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Genome-Wide Association: Current Best Practices and Perspectives

Genome-Wide Association: Current Best Practices and Perspectives. National Human Genome Research Institute. U.S. Department of Health and Human Services National Institutes of Health National Human Genome Research Institute. National Institutes of Health.

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Genome-Wide Association: Current Best Practices and Perspectives

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  1. Genome-Wide Association: Current Best Practices and Perspectives National Human Genome Research Institute U.S. Department of Health and Human Services National Institutes of Health National Human Genome Research Institute National Institutes of Health Teri A. Manolio, M.D., Ph.D.Senior Advisor to the Director, NHGRI, for Population Genomics Director, Office of Population Genomics June 22, 2007 U.S. Department of Health and Human Services

  2. Perspectives and Best Practices • Confusing nomenclature is major challenge • Collaboration is key and can promote careers • Mechanisms needed for supporting young investigators • Be sure what models and assumptions used • Large projects stimulate technology development and vice versa • Data sharing crucial • Need repositories of tissues for expression, function, methylation testing

  3. Perspectives and Best Practices • Current GWA only capturing fraction but still finding genes, and tools will only get better • Importance of translation– VKORC1 and warfarin • Importance of examining observed vs expected distributions– QQ plots • Importance of handling DNA of cases and controls similarly • Replication, replication, replication • CGEMS association data available for instant replication

  4. Perspectives and Best Practices • Genotype data are not infallible • Apply QC “filters” • Inspect cluster plots • Critical to know sample type • CNV exciting, need better detection software • More SNPs may not be better than more people • Plan for analysis: work with trial datasets similar to your platform • Most datasets too large for SAS, programs widely available such as PLINK

  5. Perspectives and Best Practices • Use of genome browsers and annotations • Unfiltered data can be very informative • Collaboration getting easier: extraction and genotyping are value added to cohorts • Multiple models of collaborations: shared, complementary, apportioned • dbGaP will accept associations as “accessions” and may soon be required by major journals • Documentation and display of study protocols like AREDS and Framingham are revolutionary

  6. Perspectives and Best Practices • Use of genome browsers and annotations • Genomics in prospective population studies must include: • participant involvement • multiple consent provisions • ethical oversight • Data sharing should be encouraged and planned in advance, participants want this • Plan for future approaches– expression, epigenetics, sequencing, all need collaboration • Changing the tenure (granting, award) system will take a very long time

  7. “Warm-Up” Genotyping Data Sets NINDS Open Access Repositoryhttp://ccr.coriell.org/ninds/ • Initial genome wide genotyping in Parkinson disease, stroke, ALS, controls HapMap Samples on GAIN Platformshttp://www.ncbi.nlm.nih.gov/sites/entrez

  8. Genetic Architecture of Complex Traits • Number of loci involved • Frequencies and effects of their alleles • Type of loci, i.e., structural or regulatory Boerwinkle and Sing, Ann Hum Genet 1987; 51:211-26.

  9. “Epidemiologic Architecture” of Genetic Variants • Population prevalence (allele frequency) • Prevalence in racial/ethnic subgroups • Relative risk of rigorously-defined, incident disease • Consistency of association across subgroups defined by age, sex, race/ethnicity, or exposures • Potential modifiability of associated risk • Correlations with other traits and exposures

  10. Rapid Investigation of Genetic Associations in Population Studies Develop novel approaches for utilizing existing prospective cohort studies and clinical trials to: • Determine population impact of putative risk variants, including prevalence, disease risk, and associations with other health characteristics • Identify modifiers of gene-trait associations, particularly those related to modifiable factors • Identify potential clues to gene function, by examining associations of putative risk variants with related phenotypic characteristics such as laboratory measures or imaging findings

  11. P Values of GWA Scan for Age-Related Macular Degeneration Klein et al, Science 2005; 308:385-389.

  12. Risk of Developing AMD by CFH Y402H and Modifiable Risk Factors Schaumberg DA et al, Arch Ophthalmol 2007; 125:55-62.

  13. Larson, G. The Complete Far Side. 2003.

  14. “The more we find, the more we see, the more we come to learn. The more that we explore, the more we shall return.” Sir Tim Rice, Aida, 2000

  15. Lessons Learned from Initial GWA Studies • This actually works! • Size matters • Luck matters • Replication matters • Collaboration matters • Controls matter, but can be shared sometimes • Non-coding SNPs and “gene deserts” matter • Current hypotheses regarding candidate genes and pathways may not matter so much • Several genes influence more than one disease

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