Gentilissimi/e, vi avviso che il seminario in oggetto è stato rimandato a data da destinarsi, a causa del grave lutto che ha colpito la nostra Sezione. Il Prof. Roberto de Marco, ordinario di Statistica presso la scuola di Medicina e Chirurgia, Università di Verona, ci ha lasciati ieri mattina. Tutti noi, orgogliosi di essere stati suoi allievi, vi diamo questo annuncio con profondo dolore. L'ultimo saluto verrà dato presso le celle dell'ospedale di Borgo Roma (Verona) lunedì 12 ottobre dalle ore 13.30 alle ore 14.30.

Alessandro Marcon

Alessandro Marcon, PhD Unit of Epidemiology & Medical Statistics Department of Diagnostics and Public Health University of Verona Strada Le Grazie 8, 37134 Verona, Italy tel. +39 045 8027668

2015-09-11 12:15 GMT+02:00 Alessandro Marcon alessandro.marcon@gmail.com:

Gentilissimi/e, vi invio un avviso di seminario, con preghiera di massima diffusione.

Sede del seminario: Istituti Biologici 2, Università di Verona, strada Le Grazie 8 (VERONA).

Distinti saluti, Alessandro Marcon

Alessandro Marcon, PhD Unit of Epidemiology & Medical Statistics Department of Diagnostics and Public Health University of Verona Strada Le Grazie 8, 37134 Verona, Italy tel. +39 045 8027668

Avviso di SEMINARIO

Lunedì 12 ottobre 2015 ore 11.00-11.45

Aula Igiene & Statistica

(Istituti Biologici 2, blocco B, secondo piano)

Dr. Cosetta Minelli

Senior Lecturer in Medical Statistics, National Heart and Lung Institute, Imperial College London

*Mendelian randomization: *

*The use of genes as instruments to strengthen causal inference in epidemiology*

Mendelian randomization is an approach that uses genes as instrumental variables to derive unconfounded estimates of the effects of risk factors (e.g. biomarkers) on disease traits, and is therefore increasingly being used in epidemiology to distinguish causal from spurious associations. Since genes are randomly allocated at conception, genetic effects on a biomarker cannot be affected by classical confounding (e.g. lifestyle factors) or reverse causation (e.g. biomarker level being influenced by the presence of disease). Demonstration that a genetic variant known to modify the biomarker also modifies the disease trait represents indirect evidence of a causal biomarker-disease association. The validity of Mendelian Randomization, however, is based on instrumental variable assumptions, the most important being absence of pleiotropy. This talk will describe the approach, discuss ways to assess pleiotropy, highlight opportunities and challenges offered by available genome-wide data, and discuss its extension to the field of epigenetics (two-step epigenetic Mendelian randomization).