This seems to imply that in each pathway, there is complete segregation of the components into those that produce qualitative vs. quantitative behavior.

Imagine an enzyme where a particular mutation causes a complete loss of function–i.e. the enzyme fails to catalyze the reaction at all, or so little that the improvement over the uncatalyzed reaction has no physiological relevance. Then it seems logical that other mutations of the same enzyme, even possibly at the same amino acid position, may have milder effects on function, giving less extreme phenotypes that still seem “normal”, just perturbed.

And this view doesn’t even take into account that qualitative changes in behavior need not necessarily arise from complete absence of any one component or interaction, even if they often do. Often, a combination of parameters will determine whether, e.g., a negative feedback is strong enough to keep a system stable.

Besides, haven’t you, or others, argued on this blog that rare variants also contribute substantially to “normal” phenotypes, i.e. things like personality types that are not considered diseases?

Re the SZ GWAS, it’s not so easy to say what they are picking up. Clearly they find a number of SNPs that are significantly associated (statistically) but with almost negligible effect on risk. Those signals could be caused by common or linked rare variants – it is not possible to distinguish. Claims that they prove a large polygenic effect in SZ caused by thousands of common variants are not justified. (For more on this, see the article referred to in the post).

I think people probably think individual/family-specific alleles contribute to disease risk. But to argue that they’re *the* source of disease risk seems like quite the leap. Not common variation at all (what is this schizophrenia GWAS picking up, if not common variants)? Not rare variants at 1% frequency?

The point is that the genetics of how a system varies is not necessarily the same as the genetics of how the system fails. (And, in my view, should not be expected to be the same).

Re height itself, the normal distribution could be caused all by common variation – there is no evolutionary reason why that should not be the case. But it could also involve a lot more rare and unique mutations that have larger effects in individuals than all their SNPs combined.

Although, for instance, type 2 diabetes may be labeled an “all or nothing” trait, there surely are healthy people with different efficiency of blood sugar regulation, i.e. if you feed them all the same amount of sugar, the immediate rise in blood glucose level may be damped to a different extent in different individuals, doesn’t that seem reasonable?

For psychological traits even more so: for instance autism is clearly a spectrum disorder, ranging from mere impairment of perceiving social nuances to the complete absence of speech and awareness of others–and sociability shows great variance even in supposedly “normal” people. Depression is even “worse”, in that tolerance of stressors greatly differs between people, with some bouncing back like springs and some staying gloomy for weeks. In fact, I’d be apt to postulate a combined internal-external feedback model for depression, where the “sum” of the ability of the neural network and one’s life circumstances to recover from loss must fall below some threshold before setbacks become self-perpetuating enough to cause clinical depression.

Furthermore, many disease phenotypes involve trade-offs. For instance, obesity involves the trade-off between famine tolerance and present bodily health, autism involves the trade-off between cooperation/integration with a group and the pursuit of novel strategies the group may have overlooked, ADHD involves the balance of rejecting extraneous information and remaining aware of shifting environments, etc. Thus, I’d say that many disease states involve selection of the wrong strategy at the wrong time, or the pursuit of one strategy to the total exclusion of others.

If diseases are really caused by different rare, partially penetrant mutations in different genes in every individual, you’re right that GWAS isn’t the best way to look for those mutations. That’s a very special model though!

Most people probably take loads of phenotype and genotype data, press three buttons, and volilà, there you have the new ‘scientific’ breakthrough.

Memory is tricky but I’m almost certain I’ve seen hippos swim.
I seem to remember a mother hippo swimming circles in a pool, its baby following close behind. I was acquainted with the curator of that zoo (Johnny Werler at Houston) and he’s now deceased. I know another guy with somewhat more extensive knowledge of hippos and, though i haven’t spoken with him in some time, will try to reach him by phone to clear this point.

What is also stated in the video clip is that the subject are selected for intelligence above the stochastic mean as follows
> 3SD to
> 4SD in math

The study is named “Gene-Trait Association Study of intelligence”. The accepted operational definition (really, up to this point the only definition that makes any sense) of intelligence is essentially the g-factor and tests which “load” on g are best measures of “intelligence”.

[[Tests]]

Since the tests or competitions stated as the requirements will screen for the cohort on the desired “intelligence” range then the tests itself are important and by definition should be acceptable measures of g but not only that, be able to measure g at > 3SD.

Let’s look at the SATs. If you look at the study carried out by Detterman & Frey on the SATs post and pre 1994 the result of the regression analyses are 2 equations to convert SAT scores to IQ (for both pre and post 1994). You will notice several things;

i. The Authors used test subjects who sat for both the ASVAB and the SAT (pre-1994) and carried out a regression analysis. They determined the correlations of about 0.82 with the IQ test and also derived an equation to convert the SAT scores to IQ.
ii. The Authors administered the RAPM to subjects who sat for the SAT (post-1994) and carried out a regression analysis. They determined the correlations of about 0.483 which was a bit low due to range restriction, but corrected it the inclusion of other subjects and ended up with r= 0.72. The authors then derived equation 2 to convert the SAT scores to IQ.

Point 1: g-factor

In both studies no factor analyses were carried out on the SAT test items to extract 1st order factors (e.g. from the math-only items) or 2nd order factors (e.g. between the math and verbal items) before extracting a higher order facto. So, the g-load of the test is inferred by correlations to standardized IQ tests (Ravens and ASVAB) and is indirect.

Point 2: ceiling

Eq. 2 (after 1994) = 0.095 x SAT-M + 0.003 X SAT-V + 50.24

= 0.095 x 800 + 0.003 X 800 + 50.24= 128.64

Std Error = 9.79 so the ceiling is 138.4.
3SD = IQ 145
The re-centered SAT does not have enough ceiling. In fact even American Mensa which has a relatively low cut-off of 2SD does not accept the SAT.

Point 3: math

If you look at equation 2, the SAT-V hardly loads on g at all as the constant for SAT-V is 0.003. This means that only SAT-M seem to be g-loaded and counts towards IQ.

Point 4: Competitions:

Physics – are there any studies carried out with high repeatability and rigorous multivariate analyses to show that the material used for physics Olympiads is g-loaded? Ditto for Informatics, the Putnam Competition?

[[Cohort & Apparent Math Focus]]

“g” (other subscribe to 2-factors gf and gc) is the general factor that explains the largest variance in many types of sub-test scores/abilities not narrow abilities like math only. If some of the subjects are selected using the SAT, Math competitions, they inadvertently have a math tilt and the study will be doing the GWAS on a cohort with very specific abilities and in the end may be identifying specific alleles (or a polygenic association e.g. thousands of SNPs which could explain the difference with the control group instead of several marker genes) that are common for mathematical ability but not “g” since “g” is a general factor that explains the largest variance in many types of sub-test scores/abilities.

[[Measurement of g at > 3SD & Spearman’s Law of Diminishing Returns (SLODR)]]

SLODR manifests as we go higher up the ability scale, i.e. the inter-correlations between sub-test scores drops dramatically (as studies by Evans, Brand, and Detterman have shown and numerous studies carried out by others have repeated these findings) since at higher levels the specificity differentiation “kicks” in, and hence less and less of psychometric g and consequently more of specificity is measured, no matter what the test (SAT, GRE e.t.c). For instance using the Otis Lennon Evans found that at the top, the average correlation was 0.35; at the low end, the average correlation was 0.50. In the slides (5) RT or ITs was briefly mentioned. In fact IQ test correlations with IT are about 0.5 for an average ability group but can drop to as low as 0.3 for a higher ability groups (about IQ115-see Chris Brand). So, even for absolute measures (IT/RT) Spearman’s Law seems to hold. I haven’t seen a paper on what the IT correlations are at 3SD or even 4SD but am pretty sure it will be very small of which the statistical significance will be brought into question.

I surmise that the test and competition requirements do not seem to have a proper psychometric basis as screening tools for “intelligence”. I will be happy to accept an alternative view if supported. A more accurate name of the study perhaps should be “Gene-Trait Association Study of Mathematical Ability” or “Gene-Trait Association Study of Scholastic competition” not “Intelligence”.

Sorry a rather long missive.

As it has happened many times in the early studies in biochemistry, it has been the study of the abnormal (the word “abnormal” from here onwards, is used in the context of that of the minority of the human population, that is the 1 in 10) that has provided the first insights into the normal biochemical situation. Thus early studies on the family tree of homosexual males pointed to the fact the genetic trait of homosexuality carried by these individuals was inherited from their heterosexual mothers and later researchers concentrated their studies on the X chromosome. A heterosexual grandmother has a 1 in 2 chance of passing this trait to her children by way of one of her two X chromosomes), whether they be sons or daughters. The sons will have a 1 in 2 chance of inheriting the trait and those that do will be genetically homosexual (the “gay” uncle). The daughters will have a 1 in 2 chance of inheriting the trait and their sons (the grandsons) will in turn have a 1 in 2 chance of inheriting the trait.

If the gene coding for “sexual attraction” is carried on the X chromosome in the gay male, is it not reasonable to conclude that the gene for determining sexual attraction in the remainder of the human population is also carried on the X chromosome? This is my starting hypothesis. The gay male has the same genetic coding as the heterosexual female – there is no mutation and no “gay gene” – just an inheritance by the male of a normal female gene. So in this 90% of the population, the two sequences of the genes on the X chromosomes (XX in the female) produce enough “product protein” to confer on the individual, an attraction to the male whilst with only one sequence there is only enough product protein to make the individual attracted to the female. The genetically gay male then would behave as if he had an XX and the lesbian would behave as a single X (in so far as this particular gene sequence is concerned and not the whole X chromosome). This could have come about in our ancestral past if there were a defect in the replication of that part of the X chromosome that produced a situation whereby the gene in question migrated from a normal sequence in the X chromosome (leaving that X chromosome deficient for that gene) and attaching itself to another normal sequenced X chromosome making it surplus by one for that gene). This postulates that there are three variants (with respect to coding for sexual orientation) of the X chromosome in the human population, one producing no product for sexual orientation because the gene sequence is missing, one producing a normal amount of product and one producing a double amount of product. I have called these X-, X and X+. As the X- and the X+ are postulated to have arisen from the same single event, it would not be surprising that they would be found (or seen to be expressed) in the population to the same extent (now believed to be about 10%).

From this, it is hypothesised that most lesbians would be XX- ; a heterosexual female would be XX or X-X+; and a nymphomaniac would be a XX+ or X+X+. Similarly for the male population, the gay male would be X+Y, the heterosexual male would be XY and the womaniser may be X-Y. As stated above and elsewhere, actual sexual behaviour of an individual will be the result of many factors, both genetic and non-genetic such as social, peer and religious pressures and could vary during the lifetime of that individual. It will be up to others, if thought worthwhile, to examine the detailed analytical genetic work of others such as Dean Hamer to determine whether it either supports or argues against (or are not capable of either) this hypothesis. I am unsure whether sequencing or other techniques are capable of determining whether a particular gene sequence does occur twice in the same chromosome or not.

If you look at studies from developing regions like Yunnan (PRC), you will see that the heritability of IQ is low in children too; but, by adulthood, it rises to .75. (It hopefully need not be said that if a lack of enriched environment had a lasting impact, then you would not have an adult heritability of 0.75 in these regions.)

Some studies have found the contrary to be true (for verbal ability): http://www.sciencedirect.com/science/article/pii/S0160289605000218