Emily Smith-Woolley

PhD student, King's College London
  • United Kingdom


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Behind the Paper

Recent Comments

Mar 25, 2018
Replying to Greg Kerr

Interesting research. I am not an expert, but I am interested.  I assume that the choice of schools that agreed to take part in this study is randomised. Or did you take the dataset from the EduYears (with genetic information) and then use it to generate the three different cohorts? I am struck by how small the genetic effect is in explaining variation in grade outcomes  (certainly compared to SES, for example), but is this because genetic effects at the level of the individual are less powerful predictors than SES, or is it because selective schools have genepools that are not that different from non-selective schools. Yes, your paper shows that there are genetic differences between selective and non-selective schools, but not that different. The paper states that Eduyears GPS explains 7.6% of variance in GCSE scores. What is the equivalent number for SES? 

Too many questions - sorry. My main interest is the intra-school results, which is not the focus of this study, I know. Every year I teach the same way and I get a range of outcomes. Most of the variation in these outcomes remains unexplained (using your four variables) and is a total mystery. Do you agree?

Would you like to come to my London-based  school to give a talk on these themes? 

Thanks very much for your interest in our paper. I’ll try and answer your questions, but let me know if there is anything I have missed!

With regards to the dataset – the sample is a large UK-based study (The Twins Early Development Study). They have been followed since birth in 1994 -1996 to the present day and have given information on educational traits such as school type, GCSE, SATs and they have also completed cognitive tests. A subsample of the total study also provided us with DNA. These 4000+ individuals are spread around the country in different schools. Because we were not looking at schools themselves, just school type averages, we pretty much have one individual per school. 

On your point about genetic effects explaining little variance in GCSE (~7%) and even less variance in the difference between average GCSE between school types - this is to be expected given that the polygenic score is still fairly ‘new’. What do I mean by that? Well, genome-wide polygenic scores are created using the information from large ‘Genome-wide association studies’ (GWAS), as these get larger, they can start to identify more of the genetic variants associated with traits (such as educational attainment). We have already seen variance in GCSE increase from the first GWAS of educational attainment (Rietveld, 2013) to the one we use in our study (Okbay, 2016). The next one is expected to explain even more variance. At the same time, 7% is still not a ‘small’ effect size for the behavioural sciences. If we think about other things we often think of as being explanatory for exam results, for example gender differences or growth mindset, these often explain less than 1% of the variance. Furthermore when you start to look at the extremes, you can start to see real-world implication. For example in a past study (Selzam et al, 2017), we showed that if you took people’s polygenic score for educational attainment and you focused on those with the lowest, only 37% went on to university, compared to 65% in the highest. 

You are right that the difference in average polygenic scores across school types is fairly small. This is because there is large variation within the school types. This means that there are people with low polygenic scores in selective schools, as well as those with higher polygenic scores, and the same goes for non-selective schools. In our paper, we are showing that on average, those students attending grammar and private schools have slightly higher scores.

In terms of the variance explained by socio-economic status in GCSE, we estimate it at 24%. If we think about what socio-economic status is: educational achievement, highest qualification, career status, career position etc, these things are all associated with educational achievement and then we can start to see why there is a moderate association.

Intra-school results is very interesting, but sadly not something we are able to look at as we only have 1 or 2 people in our sample per school. When we look at the four variables together – socio-economic status, general ability, prior achievement and the polygenic score for educational attainment – to predict GCSE scores, they explain 69% of the variance. However, there are many factors influencing GCSE scores (as I’m sure you know more than me) – home environment, behaviour, personality, health etc. My research group wrote a paper on this a few years ago (http://www.pnas.org/content/111/42/15273)

Lastly, I’d be happy to come in to talk to at your school – I’ve emailed you.