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  • 1. Social Demographic Perspectives on Behavioral Genetics: What do you get when you cross a Sociologist, a Demographer, and a Behavioral Geneticist? Jason Boardman University of Colorado Department of Sociology and Population Program, Institute of Behavioral Science Lecture prepared for the Charles B. Nam Lecture, Center for Demography and Population Health, Florida State University, February 29th. .
  • 2. Genetics and the social sciences Tension between outside or inside the body (Duster 2006). 1. Prioritization of “inside” scientific work. 2. The rapid emergence “inside” data. 3. The blocked access to “outside” data such as wealth and institutional access. 4. The “molecularization” of race
  • 3. What can “we” do about this “tension”? • This tension can be resolved, in part by stressing the obvious fact that bodies do not exist without a particular “outside”. • Thus, the structure and reproduction of the outside is paramount to the investigation of processes occurring at the molecular level.
  • 4. Genes and environment interplay: correlation and interaction • Gene-Environment Correlation: when genetic characteristics (genotype) are associated with environmental characteristics. • Gene-Environment Interaction: genotype- phenotype associations are contingent on the environment. Note: see Plomin (1994) & Deater-Deckard & Mayr (2005) for very useful reviews.
  • 5. Gene environment interplay Social Factors (E) Physical Health (Regular Smoker) Genetic factors (G)
  • 6. Gene-Environment Correlation
  • 7. Passive Correlation • Exposure to an environmental factor that is provided by a genetic relative – Parent’s with above average IQ are more likely to provide stimulating environments to their children (see Plomin 1994). Note: see Plomin (1994) & Deater-Deckard & Mayr (2005) for very useful reviews.
  • 8. Active Correlation • Environmental selection – For example, persons with above average IQ may select into more intellectually stimulating environments because these environments are more personally rewarding (Schooler & Mulatu 2001) . Note: see Plomin (1994) & Deater-Deckard & Mayr (2005) for very useful reviews.
  • 9. Reactive correlation • Genetic determinants of the environment – For example, children with elevated levels of behavior problems that are, in part, genetically oriented, will evoke rejection, hostility, and sanctions from peers, parents, and educators (O’Connor et al. 1998). Note: see Plomin (1994) & Deater-Deckard & Mayr (2005) for very useful reviews.
  • 10. Gene-environment interactions • Two variants: – The effect of the environment is conditioned by an individual’s genetic makeup. – The effect of a particular gene is conditioned by the environment.
  • 11. GxE: Constraint • The social environments may limit the potential of genetic expression for salutary outcomes in one of two ways. – Normative environment (social control): in social contexts with severe constraints on behaviors most persons will exhibit the same phenotype regardless of their genotype. – Institutional environment (resource impoverishment): genotypic characteristics may not be fully realized if important resources are unavailable. • Turkheimer et al. (2003) Genetic effects on of IQ are found to be nearly 7 times higher among siblings from HIGH SES compared to LOW SES backgrounds (same as Rowe et al. (1999))
  • 12. GxE: Triggering • The social environment triggers the expression of a particular gene. Thus the environment is structured as the “fundamental cause” – Evidence of an association only among individuals within particular environments. • Caspi et al. (2002) – MAOA activity only associated with antisocial behavior among those exposed to severe childhood maltreatment. • Caspi et al. (2003) – 5-HTT only associated with depression among those with a large number of stressful life-events
  • 13. Figure 1. Normative moderator GEI models: genetic risks for problem behaviors in average and extreme environments. Social expression Social distinction Social push Average effect Normal range Social risk Social resource Peer behaviors, norms, and attitudes
  • 14. How can we study this? • 1) Compare siblings and twins in different environments • 2) Use genetic information obtained from individuals across different social environments.
  • 15. Univariate ACE Model for a Twin Pair 1 1/.5 E C A A C E Sm1 Sm2 A (Additive Genetic)*: .48 (.22, .69) C (Shared Environment): .32 (.17, .47) E (Unshared Environment): .20 (.11, .33) Wave II of the Add Health Study (most respondents 14-19) *Mx Estimates
  • 16. More heritability estimates from Twins Phenotype rMZ rDZ BMI (age 20 yrs) .80 .42 [Fabsitz et al, 1992] IQ (age 7 years) .76 .40 [Bishop et al, 2001] IQ (age 16 years) .84 .41 [Friedman et al, 2006] Any drug, ever use .82 .75 [Rhee et al, 2003] Any drug, problem use .82 .46 [Rhee et al, 2003] Depression (Finns, female) .43 .16 [Wamboldt et al, 2000] Heart rate, resting (age 7) .65 .44 [VanHulle et al, 2000] HDL cholesterol (14 years) .81 .21 [Nance et al, 1998] Neuroticism (fem, Aus) .42 .17 [Keller et al, 2005] Extraversion (fem, Aus) .46 .18 [Keller et al, 2005] h 2 = 2(rmz − rdz ) h = 2(.80 − .42) 2 h = 2(.38) 2 h 2 = .76 16
  • 17. Quantitative Genetic Estimates Phenotype Genetic Environmental Shared Non-shared BMI (age 20 yrs) .76 .04 .20 IQ (age 7 years) .72 .04 .24 IQ (age 16 years) .82 .00 .18 Any drug, ever use .14 .68 .18 Any drug, problem use .72 .10 .18 Depression (Finns, female) .43 --- .57 Heart rate, resting (age 7) .42 .23 .35 HDL cholesterol (14 years) .81 --- .19 Neuroticism (females, Australia) .42 --- .58 Extraversion (females, Australia).46 --- .54
  • 18. Boardman, Jason D. “State-level Constraints on Genetic Tendencies to Smoke”. Revised manuscript under review at American Journal of Public Health.
  • 19. MZ (g= 1.0) p2 (score of sibling 2) DZ (g= .5) p1 (score of sibling 1) estimate of shared env. estimate of heritability p2 = a + b1 p1 + b2 g + b3 ( p1 g ) + ei
  • 20. Sibling pair data to estimate heritability (DeFries & Fulker 1985) • Genetic similarity score (g) – Identical twins =1 Fraternal twins= .5 p2 = a + b1 p1 + b2 g + b3 ( p1g) + ei • Interaction between genetic similarity and the phenotype of twin1 (b3) provides an estimate of heritability.
  • 21. Elaborating on the DF regression: the use of mixed models to identify social moderators. • Include error terms for the intercept and the slope. p2ij = a + b1 p1ij + b2 g ij + b3 ( p1ij g ij ) + eij + u0 j + u1 j ( p1ij g ij ) • Interpretation – σ u2 (random intercept). Extent to which the average level of smoking 0 varies across environments. σ u2 – (random slope). Extent to which the heritability estimate given by 1 b3 varies across environments. σu 0 ,u1 – (covariance; intercept and slope). Is heritability higher in environments with higher (social expression) or lower (genetic distinction) levels of smoking.
  • 22. Generalized linear and mixed model extension of DF ⎡ π 1ij ⎤ ⎥ = β 0 + β 1 y 2ij + β 2 g ij + β 3 hij + ∑k =1 β K X K + u 0 j + u1 j h j K log e ⎢ ⎢1 − π 1ij ⎥ ⎣ ⎦ Boardman, Jason D. Jarron M. Saint Onge, Brett C. Haberstick, David S. Timberlake, and John K. Hewitt. “Schools and the Heritability of Smoking Behaviors.” Forthcoming, Behavior Genetics
  • 23. Table 3. School-level factors that shape the direction and magnitude of the heritability of daily smoking p.e. beta s.e. t pr < Social and demographic characteristics Proportion of college educated mothers -25.23 -0.357 23.78 -1.061 0.292 Proportion non-Hispanic and white -6.55 -0.685 2.41 -2.718 0.008 Smoking norms Popular students do not smoke -6.77 -0.177 8.02 -0.845 0.401 Popular students are also smokers 51.04 1.334 8.56 5.962 0.000 Institutional control of smoking Teachers not allowed to smoke on campus -3.31 -0.144 5.05 -0.656 0.514 School penalties for smoking infractions 1.72 0.264 2.34 0.736 0.464 Smoking prevalence Proportion of students who have smoked -19.32 -0.213 174.91 -0.110 0.912 Smoking prevalence squared 31.01 0.233 236.9 0.131 0.896 Note: Cell entries are parameter estimates the latent school-level heritability factor for daily smoking regressed on various school-level factors. These models were estimated using the GEQS command in the GLLAMM procedure available in STATA 9.2. Data obtained from the sibling and twin pair sample of the National Longitudinal Study of Adolescent Health (n=1,198 pairs). Parameter estimates were weighted for individual and school-level weights. The inclusion of these estimates significantly improved overall fit (Chi-square = 16.38, df=8, p<.037). Boardman, Jason D. Jarron M. Saint Onge, Brett C. Haberstick, David S. Timberlake, and John K. Hewitt. “Schools and the Heritability of Smoking Behaviors.” Forthcoming, Behavior Genetics
  • 24. Boardman, Jason D. Jarron M. Saint Onge, Brett C. Haberstick, David S. Timberlake, and John K. Hewitt. “Schools and the Heritability of Smoking Behaviors.” Forthcoming, Behavior Genetics
  • 25. Boardman, Jason D. Jarron M. Saint Onge, Brett C. Haberstick, David S. Timberlake, and John K. Hewitt. “Schools and the Heritability of Smoking Behaviors.” Forthcoming, Behavior Genetics
  • 26. Boardman, Jason D. Jarron M. Saint Onge, Brett C. Haberstick, David S. Timberlake, and John K. Hewitt. “Schools and the Heritability of Smoking Behaviors.” Forthcoming, Behavior Genetics
  • 27. Boardman, Jason D. “State-level Constraints on Genetic Tendencies to Smoke”. Revised manuscript under review at American Journal of Public Health.
  • 28. Sociological Perspectives on Quantitative Genetics (summary) • Genetic factors operate differently across different environments. – Social institutions • Schools • Families – Social norms • Controls • Causes – Social groups (gender, race, and class) • Resources • Norms
  • 29. Why is this important?
  • 30. Collection Tube with Swabs Lysis Buffer Completed
  • 31. E (φ2 ) = α + β1φ1 + β 2π + ei φ1 = trait of proband φ 2 = trait of sibling β1 = slope (phenotypes) β 2 = slope (linkage) π = IBD sharing
  • 32. 3.5 3 LOD (multipoint) 2.5 2 LOD 1.5 VAR 1 0.5 0 D9 8 D9 6 D9 71 D9 83 D9 21 D1 17 D9 5 D9 77 BH 2 6 8 8 17 68 82 8 6 S2 S2 S1 S2 10 7S S1 S1 S1 S1 D9 Chromosome 9 (Markers) φ1 = trait of proband E (φ2 ) ij = α + β1φ1ij + β 2π ij + eij + u0 j + πu1 j φ 2 = trait of sibling β1 = slope (phenotypes) β 2 = slope (linkage) π = IBD sharing
  • 33. Genome-Wide Association Studies Thanks to Matt McQueen for this image and slide
  • 34. The human genome • 22 chromosomes • ~30,000-50,000 genes • ~8,000,000 SNPs Thanks to Matt McQueen for this image and slide
  • 35. SNP (single nucleotide polymorphism) Image borrowed from http://en.wikipedia.org/wiki/Image:Dna-SNP.svg
  • 36. Sociology and stress response • The same fundamental cause may be at the root of seemingly different processes. • In other words, social forces may lead to similar genetic responses but the EXPRESSION of the genes may look different.
  • 37. Stress exposure Stress response Internalization Externalization Women Men Alcohol and Depression Obesity Marijuana tobacco White Black White Black
  • 38. Life course perspective • Two people may look the same but they may have traveled very different paths. • Two people may have started out the same but end up in very different places.
  • 39. yij = β1 + β 2 xij + β 3 xij + β 3 xij + ζ 0 j + ζ 1 j xij + ζ 2 j xij + ζ 3 j xij + ε ij 2 3 2 3 d1 e Intercept e d2 Behavioral phenotype (d) e d3 Growth (linear) d4 e d5 e Growth (quadratic) d6 e Growth (cubic) e d7 Time (1-8) d8 e
  • 40. EB estimates Genetic effect size Weights Heritability 2 SNP 1 ζ0, ζ1, ζ2, ζ 3 α1,1, α1,2,α1,3,α1,4 w1,1, w1,2,w1,3,w1,4 h Max1 2 SNP 2 ζ0, ζ1, ζ2, ζ 3 α2,1, α2,2,α2,3,α2,4 w2,1, w2,2,w2,3,w2,4 h Max2 2 SNP 3 ζ0, ζ1, ζ2, ζ 3 α3,1, α3,2,α3,3,α3,4 w3,1, w3,2,w3,3,w3,4 h Max3 2 SNP 4 ζ0, ζ1, ζ2, ζ 3 α4,1, α4,2,α4,3,α4,4 w4,1, w4,2,w4,3,w4,4 h Max4 . . . . . . . . . . . . 2 SNP n ζ0, ζ1, ζ2, ζ 3 αn,1, αn,2,αn,3,αn,4 wn,1, wn,2,wn,3,wn,4 h Maxn Growth (cubic) Growth (quadratic) Trajectory Growth (linear) Intercept
  • 41. 100% 80% 60% E The genetic effects are nearly all GE 40% contingent upon the environment. G 20% 0% Infancy Adolsecence Adulthood Elderly 100% 80% 60% E The environmental effects are 40% GE nearly all contingent upon genotype. G 20% 0% Infancy Adolsecence Adulthood Elderly 100% 80% 60% E Differential contribution across the life 40% GE course G 20% 0% Infancy Adolsecence Adulthood Elderly
  • 42. Summary • The molecularization of individual differences is real. – Genes cause people to be different from one another. • But..the social and physical environment has a far better score card. – In terms of effect size – And reliability of findings. • And the social environment seems to structure the way that genes operate. • This prioritizes “outside” the body processes as an a priori point of initiation.
  • 43. Acknowledgements • NIH/NICHD – KO1 HD 50336: “The social determinants of genetic expression” – P01 HD31921: “The National Longitudinal Study of Adolescent Health” • Institute of Behavioral Science and CU Population Center, University of Colorado • Institute for Behavioral Genetics, University of Colorado • Center for Demography and Population Health, Florida State University
  • 44. Thank you!