Guidelines for genetic association case/control studies

Study population

  • Phenotype definitions, if possible include table of clinically relevant summary information.
  • Recruitment criteria.
  • The ancestry of the study population should be described (geographic locations of collection).
  • Efforts to ensure matching of characteristics (e.g. age, gender) between case and control groups should be stated.
  • Case control number clearly stated. While small sample sizes are unlikely to be adequately powered, a case could be made for rare conditions.

Study design

  • For candidate gene studies, the rationale for gene selection should be stated along with appropriate references.
  • If the work is attempting to validate a previously reported association the references should be clearly cited.
  • In all cases replication of association in an independent cohort will increase the weight of the submission. However in some cases this is not possible.
  • If haplotype analysis of multiple SNPs is performed, the rationale for doing so should be clearing defined.


  • Detail which SNPs were tested and the selection criteria should be clearly defined. For example, linkage-disequilibrium based tagging SNPs.
  • Genotyping platform should be described.
  • Quality control procedures clearly defined and demonstrate rigorous effort to reduce genotyping artifacts.
  • Appropriate identifiers should always be used. In the case of SNPs this should be the rs#.
  • If imputation has been used the reference panel and procedure should be clearly stated.
  • In a case/control study any differences in the processing of cases or controls should be clearly defined, as should attempts at reducing bias explained.
  • The genetic model (additive, dominant, recessive, multiplicative) being tested should be stated as should the software used to perform the analysis.
  • The rationale for secondary analyses, for example clinical sub-types, should be clearly stated.

Reporting results

  • SNP and sample call rates should be reported.
  • Results should be clearly tabulated. The table should include rs#, chromosome, base position, allele frequencies (reference allele should be defined), Hardy-Weinberg equilibrium (cases and controls),odds ratios and p-value. Raw genotype counts would also facilitate the inclusion of the results into future meta-analyses.

Statistical reporting

  • Statistical power of study design
  • Define statistical significance for study;
  • In particular this should take into account the number of tests being performed.

Interpretation of results

  • Results for haplotype analyses should be explicitly interpreted to support inclusion.
  • Inclusion of biological data from public resources, such as ENCODE, that provides insight into a biological mechanism would strengthen any manuscript.

Journal policy

All Society for Endocrinology journals require data from such studies to be deposited in public databases (e.g. MIAME or GEO) or the manuscript will not be published. The journals support the public dissemination of gene expression data.