What do we do?
In the Statistical Genetics Group, we are interested in the development of statistical methodologies in order to decipher the genetic architecture of complex human traits related to obesity. To do this, we efficiently combine large-scale genome-wide association studies (GWAS) with various -omics data. Our methods improve genetic fine-mapping, reveal gene-environment interactions, dissect genetic subtypes of obesity, enhance causal effect estimation and detect various statistical artefacts. Furthermore, we are involved in large consortia researching the genetic basis of anthropometric traits (GIANT) and longevity (LifeGen).
We have developed methods to detect collider bias (Yaghootkar et al.), gene-environment interactions (Tyrrell et al., Winkler et al., Graff et al. Justice et al.) and chromatin accessibility regulators (Lamparter et al.), to perform secure quality control (Huang et al.) and metabolome-wide QTL analysis (Rueedi et al.). We developed a new software (SS-imp) for summary statistic imputation. Group member, Sina Rueger, received the award for the best PhD student talk at EMGM 2017, and with Aaron McDaid received the best poster award at the Functional Annotation Workshop. Finally, we participated in major collaborative efforts (published in Nature, Nature Genetics, Nature Communications) to unravel the genetic basis of height, menarche, baldness, lean mass and lifespan.
Main publications 2017
- Marouli E et al. Rare and low-frequency coding variants alter human adult height. Nature 2017; 542:186-190
- McDaid AF et al. Bayesian association scan reveals loci associated with human lifespan and linked biomarkers. Nat Commun. 2017; 8:15842.
- Mace A et al. CNV-association meta-analysis in 191,161 European adults reveals new loci associated with anthropometric traits. Nat Commun. 2017; 8(1):744