PubMed · 3255044
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J H Fernández. 1988. [Genes and intelligence].. https://pubmed.ncbi.nlm.nih.gov/3255044/
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This paper provides a critical analysis of genetically informed research on relationships, with an emphasis on relationships among unrelated individuals (e.g., spouses). To date, research in this area has used traditional behavioral genetic frameworks to either partition the variance in relationship-related outcomes into genetic and environmental components, or to examine gene-environment interplay between relationship factors and other outcomes. However, this conventional approach is at odds with the long-standing understanding from the field of relationship science that both partners' characteristics matter when predicting shared outcomes-that is, outcomes that are emergent. We examine briefly the philosophical concept of emergence, and discuss ways to model dyadic outcomes in genetically informed relationships research. We also review the related topic of social genetic effects, which refer to the influence of a social partner's genotype on a proband's phenotype. A genetically informed dyadic perspective has potentially important consequences for our understanding of the pathways from genotype→shared or individual-level phenotypes, and more fully recognizes the complexity of how genetic and social/environmental factors come together to influence human behavior.
Parameters of quantitative genetic models have traditionally been estimated by either algebraic manipulation of familial correlations (or familial mean squares), biometric model fitting, or multiple-group covariance structure analysis. With few exceptions, researchers who have used these methods for the analysis of twin data have assumed that their data were multinormal and, consequently, have used normal-theory estimation methods. It is shown that normal-theory methods produce biased genetic and environmental parameter estimates when data are censored. Specifically, with censored data, (1) normal-theory estimates of narrow-sense heritability are either positively or negatively biased, whereas (2) estimates of shared-familial environmental variance are always biased downward. An alternative method for estimating genetic and environmental parameters from censored twin data is proposed. The method is called genetic Tobit factor analysis (GTFA) and is an extension of the Tobit factor analysis model developed by Muthén (Br. J. Math. Stat. Psychol. 42, 241-250, 1989). Using a Monte Carlo design, the performance of GTFA is compared to traditional quantitative genetic methods in both large and small data sets. The results of this study suggest that GTFA is the preferred method for the genetic modeling of censored data obtained from twins.
The twin method is usually credited to Francis Galton's 1875 article on twins. However, Galton did not propose the comparison between identical and fraternal twin resemblance which is the essence of the twin method. Although the twin method was "in the air" in the mid-1920s, the first descriptions of the method appeared in an article by Curtis Merriman and in a book by Hermann Siemens, both in 1924, 50 years after Galton's paper.