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Author for correspondence: Renato Polimanti, E-mail: [email protected] *
Joint first authors
†Full list of Major Depressive Disorder Working Group members appears in Acknowledgments and Supplemental Material
‡Full list of Substance Use Disorder Working Group members appears in Acknowledgments and Supplemental Material
Introduction
Major depression (MD) and alcohol dependence (AD) are common psychiatric disorders, contribute substantially to global morbidity, and often co-occur (Ferrari et al., 2013; Shield et al., 2013). Epidemiological studies report that individuals with MD are at increased risk for AD and vice versa (Kessler et al., 1997; Swendsen and Merikangas, 2000; Boden and Fergusson, 2011). Leading hypotheses suggest these associations may be due to shared risk factors (genetic and environmental) or causal processes of one disorder leading to the other, such as the self-medication hypothesis of MD (Khantzian, 1997). However, the mechanisms underlying MD-AD dual diagnosis remain unclear.
Twin studies show genetic factors influence susceptibility to MD, AD, and alcohol consumption (AC) (Sullivan et al., 2000; Vrieze et al., 2013; Verhulst et al., 2015). Large-scale genome-wide association studies (GWAS) have identified risk variants for these disorders and have revealed polygenic architectures with multiple common variants (CONVERGE consortium, 2015; Schumann et al., 2016; Clarke et al., 2017; Walters et al., 2018; Wray et al., 2018). Twin studies report moderate shared genetic liability between MD and AD, estimating the genetic correlation from 0.3 to 0.6 (Kendler et al., 1993; Prescott et al., 2000). Although emerging molecular genetic studies have reported shared genetic risk between these disorders, they have not yet illuminated mechanisms of association underlying genetic correlations (Almeida et al., 2014; Bulik-Sullivan et al., 2015a; Wium-Andersen et al., 2015; Clarke et al., 2017; Walters et al., 2018; Wray et al., 2018). That is, questions remain whether these traits show genetic correlation because of shared genetic effects independently on each trait (i.e. horizontal pleiotropy) (Hemani et al., 2018a) or because of causal processes (e.g. mediated pleiotropy).
GWAS data can be used to assess causal mechanisms by applying Mendelian randomization (MR). MR is an instrumental variables technique that uses genetic variants to index if an observational association between a risk factor (e.g....