Gene Expression QC
Quality control (QC) was conducted on NESDA subjects together with a previously reported twin
sample from the Netherlands Twin Registry 1. The expression data were required to pass standard
Affymetrix Expression Console quality metrics before further QC. All probe sequences were
mapped to the human genome (hg19) using BOWTI2 , and probes not mapped uniquely or
intersecting a polymorphic SNP (HapMap3 and 1000 Genomes Project) were removed 3,4. We
mapped and annotated all Affymetrix U219 probesets to GENCODE gene models 5, probesets not
matching any gene or transcript were removed, resulting in 45,574 probesets targeting 18,391
genes. Expression values were obtained using robust multichip averaging normalization (RMA,
Affymetrix Power Tools, v1.12.0). Samples with sex inconsistency based on chrX and chrY
probesets were removed. We verified sample identity between gene expression and SNP genotypes
using 500 of the most significant local eQTL (SNP-transcript pairs) to estimate a post mismatch
probability between gene expression and genotype profile. We examined the pairwise correlation
matrix of expression profiles using a standardized median deviation correlations (D)1 in order to
identify and remove array correlation outliers. The post mismatch probability and D were highly
correlated with many expression probesets and were used as covariates in the final models. We
evaluated the importance of multiple other potential covariates on gene expression, and chose 16
covariates for all analyses (sex, age, body mass index, smoking status, red blood cell count,
laboratory, month and hour of blood extraction, time between blood extraction and RNA
hybridization, hybridization plate and well, D, post mismatch probability, and 3 PCs derived from
genotype data, Table S1). The covariates of greatest impact (>20,000 probesets) reflected batch
effects, array processing, and RNA sampling, while subject characteristics (e.g., smoking, body
mass index, age, or sex) influenced far fewer probes (<7,100).
Linear models for MDD status and gene expression associations
Primary inference was conducted on the basis of a linear model consisting of empirical covariates
(Table S1), the appropriate MDD term as independent variables, and gene expression as dependent
variable. P-values and betas were computed for the associations between MDD and gene expression
for each of the 45,574 probesets. Two different linear models were considered: a model where the
current MDD and remitted MDD groups were combined and a model using MDD as 3 level
categorical variable (control, current MDD and remitted MDD) from which we derived overall
MDD associations with gene expression (F-test) and three pairwise MDD group comparisons
(controls vs. current MDD, controls vs. remitted MDD and remitted MDD vs. current MDD using ttests). When comparing the mean expression between the three MDD groups, gene expression was
residualized using the linear model described above (without MDD). To verify confounding effects
of immune status and antidepressants use, CRP or antidepressant use (SSRI, TCA and SNRI) were
added to the linear model. P-values and betas for the current MDD-control group comparisons were
recomputed after removal of the antidepressant users from the linear model. Control for multiple
comparisons was addressed using the false discovery rate 6 , applying a threshold of FDR<0.1. In
addition, the P-values and directions of effects for associations between MDD status and gene
expression from a recent RNA-seq study (436 cases and 459 controls) 7 were meta-analysed with
our findings using a weighted Z-score method.
Genome-wide SNP assays used for eQTL analysis
DNA was extracted from whole blood and all samples were randomized to plates. Genotyping was
conducted using Affymetrix Genome-Wide Human SNP Array 6.0 per manufacturer protocol as
described previously 1. Subjects were removed for Affymetrix contrast QC ≤ 0.4, high missingness
(> 0.05), outlying genome-wide homozygosity, outlying ancestry via principal components analysis
8
, or discrepant sex. SNP QC included removal of SNPs for non-unique probe mapping to NCBI
Build 37/UCSC hg19, low minor allele frequency (< 0.005), substantial deviation from HapMap3
CEU founder allele frequencies, deviation from Hardy-Weinberg equilibrium (PHWE < 1x10-8), or
high missingness (> 0.05).
Examine gene expression as mediator of DNA associations with MDD status
Gene-based analysis of the PGC MDD GWAS results9 was conducted using VEGAS 10 and JAG 11.
For JAG, a test statistic for each gene was computed using the sum of the -log10 P-values of the
SNPs in the gene. Empirical P-values of these test statistics were computed using 1,000,000
permutations. For VEGAS, for genes with expression differences associated with current MDD, we
selected SNPs and corresponding P-values from the PGC MDD results and used these as input to
VEGAS (URLs). The gene-based statistic used in VEGAS is the sum of the P-values from the SNPs
within the gene converted into uppertail chi-squared statistics. Monte Carlo simulations generate
chi-squared statistics with the same correlation structure as the observed statistics, induced by the
LD structure of SNPs in the gene. The empirical gene-based P-value is the proportion of simulated
test statistics that exceed the observed gene-based test statistic.
1
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