New method to analyze complex genetic data could be the key to tackling rare diseases

The findings of the new study, which are published in Physiological Genomics, explain a new method of analysing genetics, which determines the extent to which genes are involved in phenotype formation.

The previously used method extracted information using averages from different datasets, meaning that it had limitations in terms of the type of information it could provide, and what scientists could learn from it.

The study was led by Dr Cyril Rauch, an Associate Professor in Physical and Mathematical Veterinary Medicine and Science at the University of Nottingham.

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 helps scientists to understand biology, and in turn, how 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.

As the UK wants to capitalise on the success of gene editing technologies, this is something that needs urgently addressing since there can only be useful editing technologies in the cases of rare and complex traits, if precise genotype-phenotype mapping information is available. In this context, 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.

Inspired by physics theory, an interdisciplinary team of researchers at the University of Nottingham have dedicated time over the past few years to rethink and 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 Rauch says: "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."