Yurii S Aulchenko, Maksim V Struchalin, Nadezhda M Belonogova, Tatiana I Axenovich, Michael N Weedon, Albert Hofman, Andre G Uitterlinden, Manfred Kayser, Ben A Oostra, Cornelia M van Duijn, A Cecile J W Janssens, Pavel M Borodin (2009). Predicting human height by Victorian and genomic methods European Journal of Human Genetics DOI: 10.1038/ejhg.2009.5
Human height is a strongly genetic trait: in well-nourished Westerners somewhere in the vicinity of 80-90% of the variation in height is due to genetic factors; if your parents are tall, there’s a very good chance you will be too. That means that if we understood the genetic factors that influenced height we could predict the future height of a child (or even an embryo) with a reasonable degree of accuracy.
However, developing that genetic understanding has proved extremely difficult. It turns out that height is also the classic model of a genetically complex trait: a spate of very large recent genome-wide association studies has nailed down over 50 different regions of the genome that affect height, which in total explain less than five percent of the overall variation – suggesting that hundreds (if not thousands) of individual genetic variants contribute, most of them nudging us upwards or downwards by just a few millimetres.
Height is not the only human trait to demonstrate such a convoluted molecular basis; aside from a few unusual traits such as skin pigmentation, the majority of traits that vary between humans are genetically complex. This is one of the reasons why embryo screening for traits like height or IQ is unlikely to be effective in the near future.
A recent paper in the European Journal of Human Genetics (also covered by Dienekes) illustrates this point by comparing the predictive power of modern genetics with a method for height prediction developed back in 1886 by Sir Francis Galton. The results are a humbling reminder of just how much we have to learn about the genetic architecture of variable human traits.
The study looked at all 54 of the markers identified by the three studies linked above as being associated with height, and used these markers to generate a “genotypic score” – basically a count of the number of “tall” variants that each individual carried. This score was generated for 5,748 individuals of known height.
This graph shows the results: sex- and age-adjusted height are shown on the vertical axis, and genotype score on the horizontal axis.
That’s a fairly disappointing result from the best set of markers that modern genomics has to offer. Adding insult to injury, the authors go on to compare this result to the predictive power of the 123-year-old Galtonian height prediction method, which relies simply on the average deviation of the two parents from the population average (corrected for age and sex). This measure was calculated for a smaller set of 550 individuals for whom parental height data was available.
Here’s the data for this approach:
In this graph, the blue line is the best fit, and the green line has a slope of 1; the difference between these two lines is due to regression toward the mean. The correlation between this predictor and measured height is much stronger than for the genotype score above: this measure predicts around 40% of the variance in height, 6 to 10 times more than is explained by genotype score.
The two measures were highly correlated, as you would expect, meaning that adding genetic data to the Galtonian method only improved the variance explained by ~1.3%.
The difference in predictive power between these two methods (which are, in effect, measuring the same thing) is a powerful testament to our current ignorance of the genetic determinants of human traits.
Of course, that ignorance is a temporary state. I know there is at least one massive meta-analysis of height genome-wide association data that should be published early this year, and will add a few more dozen common markers; the hunt for rare height-altering variants will take a little longer but should bear fruit over the next couple of years.
So, will adding more and more genetic markers eventually provide a useful predictive test that exceeds the power of simply measuring parental heights? Well, here’s what that first graph would look like under the hypothetical scenario that we knew everything about height genetics (i.e. had markers capturing the full 80% of the heritable variance):
It’s worth noting that the better performance of the Galtonian approach is not universal. For some traits with low heritability (such as some serum lipid levels) the Galtonian approach performs even more poorly than current genetic markers; this indicates that it won’t be much longer before genetic tests are better than family history for risk prediction, at least for a subset of traits.
Still, this paper is a timely reminder of the primitive state of our current understanding of complex trait genetics, and just how far we have still to go before personal genomics can provide useful, novel predictions for the majority of human traits.