The European Drug-induced Agranulocytosis Consortium Study (The EuDAC Study)

Study type

• Study type:
  Non-interventional study
• Scope of the study:
  Other
• If ‘other’, further details on the scope of the study:
  Genome-wide association study

Non-interventional study

• Non-interventional study design:
  Case-control

Study drug and medical condition

• Anatomical Therapeutic Chemical (ATC) code:
  (A07EC) Aminosalicylic acid and similar agents

  Aminosalicylic acid and similar agents

  (H03B) ANTITHYROID PREPARATIONS

  ANTITHYROID PREPARATIONS

  (J01) ANTIBACTERIALS FOR SYSTEMIC USE

  ANTIBACTERIALS FOR SYSTEMIC USE

  (N03) ANTIEPILEPTICS

  ANTIEPILEPTICS

  (M01A) ANTIINFLAMMATORY AND ANTIRHEUMATIC PRODUCTS, NON-STEROIDS

  ANTIINFLAMMATORY AND ANTIRHEUMATIC PRODUCTS, NON-STEROIDS

  (B01AC) Platelet aggregation inhibitors excl. heparin

  Platelet aggregation inhibitors excl. heparin

  (D01B) ANTIFUNGALS FOR SYSTEMIC USE

  ANTIFUNGALS FOR SYSTEMIC USE

  (C07) BETA BLOCKING AGENTS

  BETA BLOCKING AGENTS

  (N06A) ANTIDEPRESSANTS

  ANTIDEPRESSANTS
• Medical condition to be studied:
  Agranulocytosis

Population studied

• Age groups:
  Adults (18 to < 46 years)

  Adults (46 to < 65 years)

  Adults (65 to < 75 years)

  Adults (75 to < 85 years)

  Adults (85 years and over)
• Estimated number of subjects:
  300

Study design details

• Main study objective:
  To identify genetic factors that predispose to drug-induced agranulocytosis.
• Outcomes:
  Proportion of individuals who are carriers of investigated genetic polymorphisms.
• Data analysis plan:
  Association analyses with genetic and clinical factors will be performed for the agranulocytosis group as a whole and stratified for each drug or class of drugs. A total of about 4000 population and treated controls will be used. To correct for population stratification, controls will be recruited from all countries and principal component analysis will be performed. To correct for multiple testing, the level of significance will be set at around p<1*10-8, which is equivalent to a Bonferroni correction for 1 million independent tests. We will make an effort to collect 100 new cases and controls for replication of the 10-20 top hits. We will then need to correct for 10-20 multiplied tests, i.e. a p-value of 0.0025-0.005 will suffice. We will perform single SNP tests with logistic regression with adjustment for population stratification by including significant principal components as covariates in the logistic-regression model. Results are illustrated with Q-Q and Manhattan plots.