Your argument is absolutist in tone though, by your own logic we shouldn’t accept evolution per se due to the fact the chain of transitional fossils has not been ‘completed’ yet.

Nice to you see smugly attacking Catholics and Homeopathy practitioners on your blog though. Isn’t it funny how egalitarians always pick the easiest targets to beat over the head? Not a single one would ever challenge the real dominant paradigms that rule our age, the real taboos of racial egalitarianism and how any inequality of outcome is invariably down to some kind of ‘phantom discrimination’ and can be corrected with social engineering.

Far from it, you support these paradigms and gleefully engage in the kind of character assassinations that followed Watson’s comments years ago, or Lahn’s research into Microcephalin and so on.

Fuck Zionism by the way. If you want to talk about ‘religious evil’ just read the damned Talmud, I’ve never seen so much hateful supremacist filth in all my life, the Babylonian Talmud is like Mein Kampf on steroids.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2892514/

The concordance rate for full siblings is in the 3-6% range.

http://resources.metapress.com/pdf-preview.axd?code=vjk336n5k54wtl12&size=largest

There is cross-talk between twin fetuses in utero. In cattle, there is what is known as a “free martin”, the female twin of a male calf. A free martin is usually always sterile.

The increased autism in male-male DZT is probably due to higher levels of testosterone.

Like the commenter “Miko” there, I am outside my domain competence. But in browsing the paper they only reference gender & age as assumed to have linear effects. Also, daedaulus2u, your plos link is broken.

Speaking of pre-natal effects, I have occasionally heard (such as from the second video here) that crack babies don’t actually turn out worse than comparable children not exposed to crack. That’s surprising to me, do any of you know much about it?

If you look at a gene like MeCP2, we now know that loss of it causes Rett Syndrome in females and is fatal in males (pretty strong rescue of the phenotype by interaction with an X chromosome which happens to be inactive in the cells that are rescued). The MeCP2 protein binds to methylated DNA, and so MeCP2 regulates how DNA that has been epigenetically programmed by DNA methylation is read out. MeCP2 doesn’t have any direct effects on phenotype, the only effects are secondary, mediated through differential effects on the transcription of other genes.

DNA methyl transferases also only have effects through differential regulation of other genes. SNPs of DNA methyl transferases do seem to have effects on intelligence.

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.001132

Presumably those DNA methyl transferases are having effects through their normal function, that of methylating DNA and so affecting how it is transcribed.

How about genes for transcription factors? They don’t have direct effects either, only effects mediated through differential transcription of other genes. Something like 5% of the human genome is transcription factors. They only have effects mediated through gene-gene interactions.

People have actually looked for the genes that supposedly cause intelligence to be inherited. Have they been found yet? No, they have not. Until the specific genes are found and reliably identified as influencing intelligence and under what environmental conditions, their existence remains hypothetical. Due to the level of effort put into finding such genes, and the essentially complete lack of success in finding them, to me their existence is becoming increasingly speculative.

Pretending that models of inheritance that ignore gene-gene interactions have risen to the level of “fact” is disingenuous, or as you would say, “a lie”.

Uh, we know a lot more than was known in 1950. We now know that the actual genetic material is DNA, and we now know a lot about how it is replicated, stored, regulated, epigenetically programmed, silenced and transcribed. We have actual data on similarities of twins vs siblings, and fraternal twins. We now know that fraternal twins have a higher concordance for autism than do full siblings.

http://www.ncbi.nlm.nih.gov/pubmed/19805709

Sharing an in utero environment does make fraternal twins more similar along the autism spectrum than full siblings. If fraternal twins have a higher incidence of autism, then presumably there is something about their shared in utero environment that affects the normal process of neurodevelopment and causes autism. Presumably there are also sub-clinical effects of what ever that in utero environmental process is that produce effects less than autism. Presumably those effects might include something like intelligence.

daedalus2u: “The major “data” linking genes to intelligence is from a naïve examination of monozygous twins. Monozygous twins share a lot more than just a genome, they also shared pretty much the same in utero environment where their brains grew from a single cell to 100,000,000,000 cells, a factor of 10^11. “

Evidence for the high heritability of intelligence, and other traits, is extensive, multifaceted, and unimpeachable. It is not based on one method, but on numerous, mutually reinforcing methods. Whatever working assumptions behavior genetic methods may have started with, have long since entered the realm of supported fact.

Genetic denialism is quackier than ever. We don’t even need different levels of kinship anymore (e.g. identical twins, cousins, etc), we can now demonstrate substantial heritability using nothing but full siblings! Welcome to the Post-Genomic Era; take this flyer and please remember to get your hand stamped at the door.

And not that it matters (since the assertion it was tied to was a lie), but sharing a prenatal environment overwhelmingly makes siblings less alike, not more alike.

“The HH requires modeling intelligence as a linear combination of genes and environmental influences”
The hereditarian hypothesis is that a substantial portion of the variation in intelligence is due to genetic variation (or alternatively, that the same is true for variation among population groups). If IQ was attributable purely to a massively complex interaction of countless genes, hereditarianism would be obviously correct. If IQ was purely the result of a simple linear combination of environmental factors, hereditarianism would be obviously incorrect.

“A genotype growing up in one environment may have a high IQ phenotype and in another environment may have a low IQ phenotype. One environment may produce a high IQ in one genotype and a low IQ in another.”
That can be the case even in a linear interaction model. You could correct it by specifying that in the first sentence you are speaking relative to a different genotype but the same environment, and vice versa for the second sentence. Unfortunately, we don’t yet know enough about genetics to make such comparisons. We can observe that there are still large amounts of variation for identical twins with very similar environments, but that’s compatible with a linear interaction model in which random noise plays a large role.

On stress and measurement: we can distinguish between identical twins, fraternal twins, and unrelated children raised together (or apart). This is how measures of heritability are obtained (though they can only directly shed light on contributions to within-group variation rather than between group variation, biracial children perhaps shedding light on the latter). We don’t have equivalent comparable classes for stress.

But if there are ten-dollar bills on the sidewalk researchers have overlooked in the form of unmeasured outcomes, perhaps you should start contacting researchers and offering suggestions. I often email professors out of the blue and they can be surprisingly receptive. There is a subfield of psychology devoted to childhood development, and I would expect that is your best bet.

Intelligence is not a linear combination of anything. It is highly non-linear. The problem is that the interactions between the genes and the environment are non-linear and the interactions are coupled.

The HH requires modeling intelligence as a linear combination of genes and environmental influences of the form (where G1 is gene 1 and EI2 is environmental influence 2 and g1 is the constant that determines the effect of G1):

IQ = g1(G1) + g2(G2) + … + ei1(EI1) + ei2(EI2) + …

In reality, the g’s and e’s interact with each other and with themselves. The first coefficient isn’t a constant, it is a non-linear function of all the G’s and all the E’s and all their mutual interactions that collectively influence the effect of G1 on intelligence.

It is a non-linear function with at least hundreds of genetic terms and likely thousands of environmental terms. These functions are different for each different tissue compartment and cell type that has an influence on neurodevelopment and brain function, heart; liver, kidneys, immune system and vary over time and exhibit hysteresis. It is also epigenetically programmed in utero and maybe in parental gametes.

I appreciate that the actual complexity of the genetic influence on intelligence is too complex to model given our current understanding of genetics and environmental influences. The HH requires neglecting all this complexity and ignoring all gene-gene interactions, all environment-environment interactions and all gene-environment interactions. We know that is not correct.

Let me restate it a different way; it is extremely likely that genes that affects IQ can have both positive and negative effects on IQ depending on other genes and on the environment. The effects of a particular gene on IQ are not just “small”, they can be either positive or negative depending on other things. A genotype growing up in one environment may have a high IQ phenotype and in another environment may have a low IQ phenotype. One environment may produce a high IQ in one genotype and a low IQ in another. Environments that are nominally “the same” can produce completely different results even in the same genome, as in the case of MZT being discordant for anencephalopathy.

Many of the genes that affect IQ are active in utero, when the brain grows from a single cell to 10^11 cells. Epigenetic programming of DNA in utero is known to affect animal behaviors as adults. Exposure to stress in utero is known to affect adult behaviors in animals and in humans. Are there differential levels of stress between the B/W population? Are pregnant white women exposed to more or less stress than pregnant black women? What is the basis for ignoring those differential levels of stress? Because stress levels are hard to measure? Gene frequency hasn’t been measured at all, but the HH has no difficulty attributing large effects to unmeasured and unknown genes while completely neglecting in utero stress which is known to be different, and could be measured. Certainly outcomes attributable to stressful pregnancy are known (low birth weight) and could be measured but are not.

I have never heard anyone say that IQ is the result of a single “IQ gene”. The standard take among hereditarians I’ve heard is that there are a very large number of genes with individually small effects. The addition of such large numbers of small factors results in normal distributions. There are also of course some genes with large effects that give rise to retardation.

The way that the length of a bone like the femur is determined, is primarily when the growth plates stop growing, stop adding length to the ends of the bone. That growth is determined locally, by the cells that comprise the growth plate. Skull diameter is mostly determined by brain size, but skull shape matters too. Should we expect there to be a single unitary “height gene” that synchronously controls the proportionate size of the height component of each bone independent of other physiological parameters? No, we shouldn’t.

Suppose you didn’t have the ability to measure distances longer than 1 meter, but still wanted to find the “true” or “quintessential height” (QH) of an individual by taking a weighted sum of the lengths of the individual bones. Since you are not quite sure how the different bone lengths will add up, or which bones will be important, you decide to take a weighted sum of all the bones you can measure and determine which ones are most important.

Assuming you can measure 200 bones, you measure those individual bone lengths of 100 individuals and then determine the weighting factors to use in weighting the individual bone lengths to quintessential height. You don’t know what quintessential height actually is, so you use the weighting factors that give you the “best” fit but don’t know exactly what weight to put to each of them, so you solve for the weighting factors that will give you the “best” overall fit. You now have equations of the form

k1*B1 + k2*B2 + k3*B3 + … + k200*B200 = QH (quintessential height)

You still don’t know what QH is, other than it is the “best” measure of height. So you choose the weighting factors k1, k2, k3 … k200 that minimize the “error” in your equation determining what QH is over your group of 100 individuals.

Now that you know what the k’s are and how to calculate QH, you measure more individuals, but some of them only have 190 bones, and some have 210 bones. You have already determined that k190-k200 are important, so if individuals don’t have bones B190-B200, they don’t have those components of QH and so are stunted in QH. Similarly since there is no k201-k210, bones B201-B210 are completely unimportant in QH and can be neglected.

As you accumulate data on k’s and QH over the decades, you notice that QH is creeping up, that the quintessential height of whole populations is increasing. The k factors that you thought were constant really aren’t. But no matter, because you always minimize the error when you calculate your k’s and QH, you know that your k’s and QH’s are reliable in a single cohort, even if they are not reliable between cohorts.

What does “reliable” in this context mean? Does it mean that testing an individual for QH will produce a “true” QH? No, all it means is that in a whole cohort, the average unreliability is distributed across the entire population. That on average, for every high QH there will be a QH that is low, but you can’t tell if an individual QH is low, high, or accurate.

Now in the genomic age, you try to fit genetic data to your k’s and QH’s. But which k’s and which QH? The modern ones? Or the less-modern ones? If the different B’s have changed in their importance, so too will the importance of the underlying genes (and non-genetic factors) that determine the length the individual B’s. But no matter, just pick a model that chooses k factors that minimize the “error” in the QH determination and that “proves” the answer is correct.