Thesis: Genome-Wide Association Studies & Gout


In this post I discuss genome-wide association studies in relation to gout. Genome-wide association studies are commonly used to determine the level of relationship between genetic mutations and a specific disease or phenotype. As a very, very simple analogy we might consider the height of basketball players. A study of the height of basketball players compared to ‘non-basketball players’ might reveal that those people that play basketball tend to be taller than most average people. Of course this is obvious! But this is the idea behind an association study, we simply look for things that co-occur with more-than-normal frequency. If you have been living under a rock and have never heard of the fabled association between beer and diapers, I recommend you do a quick Google! 🙂

The completion of the human genome project marks a significant milestone in scientific discovery and opens up new possibilities to research the genetic basis of biological function, health, disease and heritability (Collins et al, 2003). Over the past decade, genome-wide association studies (GWAS) have become the de facto method of investigating the underlying genetics that contribute to complex diseases such as cancer, diabetes and neurological disorders (Marchini, Donnelly and Cardon, 2005; McCarthy et al, 2008). Specifically, GWAS are aimed at mapping patterns of association between genome-phenotypes or genome-environment and have greatly added to the understanding and characterisation of simple Mendelian traits and continue to expand our understanding of more complex diseases (Chang and Keinan, 2014). However in the case of complex, multi-gene pathways, current methods of analysis fail to describe the majority of variation observed within a population suggesting that alternative methods of detecting multi-gene effects may be necessary (McCarthy et al, 2008). In this section we will briefly describe the accepted approach to genome-wide association studies with specific discussion related to the study of hyperuricemia and gout.

Determining causal links between genetic variation and the development of disease has become a key focus of genetics research. There is significant efforts to understand common genetic variants that predetermine the development of specific diseases such as gout. There is still some debate over the exact biological mechanisms that lead to many complex diseases, which are further complicated by the co-occurrence of risk factors across seemingly unrelated diseases (see Solovieff et al, 2013). Additionally, relevant biological pathways are often complex, involving many inter-related and codependent biological interactions. A change in behaviour at any point in such pathways may lead to a myriad of downstream effects and thus influence the health of an individual. Specific to gout, it is known that hyperuricemia is a necessary precursor for gout and therefore, genes relating to the transport of uric acid have been implicated as potential causal candidates for gout. However by itself, elevated serum urate levels are not sufficient for gout (Merriman 2015).

In 2013, Köttgen et al published the results of a large-scale GWAS focused on gout with a cohort of more than 140,000 individuals. Analysis of this data set revealed 28 relevant mutations, 18 of which represent previously unknown genetic variants associated with serum urate levels with implications to hyperuricemia and gout. In addition, Köttgen et al were able to show that approximately 70 % of single-nucleotide polymorphisms (SNPs) identified were specifically implicated to regions of the genome known to regulate gene expression and therefore are likely to play a causal role in the regulation of serum urate and gout (Merriman, 2015).
The findings from Köttgen et al (2013) help to confirm the important role that the metabolism of urate plays in the development of gout. Given the confirmed genetic link related to the control of serum urate levels, the obvious hypothesis therefore is that hyperuricemia and gout should show strong heritability (i.e. passed down to subsequent generations through genes). And indeed, studies suggest that there is approximately a 60 % rate of heritability for urate levels (Krishnan et al, 2012). However, only a small proportion (approximately 7 %) of the variability in urate levels can be explained by the combined genetic effects observed in the study by Köttgen et al (2013). This suggests that there are yet significant influences yet to be discovered.

It seems likely that whatever form my eventual research thesis takes, some form of association study will be at the heart of it. The challenge for me will be to construct a research direction which is both useful to Tony and his group, whilst also being relevant as a data science research question. Clearly, there are plenty of unresolved questions around the genetics of gout, all of which are interesting and valid. However I am a data science student, not a genetics student and therefore the core of my thesis needs to be framed from the perspective of a data scientist. So still a lot of work to do.

The next steps involve digging deeper into genome-wide association studies to understand how they work and whether there are any limitations that might be addressed by alternate methods of analysis. I will write about this more in my next post.


Chang, D., & Keinan, A. (2014). Principal component analysis characterizes shared pathogenetics from genome-wide association studies.

Collins, F. S., Green, E. D., Guttmacher, A. E., & Guyer, M. S. (2003). A vision for the future of genomics research. Nature, 422(6934), 835-847.

Köttgen, A., Albrecht, E., Teumer, A., Vitart, V., Krumsiek, J., Hundertmark, C., … & Lehtimäki, T. (2013). Genome-wide association analyses identify 18 new loci associated with serum urate concentrations. Nature genetics, 45(2), 145-154.

Krishnan, E., Lessov-Schlaggar, C. N., Krasnow, R. E., & Swan, G. E. (2012). Nature versus nurture in gout: a twin study. The American journal of medicine,125(5), 499-504.

Marchini, J., Donnelly, P., & Cardon, L. R. (2005). Genome-wide strategies for detecting multiple loci that influence complex diseases. Nature genetics, 37(4), 413-417.

McCarthy, M. I., Abecasis, G. R., Cardon, L. R., Goldstein, D. B., Little, J., Ioannidis, J. P., & Hirschhorn, J. N. (2008). Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature Reviews Genetics, 9(5), 356-369.

Merriman, T. R. (2015). An update on the genetic architecture of hyperuricemia and gout. Arthritis research & therapy, 17(1), 98.

Solovieff, N., Cotsapas, C., Lee, P. H., Purcell, S. M., & Smoller, J. W. (2013). Pleiotropy in complex traits: challenges and strategies. Nature Reviews Genetics, 14(7), 483-495.


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