Parents see genes at play: Parenthood and heritability attributions about "lifestyle" diseases.

Parents See Genes at Play: Parenthood and Heritability Attributions about “Lifestyle” Diseases - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Soc Sci Med . Author manuscript; available in PMC: 2026 May 31. Published in final edited form as: Soc Sci Med. 2025 May 31;381:118277. doi: 10.1016/j.socscimed.2025.118277 Parents See Genes at Play: Parenthood and Heritability Attributions about “Lifestyle” Diseases Alison Jane Martingano 1 , Susan Persky 2 Author information Article notes Copyright and License information 1. University of Wisconsin - Green Bay 2. Social and Behavioral Research Branch, National Human Genome Research Institute ✉ Address correspondence to: Susan Persky perskys@mail.nih.gov Issue date 2025 Sep. PMC Copyright notice PMCID: PMC13067377  NIHMSID: NIHMS2089424  PMID: 40505501 The publisher's version of this article is available at Soc Sci Med Abstract Parenthood offers a unique window into how genes and environments shape human characteristics. Here, we investigated whether parenthood is associated with beliefs about the heritability of various health attributes. In Study 1 ( N = 720), we found that individuals with children provided higher estimates of genetic influence for obesity, weight, and type 2 diabetes. This effect was largely driven by comparing parents and non-parents rather than by the number of children a parent has. For these attributes women were likely to more highly endorse genetic causes than men, indicating that motherhood may have a particularly strong impact on heritability beliefs. Study 2 provided a meta-analysis of existing data ( k = 5, total N = 2,353) which converged on a small but reliable overall relationship ( g = 0.11, p = .007) between parenthood and greater attributions of genetic causes, again focused on “lifestyle” attributes (specifically obesity, type 2 diabetes, and cardiovascular disease). We did not replicate the effect of gender from Study 1. Together, these findings suggest that parenthood may heighten awareness of genetic underpinnings for particular health attributes, possibly through direct observations of children’s traits or as a way to reconcile caregiving experiences with disease stigma. Implications highlight the need for targeted health messaging that recognizes parents’ more gene-focused mindsets, while also addressing the environmental and behavioral factors that contribute to health risks. Keywords: parenthood, heritability, genetic attributions, lifestyle diseases, obesity, type 2 diabetes, health beliefs “All parents are environmentalists until they have their second child” - Marvin Zuckerman (1987) The influence of parenthood on one’s mental models of inheritance has been a topic of interest among researchers and theorists for many years, as exemplified by Marvin Zuckerman’s observation quoted above (1987). Our beliefs about what shapes our minds, bodies, and behaviors develop through experience, and parents’ observations of multiple related siblings in a shared home environment provide a uniquely influential perspective on the power of heritable genetic factors in human development. Many characteristics, while also influenced by interactions with an individual’s environment and experiences, are indeed highly heritable. Personality, for example, has been estimated as being about 40% inherited ( Vukasović & Bratko, 2015 ), eating speed is estimated at about 85%, and food enjoyment at about 53% ( Llewellyn et al., 2010 ). The experience of raising multiple children may heighten parents’ awareness of inherited processes at work, wherein siblings develop both overlapping and wholly unique characteristics despite sharing formative family exposures. Despite the long-standing interest in this topic, there has been little empirical research exploring the impact of parenthood on heritability estimates, its boundary conditions, and the extent to which any insights gained through parenting generalize beyond the traits and characteristics directly observed in one’s children. The current report investigates these concepts with a focus on how parenting experiences associate with mental models about genetic influences on health factors and disease risk. When considering the source of individuals’ beliefs about the heritability of health conditions, most attention is typically given to information communicated in healthcare, educational contexts, and through the media. However, it is evident that individuals also develop beliefs about risk through everyday observation and experience, blending formal education with intuition to shape their mental models ( Menendez et al., 2023 ). Various informal sources influence causal attributions for health conditions, including family history, life experiences, and cultural forces ( McVay et al., 2016 ; O’Neill et al., 2010 ; Waters et al., 2014 ; Waters et al., 2016 ). Parenthood may similarly shape mental models of disease risk by influencing underlying beliefs about the extent and nature of genetic influence. These mental models of disease risk have significant practical implications, affecting areas such as parenting behavior, self-concept, health promotion, and disease prevention processes ( Nyman et al., 2022 ; Peetz et al., 2021 ; Persky et al., 2021 ). There are several reasons to theorize a connection between parenthood and beliefs about heritability. These theories and their implications span multiple disciplines, each approaching the concept of heritability in different ways. Zuckerman (1987) discussed environmentalist beliefs, emphasizing the environment as the primary driver of development. Other work frames these concepts as nature and nurture, internal and external locus, genetic and environmental (shared and unshared) influences. While most human traits are shaped by genetic and environmental influences in conjunction and interaction, conceptually these models are often focused on the perceived balance of internal biological-genetic factors versus external environmental ones. Development of Heritability Beliefs Social Cognitive Theory provides a foundational framework for understanding how individuals form belief systems, such as beliefs about heritability, namely, through observing others to make sense of the world ( Bandura & NIMH, 1986 ). Parents have a unique vantage point in observing the stability of similarities and differences between siblings across environmental contexts and across time ( Plomin et al., 2016 ). These observations could strengthen genetic attributions for child traits as parents consider how, when, and why siblings become more or less similar, and their ability or lack thereof to shape these outcomes. The Co-variation Theory of Attribution speaks to the development of heritability beliefs more specifically, suggesting that internal attributions (e.g., to a person’s genes) are more likely to occur when the attribute is perceived to be different from others, consistent across environments, and stable over time ( Kelley, 1973 ). These aspects may be more recognizable when a parent has multiple children to compare and contrast, along with a larger sample of non-kin children to observe through their children’s social networks. These implicit rules for shaping attributions can be particularly instructive as parents of multiple children observe how the same parenting approaches can yield different results in each child. These theories do not postulate whether parenthood will push attributions toward genetic or environmental factors. Instead, they simply highlight that parenthood and each additional child provides valuable and emotionally meaningful, although anecdotal, information for parents to develop their attributions. The formation of heritability beliefs can also be understood through a lens of motivated social cognition ( Keller, 2005 ). According to this framework, belief systems are not formed in a vacuum but are shaped by individuals’ psychological needs and motives. Jost et al., (2003) identified three primary motives involved in the process of belief formation: two types of directional motives—ideological and existential—and one non-directional motive, referred to as the need for closure. These different motivational influences work together, shaping how individuals interpret information and form beliefs. The most relevant to the formation of heritability beliefs seems likely to be existential threats. Individuals form beliefs to buffer against anxiety stemming from thoughts about their mortality ( Solomon et al., 1991 ). Believing attributes, especially diseases, are biologically predetermined and immutable may provide a sense of stability and meaning. Both parents and non-parents may look for these underlying essences in others, a phenomenon known as essentialism ( Gelman, 2003 ). Essentialism involves the “over-attribution” of genetics in explaining individual or group differences ( Heine et al., 2019 ), and arises early in development ( Rhodes & Mandalaywala, 2017 ). People appear to automatically focus on genetic factors when trying to understand other people ( Gould & Heine, 2012 ; Heine et al., 2017 ). These genetic factors, or “essences,” help categorize individuals into groups, such as who belongs to our group (ingroup) and who does not (outgroup) ( Rhodes & Gelman, 2009 ). For parents in particular, emphasizing genetic similarities would reinforce the idea that shared genetic traits are a defining characteristic of the family unit. Among the fundamental human motives is to create a strong familial group including relationships with one’s own children. At the same time, adults individuate from families of origin and while they create new nuclear families ( Aquilino, 1997 ; Colarusso, 2017; Diamond et al., 1996 ). Such motivations could increase parents likelihood of endorsing genetic explanations for their children’s characteristics as a way of reinforcing shared family identity. From a Terror Management Theory perspective, parents may especially value these essentialist beliefs because they offer a sense of “living on” through their children—by viewing their traits as passed down, they symbolically achieve continuity beyond their own lifespan ( Solomon et al., 1991 ). Finally, in contrast to non-parents, who may have less impetus to anchor their identity on generational continuity, parents’ desire to forge a cohesive family identity can heighten their focus on genetic similarities, even as they remain vigilant about their children’s future and work to guide outcomes within the bounds of perceived genetic constraints. Indeed, when faced with direct existential threats, such as illness or death, parents may increase their heritability attributions as a way of coping, reducing uncertainty, and gaining emotional security. The use of genetic explanations to downplay the impact of one’s health behaviors has precedence in the literature ( Condit, 2011 ; French et al., 2005 ; O’Neill et al., 2010 ; Smerecnik et al., 2009). On the other hand, parents may also be motivated to increase genetic attributions in order to downplay the influence of the family environment and children’s health behavior on health outcomes ( Condit, 2009 ; Keeley et al., 2009 ). Focusing on uncontrollable influences like genes could reduce self-blame and stresses of parenting in complex modern environments that promote unhealthy behavior. In conjunction, reducing emphasis on the difficult goal of guiding child health behavior may also protect parents’ perceptions that they are competent and efficacious in this key identity role ( Brandstatter & Bernecker, 2022 ; Fadjukoff et al., 2016 ). Existing literature on Parents’ Attributions Despite the discourse on this subject and its practical importance, the influence of parenthood on heritability attributions has only been evaluated in a handful of previous studies. In qualitative work, Hutchinson and colleagues (2022) noted that parents tend to organically make more genetic etiology statements about child development than non-parents. In this process, parents often use anecdotal support for their statements, suggesting that beliefs may be driven by or connected to life experience. Work by Borkenau and colleagues in 1989 more directly assessed the relationship between motherhood and potential differences in causal mental models in a sample of German women ( Borkenau & Arimond, 1989 ). This team found that mothers expressed less environmentalistic attitudes than prospective mothers, but that attitudes did not change further among mothers of two children. Borkenau and colleagues further reported that mothers of older children had a rebound in environmentalism, suggesting a temporary influence of rearing young children on biogenetic beliefs. A more contemporary evaluation in this area was conducted by Willoughby and colleagues who studied two samples of U.S. adults ( Willoughby et al., 2019 ). This study found that educated mothers with multiple children were particularly accurate in estimating the genetic contribution to a wide variety of human traits, including those not observable in young children (e.g., adult-onset disease). Notably, accuracy in this context refers to giving heritability estimates that more closely match scientific consensus, which sometimes meant providing lower heritability estimates and sometimes higher estimates when compared to the general population. While accuracy differs from the perceived degree of genetic contribution, the relationship patterns between groups provide valuable insights for the questions under examination here. Notably, Willoughby and colleagues identified gender differences in heritability estimates. Women overall were more accurate in their heritability estimates than men. Among women, accuracy improved only among mothers of two or more children, whereas women without children and mothers of one child were equally as accurate. For men, parenthood of any number of children (one or more) was associated with higher accuracy in heritability beliefs compared to non-parent men. Although these patterns suggest that parenthood influences heritability beliefs, the direction and exact nature of these changes remain unclear. It is possible that on traits for which the public generally underestimates the importance of genetic contributions (e.g., obesity, diabetes ( Willoughby et al., 2019 )) an increase in essentialist thinking might prompt higher accuracy. This would not be true for traits for which the public generally overestimates the contributions of genetics (e.g., breast cancer; Willoughby et al., 2019 ). Willoughby et al., (2019) postulated that mothers’ heightened accuracy was a result of observing their developing child (mothers in the US continue to bear the majority of parenting duties). It is not known from Willoughby et al.’s results what age the children were (although one could assume they are mostly young given that their sample of adults was mostly between 25–34). Overall, the influence of child age on parents’ views about heritability remain poorly understood. The Current Study Previous research provides preliminary support for the idea that parenthood can influence belief patterns about the heritability of human traits. However, many questions remain about the boundaries of this effect. The evidence is mixed regarding the impact of parent gender, the number of children in the family and the children’s ages on parental attributions. Does having more children necessarily lead to higher genetic attributions, or is it more related to observing children over a longer period? Additionally, it is unclear whether certain traits are more likely to be attributed to genetics as a result of parenting, and whether mothers’ attributions are uniquely impacted by parenthood in a way fathers’ are not. Study 1 Method All hypotheses, methods, and analyses plans were pre-registered with the Open Science Foundation ( https://osf.io/9hwd3/?view_only=dd241394b9a145eb9f6ef9e1184da82a ) unless otherwise noted in text. Specifically, we hypothesized 1) That the number of children a person has will be positively associated with their endorsement of genetic causes. 2) That the impact of parenthood will vary depending on the attribute in question, such that it will be larger for attributes for which the public generally underestimates heritability. Measures Participants were asked to what extent the following [attributes] are caused by genetics: type 1 diabetes, type 2 diabetes, lung cancer, melanoma, obesity, breast cancer, cardiovascular disease, hypertension, osteoporosis, stroke, and weight. Participants were given a sliding scale and asked to answer in terms of a percentage, with 0% meaning there is no genetic influence and 100% meaning that it is entirely dependent on someone’s genes. For health conditions that may have been unfamiliar, participants were given a short definition (e.g., Type 1 diabetes is a chronic condition in which the body produces little or no insulin and typically appears in adolescence). Using data from previous research 19 , obesity and type 2 diabetes were determined to be the conditions from this list whose heritability are under-estimated by the public. Participants were asked if they were a parent, and if they answered yes, they were asked to list the ages of all their children (both biological and non-biological). Participants Participants were recruited through Amazon’s Mechanical Turk with a CloudResearch overlay, oversampling for participants that CloudResearch had previously identified as having children 19 years and younger. Data included in this analysis were collected alongside a survey to validate a questionnaire about child feeding. We estimated a mid-sized effect size of 0.2 for this analysis and therefore pre-registered a target N of 620. However, recruitment of sufficient parents for the validation study required oversampling and therefore we ultimately recruited 808 participants. We excluded participants who failed an attention check and/or indicated there was a reason to exclude their data. Our final N was 720. See Table 1 for demographics. Table 1. Demographic Characteristics of Participants in Study 1. N Percent Mean SD Age 40.8 12.3 Gender Male 310 43.1 Female 403 56.0 Parent Status Parent 442 61.4 Non-parent 278 38.6 Number of Children 1.20 1.23 0 278 38.6 1 163 22.6 2 185 25.7 3+ Race 94 13.1 Asian 19 2.6 Black 73 10.1 Other 33 4.58 White Ethnicity Hispanic 595 64 82.6 8.9 Non-Hispanic Employment status 656 91.1 Full-time 440 61.1 Part-time 110 15.3 Unemployed 83 11.5 Student 9 1.3 Retired 50 6.9 Other Marital status 28 3.9 Married 335 46.5 Single 212 29.4 Widowed/Divorced/Separated 78 10.8 Cohabiting 93 12.9 Other 2 0.3 Highest Formal Education High school 85 11.8 Some college/Trade School 212 29.4 College graduate 277 38.5 Postgraduate 138 19.2 Other Mean Child Age 8 1.1 13.4 11.0 0 – 5 119 27.2 6 – 12 147 33.6 13 – 18 78 17.8” 19 – 25 17 3.9 25+ 76 17.4 Open in a new tab Note. For parents with multiple children, mean child age was calculated by averaging the ages of all children. Each parent was then categorized into one age bracket based on this mean age, rather than being represented in multiple brackets. Results Preregistered Hypothesis 1: Relationship Between Number of Children and Attributions of Genetic Causes First, we examined the relationship between number of children and participants’ attributions of genetic causes using Pearson’s r . These analyses revealed that, after applying a Bonferroni correction, there was a significant, albeit small, positive correlation between the number of children parents have and their attributions of genetic causes for type 2 diabetes ( r = 0.12, p = .001), obesity ( r = 0.18, p < .001), cardiovascular disease ( r = 0.12, p = .002), hypertension ( r = 0.11, p = .004), stroke ( r = 0.12, p = .001), and weight ( r = 0.15, p < .001). However, the correlations for lung cancer ( r = 0.08, p = .037), type 1 diabetes ( r = 0.05, p = .229), melanoma ( r = 0.04, p = .300), breast cancer ( r = 0.03, p = .483), and osteoporosis ( r = − 0.01, p = .849) were not significant after correcting for multiple comparisons. Second, we examined the relationship between number of children and participants’ attributions of genetic causes using one-way ANOVAs, by binning number of children into four categories (0, 1, 2, and 3+). When a Bonferroni correction for multiple comparisons was applied, there was only a significant impact of number of children on parents’ attributions of genetic causes for three attributes (type 2 diabetes, obesity, and weight). Together these results suggest some support for our hypothesis that parenthood and the number of children impacts attributions of genetic causes. An examination of post-hoc analyses reveals that this trend was driven by differences between non-parents and parents, rather than the impact of additional children (see Table 2 ). Table 2. Mean Endorsement of Genetic Causes for 11 attributes, by number of children. Attribute Mean Endorsement of Genetic Causes Mean (standard deviation) One-way ANOVA 0 1 2 3+ F p Lung Cancer 27.5 (21.8) 26.0 (21.8) 26.0 (21.5) 35.0 (23.9) 4.14 0.006 Breast Cancer 55.8 (25.1) 51.5 (27.0) 53.9 (26.2) 59.7 (27.8) 2.13 0.095 Stroke 32.9 (23.1) 34.5 (24.2) 36.3 (24.4) 42.6 (26.7) 3.67 0.008 Osteoporosis 47.5 (26.8) 44.6 (28.3) 43.5 (26.3) 49.6 (28.6) 1.46 0.224 Hypertension 45.4 (23.6) 46.1 (26.6) 49.3 (25.3) 55.2 (26.0) 4.08 0.007 Melanoma 32.1 (24.3) 29.6 (25.1) 30.8 (24.2) 35.3 (24.9) 1.16 0.323 Obesity * 37.1 (24.8) a 43.8 (26.6) b 49.2 (26.0) b 48.8 (25.7) b 10.18 <.001 Type 1 Diabetes 65.4 (25.7) 63.4 (26.9) 62.7 (28.7) 71.5 (26.2) 2.48 0.06 Type 2 Diabetes * 39.3 (22.1) a 46.7 (25.6) b 46.2 (26.2) b 48.3 (28.2) b 5.38 0.001 Cardiovascular disease 49.0 (23.4) 52.1 (26.1) 52.8 (23.4) 58.8 (25.2) 3.88 0.009 Weight 52.3 (24.3) a 56.7 (23.1) a b 59.1 (23.3) b 61.7 (20.6) b 5.33 0.001 Open in a new tab Note. a Means in the same row that do not share the same superscript are significantly different ( p < 0.05, Tukey’s post-hoc). b The significance threshold was adjusted using a Bonferroni correction for multiple comparisons. With 11 analyses conducted, the corrected alpha level is 0.0045. Comparisons that meet this significance level are indicated in bold. * heritability was underestimated for this condition in previous research Preregistered Hypothesis 2: Moderation by Attribute Type A Mixed ANOVA, with disease/trait type as a repeated factor, revealed that the relationship between child number and attributions of genetic causes of depended on the type of attributes in question. There was a significant main effect of number of children overall ( F = 5.39, p = 0.001) and there was also a main effect of disease type ( F = 189.21, p < .001). These main effects were qualified by a significant trait*children interaction ( F = 2.98, p < .001). The existence of a significant interaction indicates that the relationship between the number of children people have and their beliefs about genetics is not universal and differs depending on the type of attributes in question. Overall, the pattern of results (see Table 2 ) appears to support the hypothesis that number of children predicts higher endorsement of genetic causes only for attributions where the public generally underestimates heritability (e.g., obesity and type 2 diabetes). Exploratory Analysis 1: Relationship Between Gender and Attributions of Genetic Causes We examined the relationship between gender and attributions of genetic causes using 2*2 between-subjects ANOVAs, by categorizing gender into two categories (male and female) and parenthood into two categories (parents and non-parents). These analyses revealed a significant main effect of gender on attributions of genetic causes for four attributes. For all of these attributes (breast cancer, obesity, cardiovascular disease, and type 2 diabetes) women were more likely to endorse genetic causes than men (see Table 3 ). We found no significant interaction between gender and parenthood once corrections for multiple comparisons were made. Table 3. Mean Endorsement of Genetic Causes for 11 attributes, by gender and parenthood Endorsement of Genetic Causes Mean (standard deviation) Main Effect Parenthood Main Effect Gender Parenthood * Gender Attributes Parents Non-parents F p F p F p Men Women Men Women Lung Cancer 27.40 (22.80) 28.20 (22.20) 28.50 (22.00) 26.40 (22.00) 0.050 .819 0.02 .892 0.66 .41 Breast Cancer 48.60 (26.70) 57.20 (26.70) 53.60 (25.70) 58.70 (24.70) 2.370 .124 10.47 .001 0.72 .39 Stroke 36.20 (23.10) 37.30 (25.90) 32.50 (23.70) 33.90 (22.00) 3.440 .064 1.22 .270 0.00 .95 Osteoporosis 44.10 (27.00) 45.80 (28.00) 47.30 (28.20) 47.60 (25.10) 1.270 .260 0.08 .770 0.09 .76 Hypertension 47.90 (24.90) 50.10 (26.80) 43.70 (24.90) 47.80 (21.80) 2.700 .100 3.82 .051 0.23 .63 Melanoma 31.30 (24.00) 31.40 (25.10) 34.50 (24.90) 28.90 (23.50) 0.020 .878 1.45 .229 2.09 .14 Obesity 45.90 (28.20) 47.70 (25.20) 32.70 (24.70) 43.20 (24.20) 18.690 <.001 14.32 <.001 4.53 .03 Type 1 Diabetes 62.80 (27.50) 65.80 (27.70) 63.00 (25.80) 68.60 (25.00) 0.510 .480 3.23 .073 0.39 .53 Type 2 Diabetes 43.30 (26.10) 48.70 (26.40) 38.20 (22.10) 41.20 (22.30) 10.160 .001 9.82 .002 0.37 .54 Cardiovascular disease 50.50 (23.90) 55.50 (25.30) 47.30 (24.70) 51.80 (21.40) 3.220 .073 9.28 .002 0.02 .89 Weight 58.50 (22.50) 58.90 (22.80) 50.10 (24.20) 55.00 (24.80) 10.740 .001 4.21 .040 1.52 .21 Open in a new tab Note. The significance threshold was adjusted using a Bonferroni correction for multiple comparisons. With 11 analyses conducted, the corrected alpha level is .0045. Comparisons that meet this significance level are indicated in bold. Exploratory Analysis 2: Relationship Between Child Age and Attributions of Genetic Causes We examined the relationship between child age and participants’ attributions of genetic causes using one-way ANOVAs, by binning average child age into five categories (0–5, 6–12, 13–18, 19–25, and 25+). This analysis revealed no significant impact of child age on attributions of genetic causes for any of the eleven attributes (all p s > .05). Study 2 Method Study 2 combined data from five studies (see Table 4 ) between 2019 and 2022, totaling 2353 participants. More information about the study designs can be found in the original publications ( Martingano et al., 2023 ; Persky et al., 2019; Rose et al., 2019 ; Telaak et al., 2023 ) and the Supplemental Table . We also include study 1 from the current publication within our meta-analysis (labelled Study E). Participants were compensated for participation in all studies, and all studies were approved by the relevant institutional review board. These studies were selected because they asked participants to estimate the influence of genetics on at least one trait or disease, and recorded participants’ number of children. Participants who reported having more than 10 children were excluded from these analyses. Table 4. Characteristics of included research studies. Year Traits/Diseases Scale Study Purpose Study A 2019 1 Cardiovascular disease, Cancer, Overweight, Stroke, Type 2 Diabetes, Eating behavior 1 – 7 Measure influence of messages about child diet on parent feeding behavior. Study B 2021 2 Type 1 Diabetes, Type 2 Diabetes 0 – 100% Determine how causal attributions about diabetes are related to stigma and health. Study C 2023 3 Weight, Osteoporosis 1 – 7 Assess reaction to overweight and lean physician recommendations. Study D 2023 4 Obesity, Eating Behaviors 0 – 100% Measure influence of genetic educational materials on stigma and empathy. Study E 2022 5 Lung Cancer, Melanoma, Breast Cancer, Cardiovascular disease, Type 2 Diabetes, Obesity, Hypertension, Stroke, Osteoporosis, Type 1 Diabetes, Weight 0 – 100% Measuring parent’s and non–parent’s genetic causal beliefs and validation of child feeding questionnaire. Open in a new tab Note. [1] Persky et al., 2019. [2] Rose et al., 2019 . [3] Telaak et al., 2023 . [4] Martingano et al., 2023 . [5] Study 1, current manuscript. We conducted a random effects meta-analysis (DerSimonian & Laird, 1986) using Comprehensive Meta-Analysis Version 4 (Borenstein et al., 2022) to determine the overall influence of number of children on endorsement of genetic causes of attributes. We converted correlations into Hedge’s g for analysis. When multiple attributes were included in a single study, we computed a combined mean effect size and a combined variance (based on a correlation of 0.5) and entered this into the meta-analysis. This is a conservative approach to ensure that our analyses did not assign more weight to any study simply because it has more outcome measures. We provide a public copy of the CMAv4 datafiles and Excel files on OSF to allow readers to replicate these analyses. Measures Heritability Estimates. For studies B, C, and D participants were asked to estimate to what extent each attribute is caused by genetics. They were asked to answer in terms of a percentage, with 0% meaning there is no genetic influence and 100% meaning that it is entirely dependent on someone’s genes. For studies A and C participants were asked to what extent they agreed that genetics cause or contribute to a person’s risk for getting [attribute]. Participants responded on a 7-point scale from 1 (strongly disagree) to 7 (strongly agree). Parent Age and Gender. Participants were asked to report their own age and gender. Number and Age of Children. Participants were asked whether they were parents, and if so, how many children they had. Non-parents were assumed to have 0 children. In studies A, D, and E participants were also asked to report the age(s) of their children and mean child age was calculated by averaging the ages of all children. Participants Studies B, C, D, and E were conducted online and used crowdsourcing platforms and participant registries to recruit participants. Study A was a lab-based study (see original papers for details). See Table 5 for participant demographics. Table 5. Participant Demographics for Study 2. Study Total N Mean Age (SD) Gender N (%) Number of Children N (%) Child Mean Age (SD) Men Women Non-binary 0 1 2 3+ Study A 190 37.7 (5.7) 126 (66%) 64 (34%) 0 (0%) 0 (0%) 60 (32%) 81 (43%) 49 (26%) 6.04 (2.58) Study B 853 39.7 (13.8) 373 (44%) 478 (56%) 2 (0.2%) 433 (51%) 154 (18%) 153 (18%) 113 (13%) - Study C 968 41.3 (13.0) 425 (44%) 535 (55%) 8 (0.8%) 493 (51%) 141 (15%) 201 (21%) 133 (14%) - Study D 590 34.0 (11.5) 254 (43%) 322 (54%) 14 (3%) 399 (68%) 79 (13%) 74 (13%) 38 (6%) 13.2 (10.3) Study E 720 40.8 (12.3) 310 (43%) 403 (56%) 7 (1%) 278 (39%) 163 (23%) 185 (26%) 94 (13%) 13.4(11.0) Open in a new tab Results Overall, there was a weak, yet statistically significant, positive relationship between the number of children participants had and their endorsement of genetic causes of attributes ( g = 0.11, 95% CI [−0.16, 0.38], p = .007, k = 5). We can estimate that the prediction interval is −0.162 to 0.378. The true effect size in 95% of all comparable populations falls in this interval. However, it is important to note that this analysis includes only five studies. As a general rule, estimates of heterogeneity based on less than ten studies are not likely to be reliable. (Borenstein, 2019; 2020). Moderator (Meta-Regression) Analyses We examined two study-level moderators: mean parent age and percentage of women in the sample. Meta-regressions were conducted with these variables entered as continuous predictors of the Hedge’s g relationship between the number of children and attribution of genetic causes. These tests reflect between-study rather than within-study moderation. Parent Age and Gender. Meta-regression analyses were performed to determine if differences in parent demographic characteristics across studies predicted attributions of genetic causes. These analyses revealed no significant moderating effect for parent age ( B < − 0.01, p = .976, k =5), nor parent gender ( B < 0.01, p = .484, k =5). It was not possible to conduct a meta-regression with child age as too few studies collected this information ( k = 3). Moderator (Sub-Group) Analyses Type of Attribute. To explore whether parenthood impacts attributions of genetic causes differently depending upon the attribute in question we conducted subgroup moderator analyses. For this analysis, only traits for which there were two or more effect size estimates available were included (i.e., cardiovascular disease, obesity, osteoporosis, stroke, type 1 diabetes, type 2 diabetes, and weight). In order to conduct this analysis, we deviated from our conservative approach, as study data needed to be entered into multiple subgroups simultaneously, which may overestimate the precision of the effects, readers are therefore encouraged to interpret these results as preliminary. This analysis indicated that the impact of parenthood on heritability estimates differed between attributes ( Q (6) = 16.93, p =.010). There was a significant positive relationship between number of children and attributions of genetic causes for cardiovascular disease ( r = 0.11, p = .001, k = 2), obesity ( r = 0.12, p = .046, k = 2), and type 2 diabetes ( r = 0.11, p = .005, k = 3). Attributions of genetic causes for all other traits and diseases included in this analysis were not significantly related to number of children ( p > 0.05). These results are similar to those of Study 1, indicating that parenthood increases endorsement of genetic causes, but only for specific attributes. Discussion Our findings from two studies, including a mini-meta-analysis, provide compelling evidence that parenthood is significantly associated with beliefs about the heritability of specific health attributes. Our results consistently indicate that parents are more likely to endorse genetic causes for obesity and type 2 diabetes. Given that the heritability of these diseases is generally underestimated by the public, this may explain why previous research found mothers to be more accurate in their heritability estimates for these diseases ( Willoughby et al., 2019 ). We also found complementary, albeit less consistent, evidence for the impact of motherhood on the endorsement of genetic causes for other related attributes, such as cardiovascular disease and weight. It appears that attributions about so called “lifestyle diseases” may be uniquely influenced by parenthood and prompts consideration of parent’ shared life experiences that might impact beliefs about these health attributes in particular. Our results align with Social Cognitive Theory ( Bandura & NIMH, 1986 ), reinforcing the idea that parents form beliefs through observation and experience. Parenthood likely influences heritability beliefs through the direct observation of children’s traits. Parents may rely on their lived experience of raising children to develop more nuanced views on heritability. Compared to parents, non-parents, or those who are not yet parents, may expect to have more control over their children’s attributes than they actually do, thereby underestimating the role genetics plays. In reality, parents often find that despite consistent parenting efforts, children may exhibit behaviors or traits that are difficult to explain through environmental factors alone. Parenthood offers a unique vantage point for this type of learning, providing experiences that help solidify beliefs about the extent of genetic influence, particularly when environmental factors alone do not explain a child’s traits or behaviors. It is notable that we found this relationship with parenthood for so-called “lifestyle diseases” of obesity and type 2 diabetes which are both perceived to be highly related to one’s diet ( Rose et al., 2019 ; Pearl & Lebowitz, 2014 ). It seems plausible that parents, through their observations of their children’s behaviors, form genetic beliefs that are most applicable to conditions with behavioral influences, as these are easier to observe. Many dietary preferences emerge in early childhood which may also heighten the likelihood of invoking genes in their explanation ( Webber, Cooke & Wardle, 2010 ). It is also important to note that these diseases and their lifestyle correlates are highly stigmatized and are a source of perceived judgement by parents (Pescud & Pettigrew, 2012; Davis et al., 2018 ), , which may offer an alternative explanation for the results. Namely, attributing these diseases to genetics can help parents make sense of their children’s development and alleviate some of the frustration or guilt they may feel when they cannot control their children’s dietary behaviors to the extent outsiders might expect. This aligns with research on motivated cognition, which suggests that belief systems are influenced by individuals’ psychological needs ( Keller, 2005 ). It follows that the impact of motivated cognition would be especially strong for highly stigmatized traits, such as obesity and type 2 diabetes, where parents might feel judged for not doing enough prevention ( Puhl & Heuer, 2010 ). This could even include situations wherein one’s child does not currently exhibit these traits, given the future-risk focused nature of messaging about obesity prevention in children ( Puhl, Peterson, & Luedicke, 2013 ), and the guilt-laden nature of child feeding ( Yaremych & Persky, 2023 ). In this way, our results align with previous research demonstrating that genetic attributions can be driven by a desire to alleviate feelings of responsibility or guilt over traits perceived as being out of one’s control ( Condit, 2011 ; Smerecnik et al., 2009). This is also consistent with work by Webber and colleagues (2010) who found that mothers tend to attribute differences in eating behavior between siblings to genetic factors, but attribute feeding difficulties in others’ children to parenting behavior ( Webber, Cooke & Wardle, 2010 ). Our results are less consistent with the idea of parenthood enhancing essentialist beliefs, which emphasize inherent, immutable traits ( Gelman, 2003 ; Heine et al., 2019 ). Essentialism would suggest an overestimation of genetic causes across various attributes, yet in our studies, the pattern of higher heritability attribution was limited to traits that are generally underestimated by the public, such as obesity and diabetes. The lack of evidence for essentialist thinking in other attributes suggests that the observed increase in genetic attributions may be more contextual or situational—shaped by specific caregiving experiences ( Hutchinson et al., 2022 )—rather than indicative of a general tendency toward essentialism among parents. In regards to parent gender, our exploratory analyses found some evidence that women made higher heritability attributions than men. However, we did not find gender effects in Study 2. A stronger trend for mothers was also obtained in previous research ( Willoughby et al., 2019 ). It therefore remains possible, and perhaps even likely, that gender moderates the impact of parenthood on heritability estimates and that our moderator analyses in Study 2 were simply unable to detect this effect. Adherence to traditional gender roles may exacerbate these gender differences, which is a variable we were unable to control for in either of our studies. We consider it likely that the gender effects we found are driven by women’s caregiver roles as women continue to generally be the primary caretaker of children in the USA ( Catalano Weeks & Ruppanner, 2024 ; Lyttelton et al., 2022 ). The extent to which parents directly observe their children as an active caregiver should be explored as a potential moderator in future research. Interestingly, we did not find significant effects of child age on parents’ attributions of genetic causes in either of our studies. This suggests that the impact of parenthood on genetic attributions might not be driven by observing children at specific stages of development or by a need to understand changes throughout development, but rather by the simple experience of parenthood itself. This is consistent with research finding that teachers’ views on heritability do not differ depending on the age of the children they teach (Crosswaite & Asbury, 2018). It is important to note that data are correlational, and although we argue that parenthood might reasonably influence genetic attributions, our results do not allow us to conclude this definitively. Longitudinal data would be necessary to make robust claims. Additionally, our results are limited to the U.S. populations from which they were collected. The experience of parenthood varies dramatically across cultures ( Zhou & Chung, 2022 ), and to the extent that our results are a product of the experience of being a parent, we would not necessarily expect them to replicate cross-culturally. Moreover, our samples were overwhelmingly white, employed, and educated. This represents a niche experience of parenthood, unique in several ways, including likely greater access to formal childcare ( Bratsch-Hines et al., 2016 ; Van Lancker & Pavolini, 2023 ) and medical assistance ( Guerra-Reyes & Hamilton, 2016 ; Lee et al., 2021 ; Thomas, 2022 ), but potentially less close-knit community and shared childrearing responsibilities (Lansford, 2023). As we do not yet know what aspects of parenthood, if any, are driving our results, it is impossible to know to what extent they might replicate in other populations. Additionally, the inclusion of non-biological children in analysis, although rare, might have introduced some variability in our results, potentially weakening the observed effects. Further work to elucidate mechanisms may reveal important targets for intervention to support parents and promote healthy behavior. For example, studying beliefs about inheritance patterns in siblings, attitudes toward child health behavior, and concerns about self-blame and judgment could help to elucidate underlying processes in what is likely a multifaceted phenomenon. Conclusion Our research highlights that parenthood significantly shape beliefs about the genetic causation of so-called “lifestyle diseases.” Parents, and perhaps mothers in particular, are more likely to attribute genetic causes to diseases such as obesity and diabetes. These results emphasize the importance of life experiences in shaping genetic attributions and contribute to the broader literature on health belief models. Importantly, our findings suggest that concerns about fatalism or the “over-estimation” of genetic influences may not be as prominent among parents, as their increased genetic attributions were limited to conditions generally underestimated by the public. Taken together, our results are most supportive of observation-based and motivated cognition-based explanations for the relationships revealed here, however, mechanisms may vary depending on contextual factors and warrant more research. Our results direct future researchers to investigate the shared experiences of parents that may be critical in shaping genetic attributions. By addressing the underlying mechanisms behind genetic attribution, we can better inform public health strategies and educational efforts aimed at promoting health. Supplementary Material 1 NIHMS2089424-supplement-1.docx (18.6KB, docx) Figure 1. Open in a new tab Forest plot depicting the relationship between number of children and endorsement attributions of genetic causes across five studies. Highlights. Parents provide higher estimates of genetic influence for a variety of traits. Parent’s heritability estimates are particularly high for “lifestyle diseases”. These heritability estimates reflect a more accurate view of genetic influences. Footnotes We have no known conflicts of interest to disclose Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. This study was subject to ethical review by the National Institutes of Health IRB. All data and code required to replicate analyses as well as the Study 1 preregistration is available via https://osf.io/9hwd3/?view_only=dd241394b9a145eb9f6ef9e1184da82a References Aquilino WS (1997). 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