Biobanks collaborate on big data to unravel disease processes

Patients with the same illness often receive the same treatment, even if the cause of the illness is different in each person. Six Dutch universities are combining forces to chart the different disease processes for a range of common conditions. This represents a new step towards ultimately being able to offer every patient more personalized treatment. The results of this study have been published in two articles in the authoritative scientific journal Nature Genetics.

New phase

The researchers were able to make their discoveries thanks to new techniques that make it possible to simultaneously measure the regulation and activity of all the genes of thousands of people, and to link these data to millions of genetic differences in their DNA. The combined analysis of these ‘big data’ made it possible to determine which molecular processes in the body become dysregulated in a range of different diseases, from prostate cancer to inflammatory bowel disease; the dysregulation can be traced even before the individuals concerned actually become ill. “The emergence of ‘big data’, ever faster computers, and new mathematical techniques means it’s now possible to conduct extremely large-scale studies and gain an understanding of many diseases at the same time”, explains Lude Franke (UMCG), head of the genetics research team in Groningen. The researchers show how thousands of disease-related DNA differences disrupt the internal working of a cell and how their effect can be influenced by environmental factors. And all this was possible without the need for a single lab experiment.

Large-scale collaboration in the Netherlands

The success of this research is the result of the decision taken six years ago by biobanks throughout the Netherlands to share their data and biomaterials. This decision meant it became possible to gather, store and analyse data from blood samples of a very large number of volunteers. The present study illustrates the tremendous value of large-scale collaboration in the field of medical research in the Netherlands. Bas Heijmans (LUMC), research leader in Leiden and initiator of the partnership: “The Netherlands is leading the field in sharing molecular data. This enables researchers to carry out the kind of large-scale studies that are needed to gain a better understanding of the causes of diseases. This result is only just the beginning: other researchers with a good scientific idea will be given access to this enormous bank of anonymised data once their project has been approved.

Personalized approach

Mapping the various molecular causes for a disease is the first step towards a form of medical treatment that is better matched to the disease process of individual patients. However, we still have a long way to go to reach that ideal. The large-scale molecular data that have been collected for this research are the cornerstone of even bigger partnerships. The third research leader, Peter-Bram ’t Hoen (LUMC), says: “Large quantities of data should eventually make it possible to give everyone personalised health advice, and to determine the best treatment for each individual patient”.

Two articles

The two scientific articl es were published on 5th Dec ember 2016 in Nature Genetics (http://dx.doi.org/10.1038/ng.3721 & http://dx.doi.org/10.1038/ng.3737). The research has been possible thanks to the cooperation within the BBMRI biobank consortium (Biobanking and BioMolecular resources Research Infrastructure) of six long-running Dutch population studies carried out by the university medical centres in Groningen (LifeLines), Leiden (Leiden L ongevity Study), Maastricht (CODAM Study), Rotterdam (Rotterdam Study), Utrecht ( Netherlands Prospective ALS Study) and by the Vrije Universiteit (Netherlands Twin Register) plus the national centralized computational facility of SURFsara and the ErasmusMC Human Genomics Facility HuGEF. The study links in with the Personalized Medicine route of the Dutch National Research Agenda.

-Disease variants alter transcription factor levels and methylation of their binding sites by Bonder et al. full text
Most disease-associated genetic variants are noncoding, making it challenging to design experiments to understand their functional consequences ^{1, 2} . Identification of expression quantitative trait loci (eQTLs) has been a powerful approach to infer the downstream effects of disease-associated variants, but most of these variants remain unexplained ^{3, 4} . The analysis of DNA methylation, a key component of the epigenome ^{5, 6} , offers highly complementary data on the regulatory potential of genomic regions ^{7, 8} . Here we show that disease-associated variants have widespread effects on DNA methylation in transthat likely reflect differential occupancy of transbinding sites by cis-regulated transcription factors. Using multiple omics data sets from 3,841 Dutch individuals, we identified 1,907 established trait-associated SNPs that affect the methylation levels of 10,141 different CpG sites in trans (false discovery rate (FDR) < 0.05). These included SNPs that affect both the expression of a nearby transcription factor (such as NFKB1, CTCFand NKX2-3) and methylation of its respective binding site across the genome. Transmethylation QTLs effectively expose the downstream effects of disease-associated variants.

-Identification of context-dependent expression quantitative trait loci in whole blood by Zhernakova DV et al. full text
Genetic risk factors often localize to noncoding regions of the genome with unknown effects on disease etiology ^{1, 2} . Expression quantitative trait loci (eQTLs) help to explain the regulatory mechanisms underlying these genetic associations ^{3, 4, 5, 6} . Knowledge of the context that determines the nature and strength of eQTLs may help identify cell types relevant to pathophysiology and the regulatory networks underlying disease ^{7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17} . Here we generated peripheral blood RNA–seq data from 2,116 unrelated individuals and systematically identified context-dependent eQTLs using a hypothesis-free strategy that does not require previous knowledge of the identity of the modifiers. Of the 23,060 significant cis-regulated genes (false discovery rate (FDR) ≤ 0.05), 2,743 (12%) showed context-dependent eQTL effects. The majority of these effects were influenced by cell type composition. A set of 145 cis-eQTLs depended on type I interferon signaling. Others were modulated by specific transcription factors binding to the eQTL SNPs.