Lude Franke, PhD

Lude Franke (lude ludesign.nl) is a statistical geneticist working on the development and application of computational algorithms to functional genomics datasets. He has gained funding from ERC, NWO, and a Horizon Grant. He is also a graphic designer (see his PhD thesis design). This chance combination of skills can sometimes make life as a genetics/bioinformatics researcher a bit easier: if you can explain your research visually, more people can understand what you are doing. See a short film explaning his work on DNA (in Dutch) here and an interview with Lude about his work (in Dutch) here.

Students – We are always looking for bright and enthusiastic students. For more about our projects, please visit the Franke lab and Systems Genetics websites.

News

• 2022: Appointed a member of the Royal Dutch Society of Sciences (KHMW), see news article
• 2021: Awarded NWO-VICI grant (€1.5m)
• 2019: Appointed Senior Investigator in the Oncode Institute
• 2018: Appointed full professor of functional genomics
• 2016: Appointed member of The Young Academy, KNAW, more, see video interview (with English subtitles)
• 2016: Appointed Head of Research and Education in the Dept of Genetics, UMCG
• 2015: Awarded the UMCG research prize (€100k)
• 2014: ERC Starting grant (€1.5m) and NWO-VIDI grant (€800k)

For more information about his current research see the Franke lab website.

Curriculum vitae: Full CV (per Sept 2017, including collaborators, grants and prizes, full list of publications)
PhD thesis: Genome-wide approaches towards identification of susceptibility genes in complex diseases. Awarded cum laude, Utrecht University, 2008

Research achievements

My research over the last six years targets the development and application of computational algorithms to functional genomics datasets. The foundation for this work was laid during my PhD (2008, cum laude) when I developed new, cutting-edge computational and statistical methods for conducting genome-wide association studies (GWAS) and reconstructing gene networks using gene expression data.

After my graduation, I took up concurrent post-doc positions in London (Institute of Cell and Molecular Science, Queen Mary University), and Groningen (University Medical Centre Groningen). In London, I worked on the genetics of immune-mediated diseases (i.e. celiac disease) and in Groningen, on the development of methods to identify the effects of GWAS risk-SNPs on gene expression levels (Franke and Jansen, Methods Mol Bio 2009). My post-doc work resulted in a landmark paper (Dubois et al. Nature Genetics 2010) that combined my work in London and Groningen. In this paper we demonstrated that there were many independent genetic risk-variants in celiac disease and that they mostly increase disease-risk by altering gene expression levels.

As senior author, I subsequently developed various computational methods and software to increase statistical power to identify such effects on gene expression (Westra et al. Bioinformatics 2010). By using these methods, I was able to demonstrate that the genetic risk-variants for many other diseases also have an effect on gene-expression levels (Fehrmann et al. PLoS Genetics 2011), and that these genetic variants often affect gene expression levels only in specific cell types (Fu et al. PLoS Genetics 2012, Westra et al. PLoS Genetics 2015). We showed that SNPs also affect the expression levels of many long non-coding genes (lncRNAs, Kumar et al. PLoS Genetics 2013) and that they can affect poly-adenylation of genes (Zhernakova et al. PLoS Genetics 2013).

Through a large-scale, blood eQTL meta-analysis consortium that I initiated in 2010 and that I am currently leading, we then identified downstream (trans-eQTL) effects for over 100 risk-SNPs (Westra et al. Nature Genetics, 2013). We have shown that, by using functional genomics approaches, we can identify previously unknown pathways for many different diseases.

Another considerable focus point is the development of novel methods to reuse publicly available data. We recently integrated gene expression data from 80,000 microarrays to accurately predict gene functions and to gain better insight into cancer (Fehrmann et al. Nature Genetics 2015), developed DEPICT (Pers et al. Nature Communications 2015) to use these predicted gene functions to better interpret GWAS findings, and developed strategies to integrate genetic variation, gene expression and methylation data (my recent last-author papers include Zhernakova et al. Nature Genetics 2017 and Bonder et al. Nature Genetics 2017).

Currently, my group is concentrating on integrating multi-omics datasets, such as conducting large-scale trans-QTL meta-analyses in >30,000 samples, in conjunction with single-cell RNA-seq data (a paper reporting results of our single-cell RNA-seq eQTL work is under review by Nature Genetics), to identify likely causal genes for diseases that might be targetable by drugs, and to help develop computational strategies to increase the diagnostic yield in clinical genetics, by integrating RNA-seq, whole-genome sequencing data, and gene function prediction.

My group currently has 5 PhD students and 4 postdocs; 6 PhD candidates have graduated (two cum laude).

Selected papers

See all Lude Franke's papers in Google Scholar
His H-index is 93, he has co-authored >350 papers and has >40,000 citations (per March 2020)

• Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs
  MGP van der Wijst, H Brugge, DH de Vries, P Deelen, MA Swertz, ...
  Nature Genetics 2018 50(4), 493-497
• Integration of multi-omics data and deep phenotyping enables prediction of cytokine responses

  OB Bakker, R Aguirre-Gamboa, S Sanna, M Oosting, SP Smeekens, ...

  Nature Immunology 2018 19(7), 776-786
• Identification of context-dependent expression quantitative trait loci in whole blood DV Zhernakova, P Deelen, ... Nature Genetics 2017, 49 (1), 139-145
• Disease variants alter transcription factor levels and methylation of their binding sites Bonder MJ, Luijk R, Zhernakova DV, ... Franke L, Heijmans BT. Nature Genetics 2017;49(1):131-138
• Missing heritability: is the gap closing? An analysis of 32 complex traits in the Lifelines Cohort Study IM olte, PJ van der Most, BZ Alizadeh, ... EurJ Human Genetics 2017, 25 (7), 877-885
• The effect of host genetics on the gut microbiome MJ Bonder, A Kurilshikov, EF Tigchelaar, ... Nature Genetics 2016 48 (11), 1407-1412
• Refined mapping of autoimmune disease associated genetic variants with gene expression suggests an important role for non-coding RNAs I Ricaño-Ponce, DV Zhernakova, P Deelen, ... Journal of autoimmunity 2016, 68, 62-74
• Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity A Zhernakova, A Kurilshikov, MJ Bonder, EF Tigchelaar, ... Science 2016, 352 (6285), 565-569 See press releases
• GWAS for executive function and processing speed suggests involvement of the CADM2 gene CA Ibrahim-Verbaas, J Bressler, ... Molecular psychiatry 2016, 21 (2), 189-197
• Gene expression analysis identifies global gene dosage sensitivity in cancer. Fehrmann RS, Karjalainen JM, ... Wijmenga C, van Vugt MA, Franke L. Nature Genetics 2015 Press release (in English), Persbericht (in Dutch)
• Proton pump inhibitors affect the gut microbiome. F Imhann, MJ Bonder, AV Vila, J Fu, ... Gut 2015 gutjnl-2015-310376
• Biological insights from 108 schizophrenia-associated genetic loci. S Ripke, BM Neale, ... Nature 2014, 511 (7510), 421
• Systematic identification of trans eQTLs as putative drivers of known disease associations Westra HJ, Peters MJ, ..., Zhernakova A, Zhernakova DV, ... Karjalainen J, Withoff S, ... Franke L. N ature Genetics 2013, Persbericht (in Dutch). Highly cited.
• Human disease-associated genetic variation impacts large intergenic non-coding RNA expression Kumar V, Westra HJ, Karjalainen J, Zhernakova DV, ... Franke L, Wijmenga C. PLoS Genetics 2013
• Unraveling the regulatory mechanisms underlying tissue-dependent genetic variation of gene expression. Fu J, Wolfs MG, Deelen P, Westra HJ, Fehrmann RS, ... Wijmenga C, Franke L. PLoS Genetics 2012
• Trans-eQTLs reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the HLA. Fehrmann RS, Jansen RC, Veldink JH, Westra HJ, ... Wijmenga C, Te Meerman GJ, Franke L. PLoS Genetics 2011
• MixupMapper: correcting sample mix-ups in genome-wide datasets increases power to detect small genetic effects Westra HJ, Jansen RC, Fehrmann RS, te Meerman GJ, van Heel D, Wijmenga C, Franke L. Bioinformatics 2011
• Multiple common variants for celiac disease influencing immune gene expression Dubois PC, Trynka G, Franke L, Hunt KA, Romanos J, Curtotti A, Zhernakova A, ... Wijmenga C, van Heel DA. Nature Genetics 2010