Immunogenetics of lithium response and psychiatric phenotypes in patients with bipolar disorder

The link between bipolar disorder (BP) and immune dysfunction remains controversial. While epidemiological studies have long suggested an association, recent research has found only limited evidence of such a relationship. To clarify this, we performed an exploratory study of the contributions of immune-relevant genetic factors to the response to lithium (Li) treatment and the clinical presentation of BP. First, we assessed the association of a large collection of immune-related genes (4925) with Li response, defined by the Retrospective Assessment of the Lithium Response Phenotype Scale (Alda scale), and clinical characteristics in patients with BP from the International Consortium on Lithium Genetics (ConLi+Gen, N = 2374). Second, we calculated here previously published polygenic scores (PGSs) for immune-related traits and evaluated their associations with Li response and clinical features. Overall, we observed relatively weak associations (p < 1 × 10−4) with BP phenotypes within immune-related genes. Network and functional enrichment analyses of the top findings from the association analyses of Li response variables showed an overrepresentation of pathways participating in cell adhesion and intercellular communication. These appeared to converge on the well-known Li-induced inhibition of GSK-3β. Association analyses of age-at-onset, number of mood episodes, and presence of psychosis, substance abuse and/or suicidal ideation suggested modest contributions of genes such as RTN4, XKR4, NRXN1, NRG1/3 and GRK5 to disease characteristics. PGS analyses returned weak associations (p < 0.05) between inflammation markers and the studied BP phenotypes. Our results suggest a modest relationship between immunity and clinical features in BP. More research is needed to assess the potential therapeutic relevance.


Immunogenetics of lithium response and psychiatric phenotypes in patients with bipolar disorder Supplementary Results
Age-at-onset.Fifty-four associations in 21 ImmuneSet genes were found for AAO in our study (Table 3, Supplementary File 2: Table 2).These genes were enriched for negative regulation of cell death and synaptic transmission, as well as expression in the cerebellum (Supplementary File 3: Table 3).The top variant, rs1248079 (p=3.9x10 - , beta=2.75),mapped to an intronic region in GRK5 (G Protein-Coupled Receptor Kinase 5).Other important genes included PLD3, AKT2 and IL1B.The addition of eQTL genes to the functional enrichment analysis resulted in an additional overrepresentation of processes related to cellular stress responses.
Depression.With an effective sample of 692 individuals, 107 associations in 31 ImmuneSet genes for the number of depressive episodes were found (Table 3, Supplementary File 2: Table 3).These genes were enriched for synaptic processes, as well as expression in frontal and anterior cingulate cortices (Supplementary File 3: Table 4).While the top variant, rs55975329 (p=1.7x10-7 , beta=4.54),localized to an intron in BLNK (B cell linker), other important genes included PHLPP1, ZCCHC11 (TUT4) and CPPED1.The addition of eQTL genes to the functional enrichment analysis resulted in an additional overrepresentation of axonal and synaptic components.
Hypomania.Although the largest associations were found for the number of hypomanic episodes, these observations were based on only 85 individuals with available data.Therefore, we have excluded this phenotype from the figures and tables shown within this manuscript.All corresponding results are provided in the supplementary material (Supplementary File 2: Table 4, Supplementary File 3: Table 5).
Mania.With an effective sample of 665 individuals, 116 associations in 32 ImmuneSet genes for the number of manic episodes were found (Table 3, Supplementary File 2: Table 5).These genes were enriched for neuronal development and differentiation, as well as expression in spinal cord and frontal cortex (Supplementary File 3: Table 6).The top variant, rs59134172 (p=2.4x10 - , beta=1.62),localized to an intron in CNTN6 (Contactin 6).Other important genes included KALRN, PCDH9, PTK2B and CNTNAP2.Interestingly, we also found enrichment for response to serotonin re-uptake inhibitors in major depressive disorder (FDR=0.042)and serum thyroid-stimulating hormone levels (FDR=0.0028),from the GWAS Catalog trait associations, in mania-associated ImmuneSet genes.The addition of eQTL genes to the functional enrichment analysis had no impact on the overrepresented gene set classes.
The addition of eQTL genes to the functional enrichment analysis had no impact on the overrepresented processes.However, this resulted in a considerable increase in overepresented brain tissues of expression, including the hippocampus, amygdala, hipothalamus, anterior cingulate cortex, putamen and substantia nigra.
Alcohol and substance abuse.The effective sample sizes for alcohol and substance use disorders in ConLi + Gen were 835 and 832, respectively.Twenty-nine SNPs in nine genes were associated with alcohol use, with the rs7698751 SNP in SCD5 (Stearoyl-CoA Desaturase 5) being the top association (p=1.8x10 - , beta=2.61).Although no gene set enrichments were found for associations with alcohol abuse, other implicated genes included the BP-associated DGKH, NRG3 and RIMS1 (Supplementary File 2: Table 7).Interestingly, when incorporating the eQTLs genes into this functional analysis, overrepresentation of genes associated with mood swings, loneliness and anxious behaviors in the GWAS Catalog was observed (Supplementary File 3: Table 8).For substance use disorder, 78 associations implicating 17 genes were found (Supplementary File 2: Table 8).Genes were overrepresented in neurogenesis-related processes, with expression in cerebellum as well as frontal and anterior cingulate cortices (Supplementary File 3: Table 8).The top variant, rs7814474 (p=4.5x10 - , beta=6.7), was mapped to an intron in TPD52 (Tumor Protein D52).
Other genes included the BP-associated NRG1, the schizophrenia-associated PTPRM, as well as NOD1 and PRKCQ.In addition, the GWAS Catalog traits chronotype (FDR=0.011)and serum thyroid-stimulating hormone levels (FDR=0.047)were also overrepresented in substance abuse-associated ImmuneSet genes.The addition of eQTL genes to the functional enrichment analysis resulted in an additional overrepresentation of the phosphatidylinositol signaling system and expression in the amygdala, hippocampus and basal ganglia.
Suicidal ideation.Information on the presence of suicidal thoughts was available for 660 ConLi + Gen individuals.Based on these, 30 variants in seven genes were associated with suicidal ideation in the "GWAS1" sample (Table 3, Supplementary File 2: Table 9).The top SNP was rs2327882 (p=3.9x10 - , beta=0.55),located in an intron of the JARID2 (Jumonji and AT-Rich Interaction Domain Containing 2) gene.Gene set enrichment analysis found overrepresentation of functions of nuclear receptors (Supplementary File 3: Table 9).Indeed, these terms related to RARB and THRB.The addition of eQTL genes to the functional enrichment analysis had no impact on the overrepresented gene set classes.
PGSs were subjected to a supervised machine learning screening to obtain the relative importance of each PGS for Li response (dichotomized variable).

Figure S2
. Principal component analysis.Plotted principal components (PCs) of the genetic data revealed that the first PC reflected ancestry (i.e.European or East Asian) in both ConLi + Gen samples.However, all stratification by this and other factors, such as recruitment site, is completely eliminated in both GWAS1 (A) and GWAS2 (B) ConLi + Gen samples in the first 5-6 PCs.

Figure S3
. Machine learning PGS screening.Relative importance of calculated PGSs for the dichotomized response to Li treatment in ConLi+Gen "GWAS1".Various machine-learning algorithms were tested.The relative importance obtained with each algorithm is represented by the size of the bubbles.The direction of effect is colored in red when positive (i.e.favors response) and in blue when negative (i.e.favors non-response).DeepL: deep learning, FLM: fast large margin, GBTrees: gradient boosted trees, GLM: generalized linear model, Logistic: logistic regression, NB: naïve Bayes, RandomF: random forest, SVM: support vector machine, CART: decision tree.