2021-07-05

044_ Blood-based transcriptome signatures of suicidality

Research Question and Aims

Very recent data indicate that networks of gene co-expression that can be measured in the postmortem brains of neuropsychiatrically normal individuals are very well preserved in the peripheral blood;1 thus, at the level of co-expressed gene modules, the biological networks of the brain can be recapitulated from measurements made in blood, increasing the feasibility of mechanistic studies of neuropsychiatric disease. Further, we recently detected reliable within-subject relationships between principal components of peripheral-blood gene-expression levels and the expression levels of individual transcripts in the brain in the same individuals. We developed a method, called BrainGENIE (the Brain Gene Expression and Network Imputation Engine), that uses those relationships as a basis for imputing gene expression in 12 distinct brain regions based on transcriptome measures from peripheral blood. In the proposed study, we would use existing peripheral-blood-based transcriptome data (LexoGene data) from all PsyCourse subjects to identify biomarkers that correlate with state measures of suicidality, and to identify corresponding changes in 12 brain regions through imputation using BrainGENIE. In the spirit of the U.S. NIMH RDoC (Research Domain Criteria) framework, we will examine suicidality as a cross-disorder construct, so will welcome all subjects regardless of diagnosis.

Analytic Plan

The analysis will use the PsyCourse samples genotyped using the Global Screening Array (GSA). Using imputed data, polygenic risk scores for SCZ, BD, MDD, Educational Attainment and probably others based on upcoming GWASes of interest will be calculated in these subjects.
The potential effect of the interaction of PRS with Environmental factors (Childhood Trauma/Life Events) on several cross-sectional outcomes: drug use (use/not use, frequency of use) and clinical severity (as measured by psychopathological scales of positive/negative/manic/depressive symptoms) in psychiatric patients will be statistically assessed using linear/logistic models. These models will include covariates as well: age, sex, duration of disease, and ancestry components, among others. The relevance of the covariates for each model will be assessed using the AIC criteria.

Resources needed

Sociodemographic variables
v1_sex
v1_ageBL
v1_age
v1_yob
v1_seas_birth
v1_center

Diagnosis
v1_scid_dsm_dx_cat
v1_stat

Raw Medication data at each visit
v1_med_medi_1
v1_med_kategorie_1
v1_depot_medi_1
v1_depot_kategorie_1
v1_bedarf_medi_1
v1_bedarf_kategorie_1
v2_med_medi_1
v2_med_kategorie_1
v2_depot_medi_2
v2_depot_kategorie_2
v2_bedarf_medi_1
v2_bedarf_kategorie_1
v3_med_medi_1
v3_med_kategorie_1
v3_depot_medi_2
v3_depot_kategorie_2
v3_bedarf_medi_1
v3_bedarf_kategorie_1
v4_med_medi_1
v4_med_kategorie_1
v4_depot_medi_2
v4_depot_kategorie_2
v4_bedarf_medi_1
v4_bedarf_kategorie_1

Suicide-related variables
v1_scid_evr_suic_ide,v1_scid_suic_ide
v1_scid_suic_thght_mth
v1_scid_suic_note_thgts
v1_suic_attmpt
v1_scid_no_suic_attmpt
v1_prep_suic_attp_ord
v1_suic_note_attmpt
v2_scid_evr_suic_ide,v1_scid_suic_ide
v2_scid_suic_thght_mth
v2_scid_suic_note_thgts
v2_suic_attmpt
v2_scid_no_suic_attmpt
v2_prep_suic_attp_ord
v2_suic_note_attmpt
v3_scid_evr_suic_ide,v1_scid_suic_ide
v3_scid_suic_thght_mth
v3_scid_suic_note_thgts
v3_suic_attmpt
v3_scid_no_suic_attmpt
v3_prep_suic_attp_ord
v3_suic_note_attmpt
v4_scid_evr_suic_ide,v1_scid_suic_ide
v4_scid_suic_thght_mth
v4_scid_suic_note_thgts
v4_suic_attmpt
v4_scid_no_suic_attmpt
v4_prep_suic_attp_ord
v4_suic_note_attmpt
v1_idsc_itm18
v2_idsc_itm18
v3_idsc_itm18
v4_idsc_itm18
v1_bdi2_itm9
v2_bdi2_itm9
v3_bdi2_itm9
v4_bdi2_itm9

Genotypes:
Raw genotypes pre-imputation to calculate PCAs
Imputed genotypes

Transcriptomic data:
FASTQ and BAM files of Lexogen 3' RNA
Seq