Volume 1 Supplement 1
A two-stage classification approach identifies seven susceptibility genes for a simulated complex disease
© Pankratz; licensee BioMed Central Ltd. 2007
Published: 18 December 2007
The simulated data set of the Genetic Analysis Workshop 15 provided affection status, four quantitative traits, and a covariate. After studying the relationship between these variables, linkage analysis was undertaken. Analyses were performed in the first replicate only and without any prior knowledge of the underlying model. In addition to the main effect of the DR locus on chromosome 6, significant linkage was also identified on chromosomes 8, 9, 11, and 18. Notably, the power to detect linkage increased after transforming the skewed and kurtotic IgM and anti-CCP distributions. Moreover, genes on chromosome 11 could not be discerned from noise without the transformation, thus highlighting the need in real life situations for careful examination of the phenotypic data prior to genetic analysis. Significant association with one single-nucleotide polymorphism was identified for the regions on chromosome 11 and 18. Haplotype analyses were attempted for the other regions, but only the underlying variation of the DR locus could be identified. Two methods were then applied to predict classification using the factors identified so far. These methods – logistic regression and multifactor dimensionality reduction (MDR) – performed comparably for this data set. Those affected individuals that were misclassified as unaffected were then used in a genome-wide association analysis to identify additional susceptibility loci. Two additional loci were identified in this fashion, illustrating the usefulness of this two-stage classification approach.
Studies designed to identify genes contributing to complex diseases have been ongoing for many years, utilizing different study designs and methods with varied success. The simulated data set of the Genetic Analysis Workshop 15 (GAW15) provided an opportunity to evaluate different analysis techniques used to identify genes underlying complex phenotypes. I have also attempted to predict disease status given the presence of apparent "high risk" alleles (those alleles associated with disease or endophenotypes) identified in analyses, with the intent of further studying those individuals that are misclassified. Ideally, these individuals will be enriched for susceptibility genes not identified in the first round of analyses.
All analyses were performed using the simulated data set without knowledge of the underlying model, including number of genes or their location. Analyses were performed with complete genotypic and phenotypic data from Replicate 1.
The simulated data set included a number of continuous traits as well as affection status for the simulated disease. The alleles at the main susceptibility locus (DR) were also provided in the phenotype file, as was smoking history. The relationships between these variables were inferred using linear and logistic regression, and significant results were included in subsequent analyses as covariates.
First-stage genetic analysis
A genome screen was performed for affection status using the nonparametric linkage analysis methods implemented in Mapmaker/SIBS . Quantitative trait locus (QTL) analysis was also performed for age at onset, severity, IgM, and anti-CCP using both Haseman-Elston regression  and the variance components method implemented in SOLAR . Those trait distributions with unacceptable levels of kurtosis were adjusted using a natural log transformation before being analyzed.
Association analyses were performed for SNPs contained within regions with a LOD score greater than 3.0, using the pedigree disequilibrium test (PDT) . Continuous traits were analyzed with linear regression using additive models.
The provided (phased) haplotypes were used in windows increasing from one to six SNPs for each chromosomal region to determine if haplotypes were more powerful than individual SNPs at predicting any particular trait. R2 values were maximized in this way, with the understanding that a parsimonious model was preferable given comparable R2 estimates.
Prediction of phenotype
Logistic regression and multifactor dimensionality reduction (MDR)  were used to quantify the effects of the apparent high-risk alleles identified in these genetic analyses. While the MDR software is designed to classify cases and controls, logistic regression only provides a predicted value between zero and one for each individual. Therefore, a threshold for these predicted values was chosen by maximizing sensitivity and specificity and was then used to dichotomize the predicted values into cases and controls. For both methods, the dependent variable was affection status, and the number of copies for each high-risk allele was used as an independent variable. Smoking history and gender were also included where appropriate. The first affected sibling from each family was used along with an equal number of controls. The same approach using linear regression was also used for the quantitative traits.
Second-stage genetic analysis
Those affected individuals that were misclassified by the MDR algorithm were used in a genome-wide association study. PDT was used to ensure that trios from the same family would not bias the test statistic.
Affection status was significantly associated with the DR locus (OR: 25.2 for each DR_3 allele, OR: 3.0 for each DR_2 allele), gender (OR: 2.8 for females), and smoking history (OR: 2.5 for previous smoker). The overall Cox & Snell R2 for the model was 0.515, with a majority of the influence coming exclusively from the DR locus (Cox & Snell R2 = 0.493).
IgM was significantly associated with smoking and, to a lesser extent, with gender, but not with the DR alleles. Anti-CCP was only significantly associated with the DR_3 allele. Severity was not associated with any covariate. Age at onset was significantly associated with the DR_3 allele, smoking history, and female gender.
Genome screen results (LOD scores) for replicate 1
Chromosome [Location (cM)]
Trait (any covariates included)
6 (49 cM)
8 (169 cM)
9 (50 cM)
11 (115 cM)
18 (94 cM)
AgeAtOnset (including gender)
Anti-CCP† (including gender)
Anti-CCP† (including gender and DR alleles)
Anti-CCP transformed (including covariates)
IgM†(including gender and smoking history)
IgM transformed (including covariates)
PDT analysis of SNP data from each of the affected families yielded significance on chromosome 6 near the DR locus, as well as on chromosomes 11 (SNP11_387 and SNP11_389; p = 10-6 and p = 6 × 10-16) and on chromosome 18 (SNP18_269; 5 × 10-6). A Bonferonni correction for 8723 tests yields an alpha of 5 × 10-6. All other SNPs had p-values below this threshold and were therefore not significant.
Linear regression identified several SNPs that contributed to the variation in the quantitative traits. Those SNPs were then used to form haplotypes of varying sizes in an attempt to characterize their influence. SNP11_389 alone captured nearly 50% of the variance within the transformed distribution of the IgM trait, after accounting for the effects of gender and smoking, a result that did not improve with a larger haplotype. This SNP was also a significant predictor of affection status (p = 10-32).
The deleterious haplotype for the region on chromosome 18 was not obvious; however, the haplotypes resulting from SNP18_268, SNP18_269, and SNP18_270 did the best at predicting anti-CCP levels (p = 10-16). SNP18_269 by itself, however, was best at predicting affection status (p = 2 × 10-8).
Determining the specific deleterious haplotype for the region on chromosome 9 was not straightforward. The best four SNPs appear to be SNP9_185, SNP9_186, SNP9_189, and SNP9_190. When predicting severity, the R2 value was fairly low (R2 = 0.040; p = 5 × 10-9) given the size of this haplotype. The selected SNPs were not at all associated with affection status (p = 0.391). Similarly, no association with either severity or affection status could be identified for any of the SNPs under the chromosome 8 linkage peak.
Prediction of phenotype
MDR testing accuracy
Before undertaking genetic analyses
Number of DR 3 alleles
Number of DR 3 alleles, sex, smoked
Using results from first round of genetic analyses
Number of DR 3 alleles, sex, smoked, SNP11_389, SNP18_269
Using results from second round of genetic analyses
Number of DR 3 alleles, sex, SNP6_154, SNP6_162
+Smoked, SNP11_389, SNP18_269
Using SNPs/SNP haplotypes nearest the underlying loci as described in the "answers"
The SNPs nearest loci A-F, sex, smoking, DR genotype
Using the underlying loci provided in the "answers"
Number of deleterious alleles at loci A-F, sex, smoking, DR
Second-stage genetic analysis
Two loci, SNP6_154 (p = 5.2 × 10-9) and SNP6_162 (p = 1.6 × 10-6), exceeded a Bonferroni correction for 9187 tests (α = 5.6 × 10-6).
The level of kurtosis in the IgM trait was an unacceptable 33.4, but transforming the trait distribution not only brought the kurtosis (and therefore the false positive rate in SOLAR) under control, but it increased its power to detect linkage as well. Moreover, locus F on chromosome 11 could not be discerned from noise without this transformation. While the kurtosis of anti-CCP started off at -1.4, its power to detect linkage was also improved through transformation.
Incorporating information gained from linkage analysis and candidate gene association studies can create a subsample that is enriched for previously unidentified genes and thereby improves power to detect these additional loci in a whole-genome association analysis. Two loci (C and D) that were previously "over shadowed" by the DR locus were identified by factoring out information gained in the first round of analyses. While this study used linkage and candidate gene information, the source of prior information could also have come from another source, such as a previous whole-genome association (particularly one using a different type of sample, for instance cases and controls instead of trios). Similarly, other classification methods could be use instead of regression or MDR.
For these data, MDR did not outperform logistic regression. This is most likely due to the underlying simulation parameters. Since the only gene × gene or gene × environment interactions modelled were impossible to detect (r2 = 0.0 for all markers surrounding Locus A and Locus B), there were no high order interactions for MDR to identify – the software's raison d'etre. Similarly, the simulation parameters assumed the common disease, common gene hypothesis in which the same high-risk allele either arose several times independently or arose so long ago that the background haplotype was almost completely degraded. Both situations would mean that haplotype methods, particularly for the marker density provided, could not provide more information than the analysis of single SNPs independently.
An additional consequence of the underlying simulation parameters resulted from its design to mimic an existing rheumatoid arthritis data set, which appears to contain a DRB1 allele with a large effect. In the simulated data set, this large effect realistically masked the signal of two smaller genes that were rendered undetectable if the effect of the larger gene was not taken into account. The two-stage classification approach used in this paper is one way to address this issue. A strategy such as this may prove to be equally successful in a real data set.
This research was supported by NIH grant R01 NS-37167 (NP).
This article has been published as part of BMC Proceedings Volume 1 Supplement 1, 2007: Genetic Analysis Workshop 15: Gene Expression Analysis and Approaches to Detecting Multiple Functional Loci. The full contents of the supplement are available online at http://www.biomedcentral.com/1753-6561/1?issue=S1.
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