Purpose: Idiopathic intracranial hypertension (IIH) is a condition characterized by chronically increased

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1 1 Abstract Purpose: Idiopathic intracranial hypertension (IIH) is a condition characterized by chronically increased intracranial pressure without clinical evidence for space occupying lesions or hydrocephalus. Increased pressure leads to papilledema, which may lead to vision loss. Although there appear to be a variety of associated risk factors for the disease, further research is needed to identify genetic loci associated with the condition. This genome-wide association study (GWAS) was performed to further understand this disease. Methods: We analyzed 95 case subjects with IIH and 95 controls matched by sex, BMI, self-reported ethnicity, and distance to procurement site. The samples were genotyped using Illumina s Infinium HumanCoreExome v1-0 array which examined 538,448 SNPs. Data were analyzed using a generalized linear mixed model that controlled for population stratification by multidimensional scaling. Results: No SNPs were found to be significantly associated with IIH after adjusting for multiple testing using the Benjamini-Hochberg Procedure to control for false discovery rate at Quality control analysis uncovered a problem with the fourth batch of the data, resulting in an underpowered study. Conclusion: Further analysis is required to make a definitive statement of the genetic involvement to IIH. This paper exemplifies the impact of quality control analysis in GWAS. 2 Introduction Idiopathic intracranial hypertension (IIH, a.ka. psuedotumor cerebri) is a syndrome of headache and papilledema (optic disc swelling) without focal neurological signs and in the presence of normal cerebrospinal fluid. Other classic symptoms of IIH include visual disturbance, vision loss, and pulsatile tinnitus. These symptoms all occur due to the intracranial hypertension and its effect on cranial nerves. Because of pressure on the optic nerve (papilledema), 86% have visual loss and 10% develop severe visual loss [9]. A standard criterion (Dandy Criterion, Table 1) has been formulated and a diagnosis of IIH is only made once all other conditions that cause similar intracranial hypertension such as tumors, obstructive hydrocephalus, and venous sinus obstruction have been ruled out [4]. The seriousness of this syndrome merits performing studies concerning the risk factors and conditions associated with IIH Pregnant and obese women have been found to have a higher risk for developing IIH compared to the general population. The incidence of IIH is 0.9/100,00 persons, but 19/100,000 in women who weigh more than 20% above their ideal weight [2]. The underlying cause for this is not well understood. Furthermore, there is a possibility that studies performed to determine associated conditions with IIH resulted in inaccurate results due to the fact that the true cause of the symptoms originally identified as IIH were actually associated 1

2 with more common medical conditions [9]. Many of these studies did not identify IIH with the now standard Dandy criteria for IIH diagnosis. There are a series of conditions, however, that are likely to be associated with IIH. Conditions likely associated with IIH include those that decrease the flow of CSF through arachnoid granulations, obstructions to venous drainage, and certain endocrine disorders. Despite these associated conditions and their biological plausibility, no concrete genetic component of IIH has been determined. The lack of consistent data in this regard merits performing a genome wide association study (GWAS) to identify loci associated with IIH. After identification of these loci, it would be possible to perform further studies on these individual genetic elements and determine how their pathology contributes to IIH. In collaboration with the Neuro-Ophthalmology Research Disease Investigator Consortium (NORDIC), Idiopathic Intracranial Hypertension Treatment Trial (IIHT), we conducted a study of the genetics underlying IIH to gain a greater understanding of its pathogenesis. NORDIC is an extensive group of neuroophthalmologists practicing throughout the United States and Canada who have developed a structured organization to perform NIH funded prospective clinical research trials. This GWAS represents the first of its kind to explore the etiology and pathogenesis of IIH. 3 Methods 3.1 Data Collection Blood samples from 95 cases of IIH and 95 controls were obtained from research centers across the United States and Canada. IIH case status was determined by the modified Dandy criteria [4]. Controls were matched by sex, BMI, self-reported ethnicity, and distance to procurement site. The samples were genotyped using Illumina s Infinium HumanCoreExome v1-0 array which interrogated 538,448 markers representing diverse populations and a range of common conditions, with a focus on those located within the coding regions of the genome. Samples were genotyped in four batches, or plates. Shaun Purcell s PLINK [6], a free, open-source whole genome association analysis toolset was used for data management and basic analysis. 3.2 Quality Control No samples had more than 10% missing genotype data, so all samples were included in this study. Single Nucleotide Polymorphisms (SNPs) with minor allele frequency (MAF) below 1%, or genotyping rate below 90% were excluded as recommended by Turner et. al because power to detect a signal for a rare SNP is extremely low and SNP assays that fail on a large number of samples are poor assays, and are likely to result 2

3 in spurious data. In total, 2184 SNPs were excluded due to low genotyping rate, 235,252 were excluded due to low MAF. The final analysis used 301,908 SNPs. Spurious results arise not only from poorly genotyped data. Population stratification in data occurs when differences in allele frequencies of SNPs are not caused by the trait of interest, but rather by ancestral genetic differences between cases and controls [8]. Such a phenomenon may lead to spurious associations in GWAS. To control for population stratification we could incorporate self-reported ethnicity into our model. However, this variable is not always reliable or accurate. Since we have genetic data we could use it to more accurately identify ethnicity. Incorporating all half a million markers into our model would saturate it however (such a model would simply be interpolating our data). We resort to dimension reduction techniques. With PLINK, we calculate the leading Multidimensional Scaling (MDS) vectors using a matrix of pairwise distances between the subjects. Part of a class of dimension reduction techniques, MDS attempts to visualize high-dimensional data in lower dimensions. MDS is similar to Principal Component Analysis (PCA). In fact when using the Euclidean distance, classical metric MDS is the same as PCA. PLINK however uses linkage agglomerate clustering, based on identity-by-state (IBS) distances (see figure 1), to calculate the pairwise distance matrix. In general, both approaches allow for patterns to be recovered from high-dimensional data (number of SNPs) by considering the relationship between variables in the observed data. Including the leading MDS vectors with highest variance in a logistic regression will allow us to control for population stratification. 3.3 Statistical Model To assess the association between SNPs and IIH we used a generalized linear mixed model (assuming a Bernoulli response distribution and a logit link function) to model the log odds of disease given the number of minor alleles at a particular SNP. We fit a separate model for each SNP. Let Y i,j be the case status (1 for case, 0 for control) of the jth sample in the ith pair where i = 0,..., 94 and j = 0, 1. Let X i,j be the number of minor alleles of the SNP of the jth sample in the ith pair. Our model is { } πi,j log = β 0 + β 1 X i,j + β C C + β P P + γ i,j 1 π i,j where π i,j = Pr(Y i,j = 1 X) is the probability of disease, C is the matrix of MDS vectors, P is a vector of indicator variables corresponding to the plate that the jth sample was processed on, and γ i,j N(0, σ 2 γ). This model allows for random intercepts, or intercepts that are different for each pair. We incorporate this random intercept to control for the matching that is present in the data by separating out between matched subject variance and within matched subject variance. We use the R package lme4 to estimate the 3

4 model parameters. This package uses adaptive Gauss-Hermite quadrature to obtain estimates. Significantly associated SNPs are determined by a hypothesis test of β 1 = 0. Finally, to control for multiple testing, we adjust our p-values using the Benjamini-Hochberg procedure as implemented in the R function p.adjust[10]. Using this method we are able to control the false discovery rate. 4 Results Unfortunately, no SNPs were found to be significantly associated with the disease. Figure 1 shows the Manhattan plot of the p-values without the tell-tale skycraper peaks that would be indicative of significant findings. There are several key aspects of this study that contributed to this outcome. This study was particularly underpowered. Spencer et. al. in a broad overview of sample size, power, and choice of genotyping chip, illustrates that a GWAS with an effect size of 2 (relative risk per allele) would require at least 500 subjects to obtain 80% power [7]. Using Skol et. al. s power calculator for GWAS, the genotype relative risk of a SNP would have to be greater than 4.7 for our study to have a power larger than 80% [1]. Figure 2 shows another aggravating factor contributing to the lack of power. The plot illustrates a major difference between plates one through three and plate four. This discrepancy together with the fact that the samples were not randomly distributed among the four plates (plate four contained only controls) contributed to the lack of power in this study. 5 Discussion 5.1 Appropriateness of Wald Test The lme4 package uses the Wald Z-test by default for tests of single parameters. This is a traditional test in analysis of GLMs, and a convenient test statistic to calculate. However, this test statistic is only an asymptotic approximation, assuming that the sampling distributions of the parameters are multivariate normal and that the sampling distribution of the log-likelihood is proportional to a χ 2 distribution. The first assumption is a difficult assumption to accept without empirical evidence. In GLMs the failure of this assumption has been referred to as the Hauck-Donner effect [3], resulting in test statistics that tend to 0 even when the model parameters are highly significant. Instead of the Wald Z-test, authors have suggested using either MCMC methods or parametric bootstrap to obtain valid P-values. The computational time required to obtain P-values, using these methods for all of our SNPs, is too great. Additionally, MCMC methods involve additional diagnostics which can be difficult to check for many tests. We defer to the Wald Z-test to obtain our p-values, and hope that the tests are robust to deviations from the normality assumption. 4

5 5.2 Conclusion This study does highlight the impact of quality control measures on GWAS data. The high variability in gene expression readings between different batches of data encumbered an already marginally powered study. As an exploratory study of the underlying genetics of IIH, this study sought to give guidance to future experiments and provide potential leads to genetic associations that could be further explored. The etiology of idiopathic intracranial hypertension appears to be difficult to pinpoint; Wall lists several purported associations to IIH (i.e. irregular menses, oral contraceptives, multivitamins, corticosteriods, antibiotics) all of which have been found to be chance associations [9]. Our study does not shed any more light on this elusive disease. Further analysis is required to make a definitive statement of the genetic involvement to IIH. 5

6 6 Appendix Modified Dandy Criterion 1. Symptoms of raised intracranial pressure 2. No localizing signs with the exception of abducens (sixth) nerve palsy 3. The patient is awake and alert 4. Normal CT/MRI findings without evidence of thrombosis 5. LP opening pressure of > 25 cm H 2 O and normal composition of CSF 6. No other explanation for the raised intracranial pressure Table 1: Criteria necessary to classify patient as having IIH. Figure 1: An illustration of identity-by-state, the metric used to measure distances between SNPs in MDS. In this genogram, allele A2 in the first daughter has been inherited from the mother, allele A2 in the second daughter has been inherited from the father. The two alleles are said to be identical by state because they are the same allele, irregardless of the inheritance pattern. Contrast this to identical by descent which requires the alleles to have the same inheritance pattern. Adapted from [5] Figure 2: Manhattan Plot of the log 10 p-values from the logistic regression analysis. 6

7 SNPs Plate 1 Plate 2 Plate 3 Plate Proportion Missing Figure 3: Genotyping rate plotted by plate 7

8 References [1] Skol AD, Scott LJ, Abecasis GR, and Boehnke M. Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nature Genetics, 38: , [2] Binder DK, Horton JC, Lawton MT, and McDermott MW. Idiopathic intracranial hypertension. Neurosurgery, 54(3): , [3] Walter W Hauck Jr. and Allan Donner. Wald s test as applied to hypotheses in logit analysis. Journal of the American Statistical Association, 72(360a): [4] Digre KB and Corbett JJ. Idiopathic intracranial hypertension (pseudotumor cerebri): a reappraisal. Neurologist, 7:2 67, [5] Helen S. Kok, Kristel M. van Asselt, Yvonne T.van der Schouw, Petra H.M. Peeters, and Cisca Wijmenga. Genetic studies to identify genes underlying menopausal age. Human Reproduction Update, 11(5): , [6] Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ, and Sham PC. Plink: a toolset for whole-genome association and populationbased linkage analysis. American Journal of Human Genetics, 81, [7] Chris C. A. Spencer, Zhan Su, Peter Donnelly, and Jonathan Marchini. Designing genome-wide association studies: Sample size, power, imputation, and the choice of genotyping chip. PLOS Genetics, 5(5), [8] Chao Tian, Peter K. Gregersen, and Michael F. Seldin. Accounting for ancestry: population substructure and genome-wide association studies. Human Molecular Genetics, 17(R2):R143 R150. [9] Michael Wall. Ideopathic intracranial hypertension. Neurologic Clinics, 28(3): , [10] Benjamini Yoav and Hochberg Yosef. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B, 57(1):