2024-12-10

092_ Genome-wide meta-analyses of treatment response/resistance across major psychiatric disorders

Research Question and Aims

This proposal will study the genetics of treatment efficacy in major depressive disorder (MDD), bipolar disorder, and schizophrenia within the Horizon Europe project Psych-STRATA (https://psych-strata.eu). Psych-STRATA aims to study genomics, proteomics, and other omics to identify the biological mechanisms involved in treatment efficacy across mood disorders (MDD, bipolar disorder) and schizophrenia. We hypothesised that there are shared mechanisms involved in response and treatment resistance (TR), and moving beyond the study of one single disorder we may gain valuable insights in common pathways leading to poor response and TR. The possibility to include the PsyCourse cohort in the analyses would be a very valuable contribution to this project.

Analytic Plan

The sample will be analysed with other cohorts available within the Psych-STRATA consortium, having a diagnosis of MDD, bipolar disorders, or schizophrenia (e.g., Thematically Organized Psychosis (TOP), PMID: 28292279; PREFECT PMID: 33483693; Early Medication Change (EMC) PMID: 35794104; SWEBIC PMID: 38859703). First, we plan a GWAS meta-analyses within each diagnosis group, then a cross-disorder meta-analysis.
We aim to study the genetics of TR and measures of response/remission according to the data available in each sample (e.g., according to standard scales such as the Montgomery-Åsberg Depression Rating Scale, Positive and Negative Syndrome Scale, Retrospective Assessment of Response to Lithium Scale, but also other measures that are available in observational cohorts and electronic health records (EHRs) such as UK Biobank, e.g., stability of treatment over time, drug switches, or use of polypharmacy). TR will be defined according to measures available in each sample, which include non-response to ≥ two treatments according to standard scales or treatment history collected in naturalistic cohorts or EHRs (e.g., number of medication switches, e.g., PMID 33753889), ECT treatment, clozapine treatment in case of schizophrenia, or combination treatments in case of bipolar disorders. TR individuals will be compared to responders or non-TR cases; comparisons to healthy controls will be considered. Measures of functioning and quality of life/wellbeing can be considered if available.
Following standard quality control procedures (RICOPILI pipeline or equivalent) and genotype imputation, analyses will consist in genome-wide association studies (GWAS) and meta-analysis, with post-GWAS analyses (e.g., SNP-heritability estimation, fine mapping, pathway analysis, stratified linkage disequilibrium score regression, polygenic risk scores). Population stratification and other covariates will be considered (e.g., sociodemographic variables, medical and psychiatric comorbidities). Secondly, we will study the shared and distinct genetic factors between phenotypes of response/resistance across mood disorders and schizophrenia. These analyses will include a GWAS meta-analysis of response/resistance across diagnoses (multivariate GWAS, case-case-GWAS, e.g., PMID: 33686288) and post-GWAS analyses (e.g., fine mapping, prediction of effects on gene expression, global and local genetic correlations, polygenic overlap irrespective of genetic correlation (e.g., MiXeR, PMID: 31160569), polygenic risk scores, analysis of expression-weighted cell type enrichment).
Considering multi-omics analyses is one of the key objectives of Psych-STRATA, we will explore analytical approaches that link multiple omics data types. We will use methods for the individual-level imputation of omics (e.g., transcriptomics and proteomics) from genome-wide genotypes (e.g., PMID 36991119) and test their association with treatment efficacy.
Samples included in Psych-STRATA will provide genome-wide genotypes and a combination of other omics data, e.g., gene expression and proteomics. In the case of PsyCourse, use of available omics data will be valuable, as well as access to plasma samples to perform proteomic profiling with the same approach used in other samples and include PsyCourse in pQTL (protein quantitative trait locus) analyses. Proteomic profiling in Psych-STRATA includes two phases: 1) a pilot discovery phase using the Olink HT Explore panel including 5,400+ proteins, in a selected sample of 860 individuals with MDD, bipolar disorder or schizophrenia; 2) a validation phase that will prioritize a subset of proteins for a cost-effective, high throughput affinity-based proteomics approach on 4,860 samples with MDD, bipolar disorder or schizophrenia. The top 360 proteins from the Olink-based discovery phase will be analysed with the Luminex based affinity proteomics technology planned for the large-scale validation phase in the same 860 samples analysed on Olink. This intermediate validation step will ensure that all proteins prioritized for the validation campaign can be measured with sufficient accuracy with the Luminex technology. Based on these results, we will prioritize 80 proteins for validation in the 4,000 additional samples.

Resources needed

Genome-wide genotypes in plink format (pre-QC)
Plasma samples for proteomic profiling (see C. Analytic Plan)
Raw medication data

v1_id
v1_stat
v1_center
v1_tstlt
v1_sex
v1_yob
v1_school
v1_prof_dgr
v1_ed_status
v1_wrk_abs_pst_5_yrs
v1_cntr_brth
v1_outpat_psy_trm
v1_age_1st_out_trm
v1_daypat_inpat_trm
v1_age_1st_inpat_trm
v1_dur_illness
v1_1st_ep
v1_tms_daypat_outpat_trm
v1_cat_daypat_outpat_trm
v1_fam_hist
v1_chol_trig
v1_hyperten
v1_ang_pec
v1_heart_att
v1_stroke
v1_diabetes
v1_hyperthy
v1_hypothy
v1_osteopor
v1_asthma
v1_copd
v1_allerg
v1_neuroder
v1_psoriasis
v1_autoimm
v1_cancer
v1_stom_ulc
v1_kid_fail
v1_stone
v1_epilepsy
v1_migraine
v1_parkinson
v1_liv_cir_inf
v1_tbi
v1_beh
v1_eyear
v1_inf
v1_ever_smkd
v1_age_smk
v1_alc_pst12_mths
v1_lftm_alc_dep
v1_evr_ill_drg
v1_evr_hvy_usr
v1_scid_dsm_dx
v1_scid_dsm_dx_cat
v1_scid_age_MDE
v1_scid_no_MDE
v1_scid_age_mania
v1_scid_no_mania
v1_scid_age_hypomania
v1_scid_no_hypomania
v1_scid_ever_halls
v1_scid_ever_delus
v1_scid_ever_psyc
v1_scid_age_fst_psyc
v1_scid_yr_fst_psyc
v1_scid_evr_suic_ide
v1_suic_attmpt
v1_scid_no_suic_attmpt
v1_age_fst_suic_att
v1_nrpsy_mwtb
v3_cts_1
v3_cts_2
v3_cts_3
v3_cts_4
v3_cts_5
v4_opcrit
v4_alda_A
v4_alda_B1
v4_alda_B2
v4_alda_B3
v4_alda_B4
v4_alda_B5
psyc_id
gsa_id
visit
adv
age
alc_5orm
alc_pst6_mths
Antidepressants
Antipsychotics
asrm_itm1
asrm_itm2
asrm_itm3
asrm_itm4
asrm_itm5
asrm_sum
bdi2_itm1
bdi2_itm10
bdi2_itm11
bdi2_itm12
bdi2_itm13
bdi2_itm14
bdi2_itm15
bdi2_itm16
bdi2_itm17
bdi2_itm18
bdi2_itm19
bdi2_itm2
bdi2_itm20
bdi2_itm21
bdi2_itm3
bdi2_itm4
bdi2_itm5
bdi2_itm6
bdi2_itm7
bdi2_itm8
bdi2_itm9
bdi2_sum
bmi
cgi_c
cgi_s
chg_empl_stat
chg_hsng
clin_add_oth_hsp
clin_fst_ill_ep_dep
clin_fst_ill_ep_dur
clin_fst_ill_ep_hsp_dur
clin_fst_ill_ep_hsp
clin_fst_ill_ep_man
clin_fst_ill_ep_med_chg
clin_fst_ill_ep_mx
clin_fst_ill_ep_oth_end
clin_fst_ill_ep_othr
clin_fst_ill_ep_psy
clin_fst_ill_ep_slf_end
clin_fst_ill_ep_suic
clin_fst_ill_ep_symp_wrs
clin_ill_ep_snc_lst
clin_no_ep
clin_oth_hsp_dur
clin_oth_hsp_nmb
clin_othr_psy_med
clin_sec_ill_ep_dep
clin_sec_ill_ep_dur
clin_sec_ill_ep_hsp_dur
clin_sec_ill_ep_hsp
clin_sec_ill_ep_man
clin_sec_ill_ep_med_chg
clin_sec_ill_ep_mx
clin_sec_ill_ep_oth_end
clin_sec_ill_ep_othr
clin_sec_ill_ep_psy
clin_sec_ill_ep_slf_end
clin_sec_ill_ep_suic
clin_sec_ill_ep_symp_wrs
con_no_psy_hosp
cur_psy_trm
cur_work_restr
curr_paid_empl
disabl_pens
epic_id
gaf
idsc_itm1
idsc_itm10
idsc_itm11
idsc_itm12
idsc_itm13
idsc_itm14
idsc_itm15
idsc_itm16
idsc_itm17
idsc_itm18
idsc_itm19
idsc_itm2
idsc_itm20
idsc_itm21
idsc_itm22
idsc_itm23
idsc_itm24
idsc_itm25
idsc_itm26
idsc_itm27
idsc_itm28
idsc_itm29
idsc_itm3
idsc_itm30
idsc_itm4
idsc_itm5
idsc_itm6
idsc_itm7
idsc_itm8
idsc_itm9
idsc_itm9a
idsc_itm9b
idsc_sum
interv_date
leq_A_1A
leq_A_1B
leq_A_3A
leq_A_3B
leq_A_3A
leq_A_3B
leq_A_8A
leq_A_8B
leq_B_10A
leq_B_10B
leq_B_17A
leq_B_17B
leq_B_18A
leq_B_18B
leq_E_31A
leq_E_31B
leq_E_38A
leq_E_38B
leq_E_39A
leq_E_39B
leq_E_41A
leq_E_41B
leq_F_46A
leq_F_46B
leq_F_47A
leq_F_47B
leq_F_48A
leq_F_48B
leq_I_73A
leq_I_73B
lexo_id
lip_id
lith_prd
lith
liv_aln
marital_stat
med_pst_sx_mths
med_pst_wk
medchange
Mood_stabilizers
mss_itm1
mss_itm10
mss_itm11
mss_itm12
mss_itm13
mss_itm14
mss_itm15
mss_itm16
mss_itm17
mss_itm18
mss_itm19
mss_itm2
mss_itm20
mss_itm21
mss_itm22
mss_itm23
mss_itm24
mss_itm25
mss_itm26
mss_itm27
mss_itm28
mss_itm29
mss_itm3
mss_itm30
mss_itm31
mss_itm32
mss_itm33
mss_itm34
mss_itm35
mss_itm36
mss_itm37
mss_itm38
mss_itm39
mss_itm4
mss_itm40
mss_itm41
mss_itm42
mss_itm43
mss_itm44
mss_itm45
mss_itm46
mss_itm47
mss_itm48
mss_itm5
mss_itm6
mss_itm7
mss_itm8
mss_itm9
mss_sum
no_cig
no_suic_attmpt
nrpsy_com
nrpsy_dg_sym
nrpsy_dgt_sp_bck
nrpsy_dgt_sp_frw
nrpsy_lng
nrpsy_mtv
nrpsy_tmt_A_err
nrpsy_tmt_A_rt
nrpsy_tmt_B_err
nrpsy_tmt_B_rt
nrpsy_vlmt_check
nrpsy_vlmt_corr
nrpsy_vlmt_lss_d
nrpsy_vlmt_lss_t
nrpsy_vlmt_rec
Other_psychiatric
panss_g1
panss_g10
panss_g11
panss_g12
panss_g13
panss_g14
panss_g15
panss_g16
panss_g2
panss_g3
panss_g4
panss_g5
panss_g6
panss_g7
panss_g8
panss_g9
panss_n1
panss_n2
panss_n3
panss_n4
panss_n5
panss_n6
panss_n7
panss_p1
panss_p2
panss_p3
panss_p4
panss_p5
panss_p6
panss_p7
panss_sum_gen
panss_sum_neg
panss_sum_pos
panss_sum_tot
partner
prep_suic_attp_ord
pst6_ill_drg
scid_suic_ide
scid_suic_note_thgts
scid_suic_thght_mth
sf12_itm0
sf12_itm1
sf12_itm10
sf12_itm11
sf12_itm12
sf12_itm2
sf12_itm3
sf12_itm4
sf12_itm5
sf12_itm6
sf12_itm7
sf12_itm8
sf12_itm9
smk_strt_stp
smRNAome_id
spec_emp
suic_attmpt_snc_lst_vst
suic_ide_snc_lst_vst
suic_note_attmpt
Tranquilizers
waist
whoqol_dom_env
whoqol_dom_glob
whoqol_dom_phys
whoqol_dom_psy
whoqol_dom_soc
whoqol_itm1
whoqol_itm10
whoqol_itm11
whoqol_itm12
whoqol_itm13
whoqol_itm14
whoqol_itm15
whoqol_itm16
whoqol_itm17
whoqol_itm18
whoqol_itm19
whoqol_itm2
whoqol_itm20
whoqol_itm21
whoqol_itm22
whoqol_itm23
whoqol_itm24
whoqol_itm25
whoqol_itm26
whoqol_itm3
whoqol_itm4
whoqol_itm5
whoqol_itm6
whoqol_itm7
whoqol_itm8
whoqol_itm9
wrk_abs_pst_6_mths
ymrs_itm1
ymrs_itm10
ymrs_itm11
ymrs_itm2
ymrs_itm3
ymrs_itm4
ymrs_itm5
ymrs_itm6
ymrs_itm7
ymrs_itm8
ymrs_itm9
ymrs_sum