Multiple comparisons in studies of gene x gene and gene x environment interaction

The potential for genetically altered microglia to influence glioma treatment

Diffuse and unstoppable infiltration of brain and spinal cord tissue by neoplastic glial cells is the single most important therapeutic problem posed by the common glioma group of tumors: astrocytoma, oligoastrocytoma, oligodendroglioma, their malignant variants and glioblastoma. These neoplasms account for more than two thirds of all malignant central nervous system tumors. However, most glioma research focuses on an examination of the tumor cells rather than on host-specific, tumor micro-environmental cells and factors. This can explain why existing diffuse glioma therapies fail and why these tumors have remained incurable. Thus, there is a great need for innovation. We describe a novel strategy for the development of a more effective treatment of diffuse glioma. Our approach centers on gaining control over the behavior of the microglia, the defense cells of the CNS, which are manipulated by malignant glioma and support its growth. Armoring microglia against the influences from glioma is one of our research goals. We further discuss how microglia precursors may be genetically enhanced to track down infiltrating glioma cells.

A Tutorial on Interaction

In this tutorial, we provide a broad introduction to the topic of interaction between the effects of exposures. We discuss interaction on both additive and multiplicative scales using risks, and we discuss their relation to statistical models (e.g. linear, log-linear, and logistic models). We discuss and evaluate arguments that have been made for using additive or multiplicative scales to assess interaction. We further discuss approaches to presenting interaction analyses, different mechanistic forms of interaction, when interaction is robust to unmeasured confounding, interaction for continuous outcomes, qualitative or “crossover” interactions, methods for attributing effects to interactions, case-only estimators of interaction, and power and sample size calculations for additive and multiplicative interaction.

Tests for compositional epistasis under single interaction-parameter models

Compositional epistasis is said to be present when the effect of a genetic factor at one locus is masked by a variant at another locus. Although such compositional epistasis is not equivalent to the presence of an interaction in a statistical model, non-standard tests can sometimes be used to detect compositional epistasis. In this paper we consider empirical tests for compositional epistasis under models for the joint effect of two genetic factors which place no restrictions on the main effects of each factor but constrain the interactive effects of the two factors so as to be captured by a single parameter in the model. We describe the implications of these tests for cohort, case-control, case-only and family-based study designs and we illustrate the methods using an example of gene-gene interaction already reported in the literature.

The interleukin 1 beta (IL1B) gene is associated with failure to achieve remission and impaired emotion processing in major depression

BACKGROUND

Accumulating evidence suggests the involvement of inflammatory processes and cytokines in particular in the pathophysiology of major depression (MDD) and resistance to antidepressant treatment. Furthermore, amygdala and anterior cingulate cortex (ACC) responsiveness to emotional stimuli has been suggested as a predictor of treatment response. This study investigated the association between genetic variants of the interleukin 1 beta (IL1B) gene and amygdala and ACC responsiveness to emotional stimuli and response to antidepressant treatment.

METHODS

In this analysis, 256 Caucasian patients with MDD (145 women, 111 men) were genotyped for variants rs16944, rs1143643, and rs1143634 in the IL1B gene (2q14). Response to antidepressant treatment over 6 weeks was defined as remission (< or = 7 on the Hamilton Rating Scale for Depression-21-question) and response (>50% decrease on Hamilton Rating Scale for Depression-21-question). Brain activity under visual presentation of emotional faces was assessed in a subsample of 32 depressed patients by means of functional magnetic resonance imaging at 3 T.

RESULTS

Pharmacogenetic analyses show significant associations of the GG genotypes of single nucleotide polymorphisms (SNPs) rs16944 (odds ratio = 1.74; 95% confidence interval 1.2-4.3) and rs1143643 (odds ratio = 3.1; 95% confidence interval 1.3-7.8) (compared with the AA genotype) with nonremission after 6 weeks. The imaging analyses show that the number of G-alleles in both SNPs (rs16944 and rs1143643) was associated with reduced responsiveness of the amygdala and ACC to emotional stimulation.

CONCLUSIONS

The present study suggests a negative effect of the IL1B gene on pharmacological response and amygdala and ACC function involving the same genotypes of two SNPs (rs16944, rs116343), which taken together increase the risk of nonremission over 6 weeks of antidepressant treatment in MDD.

A Conservative Test for Multiple Comparison Based on Highly Correlated Test Statistics

In genetics, we often encounter a large number of highly correlated test statistics. The most famous conservative bound for multiple comparison is Bonferroni's bound, which is suitable when the test statistics are independent but not when the test statistics are highly correlated. This article proposes a new conservative bound that is easily calculated without multiple integration and is a good approximation when the test statistics are highly correlated. The performance of the proposed method is evaluated by simulation and real data analysis.

Genetic association analysis of functional impairment in chronic obstructive pulmonary disease

RATIONALE

Patients with severe chronic obstructive pulmonary disease (COPD) may have varying levels of disability despite similar levels of lung function. This variation may reflect different COPD subtypes, which may have different genetic predispositions.

OBJECTIVES

To identify genetic associations for COPD-related phenotypes, including measures of exercise capacity, pulmonary function, and respiratory symptoms.

METHODS

In 304 subjects from the National Emphysema Treatment Trial, we genotyped 80 markers in 22 positional and/or biologically plausible candidate genes. Regression models were used to test for association, using a test-replication approach to guard against false-positive results. For significant associations, effect estimates were recalculated using the entire cohort. Positive associations with dyspnea were confirmed in families from the Boston Early-Onset COPD Study.

RESULTS

The test-replication approach identified four genes-microsomal epoxide hydrolase (EPHX1), latent transforming growth factor-beta binding protein-4 (LTBP4), surfactant protein B (SFTPB), and transforming growth factor-beta1 (TGFB1)-that were associated with COPD-related phenotypes. In all subjects, single-nucleotide polymorphisms (SNPs) in EPHX1 (p < or = 0.03) and in LTBP4 (p < or = 0.03) were associated with maximal output on cardiopulmonary exercise testing. Markers in LTBP4 (p < or = 0.05) and SFTPB (p = 0.005) were associated with 6-min walk test distance. SNPs in EPHX1 were associated with carbon monoxide diffusing capacity (p < or = 0.04). Three SNPs in TGFB1 were associated with dyspnea (p < or = 0.002), one of which replicated in the family study (p = 0.02).

CONCLUSIONS

Polymorphisms in several genes seem to be associated with COPD-related traits other than FEV(1). These associations may identify genes in pathways important for COPD pathogenesis.

What makes a good genetic association study?

Genetic association studies are central to efforts to identify and characterise genomic variants underlying susceptibility to multifactorial disease. However, obtaining robust replication of initial association findings has proved difficult. Much of this inconsistency can be attributed to inadequacies in study design, implementation, and interpretation--inadequately powered sample groups are a major concern. Several additional factors affect the quality of any given association study, with appropriate sample-recruitment strategy, logical variant selection, minimum genotyping error, relevant data analysis, and valid interpretation all essential to generation of robust findings. Replication has a vital role in showing that associations that are identified reflect interesting biological processes rather than methodological quirks. For an unbiased view of the evidence for and against any particular association, study quality, rather than significance value, needs to play the dominant part.