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Erschienen in:
28.02.2020 | COMMENTARY
Opportunities, challenges and expectations management for translating biobank research to precision medicine
Erschienen in: European Journal of Epidemiology | Ausgabe 1/2020
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With the ascendance of large genomic biobanks that leverage healthcare systems and provide access to DNA, biospecimens and electronic health record (EHR) data, epidemiologists can now conduct population science research at scale in individual cohorts with sample sizes far exceeding 100,000 participants. Largescale biobanks like UK Biobank, that are nested within a national or regional health system, are creating platforms for research that holds the promise to translate genomic medicine findings into “precision” approaches for both individual patient care and population prevention. The current wave of genomic biobanks represents the most recent in a series of eras of individual smaller prospective cohort studies that began in the mid 1900s and provided initial evidence that led to targeted prediction, prevention and treatment approaches (Table 1).
Table 1
Eras of prospective cohort studies: from single population cohorts to biobanks
|
Era
|
Risk factor medicine
|
Genomic medicine
|
Precision medicine
|
|---|---|---|---|
|
Date range
|
1950–2000
|
2000–
|
2015–
|
|
Cohort design
|
Single population, multiethnic
|
Consortia of cohorts
|
Health system, mega biobank
|
|
Example
|
Framingham Heart Study, MESA
|
CHARGE, GIANT, Psychiatric Genomics
|
UK Biobank, All of Us Research Program, Million Veteran Program
|
|
Sample size
|
1000–100,000
|
100,000–500,000
|
100,000–10 Million
|
|
Risk exposure
|
Single RF, RF scores, blood & imaging biomarkers
|
Genetic/genomic, single ‘Omic profile (e.g., metabolomics)
|
Whole Genome Sequencing, Multiple ‘Omic profiles
|
|
Cohort design
|
Community, longitudinal
|
Multiple cohorts
|
Health System with Electronic Health Records
|
|
Common analysis methods
|
MV Linear/logistic regression
|
Statistical genetics, bioinformatics
|
Extension of prior methods, big data analytics
|
|
Additional analysis approaches
|
Mendelian Randomization
|
Mendelian Randomization, PheWAS, integrative genomics
|
|
|
Association Metric
|
AUC, reclassification
|
Genome-wide significance
|
Current metrics with opportunity for emerging metrics
|
|
Research collaborations
|
Rare, limited to inter-cohort
|
Global, multi-ethnic cohorts
|
Global, multi-biobank cohorts
|