham Heart Study 
BMC Proceedings  2009;3(Suppl 7):S83.
The aim of this study was to detect the effect of interactions between single-nucleotide polymorphisms (SNPs) on incidence of heart diseases. For this purpose, 2912 subjects with 350,160 SNPs from the Framingham Heart Study (FHS) were analyzed. PLINK was used to control quality and to select the 10,000 most significant SNPs. A classification tree algorithm, Generalized, Unbiased, Interaction Detection and Estimation (GUIDE), was employed to build a classification tree to detect SNP-by-SNP interactions for the selected 10 k SNPs. The classes generated by GUIDE were reexamined by a generalized estimating equations (GEE) model with the empirical variance after accounting for potential familial correlation. Overall, 17 classes were generated based on the splitting criteria in GUIDE. The prevalence of coronary heart disease (CHD) in class 16 (determined by SNPs rs1894035, rs7955732, rs2212596, and rs1417507) was the lowest (0.23%). Compared to class 16, all other classes except for class 288 (prevalence of 1.2%) had a significantly greater risk when analyzed using GEE model. This suggests the interactions of SNPs on these node paths are significant.
PMCID: PMC2795986  PMID: 20018079
19.  Evaluating epistatic interaction signals in complex traits using quantitative traits 
BMC Proceedings  2009;3(Suppl 7):S82.
Rheumatoid arthritis (RA) is a complex, chronic inflammatory disease implicated to have several plausible candidate loci; however, these may not account for all the genetic variations underlying RA. Common disorders are hypothesized to be highly complex with interaction among genes and other risk factors playing a major role in the disease process. This complexity is further magnified because such interactions may be with or without a strong independent effect and are thus difficult to detect using traditional statistical methodologies. The main challenge to analyze such gene × gene and gene × environment interaction is attributed to a phenomenon referred to as the "curse of dimensionality." Several combinatorial methodologies have been proposed to tackle this analytical challenge. Because quantitative traits underlie complex phenotypes and contain more information on the trait variation within genotypes than qualitative dichotomy, analyzing quantitative traits correlated with the affection status is a more powerful tool for mapping such trait genes. Recently, a generalized multifactor dimensionality reduction method was proposed that allows for adjustment for discrete and quantitative traits and can be used to analyze qualitative and quantitative phenotypes in a population based study design.
In this report, we evaluate the efficiency of the generalized multifactor dimensionality reduction statistical suite to decipher small interacting factors that contribute to RA disease pathogenesis.
PMCID: PMC2795985  PMID: 20018078
20.  Incorporating biological knowledge in the search for gene × gene interaction in genome-wide association studies 
BMC Proceedings  2009;3(Suppl 7):S81.
We sought to find significant gene × gene interaction in a genome-wide association analysis of rheumatoid arthritis (RA) by performing pair-wise tests of interaction among collections of single-nucleotide polymorphisms (SNPs) obtained by one of two methods. The first method involved screening the results of the genome-wide association analysis for main effects p-values < 1 × 10-4. The second method used biological databases such as the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes to define gene collections that each contained one of four genes with known associations with RA: PTPN22, STAT4, TRAF1, and C5. We used a permutation approach to determine whether any of these SNP sets had empirical enrichment of significant interaction effects. We found that the SNP set obtained by the first method was significantly enriched with significant interaction effects (empirical p = 0.003). Additionally, we found that the "protein complex assembly" collection of genes from the Gene Ontology collection containing the TRAF1 gene was significantly enriched with interaction effects with p-values < 1 × 10-8 (empirical p = 0.012).
PMCID: PMC2795984  PMID: 20018077
21.  Tests for candidate-gene interaction for longitudinal quantitative traits measured in a large cohort 
BMC Proceedings  2009;3(Suppl 7):S80.
For the Framingham Heart Study (FHS) and simulated FHS (FHSsim) data, we tested for gene-gene interaction in quantitative traits employing a longitudinal nonparametric association test (LNPT) and, for comparison, a survival analysis. We report results for the Offspring Cohort by LNPT analysis and on all longitudinal cohorts by survival analysis with cohort effect adjustment. We verified that type I errors were not inflated. We compared the power of both methods to detect in FHSsim data two sets of gene pairs that interact for the trait coronary artery calcification. In FHS, we tested eight gene pairs from a list of candidate genes for interaction effects on body mass index. Both methods found evidence for pairwise non-additive effects of mutations in the genes FTO, PON1, and PFKP on body mass index.
PMCID: PMC2795983  PMID: 20018076
22.  Genome-wide association study of rheumatoid arthritis by a score test based on wavelet transformation 
BMC Proceedings  2009;3(Suppl 7):S8.
We have conducted a genome-wide association study on the Genetic Analysis Workshop (GAW) 16 rheumatoid arthritis data using a multilocus score test based on wavelet transform proposed recently by the authors. The wavelet-based test automatically adjusts for the amount of noise suppressed from the data. The power of the test is also increased by using the genetic information contained in the spatial ordering of single-nucleotide polymorphisms on a chromosome.
After adjusting for the effect of population stratification, the test identified some previously discovered rheumatoid arthritis susceptibility loci (HLA-DRB1 and rs3761847) as well as some loci (rs2076530 and rs3130340) known to have association with sarcoidosis and bone mineral density. It was previously reported that patients with rheumatoid arthritis have elevated prevalence of sarcoidosis and have reduced bone mass.
This new test provides a useful tool in genome-wide association studies.
PMCID: PMC2795982  PMID: 20018075
23.  Identifying rheumatoid arthritis susceptibility genes using high-dimensional methods 
BMC Proceedings  2009;3(Suppl 7):S79.
Although several genes (including a strong effect in the human leukocyte antigen (HLA) region) and some environmental factors have been implicated to cause susceptibility to rheumatoid arthritis (RA), the etiology of the disease is not completely understood. The ability to screen the entire genome for association to complex diseases has great potential for identifying gene effects. However, the efficiency of gene detection in this situation may be improved by methods specifically designed for high-dimensional data. The aim of this study was to compare how three different statistical approaches, multifactor dimensionality reduction (MDR), random forests (RF), and an omnibus approach, worked in identifying gene effects (including gene-gene interaction) associated with RA. We developed a test set of genes based on previous linkage and association findings and tested all three methods. In the presence of the HLA shared-epitope factor, other genes showed weaker effects. All three methods detected SNPs in PTPN22 and TRAF1-C5 as being important. But we did not detect any new genes in this study. We conclude that the three high-dimensional methods are useful as an initial screening for gene associations to identify promising genes for further modeling and additional replication studies.
PMCID: PMC2795981  PMID: 20018074
24.  Identification of gene-gene interaction using principal components 
BMC Proceedings  2009;3(Suppl 7):S78.
After more than 200 genome-wide association studies, there have been some successful identifications of a single novel locus. Thus, the identification of single-nucleotide polymorphisms (SNP) with interaction effects is of interest. Using the Genetic Analysis Workshop 16 data from the North American Rheumatoid Arthritis Consortium, we propose an approach to screen for SNP-SNP interaction using a two-stage method and an approach for detecting gene-gene interactions using principal components. We selected a set of 17 rheumatoid arthritis candidate genes to assess both approaches. Our approach using principal components holds promise in detecting gene-gene interactions. However, further study is needed to evaluate the power and the feasibility for a whole genome-wide association analysis using the principal components approach.
PMCID: PMC2795980  PMID: 20018073
25.  Different models and single-nucleotide polymorphisms signal the simulated weak gene-gene interaction for a quantitative trait using haplotype-based and mixed models testing 
BMC Proceedings  2009;3(Suppl 7):S77.
Knowledge of simulated genetic effects facilitates interpretation of methodological studies. Genetic interactions for common disorders are likely numerous and weak. Using the 200 replicates of the Genetic Analysis Workshop 16 (GAW16) Problem 3 simulated data, we compared the statistical power to detect weak gene-gene interactions using a haplotype-based test in the UNPHASED software with genotypic mixed model (GMM) and additive mixed model (AMM) mixed linear regression model in SAS. We assumed a candidate-gene approach where a single-nucleotide polymorphism (SNP) in one gene is fixed and multiple SNPs are at the second gene. We analyzed the quantitative low-density lipoprotein trait (heritability 0.7%), modulated by simulated interaction of rs4648068 from 4q24 and another gene on 8p22, where we analyzed seven SNPs. We generally observed low power calculated per SNP (≤ 37% at the 0.05 level), with the haplotype-based test being inferior. Over all tests, the haplotype-based test performed within chance, while GMM and AMM had low power (~10%). The haplotype-based and mixed models detected signals at different SNPs. The haplotype-based test detected a signal in 50 unique replicates; GMM and AMM featured both shared and distinct SNPs and replicates (65 replicates shared, 41 GMM, 27 AMM). Overall, the statistical signal for the weak gene-gene interaction appears sensitive to the sample structure of the replicates. We conclude that using more than one statistical approach may increase power to detect such signals in studies with limited number of loci such as replications. There were no results significant at the conservative 10-7 genome-wide level.
PMCID: PMC2795979  PMID: 20018072

Results 1-25 (208)