2019-08-27

015_ Genetic background of suicide: influence of various polygenic loads on suicidal behavior

Research Question and Aims

Population-based and family studies have shown a substantial heritability of suicide behavior, with estimates from 17 to 55% (Voracek et al., 2007; Sokolowski et al., 2014). A genetic correlation between suicide attempt and several psychiatric/personality traits has recently been reported (Ruderfer et al. 2019).
Here we propose to generate polygenic scores regarding major psychiatric disorders, suicide and personality traits within the PsyCourse cohort and estimate their association with suicide-related variables within the PsyCourse cohort. As an exploration, we also perform clustering analysis on suicide-related variables and estimate their association with polygenic scores and psychiatric/personality traits.

Analytic Plan

We hypothesize that genetic/polygenic load associated to risk of major psychiatric disorders, personality traits or suicide itself will be associated with suicidal behavior in PsyCourse samples.

Participants
Data from all samples in PsyCourse who have genotype data available will be included in this study.

Polygenic risk scores
PLINK 1.90 will be used for polygenic score calculation. The most recent GWASes of major psychiatric disorders, personality traits and suicidal behavior will serve as discovery sample. Polygenic scores will be calculated based on summary statistics from the discovery dataset excluding rare SNPs (MAF < 0.5%), low quality imputed variants (info score <90%), indels, ambiguous markers (A/T and C/G). Data will be clumped in windows of 1000 kbp, discarding variants in LD (R2>.2). Scores will be calculated based on p value thresholds ranging from p < 5 x 10-8 to p < 1.

Statistical analysis
The association of polygenic scores with relevant clinical variables (medication-lithium considering 4 visits, Alda scale, suicide-related) will be studied using linear/logistic models adjusting for sex, age, treatment, ancestry components, recruitment site and any other confounding variable. Nested-cross-validation procedures will be used to identify optimal P-value thresholds to be replicated in independent, external samples.

Clustering analysis
A variety of algorithms like K-means or hierarchical clustering will be used on suicide-related variables and the association of each cluster with polygenic scores or psychiatric/personality traits will be examined by t-test or chi-square test.

Resources needed

Sociodemographic variables
v1_sex
v1_ageBL
v1_yob
v1_seas_birth
v1_center

Diagnosis
v1_scid_dsm_dx_cat
v1_stat

Medication data from Visit 1 to Visit 4
v1_med
v2_med
v3_med
v4_med

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
same variables of v2, v3, v4

Genetic data (imputed).