Research directions
Statistical approaches to unravelling the genetic architecture of complex human traits
We are developing and applying statistical methods to decipher the genetic architecture of complex human traits and their relationships. We devise and fit models integrating different omics and disease data to enhance our understanding of the genetic circuits underlying human diseases and how environmental factors may modify it. Our research aims to disentangle the intricate causal network of risk factors and cardio-metabolic traits. We are particularly active in the following research topics:
- Fine-mapping of genetic association signals (including common/rare single nucleotide and copy number variants)
- Identification of genetic subtypes of obesity and their metabolic consequences
- Genetic underpinnings of human aging
- Causal inference through Mendelian randomisation (identification of causal molecular biomarkers, effects of composite traits, development of pleiotropy robust methods, distinguishing direct/indirect genetic effects)
Recent publications:
Porcu E, Rüeger S, Lepik K; eQTLGen Consortium; BIOS Consortium, Santoni FA, Reymond A*, Kutalik Z*. (2019) Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits. Nat Commun. 2019 Jul 24;10(1):3300.
Winkler TW, Günther F, Höllerer S, Zimmermann M, Loos RJ, Kutalik Z*, Heid IM*. (2018) A joint view on genetic variants for adiposity differentiates subtypes with distinct metabolic implications. Nat Commun. 2018 May 16;9(1):1946.
McDaid AF, Joshi PK, Porcu E, Komljenovic A, Li H, Sorrentino V, Litovchenko M, Bevers RPJ, Rüeger S, Reymond A, Bochud M, Deplancke B, Williams RW, Robinson-Rechavi M, Paccaud F, Rousson V, Auwerx J, Wilson JF, Kutalik Z. (2017) Bayesian association scan reveals loci associated with human lifespan and linked biomarkers. Nat Commun. 2017 Jul 27;8:15842.
