Image: Professor Jian Yang

Image credit: Image provided by Professor Yang

Professor Jian Yang is a Professorial Research Fellow in the Institute for Molecular Bioscience, at The University of Queensland, and the recipient of the 2017 Frank Fenner Prize for Life Scientist of the Year, in the Prime Minister’s Prizes for Science. His research focuses on tracking down the genetic basis for complex inherited traits, which are those that depend on changes to more than just one or two individual genes.

“I am happy and humbled by the award of the Frank Fenner Prize, which is recognition of the work that my colleagues and I have done, to understand the genetic variation for human disposition,” says Professor Yang.

In 2010, supported by Australian Research Council (ARC) funding, Professor Yang and his colleagues published a breakthrough paper in Nature Genetics which analysed the inheritability of height, by looking at the genetic data of 3,925 unrelated, individual people.

The genetic basis of height is highly complex, and previous studies had only found gene loci that could account for about 5 per cent of height variation in the population. Professor Yang and his colleagues used a new, statistical ‘mixed linear’ model, which applies enormous computing power to large genetic datasets to estimate the accumulated effect of all genes to height.

“This research stemmed from a problem called the ‘missing heritability paradox’,” says Professor Yang. “It is estimated that 80 per cent of height variation is heritable. The previous genome-wide association studies involved tens of thousands of people, but could only account for a small fraction of the genetic variation seen. In 2010, we said that the problem is that these studies tested each genetic marker one at a time—instead, we have put the markers all together in a mixed linear model and validated our estimation method with simulations.”

Professor Yang’s team showed that 45 per cent of height variance could be explained by considering all genes (strictly speaking all genetic markers) simultaneously. Associations between these genetic markers were previously hidden within the ‘dark matter’ of the genome, escaping detection using the single-gene based methods.

“The significance is not just about a bigger proportion of height variance explained, but the underlying interpretation,” says Professor Yang. “We actually used data on a smaller number of people in our study. The underlying nature of our model shows that there must be many, many genes involved, each with a small effect.”

With a 2016 Discovery Projects grant from the ARC, Professor Yang is now investigating the phenomena known as X-chromosome inactivation, whereby females are able to switch off one of their X chromosomes, preventing it from duplicating the information contained in the other copy. 

The process controls the colourings of tortoiseshell cats, and is thought to account for females being less susceptible to X-chromosome linked conditions. These include relatively benign gene-based traits, such as red colour blindness, as well as more serious conditions like muscular dystrophy and haemophilia, all of which are more common in men, as they lack a ‘backup’ copy of the X chromosome.

 “To avoid ‘overdose’ of the X chromosome in females, we have inactivation,” says Professor Yang. “This event is interesting, but how this affects phenotype variation in the population is not clear.”

Professor Yang will apply statistical methods that search the deep genome, by analysing large datasets from genome-wide association studies, in order to map which genes on the X-chromosome are affecting traits, and what proportion of the duplicated X-located genes escape from inactivation in females.

Professor Yang says that he receives both ARC and National Health and Medical Research Council (NHMRC) funding for his work, with ARC funding supporting the initial genetic statistical analysis, and NHMRC funding supporting research that is focussed on clinical and public health research.

“My background is actually pure biology; I learnt statistical modelling when I was an undergraduate, now I do a mix of statistical and tool-driven genetic research. I think I would call myself a geneticist, rather than a statistician.”

Professor Yang says that he sees the future of his research going in several different directions, to understand the genetic basis of trait variations, but also to understand why it varies across the population. Another direction would be to use the new deeper understanding of the genome to prevent disease.

“If we are able to figure out the whole picture, we might be able to design a key therapeutic target. A very large dataset might also be able to model a person’s phenotype (how they look) in the future, and their disease risk. Using this genetic information to predict disease risk could considerably impact health care in the future, and potentially lead to public health benefits, as well as save a lot of money.