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.

Burt's argument relies on a motte-and-bailey fallacy. Burt aims to argue against the value of genetics for social science; instead she argues against certain interpretations of a specific kind of genetics tool, polygenic scores (PGSs). The limitations, previously identified by behavioural geneticists including ourselves, do not negate the value of PGSs, let alone genetics in general, for social science.

We contend that social science variables are the product of multiple partly heritable traits. Genetic associations with socioeconomic status (SES) may differ across populations, but this is a consequence of the intermediary traits associated with SES differences also varying. Furthermore, genetic data allow social scientists to make causal statements regarding the aetiology and consequences of SES.

It is a hotly contested issue whether polygenic scores should play a major role in the social sciences. Here, we defend a methodologically pluralist stance in which sociogenomics should abandon its hype and recognize that it suffers from all the methodological difficulties of the social sciences, yet nevertheless maintain an optimistic stance toward a more cautious use.

Although Burt provides a valuable critique of the scientific value of integrating genetic data into social science research, she reinforces rather than disrupts the age-old horserace between genetic effects and environmental effects. We must move past this false dichotomy to create a new ontology that recognizes the ways in which genetic and environmental processes are inextricably intertwined.

Polygenic score (PGS) computations assume an additive model of gene action because associations between phenotypes and alleles at different loci are compounded, ignoring interactions between alleles or loci let alone between genotype and environment. Consequently, PGSs are subject to the same objections that invalidated traditional heritability analyses in the 1970s. Thus, PGSs should not be used in the social sciences.

Burt formulates her critique at a general level of abstraction that highlights the methodological deficiencies of sociogenomics without also calling attention to precisely the same deficiencies in the social science model she seeks to defend against its encroachments. What might have been a methodological bulwark against the excesses of sociogenomics is instead a one-sided critique that merely renews its charter.

This commentary emphasizes two problem areas mentioned by Burt. First, that within-family designs do not eradicate stratification confounds. Second, that the linear/additive model of genetic causes of form and variation is not supported by recent progress in molecular biology. It concludes with an appeal for a (biologically and psychologically) more realistic model of such causes.

The critique of the genetics of complex social outcomes is partly well-founded, insofar as social outcomes sometimes have unreliable relations with cognitive traits. But the correct conclusion is not to dismiss the entire field altogether. Rather, the implication is to redirect geneticists' attention to the stable cognitive phenotypes that are natural candidates for genetic analysis.

Genetic studies in the social sciences could be augmented through the additional consideration of functional (transcriptome, methylome, metabolome) and/or multimodal genetic data when attempting to understand the genetics of social phenomena. Understanding the biological pathways linking genetics and the environment will allow scientists to better evaluate the functional importance of polygenic scores.

We sympathize with many of the points Burt makes in challenging the value of genetics to advance our understanding of social science. Here, we discuss how recent reflections on epistemic validity in the behavioral sciences can further contribute to a reappraisal of the role of sociogenomics to explain and predict human traits, aptitudes, and achievement.

Polygenic scores cannot elucidate the mechanisms that produce behavioral phenotypes (including “intelligence”). Therefore, they are unlikely to yield helpful interventions. Moreover, they are poor predictors of individuals' developmental outcomes. Burt's critique is well-supported by the details of molecular biology. Specifically, experiences affect epigenetic factors that influence phenotypes via how the genome functions, a fact that lends support to Burt's conclusions.

This commentary expands on Burt's concept of downward causation to include any association between genomic variants and a given outcome that is forged through social practices rather than biochemical pathways. It proposes the social stratification of population, through which endogamy over a period of generations produces allele frequency differences between socioeconomic strata, as a mechanism of downward causation.

Influences on social traits involve a tangled interplay of genetic, social, and environmental factors. Moreover, there is increasing awareness that gene–environment correlations are real and potentially measurable. Such gene–environment correlations can mislead if they are uncontrolled and genetic associations are interpreted as being purely because of direct genetic effects. This complexity is cause for more and better investigation, not a reason to refrain from researching one of the potentially important factors (genetics) influencing trait variation.

Burt's target article oddly misses the important intellectual contribution of sociogenomics to our understanding of genetic evolution in contemporary human populations. Although social scientists' immediate research agendas are often not evolutionary in nature, I call for a better appreciation of the role of sociogenomics in answering important evolutionary questions.

We generally agree with Burt's thesis. However, we note that the author did not discuss epigenetics, the study of how the environment can alter gene structure and function. Given epigenetic mechanisms, the utility of polygenic risk scores (PRS) is limited in studies of development and mental illness. Finally, in this commentary we expand upon the risks of reliance upon PRSs.

This commentary is a call to action for researchers to create and use genome-wide association studies (GWASs) with previously missed age groups (e.g., infancy, elderly), which will improve our ability to ask important developmental questions using genetic data to trace pathways across the lifespan.

Polygenic scores (PGSs) have several limitations. They are confounded with environmental effects on behavior and cannot be used to study how mutations affect brain function and behavior. For this, mutations with large effects, which often arise in only one geographical population are needed. Genome-wide association studies (GWASs), commonly used for identifying mutations, have difficulty detecting these mutations. A strategy that overcomes this challenge is discussed.

This commentary seeks to briefly outline a clear-eyed middle ground between Burt's claims that the inclusion of polygenic scores (PGSs) is essentially useless for social science and proponents' vast overstatements and over-interpretations of these scores. Current practice of including PGSs in social science is often wrong but sometimes useful.

The problems with polygenic scores (PGSs) have been understated. The fact that they are ancestry-specific means that biases related to sociodemographic factors would be impossible to avoid. Additionally, the requirement to obtain DNA would have profound impacts on study design and required resources, as well as likely introducing recruitment bias. PGSs are unhelpful for social science research.