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[OBG] Genetic and Environmental Determinants of IQ in Black, White, and Hispanic Amer

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“Generally, the B/W and H/W mean score differences in these samples were somewhat smaller than those typically found in the national population at large.”

This would indicate that perhaps some of the lower tail blacks are missing from the studies, yes? If those lower tail blacks also have the worst environments, then this can bias the h^2 estimate upwards for blacks. I know you only have a few samples, but theoretically, you can test this using r(W-B h^2 x W-B IQ d). If the depression hypothesis is right, this should be negative.

Alternatively, perhaps the W-B d is just smaller now than it used to as claimed by some. Or not fully formed at these ages.

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“Another assumption is that no assortative mating took place in the parental generation with respect to cognitive abilities.”

Is it possible to run the model with estimates of assortative mating?

Vandenberg, S. G. (1972). Assortative mating, or who marries whom?. Behavior genetics, 2(2-3), 127-157.

Cites some studies. Range is .44 to .60. Unweighted mean is 48.75. It seems foolish to conduct a study assuming a zero value for something that is known to be substantial. On the other hand, I'm not aware of any studies of assortative mating by race. If the strength of this is different, it should change the amount of genetic variance within race. This influences d's values.

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#12
(2014-Aug-19, 09:36:33)Emil Wrote: 23: sp 46: sp 67: sp 95: sp 102: sp 122: sp 126: sp 137: sp 148: sp 160: sp 216: sp 217: sp 272: sp 328: sp 339: sp 340: sp 359: sp 361: sp 421: sp 426: sp 433: sp 478: sp


When I search for spacing with word, I don't get the same number of hits. Also, your line numbers don't match with those I get, so are of no help. Anyways, I found a few more and corrected those.

Quote:Alternatively, perhaps the W-B d is just smaller now than it used to as claimed by some. Or not fully formed at these ages.

With regards to r (B/W d x B/W h^2), age and test type were major confounds; there weren't enough samples to control for these. The d-values averaged to 0.88 for the 6 non CNLSY samples. Age and test type were likely factors; birth year wasn't. Regarding Rowe and Cleveland's CNLSY sample, the d values were only 0.3 to 0.5, but this had more to do with the waves selected and the nature of the achievement tests used. Murray found no birth cohort effect for this sample.

To clarify, I added:

"Generally, the average H/W mean score difference (d-value) in these samples was of a similar size to that found nationally (0.7 versus 0.7), while the average B/W d-value was somewhat smaller (0.8 versus 1.0). The reduced magnitude of the B/W d-value from these samples was likely due to a participant age and test type effect....

Were a bio-ecological model correct, one might expect that d-values would positively correlate with heritability differences, such that when d-values were larger, the lower scoring population would show more depressed test heritabilities. Unfortunately, our samples do not allow us to robustly determine whether this is the case as they differ in participant age and test type, differences which would cofound any such analysis and which cannot be controlled for given the dearth of samples available."

Quote:I'm not aware of any studies of assortative mating by race. If the strength of this is different, it should change the amount of genetic variance within race.

I'm not either.


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#13
I've just had a really quick look at your paper. I will have a more deep reading in the coming days. In the first paragraph you wrote "In behavioral genetics, the sources of IQ variance can be partitioned into three components: additive heredity (h2), shared environment (c2), and unshared environment (e2)".
However, models sometimes also include D, which indicates dominance (non-additive) effects. It's well known that part (albeit not the majority) of the variance in IQ is also due to non-additive effects.
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#14
(2014-Aug-19, 19:33:48)Duxide Wrote: I've just had a really quick look at your paper. I will have a more deep reading in the coming days. In the first paragraph you wrote "In behavioral genetics, the sources of IQ variance can be partitioned into three components: additive heredity (h2), shared environment (c2), and unshared environment (e2)".However, models sometimes also include D, which indicates dominance (non-additive) effects. It's well known that part (albeit not the majority) of the variance in IQ is also due to non-additive effects.


I'm not sure by D changed "genetic influence" back to "additive heredity". As it is, we used Falconer's formula in which heritability is broad sense e.g., h2(b) = 2(rmz - rdz). So this should read: "genetic influence (broad heritability) (h2), shared environment (c2), and unshared environment (e2)". But maybe he had something else in mind.
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#15
Falconer's forumula gives an estimate of broad sense heritability, which is A+D. So it's better to use h2 than A, because A is only additive. Just specify that h2= A+D
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#16
I opened the file with LibreOffice. You could download that and do the same. It's free. I tried opening it with Word 2010 too. The line numbers are off, but only by 1, and not initially. Almost all the sp concern extra spaces at the end of paragraphs for no reason.
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#17
It's untrue that ACE model specifies additive heritability. It's not even clear if some experts understand it well, or not. Here's an illustration :

Haworth, C. M. A., Wright, M. J., Luciano, M., Martin, N. G., de Geus, E. J. C., van Beijsterveldt, C. E. M., ... & Plomin, R. (2010). The heritability of general cognitive ability increases linearly from childhood to young adulthood. Molecular Psychiatry, 15, 1112-1120.

Now, click on it, do CTRL+SHIFT+F, type "additive". You get 4 hits. Now, read these sentences.

Quote:These genetic and environmental effects are commonly represented as A, C and E. ‘A’ is the additive genetic effect size, also known as narrow heritability.

How is it wrong ? Well, I have written an article on income mobility where, at some point, I talked about "genoeconomics". Here's the paragraph that will help to understand this problem :

Quote:Similarly, Hyytinen et al. (2013) showed, using the Older Finnish Twin Cohort Study for Finland, with MZ and DZ pairs of 620 and 1146 for women and 494 and 1094 for men, that 24% and 54% of variance in lifetime income for women and men, respectively, is due to genetic factors, whereas shared environment is small. The authors begin to cite studies showing that schooling reforms have enhanced earnings mobility. As explained above, this is irrelevant due to its weak intergenerational transmission through the family.

The income data was from administrative registers and thus do not suffer measurement errors due to misreporting. For their analysis, they use the Defries-Fulker regression method, which can be formulated as INC1=β0+β1INC2+β2R+β3(INC2*R)+ϵ, where INC is income, β0 is the intercept, β1 is a measure of shared environment ©, β2 is a coefficient of genetic relatedness (r=1.00 for MZ and r=0.50 for DZ twins, and thus assumes full additivity), β3 the heritability, ϵ the error term (E) which includes both the non-shared environment as well as measurement error. It is possible to include a parameter for non-additive genetic effect, or dominance (D), which in this case can be denoted as β4(INC2*D), where D=1.00 for MZ and D=0.25 for DZ twins. This can be called the ADE model, where β3 and β4 evaluate the additive (i.e., narrow) and non-additive heritability for income. The sum of the additive (A) and non-additive (D) is called the broad-sense heritability. Thus the difference between ACE and ADE models is that the former, but not the latter, assumes the heritability is purely additive.

[Image: heritability-of-lifetime-income-hyytinen...able-3.png]

We read the results from their model fitting in table 3. For females, shared environment © is small (0.10) in the ACE model whereas for males the C parameter is negative and this is indicative of dominance (D) effect, since C is the mirror of D, and conversely. For both gender group, based on the model fit index Akaike (AIC) the AE has the worst fit but the ACE and ADE have equal fit. At first glance, one believes it is impossible to select among them. However, given that D is negative for women, this model is probably ill-specified and thus they have (rightly) opted for the ACE. Because C is negative in ACE and this in turn is suggestive of large D parameter, which is confirmed in ADE model, the authors have correctly chosen the ADE as the preferred model for men. In the ACE, women and men have a heritability of 0.24 and 0.77. In ADE, the A and D parameters for women amount to 0.54 and -0.20 and for men to 0.07 and 0.47. The sum of A and Z gives a broad heritability of 0.54 for men. Given that ACE should be preferred for females, their heritability is 0.24. Interestingly, they note (table A2) that the use of a broader measure of income (which includes capital income and transfers, such as unemployment benefits and parental leave benefits) has improved the heritability estimates, which were 0.42 and A+D=0.26+0.33=0.59 for women and men, given their respective preferred model, AE and ADE. This provides another illustration of how measurement errors can reduce heritability.

When they attempt to add education as covariate in the regression, thus holding education constant, the parameter estimates (A,C,D,E) remain somewhat unchanged compared to table 3, even though heritability has diminished somewhat. When education effect is deducted from income, the A, C, D and E parameters appear similar.

In other words, before talking about additive heritability, you must test ACE against ADE. If the D component is clearly larger than 0, you probably have to select it, and then look at the A component. But unless you test models vs models, you can't have any certainty about what is additive and what is not.

The A in ACE is just an assumption, and it must be tested to see if that holds or not.
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#18
(2014-Aug-21, 01:11:58)menghu1001 Wrote: The A in ACE is just an assumption, and it must be tested to see if that holds or not.


But practically speaking, can I use "A" in charts that include h^2 estimates derived from Falconer's formula? Or do I have to change this to h^2 (and defined h^2 as "genetic influence")? Specifically, are the charts I have fine in regards to the ACE labels.
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#19
My recommendation is that you should precise what study is from falconer's calculation, and what is from ACE modeling, after you have precised that the studies of race-ACE interaction did not test ACE vs ACDE (or ADE) models.

For example, in your tables, use only h2 in the columns, and just below the tables, add a note and put * for studies using falconer and ** for studies of ACE modeling, and precise that in the case where you have ** the h2 referred to the A component of ACE.
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#20
Falconer's formula does not give H2. Falconer and Mackay (1996) say that it gives some value that is closer to H2 than h2 but it's still not H2.

ACE or ADE model fitting to twin data is limited in that C and D components cannot be simultaneously estimated. Moreover, assortative mating (AM) is not considered. What this means is that the estimates from these models cannot be correct. ACE or AE models usually show the best fit to IQ data from twins, but that's in part because the biases due to non-additivity and AM have opposite effects. Non-additivity increases the MZ correlation more than the DZ correlation, but AM increases the DZ correlation without increasing the MZ correlation, so these effects tend to cancel each other out. In practice, the A estimate from ACE or AE models may be close to H2. <a href="http://link.springer.com/article/10.1007/s10519-011-9507-9/fulltext.html">Vinkhuyzen et al.</a> had data where both D and AM could be estimated, and they conclude that much of IQ heritability is non-additive but their H2 estimate comes to about 80 percent, which is similar to h2 estimates from studies that assume only additive heredity and no AM.

Because almost all IQ studies use ACE or AE models and Falconer's formula does not give H2, either, I think we should talk about h2 or A rather than H2 in the paper. We can mention the methods used in each study.

Also, MH, 'precise' is not a verb in English. Use 'clarify' or 'specify' instead.
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