Lottery, luck, or legacy. A review of "The Genetic Lottery: Why DNA matters for social equality"

Alternating between even and odd ploidy levels switches on and off the recombination control, even near the centromeres

Meiotic recombination is a key biological process in plant evolution and breeding, as it generates genetic diversity in each generation through the formation of crossovers (COs). However, due to their importance in genome stability, COs are highly regulated in frequency and distribution. We previously demonstrated that this strict regulation of COs can be modified, both in terms of CO frequency and distribution, in allotriploid Brassica hybrids (2n = 3x = 29; AAC) resulting from a cross between Brassica napus (2n = 4x = 38; AACC) and Brassica rapa (2n = 2x = 20; AA). Using the recently updated B. napus genome now including pericentromeres, we demonstrated that COs occur in these cold regions in allotriploids, as close as 375 kb from the centromere. Reverse transcription quantitative PCR (RT-qPCR) of various meiotic genes indicated that Class I COs are likely involved in the increased recombination frequency observed in allotriploids. We also demonstrated that this modified recombination landscape can be maintained via successive generations of allotriploidy (odd ploidy level). This deregulated meiotic behavior reverts to strict regulation in allotetraploid (even ploidy level) progeny in the second generation. Overall, we provide an easy way to manipulate tight recombination control in a polyploid crop.

The clinical, mechanistic, and social impacts of air pollution on atopic dermatitis

Atopic dermatitis (AD) is a complex disease characterized by dry, pruritic skin and significant atopic and psychological sequelae. Although AD has always been viewed as multifactorial, early research was dominated by overlapping genetic determinist views of either innate barrier defects leading to inflammation or innate inflammation eroding skin barrier function. Since 1970, however, the incidence of AD in the United States has increased at a pace that far exceeds genetic drift, thus suggesting a modern, environmental etiology. Another implicated factor is Staphylococcus aureus; however, a highly contagious microorganism is unlikely to be the primary etiology of a noncommunicable disease. Recently, the roles of the skin and gut microbiomes have received greater attention as potentially targetable drivers of AD. Here too, however, dysbiosis on a population scale would require induction by an environmental factor. In this review, we describe the evidence supporting the environmental hypothesis of AD etiology and detail the molecular mechanisms of each of the AD-relevant toxins. We also outline how a pollution-focused paradigm demands earnest engagement with environmental injustice if the field is to meaningfully address racial and geographic disparities. Identifying specific toxins and their mechanisms can also inform in-home and national mitigation strategies.

Confounding Fuels Misinterpretation in Human Genetics

The scientific literature has seen a resurgence of interest in genetic influences on human behavior and socioeconomic outcomes. Such studies face the central difficulty of distinguishing possible causal influences, in particular genetic and non-genetic ones. When confounding between possible influences is not rigorously addressed, it invites over- and misinterpretation of data. We illustrate the breadth of this problem through a discussion of the literature and a reanalysis of two examples. Clark (2023) suggested that patterns of similarity in social status between relatives indicate that social status is largely determined by one's DNA. We show that the paper's conclusions are based on the conflation of genetic and non-genetic transmission, such as wealth, within families. Song & Zhang (2024) posited that genetic variants underlying bisexual behavior are maintained in the population because they also affect risk-taking behavior, thereby conferring an evolutionary fitness advantage through increased sexual promiscuity. In this case, too, we show that possible explanations cannot be distinguished, but only one is chosen and presented as a conclusion. We discuss how issues of confounding apply more broadly to studies that claim to establish genetic underpinnings to human behavior and societal outcomes.

Polygenic Indices (a.k.a. Polygenic Scores) in Social Science: A Guide for Interpretation and Evaluation

Polygenic indices (PGI)-the new recommended label for polygenic scores (PGS) in social science-are genetic summary scales often used to represent an individual's liability for a disease, trait, or behavior based on the additive effects of measured genetic variants. Enthusiasm for linking genetic data with social outcomes and the inclusion of premade PGIs in social science datasets have facilitated increased uptake of PGIs in social science research-a trend that will likely continue. Yet, most social scientists lack the expertise to interpret and evaluate PGIs in social science research. Here, we provide a primer on PGIs for social scientists focusing on key concepts, unique statistical genetic considerations, and best practices in calculation, estimation, reporting, and interpretation. We summarize our recommended best practices as a checklist to aid social scientists in evaluating and interpreting studies with PGIs. We conclude by discussing the similarities between PGIs and standard social science scales and unique interpretative considerations.

Do Children Think it is Important to Predict Learning and Behaviour Problems, and Do They Think Genetic Screening Has a Role to Play in This?

This study explores how capable young children are of thinking about a potential future that uses DNA screening to assess an individual's likelihood of experiencing learning or behaviour difficulties. Puppets and a scenario-based approach were used to ask children aged 4-10 (n = 165) whether they thought DNA screening might be helpful or harmful. A content analysis derived six categories: (1) 'Worried about being - and being seen as - different'; (2) 'Beliefs about the origins of learning and behaviour'; (3) 'Testing is harmful'; (4) 'Testing could help'; (5) 'How soon is too soon for testing?'; and (6) 'What's the point?'. Findings indicate young children, as key stakeholders, can make useful contributions to public debate in this important and controversial area.

Interpreting population- and family-based genome-wide association studies in the presence of confounding

A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding and can also absorb the "indirect" genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect-size estimates are used in polygenic scores (PGSs). We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding.

Socioeconomic status and severe mental disorders: a bidirectional multivariable Mendelian randomisation study

BACKGROUND

Despite the evidence supporting the relationship between socioeconomic status (SES) and severe mental disorders (SMD), the directionality of the associations between income or education and mental disorders is still poorly understood.

OBJECTIVE

To investigate the potential bidirectional causal relationships between genetic liability to the two main components of SES (income and educational attainment (EA)) on three SMD: schizophrenia, bipolar disorder (BD) and depression.

METHODS

We performed a bidirectional, two-sample univariable Mendelian randomisation (UVMR) and multivariable Mendelian randomisation (MVMR) study using SES phenotypes (income, n=397 751 and EA, n=766 345) and SMD (schizophrenia, n=127 906; BD, n=51 710 and depression, n=500 119) genome-wide association studies summary-statistics to dissect the potential direct associations of income and EA with SMD.

FINDINGS

UVMR showed that genetic liability to higher income was associated with decreased risk of schizophrenia and depression, with a smaller reverse effect of schizophrenia and depression on income. Effects were comparable after adjusting for EA in the MVMR. UMVR showed bidirectional negative associations between genetic liability to EA and depression and positive associations between genetic liability to EA and BD, with no significant effects on schizophrenia. After accounting for income, MVMR showed a bidirectional positive direction between genetic liability to EA and BD and schizophrenia but not with depression.

CONCLUSIONS

Our results suggest a heterogeneous link pattern between SES and SMD. We found a negative bidirectional association between genetic liability to income and the risk of schizophrenia and depression. On the contrary, we found a positive bidirectional relationship of genetic liability to EA with schizophrenia and BD, which only becomes apparent after adjusting for income in the case of schizophrenia.

CLINICAL IMPLICATIONS

These findings shed light on the directional mechanisms between social determinants and mental disorders and suggest that income and EA should be studied separately in relation to mental illness.

Polygenic scores for social science: Clarification, consensus, and controversy

In this response, I focus on clarifying my arguments, highlighting consensus, and addressing competing views about the utility of polygenic scores (PGSs) for social science. I also discuss an assortment of expansions to my arguments and suggest alternative approaches. I conclude by reiterating the need for caution and appropriate scientific skepticism.

Drowning in shallow causality

It has been known for decades that inference concerning genetic causes of human behavioral phenotypes cannot be legitimately made from correlations among relatives. We claim that these inferential difficulties cannot be overcome by assigning different names to causes inferred from within-family and population-level genome-wide association studies (GWASs). For educational attainment, for example, unraveling gene-environment interactions requires more than new names for causes.

All that glisters is not gold: Genetics and social science

In their target article, Madole & Harden offer an account of "what it means for genes to be causes" of social outcomes to bolster their claim that genetics should be incorporated into social science with practical implications. Here I object to several key features of their arguments, their representation of the state of science, and claims about the utility of genetics for social science and society.

Amplification is the primary mode of gene-by-sex interaction in complex human traits

Sex differences in complex traits are suspected to be in part due to widespread gene-by-sex interactions (GxSex), but empirical evidence has been elusive. Here, we infer the mixture of ways in which polygenic effects on physiological traits covary between males and females. We find that GxSex is pervasive but acts primarily through systematic sex differences in the magnitude of many genetic effects ("amplification") rather than in the identity of causal variants. Amplification patterns account for sex differences in trait variance. In some cases, testosterone may mediate amplification. Finally, we develop a population-genetic test linking GxSex to contemporary natural selection and find evidence of sexually antagonistic selection on variants affecting testosterone levels. Our results suggest that amplification of polygenic effects is a common mode of GxSex that may contribute to sex differences and fuel their evolution.

Interpreting population and family-based genome-wide association studies in the presence of confounding

A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding, and can also absorb the 'indirect' genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of Mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect size estimates are used in polygenic scores. We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. In addition to known biases that can arise in family-based GWASs when interactions between family members are ignored, we show that biases can also arise from gene-by-environment (G×E) interactions when parental genotypes are not distributed identically across interacting environmental and genetic backgrounds. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding and interactions.

Luck, lottery, or legacy? The problem of confounding. A reply to Harden

A reply to Harden's response to Coop and Przeworski (2022).

Challenging the utility of polygenic scores for social science: Environmental confounding, downward causation, and unknown biology

The sociogenomics revolution is upon us, we are told. Whether revolutionary or not, sociogenomics is poised to flourish given the ease of incorporating polygenic scores (or PGSs) as "genetic propensities" for complex traits into social science research. Pointing to evidence of ubiquitous heritability and the accessibility of genetic data, scholars have argued that social scientists not only have an opportunity but a duty to add PGSs to social science research. Social science research that ignores genetics is, some proponents argue, at best partial and likely scientifically flawed, misleading, and wasteful. Here, I challenge arguments about the value of genetics for social science and with it the claimed necessity of incorporating PGSs into social science models as measures of genetic influences. In so doing, I discuss the impracticability of distinguishing genetic influences from environmental influences because of non-causal gene-environment correlations, especially population stratification, familial confounding, and downward causation. I explain how environmental effects masquerade as genetic influences in PGSs, which undermines their raison d'être as measures of genetic propensity, especially for complex socially contingent behaviors that are the subject of sociogenomics. Additionally, I draw attention to the partial, unknown biology, while highlighting the persistence of an implicit, unavoidable reductionist genes versus environments approach. Leaving sociopolitical and ethical concerns aside, I argue that the potential scientific rewards of adding PGSs to social science are few and greatly overstated and the scientific costs, which include obscuring structural disadvantages and cultural influences, outweigh these meager benefits for most social science applications.