Biology subjects

Hugues Aschard

Publications and source records attributed to Hugues Aschard.

3 recordsLinked to original sources

bioRxiv2016

Playing Musical Chairs in Big Data to Reveal Variables Associations

Testing for associations in big data faces the problem of multiple comparisons, with true signals buried inside the noise of all associations queried. This is particularly true in genetic association studies where a substantial proportion of the variation of human phenotypes is driven by numerous genetic variants of small effect. The current strategy to improve power to identify these weak associations consists of applying standard marginal statistical approaches and increasing study sample sizes. While successful, this approach does not leverage the environmental and genetic factors shared between the multiple phenotypes collected in contemporary cohorts. Here we develop a method that improves the power of detecting associations when a large number of correlated variables have been measured on the same samples. Our analyses over real and simulated data provide direct support that large sets of correlated variables can be leveraged to achieve dramatic increases in statistical power equivalent to a two or even three folds increase in sample size.

Genetics↗

bioRxiv2016

An Ancestry Based Approach for Detecting Interactions

IBackgroundEpistasis and gene-environment interactions are known to contribute significantly to variation of complex phenotypes in model organisms. However, their identification in human association studies remains challenging for myriad reasons. In the case of epistatic interactions, the large number of potential interacting sets of genes presents computational, multiple hypothesis correction, and other statistical power issues. In the case of gene-environment interactions, the lack of consistently measured environmental covariates in most disease studies precludes searching for interactions and creates difficulties for replicating studies.\n\nResultsIn this work, we develop a new statistical approach to address these issues that leverages genetic ancestry in admixed populations. We applied our method to gene expression and methylation data from African American and Latino admixed individuals respectively, identifying nine interactions that were significant at p < 5x10-8, we show that two of the interactions in methylation data replicate, and the remaining six are significantly enriched for low p-values (p < 1.8x10-6).\n\nConclusionWe show that genetic ancestry can be a useful proxy for unknown and unmeasured covariates in the search for interaction effects. These results have important implications for our understanding of the genetic architecture of complex traits.

Genetics↗

bioRxiv2015

A Perspective on Interaction Tests in Genetic Association Studies

The identification of gene-gene and gene-environment interaction in human traits and diseases is an active area of research that generates high expectation, and most often lead to high disappointment. This is partly explained by a misunderstanding of some of the inherent characteristics of interaction effects. Here, I untangle several theoretical aspects of standard regression-based interaction tests in genetic association studies. In particular, I discuss variables coding scheme, interpretation of effect estimate, power, and estimation of variance explained in regard of various hypothetical interaction patterns. I show first that the simplest biological interaction models--in which the magnitude of a genetic effect depends on a common exposure--are among the most difficult to identify. Then, I demonstrate the demerits of the current strategy to evaluate the contribution of interaction effects to the variance of quantitative outcomes and argue for the use of new approaches to overcome these issues. Finally I explore the advantages and limitations of multivariate models when testing for interaction between multiple SNPs and/or multiple exposures, using either a joint test, or a test of interaction based on risk score. Theoretical and simulated examples presented along the manuscript demonstrate that the application of these methods can provide a new perspective on the role of interaction in multifactorial traits.

Genetics↗