Genetic Data and Personalized Medicine


Maryam Daneshpour


Azita Zadehvakili, Ashraf Mohammad-Khani, Abdolazim Nejatizadeh, Mehdi Mirzayii


Genetic association studies have provided a powerful tool for identifying genetic variants related to health and disease, particularly for conditions influenced by many genes and environmental factors at once. Although the technique was initially used for finding genetic differences in a case-control design for particular diseases, its use quickly spread to traits measured on a continuum. The genetic committee of Iranian cohort network will try to design a guideline for:

  • Define the role of genetic study in cohorts
  • How to establish a standard genomic bank?
  • How to collect and develop family relationships in a cohort
  • Share the genetic data and define the genomic map
  • Create a database of phenotypes in existing cohort
  • How to control data quality and integration of genomic data?

Draw a road map for personalized medicine in the context of cohort studies

Quality Assurance and Quality Control in Cohort Studies


Ardeshir Khosravi


Abbas Keshtkar, Sadaf G Sepanlou, Amirabbas Momenan, Arash Ghanbarian, Akram Pourshams


Quality assurance (QA) is any systematic process or activity which is done, mainly before data gathering, to assure that a specific research project being developed meets specific requirements and the errors will be prevented or minimized. In contrast, quality control (QC) is any process or observational activity which is done, throughout a research, including data collection, data cleaning, and data analysis to identify and then, correct any error or fault. In other words, the QC is the observation techniques and activities used to fulfill requirements for quality.

For research purposes, a cohort is any group of people who are linked in some way. In medicine, a cohort study is often undertaken to obtain evidence to try to refute the existence of a suspected association between cause and effect. Since QA and QC are two major and important components of a cohort study, developing a comprehensive guideline or protocols of QA and QC is necessary when a researcher is trying to run a quality cohort study.

The QA in a cohort study has several components including:

  • Developing a precise practical methods and protocols of the study
  • Standardizing the protocols
  • Developing (or choosing) proper questionnaire for data collection and instructions for data collectors
  • Developing specific QA protocols for lab equipments
  • Developing specific checklist for laboratory QA
  • Pilot studies for data collection methods
  • Specific training courses for personnel
  • Developing standards for data banks and other software
  • Establishing local QA organization for every region.

The QC in the Persian Cohort includes the following components:

  • Evaluating executive protocols in the research field
  • Considering inclusion and exclusion criteria constantly for study population
  • Monitoring the data collection process and cleaning the data, locally and globally.
  • Study of validity and reliability of all data collection instruments
  • Running QC for numeric data such as blood pressure and anthropometric measures
  • Running QC for all paraclinic measures
  • Developing an organization chart and flowchart, specifically designed for the study QC and planning a precise timetable for every activity

QA and QC are two important and inevitable strategies to be done to assure the integrity and quality of a cohort study throughout the study. Developing and implementing a comprehensive QA and QC protocols will guarantee the quality and accuracy of data collected in each research study.

International Collaborations of ICC


Arash Etemadi

Members (in alphabetical order):

Kiarash Aramesh, Farshad Farzadfar, Roya Kelishadi, Reza Malekzadeh, Bita Mesgarpour, Afarin Rahimi-Movaghar, Nima Rezaei, Vandad Sharifi Senejani, Kazem Zendehdel


Consortial efforts are essentially international in scope, since they aim at building very large data sources and a multidisciplinary team, and a single-country consortium will limit the resources and expertise needed to accommodate such goals. However, there is still a considerable amount of underrepresentation for low-income regions in cohort cosortia. The map in figure 1 shows the geographical distribution of research groups affiliated with NCI consortia, and figure 2 shows the location of collaborating centers for another important consortium (International Childhood Cancer Cohort Consortium). These and many other evidences suggest that consortial activities are not present in all parts of the world. The most famous cohort consortium in our region is Asian Cohort Consortium ( which is mainly made up of cohort studies from Japan and Korea, and then China and Taiwan. Although Iran Cohort Consortium (ICC) aims at providing a medium for the different studies in the country to connect and interact, it is basically a limited effort without linking to larger multinational consortia, or involving international institutions. However, there are many issues that need to be discussed and defined for forming effective and fruitful collaborations. We tries to discuss these issues and questions, under the following five categories:

  1. Goals and policies: Why do we need international consortia in the first place? What are the advantages and disadvantages of forming international partnership for a consortium? Can we really have big science without international consortia? What are the different forms of collaborations (e.g. data sharing, biobanks, visiting scientists etc.)?
  2. Specific issues for Iranian scientists: What are the specific challenges facing Iranian scientists in forming or joining international collaborations? How do they cope with these challenges? How can ICC help resolve some of those issues?
  3. Ethical considerations: What are the national and global norms, regulations, and standards that govern data sharing among various studies/cohorts? What are the specific considerations regarding the specific types of research such as the ones include genomic data and biological samples? What are the ethical principles, standards, and norms governing establishing biobanks and collaboration among them? In the cases of incompatible differences among policies implemented by different collaborating national and international agencies/countries (and the way they are implemented and enforced), what are the ethical ways for reconciliation and problem-solving? Many collaborations now require data sharing to be explicitly included in the subjects’ consent froms in the participating studies, while many studies have started without having this item considered,. How should ICC face this challenge? What specific ethical considerations are important when new studies want to join international collaborations? Which IRBs are eligible and in charge of reviewing and oversighting these kinds of collaborations? The issue of intellectual property in international collaborative research.
  4. Funding and financial sources: Consortia are more successful in getting the funding they need: for example, in a study of NIH grants success rate, the percentage of funded (i.e. successful) grant applications was 48% for those submitted as part of a consortial activity, which was consistently higher than the success rates for ordinary grants (25-28%) since the year 2000. This may also reflect the fact that NIH funds primarily USA-based investigators. Why do the consortial efforts need funding? What are the international funding opportunities available to ICC? How can ICC improve its chances of being funded through international collaboration?
  5. Action plan: What steps should be taken to establish international collaborations? Who should responsible for promoting these activities? What infrastructures can ICC offer to facilitate international collaborations (e.g. data sharing website, organizing meetings, biobanking etc.)? Which cohort consortia/studies should we target?

Figure 1. Share of different countries in NCI Cohort Consortium.

Fig 2. Centers contributing to The International Childhood Cancer Cohort Consortium.

Collaboration and Authorship Agreements


Afarin Rahimi-Movaghar


Hossein Poustchi, Ehsan Bahramali, Farid Najafi


  • Conditions of authorship, universal agreements: who can and who cannot be author of a publication
  • Authorship requirements for sharing data
  • Strategies to minimize authorship disputes
  • Developing appropriate agreements

Data Management, Metadata and Data Sharing


Davood Khalili

Members (alphabetically):

Alireza Atashi, Noushin Fahimfar, Kamran Guity, Ali Kabir, Mojtaba Lotfalian, Masoud Solaymani-Dodaran, Esmaeel Vaziri


Research data form the basis of scientific knowledge. They can be more useful if they can be properly organized, managed and shared. Pooling multiple research data increases the total sample size and power to see well and detect novel findings. We are going to work on some issues under the following items:

      • Best Practices for creating data
        • Use Consistent Data Organization
        • Use Standardized Naming, Codes and Formats
        • Assign Descriptive File Names
        • Perform Basic Quality Assurance / Quality Control
        • Preserve Information – Use Scripted Languages
        • Define Contents of Data Files; Create Documentation
        • Use Consistent, Stable and Open File Formats
      • Managing data in the Data Life Cycle
        • Branching models
      • Benefits of Data Sharing
      • International approaches to Data Sharing
        • Berlin Principles, OECD, NSF, NIH, OA 2020, European Commission, NRC
      • Different challenges in Data Sharing
        • organizational structures
        • culture and behavior
        • Political and ethical issues
        • Technical issues
        • Funding
      • Different phases to develop the ICC metadata and data network
      • Pooling data and challenges in analysis