The answer to how long each of us will live is partly encoded in our genome. We have identified 16 genetic markers associated with a decreased lifespan, including 14 new to science. This is the largest set of markers of lifespan uncovered to date. About 10% of the population carries some configurations of these markers that shorten their life by over a year compared with the population average. Spearheaded by us in collaboration with scientists from the SIB Swiss Institute of Bioinformatics, the University of Lausanne and the EPFL, the study provides a powerful computational framework to uncover the genetics of our time of death, and ultimately of any disease. The study was published on 27 July in Nature Communications.
Sina Rüeger and Aaron McDaid won the poster prize at the Functional Annotation of Genome-wide variants Workshop held 14-15 March in Lausanne. Their poster was entitled “Improved summary statistics imputation for studies with variable missingness pattern”.
Our group contributed to a large international study published on 1 February in Nature, where over 300 scientists from across the globe have combined their effort to study in more than 700,000 participants what makes us shorter or taller. Adult height is mostly determined by the information encoded in our DNA: children from tall parents tend to be taller, and vice versa. “The idea is that if we can understand the genetics of a simple human trait like height, we could then apply this knowledge to develop tools to predict more complex human diseases such as diabetes or schizophrenia”, explains Zoltán Kutalik, SIB group leader and assistant professor at the Institute of Social and Preventive Medicine of the Lausanne University Hospital.
The European Society of Human Genetics (ESHG) held its yearly conference 21-24 May 2016 in Barcelona, Spain. Sina Rüeger (PhD student) received the Young Investigator Award for the best talk in statistical genetics (the Lodewijk Sandkuijl Award). She presented how summary statistics imputation can enhance approximate conditional analysis when combining different meta analysis results (e.g. SNP array and exome array results from Genetic Investigation of ANthropometric Traits). (Abstract title: “Summary statistic imputation method enables conditional analysis across meta-analysis studies: Application to GIANT height associations from exome-chip & HapMap”).
Adult body size and body shape differ substantially between men and women and change over time. More than hundred genetic variants that influence body mass index (measure of body size) or waist-to-hip ratio (measure of body shape) have been identified. While there is evidence that some genetic loci affect body shape differently in men than in women, little is known about whether genetic effects differ in older compared to younger adults, and whether such changes differ between men and women. Therefore, we conducted a systematic genome-wide search, including 114 studies (>320,000 individuals), to specifically identify genetic loci with age- and or sex-dependent effects on body size and shape. We identified 15 loci of which the effect on BMI was different in older compared to younger adults, whereas we found no evidence for loci with different effects in men compared to women. The opposite was seen for body shape as we identified 44 loci of which the effect on waist-to-hip ratio differed between men and women, but no difference between younger and older adults were observed. Our observations may provide new insights into the biology that underlies weight change with age or the sexually dimorphism of body shape. The findings have been published in this month’s PLoS Genetics.
Study Examines Association of Genetic Variants with Cognitive Impairment
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.
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).
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).