Tag Archives: agricultural_research

Implausible, given decades of debate among methodologically sophisticated scholars, that some fundamental problems in quantitative genetic estimation have been overlooked?

In recent publications (Taylor 2010, 2012) I show how the estimates made in human quantitative genetics are unreliable and typical interpretations, including interpretation of non-shared environmental influences, are unjustified.  Some readers may deem it implausible, given decades of debate among methodologically sophisticated scholars, that some fundamental problems in quantitative genetic estimation have been overlooked (Kendler 2005).  With a view to moving at least some skeptical readers to consider the full set of problems and conceptual themes I present, let me sketch the background that allows me to see the study of heredity and variation differently from most researchers and philosophers of science who have addressed quantitative genetics.

My initial research work in the mid-1970s involved the statistical analysis of large plant breeding trials, in which many cultivated varieties would be tested in each of many locations around the world.  A first step in the analysis was to partition the variation in a given trait, say, yield of wheat plants, into components related to the averages or means of the varieties (across all locations), the means of the locations (across all varieties), and so on.  (Indeed, agricultural breeding was where partitioning of variation and measuring heritability originated.)  The challenge for the plant breeders with whom I worked was to go beyond the partitioning and hypothesize what it was about any variety that led to its pattern of response across locations and what it was about any location that led to the varieties’ responses in that location compared to others.  Knowing what aspects of, say, the pedigree of the variety or of the environment conditions in the location could inform subsequent breeding or cultivation decisions.  Yet, hypothesis generation was not easy even though we had large and complete data sets to work from.  A lesson from that experience was that the limits to hypothesizing about genetic and environmental factors must be even greater when researchers partition variation for human traits.  In human studies any genetically-defined type is, at best, replicated twice—as identical twins—and different genetic types cannot be systematically raised across the same range of “locations”—families, socio-economic conditions, and so on.

Fast forward to a decade ago: I was learning about three disparate areas of quantitative research that attempt to make sense of the complexity of biological and social factors that build on each other in the development of the given trait over the life course (Taylor 2004).  I was impressed by what had been accomplished, but had some reservations about the models used in one of the areas, namely, Dickens and Flynn’s (2001) attempt to resolve the IQ paradox, in which researchers find large generation-to-generation advances in IQ test scores even though the trait is held to have high heritability.  I explained my reservations to Dickens, digested his responses, and explained my reservations about his subsequent responses.  In the course of this I found myself digger deeper into the conceptual foundations of heritability estimation and partitioning of variation.  In order to present a picture that differed from what Dickens, Flynn, and others accepted without second thought, I was explicating first principles, not disputing specialized models or mathematics. Making extensive use of perspectives and examples from the earlier plant breeding research, my exposition took a pedagogical style (Taylor 2006, 2007, 2010).

Meanwhile, my investigation continued of the other two areas—life events and difficulties research (Brown and Harris 1989) and developmental origins of chronic diseases (Barker 1998).  Barker’s work led me to life-course epidemiology (Kuh and Ben-Shlomo 2004), so I spent time with Ben-Shlomo and the active social epidemiology research group at Bristol University.  Davey Smith is a leading figure in that group and co-edits the International Journal of Epidemiology based at Bristol.  While visiting in 2007 I gave a talk on “new and old debates about genes and environment,” which touched on some of the questions about heterogeneity raised in this article.  Davey Smith’s spoken response was along the lines of his subsequent “gloomy prospect” article: epidemiologists have to accept considerable randomness at the individual level and keep the focus on modifiable causes of disease at the population level.  In his ensuing article, Davey Smith (2011) links this perspective to claims from quantitative genetics, thus providing me an opportunity to address social epidemiologists and human quantitative geneticists at the same time as I respond to his account.  In an as-yet-unpublished article bringing my interest in heterogeneity to the attention of those audiences, I extend the pedagogical style and first-principles emphasis of the other recent work and thereby speaks to philosophers of science.  My contribution to philosophy takes the form, however, of articulating conceptual themes, not dissecting specific cases on empirical, analytical, bioethical or policy grounds.  The expository approach reflects the background reviewed here, with its roots in plant breeding trials, as well as the idea that contributing to the conceptual toolbox of readers will prepare them to make their own contributions to wider discussion of heredity, variation, and heterogeneity.


Barker, D. J. P. (1998). Mothers, Babies, and Health in Later Life. Edinburgh: Churchill Livingstone.

Brown, G. W., & Harris, T. O. (Eds.) (1989). Life Events and Illness. New York: Guilford Press.

Davey Smith, G. (2011). Epidemiology, epigenetics and the ‘Gloomy Prospect’: embracing randomness in population health research and practice. International Journal of  Epidemiology, 40, 537-562.

Dickens, W. T., & Flynn, J. R. (2001). Heritability estimates versus large environmental effects: The IQ paradox resolved. Psychological review, 108, 346-369.

Kendler, K. S. (2005). Reply to J. Joseph, Research Paradigms of Psychiatric Genetics. American Journal of Psychiatry, 162, 1985-1986.

Kuh, D., & Ben-Shlomo, Y. (Eds.) (2004). A Life Course Approach to Chronic Disease Epidemiology. Oxford: Oxford University Press.

Taylor, P. J. (2004). What can we do? — Moving debates over genetic determinism in new directions. Science as Culture, 13, 331-355.

Taylor, P. J. (2006). Heritability and heterogeneity: On the limited relevance of heritability in investigating genetic and environmental factors. Biological Theory: Integrating Development, Evolution and Cognition, 1, 150-164.

Taylor, P. J. (2007). The Unreliability of High Human Heritability Estimates and Small Shared Effects of Growing Up in the Same Family Biological Theory: Integrating Development, Evolution and Cognition, 2, 387-397.

Taylor, P. J. (2010). Three puzzles and eight gaps:  What heritability studies and critical commentaries have not paid enough attention to. Biology & Philosophy, 25, 1-31.

Taylor, P. J. (2012). A gene-free formulation of classical quantitative genetics used to examine results and interpretations under three standard assumptions. Acta Biotheoretica, 60, 357-378.

Heterogeneous construction of scientific knowledge and practice: III. Six Themes Drawn from the Reconstruction of the Kerang Study

The description of the building of the KFM [in the previous posts], although brief and partial, is sufficient to introduce six themes or propositions about the processes of making science and interpretation of those processes.  These themes are put forward in the spirit of theoretical exploration, that is, to highlight important issues and orient our thinking about them.  I begin with the observation that the different agents involved in the KFM draw on diverse components from a range of domains of social action (theme 1). Themes about scientists as imaginative agents who represent-engage (themes 2 and 3) extend the idea of social-personal-scientific correlations used in in my analysis of H.T. Odum’s pioneering contributions to systems ecology (Taylor 2005, chapter 3).  The other themes take further steps towards a more general framework for analyzing the mutual relationships between scientists’ representations and actions.

Theme 1.  Science-in-the-making depends on heterogeneous webs, not unitary correspondences.  From the description above, it is clear that diverse components were involved in building the KFM—from soil quality data to the terms of reference set by the Ministry.  Moreover, they were interconnected in practice, forming what I will call heterogeneous webs (see figure).  The KFM’s assumption that farmers were subordinate to economic rationality made it easier to concentrate only on options that took the form of government policy.  The power of the government to enact its decisions rendered investigation of how farmers change less relevant, which shaped the data that needed to be collected—generalized agronomic data, rather than sociological interviews, would suffice.  This, in turn, conditioned the relationships that could appear in the model.  Similarly, the modeler’s mediated relationship with the modeled situation and his geographical separation from the region rendered it less relevant to model the novel long-term options.  Their omission from the modeling, in turn, helped ensure that such aspects of the future reality would be less realizable, and the model’s account more real.

Figure.  A schematic picture of the web of diverse, interconnected components involved in the KFM socio-environmental modeling (from Taylor 1995a).  See text for discussion.

“Technical” considerations, such as the assumption of income optimization, and “social” considerations, such as the separation of the modeler from the farmers, had implications for each other in practice.  “Local” interactions were connected with activities at a distance.  For example, the modeler and the principal investigator decided not to pursue sociological inquiry into how farmers change, which meant that the content of and conduct of the survey of farms and farmers could remain unchanged.  No one component in the web stood alone in supporting the KFM as a representation of reality; in the actual process of building the model, technical components could not be detached from social ones, nor local ones from those that spanned levels.

In this sense I would say that science is constructed; science-in-the-making is an on-going process of building from diverse components, just as a house is built over time using plans and measurements, laborers and contracts, concrete and concrete mixers, wood and saws.  This is social construction, but not merely social construction—my interpretation is not that scientific knowledge is determined by or reflects the society in which it is made.  Although it is possible to say that the model reflected all the different social components, that would be stretching the metaphor of reflection.  Given the heterogeneity of components and their inter-linkage in an ongoing process, it is difficult and uninformative to collapse science-in-the-making to a unitary idea that scientific knowledge reflects society.  Likewise for the unitary idea that scientific knowledge reflects natural reality.  Science, in practice, is heterogeneously constructed.

Theme 2.  Scientists represent-engage.  In the process of building the model, the modeler, principal investigator, and other agents linked together technical and social components in order to make a model that worked for them.  These scientific agents tended to make the different components reinforce, not undermine, each other, rendering both the model and the ongoing scientific activity more difficult for others to oppose or modify in practice (see theme 1).  This insight goes beyond observing that representations of natural reality support engagements or interventions in different domains of society (Keller 1992, 74ff), or the claim that interventions provide the basis for scientific representations (Hacking 1983).  Through the model’s heterogeneous construction, representations and engagements were formed simultaneously, and, moreover, jointly.  Interaction between “technical” and “social” considerations fails to capture this relationship in which causes are inseparable (see theme 5).  Let me instead speak of scientists representing-engaging.

Theme 3.  Scientists are practically imaginative agents.  The idea of representing-engaging implies that scientific agents are mindful both of nature and of the social realms in which they act—in which they are situated—and that they project continuously between these realms.  In focusing on scientists’ social situatedness, I am not saying that they are corrupt, fallible, lazy, or taking the path of least resistance.  On the contrary, I am affirming that all human activity is imaginative, that is, the result of a labor process that grows out of the laborer’s imagination.  Agents assess, not necessarily explicitly, the practical constraints and facilitations of possible actions in advance of their acting (Robinson 1984).

In fantasy, people envisage worlds and mentally inhabit them, escaping the practical difficulties of action.  Imagination is not like that.  Achieving some result in the material world requires human agents to be engaged with materials, tools, and other people.  The KFM modeler had to engage with pasture growth, government sponsorship, an agricultural extension system, and so on.  Moreover, materials, tools, and other people confront scientists with their recalcitrance.  So scientists project themselves into possible engagements out in the world in order to imagine what will work easily for them and what will not.  These constant projected confrontations with the components that personal and collective histories make available lie behind all the actions people take, including scientists’ representing-engaging.  Through their imagined engagements people build up knowledge about their changing capabilities for acting in relation to the conditions in which they operate (though this knowledge may not be consciously articulated).

Theme 4.  The agency of heterogeneous constructors is distributed. One consequence of focusing on agents’ contingent and ongoing mobilizing of webs of materials, tools, and other people is that the character of their agency can be interpreted as distributed over those webs, not concentrated mentally inside socially autonomous units whose ideas or beliefs are key to the order they impart on the world.  That is, although agents work with mental representations of their worlds and can speak about motivations, the malleability of those representations and motivations is not a matter simply of changing beliefs or rationality.  Instead, a heterogeneous web of materials, tools, and other people help agents act as if the world were like their representations of it.  During the Kerang study, the principal investigator may well have believed deeply that economic decision-making was of primary importance in people’s lives.  However, he was able to sustain this belief against possible challenges through many practical measures (as discussed under themes 1-3).  For example, although he knew about the sociological study of how farmers change, he did not secure access to it, and he concentrated on econometric investigations rather than developing skills in multi-objective analysis.

Theme 5.  Resources are causes.  Up to this point in my description of how the KFM was constructed, I have used the neutral term component.  But there may be little explanatory significance to some of the diverse things that scientists link into the webs as they establish support their theories and ongoing scientific activity.  Let me apply the term resource for components that make a claim or a course of action more difficult for others to modify.  By extension, a resource for one person is a constraint for another person trying to modify the first’s claim or action.  Resources make a difference; that is, when resources are deployed they function as causes.  In this light, any descriptive use of the term resource also implies a claim about causes, and such claims invite analysis.

Theme 6.  Counterfactuals are valuable for exposing causes.  The components of the construction process I have chosen to mention were significant resources in the building of the KFM—or so my account of the KFM would imply.  But how can I support the causal claims that I have thus structured into my account of the KFM?  For a start, let me note that, to support the causal claim that something made a difference logically requires an idea of what else could have been if the resource in question had been absent.  That is, causal claims involve consideration of counterfactuals—things that might have occurred but did not.

The sources for ideas about what else could have been are varied.  Sociologists and historians of science undertake conceptual analysis or historical and cross-cultural comparisons (Harwood 2000), and give rein to their sociological imagination (Hughes 1971).  They also listen to opposing parties in controversies (Collins 1981a,b) and campaigns for social change (Nelkin 1984).  Indeed, controversies and campaigns provide the clearest, most concrete evidence of alternatives, because the agents themselves identify the resources they consider important.

There is no logical reason, however, why the resources explicitly exposed during a controversy constitute the full set used by a scientist.  There are resources taken for granted and shared by opposing parties and, moreover, resources that must be mobilized even when there is no apparent controversy.  In short, ideas of what else could have been should not be limited by whether anyone actually attempted to construct the alternative situation.  For all these reasons, explicit use of counterfactuals may be needed in order to analyze a more inclusive array of resources used in the construction of science.

I began my account of the building of the KFM as a fairly neutral description.  Notice, however, that I began introducing counterfactuals once I started to draw connections among the heterogeneous components.  For example, in contrast to a single objective of maximizing income in the modeled farms, I mentioned the counterfactual possibility of multi-objective techniques.  In explaining why this was not incorporated in the KFM, I mentioned that the principal investigator’s training, his status relative to the modeler, the Institute’s specialization, and the availability of software.  These were constraints for anyone who might want to construct a multi-objective model.  By identifying them I was implying that the principal investigator’s training and so on were resources for constructing a model with a single objective function.  In this general fashion, exploring the practical constraints on counterfactual possibilities—what did not happen—can, by a logic of inversion, expose the resources that helped those who constructed what did happen (figure 2).

Figure 2 The method of exposing resources by exploring the practical constraints on counterfactual possibilities.

The emphasis on multiple, heterogeneous resources means that the relevant counterfactuals are multiple and particular.  In principle, we could formulate some all-encompassing counterfactual.  For example, an alternative to the Kerang study would be a project that was not conducive to top-down government policymaking.  However, if we were to consider the practical implications of such a counterfactual, we would be challenged to identify specific sites for possible modification of the research.  This would be all the more the case if we focused on the practical implications for the specific scientific agents involved.  In the case of the Kerang study the modeler wanted to consider sociologically realistic processes of farmers changing.   But his ability to produce results that paid attention to such processes was constrained by his distance—geographically, organizationally, and conceptually—from the farmers’ domain of social action.  The geographical and organizational distance was, in turn, related to the centralized character of government and intellectual activities in the one major city of each Australian state, something given by the previous 200 years of development.  Towards the end of the project the modeler contemplated a move counter to that centralization, namely, to live and work in the Kerang region as an agricultural consultant.  He was aware that this would raise practical issues such as purchase and maintenance of a car, long-distance access to computer facilities and libraries, ways to keep abreast of discussions about the wider state of the rural economy, and other considerations of a more personal nature.  The modeler decided not to move which meant that the representation of the Kerang region he was able to produce facilitated the making of policy based on simple economic grounds.  This outcome did not flow from a political or intellectual commitment to the economically based technocratic rationality; many practical, not only intellectual or ideological, considerations would have been entailed in producing a different result.

Extracted from Taylor, P.J. (2005) Unruly Complexity: Ecology, Interpretation, Engagement (U. Chicago Press), chapter 4.


Collins, H. M. (Ed.) (1981a). “Knowledge and contingency.” Social Studies of Science 11: 3-158.

—— (1981b). “Stages in the empirical programme of relativism.” Social Studies of Science 11: 3-10.

Hacking, I. (1983). Representing and Intervening. Cambridge: Cambridge University Press.

Harwood, J. (2000). “National differences in academic cultures: science  in Germany and the United States between the world wars,” in C. Charle, J. Schriewer and P. Wagner (Eds.), Transnational Intellectual Networks and the Cultural Logics of Nations.  Oxford: Berghahn Books.

Hughes, E. C. (1971). The Sociological Eye. Chicago: Aldine Atherton.

Keller, E. F.  (1992). “Critical Silences in Scientific Discourse: Problems of Form and Re-form,” in Secrets of Life, Secrets of Death: Essays on Language, Gender and Science.  New York: Routledge, 73-92

Nelkin, D. (Ed. (1984). Controversy:  Politics of Technical Decisions. Beverly Hills, CA: Sage.

Robinson, S. (1984). “The Art of the Possible.” Radical Science Journal 15: 122-148.

Heterogeneous construction of scientific knowledge and practice: II. Diverse components in development of the simulation model

My analysis of the Kerang project begins with the modeling because that was the part that I, as a participant, observed most closely.  I refer to myself in the third person as “the modeler” to express some distance between my position and actions in 1978–79 and my subsequent interpretive role.

My analysis of the modeling begins from the observation that the development of the Kerang Farm Model (KFM) drew on many diverse “components.” (The reason for choosing this neutral term will emerge in due course.)  These components included: data on soil quality; expected crop yields; range of farm sizes; technical assumptions used in the linear program; the status of the different agents in the project; the geographical distance between the Institute and the Kerang region; the computer packages available; the terms of reference set by the Ministry; and so on.  Moreover, many of these components link the different domains of social action or “social worlds” (Clarke 1990, 1991; Fujimura 1988) of the various agents—from the modeler to the farmers.  I need to put some order into this heterogeneity of components and assess their relative importance to the knowledge-making.  My experience as the modeler allows me to unpack parts of the processes of model building here.

Figure 4.2  A schema of the diverse components involved in the production of the Kerang Farm Model (Symbols: PI, Principal investigator; M, modeler; AgEc, agricultural economist;  AgEx, agricultural extension officers.  See text for discussion.)

Consider the central technical feature of the KFM, the use of a linear program for economic analysis.  This required the assumption that farmers operate to maximize one objective: in the KFM, this objective was income.  Furthermore, the use of a linear program for policy formation assumed that if the optimal mix of farming activities according to the KFM were different from a farmers’ existing mix, the farmer would change accordingly and immediately.  Even though the economic future of the region obviously entailed the farmers’ participation, the study was not designed to investigate why and how farmers change, how directly and readily they respond to economic signs, or the extent to which any overriding economic rationality governed their actions.  Finally, the use of the linear program assumed that all the relevant activities could be well specified.  However, the potential economic and ecological benefits of novel long-term options, such as selective reforestation and organic soil restoration, were difficult, if not impossible, to estimate without knowing the outcome of measures that has not yet been implemented, such as experimental plots, publicity, education, advocacy, and subsided loans for tree planting.

The modeler questioned these technical assumptions.  He expressed interest in techniques that incorporated more than one objective, but the principal investigator could not envisage modeling an alternative objective to income.  In any case, software for multiobjective analysis was not available at the computer center used by the Institute.  The modeler designed the KFM to allow the effect over time of new investments to be examined, but when the project approached its deadline, this part of the model development was halted.  The modeler learned of a sociological study on the factors influencing Kerang farmers to change their practices.  This study had not, however, been released at that time and the principal investigator lent no institutional support to obtain advance access to it.  Finally, the modeler located informants with experience in reforestation and organic soil restoration, but was told that these and other issues were outside the economic specialization of the Institute and best left for others to deal with.

In affirming the technical assumptions in the KFM, the principal investigator invoked his senior and permanent position at the Institute, the terms of reference and deadlines that the Ministry had set, and the Institute’s specialization in quantitative economic research.  These assumptions, in turn, had several consequences for the research.  They eliminated certain issues from investigation, e.g., farmer’s views of their objectives.  They shaped the data that needed to be collected, e.g., there was no need to investigate how farmers change or build scenarios for the novel long-term options.  And they colored the relationships put into the model—e.g., the time course of investment became a secondary issue to the farmers’ income-optimizing activities.  The authority over the young researcher exercised here by the experienced principal investigator was not extraordinary.  Nevertheless, through such exchanges the principal investigator and the modeler were negotiating the different components of what would count as a representation of reality and a guide to policy formation.

Of course, there were parties other than the principal investigator and the modeler potentially involved in accepting or disputing the KFM.  The farmers might have objected to the way their behavior was modeled.  The KFM could also have been disputed by economists interested in multi-objective techniques; by sociologists interested in how people act, interact, and change; or by agricultural policymakers interested in using the study to help change the state of farming in the region.  None of these potential disputes proved significant at the time.  The farmers were separated from the formulation and operation of the KFM.  Conversely, the KFM was insulated from the farmers by several considerations: location—the modeling was performed in the city; the chain of personnel involved—modeler–agricultural economist–senior agricultural extension officers–local agricultural extension officers–farmers; and levels of abstraction and generalization.  No one in the Institute, the principal investigator in particular, had training in multi-objective economic analysis or ready access to suitable computer software.  There were no sociologists included in the project team or advisory committee.  The Ministry, through the range of options established in the terms of reference for the study, indicated that change would be initiated by government policy based on economic and engineering criteria.  The farmers were, in effect, to be instruments, more than co-participants, in determining the future of the region.  In short, the Ministry did not dispute the KFM as a representation of reality; neither were any farmers, economists, or sociologists in a position to do so.

Extracted from Taylor, Peter J. 2005. Unruly Complexity: Ecology, Interpretation, Engagement. U. Chicago Press.


Clarke, A. (1990). “A social worlds research adventure: The case of reproductive science,” in S. E. Cozzens and T. F. Gieryn (Eds.), Theories of Science in Society.  Bloominton: Indiana University Press, 15-42.

—— (1991). “Social worlds/arenas theory as organizational theory,” in D. R. Maines (Ed.), Social Organization and Social Process: Essays in Honor of Anselm Strauss.  New York: Aldine de Gruyter, 119-158.

Fujimura, J. (1988). “The molecular biology bandwagon in cancer research:  Where social worlds meet.” Social Problems 35: 261-283.

Heterogeneous construction of scientific knowledge and practice: I. A case of simulating the future of a salt-affected agricultural region

The concept of heterogeneous construction applied to science highlights the ways that scientists mobilize a diversity of resources and, in so doing, engage with a range of social agents.  This idea is illustrated in the next three posts.  In this first post, a situation is described.
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Rehabilitating a biological notion of race? II

Sesardic (2010) makes the point that the fact that genetic variation within a group is of larger than variation between (the average of) the groups does not mean that the groups cannot be distinguished. This point is not, however, sufficient to rehabilitate a biological picture of race.  I continue from the previous post to sketch the issues we face once we delve deeper into the relevant scientific knowledge, concepts, methods, and questions for inquiry…

2. Putting point #1 aside, suppose we imagine an original human gene pool that dispersed at some point of time from its origins in Africa around the world and was not subject to subsequent breeding among widely dispersed parts of the pool. Cluster analysis techniques could be used on genetic data to divide humans into, say, N groups. Such clustering techniques are sensitive to assumptions that determine whether groups are of roughly equal size or are a mix of a few large groups. If we looked for groups that had similar within-group genetic variation, most of the N groups would be in Africa. In other words, the traditional subdivisions of human races would have to be reformulated. However, experience using cluster analysis in large agricultural data sets (Cooper and Hammer 1996) suggests that many individuals cannot be consistently be assigned to one group versus another; the grouping changes according to what traits (in our case, variants at genetic loci or SNPs) and how much each group is represented in the data.

3. Suppose we now add migration subsequent to the initial dispersal, but without cross-breeding among the groups. Picking up this last point in #2, if individuals from non-African groups outweigh those from African groups in, say, the United States, then how well could we recover from the U.S. data all the groups delineated in #2? That would be an empirical question, but the experience from agriculture warns us not to be optimistic.

4. Of course, #3 is only a thought-experiment. There has been considerable migration and cross-breeding subsequent to the initial dispersal from the place of human origin in Africa, including but not confined to the recent centuries of cross-Atlantic slavery and master-slave relations. How well could we recover from current individuals the one or more groups (as delineated in #2) that make up the individuals´ ancestry? Again, this is an empirical question. Biomedical researchers do not have to be politically biased to judge that research efforts might be more fruitfully directed along other avenues, such as those indicated by biomedical correlates of socially defined race (i.e., not the groups that would emerge from the cluster analyses in #2).

5. Perhaps, we could ask less than we have in #4. Rather than full recovery of original ancestries, we might seek simply want to predict whether an individual patient has some major biomedically relevant genes that differed, on average, among the original groups. These predictions, necessarily probabilistic, would be limited in value given the recently-emerged consensus that most medically significant traits are associated with many genes of quite small effect (McCarthy et al. 2008). Moreover, given that the groups delineated in #2 would not match the traditional subdivisions of human races or those current in the U.S.—there would be several different groups of African origin—medical practitioners would need to disregard superficial assignments to racial groups. They might just as well test directly for the presence or absence of the biomedically relevant genes.

6. If we put #1-5 aside and imagine a world in which we were able to use genetic information to assign humans to original post-dispersal groups as reliably as in the statistics class we were able to assign individuals to male and female groups. What could we do with that knowledge that there is a difference between the average genetic profiles for groups A and B when there is large within-group variation for most genetic loci (at least, for those that vary within the human species)? Let me accentuate this question with using the IQ test score case Sesardic has paid considerable attention to (2005). Suppose we knew (which we do not) that only a certain small set of genes influenced IQ test scores. What could we do with the knowledge that there is a large difference between the average IQ test score for two groups and this difference is smaller than the within-group variation? (To visualize this situation, imagine one of the axes in the Figure is IQ test score.) I would not use my ability to assign humans to original post-dispersal groups based on genetic profiles as grounds for using an individual´s membership in a group to make educational or employment decisions for the individual. But I will not speak for Sesardic; I do not know what he thinks would follow if a biological view of race were to be rehabilitated along the lines he discusses.

7. There are clearly many issues to be delved deeply into before the relevant science about human genetic variation would support a biological notion of human ancestral groupings. A biological notion of socially and historically varying racial categories lies well outside the scope of “what the best contemporary science tells us about human genetic variation.”

Cooper M, Hammer GL (eds) (1996). Plant Adaptation and Crop Improvement. CAB International, Wallingford, UK.

McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JPA, Hirschhorn JN  (2008) Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature Reviews Genetics 9: 356-369.

Sesardic N (2010) Race: A Social Destruction of a Biological Concept.  Biology and Philosophy 25:143-162.