Parental origin of the genetic code matters for obesity

High-throughput technologies have opened new perspectives to unravel the genetic cause of various diseases or physiological traits, such as body mass index (BMI). Genome-wide association studies (GWAS) measure the correlation between genetic and phenotypic variation in large groups of individuals. However, the discovered genetic associations, even combined, account for only a small fraction of the BMI heritability – in part due to the complexity of obesity. Almost all previous studies assumed that the effect of all genetic variants is the same regardless of whether they are inherited from the mother or the father.

To fill this gap, in collaboration with Dr Clive Hoggart (Imperial College London) and Dr. Carlo Rivolta (University of Lausanne), we have developed a new approach for studying variants whose impact on obesity depends on their parental origin (parent-of-origin effects, POE). The results of this study, carried out at the University Institute of Social and Preventive Medicine (IUMSP) of the Lausanne Hospital (CHUV) and the Swiss Institute of Bioinformatics (SIB), were published on 31 July in the journal PLoS Genetics.

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Metabolome Genetics

We are interested in how genotypic variability impacts molecular phenotypes and how, together with the environment, this affects complex human traits and disease susceptibility. Our focal molecular phenotype involves the concentration of small molecules underlying metabolism. These concentrations can be measured on large-scale, in body fluids, like blood and urine. In our recent article “Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links” that appeared in PLoS Genetics on 20 February 2013, we studied such data derived from the CoLaus. Below is more information on this publication and details on the our method can be found here. Our article features as News of the University of Lausanne (here), the University Hospital -CHUV (here) and the Swiss Institute of Bioinformatics (here).

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SGG joins SIB

The group has become member of the Swiss Institute of Bioinformatics (SIB) after the voting of the SIB Foundation Council on 26 June. Further details are here.

Zoltán nominated as Assistant Professor

In April 2013 Zoltán Kutalik was nominated as Assistant Professor of the Faculty of Biology and Medicine of the University of Lausanne. He will be working in the Statistics Unit of the Institute of Social and Preventive Medicine (IUMSP) of the Lausanne University Hospital (CHUV).

Leenaards prize 2013

The team of Pierre-Yves Bochud (CHUV-UNIL), Zoltán Kutalik (UNIL-SIB), Oscar Marchetti (CHUV-UNIL) and Christian van Delden (HUG & UNIGE) received the Leenaards Prize 2013 for their project “Host genome and transcriptome: new diagnostic and treatment strategies for fungal infections“.

More details can be found: UNIL News, CHUV News, SIB News, UNIGE News, HUG News, Leenaards Foundation, 24 heures, Le Temps, L’Agefi, La Liberté

Multi-SNP association method reveals allelic heterogeneity

There are many known examples of the allelic heterogeneity and imperfect tagging phenomena. The former one is of great importance in monogenic traits but has not yet been systematically investigated and quantified in complex-trait genome-wide association studies (GWASs). We devised a multi-SNP association method that estimates the effect of loci harboring multiple association signals by using GWAS summary statistics.

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Mirror extreme BMI phenotypes associated with gene dosage at the chromosome 16p11.2 locus

Underweight and obese phenotypes can both pose health risks. But whereas obesity has been associated with a number of genetic variants, little is known about the genetic basis of underweight. A large-scale screen of data from 28 cytogenetic centres in Europe and North America now shows that being underweight is frequently associated with duplication of a short region on chromosome 16. Continue reading

Novel method to estimate explained variance of GWAS hits reveals large fraction of the missing heritability

Genome-wide association studies (GWAS) are conducted with the promise to discover novel genetic variants associated with diverse traits. For most traits, associated markers individually explain just a modest fraction of the phenotypic variation, but their number can well be in the hundreds. We developed a maximum likelihood method that allows us to infer the distribution of associated variants even when many of them were missed by chance.

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