2.1 Basic genetic concepts in sociogenomics

See Table 1.

Table 1. Glossary, acronyms, and definitions for sociogenomics terms

* The term copy number variant used to be applied to all variants that had a variable number of tandem repeats, including short tandem repeats, such as the microsatellite in (D) where there are 12 or 11 copies of the CA dinucleotide. In genome sequencing projects, the term is reserved for large size changes only, such as variable numbers of repeats exceeding 50 nucleotides in the case of the 1000 Genomes Project. (Strachan & Read, Reference Strachan and Read2018)

2.2 Genetic variants, function, and prevalence

Given that sociogenomics focuses on genetic variation among people, understanding the type, prevalence, and distribution of human variation is necessary to understand what is and is not being captured in these studies. Genetic variants can be classified into three types: (1) single-nucleotide variants (SNVs), which are single-base changes (G → A); (2) indels, which are insertions of base pairs or deletions up to 50 bp and often involve tandem repeating units (e.g., GATA repeated 2–8 times); and, (3) structural variants (SVs), which are DNA rearrangements (deletions, duplications, or inversions) ranging from 50 bp to more than a million base pairs (1 Mbp). As discussed below, GWASs and PGSs analyze a subset of “common” single-nucleotide variants, known as single-nucleotide polymorphisms (SNPs), where common usually means present in at least 1% of the population (see Appendix A for more details).

Human genetic variation is extensive – all genetic variants compatible with life are likely represented in some individual living today (McClellan & King, Reference McClellan and King2010). Comparing the genomes of any two humans around the world, we would typically find between 3 and 4.5 million genetic differences between them or approximately 1 variant every 800 bases.Footnote ² Most of these genetic variants are SNPs and are non-functional. That is, they have no effects on biological functioning or differences between people. Obviously, only functional variants contribute to differences between people. Although some genetic variation is debilitating, most genetic variation in a given genome is benign, ancient, and common.

In contrast, functional variants are those that either alter gene product (the protein produced) or gene dosage (e.g., the amount of protein produced). As an example of the former, the SLC24A5 gene encodes a protein involved in epidermal melanogenesis and skin pigmentation through its intracellular potassium-dependent exchanger activity (Ginger et al., Reference Ginger, Askew, Ogborne, Wilson, Ferdinando, Dadd and Green2008). Several thousand years ago, a G → A mutation in SLC24A5 occurred among people migrating from Africa to Europe. This variant, which changes the encoded amino acid from alanine to threonine, disrupts melanogenesis and thereby results in lighter skin tone (Lamason et al., Reference Lamason, Mohideen, Mest, Wong, Norton, Aros and Humbert2005). Other variants can affect function not by changing the protein produced but, for example, by affecting the binding sites for various RNAs in a manner that reduces or increases transcription and thereby contributes to trait differences by altering gene dosage (the production of too much or too little of the functional protein).

All three variant types can be functional and contribute to differences between people. Although rare compared to SNVs and indels, evidence suggests that SVs have a disproportionate role in shaping human differences compared to other variants (Chiang et al., Reference Chiang, Scott, Davis, Tsang, Li, Kim and Montgomery2017; Collins et al., Reference Collins, Brand, Karczewski, Zhao, Alföldi, Francioli and Wang2020; Takumi & Tamada, Reference Takumi and Tamada2018). SVs can involve multiple copies of genes or the deletion of a gene and thus influence gene dosage. Sudmant et al. (Reference Sudmant, Rausch, Gardner, Handsaker, Abyzov, Huddleston and Korbel2015) estimated that SVs were 50 times more likely than SNVs to affect gene expression and three times more likely to be associated with a trait difference than an SNV.

Despite being the extreme minority among the variants we carry, we all have thousands of functional variants in our genomes. A recent deep sequencing study of diverse ancestries identified approximately 11,700 functional variants per individual genome (Taliun et al., Reference Taliun, Harris, Kessler, Carlson, Szpiech, Torres and Kang2021). Another study of roughly half a million people in the United Kingdom, Backman et al. (Reference Backman, Li, Marcketta, Sun, Mbatchou, Kessler and Balasubramanian2021) observed an average of ~600 variants, including 50 putative loss-of-function (pLOFs) variants, per gene. Backman et al. (Reference Backman, Li, Marcketta, Sun, Mbatchou, Kessler and Balasubramanian2021) estimated that on average each of us carries 214 pLOF variants as “defective” gene copies. Although this variation is non-trivial, recall that we receive two copies of our genes (excepting the male-specific genes on the Y chromosome). In addition, a host of cellular mechanisms, including those shaping gene expression, compensate for many of these loss-of-function variants and facilitate robustness to functional mutations by, for example, up-regulating transcription (thereby producing more mRNA transcripts) and slowing the rate of mRNA decay (thereby increasing the ability of the cell to generate more polypeptides from the same mRNA transcript) (see Strachan & Read, Reference Strachan and Read2018).

In addition to the several million genetic variants passed down by each of our parents, we inherit roughly 30–80 new mutations that arise during meiosis. The human population explosion over the past several hundred years has produced an abundance of new mutations as rare variants. Rare variants are disproportionately deleterious. Fu et al. (Reference Fu, O'connor, Jun, Kang, Abecasis, Leal and Shendure2013) estimated that ~86% of all deleterious SNVs are rare and recent. Many of these variants are found in only a handful of related people and are not represented in population samples. As discussed later, despite their prevalence and disease-relevance, rare variants pose a challenge for GWASs.

2.3 A brief note on ancestry and continental populations

Most sociogenomics studies at least briefly discuss ancestry and issues related thereto. A basic understanding of what this refers to is helpful (for a social science discussion, see Herd et al., Reference Herd, Mills and Dowd2021). Modern humans are, of course, a single species, which emerged some 550–750 thousand years ago (Fu et al., Reference Fu, Posth, Hajdinjak, Petr, Mallick, Fernandes and Mittnik2016). Although terminology varies, several population genetic studies classify humans roughly into five continental populations: African (AFR), European (EUR), East Asian (EAS), South Asian (SAS), and American (AMR), differentiated by their continental migration out of Africa within the last 100,000 years (The 1000 Genomes Project Consortium, 2015). Importantly, these populations are abstractions from an underlying continuum of genetic relatedness and should not be thought of as genetically distinct subpopulations (Coop & Przeworski, Reference Coop and Przeworski2022; Feldman, Lewontin, & King, Reference Feldman, Lewontin and King2003).

The vast majority of variants in an individual's genome are shared by all continental populations (The 1000 Genomes Project Consortium, 2015). Only a small proportion of the variants in an individual genome are restricted to one continental population, and these tend to be recent mutations that are also rare in the populations in which they are found. However, allele frequencies for common variants do differ across groups because of population patterns of migration and mating, shaped by physical boundaries and sociocultural influences. Furthermore, allele frequencies vary in a more fine-grained manner across subgroups of populations, especially for rare variants (Mathieson & Mcvean, Reference Mathieson and Mcvean2012). As discussed later, this variation in mostly random allele frequencies across difference groups poses a major challenge for GWASs by inducing or inflating genetic associations through confounding between genotypes and outcomes (e.g., Berg et al., Reference Berg, Harpak, Sinnott-Armstrong, Joergensen, Mostafavi, Field and Coop2019; Morris, Davies, Hemani, & Smith, Reference Morris, Davies, Hemani and Smith2020a).

3.1 What genetic differences are measured?

The complexity of GWASs/PGSs and the way that they are discussed can produce confusion over what is measured in these studies. Readers can be excused from thinking that these studies measure genes and/or causal variants that shape differences through some known biological pathway. The abstract of a recent study, for example, referenced “mothers with more education-related genes” (Armstrong-Carter et al., Reference Armstrong-Carter, Trejo, Hill, Crossley, Mason and Domingue2020). Genes are not measured in these studies. Rather, these studies measure and analyze a select subset of one form of variation in the genome: Single-nucleotide polymorphisms (SNPs) that have two alleles (e.g., A or C) (see Appendix A for a detailed discussion).Footnote ³ In this section, I describe with as much simplicity as possible what is measured in GWASs/PGSs and why. Although intricate, understanding what GWASs/PGSs do measure (SNPs) and that they do not measure (genes or causal variants) is necessary to understand the inherent limitations with this approach.

The GWAS methodology is rooted in the blocklike structure of our genome. Although technical detail is out of scope, we inherit whole chromosomes from each parent, but these chromosomes are composed of unique blends of blocks of our parents' maternal and paternal chromosomes created during the process of “crossing over” (or genetic recombination). Each chromosome we inherit is a unique blend of our parents' matching chromosomes, created when segments are exchanged in meiosis (an average of 1.5 blocks of exchange per chromosome). Helpfully, crossing over does not occur randomly across the genome but tends to occur in 1–2 kb regions, known as recombination hotspots, which occur every 50–100 kb across the genome (Myers, Bottolo, Freeman, McVean, & Donnelly, Reference Myers, Bottolo, Freeman, McVean and Donnelly2005). Consequently, blocks of chromosomal segments are passed down across many generations unbroken by recombination, and, by dint of being passed down unbroken, contain correlated SNPs (i.e., SNPs that are not inherited independently). These chromosomal segments that exist between recombination hotspots are known as haplotype blocks. The association between SNPs on a haplotype is known as linkage disequilibrium (LD) and exists as a matter of degree (as a correlation).

This haplotype structure of our genome means that there is much less variability between genomes than would occur from the random assortment of SNPs. For example, the average haplotype block contains ~50 SNPs, which would, in theory, allow 2⁵⁰ different combinations. Typically, however, most haplotype (>90%) blocks will be characterized by six or fewer combinations of alleles (The International HapMap Consortium, 2005). The combination of alleles on a haplotype block is known as haplotype and represents ancestral segments defined by common, ancient SNPs. Rarer variation exists as heterogeneity around the SNPs that define haplotype blocks (Strachan & Read, Reference Strachan and Read2018).

This haplotype structure of our genomes undergirds the GWAS methodology. Measuring and testing each of our 3 bn base pairs is impracticable. Instead, GWASs analyze a smaller number of SNPs from across the genome to tag regions of common variation (i.e., haplotypes). Contemporary GWASs scan the genome for associations between several millions of these preselected SNPs, known as “tag SNPs” and a trait. Significant SNP associations mark a genomic region (“genomic risk locus” or quantitative trait locus, QTL) in which an unknown causal variant(s) driving the association is presumed to lie. Tag SNPs are thus usually non-functional, common variants used as proxies for some unknown causal variant(s) in proximity (with which they are in LD). Proximity is relative and varying. Genomic risk loci can range in size from several hundred thousands to more than 1 Mbp.

Crucially, rare and more likely deleterious variants are not well tagged by SNPs, given that SNPs tag haplotypes defined by shared common variants, and most haplotypes will not contain the rare variants (or they wouldn't be rare)Footnote ⁴ (Backman et al., Reference Backman, Li, Marcketta, Sun, Mbatchou, Kessler and Balasubramanian2021; McClellan & King, Reference McClellan and King2010; Tam et al., Reference Tam, Patel, Turcotte, Bossé, Paré and Meyre2019). Additionally, other variant forms – indels, copy number variants (CNVs), and SVs – are not measured in GWASs, and many are not well-tagged by common SNPs (Backman et al., Reference Backman, Li, Marcketta, Sun, Mbatchou, Kessler and Balasubramanian2021; Tam et al., Reference Tam, Patel, Turcotte, Bossé, Paré and Meyre2019).

Additionally, because different ancestral groups can have different allele frequencies, different patterns of LD, and somewhat different haplotypes, tag SNPs often do not work in the same way across populations, even when the causal variant is the same (Martin et al., Reference Martin, Gignoux, Walters, Wojcik, Neale, Gravel and Kenny2017; Peterson et al., Reference Peterson, Kuchenbaecker, Walters, Chen, Popejoy, Periyasamy and Brick2019). This ancestral variation in LD and haplotypes is one biological reason why GWAS findings do not “port well” or generalize across ancestral groups (e.g., Mostafavi et al., Reference Mostafavi, Harpak, Agarwal, Conley, Pritchard and Przeworski2020).

The haplotype structure of our genome also enables GWASs by facilitating imputation. GWASs rely on large samples; however, studies vary in the genotyping platforms they use, which measure somewhat different SNPs, and contain missing data. Knowledge of haplotypes allows the probabilistic imputation of missing or untyped genotypes at adjacent SNPs using more densely genotyped samples or whole-genome reference panels.Footnote ⁵ Most genotype arrays now measure between 500,000 and 2 million SNPs, and most contemporary GWASs now include ~10 million SNPs, most imputed (Tam et al., Reference Tam, Patel, Turcotte, Bossé, Paré and Meyre2019).

The original aim of GWASs was to understand the underlying molecular basis of trait variation by tracing causal pathways from genetic variants to outcomes. The idea was that tag SNPs could be used to mark risk loci that could be followed up with fine-mapping and functional annotation to identify causal variants in genes with well-defined functions. Although GWASs have, in some cases, facilitated the identification of causal variants involved in disease pathogenesis, for reasons that are out of scope, biological interpretation is exceedingly difficult, in general, and even more so for complex social traits with increasingly numerous (>1,000) GWAS hits and miniscule effect sizes (see e.g., Backman et al., Reference Backman, Li, Marcketta, Sun, Mbatchou, Kessler and Balasubramanian2021; Crouch & Bodmer, Reference Crouch and Bodmer2020; Edwards, Beesley, French, & Dunning, Reference Edwards, Beesley, French and Dunning2013). Hence, sociogenomicists primarily use GWAS results for polygenic prediction, explicitly deemphasizing inquiry into causal variant(s) or biological pathways (but not always, see e.g., Ganna et al., Reference Ganna, Verweij, Nivard, Maier, Wedow, Busch and Lichtenstein2019). In what follows, I briefly describe the nuts and bolts of GWASs, this is followed by a discussion of PGS generation.

3.2 GWAS methodology

GWASs are a theory-free analytic approach to scan the genome for trait-associated tag SNPs. This involves testing, for each SNP one at a time, whether an allele (SNP variant) is more common in cases versus controls (or for continuous traits, across different levels). GWASs thus test for independence between genotype and outcome for each SNP, with a few controls (not including other SNPs). The 2018 educational attainment GWASs, for example, assessed whether allele frequencies – for roughly 10 million SNPs – differed (across groups stratified by) years of education (Lee et al., Reference Lee, Wedow, Okbay, Kong, Maghzian, Zacher and Linnér2018). Typically, the type of effect of interest in GWASs is variant substitution effects, which can be understood as the counterfactual change in an individual outcome that would occur from changing the individual's genotype for a particular SNP at conception (holding all else constant) (Freese, Reference Freese2008; Morris et al., Reference Morris, Davies, Hemani and Smith2020a). This counterfactual model assumes that genetic associations indicate a causal path from an individual's genotype (or allele dosage) to complex social traits, reflecting a variant substitution effect (Lawson et al., Reference Lawson, Davies, Haworth, Ashraf, Howe, Crawford and Timpson2020).

The basic form of GWASs is straightforward. Here I focus on these basics, including the familiar linear equation form that underlies the model. This model has been elaborated in recent years, but the underlying logic remains the same. Using bi-allelic SNPs and assuming additive SNP effects, genotypes for a particular SNP (e.g., AA, AC, CC) are translated to numeric allele dosage effects by counting the number of minor (or effect) alleles (0, 1, or 2) for each individual. Allele dosages for each SNP are the focal independent variable in each of these millions of regressions (again, one for each SNP examined separately), which take the following general form:

$$\eqalign{Y =& \beta _0 + \beta _1 \times {\rm SNP} + \beta _{2\;} \times {\rm Sex} + \beta _{3\;} \times {\rm Age} + \beta _{4\;}\cr &\times PC1 \ldots \;\beta _{14\;} \times PC10 + e}$$

where Y is a continuous variable (e.g., years of education), and SNP represents the allele dosage measure, controlling for age, sex, and usually 10–20 genetic ancestry principal components (PCs, discussed shortly). The outcome of interest in this model is β ₁ – the effect size for each SNP – which can be interpreted as the marginal effect of having one more minor allele (a unit increase in allele dosage) and its associated p-value. For binary outcomes, this would just approximate the form of a familiar logistic regression model. These results for the millions of separate regressions are automatically compiled into results by modern computational programming software, such as PLINK (Purcell et al., Reference Purcell, Neale, Todd-Brown, Thomas, Ferreira, Bender and Daly2007) and METAL (Willer, Li, & Abecasis, Reference Willer, Li and Abecasis2010). Focal GWAS results, as the SNP effect size estimates and p-values, are known as summary statistics, which provide the input for further analyses. Summary statistics are often considered the “data” in GWASs even as these are more accurately referred to as the results (of the first step of the analysis) (Burt & Munafò, Reference Burt and Munafò2021).

Following the estimation of the GWASs from the primary study sample or the “discovery” sample, a number of diagnostic tests (e.g., Manhattan and QQ-plots, which display p-values on a −log₁₀ scale) are performed (see Choi, Mak, & O'Reilly, Reference Choi, Mak and O'Reilly2020; Schaid, Chen, & Larson, Reference Schaid, Chen and Larson2018). Because of LD (non-independence among SNPs sharing a haplotype) and the examination of each SNP separately, there will invariably be multiple (even dozens of) SNPs marking a risk locus. Thus, follow-up analyses (e.g., clumping and thresholding) are conducted to define clusters of SNPs in high LD (often high LD is defined as r ² > 0.1Footnote ⁶) and to identify a single “lead SNP,” usually the SNP with the lowest p-value, to represent this clump and mark a risk locus. In this way, risk loci (or QTLs) are defined as trait-associated regions marked by approximately independent (“lead”) SNPs.

As noted, risk loci range in size from ~50 kbp to over 1 Mbp (e.g., Lee et al., Reference Lee, Wedow, Okbay, Kong, Maghzian, Zacher and Linnér2018). Thus, a GWAS that reports 1,237 lead SNPs can thus be understood as identifying 1,237 approximately independent risk loci defined by a lead SNP and in which the causal variant(s) responsible for the association is presumed to lie. Such risk loci, which often stretch across multiple haplotypes, usually contain thousands of SNVs along with SVs and indels, and often multiple genes (hence the difficulty of biological interpretation).

Importantly, lead SNPs for complex social traits are invariably very weakly associated with an outcome, usually accounting for less than 0.01% of the variation. In their educational attainment study, for example, Lee et al. (Reference Lee, Wedow, Okbay, Kong, Maghzian, Zacher and Linnér2018) reported that “the median effect size of the lead SNPs corresponds to 1.7 weeks of schooling per allele.” Similarly, among the five lead SNPs identified in their study of “non-heterosexuality” in the UK Biobank, Ganna et al. (Reference Ganna, Verweij, Nivard, Maier, Wedow, Busch and Lichtenstein2019) observed “very small effects”; “males with a GT genotype at the rs34730029 locus had 0.4% higher prevalence of same-sex sexual behavior than those with a TT genotype (4.0 vs. 3.6%)” (p. 3). Given the impracticability of biological interpretation and the weak prediction from any single variant or QTL, researchers have shifted to creating genetic summary scores that aggregate SNPs weighted by their effect sizes, discussed next.

3.3 Polygenic score (PGS) construction

Calculating PGSs (also called polygenic risk scores [PRSs] or genetic risk scores [GRSs], usually when referring to adverse biomedical outcomes) is now a common application of GWASs to predict complex traits (or disease risk) from weight and height to depression and educational attainment (Evans, Visscher, & Wray, Reference Evans, Visscher and Wray2009; Wray, Goddard, & Visscher, Reference Wray, Goddard and Visscher2007). PGSs operate under a massively polygenic, additive model (Boyle, Li, & Pritchard, Reference Boyle, Li and Pritchard2017). Under this model, summing the GWAS-weighted risk (or effect) allele dosages (0, 1, or 2) usually with several sophisticated statistical adjustments can provide an index of a continuous underlying (additive) genetic liability for a trait.Footnote ⁷ The human equivalent of the “breeding value” is in selective plant and animal breeding in human populations (Meuwissen, Hayes, & Goddard, Reference Meuwissen, Hayes and Goddard2001), and PGSs have been described as “summariz[ing] the cumulative effects of many variants across the genome and aim[ing] to index an individual's genetic liability for a given trait” (Domingue, Trejo, Armstrong-Carter, & Tucker-Drob, Reference Domingue, Trejo, Armstrong-Carter and Tucker-Drob2020, p. 465) or a “single quantitative measure of genetic predisposition” (Mills, Barban, & Tropf, Reference Mills, Barban and Tropf2018). The educational attainment PGS has been characterized as measuring “an individual's genetic predisposition for completing [more years of] formal schooling” (Bolyard & Savelyev, Reference Bolyard and Savelyev2020) and a “DNA-based indicator[] of propensity to succeed in education” (Harden et al., Reference Harden, Domingue, Belsky, Boardman, Crosnoe, Malanchini and Harris2020).

The specific details on PGS construction can, and have, filled articles (see Choi et al. [Reference Choi, Mak and O'Reilly2020] for more details), but the basic process is as follows: Run GWAS in discovery sample → replicate results in an independent sample → adjust for LD using a reference panel → select SNPs → adjust for LD and winner's curse → construct PGS → test PGS prediction in a target sample → assess PGS with incremental R ². It is worth noting that there are several decisions by researchers involved in PGS construction. In the “select SNPs” phase of PGS construction, researchers decide which SNPs to include in the PGS (via p-value thresholds) based on the success of prediction. Specifically, researchers evaluate several PGSs created at a variety of p-value thresholds and select the best PGS predictor (measured by R ²), which is usually the PGSs created from a p < 1 threshold (i.e., no p-value threshold) (e.g., Belsky et al., Reference Belsky, Domingue, Wedow, Arseneault, Boardman, Caspi and Herd2018; Ganna et al., Reference Ganna, Verweij, Nivard, Maier, Wedow, Busch and Lichtenstein2019; Lee et al., Reference Lee, Wedow, Okbay, Kong, Maghzian, Zacher and Linnér2018).Footnote ⁸

Thus, in what may come as a surprise to some, most PGSs are constructed from all available SNPs regardless of their statistical significance in the GWAS. Available evidence suggests that these “all SNPs” PGSs are more environmentally confounded than those that use (more stringent) p-value thresholds, such that while these may explain more variance, they do so because they capture environmental influences as well as genetic ones (Berg et al., Reference Berg, Harpak, Sinnott-Armstrong, Joergensen, Mostafavi, Field and Coop2019; Mostafavi et al., Reference Mostafavi, Harpak, Agarwal, Conley, Pritchard and Przeworski2020).

4.1 “Getting genetics out of the way”

Perhaps the most hyped value of PGSs in social science is to control for genetic heterogeneity in studies of environmental effects. According to Harden (Reference Harden2021a), many sociogenomicists are most excited about the potential of PGSs as a tool “to make genetics recede into the background, to get it out of the way” so that we can more clearly see the effects of environments (see also Conley, Reference Conley2016). Given ubiquitous heritability, proponents argue that uncontrolled genetic heterogeneity poses a serious threat to inferences about the effects of specific environments, as these ostensibly environmental causes may be biased or spurious (as actually driven by genetic differences) (Harden & Koellinger, Reference Harden and Koellinger2020; Hart et al., Reference Hart, Little and van Bergen2021). For example, rather than health or longevity being influenced by higher educational attainment, scholars have suggested, these relationships may be spurious with genetic endowment being the causal force. Similarly, sociogenomicists have asked, whether parental environments, including early childcare, causally influence educational attainment or whether these are spuriously associated because of shared genetic endowments.

Proponents also argue that incorporating PGSs as control variables into social science research can enhance the precision of environmental estimates (Cesarini & Visscher, Reference Cesarini and Visscher2017; Harden, Reference Harden2021a, Reference Harden2021b; Kweon et al., Reference Kweon, Burik, Karlsson Linnér, De Vlaming, Okbay, Martschenko and Koellinger2020). This enhanced precision may increase the power associated with randomized controlled trials, potentially shrinking their cost (Lee et al., Reference Lee, Wedow, Okbay, Kong, Maghzian, Zacher and Linnér2018; Rietveld et al., Reference Rietveld, Medland, Derringer, Yang, Esko, Martin and Agrawal2013). Controlling for genetic heterogeneity with PGSs, proponents argue, may also reveal previously obscured environmental effects. For example, some environmental influences on educational attainment may only be apparent among those at “high genetic risk” (Herd et al., Reference Herd, Mills and Dowd2021). For these reasons, proponents suggest, PGSs are valuable as a control for differential genetic propensity to illuminate more clearly and precisely the effects of environmental influences (Harden & Koellinger, Reference Harden and Koellinger2020; Trejo & Domingue, Reference Trejo and Domingue2019).

4.2 A powerful, flexible analytic tool for causal inference

Proponents also emphasize the value of PGSs as a powerful tool for causal inference (Belsky & Israel, Reference Belsky and Israel2014). This strength of PGSs, proponents argue, draws on several unique advantages of genetic data (Conley, Reference Conley2016; Harden, Reference Harden2021a). First, evidence (from twin studies of heritability) suggests that genetic differences matter. Second, “the genetic sequence of each person is fixed at conception and does not change throughout one's lifetime” (Kweon et al., Reference Kweon, Burik, Karlsson Linnér, De Vlaming, Okbay, Martschenko and Koellinger2020), which means that genotype need only be measured once. Further, once measured, PGSs can be calculated for any outcome, which need not be measured in the study, and as PGSs are updated with larger and more diverse samples, these individual scores can be created and updated (Belsky et al., Reference Belsky, Domingue, Wedow, Arseneault, Boardman, Caspi and Herd2018; Harden, Reference Harden2021a, Reference Harden2021b).

Proponents emphasize that this fixity of our DNA sequence means that reverse causality from behavior or environmental exposures to the genome can be ruled out. Given this, genetic data can serve as exogenous measures of individual characteristics, which do not change over the life course, “facilitating the tracing of developmental paths” or as a “fixed point from which to observe child development” (Belsky & Israel, Reference Belsky and Israel2014; Harden et al., Reference Harden, Domingue, Belsky, Boardman, Crosnoe, Malanchini and Harris2020). Scholars have argued that PGSs can be used as a “molecular tracer”: “Just as a radiologist might administer a radioactive tracer to track the flow of blood within the body, researchers can use genetics as a molecular tracer to get a clearer image of how students progress through the twists and turns of the educational system” (Harden et al., Reference Harden, Domingue, Belsky, Boardman, Crosnoe, Malanchini and Harris2020).

4.3 Gene–environment interplay

PGSs are also advertised as a more direct and powerful tool to explore how gene–environment interplay influences social outcomes. Broadly, gene–environment interplay with PGSs can be demarcated into three broad types: (1) PGS–environment interactions (e.g., does gender suppress “genetic potential” for educational attainment; Herd et al., Reference Herd, Freese, Sicinski, Domingue, Mullan Harris, Wei and Hauser2019), (2) PGS–environment combinatory effects (e.g., how do “nature” and “nurture” combine to shape children's resemblance to their parents in human capital accumulations over time; Harden & Koellinger, Reference Harden and Koellinger2020), and (3) PGS-through-environment pathways (e.g., through what social–psychological mechanisms does the education PGS increase educational attainment; Bolyard & Savelyev, Reference Bolyard and Savelyev2020).

Proponents have argued that PGSs can reinvigorate the study of gene–environment interactions (G × E) with “robust measures of genotype,” in contrast to the limited candidate G × E approach (Harden & Koellinger, Reference Harden and Koellinger2020; Martschenko et al., Reference Martschenko, Trejo and Domingue2019). “By applying the prism of GxE models, it is hoped that the white light of average effects will be refracted into a rainbow of genetically mediated responses that are made clear to the scholar interested in describing human behavior” (Conley, Reference Conley2016, p. 293). In addition, PGSs may also be gainfully employed in the service of understanding heterogeneous responses to social interventions, in the form of a PGS ×  intervention (Harden & Koellinger, Reference Harden and Koellinger2020).

4.4 Risk stratification and/or early identification

Although most scholars agree that PGS-based personalized programs or policies are not realistic because of poor individual prediction, PGSs are still advertised as having potential use in risk stratification, particularly for those in the upper and lower deciles of PGSs. On this view, PGSs could be used to identify “at-risk” individuals before problems manifest or become severe through the implementation of an early genetic screening system (Martschenko et al., Reference Martschenko, Trejo and Domingue2019). Such genetic screening is argued to provide an inexpensive way to more expansively identify those at high genetic risk of problems, such as lower educational attainment or physical inactivity, and intervene in advance with, for example, extra support or placement into a different learning environment (Harden & Koellinger, Reference Harden and Koellinger2020; Martschenko et al., Reference Martschenko, Trejo and Domingue2019). Similarly, PGSs could be used to identify “high potential” individuals, who could also be targeted with different learning environments.

In addition to risk stratification, proponents argue that enhanced understanding of the distribution of genetic risks could be used to study the effects of social institutions and programs. For example, in educational systems, studying the distribution of genetic risks “across schools could be used to study inequities in the current ways that the educational system under- and overdiagnoses students… thereby identifying differential diagnoses and treatment across groups” using PGSs as “indicators with some degree of objectivity” (Martschenko et al., Reference Martschenko, Trejo and Domingue2019).

4.5 Changing worldviews and approaches to social inequalities

Finally, some proponents claim that incorporating genetics into social science will change the way that social scientists think about the world. In the words of Harden and Koellinger (Reference Harden and Koellinger2020, p. 567):

On this view, GWASs and PGSs reveal the hitherto unrecognized fact that “success in life” is partly shaped by our genetic inheritances. In general, these scholars maintain that incorporating genetics into social science will stimulate new ways of thinking about and investigating our differences and inequalities, which may inform social policies to ameliorate inequalities.

4.6 Summary

Proponents tout several benefits from incorporating PGSs into social science to enhance social science research. In the next section, I scrutinize the science of sociogenomics, highlighting limitations, which I argue, undermine the utility of PGSs into social science. Most of these limitations are acknowledged by sociogenomicists; yet the full implications of these challenges are invariably unheeded in practical applications.

5.1 Relatedness confounding of PGSs

The most widespread and widely recognized form of environmental confounding is because of (genetic) relatedness and passive gene–environment correlations. Basically, people who are more genetically similar (i.e., more closely related, even distantly) also tend to develop in more similar sociocultural, political, and physical environments, which influence most complex social traits. Thus, genotype and environments are correlated for non-causal reasons. Generally, relatedness confounding is demarcated into population genetic structure and familial confounding. Both are known issues in GWASs/PGSs and steps are taken to mitigate this confounding. However, evidence is mounting that these corrections are insufficient, such that inflated or spurious genetic associations persist (e.g., Barton et al., Reference Barton, Hermisson and Nordborg2019; Berg et al., Reference Berg, Harpak, Sinnott-Armstrong, Joergensen, Mostafavi, Field and Coop2019; Haworth et al., Reference Haworth, Mitchell, Corbin, Wade, Dudding, Budu-Aggrey and Smith2019; Morris et al., Reference Morris, Davies, Hemani and Smith2020a; Mostafavi et al., Reference Mostafavi, Harpak, Agarwal, Conley, Pritchard and Przeworski2020).

5.2 Downward causation and artificial genetic signals

Downward causation – defined as sociocultural forces that sort and select individuals based on genetically influenced traits, such as skin pigmentation and height, into different environments and exposures that influence social outcomes – creates what I call artificial genetic associations, which are environmental influences masquerading as genetic influences in GWASs. Although the fact that sociocultural environments shape and filter genetic influences is understood by most, less well understood is the extent to which the causal effects of social structural and cultural forces acting on genetically influenced differences are identified as genetic influences in GWASs and PGSs.Footnote ¹⁴

Jencks' (Reference Jencks, Smith, Acland, Bane, Cohen, Gintis and Michelson1972) now classic thought experiment on discrimination by hair color can be used to illustrate downward causation creating artificial genetic associations. Jencks asks us to imagine a system where red-haired children are barred from school. In such a system, genetic variants linked to red hair would be identified by GWASs as genetic causes of educational attainment. However, neither an individuals' red hair, nor the genetic variants contributing to red hair, are appropriately conceived as causes of differences in educational attainment in this hypothetical case, in my view and that of others (Kaplan & Turkheimer, Reference Kaplan and Turkheimer2021), but see Harden (Reference Harden2021a). The “difference that makes a difference” is not red hair but the social-institutional policies excluding people with red hair, which is why a change in the rules would (over time, I presume) make hair color unrelated to educational attainment (and remove any red-hair genetic associations with education). Although explicit discriminatory exclusionary policies like this one are largely a thing of the past in most developed nations, both ongoing discrimination and the legacy of past discrimination (through intergenerational transmissions of wealth, status, social capital, etc.) continue to influence individual development and trait differences. More broadly, our environments and institutions, educational and otherwise, continue to differentially treat individuals based on a variety of genetically influenced individual traits such as height, body weight, personality, attractiveness, and skin tone into different environments and exposures and thus opportunities, achievements, and developmental outcomes (e.g., Monk, Esposito, & Lee, Reference Monk, Esposito and Lee2021; Simons, Burt, Barr, Lei, & Stewart, Reference Simons, Burt, Barr, Lei and Stewart2014).

GWASs and PGSs capture artificial genetic signals, and these artificial effects are likely to be pervasive given the extent to which we respond to phenotypic cues in our interactions with others in a manner that is unavoidably socioculturally mediated. Although casting such socioculturally driven genetic associations as genetic propensity or even “indirect genetic effects” is misguided, even more concerning is the subsequent framing of such correlations as innate individual propensities (individual “genetic fortune” or “misfortune”). Because of downward causation, genetic associations for many complex social behaviors are unavoidably environmentally confounded and are not appropriately conceived as genetic causes of outcomes.

5.3 Limited coverage of genetic variation

To serve as a control for genetic influences, in addition to not being substantially environmentally confounded, PGSs need to capture genetic influences relatively accurately and comprehensively. They do not.

5.4 Context and population specificity

That heritability studies are context- and population-specific – a point made clearly and forcefully by Lewontin (Reference Lewontin1974) nearly 50 years ago – is now widely appreciated after considerable scholarly effort and some costly misrepresentations (Jensen, Reference Jensen1967). However, that GWASs and PGSs are similarly context- and population-specific is not as widely appreciated in theory or practice (but see Kaplan & Turkheimer, Reference Kaplan and Turkheimer2021). It should be. This is particularly true for non-biological social behaviors and achievements like educational attainment or same-sex sex, which involve somewhat arbitrary institutional structures (e.g., financial resources and opportunities) as well as cultural norms.Footnote ¹⁷ For reasons expounded upon below, such genetic associations should not be understood as timeless, context-independent genetic influences. That is, even if we could disentangle the influence of genes from environments for these outcomes, these associations reflect developmental gene–environment interactions under current social arrangements in each context, not what could be in different circumstances (historical periods, social position, cultural context, etc.).

This well-known context- and population-specificity exists for two general reasons. The first is biological: Genes always interact with environments across all levels of development in their effects on complex traits. The second is sociocultural: The individual characteristics influencing traits or achievements, and thus the genetic contributors thereto, vary across historical time, society, and even across structural location. For illustration, the genetically influenced individual traits facilitating educational attainment for a woman in Saudi Arabia in 2000 versus a woman in 1870s in United States, in 2010 in India, in 2002 in Nigeria, in 1950 in Thailand, or in 2021 in United States are likely to be distinct in non-trivial ways. Although a woman going to college in the United States in 2020 would be conforming, a woman going to college in 1870s in United States would be statistically deviant. Because educational attainment reflects numerous genetically influenced traits, filtered by context and relative condition, the idea of a context-invariant “genetic propensity to” complex social outcomes like educational attainment, like crime, smoking, or same-sex sex, is misguided (Burt, Reference Burt2023).

Moreover, the search for a “winning” genetic endowment that can be measured on a unidimensional scale representing propensity for social success is also misguided, in my view (e.g., Belsky et al., Reference Belsky, Moffitt, Corcoran, Domingue, Harrington, Hogan and Williams2016). This is because our DNA is part of an interactional developmental system that responds to context- and condition-dependent stimuli (Burt, Reference Burt2018; Ellis et al., Reference Ellis, Del Giudice, Dishion, Figueredo, Gray, Griskevicius and Volk2012). Genetic differences influencing complex traits, like traits themselves, are not amenable to facile “good” or “bad,” “winning” or “losing” ratings but rather more like “it depends,” on a host of other factors (e.g., other genetic differences, other traits, historical context, social class, etc.). To use an oversimplified example, while being confident, independent, and talkative may enhance educational attainment and occupational success for an upper-middle class white male, those same traits among a minority youth from a disadvantaged background could very well impede educational attainment. Of course, confidence and independence emerge from a host of influences, but the point of this example is to reveal the oversimplified (theoretically and empirically unwarranted) model underlying an additive genetic index representing a context-independent propensity for complex social behaviors like educational attainment.

The problems with a unidimensional genetic propensity for complex biological traits are even more obvious for a phenotype of (having ever had) same-sex sex. As with people who attain higher levels of educational attainment, people who have ever had same-sex sex display remarkable diversity. From “gold star” lesbians and bisexual women to “femme” women who have same-sex sex only to please their male partners, the search for an additive, context-independent underlying continuum of genetic propensity for “having ever had same-sex sex” is empirically and theoretically unwarranted. Not only is there expansive heterogeneity within these groups, but also same-sex sex, like other social behaviors such as doing ballet, trying ecstasy (MDMA), and playing golf, is not simply the outer manifestation of some inner potentiality. Different sociocultural constraints and opportunities shape the behavioral manifestation of various traits and propensities, however genetic, which are then further altered by social responses in developmental feedback loops (including labeling and self-identification). Of course, we can impose a unidimensional propensity measure – a PGS or otherwise – for such heterogeneous and socially contingent behaviors by estimating the probability of the binary measure of having ever done so. But creating such a continuum statistically does not mean such a propensity exists biologically.

Thus, for yet another reason, PGSs cannot be thought of as “genetic potential,” inasmuch as genetic influences are not static charges where PGS effects sizes can be facilely compared across contexts or conditions. Traits that facilitate educational attainment, and any genetic contributions thereto, are dependent on sociocultural influences. For example, if physical education classes were equally emphasized with non-PE courses and graded not by effort but also by achievement, academic attainment may look noticeably different.

This context-specificity has implications for some prominent applications of PGSs. Following prior behavioral genetics work that examined how heritability estimates varied across contexts or conditions, several recent studies have used PGSs to explore how “genetic influences” are moderated by (often “constrained” or “suppressed” in) different contexts or for different social groups (Harden et al., Reference Harden, Domingue, Belsky, Boardman, Crosnoe, Malanchini and Harris2020; Trejo et al., Reference Trejo, Belsky, Boardman, Freese, Harris, Herd and Domingue2018; Wedow et al., Reference Wedow, Zacher, Huibregtse, Mullan Harris, Domingue and Boardman2018). For example, Herd et al. (Reference Herd, Freese, Sicinski, Domingue, Mullan Harris, Wei and Hauser2019) examined whether “the influence of genetics on educational attainment has changed across cohorts” and “whether this influence varies by gender” by comparing the effect sizes of the education PGS on educational attainment across cohorts (defined by historical time) and by sex. Their focal hypothesis was that among older cohorts, social structures of gender suppressed the “genetic potential for educational attainment” among women but not men, manifest in weaker education PGS prediction among women in older cohorts. To be sure, the Herd et al. study was explicitly sensitive to context, recognizing how genetic effects are “filtered, altered, and shaped by broader complex environments” (p. 1071). Even so, this approach remains insufficiently context-situated and oversimplified. This is because the study rests on the idea that PGSs capture a historically invariant genetic potential for educational attainment, such that weaker PGS prediction can be interpreted as lesser genetic influence and thus suppressed potential. However, for reasons mentioned above, as contexts and opportunities change, so too do the characteristics influencing achievements and social behaviors, and thus their genetic influences. A weaker PGS across contexts may just mean different traits matter (and would be expected in this example for statistical reasons given the lower mean and variance of educational attainment in the earlier cohorts compared to the latter ones). For all these reasons, interpreting effect size differences in PGSs as indicating that “genetic influences matter less” for social traits in different contexts or as evidence that “potential is suppressed” is unsound.

Upon deeper reflection, the extent to which research into how contexts suppress or constrain “genetic potential” (via reductions in PGS effect sizes) advances knowledge is unclear. Leaving aside my objection to the notion of a context-independent genetic potential for social traits, in general, and PGSs as an indicator of such potential, in particular, what, specifically, is the value of assessing whether “genetic potential” is suppressed by these social arrangements? Until well into the twentieth century, the potential for educational attainment for women in the United States was, of course, constrained by structures of gender that limited them to family roles in the household. We already know women's potential was suppressed, in these instances. What would it mean to say that potential was suppressed but not genetic potential? Is the null hypothesis that only “non-genetic potential” was suppressed (and what would that even mean)? Phrased alternatively, given that potential emerges from developmental systems shaped by interacting genetic and environmental forces, is there any argument that can be made that discriminatory arrangements or disadvantages constrain achievement but do not affect genetic potential? How would that work?