Christina M. Lill

Prof. Dr. Christina M. Lill

1. Translational Epidemiology Group, Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany
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Research Focus & Metrics

Academic Career Length

19Years (2008-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 September 2014 in Nature Genetics
Verified
Journal Article
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MNMike A Nalls
Mike A Nalls
NPNathan Pankratz
Nathan Pankratz
CLChristina M. Lill
Christina M. Lill
CDChuong B Do
Chuong B Do
DHDena G Hernandez
Dena G Hernandez
GDGene P D
Gene P D
Andrew Singleton and colleagues report a large-scale meta-analysis of genome-wide association data in Parkinson's disease using over 13,000 cases and 95,000 controls plus additional samples for replication. They identify 6 new risk loci and replicate 28 independent risk variants for Parkinson's disease across 24 loci. We conducted a meta-analysis of Parkinson's disease genome-wide association studies using a common set of 7,893,274 variants across 13,708 cases and 95,282 controls. Twenty-six loci were identified as having genome-wide significant association; these and 6 additional previously reported loci were then tested in an independent set of 5,353 cases and 5,551 controls. Of the 32 tested SNPs, 24 replicated, including 6 newly identified loci. Conditional analyses within loci showed that four loci, including GBA, GAK-DGKQ, SNCA and the HLA region, contain a secondary independent risk variant. In total, we identified and replicated 28 independent risk variants for Parkinson's disease across 24 loci. Although the effect of each individual locus was small, risk profile analysis showed substantial cumulative risk in a comparison of the highest and lowest quintiles of genetic risk (odds ratio (OR) = 3.31, 95% confidence interval (CI) = 2.55–4.30; P = 2 × 10−16). We also show six risk loci associated with proximal gene expression or DNA methylation.

29 September 2013 in Nature Genetics
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ABAshley H Beecham
Ashley H Beecham
NPNikolaos A Patsopoulos
Nikolaos A Patsopoulos
DXDionysia K Xifara
Dionysia K Xifara
MDMary F Davis
Mary F Davis
AKAnu Kemppinen
Anu Kemppinen
JMJacob McCauley
Jacob McCauley
The International Multiple Sclerosis Genetics Consortium reports the discovery of 48 new susceptibility variants for multiple sclerosis through targeted follow-up of immune-related loci. They also report fine mapping of association signals at established susceptibility loci and provide insights into the immune system processes underlying this disease. Using the ImmunoChip custom genotyping array, we analyzed 14,498 subjects with multiple sclerosis and 24,091 healthy controls for 161,311 autosomal variants and identified 135 potentially associated regions (P < 1.0 × 10−4). In a replication phase, we combined these data with previous genome-wide association study (GWAS) data from an independent 14,802 subjects with multiple sclerosis and 26,703 healthy controls. In these 80,094 individuals of European ancestry, we identified 48 new susceptibility variants (P < 5.0 × 10−8), 3 of which we found after conditioning on previously identified variants. Thus, there are now 110 established multiple sclerosis risk variants at 103 discrete loci outside of the major histocompatibility complex. With high-resolution Bayesian fine mapping, we identified five regions where one variant accounted for more than 50% of the posterior probability of association. This study enhances the catalog of multiple sclerosis risk variants and illustrates the value of fine mapping in the resolution of GWAS signals.

1 October 2010 in Neuron
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Journal Article
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LBLars Bertram
Lars Bertram
CLChristina M. Lill
Christina M. Lill
RTRudolph E. Tanzi
Rudolph E. Tanzi

14 January 2019 in Nature Genetics
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Journal Article
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RLRichard Karlsson Linnér
Richard Karlsson Linnér
PBPietro Biroli
Pietro Biroli
EKEdward Kong
Edward Kong
SMS. Fleur W. Meddens
S. Fleur W. Meddens
RWRobbee Wedow
Robbee Wedow
JBJonathan P. Beauchamp
Jonathan P. Beauchamp
Humans vary substantially in their willingness to take risks. In a combined sample of over 1 million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. Across all GWAS, we identified hundreds of associated loci, including 99 loci associated with general risk tolerance. We report evidence of substantial shared genetic influences across risk tolerance and the risky behaviors: 46 of the 99 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is genetically correlated (\(\left| {\hat r_{\mathrm{g}}} \right|\) ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near SNPs associated with general risk tolerance are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We found no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

15 March 2012 in PLOS Genetics
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Journal Article
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CLChristina M. Lill
Christina M. Lill
JRJohannes T. Roehr
Johannes T. Roehr
MMMatthew B. McQueen
Matthew B. McQueen
FKFotini K. Kavvoura
Fotini K. Kavvoura
SBSachin Bagade
Sachin Bagade
LBLars Bertram
Lars Bertram
More than 800 published genetic association studies have implicated dozens of potential risk loci in Parkinson's disease (PD). To facilitate the interpretation of these findings, we have created a dedicated online resource, PDGene, that comprehensively collects and meta-analyzes all published studies in the field. A systematic literature screen of ∼27,000 articles yielded 828 eligible articles from which relevant data were extracted. In addition, individual-level data from three publicly available genome-wide association studies (GWAS) were obtained and subjected to genotype imputation and analysis. Overall, we performed meta-analyses on more than seven million polymorphisms originating either from GWAS datasets and/or from smaller scale PD association studies. Meta-analyses on 147 SNPs were supplemented by unpublished GWAS data from up to 16,452 PD cases and 48,810 controls. Eleven loci showed genome-wide significant (P<5×10−8) association with disease risk: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, and SYT11/RAB25. In addition, we identified novel evidence for genome-wide significant association with a polymorphism in ITGA8 (rs7077361, OR 0.88, P = 1.3×10−8). All meta-analysis results are freely available on a dedicated online database (www.pdgene.org), which is cross-linked with a customized track on the UCSC Genome Browser. Our study provides an exhaustive and up-to-date summary of the status of PD genetics research that can be readily scaled to include the results of future large-scale genetics projects, including next-generation sequencing studies. The genetic basis of Parkinson's disease is complex, i.e. it is determined by a number of different disease-causing and disease-predisposing genes. Especially the latter have proven difficult to find, evidenced by more than 800 published genetic association studies, typically showing discrepant results. To facilitate the interpretation of this large and continuously increasing body of data, we have created a freely available online database (“PDGene”: http://www.pdgene.org) which provides an exhaustive account of all published genetic association studies in PD. One particularly useful feature is the calculation and display of up-to-date summary statistics of published data for overlapping DNA sequence variants (polymorphisms). These meta-analyses revealed eleven gene loci that showed a statistically very significant (P<5×10−8; a.k.a. genome-wide significance) association with risk for PD: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, SYT11/RAB25. In addition and purely by data-mining, we identified one novel PD susceptibility locus in a gene called ITGA8 (rs7077361, P = 1.3×10−8). We note that our continuously updated database represents the most comprehensive research synopsis of genetic association studies in PD to date. In addition to vastly facilitating the work of other PD geneticists, our approach may serve as a valuable example for other complex diseases.

1 December 2016 in Molecular and Cellular Probes
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Journal Article
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CLChristina M. Lill
Christina M. Lill