The genetics of personality Nathan Gillespie & Nick Martin

1. The genetics of personalityNathan Gillespie & Nick Martin Queensland Institute of Medical Research & University of Queensland, Joint Genetics Program, Brisbane, Australia

2. Rephrasing an old question... Free will versus determinism Autonomy of the individual Nature versus Nurture

3. A quantitative question... Philosopher: “To what extent are behavioral, biomedical, and social outcomes biased by an individual’s DNA sequences?” Psychologist: “To what extent are individual differences influenced by hereditary factors?” Geneticist: “How much of the variance is due to genes and individual / family environment ?”

4. If genetic - empirical question... Do monozygotic twins (clones) have identical outcomes ? MZ twins reared apart - note the same way of supporting their cans of beer

5. MZ concordance for human conditions Asthma 45% Eczema 84% Diabetes (type I) 56% Schizophrenia 50% Cleft lip/palate 30% Club foot 23% Homosexuality (M) 18% Homosexuality (F) 23%

6. Is concordance due to genes (G) or shared-environment (C) ? Family studies - G + C confounded MZ twins alone - G + C confounded MZ twins reared apart - Rare, atypical, selective placement? Adoptions studies - Rare, atypical, selective placement? MZ and DZ twins reared together  Extended twin design 

7. Twins typical of the population - Prevalence of psychiatric symptoms - (Virginia 30 000) - Slightly more middle class and better educated - effect stronger in males (Australian Twin Registry) Study design requirements - Appropriate sampling of MZ & DZs reared together - Correct assignment of zygosity - Comparison of means & variance in traits of interest in MZ & DZs to exclude zygosity  environment - Appropriate statistical methods to infer causes of variation

8. Model Building - Classical Twin Method Study of MZ and DZ twins reared together in same home - Most powerful method for detecting genetic & shared environmental effects: - (A) Additive genetic effects A D C E - (D) Non-additive genetic effects (dominance or epistasis) c d a e - (C) Shared environment effects - (E) Non-shared environmental effects P P = aA + dD + cC + eE

9. Path Diagram - Classical twin study 1.0 MZ / 0.25 DZ 1.0 MZ / 0.5 DZ 1.0 MZ / DZ E1 D1 C2 A2 A1 D2 E2 C1 d1 a1 a2 d2 e1 c1 c2 e2 P1 Twin1 P1 Twin2

10. 1.0 MZ / 0.5 DZ E1 E2 C1 A1 A2 a e c P1 Twin1 P1 Twin2 Tracing rules Standard rules for reading path diagrams to calculate the expected variance & covariance between variables / factors in the diagram 1.0 MZ / DZ C2 a c e

11. 1.0 MZ / DZ C2 E1 C1 E2 A1 A2 a1 a1 c2 e1 1.0 MZ / 0.5 DZ e2 c1 P1 Twin1 P1 Twin2 Expected variance / covariance matrices for MZ and DZ twin pairs under ACE model

12. E1 E2 A1 A2 1.0 MZ / 0.5 DZ a1 a1 e1 e2 P1 Twin1 P1 Twin2 Expected variance / covariance matrices for MZ and DZ twin pairs under AE model

13. 1.0MZ / .25DZ E1 D1 E2 D2 e1 d2 d1 e2 P1 Twin1 P1 Twin2 Expected variance / covariance matrices for MZ and DZ twin pairs under DE model

14. 1.0 MZ / DZ E1 C1 C2 E2 e1 c2 c1 e2 P1 Twin1 P1 Twin2 Expected variance / covariance matrices for MZ and DZ twin pairs under CE model

15. E1 E2 e1 e2 P1 Twin1 P1 Twin2 Expected variance / covariance matrices for MZ and DZ twin pairs under E model

16. Maximum Likelihood (ML) Analysis Data is typically summarized in a covariance matrix Parameter estimates are found by minimising the "maximum likelihood function". This function can be expressed in several ways all of which are equivalent FML ~= (S - Σ)'WML-1(S - Σ) S = observed covariance matrix Σ = expected covariance matrix WML = weight matrix ML analysis finds estimates of a, c, e & d which minimise the difference between the observed & expected covariance matrices

17. Genetic analysis of the dimensions of Eysenck & Cloninger N = 3127 Australian twins aged aged 18 -28 yrs

18. E1 C1 A1 a1 e1 c1 P1 Twin1

19. E1 A1 P1 Twin1 a1 e1

20. E1 C1 P1 Twin1 e1 c1

21. E1 P1 Twin1 e1

22. E1 A1 P1 Twin1 a1 e1

23. Univariate heritability estimates for dimensions of Eysenck & Cloninger N = 3127 Australian twins aged aged 18 -28 yrs

24. - Twin pairs reared together • - Separated twin pairs • - Non-twin adoptees & biological & adoptive families • - Twin pairs reared together & their relatives • Personality models • Eysenck - Psychoticism - Extraversion • - Neuroticism - Lie • Cloninger - HA, NS, RD, PERS + 3 characters • Costa & McCrae - NEO-PI FFM Application to personality research How compelling is evidence for a genetic contribution to adult and adolescent personality?

25. Sex differences & non-additivity in Neuroticism and Extraversion US, Australian & Finnish twins, their siblings, parents & spouses (N = 42,374) (Eaves et al., 1998)

26. Multivariate analysis of personality Univariate analysis - estimates the contribution of A C & E within a dimension - says nothing about the underlying genetic & environmental causes of covariation between dimensions Multivariate analysis - analyses cross-twin cross-trait correlations - determines the degree to which separate genetic & environmental factors are responsible for the correlations between variables

27. Multivariate genetic analyses 1. Cholesky Triangular Decomposition 2. Common Pathway 3. Independent Pathway

28. F Var1 Var2 Var3 e e e Multivariate Analysis

29. Multivariate Analysis G E Var1 Var2 Var3 G G E G E E

30. 1. Cholesky Triangular Decomposition A1 A3 A2 Var1 Var2 Var3 E1 E3 E2 C2 C1 C3 - Number of latent factors equals number of variables - Does not distinguish between common factor & specific factor effects except for final variable - Analogous to Principle Components Analysis

31. Multivariate genetic analysis - Cholesky Decomposition Cloninger’s 3 dimensions of character - Self-directedness (DIRECT) - Cooperativeness (COOP) - Self-transcendence (TRANS) Study of 3040 twins aged 50-96 years (Gillespie et al., submitted)

32. A1 A2 A3 DIRECT COOP TRANS E1 E2 E3 C1 C2 C3

33. A1 A2 A3 DIRECT COOP TRANS E1 E2 E3

34. C1 C2 C3 DIRECT COOP TRANS E1 E2 E3

35. DIRECT COOP TRANS E1 E2 E3

36. A2 A1 A3 -.23 -.06 -.46 .62 .16 .60 COOP TRANS DIRECT .10 .80 .87 .73 .19 -.01 E1 E2 E3

37. A C E A C E Var1 Var2 Var3 A A C E C C A E E Latent Factor Var1 Var2 Var3 A A C E C C A E E 2. Common Pathway 3. Independent Pathway

38. Multivariate genetic analysis- Common & Independent Pathway Models - 3512 female twins aged 18-45 yrs Measures of maternal / paternal behaviour Factor analysis of Parental Bonding Instrument (PBI) (Parker) - Autonomy - Overprotection - Coldness Measures of psychiatric distress Factor analysis of DSSI/sAD & SCL90 items - Depression - Anxiety - Somatic Distress

39. .28 .45 .27 A C E PARENTING .82 .65 .35 Cold Overp Auto A E A E E .30 .28 .41 .47 .33 Best fitting multivariate model for the three PBI dimensions: Coldness, Overprotecting & Autonomy

40. Best fitting multivariate model for the dimensions of Depression, Anxiety & Somatic Distress .48 .52 A E DISTRESS .69 .78 .82 Depression Anxiety Somatic A E A E E A .08 .31 .12 .22 .33 .19

41. Important applications - Recursive Models - Social Interaction - Modeling Direction of Causation - Simplex modeling - Sex limitation - Genotype  Environment Interactions - Genotype  Environment Correlations - Assortment & Cultural Transmission

42. Important applications - Recursive Models - Social Interaction - Modeling Direction of Causation - Simplex modeling - Sex limitation - Genotype  Environment Interactions - Genotype  Environment Correlations - Assortment & Cultural Transmission

43. Recursive models: Direction of Causation Under some circumstances we can model ‘direction of causation’ on cross sectional data - Genetically informative data - Qualitatively or quantitatively distinct Does parenting have an effect on psychological distress outcomes or vice a versa? Or is association between psychological distress and parenting determined by a common diathesis? - 3500 female twins aged 18-45 years - questionnaire containing SCL90, DSSI & PBI

44. MZ=(a2+d2)iB MZ=c2iB DZ=(½a2+¼d2)iB DZ=c2iB B  A A  B 1 1 E C E C C E C E eA cA cA eA eA cA cA eA At1 At2 At1 At2 iB iB iA iA Bt1 Bt2 Bt1 Bt2 eB dB dB eB eB dB dB eB aB aB aB aB A A A A D E D E E D E D 1MZ, ¼DZ 1MZ, ¼DZ 1MZ, ½DZ 1MZ, ½DZ

45. .20 .25 .38 .45 .55 .11 .11 .49 .14 .21 .26 .40 .21 .13 .36 .37 .17 Direction of causation (DOC) modeling A A E C E PARENTING DISTRESS .56 .63 .52 .49 .16 .67 COLD OVERP AUTO DEP ANX SOM A A E C A C E C E E E E

46. Measuring stability of genetic effects over time Eaves, Eysenck & Martin (1989) - Adult personality - High genetic continuity over time - Effect stronger in Neuroticism vs Extraversion Genetic continuity in adolescents? - Simplex modeling (Boomsma, 1989) - Changes in the magnitude of G & E over time? - Do same G & E effects operate throughout time?

47. ζ i-1 ζ i ζ i+1 β i+1 β i i-1 i i+1 λ i-1 λ i λ i+1 εi+1 εi εi-1 Yi Yi+1 Yi-1 Genetic Simplex modeling Autoregressive model Structural equation model ηi = βi ηi-1 + ζi var (ηi) = βi2 var (ηi-1) + var (ζi) Measurement model Yi = λi ηi + εi var (Yi) = λi2 var (ηi) + var (εi)

48. A1 A3 A2 1 1 1 1 1 1 E2 E3 E1 Extraversion - Best fitting Simplex model .38 .12 .92 .93 .23 .23 .23 E12 E14 E16 .53 .72 .35 .18 .17

49. A1 A2 A3 E1 E2 E3 Neuroticism - Best fitting Simplex model .36 .11 .11 .93 .76 1 1 1 .22 .22 .22 N12 N14 N16 1 1 1 .49 .91 .39 .22 .15

50. Genes, personality & psychopathology 1. Dopamine receptor gene (DRD4) 2. Monoamine oxidase (MAO) gene 3. Quantitative trait loci (QTLs) for Neuroticism