AUEB SEMINARS - 22/10/2014: A Retrospective Likelihood Approach for Efficient Integration of Multiple Omics Factors in Case-Control Association Studies

Η ομιλία θα γίνει την Τετάρτη 22 Οκτωβρίου στις 15.00 αντί για τις 13.00 λόγω απεργίας στα αεροδρόμια της Γερμανίας από όπου έρχεται η ομιλήτρια.

Brunilda Balliu
Department of Medical Statistics,
Leiden University Medical Centre


A Retrospective Likelihood Approach for Efficient Integration of Multiple Omics Factors in Case-Control Association Studies


Wednesday 22/10/2014
13:00 – 14:00
15:00-16:00

ROOM 607, 6th FLOOR,
POSTGRADUATE STUDIES BUILDING
(EVELPIDON & LEFKADOS)


ABSTRACT

Integrative omics, the joint analysis of outcome and multiple types of omics data, such as genomics, epigenomics and transcriptomics data, constitute a promising approach for powerful and biologically relevant association studies. These studies often employ a case-control design, and often include non-omics covariates, such as age and gender, that may modify the underlying omics risk factors. An open question is how to best integrate multiple omics and non-omics information to maximize statistical power in case-control studies that ascertain individuals based on the phenotype. Recent works on integrative omics have used prospective approaches, modeling case-control status conditional on omics and non-omics risk factors. Compared to univariate approaches, jointly analyzing multiple risk factors with a prospective approach increases power in non-ascertained cohorts. However, these prospective approaches often lose power in case-control studies. In this article, we propose a novel statistical method for integrating multiple omics and non-omics factors in case-control association studies. Our method is based on a retrospective likelihood function that models the joint distribution of omics and non-omics factors conditional on case-control status. The new method provides accurate control of Type I error rate and has increased efficiency over prospective approaches in both simulated and real data.