Searching For Hidden Socioeconomic Layers In The Heritability Of Child Cortical Structure - Info and Reading Options

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Human traits are a result of a complex interplay between genetic and non-genetic influences. Heritability quantifies the extent to which genetic differences contribute to individual variation in a particular trait in a population. Twin studies indicate heritability estimates for cognitive abilities as high as 50% -70%, and that some of the variability is age dependent. Single nucleotide polymorphism (SNP)-based analyses tend to present somewhat lower estimates and broader variability, also depending on the cognitive ability examined (Bergen et al., 2007; Briley & Tucker-Drob, 2013; Haworth et al., 2010; Smith et al., 2023). The contributions of non-genetic (“environmental”) factors in the shaping of cognitive traits is likely both dynamic and unique to each individual, as a child’s genetic predispositions will influence the environments they seek and the responses elicited from others (Plomin et al., 1977; Scarr & McCartney, 1983). These time- and context-dependent environmental effects will further impact the child’s traits, and a dynamic translational process between the environment and relevant (initially genetically predisposed) traits will evolve over time (Tucker-Drob et al., 2013). Such translational processes assume that a child can encounter a variety of environments, yet, for some children such diversity is limited. At the group level, youths from low socioeconomic status (SES) environments are less likely to receive opportunities and resources based on their genetically influenced traits and abilities. In this context, genetic influences might only be maximized in higher SES settings (Harden et al., 2007; Tucker-Drob et al., 2011). This notion known as the “Scarr-Rowe hypothesis” was first observed in the US in the 1970s (Scarr-Salapatek, 1971) and replicated both in the US (Rowe et al., 1999) and Sweden (Fischbein, 1980). Recent findings have however been mixed, with some indications that the hypothesis may be US-specific (Tucker-Drob & Bates, 2016), but this is also debated (Figlio et al., 2017; Gottschling et al., 2019). Of note the somewhat related “Pareto hypothesis” posits that genetic potential may peak among the middle class and be less realized in both poor and affluent environments, due to restrictive- and buffering protective factors. The “Saunders hypothesis” focuses on the protective factors of high SES only, stating that this may lead to lesser realization of genetic potential, thereby directly contrasting the Scarr-Rowe hypothesis (Lin, 2020). Other important factors include historical shifts, e.g. that individuals in the past had fewer opportunities for personalized development compared to individuals currently, and that macro-level factors, including educational systems and welfare policies, have been shown to modulate genetic potential (Baier et al., 2022). Extensive research has been conducted on the “Scarr-Rowe hypothesis”, however to our knowledge, only one study has tested whether SES moderates the heritability of brain imaging features. In a small study of 705 predominantly adult individuals, Chiang et al. (2011) reported that higher SES was associated both with greater and lesser heritability of fractional anisotropy in specific white matter tracts. When assessed through magnetic resonance imaging (MRI) the cerebral cortex, essential for human cognitive abilities (Harris & Shepherd, 2015; Rakic, 2009), is found to have region- and metric-specific heritability that varies extensively, but can reach up to about 65% for surface area, 55% for cortical thickness, and similar estimates for different sulcal features, with family-based studies generally showing higher estimates than SNP-based (Pizzagalli et al., 2020; Strike et al., 2019). Yet, no imaging studies have explored whether SES moderates the heritability of cortical structure. In the current study we will investigate whether the heritability of cortical macro- and microstructure is moderated by a child’s socioeconomic and neighborhood environment. We plan to use data from about 9000 children aged 9-11 years from the Adolescent Brain Cognitive Development (ABCD) Study®. Cortical structure will be measured by global and regional assessments of cortical thickness, surface area, curvature and grey/white-matter contrast (GWC). We will model the interaction between the child’s socioeconomic and neighborhood environment and genetic and environmental contributions to cortical structure using linear mixed effects models.

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