Sociogenomics in the 21st century: An introduction to the history and potential of genetically informed social science

Braudt, David B.

doi:10.1111/soc4.12626

Cited by 69 publications

(17 citation statements)

References 77 publications

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“…The results from our study also contribute to ongoing discussions regarding sociogenomics (Braudt, 2018) and highlight the need for designs in which it is possible to disentangle purported social-genetic effects from a correlated rearing environment. In view of the pervasive evidence for passive gene–environment correlation for many behaviors (Jaffee & Price, 2007), caution is warranted when interpreting evidence for social-genetic effects.…”

Section: Discussionsupporting

confidence: 53%

Disentangling Social-Genetic From Rearing-Environment Effects for Alcohol Use Disorder Using Swedish National Data

et al. 2020

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Investigations of social-genetic effects, whereby a social partner’s genotype affects another’s outcomes, can be confounded by the influence of the social partner’s rearing environment. We used marital information on more than 300,000 couples from Swedish national data to disentangle social-genetic from rearing-environment effects for alcohol use disorder (AUD). Using observational and extended-family designs, we found that (a) marriage to a spouse with a predisposition toward AUD (as indexed by a parental history of AUD) increased risk for developing AUD; (b) this increased risk was not explained by socioeconomic status, the spouse’s AUD status, or contact with the spouse’s parents; and (c) this increased risk reflected the psychological consequences of the spouse having grown up with an AUD-affected parent (i.e., a rearing-environment effect) rather than a social-genetic effect. Findings illustrate that a spouse’s rearing-environment exposures may confer risk for AUD.

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Section: Discussionsupporting

confidence: 53%

Disentangling Social-Genetic From Rearing-Environment Effects for Alcohol Use Disorder Using Swedish National Data

et al. 2020

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“…Genome-wide association studies (GWAS) have discovered genetic loci associated with BMI (Locke et al, 2015;Speliotes et al, 2010;Yengo et al, 2018). Single nucleotide polymorphisms (SNPs) discovered in GWAS of BMI phenotypes have relatively small effects; therefore, polygenic scores (PGSs) have been used to combine information from multiple GWASdiscovered SNPs (Belsky & Israel, 2014;Braudt, 2018;Dudbridge, 2016). A BMI PGS focused on samples of European ancestry has been shown to be predictive of BMI, including in young adults in the United States (Belsky et al, 2012;Domingue et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Genetic risk, body mass index, and weight control behaviors: Unlocking the triad

Braudt

Domingue²,

Bibbins‐Domingo

et al. 2019

Intl J Eating Disorders

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Background: The relationship between genetic risk for body mass index (BMI) and weight control behaviors remains unknown. The objectives of this study were to determine the association between genetic risk for BMI and weight control behaviors in young adults, and to examine actual measured BMI as a potential mediator variable.Method: We analyzed data from three data collection waves of the National Longitudinal Study of Adolescent to Adult Health. The BMI polygenic score (PGS) was based on published genome-wide association studies for BMI. BMI was collected at 11-18 years and 18-26 years. Weight control behaviors included self-reported:(a) weight loss behaviors (dieting, vomiting, fasting/skipping meals, diet pills, laxatives, or diuretic use to lose weight) and (b) weight gain behaviors (eating more or different foods than normal, taking supplements to gain weight).Results: Among 4,397 participants, the BMI PGS was associated with higher odds of weight loss behaviors in females (OR 1.24, 95% CI 1.14-1.35) and males (OR 1.43, 95% CI 1.26-1.62), and this association was mediated by BMI (indirect effect 0.04, 95% CI 0.03-0.05 in females and 0.03, 95% CI 0.03-0.04 in males). The BMI PGS was associated with lower odds of weight gain behaviors in females and males, which was also mediated by actual BMI. Conclusions:The BMI PGS was associated with weight loss behaviors in both males and females, and this association was mediated by actual measured BMI. Clinical interventions to prevent high BMI, particularly for individuals with genetic risk, may also prevent subsequent development of potentially unhealthy weight loss behaviors. K E Y W O R D Sbody mass index, disordered eating behaviors, eating behaviors, genetics, obesity, young adult

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“…Here, I argue that a genetically informed perspective provides a more complete theoretical picture of the formation of educational expectations by shedding light on processes that would not be captured in a purely phenotypic design. Behavioral genetics research has shown that there are genetic influences on virtually all human behavior (Braudt 2018;Harden and Koellinger 2020;Polderman et al 2015). According to the model of the "phenotypic bottleneck" (Freese 2008) genetic influences on distal social outcomes, such as educational expectations, are strictly mediated by embodied phenotypes, such as height, cognitive ability or personality traits (ibid.…”

Section: The Role Of Genesmentioning

confidence: 99%

A Genetically Informed Perspective on the Formation of Educational Expectations

Ruks¹

2022

Preprint

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In this article I test whether students’ educational expectations respond to signalsof academic potential and whether this responsiveness varies by socio-economicstatus (SES). I address these questions by combining sociological and behavioralgenetics perspectives which comes with two advantages. First, it provides a moredetailed theoretical picture of the mechanisms of the formation of educational expectations by disentangling whether expectations respond to prior academic performance as an observed signal of academic potential or other unobserved genetically influenced signals of academic potential. Second, it provides a more accurate estimate of the responsiveness to academic performance by controlling for unobserved genetic and shared environmental confounders. Using panel data from the German TwinLife survey I find that the responsiveness to prior performance is entirely explained by genetic und shared environmental confounding. Instead, students update their expectations based on unobserved genetically influenced signals of academic potential. Students with a high SES are less responsive to these signals.

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Sociogenomics in the 21st century: An introduction to the history and potential of genetically informed social science

Cited by 69 publications

References 77 publications

Disentangling Social-Genetic From Rearing-Environment Effects for Alcohol Use Disorder Using Swedish National Data

Disentangling Social-Genetic From Rearing-Environment Effects for Alcohol Use Disorder Using Swedish National Data

Genetic risk, body mass index, and weight control behaviors: Unlocking the triad

A Genetically Informed Perspective on the Formation of Educational Expectations

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