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- Twin Data in an Easily-Searchable (Free!) Database...
Twin Data in an Easily-Searchable (Free!) Database: MaTCH
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Did you ever wish you had access to a searchable database of twin correlations and trait heritability statistics? If not, once you see this, you will wonder why you hadn’t been looking for this kind of resource. Shout out to David Myers (Hope College) for pointing me toward MaTCH.
Let’s take height as an example. From the first drop-down menu, select “ICF/ICD10 Subch” and then from the second drop-down menu, select “Height (297). The number in parentheses refers to the number of studies included in the displayed data.
This is the first chart that is generated.
If one identical (mz = monozygotic) twin is tall, there is a very good chance the other will be as well. If one is short, there is a very good chance the other will be as well. The correlation between being a twin and height is .91. The chart also gives correlations for just male identical twins (mzm = monozygotic male) and female identical twins (mzf = monozygotic female). If one fraternal (dz = dizygotic) twin is tall, there is a smaller chance the other will be as well – correlation of .54. Correlations are also given for all same-sex fraternal twins (dzss), just male fraternal twins (dzm), just female fraternal twins (dzf), and all other-sex fraternal twins (dos).
Below the chart is this table.
“Est.” is the estimated correlation based on the data from all of the studies included in the dataset. These are the correlations reported in the bar chart. “SE” is the standard error – the smaller the number, the more confident we are that the data reflect what’s true in the population. “Ntraits” are the number of studies in the dataset. “Npairs” are how many pairs of twins were included.
While the correlations are interesting – and can certainly provide you with some interesting correlations when covering research methods – the real interesting stuff in this website comes from the last chart.
This is where we get the “Reported ACE” – the heritability data. ACE is a model used among heritability researchers. A is additive genetics (the contribution of genes), C is common environment (the contribution of experiencing a shared environment), and E is [unique] environment (the contribution of our own, individual experiences).
Before we get into the data, let’s a do a quick refresher of what heritability – and the ACE model – is. Within a population, people vary, say, in height. In the United States, the average height for adult females is about 5’ 4” (Onion, 2016). Some women are taller than that average, while others are shorter. It’s that difference between the shortest and the tallest – the variance – that ACE addresses.
Let’s look at the “Reported ACE” chart for height.
Picture this. Let’s say that we got all of the women in the United States together in one space. We measured each of their heights. A few would be less than 3 feet tall and a few would be more than 8 feet tall. Most would probably fall between 4’ 6” and 6’ 3 inches. The ACE model addresses where those differences in height come from. We are all going to be of some height just by virtue of being born. But what explains the differences in height among us? This article provides a nice explanation of heritability (Adam, 2012).
“h2_all” is the heritability estimate for everybody based on the twin data. This means that 63% of the difference (the variability) in the height among all of us is due to genetics. “c2_all” is the estimate of the role played by a shared, common environment. This means that 30% of the difference in the height among all of us is due to a shared environment. Those two variables, genetics and common environment, together account for 93% (63% plus 30%) of the differences in our heights. The remaining 7%? That’s due to our unique environmental experiences.
Please note that this says nothing about our own individual height. As a 5’ 4” female from the United States, this does NOT mean that 63% of my height is due to genetics. These numbers are only meaningful in explaining the differences in our heights across a population.
To emphasize how population-driven heritability estimates are, on MaTCH’s left navigation menu, click on “Country.” Here you will see the data for height (if you were looking at the height variable) broken down by country. The ‘r’s are the correlations. Scroll to the right to see the heritability and common environment numbers. Canada, for example, shows 34% heritability for height and 60% for common environment, leaving 6% for unique environment. These numbers are very different from, say, the data for the United States. The U.S. shows 85% for heritability and 8% for common environment, leaving 7% for unique environment. Why might this be? Maybe Canadians are more genetically alike than are people in the U.S., thus differences amongst Canadians in their height must be more due to environment. Or maybe there just isn’t enough Canadian data. In the second column of that table, we see that three studies were used to calculate the Canadian estimates whereas 29 studies were used to calculate the U.S. data.
There is much data here to explore. Before you dive too deeply into this website, watch this 15-minute tutorial video.
If you want to tackle this with your Intro Psych students, perhaps wherever you cover genetics, send your students to the MaTCH website to choose a psychologically relevant trait. Give your students a template like this to complete.
The correlation for identical twins (mzall) on ______________ (enter trait name) is ________ (first line in the blue chart).
The correlation for fraternal twins (dzall) on ______________ (enter trait name) is ________ (fourth line in the blue chart).
The differences in ______________ (enter trait name) within a population are _____% (h2_all) due to genetics, _____% (c2_all) due to a shared environment, and _____% (100 minus h2_all minus c2_all) due to a unique environment.
If students can’t find the trait they are interested in from the drop-down menu, they can click on “Find my Trait” in the top navigation bar. Searching on “intelligence” for example, tells us that that trait is lumped under “Higher-Level Cognitive Functions”.
Adam, G. (2012, September 6). What is heritability? Retrieved from Science 2.0: Join the Revolution: http://www.science20.com/gerhard_adam/what_heritability-93424
Onion, A. (2016, July 3). Why have Americans stopped growing taller? Retrieved from ABC News: http://abcnews.go.com/Technology/story?id=98438&page=1
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