ACTN3 Gene and Sports Performance – A Look into 1,750 Genomes [OpenSNP]

While phenotypes are most often defined by a combination of genetic mutations (SNPs and other), there are single gene modifications that seem to have powerful phenotypic effects – think of diseases driven by single nucleotide polymorphisms. In such circumstances, you can’t do much on the ‘nurture’ side of things – when the ‘nature’ or the genetics side of it is so determining.

We’re going to take a brief look at a phenotype that seems to be strongly affected by mutations in the alpha-actin-3 or ACTN3 gene. Specifically, we take advantage of the ‘opennes’ of the OpenSNP platform where users share genetic and phenotype data.

Even more specifically, we’re looking at rs1815739 (SNP) which refers to the coding of a premature stop codon in ACTN3, which is a muscle protein located on chromosome 11. This genetic mutation seems to affect muscle performance.

There are currently ~1,750 users on the OpenSNP platform who shared their genotype for this SNP.

From SNPedia we find out the following associations:

CC – Better performing muscles. Likely sprinter.
CT – Mix of muscle types. Likely sprinter.
TT – Impaired muscle performance. Likely endurance athlete.

There are numerous studies that have looked into these associations and some of them could not be replicated. If you’re interested, go to the SNPpedia page to learn more about this SNP.

In our cohort of ~1,750 people, a large portion (79%) share the CC and CT phenotype, making them more likely to have good muscle performance, while only 21% are TT.

There are many analyses we could conduct on our cohort, courtesy of the fact that we have some self-reported phenotypic data.

Here we’re only going to briefly organize the data for the ‘sports interest’ phenotype, which has been reported by 267 people. The ‘jogger’ phenotype (reported by 216 people) would also be interesting to explore…

Here’s what people report for the ‘sports interest’ phenotype:

– never
– no interest in sports
– somewhat
– snowmobiling
– baseball
– martial arts
– gym
– weightlifting
– strongly

Whoever made this categorization in the first place was definitely ‘creative’. I’ll reformulate and modify it the best I can, for relevance.

‘Martial arts’ has been reported by 1 person, ‘baseball’ by 1, ‘gym’ by 13, ‘never’ by 6, ‘snowmobiling’ by 2, ‘weightlifting’ by 2, while the bulk is as follows:

– somewhat – 84
– strongly – 38
– no interest in sports – 120

So, these seem more relevant. Hence, I’ll include ‘weightlifting’, ‘martial arts’ and ‘gym’ into the ‘strongly’ group, ‘never’ into the ‘no interest in sports’, and ‘baseball’ and ‘snowmobile’ into the ‘somewhat’ group.

Thus, the new categories:

– somewhat – 84+1+2 = 87
– strongly – 38+2+1+13 = 54
– no interest in sports – 120 + 6 = 126

Okay, we have three broad categories and the overall picture seems to look a bit more balanced.

Conclusion

It is likely that in a future post, if I find can make time for this, I will use Python for data analysis and go through each of these 267 genomes at the location of this mutation and see how it correlates with the phenotype reported by the user.

If any of you reading this post would like to take it from here, please! I am willing to help. A step-by-step tutorial (combining programming and data analysis) that I’ve written a few months ago can guide you along…


Images: here and here.


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