by Mark James Adams
A worker in quantitative genetics, primate psychology, and cooperative breeding. I research the evolutionary dynamics of correlated suites of behavior in wild animals. I am trying to answer the question Why do our personalities differ?
"Models of data have a deep inﬂuence on the kinds of theorising that researchers do. A structural equation model with latent variables named Shifting, Updating, and Inhibition (Miyake et al. 2000) might suggest a view of the mind as inter-connected Gaussian distributed variables. These statistical constructs are driven by correlations between variables, rather than by the underlying cognitive processes – though the latter were used to select the measures used. Davelaar and Cooper (2010) argued, using a more cognitive-process-based mathematical model of the Stop Signal task and the Stroop task, that the inhibition part of the statistical model does not actually model inhibition, but rather models the strength of the pre-potent response channel. Returning to the older example introduced earlier of g (Spearman 1904), although the scores from a variety of tasks are positively correlated, this need not imply that the correlations are generated by a single cognitive (or social, or genetic, or whatever) process. The dynamical model proposed by van der Mass et al. (2006) shows that correlations can emerge due to mutually beneﬁcial interactions between quite distinct processes."
"Timeline for the fields of molecular and quantitative genetics. The figure illustrates how the new synthesis by Fisher during the early 20th century provided a unified theory for Mendelian and biometrical genetics, how several key discoveries within the fields facilitated the interdisciplinary connections leading to two of the most groundbreaking discoveries in genetics over the past decade, genetic mapping and genomic prediction, and why we believe a new synthesis is needed to provide a common theory that embraces the full width of these two fields. Abbreviations: QTL, quantitative trait locus; RFLP, restriction fragment length polymorphism; SNP, single nucleotide polymorphism."
The figure shows the predictive power of polygenic risk scores under different models (M1-M7) of genetic architecture. The text says “If these models are true, genotyping panels of hundreds (rather than tens of thousands) of SNPs may have the potential for use in genetic risk prediction”. If you are in favor of GWAS-based methods, you might be interested in the number of SNPs and sample-sizes necessary to explain variation in the prediction set. If you are wary of GWASs’ utility, you’ll note that the R2 tops out at 0.3.
"Taken together, these nine predictions represent a very stringent test of the theory, because all nine originate from a single theoretical framework. None of these predictions can be varied independently of the other eight in order to fit a particular set of data—the data must confirm them all, or the theory is ruled out."
"[research] shows how good teachers improve reading standards for all but this means that the variance that remains is more due to genetic differences. This leads to a conclusion almost completely at odds with prevailing conventional wisdom in political and academic debates over education."