Human Genetics Grant Application Guidelines

Researchers are strongly encouraged to present a rationale that balances substantive, methodological, and budgetary issues that addresses the following considerations, as appropriate to each study:

Research Design:

  • Substantive focus with clear hypothesis (e.g., specific heritable phenotypes, alternative phenotypes, endophenotypes)
  • Sample selection (e.g., particularly informative sub-samples and subgroups, understudied populations) and generalizability (i.e., population to which findings can be applied)
  • Research design (e.g., genetically-informative, birth cohort, multi-generational, selection by prenatal exposure, case-control)
  • Statistical power (e.g., ability to detect effects within the proposed study or in combination with other studies) and population stratification
  • Methods and tools development, data integration
  • Cost effectiveness

Phenotypic Assessment:

  • Number and timing of assessments when applicable (e.g., multiple waves ranging from pre- to post-drug exposure, or from disorder onset to remission)
  • Selection and quality of assessments (e.g., diagnostic and/or symptom-based scales, detailed measurement of salient individual and environmental factors)
  • The NIDA Genetics Consortium has developed a series of core elements for assessing and harmonizing demographic information and substance use history for genetic studies. The core elements can be accessed here. NIDA strongly encourages all applicants to discuss how these core elements are addressed in their data sharing plan by either assessing them directly or indirectly by proxy measures
  • Relevant tools for phenotyping substance abuse can be found by searching “substance abuse” on the phenx tool website,
  • Comorbidity (e.g., psychiatric, polysubstance, physical illness)
  • Flexibility for future use of the data (e.g., consent forms have potential for subsequent inquiry)
  • Compatibility/overlap with extant studies in the NIDA Genetics Consortium (for a description of those studies see

Data Sharing: