How to kickstart a national biobanking infrastructure – experiences and prospects of BBMRI-NL

Correction for both common and rare cell types in blood is important to identify genes that correlate with age

BACKGROUND

Aging is a multifactorial process that affects multiple tissues and is characterized by changes in homeostasis over time, leading to increased morbidity. Whole blood gene expression signatures have been associated with aging and have been used to gain information on its biological mechanisms, which are still not fully understood. However, blood is composed of many cell types whose proportions in blood vary with age. As a result, previously observed associations between gene expression levels and aging might be driven by cell type composition rather than intracellular aging mechanisms. To overcome this, previous aging studies already accounted for major cell types, but the possibility that the reported associations are false positives driven by less prevalent cell subtypes remains.

RESULTS

Here, we compared the regression model from our previous work to an extended model that corrects for 33 additional white blood cell subtypes. Both models were applied to whole blood gene expression data from 3165 individuals belonging to the general population (age range of 18-81 years). We evaluated that the new model is a better fit for the data and it identified fewer genes associated with aging (625, compared to the 2808 of the initial model; P ≤ 2.5⨯10^-6). Moreover, 511 genes (~ 18% of the 2808 genes identified by the initial model) were found using both models, indicating that the other previously reported genes could be proxies for less abundant cell types. In particular, functional enrichment of the genes identified by the new model highlighted pathways and GO terms specifically associated with platelet activity.

CONCLUSIONS

We conclude that gene expression analyses in blood strongly benefit from correction for both common and rare blood cell types, and recommend using blood-cell count estimates as standard covariates when studying whole blood gene expression.

Governing the research-care divide in clinical biobanking: Dutch perspectives

Biobanking, the large-scale, systematic collection of data and tissue for open-ended research purposes, is on the rise, particularly in clinical research. The infrastructures for the systematic procurement, management and eventual use of human tissue and data are positioned between healthcare and research. However, the positioning of biobanking infrastructures and transfer of tissue and data between research and care is not an innocuous go-between. Instead, it involves changes in both domains and raises issues about how distinctions between research and care are drawn and policed. Based on an analysis of the emergence and development of clinical biobanking in the Netherlands, this article explores how processes of bio-objectification associated with biobanking arise, redefining the ways in which distinctions between research and clinical care are governed.

Comparing the hub-and-spoke model practices of the LifeLines Study in the Netherlands and the H3Africa Initiative

Biobank sample storage is critical in population health and epidemiology studies. Biobanks bridge two very different worlds: they connect to the participants and patients at an individual level, but they also aggregate information and represent the cutting edge of scientific discovery. In this brief report, we describe how the LifeLines study in the Netherlands manages its resources for communication and services, and how it can serve as a model for the Human Heredity and Health in Africa Initiative (H3Africa Initiative).

The Genome of the Netherlands: design, and project goals

Within the Netherlands a national network of biobanks has been established (Biobanking and Biomolecular Research Infrastructure-Netherlands (BBMRI-NL)) as a national node of the European BBMRI. One of the aims of BBMRI-NL is to enrich biobanks with different types of molecular and phenotype data. Here, we describe the Genome of the Netherlands (GoNL), one of the projects within BBMRI-NL. GoNL is a whole-genome-sequencing project in a representative sample consisting of 250 trio-families from all provinces in the Netherlands, which aims to characterize DNA sequence variation in the Dutch population. The parent-offspring trios include adult individuals ranging in age from 19 to 87 years (mean=53 years; SD=16 years) from birth cohorts 1910-1994. Sequencing was done on blood-derived DNA from uncultured cells and accomplished coverage was 14-15x. The family-based design represents a unique resource to assess the frequency of regional variants, accurately reconstruct haplotypes by family-based phasing, characterize short indels and complex structural variants, and establish the rate of de novo mutational events. GoNL will also serve as a reference panel for imputation in the available genome-wide association studies in Dutch and other cohorts to refine association signals and uncover population-specific variants. GoNL will create a catalog of human genetic variation in this sample that is uniquely characterized with respect to micro-geographic location and a wide range of phenotypes. The resource will be made available to the research and medical community to guide the interpretation of sequencing projects. The present paper summarizes the global characteristics of the project.