The six empirical patterns painted in Chapter 6 capture basic features of global human genomic variation. In truth, there are seven such patterns. However, the seventh pattern – natural selection – is so complex and interesting that it merits a longer investigation. In human evolutionary genomics, findings about natural selection emerge, as in the preceding six empirical patterns, primarily from analyses of contemporary, global, and populational genomic variation, even if the genomic investigation of remnants of ancient Homo sapiens and of archaic hominins is also increasingly relevant. Although all patterns provide evidence that our species is much closer to Planet Unity than to Galápagos-Writ-Large (see Figure 4.1), relative to the other six patterns, the signatures of natural selection provide far more, and perhaps clearer, evidence for some Galápagos-Writ-Large in our genomes. This is one reason why natural selection has served as the speculative vehicle for some interlocutors to rashly posit that unique selection regimes in local environments are responsible for racial differentiation, not to say racial adaptation.

In Chapter 7, we explored dichotomies between natural selection and various forces, explanations, and paradigms complementary to natural selection, such as random genetic drift and developmental mechanisms. Both poles of each of these multiple binaries need to be respected and, ultimately, integrated. As we have discovered throughout Our Genes, the dichotomy between individual and population calls for continuous measurement as well as careful comparison and conceptualization. In light of populational statistical analysis, genomics shows us that individuals differ but that individuals are also, in effect, almost the same, and certainly more similar than what social discourse typically suggests.

At their most basic, the answers provided by modern genomic science to questions about populations depend on data. Data offer such a crucial lens through which to observe and begin to understand our similarities to, and differences from, one another that one of my major arguments in Our Genes is simply this: Data matter. Field data, yielded by measuring features of natural populations, help make evolutionary theory mathematically and statistically explicit by viewing evolution as the change of allele frequencies across generations. Evolutionary geneticists use many kinds of data to develop and select among mathematical models, and to subject explanatory and predictive evolutionary genetic theory to empirical tests.1To test and improve such models, evolutionary geneticists depend on measurements and data provided by field samples and laboratory experiments, fed through statistical machinery.

Clearly, when we stand at the intersection of human evolutionary genomics and philosophy and ask Who are we? we implicitly acknowledge that in body, mind, and history, we are fundamentally relational. Perhaps paradoxically and certainly provocatively, we are not really individuals at all: Connections, relations, and larger holistic and pluralistic systems are as important to your and my reality as the internal subjectivity many of us feel. In terms of the genetic paradigm, a single individual out of relationship with others is of little evolutionary or ecological significance. A single individual needs other individuals for behavioral and psychological interactions, competition and cooperation, and reproduction (Figure 3.1, chapter opener). Populations are a unique organizational level because most, if not all, local interaction happens at this level.

As we have learned throughout these pages, defining, investigating, and interpreting identity is complex. Many of our questions about Who am I? refer us to our local, geographic population(s) of origins, but just as many refer us to each other, as a whole, and to the human genome we each carry. This chapter zooms out from case study analysis to consider another form of identity that genomics helps us define and investigate – race (Figure 9.1, chapter opener). A book that approaches human evolutionary genomics through a philosophical lens would be incomplete without considering the particularly contentious analyses and interpretations associated with the genomic study of race. Conceived of as a social indicator of varying importance, race is linked to social structures and, thus, politics. Racism is the process of frequent and systemic judgments and prejudices based on perceived racial differences leading to differential access to social goods, including dignity, trust, and opportunity. We can work to ignore race, insist race does not matter, and strive for the post-racial future some of us may long to inhabit, but we do not live there yet, and a variety of futures are possible.

This chapter synopsizes our discussions by taking methodological step back to statistics, and to the irreducible entanglement of explanatory paradigms. We will never fully resolve the philosophical themes central to Our Genes, but we can certainly reflect on them. So far, we have investigated themes related to causation and explanation; the multivalent, ambiguous role of theoretical assumptions; the importance of data and empiricism; and the power of philosophy – for example, conceptual analysis, tracking ethics and power, critique, and imagining “What if…?” – in scientific investigation.

In pursuit of more nuanced answers about ourselves and each other, we must shift from metrics and models to interpretation and applications of genomic analyses, both in scientific and medical contexts, and in broader culture and politics. Accordingly, this chapter presents six general empirical patterns of human genomic variation. The patterns emerge from many data points, layered through measures and metrics such as heterozygosity and Euclidean genetic distance, statistical modeling machinery, such as variance partitioning protocols and Bayesian clustering algorithms, and theoretical concepts, such as population and genetic variation. Because the six patterns form an indelible, complex mix of genomic data, statistical methods, and evolutionary genetic theory, I call them neither facts nor data, nor empirical results nor states of affairs. They are also neither models nor methodologies. They are much too complex and unique for such simple and straightforward denominations.