Article

The mayo clinic biobank: a building block for individualized medicine.

Department of Health Sciences Research, Mayo Clinic, Rochester, MN. Electronic address: .
Mayo Clinic Proceedings (Impact Factor: 5.79). 09/2013; 88(9):952-62. DOI: 10.1016/j.mayocp.2013.06.006
Source: PubMed

ABSTRACT To report the design and implementation of the first 3 years of enrollment of the Mayo Clinic Biobank.
Preparations for this biobank began with a 4-day Deliberative Community Engagement with local residents to obtain community input into the design and governance of the biobank. Recruitment, which began in April 2009, is ongoing, with a target goal of 50,000. Any Mayo Clinic patient who is 18 years or older, able to consent, and a US resident is eligible to participate. Each participant completes a health history questionnaire, provides a blood sample, and allows access to existing tissue specimens and all data from their Mayo Clinic electronic medical record. A community advisory board provides ongoing advice and guidance on complex decisions.
After 3 years of recruitment, 21,736 individuals have enrolled. Fifty-eight percent (12,498) of participants are female and 95% (20,541) of European ancestry. Median participant age is 62 years. Seventy-four percent (16,171) live in Minnesota, with 42% (9157) from Olmsted County, where the Mayo Clinic in Rochester, Minnesota, is located. The 5 most commonly self-reported conditions are hyperlipidemia (8979, 41%), hypertension (8174, 38%), osteoarthritis (6448, 30%), any cancer (6224, 29%), and gastroesophageal reflux disease (5669, 26%). Among patients with self-reported cancer, the 5 most common types are nonmelanoma skin cancer (2950, 14%), prostate cancer (1107, 12% in men), breast cancer (941, 4%), melanoma (692, 3%), and cervical cancer (240, 2% in women). Fifty-six percent (12,115) of participants have at least 15 years of electronic medical record history. To date, more than 60 projects and more than 69,000 samples have been approved for use.
The Mayo Clinic Biobank has quickly been established as a valuable resource for researchers.

1 Bookmark
 · 
88 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Background Bipolar disorder (BD) is a highly heritable disease. While genome-wide association (GWA) studies have identified several genetic risk factors for BD, few of these studies have investigated the genetic etiology of specific disease subtypes. In particular, BD is positively associated with eating dysregulation traits such as binge eating behavior (BE), yet the genetic risk factors underlying BD with comorbid BE have not been investigated. Methods Utilizing data from the Genetic Association Information Network study of BD, which included 729,454 single nucleotide polymorphisms (SNPs) genotyped in 1001 European American bipolar cases and 1034 controls, we performed GWA analyses of bipolar subtypes defined by the presence or absence of BE history, and performed a case-only analysis comparing BD subjects with and without BE history. Association signals were refined using imputation, and network analysis was performed with Ingenuity Pathway Analysis software. Based on these results, candidate SNPs were selected for replication in an independent sample of 855 cases and 857 controls. Results Top ranking SNPs in the discovery set included rs6006893 in PRR5, rs17045162 in ANK2, rs13233490 near PER4, rs4665788 and rs10198175 downstream of APOB, rs2367911 in CACNA2D1, and rs7249968 near ZNF536. Rs10198175 in APOB also demonstrated evidence of association in the replication sample and a meta-analysis of the two samples. Limitations Without information of BE history in controls, it is not possible to determine whether the observed association with APOB reflects a risk factor for BE behavior in general or a risk factor for a subtype of BD with BE. Further longitudinal and functional studies are needed to determine the causal pathways underlying the observed associations. Conclusions This study identified new potential BD-susceptibility genes, highlighting the advantages of phenotypic sub-classification in genetic research and clinical practice.
    Journal of Affective Disorders 08/2014; 165:151–158. · 3.71 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Paul Appelbaum and colleagues propose four models of informed consent to research that deploys whole genome sequencing and may generate incidental findings. They base their analysis on empirical data that suggests that research participants want to be offered incidental findings and on a normative consensus that researchers incur a duty to offer them. Their models will contribute to the heated policy debate about return of incidental findings. But in my view, they do not ask the foundational question, In the context of genome sequencing, how much work can consent be asked to do?
    Hastings Center Report 07/2014; 44(4). · 1.08 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: We provide a mini-review of how biobanks can support clinical genetics in the era of personalized medicine. We discuss types of biobanks, including disease specific and general biobanks not focused on one disease. We present considerations in setting up a biobank, including consenting and governance, biospecimens, risk factor and related data, informatics, and linkage to electronic health records for phenotyping. We also discuss the uses of biobanks and ongoing considerations, including genotype driven recruitment, investigations of gene-environment associations, and the re-use of data generated from studies. Finally, we present a brief discussion of some of the unresolved issues, such as return of research results and sustaining biobanks over time. In summary, carefully designed biobanks can provide critical research and infrastructure support for clinical genetics in the era of personalized medicine.
    Clinical Genetics 03/2014; · 3.65 Impact Factor