Yesterday I arrived in Uppsala, Sweden, after a reasonably harrowing day which included getting up at 3.45am, and attempting to make small talk with a taxi driver on the way to the airport. Not only that, but I failed to read the email from the hostel which informed me of the minimal reception hours. This meant that I spent the first 2 hours of my time in Uppsala dragging my large suitcase around the city, through the snow, looking like a dick. After I got in and dropped it off, however, I went for a nice walk to the castle, gazed up at the stars, and slid down some snow-covered hills in a fever of excitement.
I may be 30, but that doesn’t mean that I’m not still AWESOME.
My reason for being here is not just so that I can have my opinion of long-johns changed forever (holy crap, they’re THE BEST), but also because I am taking a two-week quantitative genetics course being run by Dr Bruce Walsh of the University of Arizona (Note: replying to a taxi driver’s query as to whether you’re going to Sweden on holiday with, “No, I’m going on a two-week quantitative genetics course” is a guaranteed small-talk death blow).
I had planned to write short blog posts most days to cover briefly what he has been teaching us, but today has shown me the downright stupidity of such thinking. In just the first day, we’ve already covered pretty much everything I’ve struggled to teach myself over the past year and a half; one of the post-docs I chatted to at the morning break also informed me that he went on a 3-day course last year, and Walsh covered the entirety of that content in the first half hour.
So, yeah, it’s pretty intense.
Instead, I’ll give an extremely brief overview of what quantitative genetics is (and links to more comprehensive information), and hopefully continue along some of the basics – and how I am planning to use such techniques in my own research – over the next couple of weeks…
Quantitative genetics uses the insight that the expression of a single trait may be influenced by multiple genes, in addition to environmental factors, as a way to analyse phenotypic variation and evolution. Put simply, it enables us to study both nature and nurture! ‘Variation’ or ‘variance’ constitute almost every second word in the literature; we are never given the opportunity to forget that it is populations which evolve, not individuals, and therefore we are always interested in the phenotypic and genetic variation within populations.
There are a number of people who can be thanked for the development of this field, including Gregor Mendel, Francis Galton, Karl Pearson, and Sewall Wright, but one of the foremost is the statistician, biologist and all-round badass polymath that is Sir Ronald Fisher.
Anyone who has idly flicked through a biostatistics textbook (come on, we’ve all been there) will have seen mention of ANOVA; this ‘analysis of variance’ is based upon the concept of variance partitioning outlined by Fisher in his 1918 paper ‘The Correlation Between Relatives on the Supposition of Mendelian Inheritance’.
The very title of this paper gives you a good idea of what much of quantitative genetics entails: applying Mendelian principles of genetic inheritance in order to compare the phenotypes of individuals whose relatedness is known. The extent to which relatives resemble one another depends on how much the expression of the phenotype is determined by shared genes, as opposed to random environmental effects. The expression of a single phenotype (or a particular phenotypic trait) can be written mathematically as:
P = G + E
Where P is the phenotypic value, G is the genotypic value, and E is the deviance from this genotypic value caused by environmental effects.
Sounds pretty easy, right?
[EDIT: This video is supposed to start at the bit where Arnie says ‘WRONG’ and then shoots a guy in the face. It doesn’t, though. You don’t have to watch it. Oh, and if you haven’t seen Commando, *spoiler alert*]
Firstly, recall that we must deal with a population, so we have to think in terms of variation:
V(P) = V(G) + V(E)
The above are variances of phenotype, genotype, and environmental effects.
Next, we find that there are several components of genetic variation:
V(G) = V(A) + V(D) + V(I)
These components are additive genetic variance, dominance variance, and epistatic variance; only additive genetic variance is heritable, so this is the really crucial part. For more information on the others, and variance in general, I recommend this short paper in Nature.
Next up: environmental variation. These come in two broad forms – general environmental effects and special environmental effects – along with a special added bonus:
V(E) = V(Eg) + V(Es) + V(GxE)
But that will have to wait until another day, because it’s late and I’m tired and I’m going to bed.
Yes, probably still wearing my long-johns.