“The same mutation can have a different outcome in different individuals”

The English researcher Ben Lehner started as a junior group leader at the CRG in December 2006 and has been an ICREA Professor since 2009. His lab, Genetics Systems, consists of five postdoctoral fellows, four PhD students, and a technician who hail from Italy, the UK, Germany, the Netherlands, Poland, Chile, Peru, Canada and Switzerland. About half of the group members are computational biologists and the rest work primarily in the ‘wet’ lab. They all have the same aim – to understand basic questions in genetics – but they use diverse approaches and model systems.

From individual genome sequences to individual phenotypes 

“In humans many mutations in genes are associated with an increased risk of particular diseases such as cancer”, the scientist explains. “But human geneticists are terrible at predicting disease risk. Most people with disease mutations never get the disease”. One aim of Lehner’s group is to better understand how the thousands of mutations in the genome of a particular individual interact to influence phenotypes such as disease risk. “What causes the same mutation to have a different outcome in different individuals? That’s one of our favourite questions”, states the researcher. This means understanding how genetics, the environment and ‘chance’ influence the outcome of particular genetic variants.
Recent research has focused on finding ways to predict the ‘normal’ outcome when genes are inhibited. In collaboration with labs from Korea, Toronto and Texas the group has created prediction models using generalisations such as the shared function of genes. “If two proteins interact physically, one can assume that they are involved in similar processes”, Lehner explains. The consequence of this hypothesis is that a mutation in either of the genes is likely to result in a similar phenotype, according to their shared function. By assuming this, one can generalise and expand the findings by using all available information on physical data, genetic interaction and co-evolution of every single gene analysed.

The next step for the group is to understand how the thousands of mutations in an individual’s genome combine to influence their characteristics. “Two humans differ by thousands of mutations, so how do we evaluate the outcome of all of this genetic variation in one go?” Beyond this they are also trying to understand why it can be impossible to predict disease phenotypes from a genome sequence. “Even genetically ‘identical’ twins are not identical when it comes to disease susceptibility. The same is true in simple organisms – if you control the genetics and you control the environment, you still cannot predict what will happen. We are working to understand why this is”, says the head of the lab.

Focus on basic problems 

In their studies the group uses experimentally tractable model organisms like yeast or the transparent roundworm C. elegans, but they also use existing data from many different sources. “Rather than working with a single system or approach, we like to choose the best system to study a particular problem with. And if you look at the history of biology, ‘best’ normally means ‘simplest’”, explains the biologist. “The problem is the important thing – it doesn’t really matter how you choose to solve it. But the problem must be basic and general, and one that you can actually solve!”.

This article was published in the El·lipse publication of the PRBB.

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