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Genome-wide scan of depressive symptomatology in tworepresentative cohorts in the United States and the UnitedKingdomDOI:10.1016/j.jpsychires.2018.01.016

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Citation for published version (APA):Mekli, K., Phillips, D., Arpawong, T., Vanhoutte, B., Tampubolon, G., Nazroo, J. Y., Lee, J., Prescott, C. A.,Stevens, F., & Pendleton, N. (2018). Genome-wide scan of depressive symptomatology in two representativecohorts in the United States and the United Kingdom. Journal of Psychiatric Research.https://doi.org/10.1016/j.jpsychires.2018.01.016Published in:Journal of Psychiatric Research

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Accepted Manuscript

Genome-wide scan of depressive symptomatology in two representative cohorts inthe United States and the United Kingdom

Krisztina Mekli, Drystan F. Phillips, Thalida E. Arpawong, Bram Vanhoutte, GindoTampubolon, James Y. Nazroo, Jinkook Lee, Carol A. Prescott, Adam Stevens, NeilPendleton

PII: S0022-3956(17)30321-7

DOI: 10.1016/j.jpsychires.2018.01.016

Reference: PIAT 3294

To appear in: Journal of Psychiatric Research

Received Date: 17 March 2017

Revised Date: 29 January 2018

Accepted Date: 31 January 2018

Please cite this article as: Mekli K, Phillips DF, Arpawong TE, Vanhoutte B, Tampubolon G, Nazroo JY,Lee J, Prescott CA, Stevens A, Pendleton N, Genome-wide scan of depressive symptomatology in tworepresentative cohorts in the United States and the United Kingdom, Journal of Psychiatric Research(2018), doi: 10.1016/j.jpsychires.2018.01.016.

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Genome-wide scan of depressive symptomatology in two representative cohorts in the United

States and the United Kingdom

Authors:

Krisztina Meklia,*

, Drystan F. Phillipsb,c

, Thalida E. Arpawongd, Bram Vanhoutte

a, Gindo Tampubolon

e,

James Y. Nazrooa, Jinkook Lee

b,c, Carol A. Prescott

d,f, Adam Stevens

g, Neil Pendleton

h

aCathie Marsh Institute for Social Research, The University of Manchester, Manchester, United

Kingdom bDornsife Center for Economic and Social Research, University of Southern California, Los Angeles,

CA, USA cRAND Corporation, Santa Monica, CA, USA

dDepartment of Psychology, Dornsife College of Letters, Arts and Sciences, University of Southern

California, Los Angeles, CA, USA eInstitute for Social Change, The University of Manchester, Manchester, United Kingdom

fDavis School of Gerontology, University of Southern California, Los Angeles, CA, USA

gDivision of Developmental Biology and Medicine, The University of Manchester, Manchester, United

Kingdom hDivision of Neuroscience and Experimental Psychology, The University of Manchester, Manchester,

United Kingdom*Corresponding author, address: Krisztina Mekli, G.21, Cathie Marsh Institute for

Social Research, Humanities Bridgeford Street, Oxford Road, The University of Manchester,

Manchester, United Kingdom, M13 9PL. Telephone: +44(0)1612751098, Fax: +44(0)2754269, e-mail:

[email protected].

E-mail addresses of Authors:

Drystan F. Phillips: [email protected]

Thalida E. Arpawong: [email protected]

Bram Vanhoutte: [email protected]

Gindo Tampubolon: [email protected]

James Y. Nazroo: [email protected]

Jinkook Lee: [email protected]

Carol A. Prescott: [email protected]

Adam Stevens: [email protected]

Neil Pendleton: [email protected]

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Abstract

Unlike the diagnosed Major Depressive Disorder, depressive symptomatology in the general

population has received less attention in genome-wide association scan (GWAS) studies.

Here we report a GWAS study on depressive symptomatology using a discovery-replication

design and the following approaches: To improve the robustness of the phenotypic measure, we

used longitudinal data and calculated mean scores for at least 3 observations for each individual. To

maximize replicability, we used nearly identical genotyping platforms and identically constructed

phenotypic measures in both the Discovery and Replication samples.

We report one genome-wide significant hit; rs58682566 in the EPG5 gene was associated

(p=3.25E-08) with the mean of the depression symptom in the Discovery sample, without

confirmation in the Replication sample. We also report 4 hits exceeding the genome-wide suggestive

significance level with nominal replications. Rs11774887, rs4147527 and rs1379328, close to the

SAMD12 gene, were associated with the mean depression symptom score (P-values in Discovery

sample: 4.58E-06, 7.65E-06 and 7.66E-06; Replication sample: 0.049, 0.029 and 0.030, respectively).

Rs13250896, located in an intergenic region, was associated with the mean score of the three

somatic items of the depression symptoms score (p= 3.31E-07 and 0.042 for the Discovery and

Replication samples). These results were not supported by evidence in the literature.

We conclude that despite the strengths of our approach, using robust phenotypic measures

and samples that maximize replicability potential, this study does not provide compelling evidence

of a single genetic variant’s significant role in depressive symptomatology.

Key words: genetics, depressive symptomatology, genome-wide association, ageing

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Introduction

Major depression is the most common psychiatric disorder with the lifetime prevalence of

16% in the community (Kessler et al., 2003) and also moderately heritable. Studies have estimated

the heritability of depression between 16-37%, depending on the type of depression and the study

design (Gatz et al., 1992; Sullivan et al., 2000). These heritability estimates are now being

supplemented with association studies that set out to identify genes with a possible role in

susceptibility to depression (Kohli et al., 2011; Wray et al., 2012; Ripke et al., 2013; Hek et al., 2013;

CONVERGE Consortium, 2015; Hyde et al., 2016). As for underlying biological mechanisms of

depression, recent research suggests that they may differ across the life course. In later life,

plausible mechanisms include those that are characteristic to the ageing process, such as endocrine

changes (for example decreasing testosterone levels) (Blazer & Hybels, 2005), vascular risk factors

(hypertension, diabetes and atherosclerosis) and neurodegenerative diseases (Alzheimer disease

and dementia) (Weisenbach & Kumar, 2014).

On the other hand, depressive symptoms and trait depression have received less attention

as phenotypic measures in GWAS studies (Terracciano et al., 2010), although literature suggests that

diagnosable depression exists on a continuum with subthreshold depressive symptoms (Lewinsohn

et al., 2000). Moreover, the presence of depressive symptoms is important on its own, as they are

affecting the individual’s well-being and associated with cognitive (Wilson et al., 2002) and physical

decline (Penninx et al., 1998) among others.

Here we report the results of a genome-wide association scan (GWAS) study on depressive

symptoms using a discovery-replication design with two independent community representative

samples of older adults. The aim was to attempt replication with nearly identical genotyping

platforms and carefully constructed identical phenotypic measures in two large independent

samples. As a phenotypic measure, we used a self-report scale designed to measure the frequency

of depressive symptomatology in the general population, the Center for Epidemiologic Studies

Depression Scale (CESD; Radloff, 1977).The CESD scale assesses the level of psychological distress by

marking the presence or absence of symptoms of depression. Although the CESD was not designed

to measure the prevalence of depression, it performs well against other depression diagnostic

inventories. Using 3 as cut-off point for caseness on the short version of CESD, its specificity and

sensitivity were over 70% against the World Health Organisation’s Composite International

Diagnostic Interview’s measure, which is commonly used as a substitute for a clinician’s diagnosis

(HRS Health Working Group, 2000). However, the CES-D questions do not match to the Diagnostic

and Statistical Manual (DSM) criteria for depressive disorder in many ways. For example, they do not

address duration and intensity, which are important components for a diagnosis of disorder. They

also ignore the possibility of other psychological disorders that have similar symptoms similar to

depression, such as anxiety disorders (HRS Health Working Group, 2000). We used the shorter, eight

items version of the CESD scale as a continuum from low to high psychological distress, rather than

dichotimising the scores into depressed and not depressed.

We used longitudinal data to construct phenotypic measures that did not represent a single

time point, rather was constructed to represent a more stable pattern of symptomatology over the

observed period. This approach allowed us to reduce variability due to both temporary effects and

random error, and critically, to obtain a more robust phenotypic measure that reflects a trait rather

than a state for elevated or reduced symptomatology. This approach minimizes problems with prior

GWAS studies that use single assessments in which the phenotype reflects transitory depression,

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and fluctuations likely reflect environmental influences. Our approach of using a more stable

phenotype, that draws from repeat measures data, is preferable and more robust for genetic

association studies.

We also conducted confirmatory factor analysis to ensure measurement equivalence in the

two samples. We used relatively large sample sizes with over 10,000 individuals in the Discovery

sample and over 5,000 individuals in the Replication sample. The genotyping platform was nearly

identical between the two samples using 2.5 million single nucleotide polymorphisms (SNPs). Using

population representative cohorts of older adults, we anticipated that the depressive

symptomatology-associated genes will be relevant to emotional health in later life.

Materials and methods

Sample

We used the discovery and replication sample approach. The Discovery sample was drawn

from the Health and Retirement Study (HRS), a nationally representative sample of households of

older Americans in the United States (http://hrsonline.isr.umich.edu/). We included all participants

who were interviewed in wave 2 (1994) through 10 (2010) and were at least 50 years old at any

wave of data collection. All participants were provided with written consent forms, and ethical

approval was granted by the University of Michigan Institutional Review Board.

The Replication sample was the English Longitudinal Study of Ageing in the United Kingdom

(ELSA, http://www.elsa-project.ac.uk/), a nationally representative cohort of individuals living in

England aged 50 and older (Steptoe et al., 2013). In this study, we included individuals who were

interviewed in wave 1 (2002) through 5 (2010) and were 50 years old or older at first observation. All

participants provided signed consent and ethical approval was granted by the London Multi-Centre

Research Ethics Committee.

The investigation was carried out in accordance with the latest version of the Declaration of

Helsinki. Both samples contain only individuals with at least 3 observations for any phenotypic

measure, even if the observed waves were not consecutive.

Genetic data

The genetic data was obtained by using Illumina's Human Omni2.5-Quad BeadChip

methodology (Illumina, San Diego, CA, USA). Genotyping was performed by the NIH Center for

Inherited Disease Research (Johns Hopkins University, Baltimore, MD, USA) using HumanOmni2.5-

4v1 platform for the Discovery sample. The University College London Genomics (London, UK)

performed the genotyping on HumanOmni2.5-8v1 for the Replication sample. Quality control on the

genetic data was performed by the data holder.

In brief from the 2,443,179 SNPs in the Discovery sample, 241,808 were excluded based on

Hardy-Weinberg equilibrium p-value in European samples <0.0001 or missingness >=2% or minor

allele frequency =0% or other QC discrepancies

(http://hrsonline.isr.umich.edu/sitedocs/genetics/HRS_QC_REPORT_MAR2012.pdf). During the

analysis, SNPs with minor allele frequency below 5% were also excluded.

For the Replication sample similar quality filters were applied by the University College

London, Genetics Institute, apart from SNP missingness, which was >5%. As a result of quality

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control, the number of SNPs analysed were 1,223,220 and 1,490,612 for the Discovery and

Replication samples, respectively.

In the Discovery sample, participants were excluded based on relatedness (n=88) and

chromosome anomalies (n=79), yielding 12,341 individuals with genotype data. In the Replication

sample 41 individuals were excluded for non-white ethnicity and sex discrepancies.

To minimize population stratification and maximise genetic similarity between the samples,

we only analysed self-reported white individuals. As a result, the number of individuals in the

analysis was 10,123, (including 773 Hispanic whites) in the Discovery sample and 5,251 in the

Replication sample.

Phenotypic measures: 8-item, 6-item measures and separate mood and somatic measures

Eight-item measure. We used a shortened, eight-item version scale of the CESD. The 8 items

were selected for their psychometric properties in assessing the continuum of depressive symptoms

compared to the full scale (Kohout et al., 1993), and demonstrate very similar construct and external

validity to the original inventory (HRS Health Working Group, 2000; Turvey et al., 1999). These 8

questions ask whether the following was true for the respondent much of the time during the past

week, that (s)he:

felt depressed,

felt like everything (s)he did was an effort,

his/her sleep was restless,

was happy,

felt lonely,

enjoyed life,

felt sad,

could not get going.

We counted the number of symptoms for each wave (0-8). The maximum number of available waves

was 9 for the Discovery and 5 for the Replication sample. Our main phenotypic variable is the mean

of the individual scores of each wave, a continuous variable on a scale of 0-8.

Six-item measure. We investigated the measurement invariance across the two samples

using multigroup confirmatory factor analysis (Chung & Rensvold, 1999, 2002). This enabled us to

ensure that the factors are measuring the same underlying latent construct within each sample. This

analysis suggested that the ‘respondent’s sleep was restless’ and ‘respondent did not enjoy life’

items differed significantly between the Discovery and Replication samples; therefore we also

developed a mean CESD score that excluded these two items.

Separate mean mood and mean somatic measures. Based on earlier confirmatory factor

analysis results (Vanhoutte, 2014), we divided the CESD scores into mood items (respondent felt

depressed, felt lonely, felt sad, was unhappy and not enjoyed life) and somatic items (respondent

felt everything was an effort, his/her sleep was restless and could not get going) and calculated the

mean scores separately, thus developing two additional phenotypic measures.

Statistical analysis

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Phenotypic measures were developed using Stata12 software (Stata Corporation,

http://www.stata.com/).

We performed association analysis against four phenotypic measures using linear regression

and with sex included as a covariate to correct for observed gender differences in depression (Karim

et al. 2015). For the meta-analysis we chose 64399 SNPs with p<=0.05 in the Discovery sample and

the mean of the total CESD score. These analyses were performed using Plink software (Chang et al.,

2015, Purcell et al., 2007).

To avoid spurious association results arising from unadjusted population substructure we

used the first six Eigenvectors as covariates in the analyses. A scree plot generated during the quality

control of the genetic data

(http://hrsonline.isr.umich.edu/sitedocs/genetics/HRS_QC_REPORT_MAR2012.pdf) shows that the

fraction of variance accounted for is less than 4%, and approximately levels after the first two

vectors, indicating that the use of the first six components is sufficient. The Replication sample

contained only white individuals and literature indicates only modest population stratification in the

British population (The Wellcome Trust Case Control Consortium, 2007); therefore only sex was used

as covariate in the initial analyses. However, we repeated all analyses in the Replication sample with

the first 4 Eigenvectors also included as covariates.

Genomic inflation factor was calculated by R (http://cran.us.r-project.org/).

LD score regression intercepts were calculated with LDSC software tool

(https://github.com/bulik/ldsc, Bulik-Sullivan et al., 2015).

Genetic results annotation was performed by the Ensembl Variant Effect Predictor,

Assembly: GRCh38.p5 (McLaren et al., 2010).

Search for past associations available for SNPs in GWAS studies, GRASP database query was

performed (Leslie et al., 2014).

Confirmatory factor analysis was performed using Mplus version 7.2

(https://www.statmodel.com/).

Gene-based association analysis was performed by using VEGAS web platform

(https://vegas2.qimrberghofer.edu.au/). We used the defaults settings, chose CEU for the Discovery

sample and GBR for the Replication sample as subpopulations and defined gene boundaries as +/-

10000 kb outside of the gene.

The replication criteria for nominal replication in the Replication sample was p<0.05 and the

same direction of beta value as in the Discovery cohort.

Results

Demographic and phenotypic results

Discovery sample Replication sample

Males (%) 4,210 (41.59) 2,397 (45.65)

Females (%) 5,913 (58.41) 2,854 (54.35)

Age at first CESD observation (years) 58.59*

SE: 0.088

63.18*

SE: 0.134

Mean total CESD score (8 item scale) Mean: 1.287*

SE: 0.014

Mean: 1.385*

SE: 0.021

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Mean mood CESD score (5 mood items only) Mean: 0.622

SE: 0.009

Mean: 0.619

SE: 0.013

Mean somatic CESD score (3 somatic items only) Mean: 0.665*

SE: 0.007

Mean: 0.767*

SE: 0.010

Mean reduced CESD score (6 item scale) Mean: 0.92

SE: 0.011

Mean: 0.913

SE: 0.017

Table 1 Demographic and phenotypic results. The significant differences between means are marked

with asterisks.

Table 1 shows that in both samples, there were more female participants than males.

Individuals at first CESD observation were significantly younger in the Discovery sample, compared

to the Replication sample. The mean CESD scores and the mean scores of the 3 somatic items were

significantly different between the Discovery and the Replication samples (p<0.001).

Confirmatory factor analysis results

To investigate measurement invariance by using confirmatory factor analysis, we divided

one cross sectional wave (wave 6 of the Discovery sample and wave 1 of the Replication sample,

combined n=23,975) of our sample into four groups by gender and country. Results indicated that

two items, ‘enjoying life’ and ‘restless sleep’, introduce country and gender bias. When allowing the

factor loading of enjoying life, and the intercept for restless sleep to vary across gender and country,

partial measurement equivalence is established (RMSEA=0.047, CFI =0.97) (Cheung and Rensvold,

1999, 2002). (CFI=comparative fit index, RMSEA=root mean squared error of approximation).

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Genetic association results

The top 5 hits for each of the four phenotypic measures are shown in Table 2.


Chr BP SNP Allele Beta P-value MAF Beta P-value MAF Nearest gene

18 43531868 rs58682566 G 0.2009 3.25E-08 0.082 -0.07645 0.218 0.062 EPG5, intronic

18 43533220 rs11082501 A 0.1946 6.02E-08 0.086 -0.07795 0.206 0.063 EPG5, intronic



8 58844022 rs13250896 A -0.1583 1.31E-07 0.120 -0.08396 0.072 0.114 intergenic

Table 2a Phenotype: Mean CESD score (eight item scale)




8 58844022 rs13250896 A -0.1248 1.02E-07 0.120 -0.05297 0.147 0.114 intergenic



8 58843535 rs11774225 C -0.1186 1.73E-07 0.129 -0.05074 0.152 0.122 intergenic

Table 2b Phenotype: Mean CESD score (six item scale)

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4 38871385 rs73236646 G 0.0723 1.74E-06 0.192 0.0007874 0.974 0.177 FAM114A1,

intronic


9 32744916 rs13297009 C* -0.05643 2.62E-06 0.444 -0.02274 0.213 0.455 intergenic

20 11138229 rs6108847 G 0.05746 2.74E-06 0.383 0.008241 0.662 0.370 intergenic

Table 2c Phenotype: Mean CESD score (five mood items)

*In ELSA the minor allele is G







8 58844022 rs13250896 A -0.07387 3.31E-07 0.120 -0.04725 0.042* 0.114 intergenic

Table 2d Phenotype: Mean CESD score (three somatic items)

*Nominally replicated result

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We observed only one genome-wide significant association, between rs58682566 and the

total mean CESD score, without confirmation in the Replication sample.

Considering the suggestive level (p<1E-05), we had 48 SNPs significantly associated with the

mean total CESD score, 34 with the reduced CESD score, 25 with the mood items and 29 with the

somatic items in the Discovery sample by 74 unique SNPs (results can be seen in Appendix). Among

these results, we found 4 nominal replications; they are presented in Table 3. However, three of

them (rs11774887, rs4147527 and rs1379328) span a ~5,500 bp region of chromosome 8, and thus

likely represent the same signal (r2>0.98 for each SNP pair). The fourth SNP (rs13250896) is also

located on chromosome 8 but resides 60 million bp away, therefore is possibly an independent

signal.

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Chr BP SNP Allele Beta P-value MAF Beta P-value MAF Nearest

gene

Phenotype

8 119171312 rs11774887 A 0.1186 4.58E-06 0.174 0.7702 0.049 0.172 SAMD12 Mean CESD



8 58844022 rs13250896 A -0.07387 3.31E-07 0.120 -0.04725 0.042 0.114 intergenic Mean CESD

somatic

Table 3 Nominally replicated SNP results

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On the suggestive level, we found good overlap of associated SNPs between the total and

the reduced CESD scores (29 SNPs) and between the total score and either the mood or somatic

items (26 SNPs). However, we only found 7 SNPs in common between the mood and somatic items

analyses. We also observed 7 SNPs (rs12232683, rs12604492, rs58682566, rs73236646, rs1047115,

rs11082501, rs13250896) that were significant in every analysis in the Discovery sample (p<1E-05),

but only one of them replicated on a nominal level in the Replication sample (rs13250896). All

suggestive level results can be seen in Appendix.

Meta-analysis results

In the meta-analysis, assuming a fixed effect, none of the SNPs reached genome-wide

significance. The most significant association was between rs13250896 and the total mean CESD

score (p=6.14E-08, beta=-0.137). The same SNP yielded the most significant result in two other

analyses, namely in the reduced CESD score and somatic items analyses. This was the only SNP

which reached a suggestive level significance in all four analyses (Results are presented in Table 4).

Altogether we found 54 SNPs which reached at suggestive genome-wide significance in any of the

four analyses. These results are presented in the Appendix.

Gene based analysis

In the gene-based analysis the most significantly associated genes with the total mean CESD

score were EPG5 (p=6.0E-06, represented by 42 SNPs) and TLR1 (p=9.0E-06, represented by 8 SNPs).

These p-values do not reach genome-wide significance of 2.5E-06 (=0.5/20000), and were not

significant in the Replication sample.

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MeanCESD MeanCESD 6 items Mood items Somatic items

Chr BP SNP Allele P-value beta P-value beta P-value beta P-value beta

8 58844022 rs13250896 A 6.14E-08 -0.137 1.39E-07 -0.104 5.49E-06 -0.070 6.24E-08 -0.067

Table 4 Fixed effects meta-analysis results for rs13250896.

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The genomic inflation factor values for the Discovery sample vary between 1.017 - 1.022 and

between 0.996 - 1.008 for the Replication sample. These results did not indicate serious population

stratification in the samples and the literature suggests that polygenicity (many small genetic effects)

accounts for the majority of inflation in test statistics in GWAS studies (Bulik-Sullivan et al., 2015).

LD score regression intercepts range between 1.006-1.016 (SE 0.0064-0.0069) in the Discovery

sample and 1.002-1.010 (SE 0.0059-0.0068) in the Replication sample, also indicating adequate

control for population stratification.

Manhattan and QQ plots for each analysis are provided in the Appendix.

Discussion

In this study we conducted a genome-wide association study on depressive

symptomatology, in two representative cohorts. The aim was to attempt replication with highly

comparable genotype platforms and identical phenotypes in the two independent samples. To take

advantage of the repeated measures and longitudinal design of the study cohorts, we used the mean

CESD score across waves for individuals who had at least three observations on the full eight-item

scale. With this approach, our phenotype did not represent only a single point in time, which would

be a disadvantage because CESD scores fluctuate over time; rather the phenotype represents more

of a stable trait, which is preferable for discovering underlying genetic associations. We also

developed three further phenotypic measures: (a) a six-item phenotype to ensure measurement

invariance between the samples and (b) separate mood and (c) somatic scores in order to

differentiate between emotional and somatic domains of depressive symptomatology. We corrected

for population substructure by using only white participants and the first six Eigenvectors.

In these analyses, there was one genome-wide significant association, but it did not

replicate. Considering analysis of all four phenotypes, altogether, we had 74 unique SNPs yielding

136 associations below the genome-wide suggestive level (p<1E-05). Of these associations, four

were nominally replicated in the Replication sample but they correspond to only 2 independent

signals. The meta-analyses enhanced the significance of one of the nominally replicated SNP,

rs13250896 but it still not reached the genome-wide significance level. We found no replication

after performing gene-based association, either. We also concluded from our results that the lack of

replication is unlikely to be attributed to phenotypic measurement differences between the two

samples. Despite our expectation, we could not provide evidence of a single genetic variant’s

association with the depressive symptomatology.

The SNP yielding genome-wide significant association in the Discovery sample rs58682566 is

an intronic variant within the EPG5 (Ectopic P-Granules Autophagy Protein 5 Homolog (C. elegans)

gene on chromosome 18. This gene is the human homologue of the metazoan-specific autophagy

gene Epg5 which encodes the key autophagy protein Epg5. Despite the indications of the

importance of this gene in neurological processes (Cullup et al., 2013), neither the gene nor

rs58682566 has been associated with emotional health phenotypes in the literature previously.

All four SNPs that considered as nominally replicated are located on chromosome 8. Three of

these (rs11774887, rs4147527, rs1379328) are close to the SAMD12 (Sterile Alpha Motif Domain

Containing 12) gene. The fourth SNP, rs13250896 occupies an intergenic region of the genome.

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The role of SAMD12 is not well characterized in the literature. A study found that

transcription of the SAMD12 gene was down-regulated in response to Tat protein in human T cells

(Johnson et al., 2013). Tat is a viral protein crucial for HIV replication and present at reasonably high

levels in the brain and cerebrospinal fluid. Tat was also found to upregulate proinflammatory

cytokines (such as IL1B and IL8) and dysregulate IL-17 signaling (Johnson et al., 2013). This suggests

that a SAMD12 gene product may take part in immunological processes, and this is notable with the

growing interest in immune mediated theories for depression (Furtado & Katzman, 2015; van

Dooren et al., 2016). It worth mentioning, that another gene, EXT1 (Exostosin Glycosyltransferase 1)

can be found in the region of the three SNPs. This gene’s product is involved in the chain elongation

step of heparan sulphate biosynthesis. Interestingly, heparan sulphate proteoglycans are associated

with the pathology of common neurodegenerative disorders, such as Alzheimer’s disease and

Parkinson’s disease (Nadanaka & Kitagawa, 2008).

Also, there are some indications for past associations in GWAS studies for 3 of our 4

nominally replicated SNPs in the literature with the associated phenotypes covering a wide range.

Rs4147527 and rs1379328 have shown associations with autism (Anney et al., 2010), late onset

Alzheimer disease (Hu et al., 2011), and age-related macular degeneration (Fritsche et al., 2013).

Rs13250896 has shown associations with cardiovascular risk-related phenotypes, such as carotid

artery thickness (Melton et al., 2013), LDL and total cholesterol levels (Teslovich et al., 2010), and

diastolic blood pressure (Ehret et al., 2011) in GWAS studies. However, none of these associations

were below the p=1.0E-4 levels (GRASP database of GWAS studies, Leslie et al., 2014).

We compared our results against the publicly available results of a GWAS study on MDD by

the Psychiatric Genetic Consortium (PGC) (Ripke et al., 2013). Our top hit, rs58682566 was not

present among the genotyped PGC SNPs, but two SNPs were imputed from this area of the genome

(rs9964220 and rs8090930, spanning 9500 bps, with rs58682566 located between them) and their

significance was 0.368 and 0.88. Of our four nominally replicated SNPs, rs4147527, rs1379328 and

rs13250896 were among the genotyped PGC results, with p values of 0.412, 0.4311 and 0.2714,

respectively. Therefore comparison of our results against PGC’s MDD results did not provide

supporting evidence for SNPs identified in this study.

For the results presented in the present study, the GWAS literature provided some evidence

for the associations in GWAS studies of the identified SNPs, but failed to confirm their role in

influencing emotional health; this does not provide confidence for drawing conclusions on the

relationships between these SNPs and depressive symptomatology from our weak replications.

The lack of success in replication can result from many factors. It is generally accepted that the

effects of common genetic variants for a complex phenotype such as depressive symptomatology is

very small (Ripke et al., 2013) and many studies suffer from insufficient power to detect these small

effect sizes. In a recent study using depressive symptoms as phenotypic measure in a meta-analysis

of 180,866 individuals, only 2 SNPs exceeded the genome-wide significance level and replicated

successfully in a companion cohort (Okbay et al., 2016). This gives an indication of the magnitude of

sample size required for successful detections of true findings. This study using heterogeneous

phenotypic measures found an effect size of 0.002%. This value is 250 times smaller than the 0.5%

that our study supports. Even if we consider that the phenotypic measure we used is homogenous

and measured at several time points (70% of the participants have at least 7 observations in the

Discovery sample), our study is likely to be underpowered.

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On the other hand, there is example in the literature for successful identification and

replication of MDD-associated SNPs with much smaller sample size with carefully selected subjects

for whom known sources of phenotypic and genetic heterogeneity were minimized (CONVERGE

Consortium, 2015).

Recently, the proportion of phenotypic variance explained by common SNPs (Yang et al.,

2011) of the CESD score was found to be 0.35 in a univariate model (Boardman et al., 2015).

However, after controlling for the first five Principal Components, the heritability estimate dropped

to 0.19. This relatively moderate heritability explained by common SNPs indicates that other genetic

(Glessner et al., 2010) or non-genetic factors may play a significant role in depression.

The non-genetic environmental factors may co-act in an additive way or interact with the

genetic factors. For example, Caspi and colleagues (2003) showed that functional polymorphism (5-

HTTLPR) in the promoter region of the serotonin transporter gene (SLC6A4) was only associated with

depression in an interaction with life events, not on its own. This demonstrates that the risk allele

only confers susceptibility to depression if the carrier has been exposed to stressful life events, and

provides evidence for a gene-environment interaction. The Caspi finding has been replicated in some

studies since then, although not consistently (Cohen-Woods et al., 2013). We did not take into

consideration current or past stressful life events in our analyses, despite the likely influences of a

gene-environment interaction within individuals and across the sample.

Moreover, there could be larger-scale environmental effects present, as the sample mean

CESD was higher in some waves than in others. In the Discovery sample in wave 3, the sample mean

of the mean of CESD measure was 1.021 (lowest), whereas in wave 8 it was 1.35 (highest). This

range in the Replication sample was 1.29 (lowest, wave 4) – 1.42 (highest, wave 2 and wave 5). We

have no explanation for the origin of this difference between waves but it could have contributed to

the failure of replication.

In addition, the CESD score is a subjective measure, with the possibility of individual

variation in interpretation of and response to particular items, making it more difficult to achieve

successful replication. However, here we have used a version of the measure that has measurement

equivalence in the discovery and replication samples (Vanhoutte, 2014) and the measure has been

shown to have good validity and reliability (HRS Health Working Group, 2000).

Further concern could be the robustness of our phenotypic measure. We calculated the

association between the individual wave-specific CESD scores and the overall mean CESD scores in

the Discovery sample. The results showed high correlation (range: 0.70-0.79), indicating the

robustness of the mean CESD score.

Despite the lack of convicting replications, our study has a number of strengths. It uses two

nationally representative samples with large sample sizes in both the Discovery (n=10,123) and the

Replication sample (n=5,251). We used a continuous scale phenotypic measure, which increases the

phenotypic variance compared to binary phenotypic measures. Also, phenotypes were constructed

by including repeated assessments of depressive symptomatology over at least three waves in order

to provide some degree of stability to the measure. This is relevant because symptomatology can

fluctuate widely over time, partially due to environmental exposures. Additionally, by using the same

questions in both samples, we attempted to ensure phenotypic homogeneity between the two

samples. A further notable strength of our study design is the use of the highly comparable genotype

platform for the Discovery and Replication samples.

Among the weaknesses of the study include the use of the community samples, as the

number of individuals with higher depressive symptoms scores may be lower than in a specifically

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recruited sample of depressed individuals. In relation to this, although we have 40% of individuals in

the Discovery sample and 70% in the Replication sample with maximum number of observations,

the missing data may be due to a depressive episode, therefore the CESD measure is somewhat

biased. Moreover, the CESD questions ask about the past week, which may not be typical of the

individual’s life in general; however, by using at least three separate observations for each

individual, we are more likely to capture a trait that is characteristic to that individual, rather than a

labile state.

We also acknowledge that one of the observed four nominal replications was in relation to

the mean of the three somatic items measure. The already weak replication is further questioned by

the fact, that one of the items in this measure was among the two with different measurement

properties in the two samples as indicated by the multigroup confirmatory factor analysis.

A further weakness of the study is that depressive symptomatology is heavily influenced by

the environment, such as by stressful life events (Lotrich, 2011), and can be affected by cultural and

health-care access differences between the two countries; variables we did not measure and correct

for.

Although studies have demonstrated the importance of epigenetic regulation in depression-

related behavior or its treatment (Sun et al., 2013), epigenetic mechanisms cannot be interrogated

within a framework of a conventional GWAS study.

The different number of available waves in the two samples may cause differences in the

accuracy of the phenotypic measures. Moreover, these waves span different calendar years in the

two samples and this may pose cohort effects. Lastly, the multigroup confirmatory factor analysis

results indicated differences in the mean CESD measures between the eight or six item scales.

However, that difference was not supported by the genetic association results; in fact one of the

nominal replications was observed in association with the somatic measure which contains the

‘restless sleep’ item.

In summary, here we report the results of a genome-wide association scan study of

depressive symptomatology with two large population representative samples, paying particular

attention to independent replication. Similar to previous studies investigating MDD and depression

as a trait, we cannot report robust and replicable findings. We had one association that exceeded

the genome-wide significance threshold, although it did not replicate. This suggests future studies of

genetic determinants of depressive symptomatology may require alternate study designs, such as

incorporating larger sample sizes, gene x environment interactions, or epigenetic approaches.

All the pheno- and genotype data are publicly available.

HRS: https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000428.v2.p2

ELSA: https://www.ebi.ac.uk/ega/studies/EGAS00001001036

All authors have approved the final article.

Acknowledgements

Mr. John Mcloughlin for his technical assistance, programming and Linux administration.

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All participants in HRS and ELSA.

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Appendix

Manhattan and QQ plots of analyses

Sample: Discovery sample- HRS

Phenotype: mean CESD, min 3 observations

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Phenotype: mean CESD 6 items, min 3 obs

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Phenotype: mean of the 5 mood items, min 3 obs

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Phenotype: mean of the 3 somatic items, min 3 obs

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Sample: Replication sample, ELSA

Phenotype: mean CESD, min 3 observations

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Phenotype: mean CESD 6items, min 3 observations

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Phenotype: mean of the 5 mood items, min 3 obs

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Phenotype: mean of the 3 somatic items, min 3 observations

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Wave characteristics

HRS HRS ELSA ELSA

CESD measure Age CESD measure Age

Mean SE Median Mean SE Mean SE Median Mean SE

w1 not included in analysis 1.349 0.027 1 63.15 0.133

w2 1.043 0.023 0 59.53 0.108 1.42 0.027 1 65.46 0.133

w3 1.021 0.022 0 61.49 0.108 1.352 0.028 1 67.33 0.134

w4 1.295 0.02 1 62.44 0.1 1.294 0.029 1 69.39 0.134

w5 1.263 0.02 1 64.32 0.101 1.42 0.031 1 71.43 0.134

w6 1.264 0.021 1 66.39 0.1

w7 1.26 0.019 0 65.72 0.105

w8 1.348 0.019 1 67.71 0.105

w9 1.315 0.019 1 69.43 0.105

w10 1.283 0.021 0 71.15 0.108

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mean CESD, min 3 observations mean of the 6 CESD items, min 3 observations mean of the 5 mood CESD items, min 3 observations mean of the 3 somatic CESD items, min 3 observations

Discovery sample Replication sample Discovery sample Replication sample Discovery sample Replication sample Discovery sample Replication sample replication indicates nominal replication

MeanCESD no EVS MeanCESD 4EVs MeanCESD6it no EVs MeanCESD6it 4EVs MoodCESD no EVs MoodCESD 4EVs SomCESD no EVs SomCESD 4EVs p<0.05 and consitent directions of beta

chr snp bp A1_hrs a1_elsa beta_hrs_1ap_hrs_1a beta_elsa_1ap_elsa_1a beta_elsa p_elsa beta_hrs_2ap_hrs_2a beta_elsa_2ap_elsa_2a beta_elsa p_elsa beta_hrs_3ap_hrs_3a beta_elsa_3ap_elsa_3a beta_elsa p_elsa beta_hrs_4ap_hrs_4a beta_elsa_4ap_elsa_4a beta_elsa p_elsa pheno_1a pheno_2a pheno_3a pheno_4a repl_1a repl_2a repl_3a repl_4a final_rs symbol rs_ID1 final_rs

0 kgp22839617 0 A A 0.1129 5.95E-06 -0.006134 0.8754 -0.008124 0.8358 0.08778 6.69E-06 -0.001531 0.9601 -0.003299 0.9142 meanCESD meanCESD6it kgp22839617 kgp22839617

2 kgp246664 42310860 G G 0.08301 8.79E-06 -0.02542 0.352 -0.02509 0.359 3som rs72794551 LOC105374531 rs72794551rs72794551

2 kgp4709971 42311276 A A 0.08412 7.57E-06 -0.02317 0.3975 -0.02283 0.4051 3som rs72794553 LOC105374531 rs72794553rs72794553

2 kgp1723717 42313030 G G 0.08546 4.89E-06 -0.02065 0.4492 -0.0203 0.4575 3som rs72794554 LOC105374531 rs72794554rs72794554

2 rs7594227 97605748 G G 0.1368 7.27E-06 0.02319 0.6231 0.02385 0.6143 meanCESD rs7594227 FAM178B rs7594227

2 kgp1036511 97632449 A A 0.1401 5.65E-06 0.03649 0.4415 0.03722 0.4337 meanCESD rs11891457 FAM178B rs11891457rs11891457

2 kgp5914253 97752054 G 0.1504 4.77E-06 meanCESD rs115507803 FAHD2B rs115507803rs115507803

2 kgp3889003 184591449 G G 0.1833 9.81E-06 -0.1277 0.06153 -0.1314 0.05496 meanCESD rs77174609 - rs77174609rs77174609

3 rs923572 72697011 G 0.1681 2.35E-06 0.1352 1.19E-06 0.1012 3.90E-06 meanCESD meanCESD6it 5mood rs923572 LOC105377161 rs923572

3 rs1047115 186358366 C C 0.1433 7.77E-07 0.01503 0.7283 0.01536 0.7231 0.1178 2.05E-07 0.01209 0.7207 0.01293 0.7027 0.08153 4.90E-06 0.009107 0.7313 0.009262 0.7274 0.06181 9.94E-06 0.005923 0.7831 0.006094 0.7775 meanCESD meanCESD6it 5mood 3som rs1047115 RP11-573D15.8 rs1047115

4 kgp4354243 38763359 G G 0.09706 9.58E-06 0.03255 0.3473 0.03384 0.3295 0.07831 4.87E-06 0.02603 0.3364 0.02668 0.3255 meanCESD meanCESD6it rs10013453 RNA5SP158 rs10013453rs10013453

4 rs11725309 38783848 G G 0.1142 4.77E-06 -0.004625 0.9059 -0.006596 0.8664 0.08886 5.26E-06 -0.000641 0.9833 -0.002392 0.9378 0.06859 7.96E-06 -0.001523 0.9494 -0.002831 0.9062 meanCESD meanCESD6it 5mood rs11725309 TLR10 rs11725309

4 kgp240399 38792340 G 0.1008 8.66E-06 0.0784 9.64E-06 meanCESD meanCESD6it rs6531663 TLR1 rs6531663 rs6531663

4 kgp274574 38806096 G G 0.1114 7.35E-06 -0.004479 0.9086 -0.0103 0.7921 0.08721 7.06E-06 -0.000885 0.9769 -0.005648 0.8533 meanCESD meanCESD6it rs5743562 TLR1 rs5743562 rs5743562

4 kgp22736387 38806435 G G 0.1157 3.91E-06 -0.006125 0.8754 -0.008094 0.836 0.09131 3.12E-06 -0.001915 0.95 -0.003668 0.9045 0.06925 6.90E-06 -0.001947 0.9352 -0.003255 0.892 meanCESD meanCESD6it 5mood kgp22736387 kgp22736387

4 rs5743551 38807654 G G 0.07767 7.92E-06 0.02322 0.4017 0.02384 0.3905 meanCESD6it rs5743551 TLR1 rs5743551

4 rs17616434 38812876 G G 0.0789 5.81E-06 0.02934 0.2862 0.02998 0.277 meanCESD6it rs17616434 TLR1 rs17616434

4 kgp2256522 38867427 G G 0.1175 1.35E-06 0.007566 0.8441 0.006952 0.8568 0.08894 2.88E-06 0.007496 0.8032 0.006669 0.8248 0.05185 9.81E-06 0.003731 0.8455 0.003787 0.8434 meanCESD meanCESD6it 3som rs17582830 FAM114A1 rs17582830rs17582830

4 kgp9123948 38871385 G G 0.128 1.91E-07 0.00583 0.8801 0.005174 0.8937 0.09733 4.05E-07 0.005106 0.8659 0.004247 0.8884 0.0723 1.74E-06 0.000787 0.9735 9.83E-05 0.9967 0.0557 2.62E-06 0.005042 0.7933 0.005076 0.7922 meanCESD meanCESD6it 5mood 3som rs73236646 FAM114A1 rs73236646rs73236646

4 kgp4956717 38894380 G G 0.09854 9.78E-06 0.008608 0.8058 0.009367 0.7895 meanCESD rs6835514 FAM114A1 rs6835514 rs6835514

4 kgp2519194 187813486 A A -0.04637 3.59E-06 0.001102 0.9436 0.001118 0.9428 3som rs66691853 LOC105377599 rs66691853rs66691853

5 kgp9192558 4409707 A A 0.09679 8.72E-06 0.001788 0.958 0.002867 0.9328 0.0799 2.64E-06 -0.002775 0.9168 -0.001739 0.9479 0.06071 5.79E-06 0.00577 0.7817 0.006482 0.7559 meanCESD meanCESD6it 5mood rs9942351 - rs9942351 rs9942351

5 kgp11164084 180043388 A A -0.09877 4.90E-06 0.001021 0.9752 0.001991 0.9518 -0.07662 5.75E-06 0.003125 0.9033 0.003695 0.8858 meanCESD meanCESD6it rs1130378 FLT4 rs1130378 rs1130378

7 rs4722536 25921083 A A -0.04636 2.88E-06 -0.01068 0.4923 -0.01045 0.5021 3som rs4722536 - rs4722536

7 rs17152874 25923381 A A -0.05126 3.91E-06 -0.02117 0.2195 -0.02103 0.2228 3som rs17152874 - rs17152874

7 rs896312 25935030 G G -0.04444 6.71E-06 -0.008649 0.5763 -0.008405 0.5877 3som rs896312 - rs896312

7 kgp2219897 28790220 G G -0.04209 7.04E-06 0.01835 0.2192 0.01836 0.2192 3som kgp2219897 kgp2219897

7 rs218094 31500579 G G 0.04504 5.64E-06 -0.004398 0.7829 -0.005572 0.7276 3som rs218094 - rs218094

7 rs10271006 42356814 G G -0.04386 8.92E-06 0.02568 0.1041 0.02528 0.1102 3som rs10271006 - rs10271006

8 rs1058914 24212040 G G 0.07307 7.96E-06 -0.01632 0.4954 -0.01645 0.4924 5mood rs1058914 ADAM28 rs1058914

8 kgp3825727 58843535 C C -0.1491 2.80E-07 -0.07782 0.08617 -0.08378 0.06548 -0.1186 1.73E-07 -0.05074 0.1524 -0.05529 0.12 -0.07102 3.93E-07 -0.04325 0.05523 -0.04638 0.04034 meanCESD meanCESD6it 3som rs11774225 LOC105375856 rs11774225rs11774225

8 rs13250896 58844022 A A -0.1583 1.31E-07 -0.08396 0.07212 -0.08935 0.05607 -0.1248 1.02E-07 -0.05297 0.1467 -0.05737 0.1166 -0.08446 4.72E-06 -0.03672 0.1995 -0.03981 0.1649 -0.07387 3.31E-07 -0.04725 0.04202 -0.04954 0.03326 meanCESD meanCESD6it 5mood 3som 1 rs13250896 LOC105375856 rs13250896

8 kgp12363344 118824153 A A -0.07237 9.60E-06 0.01948 0.4438 0.01813 0.4771 meanCESD6it rs4876391 EXT1 rs4876391 rs4876391

8 rs11989122 118827839 G G -0.0741 6.00E-06 0.01604 0.5254 0.01432 0.5714 meanCESD6it rs11989122 EXT1 rs11989122

8 kgp3204231 119165749 A A 0.1157 7.65E-06 0.08539 0.02853 0.08342 0.03244 meanCESD 1 rs4147527 SAMD12 rs4147527 rs4147527

8 kgp4888490 119165984 A A 0.1157 7.66E-06 0.08448 0.03031 0.08473 0.02999 meanCESD 1 rs1379328 SAMD12 rs1379328 rs1379328

8 rs11774887 119171312 A A 0.1186 4.58E-06 0.07702 0.04873 0.07726 0.04825 0.0719 6.30E-06 0.03933 0.1007 0.03991 0.09611 meanCESD 5mood 1 rs11774887 SAMD12 rs11774887

8 kgp10557436 119174045 A A 0.1166 5.92E-06 0.07149 0.08413 0.0717 0.08357 0.07069 8.06E-06 0.04506 0.07631 0.0457 0.0725 meanCESD 5mood rs4876798 SAMD12 rs4876798 rs4876798

9 kgp3087254 32744916 C G -0.09191 2.51E-06 -0.02896 0.3305 -0.03132 0.2932 -0.07552 7.41E-07 -0.02865 0.2181 -0.03061 0.1888 -0.05643 2.62E-06 -0.02274 0.2125 -0.02399 0.189 meanCESD meanCESD6it 5mood rs13297009 LOC105376016 rs13297009rs13297009

9 kgp610756 112690677 C C -0.07688 7.45E-06 0.05531 0.03977 0.05301 0.04922 3som rs57962777 PALM2-AKAP2 rs57962777rs57962777

9 kgp8654473 112695036 A A -0.07642 8.78E-06 0.0568 0.03545 0.05448 0.04407 3som rs62580181 PALM2-AKAP2 rs62580181rs62580181

11 kgp12329752 108836125 A A 0.08793 9.54E-06 -0.01379 0.6508 -0.01346 0.6592 meanCESD6it kgp12329752 kgp12329752

12 rs4763800 12463407 A A 0.07305 8.81E-06 -0.005469 0.8304 -0.007293 0.7756 5mood rs4763800 - rs4763800

12 rs12821369 70978590 G G -0.1967 9.42E-06 0.007803 0.9214 0.01212 0.8784 meanCESD rs12821369 PTPRB rs12821369

12 rs7309603 84392473 A A 0.06507 9.89E-06 0.001481 0.9467 0.001196 0.9571 5mood rs7309603 - rs7309603

12 rs11059266 128054753 A A -0.06325 1.51E-06 0.04605 0.02941 0.04707 0.02613 3som rs11059266 - rs11059266

13 kgp999574 111471350 A A -0.09465 8.60E-06 -0.02058 0.5268 -0.02086 0.5213 meanCESD rs328819 LOC105370362 rs328819 rs328819

13 kgp6173565 111472875 A A -0.09828 3.90E-06 -0.02005 0.5372 -0.02064 0.5256 -0.07387 9.00E-06 -0.01248 0.6233 -0.01296 0.6101 meanCESD meanCESD6it rs437893 LOC105370362 rs437893 rs437893

13 rs9588267 111474921 A A -0.09881 4.27E-06 -0.02078 0.5287 -0.02146 0.5158 -0.04701 5.73E-06 -0.01169 0.4764 -0.01167 0.4776 meanCESD 3som rs9588267 LOC105370362 rs9588267

15 kgp10139508 94083217 C C 0.1452 6.21E-06 0.07085 0.17 0.06864 0.1841 0.1144 5.24E-06 0.05146 0.2023 0.04915 0.2237 0.09028 4.94E-06 0.0504 0.1113 0.04884 0.1232 meanCESD meanCESD6it 5mood rs1027118 RP11-266O8.1 rs1027118 rs1027118

16 rs1478708 7139879 G G -0.1345 7.57E-06 0.00419 0.9263 0.002696 0.9526 -0.08568 3.51E-06 0.01647 0.553 0.01528 0.5822 meanCESD 5mood rs1478708 RBFOX1 rs1478708

16 kgp3464645 7142456 A A -0.1368 6.94E-06 0.007736 0.8653 0.006199 0.892 -0.08585 4.48E-06 0.01948 0.4859 0.01828 0.5136 meanCESD 5mood rs1551960 RBFOX1 rs1551960 rs1551960

16 rs12921740 20312032 G G 0.09442 1.30E-06 -0.01176 0.6941 -0.0141 0.6378 0.06831 7.49E-06 -0.007509 0.7483 -0.009196 0.6947 0.04684 6.33E-07 -0.001398 0.9253 -0.002604 0.8615 meanCESD meanCESD6it 3som rs12921740 - rs12921740

16 rs9933126 20312192 A A -0.08871 4.03E-06 0.02148 0.4702 0.02282 0.4434 -0.04438 1.73E-06 0.006859 0.6431 0.007506 0.6123 meanCESD 3som rs9933126 - rs9933126

16 rs4238593 20314682 G G 0.09157 2.05E-06 -0.002001 0.9466 -0.004004 0.8934 0.068 6.44E-06 0.000508 0.9826 -0.0008536 0.9709 0.04545 1.02E-06 -0.000204 0.9891 -0.001289 0.9309 meanCESD meanCESD6it 3som rs4238593 - rs4238593

18 kgp5741699 43445342 A A 0.06276 7.75E-06 -0.009385 0.6882 -0.008868 0.7048 3som rs12456289 EPG5 rs12456289rs12456289

18 rs11082492 43455229 A A 0.06076 4.42E-06 0.01016 0.637 0.01077 0.6173 3som rs11082492 EPG5 rs11082492

18 rs9965433 43508800 A A 0.18 3.20E-07 -0.06551 0.2667 -0.06386 0.2795 0.137 6.42E-07 -0.03427 0.4574 -0.0322 0.4855 0.08618 3.94E-07 -0.04504 0.1248 -0.0449 0.1265 meanCESD meanCESD6it 3som rs9965433 EPG5 rs9965433

18 kgp781834 43520214 A A 0.1929 7.32E-08 -0.06953 0.2573 -0.06786 0.2696 0.145 2.24E-07 -0.03494 0.4666 -0.03277 0.4953 0.1032 2.84E-06 -0.01651 0.6608 -0.01489 0.6926 0.08971 2.10E-07 -0.05302 0.08259 -0.05297 0.08325 meanCESD meanCESD6it 5mood 3som rs12604492 EPG5 rs12604492rs12604492

18 kgp4318744 43522732 G G 0.1931 6.60E-08 -0.07217 0.2392 -0.07051 0.2509 0.1471 1.40E-07 -0.03745 0.4346 -0.03529 0.4622 0.1046 1.99E-06 -0.0172 0.6473 -0.01559 0.6788 0.08856 2.81E-07 -0.05497 0.07168 -0.05493 0.07226 meanCESD meanCESD6it 5mood 3som rs12232683 EPG5 rs12232683rs12232683

18 kgp8272658 43531868 G G 0.2009 3.25E-08 -0.07645 0.2175 -0.07479 0.2283 0.1526 7.79E-08 -0.04222 0.3837 -0.04004 0.4093 0.1076 1.51E-06 -0.02044 0.5907 -0.01881 0.6211 0.09336 1.01E-07 -0.05601 0.06941 -0.05598 0.06991 meanCESD meanCESD6it 5mood 3som rs58682566 EPG5 rs58682566rs58682566

18 rs11082501 43533220 A A 0.1946 6.02E-08 -0.07795 0.206 -0.07629 0.2164 0.1477 1.43E-07 -0.04148 0.3893 -0.03932 0.4151 0.1031 3.06E-06 -0.01865 0.6216 -0.01703 0.6526 0.09154 1.28E-07 -0.0593 0.05313 -0.05926 0.05357 meanCESD meanCESD6it 5mood 3som rs11082501 EPG5 rs11082501

20 rs6040369 11107386 A A 0.05502 9.04E-06 0.01026 0.5914 0.01035 0.5888 5mood rs6040369 - rs6040369

20 rs6108837 11123105 G G 0.05559 6.99E-06 0.01241 0.5133 0.0125 0.5108 5mood rs6108837 - rs6108837

20 rs6108845 11135914 A A 0.09044 9.18E-06 0.02087 0.5017 0.02119 0.4957 0.05787 3.92E-06 0.007144 0.7076 0.007315 0.7012 meanCESD 5mood rs6108845 LOC105372527 rs6108845

20 rs6108847 11138229 G G 0.08987 6.48E-06 0.01703 0.5793 0.01745 0.5705 0.05746 2.75E-06 0.008241 0.6618 0.00845 0.6541 meanCESD 5mood rs6108847 LOC105372527 rs6108847

23 rs5955583 20048947 G G -0.1156 4.52E-06 -0.04391 0.2674 -0.04462 0.2608 -0.08907 6.14E-06 -0.03773 0.2229 -0.03767 0.2246 meanCESD meanCESD6it rs5955583 MAP7D2 rs5955583

23 kgp22790528 20066256 A A -0.1175 2.65E-06 -0.03939 0.3138 -0.04194 0.2845 -0.09172 2.71E-06 -0.03705 0.2255 -0.03869 0.2066 meanCESD meanCESD6it rs7885754 MAP7D2 rs7885754 rs7885754

23 kgp22730196 20067879 G G -0.1168 2.82E-06 -0.04281 0.2767 -0.04504 0.2533 -0.09122 2.87E-06 -0.03807 0.2159 -0.03947 0.2004 meanCESD meanCESD6it rs60349697 MAP7D2 rs60349697rs60349697

23 kgp22825330 20071046 G G -0.1178 1.86E-06 -0.02471 0.5231 -0.02711 0.4845 -0.09188 1.94E-06 -0.0246 0.4162 -0.02614 0.3886 meanCESD meanCESD6it rs34519770 MAP7D2 rs34519770rs34519770

23 kgp22768726 20075730 A A -0.1148 8.82E-06 -0.05258 0.1908 -0.0534 0.1847 meanCESD rs112786451 MAP7D2 rs112786451rs112786451

23 rs4604640 20100619 A A -0.1143 9.71E-06 -0.03955 0.3249 -0.04032 0.3167 meanCESD rs4604640 MAP7D2 rs4604640

23 kgp22750556 20139661 A A -0.1183 2.14E-06 -0.04536 0.2466 -0.04793 0.2218 -0.09253 2.09E-06 -0.04144 0.1757 -0.0431 0.1599 meanCESD meanCESD6it rs7059528 MAP7D2 rs7059528 rs7059528

23 kgp22730997 29221033 G G 0.09082 3.04E-06 0.05792 0.07743 0.05618 0.08704 5mood rs6628418 IL1RAPL1 rs6628418 rs6628418

MANUSCRIP

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ACCEPTED

ACCEPTED MANUSCRIPT

Meta-analysis results MeanCESD MeanCESD6items Mean 5 mood items Mean 3 somatic items

CHR Chromosome code chr bp snp a1 a2 n p pr beta betar q i n p pr beta betar q i n p pr beta betar q i n p pr beta betar q i final_rs Symbol

BP Basepair position 0 0 kgp22837785 A G 2 7.86E-06 7.86E-06 -0.0496 -0.0496 0.5159 0 kgp22837785

SNP SNP identifier 1 15,88,71,474 kgp3071092 A G 2 3.63E-06 3.63E-06 -0.0754 -0.0754 0.7755 0 2 2.02E-06 2.02E-06 -0.0604 -0.0604 0.9617 0 2 3.33E-06 3.33E-06 -0.0368 -0.0368 0.6678 0 rs2427816 -

A1 First allele code 1 22,42,32,423 kgp11402148 A C 2 8.90E-06 8.90E-06 0.0466 0.0466 0.7586 0 rs35773430-

A2 Second allele code 1 22,42,33,718 kgp1605336 A G 2 7.66E-06 7.66E-06 0.0469 0.0469 0.8319 0 rs34479169-

N Number of valid studies for this SNP 1 22,42,37,005 kgp8682297 C A 2 8.71E-06 8.71E-06 0.0466 0.0466 0.7415 0 rs12725098-

P Fixed-effects meta-analysis p-value 1 22,42,38,495 kgp2913569 C A 2 7.75E-06 7.75E-06 0.045 0.045 0.4266 0 rs36067040-

P(R) Random-effects meta-analysis p-value 1 22,42,38,908 kgp3667676 A G 2 7.88E-06 7.88E-06 0.0468 0.0468 0.7148 0 rs11588064-

OR Fixed-effects OR estimate 1 23,80,13,174 rs16835926 G A 2 4.12E-06 4.12E-06 0.0482 0.0482 0.7527 0 rs16835926-

OR(R) Random-effects OR estimate 2 6,74,24,193 rs7590628 A C 2 3.69E-06 3.69E-06 0.046 0.046 0.4213 0 rs7590628 AC078941.1

Q p-value for Cochrane's Q statistic 3 12,70,95,040 kgp9378085 A G 2 5.79E-06 5.79E-06 -0.0378 -0.0378 0.4764 0 rs11714710RP11-88I21.2

I I^2 heterogeneity index (0-100) 3 12,70,95,773 kgp12250447 G A 2 4.41E-06 4.41E-06 -0.0383 -0.0383 0.4108 0 rs60681626RP11-88I21.2

3 12,71,39,117 rs10934813 A G 2 5.17E-06 5.17E-06 -0.0378 -0.0378 0.6571 0 rs10934813-

3 18,63,58,366 rs1047115 C A 2 6.25E-06 0.198 0.085 0.0679 0.0094 85.17 rs1047115 FETUB

4 3,85,94,895 kgp9212403 G A 2 6.91E-06 6.91E-06 -0.0771 -0.0771 0.695 0 2 9.38E-06 9.38E-06 -0.037 -0.037 0.8597 0 kgp9212403intergenic

4 3,86,04,470 rs337637 A G 2 7.14E-06 7.14E-06 -0.0765 -0.0765 0.5453 0 2 8.29E-06 8.29E-06 -0.0594 -0.0594 0.6288 0 rs337637 -

4 3,88,71,385 kgp9123948 G A 2 7.47E-06 0.2472 0.0929 0.0706 0.0076 85.94 rs73236646FAM114A1

5 15,75,25,572 kgp1423289 A G 2 1.00E-06 1.00E-06 -0.0399 -0.0399 0.9691 0 rs1022794 -

6 8,00,65,225 kgp9655634 A G 2 4.02E-06 4.02E-06 0.1085 0.1085 0.8908 0 2 9.80E-06 9.80E-06 0.0813 0.0813 0.9039 0 rs4706778 RP1-232L24.2

6 9,06,68,158 kgp12291530 G A 2 3.37E-06 3.37E-06 0.0699 0.0699 0.6863 0 kgp12291530BACH2

6 15,40,17,226 kgp7779776 A G 2 7.94E-06 5.85E-05 0.0486 0.0495 0.2722 17.05 rs1937599 -

6 15,41,97,500 kgp6601817 A G 2 3.30E-06 3.30E-06 0.0591 0.0591 0.7884 0 rs9397669 -

6 15,42,19,177 rs9384159 A G 2 3.44E-06 3.44E-06 0.0591 0.0591 0.8149 0 rs9384159 -

6 15,43,32,947 kgp2793872 G A 2 8.33E-06 0.000229 0.0483 0.05 0.2333 29.62 rs1294093 OPRM1

6 15,43,35,150 rs712244 G A 2 2.45E-06 0.002398 0.0539 0.059 0.1202 58.59 rs712244 OPRM1

7 2,59,23,381 rs17152874 A C 2 5.43E-06 0.008081 -0.0424 -0.0391 0.1422 53.56 rs17152874-

7 8,00,85,505 kgp8222655 C A 2 8.15E-06 8.15E-06 -0.0457 -0.0457 0.4429 0 rs62461533GNAT3

8 1,44,17,581 kgp6159192 C A 2 4.69E-06 4.69E-06 0.0675 0.0675 0.5423 0 rs59299000SGCZ

8 5,88,43,535 kgp3825727 C A 2 1.49E-07 0.000424 -0.1284 -0.122 0.1854 42.98 2 2.24E-07 0.007103 -0.0989 -0.0901 0.1068 61.55 2 1.01E-07 7.52E-07 -0.0633 -0.0628 0.2955 8.62 rs11774225-

8 5,88,44,022 rs13250896 A G 2 6.14E-08 0.000333 -0.1366 -0.1297 0.1802 44.31 2 1.39E-07 0.007877 -0.1038 -0.0943 0.0976 63.55 2 5.49E-06 0.00494 -0.0704 -0.0656 0.1607 49.17 2 6.24E-08 6.24E-08 -0.0664 -0.0664 0.3306 0 rs13250896-

8 5,88,78,815 kgp4910022 A G 2 6.12E-06 6.12E-06 -0.0472 -0.0472 0.5192 0 rs4237022 -

8 11,91,65,749 kgp3204231 A G 2 7.78E-07 7.78E-07 0.1064 0.1064 0.517 0 2 5.08E-06 5.08E-06 0.0768 0.0768 0.5373 0 2 2.56E-06 2.56E-06 0.0623 0.0623 0.3878 0 rs4147527 -

8 11,91,65,984 kgp4888490 A G 2 8.25E-07 8.25E-07 0.1062 0.1062 0.5045 0 2 5.43E-06 5.43E-06 0.0766 0.0766 0.5283 0 2 2.78E-06 2.78E-06 0.062 0.062 0.3737 0 rs1379328 -

8 11,91,71,312 rs11774887 A G 2 9.07E-07 9.07E-07 0.1059 0.1059 0.3749 0 2 6.34E-06 6.34E-06 0.0761 0.0761 0.3964 0 2 2.97E-06 9.64E-05 0.0619 0.0605 0.2573 22.07 rs11774887-

8 11,91,74,045 kgp10557436 A G 2 1.95E-06 1.95E-06 0.104 0.104 0.3546 0 2 6.65E-06 6.65E-06 0.077 0.077 0.4674 0 2 2.27E-06 2.27E-06 0.0635 0.0635 0.3921 0 rs4876798 -

9 22,02,331 kgp1254185 A G 2 4.72E-06 4.72E-06 -0.0502 -0.0502 0.7153 0 rs4741653 -

9 47,32,757 kgp1871860 C A 2 5.55E-06 0.002374 0.0751 0.0809 0.1312 56.1 rs7861716 AK3

9 3,27,44,916 kgp3087254 G C 2 7.70E-06 0.03898 -0.073 -0.0644 0.0769 68.04 2 1.46E-06 0.01701 -0.0614 -0.0554 0.092 64.79 2 3.95E-06 0.01072 -0.0463 -0.0424 0.1228 58 rs13297009-

11 4,44,32,418 rs6485523 A G 2 9.10E-06 9.10E-06 -0.0493 -0.0493 0.5004 0 rs6485523 -

15 9,40,83,217 kgp10139508 C A 2 5.00E-06 0.000805 0.1245 0.119 0.2213 33.15 2 5.53E-06 0.002847 0.0968 0.0909 0.1854 42.98 2 2.37E-06 2.27E-05 0.0791 0.078 0.285 12.5 rs1027118 RP11-266O8.1

19 45,57,056 kgp2132781 A C 2 4.95E-06 4.95E-06 0.0525 0.0525 0.3655 0 rs10422881SEMA6B

21 2,88,17,836 rs11701365 A G 2 9.42E-06 9.42E-06 0.0582 0.0582 0.7422 0 rs11701365AP001604.3

23 2,00,48,947 rs5955583 G A 2 7.92E-06 0.0144 -0.0949 -0.0863 0.1266 57.16 2 7.78E-06 0.005936 -0.0743 -0.069 0.1616 48.95 2 9.24E-06 9.24E-06 -0.0579 -0.0579 0.3697 0 rs5955583 MAP7D2

23 2,00,66,256 kgp22790528 A C 2 6.72E-06 0.0292 -0.0948 -0.0842 0.0924 64.7 2 4.07E-06 0.009749 -0.0759 -0.0694 0.1318 55.98 2 4.63E-06 4.63E-06 -0.0593 -0.0593 0.3529 0 rs7885754 MAP7D2

23 2,00,67,879 kgp22730196 G A 2 5.58E-06 0.01824 -0.0956 -0.0861 0.1122 60.37 2 3.87E-06 0.007192 -0.076 -0.07 0.1443 53.07 2 6.14E-06 6.14E-06 -0.0585 -0.0585 0.3656 0 rs60349697MAP7D2

23 2,00,71,046 kgp22825330 G A 2 8.46E-06 0.06223 -0.0724 -0.0623 0.0608 71.56 rs34519770MAP7D2

23 2,00,75,730 kgp22768726 A G 2 8.74E-06 0.002471 -0.0966 -0.0913 0.1927 41.06 2 8.19E-06 0.000242 -0.0758 -0.0738 0.2557 22.59 rs112786451MAP7D2

23 2,01,39,661 kgp22750556 A G 2 3.78E-06 0.01425 -0.0972 -0.0881 0.116 59.51 2 2.22E-06 0.003706 -0.0778 -0.0724 0.1591 49.57 2 3.15E-06 3.15E-06 -0.0602 -0.0602 0.3698 0 rs7059528 EIF1AX

23 2,92,21,033 kgp22730997 G A 2 8.68E-07 8.68E-07 0.0823 0.0823 0.3881 0 rs6628418 IL1RAPL1

23 15,13,71,107 kgp22829282 G A 2 6.27E-06 0.09951 0.0571 0.0751 0.0012 90.53 rs7062484 GABRA3

23 15,13,71,817 kgp22755075 A G 2 5.28E-06 0.08457 0.0575 0.0745 0.0021 89.42 rs4828692 GABRA3

23 15,13,82,114 kgp22820808 A G 2 8.84E-06 0.08892 0.0571 0.0745 0.0022 89.29 rs11797985GABRA3

23 15,13,84,118 kgp22756678 A G 2 9.99E-06 0.1007 0.057 0.0751 0.0014 90.17 kgp22756678GABRA3

23 15,13,87,604 kgp22771567 G A 2 5.23E-06 0.07883 0.0585 0.0754 0.0027 88.88 kgp22771567GABRA3

23 15,14,14,299 kgp22766776 A G 2 8.70E-06 0.08016 0.0572 0.0736 0.0033 88.43 rs2131190 GABRA3

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Conflict of interest

The authors report no conflict of interest.

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The authors’ contribution

Study design: JL, NP, JYN

Analysis: KM, DFP, TEA, BV

Writing the manuscript: KM, DFP, TEA, BV, GT, JYN, JL, CAP, AS, NP

Funding source

This work was supported by the Medical Research Council [grant number: G1001375] in the United

Kingdom and the National Institute on Aging [grant numbers: R01 AG030153 and F32 AG048681] in

the United States. The funding sources had no further role in conducting the research or in

publication.

The authors report no conflict of interest.

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