Robustness of meta-analyses in finding gene × environment interactions

<div><p>Meta-analyses that synthesize statistical evidence across studies have become important analytical tools for genetic studies. Inspired by the success of genome-wide association studies of the genetic main effect, researchers are searching for gene × environment interactions. Confounders are routinely included in the genome-wide gene × environment interaction analysis as covariates; however, this does not control for any confounding effects on the results if covariate × environment interactions are present. We carried out simulation studies to evaluate the robustness to the covariate × environment confounder for meta-regression and joint meta-analysis, which are two commonly used meta-analysis methods for testing the gene × environment interaction or the genetic main effect and interaction jointly. Here we show that meta-regression is robust to the covariate × environment confounder while joint meta-analysis is subject to the confounding effect with inflated type I error rates. Given vast sample sizes employed in genome-wide gene × environment interaction studies, non-significant covariate × environment interactions at the study level could substantially elevate the type I error rate at the consortium level. When covariate × environment confounders are present, type I errors can be controlled in joint meta-analysis by including the covariate × environment terms in the analysis at the study level. Alternatively, meta-regression can be applied, which is robust to potential covariate × environment confounders.</p></div

Tests of interaction with controlling for the C×E confounder, <i>f</i>₁ = 0.3, <i>f</i>₂ = 0.1.

<p>MR_I: MR test of interaction; MR_G&I: MR joint test of the genetic main effect and interaction; JMA_I: JMA test of interaction; JMA_G&I: JMA joint test of the genetic main effect and interaction; MEGA_I: mega-analysis test of interaction; and MEGA_G&I: mega-analysis joint test of the genetic main effect and interaction.</p

Tests of interaction without controlling for the C×E confounder, <i>f</i>₁ = 0.3, <i>f</i>₂ = 0.1; confounder is present in half of the studies.

<p>MR_I: MR test of interaction; MR_G&I: MR joint test of the genetic main effect and interaction; JMA_I: JMA test of interaction; JMA_G&I: JMA joint test of the genetic main effect and interaction; MEGA_I: mega-analysis test of interaction; and MEGA_G&I: mega-analysis joint test of the genetic main effect and interaction.</p

Statistical power of tests of interaction, .

<p>MR5_I: MR test of interaction with 5 strata within each study; MR5_G&I: MR joint test of the genetic main effect and interaction with 5 strata within each study; MR10_I: MR test of interaction with 10 strata within each study; MR10_G&I: MR joint test of the genetic main effect and interaction with 10 strata within each study; JMA_I: JMA test of interaction; JMA_G&I: JMA joint test of the genetic main effect and interaction; and MEGA_I: mega-analysis test of interaction; and MEGA_G&I: mega-analysis joint test of the genetic main effect and interaction.</p

Tests of interaction without controlling for the C×E confounder, <i>f</i>₁ = <i>f</i>₂ = 0.3.

<p>MR_I: MR test of interaction; MR_G&I: MR joint test of the genetic main effect and interaction; JMA_I: JMA test of interaction; JMA_G&I: JMA joint test of the genetic main effect and interaction; MEGA_I: mega-analysis test of interaction; and MEGA_G&I: mega-analysis joint test of the genetic main effect and interaction.</p

Tests of genetic main effect without controlling for the C×E confounder, <i>f</i>₁ = 0.3, <i>f</i>₂ = 0.1.

<p>MR_G: MR test of the genetic main effect; IV_G: inverse variance test of the genetic main effect; and MEGA_G: mega-analysis test of the genetic main effect.</p

Tests of interaction without controlling for the C×E confounder, <i>f</i>₁ = 0.3, <i>f</i>₂ = 0.1.

<p>MR_I: MR test of interaction; MR_G&I: MR joint test of the genetic main effect and interaction; JMA_I: JMA test of interaction; JMA_G&I: JMA joint test of the genetic main effect and interaction; MEGA_I: mega-analysis test of interaction; and MEGA_G&I: mega-analysis joint test of the genetic main effect and interaction.</p

Simple Model for Predicting the Cutting Temperature between Light and Heavy Fractions in Fluid Catalytic Cracking Naphtha Selective Hydrodesulfurization Processes

In selective hydrodesulfurization processes for hydro-upgrading fluid catalytic cracking (FCC) naphtha with high olefin and sulfur contents, it is a common practice to split the feeding full-range FCC naphtha into a light fraction and a heavy fraction. This operation can effectively alleviate olefin saturation and thereby octane loss. Thus, the determination of a suitable cutting temperature plays a vital role in guaranteeing the success of the operation. Starting by fractionating two FCC naphthas into nine narrow cuts, this paper shows that, despite the great differences in the properties of the two FCC naphthas, both hydrocarbons and sulfides have almost the same distributions in the nine cuts. More importantly, it was observed that the distribution of sulfides in the narrow cuts is irrelevant to their true boiling points because of the formation of azeotropes between sulfides and hydrocarbons. On the basis of these findings, a simple model for estimating thiophene content in light FCC naphtha and, thereby, determining the cutting temperature was deduced and its applicability was verified using three other FCC naphthas sampled from different refineries. The salient feature of the model lies in that it only uses the total thiophene content of the feeding FCC naphtha to perform the estimation without the necessity to carry out time-consuming and cost-expensive pre-hydrogenation and fractionation tests. Thus, it can provide in-prior estimation for the design and operation optimization of FCC naphtha hydro-upgrading processes

Fabrication of Antireflective Compound Eyes by Imprinting

In this article, we demonstrate a simple and cost-effective approach to fabricate antireflective polymer coatings. The antireflective surfaces have 3D structures that mimick moth compound eyes. The fabrication is easily performed via a one-step imprinting process. The 3D arrays exhibit better antireflective performance than 2D arrays over most wavelengths from 400 to 2400 nm. The reflectivity of the 3D arrays is lower than 5.7% over the all of the wavelengths, and the minimum reflectivity is 0.27% at a wavelength of around 1000 nm

Supplement Number 1

Figures of XRD, resistivity, current induced switching sequence and spin Hall conductivit