Solving the genetics jigsaw puzzle
19 May 2011
A method which could help pinpoint the 'missing pieces' of the genetic makeup underlying complex traits, such as height, skin colour and diseases including cancer, diabetes and heart disease, has been developed by researchers at the University of Nottingham.
Despite huge advances in recent years, one of the central challenges of modern genetics is to identify all the genes involved in complex traits and how they interact to bring about variation within a population.
So-called 'mapping' studies, which identify areas of a genome (loci) that are associated with specific traits, for example those that influence disease risk, tend to leave many questions unanswered. Despite the large numbers of loci that have been detected, the effects are almost all modest, and explain only a small portion of the variability.
According to geneticist Dr Gianni Liti from the University of Nottingham, the problem is largely one of 'missing heritability':
"Mapping studies typically detect only a fraction of the loci underlying heritable traits," says Liti. "And of those trait loci that are found, the association peaks generally span a large region of the genome, and do not point to the mechanism responsible for the association."
In a recently published study, Dr Liti and colleagues at University of Nottingham, the Wellcome Trust Sanger Institute and the Universities of Gothenburg and Toronto have developed a highly sensitive method to detect trait loci, in some cases down to the level of single genes.
Finding all the pieces
Copyright: Institute of Food Research
Working with yeast - a model organism - Liti and his team crossed two different strains, one that was heat tolerant and one that was heat sensitive. After multiple rounds of 'intercrossing' they had generated a pool of 100 million cells with a variety of genetic backgrounds.
The team then selected for heat tolerant individuals by growing the resulting pools of cells asexually at 40°C and used next generation sequencing methods to assess changes in the frequency of beneficial 'alleles' over a period of 12 days.
"We mapped 21 loci with significant changes in genetic background in response to selection. This is several times more than found with traditional linkage methods," says Liti. "Nine of these regions contain two or fewer genes, yielding much higher resolution than previous genomic linkage studies."
According to Liti, there are two reasons for the enhanced sensitivity of their new method. Firstly, the multiple rounds of intercrossing introduce recombination between the genomes of the mating cells, which in turn increases the mapping resolution by stretching the length of the entire genome and reducing the linkage between individual loci. Secondly, by applying a selective pressure and monitoring changes over time the team was able to detect alleles with minor fitness effects, which had risen in frequency to become detectable.
Liti explains, "Our dynamic monitoring means that we are more likely to find all the pieces in the puzzle, even the very small ones. This approach was only possible because of recent advances in next generation sequencing.
"It's a whole new way of looking at things: instead of looking at changes in the genomes of individuals we are directly detecting changes within a population."
Building a complete picture
And because they can pinpoint trait genes in yeast, they can also look at how they interact.
"One of the good things about yeast is that we can engineer variation into the population to experimentally measure the effect on the phenotype and how trait genes interact with each other," says Liti. "Once we can quantify the total genetic basis of a trait and the contribution from the environment, we will be in a position to make predictions about an organism's biology from its genome sequence.
This could have important implications for the study of human diseases like type 1 diabetes where it is not certain what all the genetic factors are.
Dr Leopold Parts and Professor Richard Durbin at the Wellcome Trust Sanger Institute who together with Liti's team developed the approach and analysis methods are now studying the implications of these findings to understand the genetic makeup of human disease.
The possibilities are endless
Back in Nottingham, Liti, together with Professor Edward Louis, is looking to use the method to generate robust yeast strains for ethanol production, as part of a BBSRC Sustainable Bioenergy Centre (BSBEC) programme. They hope to be able to select strains that are better at overcoming pH and temperature inhibition during the fermentation process as well as those that are better able to metabolise xylose and cellulose sugars found in waste plant material.
"Our method can be applied to any selectable trait, including ones that do not affect fitness. For example, cell sorting to select for cell size, GFP expression on specific promoters or washing the plate to detect cell adhesion traits," says Liti. "And a similar approach could be applied to other model genetic systems, including Drosophila and C. elegans, that are amenable to crossing in bulk and large experimental population sizes."
L. Parts et al. Revealing the genetic structure of a trait by sequencing a population under selection. 2011 Genome Research. DOI: 10.1101/gr.116731.110.
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