Scientists have developed a new method of genetic analysis which extracts more precise data from DNA and could help to tackle rare and complex diseases.
The findings of the new study explain a new method of analysing genetics, which determines the extent to which genes are involved in phenotype formation.
Previously used methods extracted information using averages from different datasets, meaning they had limitations in terms of the type of information they could provide, and what scientists could learn from it.
Genome-wide association studies (GWASs) provide a method to map genotypes – the genetic makeup of an organism, and phenotypes – observational traits such as height or hair colour. This biological data can help to treat certain diseases.
Although genomic technologies have advanced quickly, the statistical model used to analyse genotype and phenotype association are based on works developed by scientist R. A Fisher more than 100 years ago.
However, there is an ongoing debate in the scientific community over whether this method has reached its limit for truly understanding the genetic basis of rare and complex traits – such as rare disease.
Useful editing technologies in the cases of rare and complex traits require precise genotype-phenotype mapping information. New and more accurate statistical methods maximising the investigative power of biological or medical data are needed to help define gene targets and future treatments precisely.
Researchers at the University of Nottingham, UK, have sought to change the mathematical foundations of classic GWA methods, so they can maximise the investigative power of genotype/phenotype datasets.
This has resulted in a new method called Genomic Informational Theory (GIFT) that has now been applied successfully to a range of datasets. By removing the informational barrier linked to dataset categories, the team have demonstrated that it is possible to extract more information using GIFT than the previously used GWAS.
Dr Cyril Rauch, who led the study, said: “One way to represent the difference in the investigative or informational powers of GIFT relative to GWASs is to use an analogy with the magnification power of microscopes.
“Our results show that comparing the informational (resolution) powers of GIFT relative to GWASs is like comparing an electron microscope (GIFT) to a light microscope (GWASs).
“With increased informational power, GIFT can be applied to relatively large datasets to extract further information and/or to small datasets to extract novel information where GWASs were unable to do so previously. GIFT is particularly well suited for applications in fields were building datasets is difficult, for example rare diseases.”
