Genes & Reading Ability

Nature, nurture, and expertise
Robert Plomin et al.; 2013
http://www.sciencedirect.com/science/article/pii/S0160289613000810


Abstract

Rather than investigating the extent to which training can improve performance under experimental conditions (‘what could be’), we ask about the origins of expertise as it exists in the world (‘what is’). We used the twin method to investigate the genetic and environmental origins of exceptional performance in reading, a skill that is a major focus of educational training in the early school years. Selecting reading experts as the top 5% from a sample of 10,000 12-year-old twins assessed on a battery of reading tests, three findings stand out. First, we found that genetic factors account for more than half of the difference in performance between expert and normal readers. Second, our results suggest that reading expertise is the quantitative extreme of the same genetic and environmental factors that affect reading performance for normal readers. Third, growing up in the same family and attending the same schools account for less than a fifth of the difference between expert and normal readers. We discuss implications and interpretations (‘what is inherited is DNA sequence variation’; ‘the abnormal is normal’). Finally, although there is no necessary relationship between ‘what is’ and ‘what could be’, the most far-reaching issues about the acquisition of expertise lie at the interface between them (‘the nature of nurture: from a passive model of imposed environments to an active model of shaped experience’).

The breeder's equation

http://westhunt.wordpress.com/2013/06/07/the-breeders-equation/

On the genetic basis of face cognition and its relation to fluid cognitive abilities

On the genetic basis of face cognition and its relation to fluid cognitive abilities
A. Kiy et al.; 2013
Genes, Brain & Behavior


Abstract

The oxytocin and the dopaminergic systems have turned out to be highly relevant for social abilities and cognition. Therefore, we examined the association between two functional gene polymorphisms and face cognition (FC) in a multivariate study (N = 250) by applying structural equation modeling. The catechol-O-methyltransferase (COMT) val158met polymorphism influences the enzyme activity of COMT, which affects the prefrontal dopamine concentration. The rs226849 is a single-nucleotide polymorphism located in the promoter region of the oxytocin receptor (OXTR) gene, modulating the mRNA expression. By modeling a general fluid ability factor (defined by working memory and reasoning) and nested FC factors, we tested genetic contributions to FC, after controlling for variance in FC that was also associated with fluid abilities. In line with several previous studies, we found a significant association between the COMT genotype and fluid abilities (Gf) but not with FC. The association between the oxytocin polymorphism and Gf was opposite in direction for men and women. Women with the C⁺ genotype performed better on Gf tasks than those with the C⁻genotype. Conversely, men with the C⁻ genotype performed better than those with the C⁺ genotype. There was no significant association between OXTR and the nested FC factor. Therefore, the relationship between the oxytocin polymorphism and FC can be fully accounted for by Gf. The sex specificity of this relationship is a novel finding and warrants a mechanistic explanation.

A post-genomic view of behavioral development and adaptation to the environment

A post-genomic view of behavioral development and adaptation to the environment
Peter LaFreniere and Kevin Mac Donald; 2013


Abstract

Recent advances in molecular genetics are reviewed that have major implications for the biobehavioral sciences and for understanding how organisms adapt to their environments at both phylogenetic and ontogenic levels. From a post-genomics perspective, the environment is as crucial as the DNA sequence for constructing the phenotype, and as a source of information in trying to predict phenotypes. The review is organized with respect to five basic processes by which phenotypes adapt to both recurrent and novel environmental challenges, with an emphasis on the data for humans: 1) developmental plasticity, 2) epigenetic mechanisms, 3) genotypeenvironment correlations, 4) gene x environment interactions, and 5) domain-general psychological mechanisms.

Genetic Contributions to Antisocial Personality and Behavior: A Meta-Analytic Review From an Evolutionary Perspective

Genetic Contributions to Antisocial  Personality and Behavior: A Meta-Analytic  Review From an Evolutionary Perspective
Christopher J Ferguson; 2010
http://www.tamiu.edu/~cferguson/evmeta.pdf


Abstract

Evidence from behavioral genetics supports the conclusion that a significant amount of the variance in antisocial personality and behavior (APB) is due to genetic contributions. Many scientific fields such as psychology, medicine, and criminal justice struggle to incorporate this information with preexisting paradigms that focused exclusively on external or learned etiology of antisocial behavior. The current paper presents a meta-analytic review of behavioral genetic etiological studies of APB. Results indicated that 56% of the variance in APB can be explained through genetic influences, with 11% due to shared non-genetic influences, and 31% due to unique non-genetic influences. This data is discussed in relation to evolutionary psychological theory.

Prefrontal and striatal dopaminergic genes predict individual differences in exploration and exploitation

Prefrontal and striatal dopaminergic genes predict individual differences in exploration and exploitation
Michael J Frank et al., 2009
http://diyhpl.us/~bryan/papers2/neuro/Prefrontal%20and%20striatal%20dopaminergic%20genes%20predict%20individual%20differences%20in%20exploration%20and%20exploitation.pdf


Abstract

The basal ganglia support learning to exploit decisions that have yielded positive outcomes in the past. In contrast, limited evidence implicates the prefrontal cortex in the process of making strategic exploratory decisions when the magnitude of potential outcomes is unknown. Here we examine neurogenetic contributions to individual differences in these distinct aspects of motivated human behavior, using a temporal decision-making task and computational analysis. We show that two genes controlling striatal dopamine function, DARPP-32 (also called PPP1R1B) and DRD2, are associated with exploitative learning to adjust response times incrementally as a function of positive and negative decision outcomes. In contrast, a gene primarily controlling prefrontal dopamine function (COMT) is associated with a particular type of ‘directed exploration’, in which exploratory decisions are made in proportion to Bayesian uncertainty about whether other choices might produce outcomes that are better than the status quo. Quantitative model fits reveal that genetic factors modulate independent parameters of a reinforcement learning system.

Sources of Cognitive Exploration: Genetic Variation in the Prefrontal Dopamine System Predicts Openness/Intellect

Sources of Cognitive Exploration: Genetic Variation in the Prefrontal Dopamine System Predicts Openness/Intellect

Colin G DeYoung et al; 2012

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3143482/


Abstract

The personality trait Openness/Intellect reflects the tendency to be imaginative, curious, perceptive, artistic, and intellectual—all characteristics that involve cognitive exploration. Little is known about the biological basis of Openness/Intellect, but the trait has been linked to cognitive functions of prefrontal cortex, and the neurotransmitter dopamine plays a key role in motivation to explore. The hypothesis that dopamine is involved in Openness/Intellect was supported by examining its association with two genes that are central components of the prefrontal dopaminergic system. In two demographically different samples (children: N = 608; adults: N = 214), variation in the dopamine D4 receptor gene (DRD4) and the catechol-O-methyltransferase gene (COMT) predicted Openness/Intellect, as main effects in the child sample and in interaction in adults.

The Heritability of Life History Strategy

The Heritability of Life History Strategy: The K-Factor, Covitality, and Personality

A J Figueredo et al., 2004

http://midus.wisc.edu/findings/pdfs/811.pdf

Abstract

Archival data from the MIDUS survey (Brim et al., 2000), a nationally representative sample, on 309 MZ and 333 DZ twin pairs aged 25-74 years were used to test the psychometrics and behavioral genetics of life history strategy. We organized 253 of the originally administered 2,000 questions into 30 scales measuring life history traits (e.g., quality of family relationships and altruism towards kin), medical symptoms (e.g., thyroid problems), personality traits (e.g., neuroticism, extraversion, conscientiousness), and social background (e.g., financial security). A single higher-order factor, indicating a general life history strategy, composed of three lower-order factors, was replicated. Factor analyses were then performed on the genetic variance-covariance matrices. We found that (a) a single higher-order factor explained the preponderance of the genetic correlations among the scales and (b) this higher-order factor was itself 68 percent heritable and accounted for 82 percent of the genetic variance among the three component lower-order factors.

[http://digressionality.blogspot.co.at/2013/03/the-heritability-of-life-history.html]

Genetic Influence on Human Psychological Traits:

http://www18.homepage.villanova.edu/diego.fernandezduque/Teaching/PhysiologicalPsychology/zCurrDir4200/CurrDirGeneticsTraits.pdf
Thomas J. Bouchard Jr., Genetic influence oh human psychological traits, 2004

[Sehr guter Überblick: table 1]

 

siehe auch: http://jaymans.wordpress.com/2012/12/31/all-human-behavioral-traits-are-heritable/

The Relevance of Noncoding DNA Regions:

[M]ost of the genome that does something significant and can thus mutate in ways that cause harm is noncoding. We don’t understand that component as well. Certainly I don’t. A rough estimate would be that 1.5% of the genome makes proteins while another 3.5% does something else functional - regulation of protein expression, regulation of other regulators, etc.

Gregory Cochran

http://westhunt.wordpress.com/2012/10/17/more-to-go-wrong/