Intersecting Processes

complexity & change in environment, biomedicine & society

February 18, 2011
by peter.taylor
0 comments

Taxonomy of heterogeneities

Contention motivating this taxonomizing: Research as well as the application of knowledge resulting from research are untroubled by heterogeneity to the extent that populations are well controlled. Such control can only be established and maintained with considerable effort or social infrastructure, which invites attention to possibilities for participation instead of control of human subjects.

The taxonomizing is an incomplete work in progress; comments welcome.

Kinds of heterogeneity

Static 1. There is an assortment, each a separate type (“cabinet of curiosities”)
or 2. Mixture of types (e.g., allelic heterogeneity & locus heterogeneity in genetics)
Variational 3. Trait = composite of types (analogy: the 3 components of a triathalon)
4. There is variation, not types
5. Variation in a set of traits involves a composite of variance/covariance structures (statistical heterogeneity)
6. When similar responses of different individual (e.g., genetic) types are observed, it is not necessarily the case that similar conjunctions of risk or protective factors have been involved in producing those responses (=possibility of “underlying heterogeneity”)
Dynamic 7. Variation produces qualitative changes in results from standard theory based on uniform units (e.g., theory about Malthusian population growth, tragedy of the commons, prisoner’s dilemma)
8. “Unruly complexity,” which arises whenever there is ongoing change in the structure of situations that have built up over time from heterogeneous components and are embedded or situated within wider dynamics. (Synonym: “intersecting processes”)
8a. In heterogeneous construction researchers establish knowledge and technological reliability through practices that are developed through diverse and often modest practical choices. This is the same as saying they are involved in contingent and on-going mobilizing of diverse materials, tools, people, and other resources into webs of interconnected resources.
Dynamic-participatory 9. Multiple points of engagement allow for participatory restructuring of unruly complexity or heterogeneous construction
10. Participatory restructuring, which occurs in tension with deployment or withholding of trans-local knowledge and resources.

Actions corresponding to each kind of heterogeneity

including the control (C) that allows one not to be troubled by the heterogeneity and possibilities for participation (P)

1. Question [P] (or suppress the question [C]) about why this assortment has been collected into one list.
2. In medical sociology Brown & Harris find common meaning despite different types of experience (through coding of sameness despite surface heterogeneity).
3. Disaggregate/decompose into separate phenomena
4. C: Make people fit types (stereotyping, panopticon, screening & surveillance, public health measures, diagnostic manuals, reassignment surgery…) Control/ignore non-conformers.
5.
6. C: Look for subclasses in which underlying factors are uniform. If found, use to probe or extrapolate (perhaps unsuccessfully) back to other subclasses.
7.
8. Diagramming of intersecting processes, which exposes multiple points of engagement->8a
8a. Mapping by researchers of situations and situatedness [P]
9. Well-facilitated participatory processes
10.

References

Taylor, P. J. (2005). Unruly Complexity: Ecology, Interpretation, Engagement. Chicago, University of Chicago Press.
Taylor, P. J. (2009). “Infrastructure and Scaffolding: Interpretation and Change of Research Involving Human Genetic Information.” Science as Culture, 18(4):435-459.
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. (DOI 10.1007/s10539-009-9174-x).

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February 17, 2011
by peter.taylor
2 Comments

"Race: A Social Construct or a Scientific Reality?"

Discussion on WUMB Commonwealth Journal  based on new exhibit at the Boston Museum of Science exhibit, Race: Are we so different?

Broadcast on Sunday February 13, 2011.  Speakers: Peter Taylor, Nina Nolan, Chair, RACE Education Team, Boston Museum of Science, and WUMB host, Janis Pryor. Click here to listen to  Podcast.

Afterthoughts on the discussion:

1.  The host did well to launch us into the discussion without precirculation of questions.  The broadcast ended up cutting out only about 5 minutes of the recorded discussion.

2. I was cast as the scientist who would supply the definitive answers about the biological (genetic) basis of race, as if the answer to the rhetorical question in the title given to the broadcast was there’s no scientific reality to race (with the implication that those who say there is are part of the longstanding, historically given problem of racism in the USA).

3. I tried to take the role of someone who was informed by the science, but wanted all listeners to be able to think about the complexities of the issues around race.

4. One of these issues is helping people who think it’s plausible that average differences in, say, IQ test scores, among social/racial groups could be explained by genetic differences see the problems in supporting such an idea with evidence.

5. Another of the issues is the fact that, even if races are defined by (shifting unreliable) social definitions, the experience of living with such definitions can have a significant impact on one’s biology and psychology–that is, it becomes a scientific reality in another sense.

6. I tried to do #4 on the radio and they didn’t edit it out, but visual aids would have helped and even then I need more practice if the take-home point is to come across.  #5 isn’t so hard to convey, but I didn’t get into countering the rejoinders that hypothesize that average differences in susceptibility to illness among social/racial groups could be explained by genetic differences.

7. Re: the passage at the end where I try to speak of the cost of a racially divided society even to those that benefit from it, I need to keep working on how to express this to have impact (and not seem to discount the far greater costs to minorities).

Comments welcome.

January 31, 2011
by peter.taylor
0 comments

Towards an agent-oriented focus to social epidemiology

Under the life-course perspective that has developed in social and psychological epidemiology since the 1990s, researchers seek to reconstruct the complex causal processes that generate specific diseases and behavioral attributes (Kendler et al. 2005, Kuh and Ben-Shlomo 2004). However, some prominent social epidemiologists are becoming skeptical about the availability of the kinds of data and analyses needed to separate the effects of diverse biological and social factors that operate on a range of temporal and spatial scales and build up over a person’s life course (Davey-Smith 2007), or more generally, to “to identify modifiable causes of disease that can be utilized to leverage improved population health” (Davey-Smith 2008a, b; but see Lynch 2007).   Grounds for such skepticism are amplified by the possibility of heterogeneity, that is, when similar responses of different individual (e.g., genetic) types are observed, researchers need not assume that similar conjunctions of risk or protective factors have been involved in producing those responses.

This state of play leads me to emphasize the possibility of an agent-oriented focus, in which researchers depart from the traditional emphasis on exposures impinging on subjects and, instead, elucidate people’s resilience and reorganization of their lives and communities in response to social changes (Sampson et al. 1997).  The patterns those studies establish might not extrapolate readily over time, place, and scale.  They can, however, provide a point of departure for research and policy engagements in the next situation studied.  An agent-oriented epidemiologist would need to be conversant with studies of resilience and reorganization in communities, but also train in participant observation and qualitative methods for research on population health changes that arise through grassroots and professional initiatives and grow into loosely-knit social movements, e.g., around innovations in short-term therapy for depression (e.g., Griffin and Tyrell 2003, White and Denborough 1998).

I am interested in conversations with others who wish to examine the epidemiological significance of an agent-oriented focus.

References

Davey Smith, G. (2007). “Lifecourse epidemiology of disease: a tractable problem?” International Journal of  Epidemiology 36(3): 479-480.

Davey Smith, G. (2008a). “Epidemiology and the ‘gloomy prospect’: why epidemiologists are not in the business of understanding individual-level risks” (Lecture, January, Department of Social Medicine), University of Bristol.

Davey-Smith, G. (2008b). “‘Something funny seems to happen’: J.B.S. Haldane and our chaotic, complex but understandable world.” International Journal of Epidemiology 37: 423-426.

Griffin, J. & I. Tyrrell. (2003). Human Givens: A New Approach to Emotional Health and Clear Thinking. Human Givens Pub.

Kendler, K. S., C. O. Gardner, C. A. Prescott (2002). “Towards a comprehensive developmental model for major depression in women.” American Journal of Psychiatry 159: 1133-1145.

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

Lynch, J. W. (2007). “Relevant Risk, Revolution and Revisiting Rose – Causes of Population Levels and Social Inequalities in Health.” http://www.sph.umn.edu/ce/roundtable/Roundtable_032307.asp. (viewed 9 Sept. 2009)

Sampson, R. J., S. W. Raudenbush, F. Earls. (1997). “Neighborhoods and Violent Crime: A Multilevel Study of Collective Efficacy.” Science 277(5328): 918-924.

White, C. & D. Denborough, eds. (1998). Introducing Narrative Therapy. Dulwich Centre Publications

January 30, 2011
by peter.taylor
0 comments

A non-technical introduction to path analysis and structural equation modeling II: Heritability studies

In studies of heritability, a field in which path analysis originated, there are no measured variables except the observed focal variable (e.g., height). Path analysis can still be used if we convert the additive model on which any given Analysis of Variance (AOV or ANOVA) is based into an additive model of constructed variables that take the values of the contributions fitted to the first model.

For example, in an agricultural evaluation trial of many varieties replicated one of more times in each of many locations, the AOV model is

Yijk = M +Vi +Lj +VLij +Eijk (eqn. 1)

where Yijk denotes the measured trait y for the ith variety in the jth location and kth replication;
M is a base level for the trait;
Vi is the contribution of the ith variety;
Lj is the contribution of the jth location;
VLij is an additional contribution from the i,jth variety-location combination—in statistical terms, the “variety-location-interaction” contribution; and
Eijk is a noise contribution adding to the trait measurement.

The path model equivalent to equation 1 is
Yx = M +Z1x +Z2x +Z3x +Ex (eqn. 2)

where
Y is the measured trait as before and x denotes the replicates
Z1x = Vi if x if a replicate of variety i, or 0 otherwise
Z2x = Lj if x if a replicate in location j, or 0 otherwise
Z3x = VLij if x if a replicate of variety i in location j, or 0 otherwise
Ex = Eijk where x is replicate k of variety i in location j

The path coefficients are then set to equal the square root of the ratio of the variance of the contribution (Vi, etc.) to the total variance for the trait (Y). The equation of complete determination becomes
1 = Sum (over w’s) of variance (Zw) / var(Y) (eqn. 3)
where w denotes the different contributions in the Analysis of Variance model.

For the agricultural trial this equation might be written
1 = [var(V) + var(L) + var(VL) + var(E)] / var(Y) (eqn. 4)
where V = variance of the vi terms, etc.

In human studies the var(VL) is ignored or discounted (which is a shortcoming) and this is expressed as
1 = heritability + shared environmental effect + non-shared environmental effect (eqn. 5)

When the same trait is observed in two relatives, their separate path analyses can be linked in one network and the correlation between the relatives calculated (Lynch & Walsh 1998, 826)—provided it is assumed that the contributions (and path coefficients) apply to both and that the noise contributions are uncorrelated. If we have data on correlations for different kinds of relatives (e.g., identical vs. fraternal twins), we can estimate the relative size of the contributions in equations such as 4 and 5. That’s the crux of heritability studies.
(This post is a second supplement [see previous post] to a series laying out a sequence of basic ideas in thinking like epidemiologists, especially epidemiologists who pay attention to possible social influences on the development and unequal distribution of diseases and behaviors in populations [see first post in series and contribute to open-source curriculum http://bit.ly/EpiContribute].)
References
Lynch, M. and B. Walsh (1998). Genetics and Analysis of Quantitative Traits. Sunderland, MA, Sinauer.

January 29, 2011
by peter.taylor
0 comments

A non-technical introduction to path analysis and structural equation modeling

Path analysis is a data analysis technique that quantifies the relative contributions of variables (“path coefficients”) to the variation in a focal variable once a certain network of interrelated variables has been specified (Lynch & Walsh 1998, 823). Some of these contributions are direct and some mediated through other variables, i.e., indirect. Although some researchers interpret “contribution” in causal terms (e.g., Pearl 2000, 135 & 344-5), others criticize such an interpretation (e.g., Freedman 2005). Here, contribution refers neutrally to the term of an additive model fitted to data.

This post is a first attempt at a non-technical introduction to path analysis and structural equation modeling (alternatives expositions welcome).

The conceptual starting point for path analysis is an additive regression model that associates the focal (“dependent”) variable with several other measured (“independent” or “exogenous”) variables. (The vertical lines in these figures indicates that the separate horizontal lines are combined together.)

X1 —-|
X2 —-|—-> Y
X3 —-|

Technically, the additive model is transformed by subtracting the mean from every term, squaring the expression (so it is an equation for the variance), and dividing by the variance of the focal (“dependent”) variable. The result is the “equation of complete determination,” with the regression coefficients being multiplied by the SD of the other “independent” variables and divided by the SD of the focal variable to arrive at the path coefficient.

The next step is to consider more than one focal, “endogenous” variable and networks of exogenous and endogenous variables that you have reason to think are associated with one another. Indeed, the focal variable of one regression may be among the variables associated with a second focal variable and so on. In the figure below X3 has a direct link with Y2 and an indirect one through Y1.

X1 —-|
X2 —-|—-> Y1 -|–> Y2
X3 —-|————|

The software (e.g., LISREL) can solve these linked regression equations, but it is up to you to compare the results using the network you specify with plausible (theoretically-justified) alternatives that may link exogenous, independent variables and endogenous variables differently. Unlike multiple regression, we do not arrive at our idea of what should be in the regression by adding or subtracting variables in some stepwise procedure.

Structural equation modeling extends path analysis to include latent (a.k.a. unmeasured) variables or “constructs.” These latent variables are sometimes the presumed real underlying variable of which the measured one is an imperfect marker. For example, birth weight at full term and the neonate APGAR scores might be the measured variables but the model might include degree of fetal under-nutrition as a latent variable. Latent variables can also be constructed by the software in the same way that they are in factor analyses, namely, as economical (dimension-reducing) linear combinations of measured variables. Calling the networks of linked variables “structural” is meant to suggest that we can give the pathways causal interpretations, but SEM and path analysis has no trick that overcomes the problems that regression and factor analyses have in exposing causes.

(This post is a supplement to a series laying out a sequence of basic ideas in thinking like epidemiologists, especially epidemiologists who pay attention to possible social influences on the development and unequal distribution of diseases and behaviors in populations [see first post in series and contribute to open-source curriculum http://bit.ly/EpiContribute].)

References

Freedman, D. A. (2005). Linear statistical models for causation: A critical review. Encyclopedia of Statistics in the Behavioral Sciences. B. Everitt and D. Howell. Chichester, Wiley.
Lynch, M. and B. Walsh (1998). Genetics and Analysis of Quantitative Traits. Sunderland, MA, Sinauer.
Pearl, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge, Cambridge University Press.
http://en.wikipedia.org/wiki/Apgar_score

January 28, 2011
by peter.taylor
2 Comments

Popular epidemiology and health-based social movements

Idea: The traditional subjects of epidemiology become agents when: a. they draw attention of trained epidemiologists to fine scale patterns of disease in that community and otherwise contribute to initiation and completion of studies; b. their resilience and reorganization of their lives and communities in response to social changes displaces or complements researchers’ traditional emphasis on exposures impinging on subjects; and c. when their responses to health risks displays rationalities not taken into account by epidemiologists, health educators, and policy makers.

Compare & Contrast these works from early 1990s– Brown: Popular epidemiology (USA) and Davison: Lay epidemiology (UK)
(Brown 2006 provides a more recent contribution to popular epidemiology, and Lawlor 2003 to lay epidemiology.)

Epstein shows how AIDS activists influenced AIDS science—AZT vs. AIDSVAX
Schienke shows the possibilities for citizen surveillance of exposures

Black discusses evidence-based policy (which provides us a contrast)

(This post concludes a series laying out a sequence of basic ideas in thinking like epidemiologists, especially epidemiologists who pay attention to possible social influences on the development and unequal distribution of diseases and behaviors in populations [see first post in series and contribute to open-source curriculum http://bit.ly/EpiContribute].)

References

Black, N. (2001). “Evidence based policy: proceed with care,” BMJ 323: 275-279.
Brown, P. (1992). “Popular Epidemiology and Toxic Waste Contamination: Lay and Professional Ways of Knowing.” Journal of Health and Social Behavior 33: 267-281.
Brown, P., S. McCormick, et al. (2006). “‘A lab of our own’: Environmental causation of breast cancer and challenges to the dominant epidemiological paradigm.” Science, Technology, & Human Values 31(5): 499-536.
Davison, C., G. Davey-Smith, et al. (1991). “Lay epidemiology and the prevention paradox: The implications of coronary candidacy for health education.” Sociology of Health and Illness 13: 1-19.
Epstein, S. (1995). “The construction of lay expertise: AIDS activism and the forging of credibility in the reform of clinical trials.” Science, Technology, & Human Values 20(4): 408-437.
Lawlor, D. A., S. Frankel, et al. (2003). “Smoking and Ill Health: Does Lay Epidemiology Explain the Failure of Smoking Cessation Programs Among Deprived Populations?” American Journal of Public Health 93(2): 266-270.
Schienke, E. (2001). Bill Pease/ An original developer of scorecard.org / 2001. Troy, NY, Center for Ethics in Complex Systems.

January 27, 2011
by peter.taylor
0 comments

Genetic diagnosis, treatment, monitoring, and surveillance

Idea: Genetic analysis has begun to identify genetic risk factors. We need to consider the social infrastructure needed to keep track of the genetic and environmental exposures with a view to useful epidemiological analysis and subsequent healthcare measures. Even in cases where the condition has a clear-cut link to a single changed gene and treatment is possible, there is complexity in sustaining that treatment.

For a few years this decade, genome-wide association studies seemed to hold promise for detecting genes related to diseases and for the invention of drug-based treatments.

But, 18 months ago, http://www.nytimes.com/2009/11/18/business/18gene.html?_r=1&scp=1&sq=decode&st=cse , A Genetics Company Fails, Its Research Too Complex

In 2009, Khoury et al. were concerned that the promises were not over-stated. Look at the table giving their quality control proposal.

In 2005, Frank cautions that epidemiology needs as much data about environmental factors as genes, but observes that the playing field is not level. (Give credit of you ever cite this powerpoint.)

Even for (rare) diseases governed by single genes, the path from genetic diagnosis to therapy is complicated as the poster-child case of PKU shows. From Taylor (2009):

  • Diane Paul’s (1998) history of PKU screening describes, the certainty of severe retardation has been replaced by a chronic disease with a new set of problems. Screening of newborns became routine quite rapidly during the 1960s and 70s, but there remains an ongoing struggle in the USA to secure health insurance coverage for the special diet and to enlist family and peers to support PKU individuals staying on that diet through adolescence and into adulthood. For women who do not maintain the diet well and become pregnant, high levels of phenylalanine adversely affect the development of their non-PKU fetuses. This so-called maternal PKU is a public health concern that did not previously exist. In short, a more complex picture of development in a social environment is needed for anyone to make use of the knowledge that the fate of individuals with the PKU gene is not determined at birth.

Genetic analysis

This post’s readings deal with analysis of actual genes, whereas most of the previous post’s readings about heritability looked at variation in some observed trait. With the expanding role of genetic analysis comes the challenge of how to fit this into our healthcare system. Genetic risk factors associated with common diseases, individually may only have a weak risk-ratio, but combinations of these factors may, researchers hope, have a larger impact of the population.
Khoury et al try to make a case for establishing standards for “presenting and interpreting cumulative evidence on gene-disease associations.” They describe problems such as publication and selection biases, differences in collection and analysis of samples and the presence of undetected gene-environment interactions among studies of genome-wide analysis. This has lead to a high incidence of type 1 errors in GWA studies (false positives). Networks are attempting to establish consensus guidelines for reporting and publishing gene-disease associations to reduce this risk. (Do a web search to see how things have developed in the two years since.)
Bowcock describes how a consortium of 50 British groups examined genetic variance in a genome-wide association study. They examined the genetic issues for 7 common diseases including RA, CAD, bipolar disorders, diabetes, hypertension and Crohn’s disease. To identify the genetic risk factors for these common diseases, they examined 500,000 genetic markers(or SNPs-single nucleotide polymorphisms) from the genomes of 17,000 individuals. They found very little difference between controls and cases, but they did find some SNPs that can be considered genetic risk factors for a particular disease, some confirming previous studies, but others identifying unique genes that affect susceptibility to a disease.
The more advanced the genetic analysis becomes, the issue of how this information is going to be utilized for the treatment or monitoring of a person’s risk for disease and what part prevention and screening plays in the individual’s health status presents itself. Bowcock cautions that translating someone’s risk into “medical practice” should not be done without “larger patient populations, well-annotated clinical databases and sophisticated environmental assessment.”
Frank’s powerpoints remind us that knowledge about environmental factors is needed as well, but because it costs more to collect and store, it tends not to be collected. This will make some epi. research questions impossible to address and shape the kinds of knowledge that can be put into biomedical practice and social policy.

Social application of knowledge about genes
The Paul article gives us an example of how a rare genetic disorder, Phenylketonuria (PKU) has been managed. Even though the incidence is between 1 in 11,000 and 1 in 15,000 births, all newborns are tested for it in the US, Canada, Australia, New Zealand, Japan and most other Western and Eastern European countries. The article chronicles the history of instituting the screening procedures for PKU. PKU was described as a “treatable genetic disease.” If left untreated, it results in severe mental retardation and behavioral abnormalities. PKU can be treated by a special diet which eliminates phenylalanine toxicity in the blood of those with PKU. There were policy issues involved in the PKU screening process that warrant examination. What were the societal factors that contributed to the federal initiative in the US in 1961? Not everyone was a proponent of the testing of every newborn for such a rare genetic disease. Problems of treatment efficacy and the question of the “cost” of the program are also addressed.
As we become more advanced in genetic analysis, many similar issues may be encountered for other conditions. One current related topic is the role of BRCA1 and BRAC2 inherited breast cancer gene abnormalities. Although they only account for about 10% of all breast cancers, there is much discussion about the Pros and Cons of seeking your genetic profile for breast cancer. Issues of prophylactic breast removal surgery, discrimination by health insurers and stress and anxiety associated with knowing your genetic profile are all ones that can be related to other genetic testing.
Taylor (2009) looks in broad brush at the overall project of application of genetic information.

(This post continues a series laying out a sequence of basic ideas in thinking like epidemiologists, especially epidemiologists who pay attention to possible social influences on the development and unequal distribution of diseases and behaviors in populations [see first post in series and contribute to open-source curriculum http://bit.ly/EpiContribute].)

References

Bowcock, A. M. (2007). “Guilt by association.” Nature 447: 645-646.
Frank, J. (2005). “A Tale of (More Than ?) Two Cohorts – from Canada.” 3rd International Conference on Developmental Origins of Health and Disease.
Khoury, M. J., J. Little, M. Gwinn and J. P. Ioannidis (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.
Paul, D. (1998). The history of newborn phenylketonuria screening in the U.S. Final Report of the Task on Genetic Testing. Baltimore, Johns Hopkins University Press: 1-13.
Taylor, P. J. (2009). “Infrastructure and Scaffolding: Interpretation and Change of Research Involving Human Genetic Information.” Science as Culture, 18(4):435-459.

January 26, 2011
by peter.taylor
3 Comments

Heritability, heterogeneity, and group differences

Idea: As conventionally interpreted, heritability indicates the fraction of variation in a trait associated with “genetic differences.” A high value indicates a strong genetic contribution to the trait and “makes the trait a potentially worthwhile candidate for molecular research” that might identify the specific genetic factors involved. I contest the conventional interpretation and contend that there is nothing reliable that anyone can do on the basis of estimates of heritability for human traits. While some have moved their focus to cases in which measurable genetic and environmental factors are involved, others see the need to bring genetics into the explanation of differences among the averages for groups, especially racial groups.

a. Heritability & critique
Heritability is a quantity derived from analysis of variation in traits of humans, other animals, or plants in ways that take account of the genealogical relatedness of the individuals whose traits are observed. Such “quantitative genetic” analysis does not require any knowledge of the genes or “measurable genetic factors” involved.
Turkheimer is “on the left” of behavioral genetics, being much less gung ho about the implications of its findings. Here he gives a clear overview of what the field has shown.
Plomin articulates the confident consensus of behavior genetics, namely, that they’ve debunked the supposed environmentalist orthodoxy in social science that says that everything is social and have established a basis for connecting with molecular genetics to identify the actual genetic factors.
Rutter, a senior psychological researcher (who once worked with Brown on social determinants of mental illness), tries to moderate the “polarizing claims” and “unwarranted extrapolations.”
Taylor 2010 casts doubt on the findings that underlie both Turkheimer and Plomin’s articles by exposing problems with the concepts and methods used to arrive at those findings. Taylor ends with a nudge towards methods that use measured genetic factors as well as measured environmental factors (the latter being the staple of social epidemiology).

b. Interaction of measured genes and measured environments
Moffitt 2005 provides a review of what’s involved in trying to identify interactions between measured genetic and environmental factors. (Use Taylor 2010 to get clear about the difference between this kind of interaction and the classical genotype x environment interaction in quantitative genetics.) Caspi 2002 is one of two 2002 papers that caused a lot of splash. Davey-Smith picks up on the current consensus that the 2002 studies have been hard to replicate and invokes Mendelian randomization as a way to strengthen causal inference about interactions between measured genetic and environmental factors.

c. Data & models about heritability & change (or lack of it)
Dickens 2001 provides a resolution of the paradox that heritability of IQ test scores is reported to be high, but there has been a large increase in average IQ test scores from one generation to the next. We know that genes haven’t changed from one generation to the next, so Dickens’ account is also exposing a flaw in the logic that because heritability of IQ test scores is high within racially defined groups and because there is a large difference in average IQ test scores between whites and blacks, genetic factors are probably involved in that difference.
Rushton 2005 however thinks that 30 years of research has validated that idea.
Taylor 2010 refers to Dickens 2001, but gives a somewhat different spin on its implications.

(This post continues a series laying out a sequence of basic ideas in thinking like epidemiologists, especially epidemiologists who pay attention to possible social influences on the development and unequal distribution of diseases and behaviors in populations [see first post in series and contribute to open-source curriculum http://bit.ly/EpiContribute].)

References

Caspi, A., J. McClay, et al. (2002). “Role of Genotype in the Cycle of Violence in Maltreated Children.” Science 297(5582): 851-854.
Davey-Smith, G. (2009). “Mendelian randomization for strengthening causal inference in observational studies: Application to gene by environment interaction.” Perspectives on Psychological Science, in press.
Dickens, W. T. and J. R. Flynn (2001). “Heritability estimates versus large environmental effects: The IQ paradox resolved.” Psychological Review 108(2): 346-369.
Moffitt, T. E., A. Caspi, et al. (2005). “Strategy for investigating interactions between measured genes and measured environments.” Archives of General Psychiatry 62(5): 473-481.
Plomin, R. and K. Asbury (2006). “Nature and Nurture: Genetic and Environmental Influences on Behavior.” The Annals of the American Academy of Political and Social Science 600(1): 86-98.
Rushton, J. P. and A. R. Jensen (2005). “Thirty years of research on race differences in cognitive ability.” Psychology, Public Policy, and Law 11: 235-294.
Rutter, M. (2002). “Nature, nurture, and development: From evangelism through science toward policy and practice.” Child Development 73(1): 1-21.
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. (DOI 10.1007/s10539-009-9174-x).
Turkheimer, E. (2000). “Three laws of behavior genetics and what they mean.” Current Directions in Psychological Science 9(5): 160-164.

January 25, 2011
by peter.taylor
1 Comment

Life course epidemiology

Idea: How do we identify and disentangle the biological and social factors that build on each other over the life course from gestation through to old age?

The references below mostly relate to “life course epidemiology,” that is, fetal and developmental origins of diseases in late life (Barker being generalized by Ben-Shlomo), in some tension with development over the life course (incl. Berney reviewing lifetime accumulation of hazards in relation to health in old age). In contrast to this approach, we have Brown on life course influences on depression (not necessarily in old age).  A pertinent question: In what ways could either side usefully draw methods, data, results from the other?

(This post continues a series laying out a sequence of basic ideas in thinking like epidemiologists, especially epidemiologists who pay attention to possible social influences on the development and unequal distribution of diseases and behaviors in populations [see first post in series and contribute to open-source curriculum http://bit.ly/EpiContribute].)

References

Barker, D. J. P. (1998). Mothers, Babies, and Health in Later Life. Edinburgh, Churchill Livingstone.
Ben-Shlomo, Y. and D. Kuh (2002). “A life course approach to chronic disease epidemiology: Conceptual models, empirical challenges and interdisciplinary perspectives.” International Journal of Epidemiology 31: 285-293.
Berney, L., D. Blane, et al. (2000). Life course influences on health in old age. Understanding health inequalities. H. Graham. Buckingham [England], Open University Press: 79-95.
Brown, G. W. and T. O. Harris (1978). Sociology and the aetiology of depression; Depression and Loss; A Model of Depression; Summary and conclusions. Social Origins of Depression: a Study of Psychiatric Disorder in Women. New York, Free Press: 3-20; 233-293.
Davey-Smith, G. (2007). “Life-course approaches to inequalities in adult chronic disease risk.” Proceedings of the Nutrition Society 66: 216-236.
Krieger, N., J. T. Chen, et al. (2005b). “Lifetime socioeconomic position and twins’ health: An analysis of 308 pairs of United States women twins.” PLoS Med 2(7): e162.
Kuh, D., Y. Ben-Shlomo, et al. (2003). “Life course epidemiology.” Journal of Epidemiology and Community Health 57: 778-783.
Lynch, J. and G. Davey-Smith (2005). “A Life Course Approach to Chronic Disease Epidemiology.” Annual Review of Public Health 26: 1-35.

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