Sophistication of QTL Search for IQ Heritability through Multivariate Analysis

As humans continue to evolve, both technologically and physically, the speculation of scientists has become how technologies that allow artificial modification of life can be applied to the focus on the advantages of this evolution specifically addressing the values of today’s society: intelligence and the leadership qualities that it fosters that can fuel innovation.  At one time in history, the solution to this was the technique used by a variety of leaders considered by modern society to have been the evilest people to have lived: eugenics through genocide.  Today, however, studies are increasingly showing that variance in such qualities in the category as IQ, a quantification of mental capability which is considered to be a major contributor to the also quantifiable General Cognitive Ability (GCA), can be attributed to genes in the newly sequenced human genome (Johnson).  Nevertheless, finding genes specifically responsible for this variability has been excessively difficult, due to their qualities of involving a wide variety of environmental factors and manifestations of both struggle and success in multiple distinct ways (Rose).  Thus, significant progress towards this end will be achieved through two factors: improvement of the location of Quantitative Trait Loci (QTL) for simpler and more easily measurable traits, and consideration of IQ as simply one effect of genes that affect multiple traits, including, per research thus far, most distinctly, reactivity to stimuli alongside the information processing ability defined by IQ.

Research and experimentation regarding the heritability of IQ has existed for nearly a century, but the unique approaches that are posing unique possibilities are fairly recent.  The majority of these earlier experiments were twin and sibling studies which sought to detect correlation in the variation of IQ from mean IQ scores for the entire population, between relatives.  The conclusion elucidated by a high correlation would be that the variation reflected by both subjects would be similar, and thus caused by genetic and/or environmental factors (Jensen).  A summary of a large number of studies in Alan S. Kaufman’s work IQ Testing 101 is shown by the chart below.

 Correlation of Variation in IQ

Two important points to note here are that the correlation for identical twins and fraternal twins/siblings are capped at .90-.95 and .5, respectively, assuming that genetics are the sole factor, based on the overall reliability of IQ tests and the fact that siblings share about half their genes (Jensen).  Basic analysis of this data would seem to show that variation certainly is apparent, even if not ideal in numerical value.  Each data point doesn’t quite reach the theoretical value presented by the previous claim (.86 versus .90-.95 for identical twins reared together and .47 versus .50 for biological siblings reared together seem to be the closest), excepting one: fraternal twins reared together.  A correlation of .55 in fact exceeds the predicted value of .50, designating alongside the definite gap in each relationship based on being reared together and reared apart, an environmental impact on variation.  This is the core idea behind a recent, innovative claim made by Wendy Johnson in her paper for Current Directions in Psychological Science titled “Understanding the Genetics of Intelligence: Can Height Help? Can Corn Oil?”: Control over environment increases with age, thus decreasing environmental variation and maximizing gene-based variation.  Thinking aligned with this idea that environmental factors may be skewing data, has, throughout time, triggered a focus on alternative research to consider this specifically and thus better narrow down genetic effects.

A well-known example of this sort of study was published in 1997 in Nature by B. Devlin, Michael Daniels, and Kathryn Roeder.  This study examined “maternal effects,” that is, the effect of conditions within the womb, on correlation that it concluded were the root of the high correlation between twins as opposed to siblings and non-biologically-related siblings raised in the same environment.  A 20% covariance between twins and 5% between siblings was found to be based on these maternal effects, reducing these two measures of heritability (further taking into account other major environmental factors) to below 50%.  Nevertheless, this impact remains and may be studied from a genetic perspective.

In his commentary on Heritability estimates titled “Long Past Their Sell-By Date” for the International Journal of Epidemiology, Steven P. R. Rose describes the efforts being made towards this end, then criticizes them for analyzing complex organisms linearly.  The starting point is the original 1969 article by the aforementioned author, Arthur Jensen, titled How much can we boost IQ and scholastic achievement?  The claim presented in this article that IQ has an approximately 80% heritability (assuming, prior to the research by Devlin, Daniels, and Roeder, that only 20% of variability was by chance or environmental factors) is cited by Rose to be the basis for research for specific genes that affect IQ.  The primary method which is used is the mining of massive amounts of DNA data to align phenotypic patterns of variation from population means with specific quantitative trait loci (QTLs).  QTLs are specific to genes which express phenotypes in varying degrees, like IQ.

Unfortunately, there are three major factors discussed by Rose and elaborated on by Johnson that make this sort of research difficult, reliant on assumption, and problematic.  The first of these is the fact that the majority of effective research at this point in time has been forced to diverge from the “mutations in coding regions or single nucleotide polymorphism in non-coding regions” (Rose) in favor of genetic risk factors determined by a wide variety of genes.  I refer to this, of course, in the context of the field of gene identification in general, rather than the search for genes that affect IQ.  This, in turn, must take into account, on one hand, Rose’s point that “it is not generally recognized that QTL analysis itself relies on a prior assumption of significant heritability,” and, on the other hand, the simple idea that the human genome itself is so expansive that determining specific points to examine is quite difficult and time-consuming.  Of course, as has been discussed, technology (i.e. BLAST) is making this a simpler task that, as research on this topic becomes more sophisticated, has great potential to speed up progress in finding specific QTLs that may seem to consistently impact variation.  The final factor is an idea referenced by Rose as having been developed initially by the Russian zoologist and evolutionist Ivan Ivanovich Schmalhausen and later by Ukrainian-American geneticist and evolutionary biologist Theodosius Grygorovych Dobzhansky: norm of reaction.  Rose states that the first factor presented here represents a situation in which this theory is invalidated, but it is still important to acknowledge.  This concept states that “the phenotypic effect of any gene may vary continuously but non-linearly and often unpredictably across a range of environments” (Rose).  Though Rose does clearly state this to be a problem in this sort of research, he begins to introduce the idea upon which Johnson elaborate that the consideration of the “entire genome” (Rose) working differently on “multiple environmental levels” (Rose) is playing a large part in modern research.

Johnson’s reference to this problem with norms of reaction is based, on one hand, in the idea presented earlier that, as adults, when heritability of IQ seems to be the highest (reaching, at points, 80%), control of one’s environment is maximized and thus effects from it are minimized due to the increased suitability for one’s genetic makeup for their environment through choice.  On the other hand, her argument is centered on the idea that “environmental variables may contribute directly to changes in means without contributing to variances” (Johnson 178), thus having the same effect on all members of a population and thus being irrelevant to consideration of the norms of reaction in the same way that Rose states them to be in the case of genetic risk factor search.  Nevertheless, nuances in the specific case of IQ, or, in the case of Johnson’s paper, GCA, maintain the importance of these norms of reaction.  Johnson presents the example “those who are brighter tend to use larger vocabularies in talking to their children, read more books to them, and are more likely to expose them to intellectual experiences of all kinds” (Johnson 179), thus enhancing genetic effects on variance.  Johnson conjectures that a good portion of the variance of the population as a whole, 35%, is likely based off of these sort of “gene-environment correlations.”  Thus, she concludes that the real progress that can be made in research in this area would be in sophistication of the criteria by which populations are divided, such that this population variance may be minimized for each sample and genetic effects can truly be measured against each other.  This may be exceedingly difficult, considering the isolated nature of such environments as those discussed in the immediately preceding example, in terms of genetic diversity, but is certainly something which scientists can and will take into account.

The perfect example of an approach that explicitly takes these ideas into account is chronicled in the paper “DNA evidence for strong genetic stability and increasing heritability of intelligence from age 7 to 12” by M Trzaskowski, J Yang, PM Visscher, and R Plomin in Molecular Psychiatry.  The thesis which this study works to prove is a somewhat different situation, but provides an example for how studies following Johnson’s criteria may be structured.  The thesis of this paper is that GCA (referred to as the g factor) is impacted by the same genes throughout development (ages 7-12), despite the fact that heritability, according to previous studies, increases with age (likely due to Johnson’s claim that environmental control increases).  The technique that these scientists make use of is known as Genome-wide complex trait analysis (GCTA) and is based on Johnson’s final claim that, based on the theory of norms of reaction, the key to finding genetic results is to look for the cooperation of vast numbers of genes through environmental isolation, rather than the single, or even limited multiple, polymorphisms or mutations.  The application of GCTA here involves the application of 1.7 million DNA markers to the examination of patterns in g factor, as compared between results at ages 7 and 12 for single participants, and between twins (Trzaskowski).  GCTA, according to the authors of this paper, significantly reduces assumption in research, and thus succeeds in eliminating at least Rose’s final factor in the problem of genetic analysis for heritability.  The unique aspect of this study which can definitely be applied to upcoming research is the use of bivariate analysis to deepen analysis in such a way that proven points can be taken into account such that present research can truly be more sophisticated.  The two variables analyzed by Trzaskowski, Yang, Visscher, and Plomin are stability between ages 7 and 12 and already proven increasing heritability.  Future studies may easily apply such concepts to make much larger inferences about much bigger populations.

Johnson’s most intriguing claim which may be applied to GCTA and the accompanying study techniques is centered around the idea that, based on separate, non-genetic research regarding IQ, it is possible to determine much more specific phenotypes to examine.  The most prominent, yet also most abstract, possibility is what Johnson believes is the GCA equivalent of optimal body size for height: reactivity to stimuli, as contrasted to the information processing capacity that is defined by GCA and IQ.  Of course, this is a rather vague possibility which Johnson does not cite specific examples of, but its consideration by researchers in situations of bivariate, or better multivariate (as allowed by mathematical sophistication), analysis in terms of this situation will lead to distinct sophistication and improvement in research.  Beyond this vague concept, Johnson also presents much more tangible phenotypes that have been directly proven to have a positive correlation with IQ: “brain size, white matter integrity and volume, gray matter volume, and cortical thickness” (Johnson 177).  While initial research based off of this idea may definitely focus on bivariate correlation of each of these as compared against genome-wide trait cooperation, but the two specific techniques that will improve research will be specific research on SNPs (single nucleotide polymorphisms) that impact these highly tangible phenotypes and complex multivariate analysis of many phenotype variables in search of genes that have across-the-board application.  The combination of these with increased effort in this field will provide definite progress in the detection of specific genes that may contribute directly to variation in IQ.

Analysis and understanding of GCTA in the context of multivariate analysis of the many contributions to IQ and search of specific QTLs will better allow focused research.  Science that works to explain this through specific genetic evidence both poses a danger in the potential possibilities for achievement and the potential for benefit through the potential to address specific detriments to IQ that have been discovered.  The latter is often a basic starting point due to the fact that many are defined by SNPs, so true progress will be reflected by larger research.  As research as to specific genetic modification of humans beyond selection as manifested in in vitro fertilization progresses, the concept of genetic modification for the sake of elimination of disorders or enhancement of IQ variation, thus changing population mean.  Of course, the much more accessible factor is environmental, which is something that can much more quickly be achieved outside of science.  Perhaps there is a future in this idea, which we will soon observe.



Works Cited

Devlin, B., Michael Daniels, and Kathryn Roeder. “The Heritability of IQ.” Nature 388.6641 (1997): 468-71. Nature. Macmillan Publishers Limited, 31 July 1997. Web. 4 May 2014.

Jensen, Arthur Robert. The G Factor: The Science of Mental Ability. Westport, CT: Praeger, 1998. Print.

Johnson, Wendy. “Understanding the Genetics of Intelligence: Can Height Help? Can Corn Oil?” Current Directions in Psychological Science 19.3 (2010): 177-82. Psyc621 Clinical Assessment. University of Arizona, 17 June 2010. Web. 4 May 2014.

Kaufman, Alan S. IQ Testing 101. New York, NY: Springer Pub., 2009. Print.

Plomin, R., N. L. Pedersen, P. Lichtenstein, and G. E. Mcclearn. “Variability and Stability in Cognitive Abilities Are Largely Genetic Later in Life.” Behavior Genetics 24.3 (1994): 207-15. Print.

Rose, S. P R. “Commentary: Heritability Estimates–long past Their Sell-by Date.” International Journal of Epidemiology 35.3 (2006): 525-27. Print.

Trzaskowski, M., J. Yang, P. M. Visscher, and R. Plomin. “DNA Evidence for Strong Genetic Stability and Increasing Heritability of Intelligence from Age 7 to 12.” Molecular Psychiatry 19.3 (2013): 380-84. 29 Jan. 2013. Web. 5 May 2014.


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