Correcting Systematic Inflation in Genetic Association Tests That Consider Interaction Effects

Almli, Lynn M.; Duncan, Richard; Feng, Hao; Ghosh, Debashis; Binder, Elisabeth B.; Bradley, Bekh; Ressler, Kerry J.; Conneely, Karen N.; Epstein, Michael P.

doi:10.1001/jamapsychiatry.2014.1339

Cited by 43 publications

(40 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This demonstrates that fundamental approaches to G × E are maturing, and the development of genome-wide approaches to G × E will help us evaluate the significance of current findings in a more rigorous, unbiased manner. Recent nonlinear statistical approaches to whole-genome G × E and GWAS analytic methods are making progress (Almli et al 2014). Issues of power and sample size require consortia efforts, and we should pay attention to the standardization of environmental measures; however, advances in fundamental statistical methodologies should be applied to all future studies and reanalyses of past studies.…”

Section: An Overview Of Genetic Association Studiesmentioning

confidence: 99%

Gene × Environment Determinants of Stress- and Anxiety-Related Disorders

Sharma

Powers

Bradley

et al. 2016

Annu. Rev. Psychol.

Self Cite

112

View full text Add to dashboard Cite

The burgeoning field of gene-by-environment (G × E) interactions has revealed fascinating biological insights, particularly in the realm of stress-, anxiety-, and depression-related disorders. In this review we present an integrated view of the study of G × E interactions in stress and anxiety disorders, including the evolution of genetic association studies from genetic epidemiology to contemporary large-scale genome-wide association studies and G × E studies. We convey the importance of consortia efforts and collaboration to gain the large sample sizes needed to move the field forward. Finally, we discuss several robust and well-reproduced G × E interactions and demonstrate how epidemiological identification of G × E interactions has naturally led to a plethora of basic research elucidating the mechanisms of high-impact genetic variants.

show abstract

Section: An Overview Of Genetic Association Studiesmentioning

confidence: 99%

Gene × Environment Determinants of Stress- and Anxiety-Related Disorders

Sharma

Powers

Bradley

et al. 2016

Annu. Rev. Psychol.

Self Cite

112

View full text Add to dashboard Cite

show abstract

“…Almli et al (2014) studied GxE interaction models in a post-traumatic stress disorder dataset and reported that the presence of heteroscedasticity was invalidating their inference. The authors found that the residual variance was a function of the environment, which led a QQ-plot to show heavily inflated p-values when performing genome-wide interaction testing with the standard GxE model.…”

Section: Exposure Misspecification In Gxe Inferencementioning

confidence: 99%

“…Therefore the exposure effects can be misspecified, resulting in invalid model-based inference, as presented in the example of Cornelis et al (2012). Environment misspecification may also cause the appearance of heteroscedasticity with respect to the exposure, which can similarly invalidate inference (Almli et al, 2014). …”

Section: Introductionmentioning

confidence: 99%

“…These diagnostics suggest misspecification of the GxE mean model, as described and explained in detail by Voorman et al (2011). Surprisingly, GxE inference which utilizes the Huber-White ’sandwich’ variance estimator, a commonly-proposed remedy for incorrect inference in GWEIS (Voorman et al, 2011; Tchetgen and Kraft, 2011; Almli et al, 2014) often produces inflated p-values that show a larger departure from uniformity than p-values calculated from model-based standard errors. Again, see Figure 3 for an example.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Testing for Gene–Environment Interaction under Exposure Misspecification

Sun

Carroll

Christiani³

et al. 2017

Biometrics

View full text Add to dashboard Cite

Summary Complex interplay between genetic and environmental factors characterizes the etiology of many diseases. Modeling gene-environment (GxE) interactions is often challenged by the unknown functional form of the environment term in the true data-generating mechanism. We study the impact of misspecification of the environmental exposure effect on inference for the GxE interaction term in linear and logistic regression models. We first examine the asymptotic bias of the GxE interaction regression coefficient, allowing for confounders as well as arbitrary misspecification of the exposure and confounder effects. For linear regression, we show that under gene-environment independence and some confounder-dependent conditions, when the environment effect is misspecified, the regression coefficient of the GxE interaction can be unbiased. However, inference on the GxE interaction is still often incorrect. In logistic regression, we show that the regression coefficient is generally biased if the genetic factor is associated with the outcome directly or indirectly. Further we show that the standard robust sandwich variance estimator for the GxE interaction does not perform well in practical GxE studies, and we provide an alternative testing procedure that has better finite sample properties.

show abstract

“…First, although PTSD clearly results from the interaction of trauma with genetic predisposition, it is unclear whether or not the biological realities of such an interaction are captured by testing deviations from a multiplicative logistic regression model (Thompson, 1991). Second, the significance of the GxE interaction term estimated using standard regression models can be inflated under commonly occurring conditions (Almli et al, 2014a;Voorman et al, 2011). Third, obtaining reasonable power in GxE analysis takes sample sizes larger than those required for main effect analyses.…”

Section: Gxe Analysesmentioning

confidence: 99%

The Psychiatric Genomics Consortium Posttraumatic Stress Disorder Workgroup: Posttraumatic Stress Disorder Enters the Age of Large-Scale Genomic Collaboration

Logue

Amstadter

Baker

et al. 2015

Neuropsychopharmacol

119

101

View full text Add to dashboard Cite

The development of posttraumatic stress disorder (PTSD) is influenced by genetic factors. Although there have been some replicated candidates, the identification of risk variants for PTSD has lagged behind genetic research of other psychiatric disorders such as schizophrenia, autism, and bipolar disorder. Psychiatric genetics has moved beyond examination of specific candidate genes in favor of the genome-wide association study (GWAS) strategy of very large numbers of samples, which allows for the discovery of previously unsuspected genes and molecular pathways. The successes of genetic studies of schizophrenia and bipolar disorder have been aided by the formation of a large-scale GWAS consortium: the Psychiatric Genomics Consortium (PGC). In contrast, only a handful of GWAS of PTSD have appeared in the literature to date. Here we describe the formation of a group dedicated to large-scale study of PTSD genetics: the PGC-PTSD. The PGC-PTSD faces challenges related to the contingency on trauma exposure and the large degree of ancestral genetic diversity within and across participating studies. Using the PGC analysis pipeline supplemented by analyses tailored to address these challenges, we anticipate that our first large-scale GWAS of PTSD will comprise over 10 000 cases and 30 000 trauma-exposed controls. Following in the footsteps of our PGC forerunners, this collaboration-of a scope that is unprecedented in the field of traumatic stress-will lead the search for replicable genetic associations and new insights into the biological underpinnings of PTSD.

show abstract

Correcting Systematic Inflation in Genetic Association Tests That Consider Interaction Effects

Cited by 43 publications

References 51 publications

Gene × Environment Determinants of Stress- and Anxiety-Related Disorders

Gene × Environment Determinants of Stress- and Anxiety-Related Disorders

Testing for Gene–Environment Interaction under Exposure Misspecification

The Psychiatric Genomics Consortium Posttraumatic Stress Disorder Workgroup: Posttraumatic Stress Disorder Enters the Age of Large-Scale Genomic Collaboration

Contact Info

Product

Resources

About