Meta-analysis of genetic association studies : overview of the methodological issues and proposal of guidelines

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Schematic diagram illustrating the different steps of the VOI approach.

<p>Schematic diagram illustrating the different steps of the VOI approach.</p

Additional file 1: Table S1. of Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology

Reports that described the development of a method to quantify, or correct for measurement error (RTF 268 kb

Mendelian Randomization using Public Data from Genetic Consortia

Mendelian randomization (MR) is a technique that seeks to establish causation between an exposure and an outcome using observational data. It is an instrumental variable analysis in which genetic variants are used as the instruments. Many consortia have meta-analysed genome-wide associations between variants and specific traits and made their results publicly available. Using such data, it is possible to derive genetic risk scores for one trait and to deduce the association of that same risk score with a second trait. The properties of this approach are investigated by simulation and by evaluating the potentially causal effect of birth weight on adult glucose level. In such analyses, it is important to decide whether one is interested in the risk score based on a set of estimated regression coefficients or the score based on the true underlying coefficients. MR is primarily concerned with the latter. Methods designed for the former question will under-estimate the variance if used for MR. This variance can be corrected but it needs to be done with care to avoid introducing bias. MR based on public data sources is useful and easy to perform, but care must be taken to avoid false precision or bias

Additional file 1: Table S1. of Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology

Reports that described the development of a method to quantify, or correct for measurement error (RTF 268 kb

Bayesian analysis of censored response data in family-based genetic association studies

Biomarkers are subject to censoring whenever some measurements are not quantifiable given a laboratory detection limit. Methods for handling censoring have received less attention in genetic epidemiology, and censored data are still often replaced with a fixed value. We compared different strategies for handling a left-censored continuous biomarker in a family-based study, where the biomarker is tested for association with a genetic variant, S, adjusting for a covariate, X. Allowing different correlations between X and S, we compared simple substitution of censored observations with the detection limit followed by a linear mixed effect model (LMM), Bayesian model with noninformative priors, Tobit model with robust standard errors, the multiple imputation (MI) with and without S in the imputation followed by a LMM. Our comparison was based on real and simulated data in which 20% and 40% censoring were artificially induced. The complete data were also analyzed with a LMM. In the MICROS study, the Bayesian model gave results closer to those obtained with the complete data. In the simulations, simple substitution was always the most biased method, the Tobit approach gave the least biased estimates at all censoring levels and correlation values, the Bayesian model and both MI approaches gave slightly biased estimates but smaller root mean square errors. On the basis of these results the Bayesian approach is highly recommended for candidate gene studies; however, the computationally simpler Tobit and the MI without S are both good options for genome-wide studies

Characteristics of the DELBYA study population.

<p>Characteristics of the DELBYA study population.</p

Crude and adjusted difference in L (FVC) or % (FEV1/FVC) of South Asian British vs White participants.

<p>“Baseline” refers to adjustment for height and sex. The adjusted model includes adult height, sex, demispan, father’s occupation, birth weight, maternal educational attainment and maternal upbringing.</p

Lung function and respiratory symptoms by ethnicity.

<p>Lung function and respiratory symptoms by ethnicity.</p

Crude and adjusted difference in FVC and FEV1/FVC across categories (categorical variables) and per unit (continuous variables).

<p>Crude and adjusted difference in FVC and FEV1/FVC across categories (categorical variables) and per unit (continuous variables).</p