I’ve been thinking about how the anti-science label tends to get assigned to anyone who tries to move the GMO debate to the level of political economy. (That is, away from whether GMO food is safe and towards who controls production and prices.) Continue reading
a longstanding tension within agricultural research between, broadly speaking, breeders and physiologists. Breeders seek improvement through selection of varieties combined with plant or animal husbandry appropriate to those varieties. Physiologists focus on determining and manipulating the specific genetic and environmental factors underlying the development of the trait in question. (In this era of genomics, breeders may also be physiologists, but let [us] continue distinguishing the two ideal types.) Breeders are not uninterested in the underlying factors. They make hypotheses about such factors based on… trials [of multiple varieties grown in multiple locations] as well as sources other than the data analysis, then use these hypotheses to plan the next set of varieties and locations on which to collect data… Physiologists make much less use of variety-location trials to generate hypotheses; instead they focus on experiments under controlled conditions. Since the advent of DNA technologies, their experiments have included modification of specific genetic factors.
In agricultural trials, where a number of varieties or animals or plants can be raised or grown in multiple replicates in many locations, varieties can be grouped by similarity in responses across all locations using techniques of cluster analysis (Byth et al. 1976). Varieties in any resulting group tend to be above average for a location in the same locations and below average in the same location… The wider the range of locations in the measurements on which the grouping is based, the more likely it is that the ups and downs shared by varieties in a group are produced by the same conjunctions of measurable factors [genes and environmental factors].
Introduction to my essay review of
Biology Under the Influence: Dialectical Essays on Ecology, Agriculture, and Health, by Richard Lewontin and Richard Levins, Monthly Review Press, 2007
In “A Program for Biology,” one of this collection’s thirty-one essays, the Marxist biologists Richard Lewontin and Richard Levins (hereon: L&L) list recent “big mistakes” in scientific approaches to complex phenomena: “the green revolution, the epidemiological transition [from infectious to chronic diseases], sociobiology, the reification of intelligence testing, and the current fetishism of the genome.” They attribute such mistakes to the “posing [of] problems too narrowly, treating what is variable as if it were constant and even universal, and offering answers on a single level only” (p.81). What they point to is not simply the “philosophical tradition of reductionism,” but also “the institutional fragmentation of research, and the political economy of knowledge as a commodity” (p.9). Indeed, their critical position extends beyond science to rejection of “the greed and brutality and smugness of late capitalism” (p.373).
Their anti-capitalist stance notwithstanding, the foci or starting points of L&L’s essays, like their 1986 collection, The Dialectical Biologist, lies in research in the life sciences. Regarding the green revolution, for example, L&L see:
…that a view based on unidirectional causation leads to the expectation that since grasses need nitrogen, a genotype that takes up more nitrogen would be more productive; since pesticides kill pests, their widespread use would protect crops; and since people eat food, increased yields would alleviate hunger (p.84).
The actual outcomes did not end up matching such simple causation because:
…the increase in wheat yield was partly achieved by breeding for dwarf plants that are more vulnerable to weeds and to flooding; the killing of pests was accompanied by the killing of their natural enemies, their replacement by other pests, and the evolution of pesticide resistance. The successful yield increases encouraged the diversion of land from legumes. The technical packages of fertilizers, pesticides, irrigation, and mechanization promoted class differentiation in the countryside and displacement of peasants (p.84).
“A Program for Biology” ends with three fundamental questions for the study of complexity:
Why are things the way they are instead of a little bit different (the question of homeostasis, self-regulation, and stability)? Why are things the way they are instead of very different (the question of evolution, history, and development)? And what is the relevance to the rest of the world? (p.86)
The third question, rephrased in a later essay as “how [do we] intervene in these complex processes to make things better for us”? (p.115), invites… readers to ask what L&L’s essays tell us about having an effect—direct or indirect—on the complex processes of the production and application of scientific knowledge. The essay review approaches this third question as it relates to social studies of science and technology and L&L’s contributions from four angles:
- a more vigorous culture of science criticism;
- a visible college of Marxist scientists in the USA;
- inquiries into the diverse social influences shaping science; and
- motivating readers who want to pursue their science as a political project.
Indirect contributions—influences on and appropriations by other actors in the wider realm of biology as politics—are discussed as well as the more direct effects.
Heritability is a technical term, which originated in agriculture before World War 2, that has no obvious relationship with the colloquial idea of a parent passing on genes to an offspring. The technical meaning can be presented in a not-so-mathematical way and thus made accessible for non-specialists. (That, in turn, makes it easier to speak clearly about the limitations of research on the heritability of human characteristics, but that is not a topic for this post.)
In the picture below, we imagine many genetically defined and reproducible varieties of, say, plants, raised in many locations. (Genetically defined is easy to visualize if individuals of the variety are clones of each other, as they are when a plant is grown from a cutting of another plant. It can also be thought of as genealogically defined, e.g., each individual is a grandchild of the same two pairs of grandparents.)
In each location multiple individuals—”replicates”—are raised. The individuals in all the locations for all the varieties are all measured for, say, height, and there is variation among those measures—thus the different sized curly brackets below.
The average or mean height for each variety can be calculated and there is variation among those variety means. Heritability is the ratio of variation among the variety means and the total variation across the whole data set. That’s it. Notice that heritability is derived from measurements of an observed trait; there has been no mention of genetic or environmental factors involved in producing the trait.
With humans, we can replicate a variety in the form of identical or fraternal twins, so, with only two individuals in each genetically defined variety, there will be many, many points in the diagram below for which there is no measured value. That means that the estimate of heritability must be more approximate.
According to the perspective of heterogeneous construction, scientists mobilize a diversity of resources and, in so doing, engage with a range of social agents. Similarly, when interpreters of science delimit the relevant resources and agents, they also mobilize resources and engage with diverse social agents (Taylor 2005, Chapter 5, section A). Interpreters of science who recognize this might then reflect explicitly on the practical conditions that enable them to build and gain support for their interpretations. Applying the same interpretive framework to one’s own research should enhance the plausibility of their reconstructions of the work of scientists.
There might be more direct way that heterogeneous constructionist interpretation might influence science productively. Instead of relying on some second party to do the reconstruction, could scientists—or indeed any researchers—interpret their own heterogeneous webs? Could researchers reflect explicitly on how their own social embeddedness or situatedness affects their ability to study the situations that interest them? Could they attempt to identify multiple potential sites of engagement and change for themselves? If so, this would cut through some of complexities arising from interpreters trying to model practical reflexivity.
Mapping, Map-makers, and Maps
To explore this possibility with a number of ecologists and natural resource researchers, I convened two “mapping workshops”—the first in Helsinki, co-led with ecologist-philosopher Yrjö Haila; the second in Berkeley. These workshops were designed to proceed as follows. Each researcher would focus on a key issue—a question, dispute, or action in which the researcher was strongly motivated to know more or act more effectively. All researchers would identify “connections”—things that motivated, facilitated, or constrained their inquiry and action. These might include theoretical themes, empirical regularities, methodological tactics, organisms, events, localities, agents, institutional facilities, disputes, debates, and so on. Researchers would then draw their “maps”—pictorial depictions employing conventions of size, spatial arrangement, and perhaps color that allow many connections to be viewed simultaneously. The map metaphor was meant to connote not a scaled-down representation of reality but a device that shows the way—a guide for further inquiry or action (Taylor and Haila 1989; Taylor 1990).
Over a series of sessions the workshop participants would present these maps and be questioned by other participants. As a result they might clarify and filter the connections and reorganize their maps so as to indicate which connections were actually significant resources. The ideal was that researchers would self-consciously modify their social situations and their research together, perhaps in collaborations formed among the workshop participants. Of course, given that mapping was an experiment, it was not surprising that the ideal was not realized in these initial two workshops.
Three maps from the workshops illustrate the map making that resulted. Figure 1, by a Finnish ecologist I will call “E,” was the most orderly of the maps, having been streamlined and redrawn on a computer. As such it does not do justice to the real-time experience of its production during an actual workshop. Indeed, when viewed on their own all the maps appear schematic; valuable history, emphasis, and substance were added when the mapmakers presented their maps to other workshop participants.
The central issue on E’s map is very broad, namely, to understand the ecology of carabid beetles living in the leaf litter under trees in urban environments. On the map below this issue are many theoretical and methodological sub-problems, which reflect the conventional emphasis in science on refining one’s issue into specialized questions amenable to investigation. Above the central issue are various background considerations, larger and less specific issues, situations, and assumptions that either motivated work on the central issue or were related to securing support for the research. E’s research alone would not transform the urban public into recognizing that “nature is everywhere—including in the cities,” but by combining the upward and downward connections, he reminded himself that work on the background issues, not only refining a working hypothesis, would be necessary to be able to keep doing his research.
In narrating his map, E mentioned some additional history. Many of the ecologists with whom he collaborated had been studying a forest area, but the group lost their funding when the Forestry Department asserted that forest ecology was their own domain. It did not matter that animals are barely mentioned in the ecology of forestry scientists. The ecologists self-consciously, but of necessity, turned their attention to the interconnected patches of forest that extend almost to the center of Helsinki, and explored novel sources of funding and publicity, including a TV documentary. The upward connections were thus a recurrent, if not persistent, influence on E as he defined his specific research questions.
Historical background depicted in a narrative format is more evident in a large map by “R,” a Mexican who had come to specialize in the economic and agronomic dynamics which lead to impoverishment of peasants, their migration into forest areas, and subsequent clearing of those forests. Figure 2 is only one section of that map. Although radically different from E’s redrawn map, R’s map also highlighted simultaneous issues of building the disciplinary and collaborative context in which to pursue his many concerns. As a biologist he wanted to stem rainforest destruction; as a political activist he wanted to reduce rural poverty; and as a resource economics graduate student in the U.S. he needed to frame technical questions that could be answered.
In Figure 3 “M,” an American studying land degradation and impoverishment among nomadic pastoralists in West Africa, depicted a more conventional conception of research. Questions form the bulk of the map and are separated from methods—the strip along the bottom. M omitted the movements, arrangements, alliances, and negotiations he built in order to monitor milk production, elicit from the herders rules governing herd movement, assess herd ownership, measure the effect of grazing on pasture growth, complete surveys to “ground truth” satellite images, and so on. M’s map also located him in his remote field area, and omitted the audiences in the U.S.—sponsors and critics alike—for his current and future research. In short, notwithstanding the guidelines I had given to mapmakers, M included the situation he studied and left himself out.
To what extent, recalling the goals of mapping workshops, did the workshops lead participants to “clarify and filter the connections and to reorganize their maps”? It took considerable time to prepare maps, and the mapmakers did not devote more time to redraw their maps in response to interaction during the mapping sessions. M, for example, did not redraw his map to include his own context. To what extent then did researchers realize the ideal of “self-consciously modify[ing] their social situations and their research together, perhaps in collaborations formed among the workshop participants”? Several participants, at the Helsinki workshop in particular, claimed that the mapping workshop had expanded the range of influences, both theoretical and methodological, that they would bring into planning their future work. One workshop participant commented that mapping made it impossible “simply to continue along previous lines.” Nevertheless, although the workshops provided the opportunity to link up with others around revealed affinities, no new coalitions emerged; changes in the researchers’ work were not so dramatic.
Extracted from Taylor, P.J. (2005) Unruly Complexity: Ecology, Interpretation, Engagement (U. Chicago Press), chapter 5, Part B.
Taylor (1990). “Mapping ecologists’ ecologies of knowledge.” Philosophy of Science Association 2: 95-109.
Taylor and Y. Haila (1989). “Mapping Workshops for Teaching Ecology.” Bulletin of the Ecological Society of America 70(2): 123-125.
What can researchers do on the basis of knowing a trait’s heritability if the genetic and environmental factors underlying the observed trait are heterogeneous, or if the method of data analysis does not allow researchers to rule out the possibility of underlying heterogeneity? What steps and conditions are needed for researchers to bridge or circumvent the knowledge that underlying factors may be heterogeneous? There seem to be six directions that researchers might pursue:
a. Undertake research to identify the specific, measurable genetic and environmental factors without reference to the trait’s heritability or the other fractions of the total variance (e.g., Moffitt et al. 2005, Davey Smith and Ebrahim 2007, Khoury et al. 2007). Discussion of this direction of research takes us beyond heritability studies.
b. Use high heritability to guide molecular research to identify the specific genetic factors involved. There may be traits for which the underlying factors are not heterogeneous. These might be worth finding even if researchers do not know in advance the proportion of fruitful investigations compared with those confounded by the underlying heterogeneity. The search here is not for high penetrance major genes; these can be detected through examination of family trees; heritability analysis need not be involved. Rather, researchers need to find traits in which many underlying genetic factors each of small influence turn out to be similar for all individuals who show the same value for the trait within some defined population.
c. Restrict attention to within a set of relatives. Even if the underlying factors are not yet known, high heritability still means that if one twin develops the trait (e.g., type 1 diabetes), the other twin is more likely to as well. This information might stimulate the second twin to take measures to reduce the health impact if and when the disease starts to appear. However, notice that this scenario assumes that the timing of getting the condition differs from the first twin to the second. Researchers might well ask: What factors influence the timing? How changeable are these? How much reduction in risk comes from changing them? To address these issues researchers have to identify the genetic and environmental factors involved in the development of the trait and to secure larger sample sizes than any single set of relatives allows. The question then arises whether the results can be extrapolated from one set of relatives to others. The possibility of underlying heterogeneity has not, therefore, gone away as an issue. The answer to the question of extrapolation is an empirical one; there is a risk, as before, that the proportion of fruitful investigations will be low compared to those confounded by factors not extrapolating well from the initial set of relatives.
d. Put aside the search for measurable factors. Instead, focus on heritability as a fraction of the variation among measurements. This focus is useful in agricultural and laboratory breeding. If the actual advance under selective breeding is less than predicted, one source of the discrepancy might be the underlying heterogeneity of genetic factors and their reassortment through mating. Again, this matters little for breeders because they can always compensate for discrepancies: they discard the undesired offspring, breed the desired ones, and continue. Of course, selective breeding is not an acceptable option for humans. What is left is the intuition that genetic factors have a larger influence than environmental factors for high heritability traits. This is problematic (Taylor 2010, p. 13ff), even more so when researchers consider models that allow for heterogeneous factors to underlie the trait.
e. Reduce the possibility of underlying heterogeneity by restricting the range of varieties or locations. Agricultural researchers can reduce the possibility of underlying heterogeneity by restricting the range of locations in which a variety is raised or grown. They can also control environmental conditions, such as, for animals, the regimes of feeding and husbandry or, for plants, the application of fertilizer and irrigated water. Agricultural breeders can also produce inbred lines and thereby eliminate the heterogeneity of genetic factors that exists within outbred varieties. However, to envisage taking action on the basis of research conducted under restrictive conditions is to presume that the restrictive conditions can be replicated. This presumption is most apparent when plant breeders recommend varieties to be grown only in defined regions and under prescribed techniques of cultivation, or when animal breeders specify the optimal feeding and husbandry for each variety. In the study of human traits, however, it is not feasible to control the full range of relevant environmental conditions or to breed for genetic uniformity. It may be possible to restrict the locations included in a human study (e.g., to include only families of low socioeconomic status; Turkheimer et al. 2003). The heritability estimates would be reliable to the extent that these restrictions were replicated in subsequent research or policy. That is, the research could be applied even though the environmental factors underlying those locations had not been identified.
f. Reduce the possibility of underlying heterogeneity by grouping varieties that are similar in responses across locations. (Note that when analyzing measurements from studies of human twins because such studies have only two replicates [twins] in one or at most two locations [families], so this is not a feasible direction by which research on human variation can bridge or circumvent the knowledge that underlying factors may be heterogeneous.) In agricultural trials, where a number of varieties or animals or plants can be raised or grown in multiple replicates in many locations, varieties can be grouped by similarity in responses across all locations (using techniques of cluster analysis; Byth et al. 1976). (Similarly, locations can be grouped by similarity in responses elicited from varieties grown across those locations.) Varieties in any resulting group tend to be above average for a location in the same locations and below average in the same location (Taylor 2010). The wider the range of locations in the measurements on which the grouping is based, the more likely it is that the ups and downs shared by varieties in a group are produced by the same conjunctions of underlying measurable factors. This gives researchers more license to discount the possibility of underlying heterogeneity within a group. If the underlying factors are assumed to be homogeneous within each of the groups, researchers can hypothesize about the group averages—about what factors in the locations elicited basically the same response from varieties in a particular variety group that distinguishes them from other groups. (It should be noted that knowledge from sources other than the data analysis is always needed to help researchers generate any hypotheses about genetic and environmental factors.)
In summary, unless you can think of directions of research other than the six above, there is very little that researchers on human variation can do that is reliable on the basis of knowing a trait’s heritability if the genetic and environmental factors underlying the observed trait are heterogeneous. Agricultural researchers can do more because they have greater control of their varieties and conditions in test locations.
Adapted from Taylor (2010) and Nature-Nurture? No… A Short, but Expanding Guide to Variation and Heredity (work in progress)
Byth, D.E., Eisemann, R.L. and DeLacy, I.H.: 1976, Two-Way Pattern Analysis of a Large Data Set to Evaluate Genotypic Adaptation. Heredity 37(2), 215-230.
Davey-Smith, G. and Ebrahim, S.: 2007, Mendelian randomization: Genetic variants as instruments for strengthening causal influences in observational studies, in Weinstein, M., Vaupel, J. W., Wachter, K.W. (eds.) Biosocial Surveys. Washington, DC, National Academies Press, pp. 336-366.
Khoury, M.J., Little, J., Gwinn, M. and Ioannidis, J.P.: 2007, On the Synthesis and Interpretation of Consistent but Weak Gene-Disease Associations in the Era of Genome-Wide Association Studies. International Journal of Epidemiology, 36, 439-445.
Moffitt, T.E., Caspi, A. and Rutter, M.: 2005, Strategy for Investigating Interactions between Measured Genes and Measured Environments. Archives of General Psychiatry, 62(5), 473-481.
Taylor, P. “Three puzzles and eight gaps: What heritability studies and critical commentaries have not paid enough attention to,” Biology & Philosophy, 25:1-31, 2010
Turkheimer, E., Haley, A., Waldron, M., D’Onofrio, B. and Gottesman, I.I.: 2003, Socioeconomic Status Modifies Heritability of IQ in Young Children, Psychological Science 16(6), 623-628