2023-06-10

066_ Assessment of LipidQTLs in the PsyCourse Study (amendment to 031)

Research Question and Aims

As the most abundant compounds in the central nervous system, lipids are known to play an important yet often times disregarded role in neuronal structure and function. This role includes tasks in the regulation of membrane fluidity and permeability, vesicle formation and transport, neurotransmitter release, cell integrity and plasticity. Dysfunction of these processes has been implicated in the pathogenesis of numerous psychiatric disorders (reviewed in PMID 27054615).
There is mounting evidence that lipid species are altered both in the central nervous system and the periphery of individuals with severe psychiatric disorders such as bipolar disorder (BPD) or schizophrenia (SCZ) compared to controls (PMID 30089790, own unpublished observation).
As comprehensive lipidomics are a still-emerging field, few studies have assessed the genetic underpinnings of lipid profiles in a comprehensive fashion. A recent study performed a plasma lipid species-based quantitative trait locus (lipidQTL) analysis and identified 42 genome-wide significant associations between single nucleotide polymorphisms (SNPs) and molecular lipid species (PMID 31551469). Interestingly, 19 of the proposed gene candidates at these lipid-species-associated loci also harbor common genetic variants associated with psychiatric phenotypes ranging from cognitive phenotypes and major psychiatric disorders such as BPD or SCZ to transdiagnostic phenotypes (cf. GWAS Catalog, www.ebi.ac.uk/gwas/).
Accordingly, herein, we propose to perform a lipidQTL analysis, individually or as a low-dimensional representation of the lipidomic and proteomic datasets, in plasma samples of individuals belonging to the PsyCourse Study for whom plasma lipidomic profiles (n=310 BPD, n=234 SCZ, n=266 controls) and genotype data are available. Aim one would be to replicate the findings of the performed lipidQTL study above in an independent data set and to identify potential lQTLs of particular importance to severe mental health disorders. We will further employ Mendelian Randomization strategies to understand the causal relationship between plasma lipid levels and severe mental health diagnoses. The second aim would be to use lipidQTLs to identify novel pathways or pathomechanisms involved in BPD and SCZ.

Analytic Plan

We postulate that plasma lipidQTLs may represent a tool to uncover pathomechanisms underlying BPD and SCZ. We will use SCID-assessed DSM-IV diagnoses for phenotype definition.
Available lipidomics data have been quality controlled by the collaborating lab, quality control of genotype data will performed as described previously in studies in which the applicants were involved (e.g. PMID 26806518, PMID 31712617).
LipidQTL analysis will be performed by running individual GWAS for lipid levels. PCA will be performed to address potential confounders. Analyses will be performed in Python or R as applicable. The identified lipidQTLs will be blasted against large databases of know genetic risk loci not only for mental disorders but many other human traits with the aim to identify genetic overlaps. Moreover, depending on the outcomes, a phenome-wide analysis of the top variants on selected phenotypes will be carried out using resources like the UK Biobank and both one- and two sample Mendelian Randomization will help us identify potential causal relationships between lipid levels and severe mental health phenotypes (e.g. PMID 35239653). Analyses will be performed in Python or R as applicable.

Resources needed

v1_age
v1_yob
v1_sex
v1_center
v1_cur_psy_trm
v1_age_1st_out_trm
v1_age_1st_inpat_trm
v1_dur_illness
raw medication data sets (v1_med_clin_orig, v1_med_con_orig)
v1_fam_hist
v1_lith
v1_lith_prd
v1_waist
v1_bmi
v1_weight
v1_height
v1_chol_trig
v1_hyperten
v1_ang_pec
v1_heart_att
v1_stroke
v1_diabetes
v1_scid_dsm_dx
v1_scid_dsm_dx_cat
v1_med_pst_wk
v1_ever_smkd

v2_age
v2_waist
v2_bmi
v2_weight
v2_lith
v2_lith_prd
raw medication data sets (v2_med_clin_orig, v2_med_con_orig)
v2_smk_strt_stp
v2_med_pst_wk

v3_age
v3_waist
v3_bmi
v3_weight
v3_lith
v3_lith_prd
raw medication data sets (v3_med_clin_orig, v3_med_con_orig)
v3_smk_strt_stp
v3_med_pst_wk

v4_age
v4_waist
v4_bmi
v4_weight
v4_lith
v4_lith_prd
raw medication data sets (v4_med_clin_orig, v4_med_con_orig)
v4_smk_strt_stp
v4_med_pst_wk
gwas_id
v1_lip_id
v2_lip_id
v3_lip_id
v4_lip_id
v1_prot_id
v2_prot_id
v3_prot_id
v4_prot_id
v1_ab_prof_id
v2_ab_prof_id
v3_ab_prof_id

Genetic data:
Raw genotypes to calculate PCAs
Imputed genotypes for the calculation of PRS