1. Behavior Genetics The Study of Variation and Heredity
Prof. Timothy Bates

2. Overview Introduce behavioural genetic methodology
Adoption studies
Twin studies
Family studies
Introduce concepts of
Genetic variance
Genetic correlation
Examples of cognitive ability

3. Motivation: Why look at genetics?

4. Causality: genetics uniquely untangles causality
Does Parental Neglect cause Anti-Social Behavior?
Eaves et al., 2010

5. Why aren’t we all the same?
σ2 = variance
Sum((x-mean)2) > 0

6. Applies to Continuous Variation…

7. and to categorical variation: Thresholded Continuous trait

8. In BG, categories like “depression” are often viewed as thresholds on a liability scale

9. Why do things run in families?
Shared genes + Shared environment

10. Genes, Environment and Intelligence
07/03/2014
Genes, Environment and Intelligence
Sir Francis Galton (Victorian polymath)
Half cousin of Charles Darwin
Genealogical studies of eminent families
Hereditary Genius (1869)
Intelligence is inherited
"Twins have a special claim upon our attention; it is, that their history affords means of distinguishing between the effects of tendencies received at birth, and those that were imposed by the special circumstances of their after lives."

11. Behavioural Genetics Methods
07/03/2014
Behavioural Genetics Methods
Use known genetic relatedness to estimate variability in a trait due to genes (heritability)
Children share 50% of genes with each parent
Identical twins share 100%
Non-identical twins/siblings 50%
Grandparents 25%, cousins 12.5%
Unrelated people, no genetic sharing

12. Resemblance for Cognitive Ability in family members
22/11/2016
Resemblance for Cognitive Ability in family members
Behavioral Genetics (2016).
Valerie S. Knopik,‎ Jenae M. Neiderhiser,‎ John C. DeFries,‎ Robert Plomin

13. Estimating h2 from extended Pedigree Studies
22/11/2016
Estimating h2 from extended Pedigree Studies
Components of genetic and environmental variance are estimated by univariate linear mixed models Luciano et al., (2010) Intelligence, 38:

14. Estimating h2 from Adoption Studies
07/03/2014
Estimating h2 from Adoption Studies
If adopted offspring are more similar to biological than adoptive parent indicates genetic influence
e.g., adoptees with schizophrenia show concordance rate of 12% with biological family members versus 3% with adoptive family members
Example to the right is height MZ twin- pair members in Tom Bouchard’s MISTRA study of twins reared apart

15. Similarity for cognitive ability across adoptive family classes
22/11/2016
Similarity for cognitive ability across adoptive family classes

16. Estimating h2 from Twin Studies
07/03/2014
Estimating h2 from Twin Studies
Compare trait similarity in identical and fraternal twins
1/250 births are identical twins
1/150 are fraternal twins

17. Twins Reared Apart Design
MZs reared apart
σ2 = A+C+E
cov= A (=h2)
Modern example
Tom Bouchard
Minnesota Study of Twins Reared Apart (MISTRA)

18. Genes, Environment and IQ
07/03/2014
Genes, Environment and IQ

19. The Classical Twin Design: Variance decomposition
MZ: Monozygotic
Share 100% of genes (A)
Share family (C)
Have unique experiences (E)
DZ: Dizygotic
Share (on average) 50% of genes (1/2 A)
σ2 = A+ C + E rMZ = A + C rDZ = .5A + C E = 1 – rMZ A = 2 * (rMZ – rDZ) C = 1 – (A + E)
Falconer’s formula

20. 22/11/2016
Heritability
The proportion (ratio) of population variance that can be attributed to genetic influences
This genetic contribution usually represents the cumulative effects of many genes (“polygenic” “complex trait genetics”)
Narrow sense:
Broad sense:

21. Genetic Variation Genetic additivity (A):
Allelic effects sum within and across loci
Genetic non-additivity 1: (Dominance):
Interaction of the effects of alleles at a locus
Not shared between parents and offspring
Genetic non-additivity 2: (Epistasis)
Interaction of the effects of alleles across loci

22. Twin Modelling in SEM (Structural Equation Modelling)
ACE model

23. Specifying the ACE model
Use two groups:
MZs (with A1 <-> A2 path = 1.0)
DZs, with A1 <-> A2 path = .5

24. SEM for Twin Modelling OpenMx umx R package, on CRAN
Open Source:
umx
Open Source
High-level assistance for twin and general modeling
umxACE()

25. Specifying the ACE model in OpenMx

26. How heritable is height? library(umx) & umxACE()
data(twinData); df = twinData selDVs = c("ht1", "ht2") mzData <- df[df$zygosity %in% "MZFF", ] dzData <- df[df$zygosity %in% "DZFF", ] m1 = umxACE(selDVs= selDVs, dzData= dzData, mzData= mzData)

27. ACE Model Assumptions Twins are ‘ordinary folks’
22/11/2016
ACE Model Assumptions
Twins are ‘ordinary folks’
Similar to singletons despite greater gestational difficulties
No particular effects of growing up side by side
No assortative mating
MZ and DZ twins experience environmental influences equally

28. 22/11/2016
ACE Model Assumptions
Genetic and environmental influences are independent and additive
No gene-environment correlation
Exposure to environmental conditions depends on an individual's genotype
No gene-environment interaction
Environmental exposure does not moderate effects of genes on behaviour

29. Genes, Environment and IQ
07/03/2014
Genes, Environment and IQ
Gene by environment interaction
Heritability value can change depending on the group(s) measured
e.g. Higher heritability in high SES vs low SES (near or below poverty line in the US)
Turkheimer E et al. Psychological Science (2003)
Bates T C et al. Psychological Science (2013)

30. Genes, Environment and IQ
07/03/2014
Genes, Environment and IQ
Heritability generally increases with the age of the sample measured: ~0.30 in childhood to 0.50 in middle adulthood to 0.70 in old age
Haworth et al. (2010) Mol Psychiatry, 15: 1112–1120.

31. More than one Trait Traits covary We can explain association via:
ADHD E1 E2 A1 A2
a11 IQ rG C1 C2 rC 1 rE
c11
a22
c22
e11
e22
Traits covary
We can explain association via:
Additive genetic factors (rG)
Shared environment (rC)
Non-shared environment (rE)
e.g. Kuntsi et al. (2004) Am J Med Genet B, 124:41

32. Cholesky Decomposition
Twin 1
Phenotype 1 A1 A2 E1 E2
a11
a22
e11
e22 1
Phenotype 2 C1 C2
c11
c22

33. Cholesky Decomposition
Twin 1
Phenotype 1 A1 A2 E1 E2
a11
a21
a22
e11
e21
e22 1
Phenotype 2 C1 C2
c11
c21
c22

34. Cholesky Decomposition
Twin 1
Phenotype 1 A1 A2 E1 E2
a11
a21
a22
e11
e21
e22 1
Phenotype 2 C1 C2
c11
c21
c22
Twin 2
1/0.5

35. Genetic Correlation

36. rG vs. robserved
If the rG = 1, the two sets of genes overlap completely
If however a11 and a22 are near to zero, genes do not contribute (much) to the observed correlation
The contribution to the observed correlation (phenotypic) is a function of rG and both heritabilities

37. Proportion of rP due to additive genetics: ADHD and IQ
a2 x IQ rg 1
a2y
Heritability of ADHD
Genetic correlation between ADHD and IQ
Heritability of IQ
Phenotypic corr. ADHD & IQ
Proportion of pheno.corr. due to genetic factors

38. Multivariate Modelling
Luciano et al., (2001). Behavior Genetics, 31(6),

39. (Many) Other important Types of Models
Common Pathway Model
Independent Pathways Model
Longitudinal Data
Latent Growth models
Latent Factor models
Interaction models
G*E
Measured Environment, Measured Genes
Direction of Causation

40. Latent Growth Model AI CI EI As 2 Cs Es G1 G2 Genotypes G3 Phenotype
Time 1 AI
a11
c11
e11
Intercept
Time 2
Time 3
AS2
CS2
ES2
AS3
CS3
ES3
AS1
CS1
ES1 CI EI As
a22
c22
e22
Slope 2 Cs Es
a21
c21
e21 im sm
as11
as22
as33
cs11
es11
cs22
es22
cs33
es33
Twin 1 Bi Bs
Phenotype
Age G1 G2 G3
Genotypes

41. Quantitative and Qualitative Sex Effects
Females
aF = aM? cF = cM? eF = eM?
Does the magnitude of genetic/environmental effects differ in males and females? (QUANTITATIVE)
With OS pairs: Are the same genes/environments acting in males and females (QUALITATIVE)
Compare correlations from
DZ like-sex
DZ opposite-sex pairs
Sex-specific effects suggested when OS DZr < Same-sex DZr.

42. Bivariate Direction of Causation Modelling

43. Extending theTwin Design
Adding other relatives
Siblings
Cousins
Children of twins
Parents, grandparents
Uncles, Aunts…

44. Thank you! Questions Please!