How Technology Changes Ethics: New Issues Raised by Genome Wide Association Studies GWAS

1. How Technology Changes Ethics: New Issues Raised by Genome Wide Association Studies (GWAS) Nancy Press, Ph.D. Schools of Nursing and Medicine Oregon Health & Science University

2. �Current developments in genomics challenge the established framework of biomedical ethics because the empirical facts of the genomic science change too fast for the reflections of ethics to keep pace with. At the same time, as practical applications of new technologies are being developed, scientists call for pragmatic moral guidance.� Lunshof JE, Chadwick R, Vorhaus DB, Church GM. From genetic privacy to open consent. Nature Reviews Genetics. (2008) Volume 9:406-411.

3. What ARE Genome Wide Association Studies? Genome wide association studies (GWAS) explore genetic variation across the entire human genome in order to find connections between specific genes (genotype information), and their outward expression (phenotype information) The hope is that results will accelerate the development of better diagnostic tools and the design of new, safe and highly effective treatments. GWAS take the form of case-control studies where one group has the phenotype of interest and the other group does not GWAS focus on complex conditions where it is assumed there is very significant gene X gene and gene X environment interactions Corollary: the contribution of any one gene locus in causation of the condition will be small Corollary: GWAS studies must look at multiple genes and multiple environmental factors at the same time. It is assumed that the effect size for any genetic or environmental factor is going to be small THUS: GWAS requires large samples

4. Why GWAS Now? The technology now exists GWAS depends on the technical ability to do �high throughput� � that is, to very rapidly and reasonably inexpensively conduct multiple genetic tests on a sample With all the enthusiasm about genetics, the health outcome benefits of other methods have been less useful than hoped for: Candidate gene studies Gene therapy Pharmacogenomics A new approach was needed Greater realization of the complexity of interactions in the complex disease and trait phenotypes These new models seem �right� in terms of what we know about the complexity of biology.

5. How Large Should GWAS Samples Be?? 2000 cases and 2000 controls are said by some biostatisticians to be a minimum just to find possible leads Follow-up studies would then need to be orders of magnitude larger 10,000 cases and 10,000 controls is probably a statistically better design Cohorts as large as 500,000 are already occurring Some biostatisticians feel that this will only pick up �loud� signals Thus, GWAS will miss smaller, but significant genetic effects As samples get bigger, the number of �true positives� increase � because the signal from an underlying reality is becoming more audible. At the same time the, rate of �false positives� remains the same ..

6. What Are the Implications of Large GWAS Samples? Public resources spent are great The burden on the public is large Vast amounts of data are generated: Taken together these factors have led to data sharing policies that are a crucial part of the ethical issues involved in GWAS studies

7. Biobanks, Data sharing, and dbGaP The size of GWAS encourages collaboration If diseases are rare, studies can be combined If diseases are common, large cohorts can be quickly assembled from multiple investigators The expense of GWAS encourages collaboration If public funds are going to be used, data should be put to as much use as possible The amount of data encourages collaboration There will be more findings than any one team can investigate

8. Current NIH Policy on GWAS �To facilitate broad and consistent access to NIH-supported GWAS datasets, the NIH has developed a central NIH GWAS data repository � All investigators who receive NIH support are expected to submit descriptive information about their studies. Submissions should include the following: Study protocols Questionnaires Study manuals Variables measured NOTE: These data will be included in the open access portion of the data repository. All investigators are �strongly encouraged� to submit coded data of the following types: Genotypes (coded and de-identified) Pedigree data Phenotypes (according to standardized phenotype variables) Exposure data NOTE: These data will be available through a controlled access process which will be protected by various bioinformatic techniques on the NIH side and by the ethics oversight of the local Institutional Review Boards on the investigator side The investigator will keep the code that links the individual participants to their data. This code will never go to NIH and will not be released to any secondary source unless appropriate measures are taken and approvals granted. Will this kind of data sharing become mandatory internationally? Is there anything similar in China?

9. Study Designs in GWAS Prospective collection of samples Retrospective collection of samples