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. Author manuscript; available in PMC: 2019 Jun 14.
Published in final edited form as: Psychol Med. 2019 Apr 1;49(7):1218–1226. doi: 10.1017/S0033291719000667

Evidence of causal effect of major depression on alcohol dependence: findings from the psychiatric genomics consortium

Renato Polimanti 1,*, Roseann E Peterson 2,*, Jue-Sheng Ong 3, Stuart MacGregor 3, Alexis C Edwards 2, Toni-Kim Clarke 4, Josef Frank 5, Zachary Gerring 6, Nathan A Gillespie 2, Penelope A Lind 8, Hermine H Maes 2,7, Nicholas G Martin 9, Hamdi Mbarek 10, Sarah E Medland 8, Fabian Streit 5; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium†; Substance Use Disorder Working Group of the Psychiatric Genomics Consortium‡; 23andMe Research Team11, Arpana Agrawal 12, Howard J Edenberg 13, Kenneth S Kendler 2, Cathryn M Lewis 14, Patrick F Sullivan 15,16,17, Naomi R Wray 18,19, Joel Gelernter 1,20, Eske M Derks 6
PMCID: PMC6565601  NIHMSID: NIHMS1034150  PMID: 30929657

Abstract

Background.

Despite established clinical associations among major depression (MD), alcohol dependence (AD), and alcohol consumption (AC), the nature of the causal relationship between them is not completely understood. We leveraged genome-wide data from the Psychiatric Genomics Consortium (PGC) and UK Biobank to test for the presence of shared genetic mechanisms and causal relationships among MD, AD, and AC.

Methods.

Linkage disequilibrium score regression and Mendelian randomization (MR) were performed using genome-wide data from the PGC (MD: 135 458 cases and 344 901 controls; AD: 10 206 cases and 28 480 controls) and UK Biobank (AC-frequency: 438 308 individuals; AC-quantity: 307 098 individuals).

Results.

Positive genetic correlation was observed between MD and AD (rgMD−AD = + 0.47, P = 6.6 × 10−10). AC-quantity showed positive genetic correlation with both AD (rgAD−AC quantity = + 0.75, P = 1.8 × 10−14) and MD (rgMD−AC quantity = + 0.14, P = 2.9 × 10−7), while there was negative correlation of AC-frequency with MD (rgMD−AC frequency = −0.17, P = 1.5 × 10−10) and a non-significant result with AD. MR analyses confirmed the presence of pleiotropy among these four traits. However, the MD-AD results reflect a mediated-pleiotropy mechanism (i.e. causal relationship) with an effect of MD on AD (beta = 0.28, P = 1.29 × 10−6). There was no evidence for reverse causation.

Conclusion.

This study supports a causal role for genetic liability of MD on AD based on genetic datasets including thousands of individuals. Understanding mechanisms underlying MD-AD comorbidity addresses important public health concerns and has the potential to facilitate prevention and intervention efforts.

Keywords: Alcohol consumption, alcohol dependence, genetic correlation, genome-wide association, major depression, Mendelian randomization

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. MD) and an outcome (e.g. AD) is consistent with a causal effect (e.g. MD causes AD). MR relies on random assortment of genetic variants during meiosis which are typically unassociated with confounders since they are randomly distributed in the population at birth. The differences in outcome between those who carry genetic risk variants and those who do not can be attributed to the difference in the risk factor. The validity of the genetic instrument is dependent on meeting three core assumptions: (i) the genetic variant is associated with the risk factor/exposure; (ii) the genetic variant is not associated with confounders; and (iii) the genetic variant influences the outcome only through the risk factor. Although random controlled trials (RCTs) are considered the gold standard for establishing causality, MR is a viable alternative to provide support for causal mechanisms, especially when RCTs are not possible or ethical.

Two studies have previously evaluated the causal effect of AC on depression using MR. Almeida et al., investigated the impact of ADH1B rs1229984 in a sample of 3873 men and did not find evidence of a causal influence on depression (Almeida et al., 2014). In another study, the causal influence of two alcohol dehydrogenase (ADH) genes, ADH1B (rs1229984) and ADH1C (rs698) on depression was assessed in a sample of 68 486 participants from the general population and reported a lack of evidence for a causal influence on depression (Wium-Andersen et al., 2015). However, these studies did not investigate the causal influence of MD on AC or on AD risk. Furthermore, explorations of the causal nature were based on ADH candidate genetic variants only, which does not model the polygenic nature of these disorders.

Here, we leverage GWAS summary statistics generated by large datasets from the Psychiatric Genomics Consortium (PGC) and the UK Biobank to estimate genetic correlations between MD, AD, and two measures of AC (AC-quantity, AC-frequency) via linkage disequilibrium (LD) score regression (Bulik-Sullivan et al., 2015a, 2015b). Further, we investigated support for causal mechanisms linking these psychiatric disorders and AC via two-sample MR analyses, which use genetic variants to assess whether an exposure has a causal effect on an outcome in a non-experimental setting (Burgess et al., 2015).

Materials and methods

Samples

  1. Major depression (Wray et al., 2018)

    MD summary association data were obtained from the latest GWAS meta-analysis including 135 458 MD cases and 344 901 controls from the MD working group of the PGC (PGC-MDD2), which included seven cohorts. A detailed description of the cohorts is reported in the main GWAS analysis and a summary appears in the Supplemental Methods.

  2. Alcohol Dependence (Walters et al., 2018)

    AD summary association data from unrelated subjects of European descent (10 206 cases; 28 480 controls) were obtained from GWAS meta-analysis of 14 cohorts conducted by the PGC Substance Use Disorder Workgroup. Detailed descriptions of the AD samples have been previously reported and a summary appears in the Supplemental Methods.

  3. UK Biobank – AC

    The UK Biobank cohort consists of 502 000 middle-aged (40–69 years) individuals recruited from the UK. Information on alcohol intake was obtained through various self-report questionnaires. Frequency of consumption (AC-frequency) was assessed in 501 718 participants (UK Biobank field IDs: 1558) with the item ‘About how often do you drink alcohol?’. Frequency was originally assessed at a scale ranging from 1 (daily or almost daily) to 6 (never), but was reverse coded so that a lower score represented less frequent drinking. Online Supplemental Figure S1 shows the distribution in the UK Biobank population. In those who drink at least once or twice a week, information on the quantity of consumption (AC-quantity) was assessed (n = 348 039). Details can be found in the online Supplemental Methods.

    Approximately 488 000 participants were genotyped and imputed using Haplotype Reference Consortium (HRC) and UK10 K haplotype resources (The UK10K Consortium et al., 2015; McCarthy et al., 2016; Bycroft et al., 2018). Due to the UK Biobank’s reported QC issues with non-HRC single nucleotide polymorphisms (SNPs), we retained only the ∼40 M HRC SNPs for analysis. In light of a large number of related individuals in the UK Biobank cohort, the GWAS was performed using BOLT-LMM (Loh et al., 2015). Using the criteria reported in the supplemental methods, we identified 438 870 individuals for this study who are genetically similar to those of white-British ancestry. After exclusion of ethnic outliers, we included 438 308 participants in the AC-frequency and 307 098 participants in the AC-quantity GWAS.

SNP-based heritability analysis

The proportion of variance in phenotypic liability that could be explained by the aggregated effect of all SNPs (h2-SNP) was estimated using LD-Score Regression analysis (online Supplemental Methods) (Bulik-Sullivan et al., 2015b). For this analysis, we included in the regression 1 217 311 SNPs that were present in the HapMap 3 reference panel. Analyses were performed using pre-computed LD scores based on 1000 Genomes Project reference data on individuals of European ancestry. The h2-SNP estimates for the two binary traits were converted to the liability scale, using sample prevalence of 0.159 for AD and 0.15 for MD.

Genetic correlations between MD, AD, AC-quantity, and AC-frequency

We used cross-trait LD-Score regression to estimate the bivariate genetic correlations between MD, AD, and AC using GWA summary statistics (Bulik-Sullivan et al., 2015a). For each pair of traits, the genetic covariance is estimated using the slope from the regression of the product of z-scores from two GWA studies on the LD score. The estimate represents the genetic covariation between the two traits based on all polygenic effects captured by SNPs. To correct for multiple testing, we adopted a Bonferroni corrected P-value threshold of significance of 0.05/ 6 = 0.0083.

Mendelian randomization

To assess causality among MD, AD, AC-quantity, and AC-frequency, we used GWAS summary association data to conduct two-sample MR analyses (Davey Smith and Hemani, 2014; Burgess et al., 2015). Since different MR methods have sensitivities to different potential issues, accommodate different scenarios, and vary in their statistical efficiency (Polimanti et al., 2017, 2018; Ravera et al., 2018; Wendt et al., 2018), we considered multiple MR methods (online Supplemental Table S1). These include methods based on median (Bowden et al., 2017), mean (Bowden et al., 2016), and mode (Hartwig et al., 2017), and various adjustments, such as fixed v. random effects (Bowden et al., 2017), Rucker framework (Rucker et al., 2011), and Steiger filtering (Hemani et al., 2017). We verified the stability of the results, comparing the effect directions across the different MR-variant filtering methods (online Supplemental Table S1). MR-Egger regression intercept was considered to verify the presence of pleiotropic effects of the SNPs on the outcome (i.e. to verify whether the instrumental variable is associated with the outcome independently from its association with the exposure) (Bowden et al., 2015). In total we performed 17 MR tests (online Supplemental Table S2). This number is due to the fact we were not able to test AD using a genetic instrument based on genome-wide significant (GWS) loci and, since we are conducting a two-sample MR analysis, we did not test causal relationship between AC-quantity and AC-frequency because they are based on UK Biobank cohort. For the variants included in the instrumental variable, we performed LD clumping by excluding alleles that have R2 ≥ 0.01 with another variant with a smaller association P-value considering a 1Mb window. Additionally, during the harmonization of exposure and outcome data, palindromic variants with an ambiguous allele frequency (i.e. minor allele frequency close to 50%) were removed from the analysis to avoid possible issues (Bowden et al., 2015; Hartwig et al., 2016). The variants included in each genetic instrument used in the present analysis are listed in online Supplemental Table S3. For each exposure, two instrumental variables were built considering GWS loci (P < 5× 10−8) and suggestive loci (P < 5× 10−5). We verified these MR estimates using the MR-RAPS approach, which is a method designed to identify and estimate confounded causal effects using weak genetic instrumental variables (Zhang et al., 2018). To ensure the reliability of the significant findings, we performed heterogeneity tests based on three different methods: inverse-variance weighted, MR-Egger regression, and maximum likelihood (online Supplemental Table S4). To further confirm the absence of possible distortions due to heterogeneity and pleiotropy, we tested the presence of horizontal pleiotropy among the variants included in the genetic instrument using MR-PRESSO (Verbanck et al., 2018). Finally, the funnel plot and leave-one-out analysis were conducted to identify potential outliers among the variants included in the genetic instruments tested. The MR analyses were conducted using the TwoSampleMR R package (Hemani et al., 2018b).

Data availability

MD GWAS:

The PGC’s policy is to make genome-wide summary results public. Summary statistics for a combined meta-analysis of PGC29 with five of the six expanded samples (deCODE, Generation Scotland, GERA, iPSYCH, and UK Biobank) are available on the PGC web site (https://www.med.unc.edu/pgc/results-and-downloads). Results for 10 000 SNPs for all seven cohorts are also available on the PGC web site. GWA summary statistics for the Hyde et al., cohort (23andMe, Inc.) must be obtained separately. These can be obtained by qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. Contact David Hinds (dhinds@23andme.com) to apply for access to the data. Researchers who have the 23andMe summary statistics can readily recreate our results by meta-analyzing the six-cohort results file with the Hyde et al., results file from 23andMe.

AD GWAS:

The PGC’s policy is to make genome-wide summary results public. Summary statistics are available on the PGC web site is (https://www.med.unc.edu/pgc/results-and-downloads).

AC quantity and frequency GWAS: Summary statistics will be made publicly available through LD hub http://ldsc.broadinstitute.org/ldhub/ before publication of this paper or can be obtained upon request from the corresponding author.

Results

SNP-based heritabilities and genetic correlations

We confirmed previously reported heritability estimates of MD (h2-SNP = 8.5%, s.e. = 0.003, K = 0.15) and AD (h2-SNP = 9.0%, s.e. = 0.019, K = 0.16), with K defined as the disease prevalence in the population (Walters et al., 2018; Wray et al., 2018). The h2-SNP of AC-frequency, which has not been previously reported, was estimated at 8.0% (s.e. = 0.003). The h2-SNP of AC-quantity using LD-score regression was estimated at 6.9% (s.e. = 0.004), which is lower than the GCTA-estimate reported by Clarke et al. (13%) who analyzed a smaller subset (n = 112 117) (Clarke et al., 2017) from the current data (n = 307 098). The lower estimate may be explained by differential methodology (i.e. LD-score regression v. GCTA) and by the fact that the first release of UK Biobank included a subset of individuals that was selected based on smoking and may be less representative of the general population than the current sample (Wain et al., 2015). These h2-SNP estimates are capturing 17–23% of heritabilities reported by twin studies (Sullivan et al., 2000; Verhulst et al., 2015).

The high genetic correlation between AD and AC-quantity (rgAD−AC quantity = + 0.75, 95%CI = (0.56, 0.94), P = 1.8 × 10−14) (Fig. 1) suggests that these phenotypes capture overlapping constructs and that quantity of consumption is an indicator of problematic alcohol use. Of note, the genetic correlation between AD and AC-frequency is not significantly different from zero, indicating that it is not a reliable indicator of genetic risk for AD.

Fig. 1.

Fig. 1

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Genetic correlations of major depression (MD), alcohol dependence (AD), and alcohol consumption quantity (ACQ), and alcohol consumption frequency (ACF).

Consistent with twin studies, MD and AD show moderate overlap of genetic factors [rgMD−AD = + 0.47, 95% CI = (0.32, 0.62), P = 6.6 × 10−10] (Kendler et al., 1993; Prescott et al., 2000). A significant genetic correlation between AC-quantity and AC-frequency was observed [rgAC quantity−AC frequency = + 0.52, 95% CI = (0.48, 0.56), P = 1.3 × 10−149], but MD showed significant correlations with these traits in opposite directions [rgMD−AC quantity = + 0.14, 95% CI = (0.09, 0.20), P = 2.9 × 10−7; rgMD−AC frequency = −0.17, 95% CI = (−0.22, −0.11), P = 1.5 × 10−10].

Mendelian randomization

We investigated the presence of mediated pleiotropy via two-sample MR as this allows us to test for causative mechanisms linking MD, AD, AC-frequency, and AC-quantity. This is a strategy to investigate causal relationships in which evidence on the associations of genetic variants (i.e. instrumental variable) with the risk factor (i.e. exposure) and with the outcome are derived from two samples (Davey Smith and Hemani, 2014; Burgess et al., 2015). The instrumental variables were built considering GWS loci (P < 5× 10−8) and suggestive loci (P < 5× 10−5). Since different MR methods have sensitivities to different potential issues, we considered 28 MR/variant-filtering approaches (online Supplemental Table S1). A Bonferroni correction (P < 1.05 × 10−4) was applied to correct for the number of MR tests performed (n = 17; online Supplemental Table S2) and the number of methods/variant-filtering considered for each test (n = 28; online Supplemental Table S1). Of the 17 MR tests conducted, we observed that 14 survived multiple testing correction (Table 1). This outcome was expected due to the strong genetic correlations observed among the traits investigated. To verify that the significant results were not due to the presence of biases in the genetic instruments, we conducted three main sensitivity analyses: (i) inspected consistency of direction of effects across MR methods (online Supplemental Table S5); (ii) tests of horizontal pleiotropy between the exposure and the outcome (MR-Egger regression intercept P > 0.1; online Supplemental Table S6); (iii) assessed heterogeneity of effect sizes among the variants included in the genetic instrument (heterogeneity test P > 0.05; online Supplemental Table S3). Of 14 MR tests surviving Bonferroni multiple testing correction, only the causal relationship of MD on AD passed all three sensitivity analyses. We observed that the MD instrumental variable based on suggestive variants (259 SNPs) was associated with AD (fixed-effect inverse-variance weighted method: β = 0.28, P = 1.3 × 10−6; Fig. 2). Since this causal estimate was generated from a genetic instrument including suggestive variants, we confirmed this result using the MR-RAPS method: β = 0.28, P = 5.6 × 10−5. A similar effect size was also observed for the MD instrumental variable based on GWS loci (40 SNPs; fixed-effect inverse-variance weighted method: β = 0.27, P = 0.054). Results indicated that MD is associated with a 32% increase in the odds for AD risk per unit increase in the log(OR) for MD (95% CI 18–48%) and were consistent across multiple MR approaches (online Supplemental Table S5.17). As mentioned above, the MD genetic instrument did not show evidence of horizontal pleiotropic effects as demonstrated by MR-Egger regression intercept (P = 0.297, online Supplemental Table S6), confirming that the causal effect of MD on AD does not appear to be biased by horizontal pleiotropy. The heterogeneity tests indicated no evidence of heterogeneity in the MD-AD result (P > 0.13; online Supplemental Table S3). The MR-PRESSO global test (Verbanck et al., 2018) also supported the absence of horizontal pleiotropy (RSSobs = 285.6, P = 0.143). The MR-RAPS overdispersion test did not observe significant horizontal pleiotropy (estimated pleiotropy variance = 1 × 10−4; P = 0.249). Finally, the funnel plot and leave-one-out analyses provided additional support that the MD-AD result was not biased by outliers included in the genetic instrument (online Supplemental Figure S2). The same MD genetic instrument also showed significant effects on AC-quantity and AC-frequency (Table 1), but, in contrast to the AD outcome, these causal effects showed evidence of non-consistency across MR methods, heterogeneity, and horizontal pleiotropy (online Supplemental Table S3, S5, S6). No reverse causal effect was observed between AD genetic instrument and MD (fixed-effect inverse-variance weighted method: β = 0.01, P = 0.1), which also showed non-concordant direction of effects across MR methods (online Supplemental Figure S3). Conversely, the AD genetic instrument showed significant effects on AC-quantity and AC-frequency but was affected by heterogeneity and horizontal pleiotropy (Table 1; online Supplemental Table S3, S6).

Table 1.

Results of the most significant MR approach among those surviving Bonferroni multiple testing correction for each of the MR tests conducted

Test Method SNP n Estimate P Concordance Het. (P > 0.05) MR-Egger intercept (P > 0.1)
ACF→AD Egger 92 −1.97 2.51×10−11 pass violated violated
ACF×10−5→AD Egger 773 −0.75 3.24×10−06 pass violated violated
ACF×10−5→MD IVW 795 0.07 6.50×10−20 violated violated violated
ACQ→AD IVW 31 0.12 3.81×10−11 pass violated violated
ACQ→MD IVW 30 0.01 8.06×10−05 violated violated pass
ACQ×10−5→AD IVW 385 0.06 7.34×10−20 pass violated violated
ACQ×10−5→MD IVW 405 0.01 2.43×10−17 violated violated violated
AD×10−5→ACF Egger 96 −0.05 9.11×10−48 pass violated violated
AD×10−5→ACQ IVW 95 0.26 3.26×10−36 pass violated pass
MD→ACF IVW 36 0.05 4.06×10−08 pass violated pass
MD→ACQ Egger 36 −4.77 1.93×10−10 pass violated violated
MD×10−5→ACF IVW 252 0.02 1.27×10−08 violated violated pass
MD×10−5→ACQ IVW 251 0.31 1.46×10−06 violated violated violated
MD×10−5→AD IVW 259 0.28 1.29×10−06 pass pass pass
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ACF, alcohol consumption frequency; ACQ, alcohol consumption quantity; AD, alcohol dependence; MD, major depression; Het., heterogeneity test; suggestive loci (P < 5× 10−5). All top-results reported in the table were obtained using fixed effects and tophits adjustments (see online Supplemental Table S2)

Fig. 2.

Fig. 2

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Single nucleotide polymorphism (SNP)-exposure (major depression (MD) associations, logOR) and SNP-outcome (alcohol dependence (AD) associations, logOR) coefficients used in the Mendelian randomization (MR) analysis. Error bars (95% CIs) are reported for each association.

Discussion

Data from large-scale GWAS are redefining the boundaries of psychiatric disorders, identifying the contribution of common risk alleles and pervasive genetic correlations. Here, leveraging the polygenic architecture of these complex traits and the large sample size of GWAS results from the PGC and UK Biobank, we observed genetic overlap between MD and AD and provide support for a causal effect of MD on AD, which does not appear to be affected by horizontal pleiotropy or other detectable biases. To our knowledge, this is the first report of causality between MD and AD based on molecular genetic information. Consistent with the two previously published MR studies (Almeida et al., 2014; Wium-Andersen et al., 2015), we did not find a robust causal influence of AC on depression.

We detected significant genetic correlations between all pairs of phenotypes, except for AD and AC-frequency, suggesting that frequency of AC is not a good proxy for AD. In contrast, the genetic correlation between AD and AC-quantity is high at rg = 0.75. Of note, our estimate is higher than previously reported by Walters et al. (rg = 0.37) who used the same GWAS results for AD (Walters et al., 2018) but the earlier, smaller subset of the UK Biobank data on consumption (Clarke et al., 2017). However, our estimate is comparable to the estimate reported by Walters et al. (rgAD−AC quantity = + 0.70) who calculated the genetic correlation between AD and AC from the Alcohol Genome-wide Association and Cohorts for Aging and Research in Genomic Epidemiology Plus consortium (Schumann et al., 2016), suggesting that the genetic correlation between these traits is indeed high (Walters et al., 2018).

The finding of a negative correlation between AC-frequency and MD may seem counterintuitive, but is supported by earlier studies that report opposing effects of quantity and frequency on health. For example, high drinking quantity is associated with increased all-cause mortality risk while the frequency of drinking does not show such an association (Breslow and Graubard, 2008). Additionally, in line with our findings, it was recently shown that MD is associated in opposite directions with two aspects of problematic drinking as assessed by the AUDIT: AUDIT-consumption (i.e. assessing frequency of consumption) (rg = −0.23) and AUDIT-problematic consequences (rg = + 0.26) (Sanchez-Roige et al., 2018). Our findings support the notion that MD is genetically positively correlated with measures of problematic drinking (i.e. AD and AC-quantity), but is negatively correlated with the frequency of consumption.

In contrast to some epidemiological reports (Boden and Fergusson, 2011), our results do not support evidence of reverse causation, that is, AD causing MD. One could posit that this is due to the relative power of the AD instrumental variable compared to those for MD and AC given the greater number of GWS variants detected for those traits. However, we would like to bring forward three arguments that support the notion that the null AD→MD result is due to the absence of a causal effect of AD on MD rather than a lack of power. First, our findings are in line with the results of an earlier MR study that explored the causal effect of ADH1B rs1229984 on depression and reported no significant association (Almeida et al., 2014). Since this variant is significantly associated with AD risk (Walters et al., 2018), this supports our premise that AD does not have a causal influence on MD. Second, the AD genetic instrument showed different associations between the traits tested: significant causal effect with respect to AC scales, while no effect on MD. Third, the genetic-correlation results indicated that AD is informative of the pleiotropy (mediated or horizontal) with ACQ and MD. Therefore, although the AD GWAS has a smaller sample size than the other GWAS used in the present analysis, it is informative of AD polygenic architecture as indicated by quantifiable and statistically significant SNP-based heritability and genetic correlation results. In particular, MD showed a much stronger genetic correlation with AD than that observed with AC scales. However, we note that larger AD and MD datasets will be required to confirm the current findings using genetic instruments based on genetic variants that reached the more conservative genome-wide significance threshold.

Conclusions

These results support the utility of using genetic approaches to advance the understanding of complex trait comorbidities. Given the significant morbidity and mortality associated with the comorbid conditions, AD and MD, understanding mechanisms underlying these associations not only address important public health concerns but also has the potential to facilitate prevention and intervention efforts. As discovery GWAS increase in sample size, future research will have the power to examine patterns of genetic correlation and causal mechanisms by important stratifications such as across diverse ancestries and sex.

Supplementary Material

1

Acknowledgements.

The Psychiatric Genomics Consortium (PGC): We are deeply indebted to the investigators who comprise the PGC, and to the hundreds of thousands of subjects who have shared their life experiences with PGC investigators. This work was conducted using the UK Biobank Resource (application number 25331). The PGC has received major funding from the National Institute of Mental Health and the National Institute on Drug Abuse (PGC3: U01 MH109528 and U01 MH109532, PGC2: U01 MH094421, PGC1:U01MH085520). The Substance Use Disorders Working Group of the Psychiatric Genomics Consortium (PGC-SUD) is supported by funds from NIDA and NIMH to MH109532 and, previously, with analyst support from NIAAA to U01AA008401 (COGA). We gratefully acknowledge the contributing studies and the participants in those studies without whom this effort would not be possible. For a full list of acknowledgements of all individual cohorts included in the PGC-SUD and PGC-MD groups, please see the original publications. Statistical analyses were carried out on the Genetic Cluster Computer (http://www.geneticcluster.org) hosted by SURFsara, which is financially supported by the Netherlands Scientific Organization (NWO 480-05-003) along with a supplement from the Dutch Brain Foundation and the VU University Amsterdam. Renato Polimanti was supported by a Young Investigator Grant from the American Foundation for Suicide Prevention. Roseann E. Peterson was supported by National Institutes of Health K01 grant MH113848. Nathan A. Gillespie was supported by National Institutes of Health R00 grant R00DA023549. This paper represents independent research part-funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

Footnotes

Supplementary material. The supplementary material for this article can be found at https://doi.org/10.1017/S0033291719000667

Ethical standards. Contributing studies provided individual-level genotype data or summary statistics consistent with their institutional review board-approved protocols. All procedures complied with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

Conflict of interest. 23andMe Research Team are employees of 23andMe, Inc. and hold stock or stock options in 23andMe. The other authors reported no biomedical financial interests or potential conflicts of interest.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS1034150-supplement-1.pdf (981.8KB, pdf)

Data Availability Statement

MD GWAS:

The PGC’s policy is to make genome-wide summary results public. Summary statistics for a combined meta-analysis of PGC29 with five of the six expanded samples (deCODE, Generation Scotland, GERA, iPSYCH, and UK Biobank) are available on the PGC web site (https://www.med.unc.edu/pgc/results-and-downloads). Results for 10 000 SNPs for all seven cohorts are also available on the PGC web site. GWA summary statistics for the Hyde et al., cohort (23andMe, Inc.) must be obtained separately. These can be obtained by qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. Contact David Hinds (dhinds@23andme.com) to apply for access to the data. Researchers who have the 23andMe summary statistics can readily recreate our results by meta-analyzing the six-cohort results file with the Hyde et al., results file from 23andMe.

AD GWAS:

The PGC’s policy is to make genome-wide summary results public. Summary statistics are available on the PGC web site is (https://www.med.unc.edu/pgc/results-and-downloads).

AC quantity and frequency GWAS: Summary statistics will be made publicly available through LD hub http://ldsc.broadinstitute.org/ldhub/ before publication of this paper or can be obtained upon request from the corresponding author.

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