Molecular traits on the other hand are more neutral (because selection acts on the organism), thus are less confounded by population structure, which we believe further enhances ML prediction, because it allows the effect of distinct markers to be independent across populations.

Another interesting insight we make is the effect of ancestral selection on genomic predictions. We show how selection acts more strongly on complex traits, (like flowing time) causing variance in the phenotype to be collinear with population structure, which confounds genomic prediction

We find that ML applications best suit molecular traits. These traits tend to have simpler genetic architectures. We argue that with limited of observations, epistasis is more easily modelled when the variance in a trait is controlled by a few (e.g. 10) SNPs.

Photos:
1. Media training
2. All of the Australians in attendance (I think we were the most represented nation per-capita)
3: Me and my friend Nikil outside the Heidelberg castle
4: Pat Hanrahan and some more friends

There were also opportunities to develop research adjacent skills inc. a media training workshop. I also presented an artistic representation of my research as apart of the “intercultural art project” that runs each year as apart of the conference. I highly recommend the conference!

It was amazing to also meet the other young researchers – every one there had something to teach me, and I learned about all sorts of fields from maths and compsci. I hope to stay in touch with the friends I made there!