Pathomechanisms and Signatures in the Longitudinal Course of Psychosis

13.01.2015

2021-12-10

048_ Multi-level integrative omics to identify biomarkers in schizophrenia and other major psychoses (MulioBio): Classification based on psychopathology and cognitive profile

Research Question and Aims

Despite many years of research and many promising candidates there are no validated and reliable biomarkers (BM) in clinical use for schizophrenia or other major psychoses (MP). In the MulioBio project (see e.g. proposal 024_ Preparatory work for the MulioBio project and 037_ Multi-level integrative omics to identify biomarkers in a Schizophrenia and other major psychoses (MulioBio): SmallRNAome comparison of broad diagnostic groups), we postulate that phenotype definition is the reason BM could not be discovered. To resolve this, we intended to classify patients of the longitudinal PsyCourse cohort, that show the same behavioral profiles over time, into transdiagnostic groups. In the MulioBio study, former PsyCourse participants will be re-contacted, PBMCs will be obtained from them under the auspices of the MulioBio study, and a multi-level BM screening of molecular phenotypes will be performed (RNAome, smallRNAome, epigenome, proteome), eventually leading to potential biomarkers able to differentiate between the transdiagnostic groups.
The idea of the MulioBio project is that while the overlap of symptoms makes it difficult to identify biomarkers of specific disorders, it should be possible to identify biomarkers of psychosis subgroups with a similar course over time. To achieve this we will use the longmixr package (https://github.com/cellmapslab/longmixr, previously known as PhenEndo) to classify PsyCourse participants according to both the course of their psychopathology and their cognitive profile. This package realizes a clustering using longitudinal data.

Analytic Plan

We intent to use data from the 4 visits of the PsyCourse study to create transdiagnostic groups using the longmixr R package adjusting for age at baseline. We will further characterize each group based on both phenotypic cross-sectional data (see phenotypic variables below) and PRS for schizophrenia1, bipolar disorder2, depression3 as well as a transdiagnostic PRS (calculated using PRS-CS with the summary statistics of the largest GWAS to date). To do so, we compare them using ANOVAs, t-tests, ChiČ tests, or model comparisons depending on the number of clusters and the respective variables.
The groups and the data used to generate them will eventually also be used for the analysis of newly collected data from the MulioBio project.

Resources needed

curr_paid_empl
partner
dur_illness
evr_ill_drg
ever_smkd,
age_smk,
no_cig
alc_pst12_mths,
alc_5orm,
lftm_alc_dep
evr_ill_drg
Antidepressants
Antipsychotics
Mood_stabilizers
Tranquilizers
gaf
idsc_itm1
idsc_itm10
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_11_12
idsc_13_14
panss_p1
panss_p2
panss_p3
panss_p4
panss_p5
panss_p6
panss_p7
panss_n1
panss_n2
panss_n3
panss_n4
panss_n5
panss_n6
panss_n7
panss_g1
panss_g2
panss_g3
panss_g4
panss_g5
panss_g6
panss_g7
panss_g8
panss_g9
panss_g10
panss_g11
panss_g12
panss_g13
panss_g14
panss_g15
panss_g16
ymrs_itm1
ymrs_itm2
ymrs_itm3
ymrs_itm4
ymrs_itm5
ymrs_itm6
ymrs_itm7
ymrs_itm8
ymrs_itm9
ymrs_itm10
ymrs_itm11
nrpsy_lng
nrpsy_mtv
nrpsy_tmt_A_rt
nrpsy_tmt_A_err
nrpsy_tmt_B_rt
nrpsy_tmt_B_err
nrpsy_dgt_sp_frw
nrpsy_dgt_sp_bck
nrpsy_dg_sym

To characterize the cluster:
idsc_sum
panss_sum_pos
panss_sum_neg
ymrs_sum
panss_sum_gen
panss_sum_tot
v1_id
v1_scid_dsm_dx
v1_scid_dsm_dx_cat
v1_sex
v1_age
v1_nrpsy_mwtb
v4_opcrit
scid_suic_ide
scid_suic_thght_mth
v3_cts_els_dic
v3_cts_1_dic
v3_cts_2_dic
v3_cts_3_dic
v3_cts_4_dic
v3_cts_5_dic
med_pst_wk
med_pst_sx_mths

Biological data:
PRS-CS for schizophrenia, bipolar disorder, depression
The transdiagnostic PRS will be obtained by subjecting the disorder specific PRS to a principal component analysis, and extracting the first principal component.