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Statistical methods for testing X chromosome variant associations: application to sex-specific characteristics of bipolar disorder.

ABSTRACT: BACKGROUND:Bipolar disorder (BD) affects both sexes, but important sex differences exist with respect to its symptoms and comorbidities. For example, rapid cycling (RC) is more prevalent in females, and alcohol use disorder (AUD) is more prevalent in males. We hypothesize that X chromosome variants may be associated with sex-specific characteristics of BD. Few studies have explored the role of the X chromosome in BD, which is complicated by X chromosome inactivation (XCI). This process achieves "dosage compensation" for many X chromosome genes by silencing one of the two copies in females, and most statistical methods either ignore that XCI occurs or falsely assume that one copy is inactivated at all loci. We introduce new statistical methods that do not make these assumptions. METHODS:We investigated this hypothesis in 1001 BD patients from the Genetic Association Information Network (GAIN) and 957 BD patients from the Mayo Clinic Bipolar Disorder Biobank. We examined the association of over 14,000 X chromosome single nucleotide polymorphisms (SNPs) with sex-associated BD traits using two statistical approaches that account for whether a SNP may be undergoing or escaping XCI. In the "XCI-informed approach," we fit a sex-adjusted logistic regression model assuming additive genetic effects where we coded the SNP either assuming one copy is expressed or two copies are expressed based on prior knowledge about which regions are inactivated. In the "XCI-robust approach," we fit a logistic regression model with sex, SNP, and SNP-sex interaction effects that is flexible to whether the region is inactivated or escaping XCI. RESULTS:Using the "XCI-informed approach," which considers only the main effect of SNP and does not allow the SNP effect to differ by sex, no significant associations were identified for any of the phenotypes. Using the "XCI-robust approach," intergenic SNP rs5932307 was associated with BD (P = 8.3 × 10-8), with a stronger effect in females (odds ratio in males (ORM) = 1.13, odds ratio in females for a change of two allele copies (ORW2) = 3.86). CONCLUSION:X chromosome association studies should employ methods which account for its unique biology. Future work is needed to validate the identified associations with BD, to formally assess the performance of both approaches under different true genetic architectures, and to apply these approaches to study sex differences in other conditions.

SUBMITTER: Jons WA

PROVIDER: S-EPMC6902568 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Statistical methods for testing X chromosome variant associations: application to sex-specific characteristics of bipolar disorder.

Jons William A WA Colby Colin L CL McElroy Susan L SL Frye Mark A MA Biernacka Joanna M JM Winham Stacey J SJ

Biology of sex differences 20191209 1

<h4>Background</h4>Bipolar disorder (BD) affects both sexes, but important sex differences exist with respect to its symptoms and comorbidities. For example, rapid cycling (RC) is more prevalent in females, and alcohol use disorder (AUD) is more prevalent in males. We hypothesize that X chromosome variants may be associated with sex-specific characteristics of BD. Few studies have explored the role of the X chromosome in BD, which is complicated by X chromosome inactivation (XCI). This process a ...[more]

PMID: 31818333

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Project description:BackgroundModern biology has shifted from "one gene" approaches to methods for genomic-scale analysis like microarray technology, which allow simultaneous measurement of thousands of genes. This has created a need for tools facilitating interpretation of biological data in "batch" mode. However, such tools often leave the investigator with large volumes of apparently unorganized information. To meet this interpretation challenge, gene-set, or cluster testing has become a popular analytical tool. Many gene-set testing methods and software packages are now available, most of which use a variety of statistical tests to assess the genes in a set for biological information. However, the field is still evolving, and there is a great need for "integrated" solutions.ResultsGeneTools is a web-service providing access to a database that brings together information from a broad range of resources. The annotation data are updated weekly, guaranteeing that users get data most recently available. Data submitted by the user are stored in the database, where it can easily be updated, shared between users and exported in various formats. GeneTools provides three different tools: i) NMC Annotation Tool, which offers annotations from several databases like UniGene, Entrez Gene, SwissProt and GeneOntology, in both single- and batch search mode. ii) GO Annotator Tool, where users can add new gene ontology (GO) annotations to genes of interest. These user defined GO annotations can be used in further analysis or exported for public distribution. iii) eGOn, a tool for visualization and statistical hypothesis testing of GO category representation. As the first GO tool, eGOn supports hypothesis testing for three different situations (master-target situation, mutually exclusive target-target situation and intersecting target-target situation). An important additional function is an evidence-code filter that allows users, to select the GO annotations for the analysis.ConclusionGeneTools is the first "all in one" annotation tool, providing users with a rapid extraction of highly relevant gene annotation data for e.g. thousands of genes or clones at once. It allows a user to define and archive new GO annotations and it supports hypothesis testing related to GO category representations. GeneTools is freely available through www.genetools.no

Project description:BackgroundThere is growing interest in examining the simultaneous effects of multiple exposures and, more generally, the effects of mixtures of exposures, as part of the exposome concept (being defined as the totality of human environmental exposures from conception onwards). Uncovering such combined effects is challenging owing to the large number of exposures, several of them being highly correlated. We performed a simulation study in an exposome context to compare the performance of several statistical methods that have been proposed to detect statistical interactions.MethodsSimulations were based on an exposome including 237 exposures with a realistic correlation structure. We considered several statistical regression-based methods, including two-step Environment-Wide Association Study (EWAS2), the Deletion/Substitution/Addition (DSA) algorithm, the Least Absolute Shrinkage and Selection Operator (LASSO), Group-Lasso INTERaction-NET (GLINTERNET), a three-step method based on regression trees and finally Boosted Regression Trees (BRT). We assessed the performance of each method in terms of model size, predictive ability, sensitivity and false discovery rate.ResultsGLINTERNET and DSA had better overall performance than the other methods, with GLINTERNET having better properties in terms of selecting the true predictors (sensitivity) and of predictive ability, while DSA had a lower number of false positives. In terms of ability to capture interaction terms, GLINTERNET and DSA had again the best performances, with the same trade-off between sensitivity and false discovery proportion. When GLINTERNET and DSA failed to select an exposure truly associated with the outcome, they tended to select a highly correlated one. When interactions were not present in the data, using variable selection methods that allowed for interactions had only slight costs in performance compared to methods that only searched for main effects.ConclusionsGLINTERNET and DSA provided better performance in detecting two-way interactions, compared to other existing methods.

Project description:IntroductionSchizophrenia(SCZ) and Bipolar disorder (BD) are frequently occurring and impairing disorders that affect around 1% of the population. Important endophenotypes in the genetic research of SCZ and BD are cognitive functions. Core symptoms for SCZ and BD are impairments in working memory, declarative memory and attention, all of which fulfill the criteria for an endophenotype. The FK506 Binding Protein 5 (FKBP5) gene codes for a co-chaperone of the glucocorticoid receptor and has been reported to be associated with cognition.AimThe aims of our research were to determine the degree of cognitive impairment in patients suffering from SCZ and BD and to explore the association of the FKBP5 variant rs3800373 genotype with the cognitive endophenotypes.Material and methodsPatients and healthy controls were recruited over a period of two years from the Psychiatric Clinic, Clinical Center University of Sarajevo. Genotyping and neuropsychological assessments were performed for 263 subjects (129 SCZ, 53 BD, and 81 healthy controls [HC]). Neuropsychological assessments were performed for all patients with the Trail Making Test-A&B (TMT-A&B) and Digit-span forward&backwards tasks. The single nucleotide polymorphism (SNP) rs3800373 in the FKBP5 gene was genotyped using Infinium PsychArray Bead Chips.Results and conclusionSCZ and BD patients performed lower than HC in the TMT-A&B and in the Digit-span backwards task, while no differences were observed between SCZ and BD patients. While SCZ patients performed lower than HC in the Digit-span forwards task, there were no differences between BD and HC or between BD and SCZ. Rs 3800373 was not associated with performance in the TMT-A&B or Digit-span forwards&backwards tasks. SCZ and BD share largely overlapping neurocognitive characteristics. Rs3800373 was not associated with performance in the neuropsychological tests. However, given the limited sample size, the results do not exclude an association with the rs3800373 variant in a larger sample. Furthermore, as the analysis was limited to one SNP, the results cannot be generalized to other genetic variants in FKBP5.

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Statistical methods for testing X chromosome variant associations: application to sex-specific characteristics of bipolar disorder.

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Statistical methods for testing X chromosome variant associations: application to sex-specific characteristics of bipolar disorder.

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