Liis Leitsalu

Cohort Profile: Estonian Biobank of the Estonian Genome Center, University of Tartu

The Estonian Biobank cohort is a volunteer-based sample of the Estonian resident adult population (aged ≥18 years). The current number of participants-close to 52000--represents a large proportion, 5%, of the Estonian adult population, making it ideally suited to population-based studies. General practitioners (GPs) and medical personnel in the special recruitment offices have recruited participants throughout the country. At baseline, the GPs performed a standardized health examination of the participants, who also donated blood samples for DNA, white blood cells and plasma tests and filled out a 16-module questionnaire on health-related topics such as lifestyle, diet and clinical diagnoses described in WHO ICD-10. A significant part of the cohort has whole genome sequencing (100), genome-wide single nucleotide polymorphism (SNP) array data (20 000) and/or NMR metabolome data (11 000) available (http://www.geenivaramu.ee/for-scientists/data-release/). The data are continuously updated through periodical linking to national electronic databases and registries. A part of the cohort has been re-contacted for follow-up purposes and resampling, and targeted invitations are possible for specific purposes, for example people with a specific diagnosis. The Estonian Genome Center of the University of Tartu is actively collaborating with many universities, research institutes and consortia and encourages fellow scientists worldwide to co-initiate new academic or industrial joint projects with us.

The role of a bioresource research impact factor as an incentive to share human bioresources

The role of a bioresource research impact factor as an incentive to share human bioresources

An epidemiological perspective of personalized medicine: the Estonian experience

The Estonian Biobank and several other biobanks established over a decade ago are now starting to yield valuable longitudinal follow‐up data for large numbers of individuals. These samples have been used in hundreds of different genome‐wide association studies, resulting in the identification of reliable disease‐associated variants. The focus of genomic research has started to shift from identifying genetic and nongenetic risk factors associated with common complex diseases to understanding the underlying mechanisms of the diseases and suggesting novel targets for therapy. However, translation of findings from genomic research into medical practice is still lagging, mainly due to insufficient evidence of clinical validity and utility. In this review, we examine the different elements required for the implementation of personalized medicine based on genomic information. First, biobanks and genome centres are required and have been established for the high‐throughput genomic screening of large numbers of samples. Secondly, the combination of susceptibility alleles into polygenic risk scores has improved risk prediction of cardiovascular disease, breast cancer and several other diseases. Finally, national health information systems are being developed internationally, to combine data from electronic medical records from different sources, and also to gradually incorporate genomic information. We focus on the experience in Estonia, one of several countries with national goals towards more personalized health care based on genomic information, where the unique combination of elements required to accomplish this goal are already in place.

Linking a population biobank with national health registries-the estonian experience.

The Estonian population-based biobank, with 52,000 participants’ genetic and health data, is the largest epidemiological cohort in the Baltic region. Participants were recruited through a network of medical professionals throughout Estonia (population 1.34 million). Unique legislation as well as a broad consent form give the Estonian Genome Center, a research institute of the University of Tartu, permission to re-contact participants and to retrieve participants’ data from national registries and databases. In addition to two re-contacting projects to update the health data of participants, extensive clinical characterizations have been retrieved from national registries and hospital databases regularly since 2010. Acquiring data from electronic health records and registries has provided a means to update and enhance the database of the Genome Center in a timely manner and at low cost. The resulting database allows a wide spectrum of genomic and epidemiological research to be conducted with the aim of benefitting public health. Future plans include linking the genome center database with the national health information system through X-road and exchanging data in real time, as well as using the genetic data and the technical infrastructure available for piloting personalized medicine in Estonia.

Giving and Withholding of Information following Genomic Screening: Challenges Identified in a Study of Primary Care Physicians in Estonia

The use of predictive genomic information to improve medical care remains a contentious topic. However, it is generally agreed that the potential of genomics to improve medicine relies on medical care providers’ ability to effectively communicate and put in context the meaning of test results. As the amount of information available increasingly outstrips providers’ ability to offer qualified judgments on what the information means, consumers inevitably will be faced with test results of uncertain significance, as well as difficult questions about what they do or do not wish to know. Results of this survey of 64 primary care doctors in Estonia suggests that it may be inherently difficult for physicians to withhold genetic information obtained by genome scans or sequencing, even when they believe that having that information is not in the best interests of their patient. The descriptive data suggest introducing genomic medicine through primary care physicians, as proposed by the Estonian Genome Center of the University of Tartu, will require further genetics education as well as a carefully developed set of guidelines for determining where, when and how to use test results.

MixFit: Methodology for Computing Ancestry-Related Genetic Scores at the Individual Level and Its Application to the Estonian and Finnish Population Studies

Ancestry information at the individual level can be a valuable resource for personalized medicine, medical, demographical and history research, as well as for tracing back personal history. We report a new method for quantitatively determining personal genetic ancestry based on genome-wide data. Numerical ancestry component scores are assigned to individuals based on comparisons with reference populations. These comparisons are conducted with an existing analytical pipeline making use of genotype phasing, similarity matrix computation and our addition—multidimensional best fitting by MixFit. The method is demonstrated by studying Estonian and Finnish populations in geographical context. We show the main differences in the genetic composition of these otherwise close European populations and how they have influenced each other. The components of our analytical pipeline are freely available computer programs and scripts one of which was developed in house (available at: www.geenivaramu.ee/en/tools/mixfit).

From Biobanking to Precision Medicine: The Estonian Experience

Abstract The Estonian Biobank was founded in 2000 as a population-based biobank. A decade later, the biobank includes a collection of health and genetics data of around 5% of the adult population of Estonia. The Human Genes Research Act allows regular updating of data through linkage to national registries enabling long-term follow-up of the cohort. In addition to promoting the development of genetic research, the EGCUT has used data available in the Estonian Biobank for a wide variety of research projects nationally and through international collaborations. In the past few years increasing amount of attention has been placed on translating the results of genetic research to improve public health. In 2014, the Estonian Government supported a plan for a shift toward precision medicine based on modern genetic technology.The Estonian Biobank was founded in 2000 as a population-based biobank. A decade later, the biobank includes a collection of health and genetics data of around 5% of the adult population of Estonia. The Human Genes Research Act allows regular updating of data through linkage to national registries enabling long-term follow-up of the cohort. In addition to promoting the development of genetic research, the EGCUT has used data available in the Estonian Biobank for a wide variety of research projects nationally and through international collaborations. In the past few years increasing amount of attention has been placed on translating the results of genetic research to improve public health. In 2014, the Estonian Government supported a plan for a shift toward precision medicine based on modern genetic technology.

Recall by genotype and cascade screening for familial hypercholesterolemia in a population-based biobank from Estonia

PurposeLarge-scale, population-based biobanks integrating health records and genomic profiles may provide a platform to identify individuals with disease-predisposing genetic variants. Here, we recall probands carrying familial hypercholesterolemia (FH)-associated variants, perform cascade screening of family members, and describe health outcomes affected by such a strategy.MethodsThe Estonian Biobank of Estonian Genome Center, University of Tartu, comprises 52,274 individuals. Among 4776 participants with exome or genome sequences, we identified 27 individuals who carried FH-associated variants in the LDLR, APOB, or PCSK9 genes. Cascade screening of 64 family members identified an additional 20 carriers of FH-associated variants.ResultsVia genetic counseling and clinical management of carriers, we were able to reclassify 51% of the study participants from having previously established nonspecific hypercholesterolemia to having FH and identify 32% who were completely unaware of harboring a high-risk disease-associated genetic variant. Imaging-based risk stratification targeted 86% of the variant carriers for statin treatment recommendations.ConclusionGenotype-guided recall of probands and subsequent cascade screening for familial hypercholesterolemia is feasible within a population-based biobank and may facilitate more appropriate clinical management.

Reporting incidental findings of genomic disorder-associated copy number variants to unselected biobank participants

BACKGROUND Procedural guidelines for disclosure of incidental genomic information are lacking. METHODS We introduce a method and evaluated the impact of returning results to population biobank participants with 16p11.2 copy number variants, which are commonly associated with neurodevelopmental disorders and BMI imbalance. Of the 7877 participants, 11 carriers were detected. Eight participants were informed of their carrier status and surveyed 11-17 months later. RESULTS All participants demonstrated preference for disclosure. Although two participants experienced worry, all five survey respondents rated receiving this information favorably. One participant reported modifications in treatment and three felt that their treatment/condition had since improved. CONCLUSION This approach can be adapted and applied for the return of incidental findings to biobank participants.