The paper begins by highlighting the expanding scope of genomics research, moving beyond disease to encompass social behavior. This has led to a renewed focus on the nature versus nurture debate and to the use of genomic data to investigate social outcomes like educational attainment and social stratification. However, this approach is not without controversy, as exemplified by the first large-scale GWAS on sexual orientation, which identified genetic variants associated with same-sex sexual behavior. This study serves as the central case for the authors' analysis, which explores the complex interplay between genomics and social science. The authors emphasize the importance of considering how societal relations, understandings, and categorizations are embedded in and reproduced by genomic research, particularly in the context of big data and datafication of the human body. They introduce a critical framework for analyzing these processes, focusing on the role of theory and methodological choices in shaping data-driven research.

The authors draw upon several sociological theories to contextualize their analysis. They discuss Bourdieu's theory of habitus and the embodied nature of social categories, emphasizing how power relations shape classifications and understandings of the body. Foucault's concept of the medical gaze, illustrating the historically contingent nature of knowledge production about the body, is also relevant to their discussion. Furthermore, feminist perspectives on the social construction of sex and gender are incorporated, highlighting the complex relationship between biological sex and socially constructed gender. The authors also touch on Science and Technology Studies (STS) literature on classifications, showing how categories create social order, which is not naturally given but rather a product of social dynamics.

The authors employ a critical analysis of a specific case study – a large-scale GWAS on same-sex sexual behavior (Ganna et al., 2019). This study, while cautioning against simplistic interpretations, has been met with criticism for its limitations and the potential for misinterpretations. The authors analyze the methodology of this study and the assumptions made about the relationship between genetics and sexual orientation. Their analysis focuses on three angles. First, they examine how social relations and categorizations are employed and integrated into the research process itself, and then incorporated into the results and conclusions. Second, the paper discusses the increasing reliance on data-driven research and its departure from theoretical frameworks in social sciences. Finally, they analyze how the assumption of 'theory-free' big data research overlooks crucial choices regarding data selection and interpretation which inevitably shape the findings. The authors use this GWAS as a lens to discuss the challenges of integrating social theory and established methodology into big data research, specifically emphasizing the problematic assumptions made within the study about sexual orientation and its relationship to social and biological factors.

The analysis of the GWAS on same-sex sexual behavior reveals several key issues: The study's reliance on a binary understanding of sex and gender and on self-reported data about sexual behavior (rather than sexual attraction or desire) limits its generalizability and potentially reinforces existing societal biases. Furthermore, the data used came primarily from biobanks, with inherent limitations in sample representation and potential biases related to age, ancestry, and self-selection. The study identifies only a small percentage of variance in same-sex sexual behavior that can be attributed to genetics, while largely ignoring the influence of environmental factors which were noted to be critical. The authors highlight the tension between the need to find a genetic basis for a complex social phenomenon and the limitations of currently available data. They discuss the implications of the study's findings for the public understanding of sexual orientation, noting the potential for genetic essentialism and misinterpretation. The limitations of the data used (e.g., SNPs only, limitations in sample representation) restrict the conclusions that can be made about the complex interplay between genes and environment. In particular, a lack of data regarding epigenetics and social interactions prevent broader conclusions about causal effects. The authors highlight the potential for such studies to lead to genetic essentialism and oversimplified interpretations of complex social phenomena.

The authors argue that the increasing reliance on big data in genomics research has led to a neglect of social theory and established sociological methodologies. This 'theory-free' approach, however, ignores the fact that data itself is inherently shaped by theoretical assumptions, methodological choices, and technological constraints. The authors emphasize the importance of considering the social context and the potential for bias in data collection and interpretation. They advocate for an interdisciplinary approach that incorporates sociological perspectives into genomic research, thereby enriching the understanding of complex social phenomena. The discussion reinforces the need to move beyond simply identifying correlations between genetic markers and social outcomes, which risks oversimplification and inaccurate conclusions. The study further argues that neglecting social science can lead to distorted interpretations of complex social phenomena and may have significant real-world consequences.

The paper concludes with a call for a renewed focus on the social in big data-driven genomic research. The authors emphasize the need for a multi-perspectival approach that integrates social science perspectives with biological research. This would encompass recognizing the inherent contingency of research choices, taking on ethical responsibilities beyond procedural requirements, and fostering interdisciplinarity in analytical practice. This framework should move beyond the limitations of traditional ELSI approaches and foster genuine collaboration between different disciplines.

The authors acknowledge that their analysis focuses on a single case study, the GWAS on sexual orientation. While this provides a valuable illustration of their argument, it cannot encompass the full breadth of genomic research on social behavior. Further research is needed to investigate the broader implications of data-driven research in genomics across diverse social and biological contexts. The focus on one case study means findings may not be generalizable to other areas of social genomics. However, the authors argue the principles of their analysis are applicable more broadly.