Genes and Social Networks. James H. Fowler UC San Diego

Size: px

Start display at page:

Download "Genes and Social Networks. James H. Fowler UC San Diego"

Octavia Carr
5 years ago
Views:

1 Genes and Social Networks James H. Fowler UC San Diego

3 Turnout in a Small World Social Logic of Poli,cs 2005 The Watts-Strogatz (WS) Model Order Chaos Real Social Network

4 ! = 0.71! = 0.50

6 ParEsanship, VoEng, & the Dopamine D2 Receptor Gene Christopher T. Dawes, James H. Fowler Change in Likelihoodn of Being a Partisan (%) One Two Number of A2 Alleles

7 Friendships Moderate an AssociaEon Between a Dopamine Gene Variant and PoliEcal Ideology Jaime E. SeJle, Christopher T. Dawes, Peter K. Hatemi, Nicholas A. Christakis, James H. Fowler

16 Big Picture Behaviors Social ParEcipaEon Obesity Smoking Happiness CooperaEon Networks

17 Big Picture Supergenes Dopamine Serotonin Behaviors ParEcipaEon Obesity Smoking Happiness CooperaEon

18 Big Picture Supergenes Dopamine Serotonin? Social Networks? Behaviors ParEcipaEon Obesity Smoking Happiness CooperaEon

19 Today s Piece of the Puzzle Genes Twin Study Method Network Structure is Heritable Reject All ExisEng Models of Human Social Networks Develop AlternaEve AJract & Introduce Model Social Networks

(fraternal) share 50% of their genes on average Decompose

20 Twin Studies Compare the behavior of - monozygoec (MZ) twins (idenecal) share 100% of their genes - dizygoec (DZ) twins (fraternal) share 50% of their genes on average Decompose variance - A geneec - C common environment - E unshared environment

21 Twin Study Method Structural EquaEons Model (SEM) to esemate - P = total variance - A = geneec - C = common envrionment - E = unshared environment - MZ = monozygoec (idenecal) twins - DZ = dizygoec (fraternal) twins

CriEcism of Twin Studies MZ and DZ environments may not be comparable - MZ twins may be more strongly affiliated than DZ twins However - Studies of twins reared apart validated by other methods

22 CriEcism of Twin Studies MZ and DZ environments may not be comparable - MZ twins may be more strongly affiliated than DZ twins However - Studies of twins reared apart validated by other methods Bouchard 1998; Visscher et al Differences between MZ and DZ twins persist even among twins whose zygosity has been miscategorized by their parents Bouchard and McGue MZ twins are someemes in more frequent contact, but this results from rather than causes greater similarty Posner et al MZ twins living apart become more similar with age Bouchard and McGue 2003

sample 90,118 students in 142 schools - Name up to 5 male and 5

23 Add Health Data NaEonal Longitudinal Study of Adolescent Health - 3 waves of health, social network, and geneec data - In school sample 90,118 students in 142 schools - Name up to 5 male and 5 female friends - Oversampled twins 307 MZ pairs 248 same sex DZ pairs

24 Egocentric Social Network Measures in degree (0 to N 1) out degree (0 to 10) - 10% name max. - mean = 3.8, SD = 3.7 transievity (0 to 1, mean=0.18) - Pr( j k i j, i k ) betweeness centrality -

25 Twin Study Results

A (Very) Brief History of Social Network Models Focus on generaeng variaeon via endogenous processes aceng on idenecal individuals - Physics: scale free

26 A (Very) Brief History of Social Network Models Focus on generaeng variaeon via endogenous processes aceng on idenecal individuals - Physics: scale free model - Economics: conneceons model - Sociology: homophily, structural balance models Ignore intrinsic heterogeneity Our most intrinsic characterisecs are genes

27 Candidate Social Network Models Only models with intrinsic node characterisecs can generate significant heritability PossibiliEes include: - Fitness model (Park Barbási 2007) - Social Space model (Boguñá et al. 2004) - ERGM exponeneal random graph model (Snijders et al. 2006) - Erdos Renyi model used as a baseline How to test?

28 The Mirror Network Method 1. Generate first set of intrinsic node characterisecs 2. Simulate network from first set 3. Generate second set of intrinsic node characterisecs 4. Sample one individual from first set and one individual from second set (the twins )

29 The Mirror Network Method 5. Copy node characterisecs from sampled twin from first set to sampled twin from second set 6. Simulate mirror network from second set 7. Calculate and store egocentric network measures for each twin 8. Repeat Emes, calculate twin correlaeon

30 Percent Variance Explained by Intrinsic Node Characteristics (Heritability) Heritability Comparison * * Real Data (Add Health) Attract & Introduce Model Social Space Model Fitness Model Exp. Random Graph Model Erdos-Renyi Model * In-Degree Transitivity Centrality Out-Degree

31 AJract & Introduce Model N nodes and E edges Each node endowed with two characterisecs Draw nodes i and j Probability p j ajract a social Ee from i to j forms - If so, then with probability p i introduce, i introduces j to all of his friends If so, then each friend sends a Ee to j with probability p j ajract and j sends a Ee to each kth friend with probability p k ajract Repeat unel at least E Ees are generated

32 Percent Variance Explained by Intrinsic Node Characteristics (Heritability) Heritability Comparison * * Real Data (Add Health) Attract & Introduce Model Social Space Model Fitness Model Exp. Random Graph Model Erdos-Renyi Model * In-Degree Transitivity Centrality Out-Degree

33 Real Data (Add Health) Attract and Introduce 1 - Cumulative Distribution Degree DistribuEons Social Space Fitness ERGM 142 Erdos- Renyi Degree

34 Node Degree vs. TransiEvity Mean Node Transitivity Real Data (Add Health) Attract & Introduce Model Social Space Model Fitness Model Exp. Random Graph Model Erdos Renyi Model Node Degree

35 Modularity Comparison Degree degree correla5on = 0.18

36 k MoEf Fingerprint Method Use staesecal distribueon of pajerns among k nodes (moefs) to idenefy networks 16 3 moefs; moefs Simulate network 100 Emes Calculate mean and variance of each moef Generate mulevariate beta density Calculate likelihood from densiees Correctly idenefied candidate network in 10,000 tests MulEnomial Dirichlet bejer but convergence difficult

37 MoEf Fingerprint Comparison Adjusted Log Log Likelihood Real Data (Add Health) Attract & Introduce Model Social Space Model Fitness Model Exp. Random Graph Model Erdos Renyi Model 3 motifs Adjusted Log Log Likelihood Real Data (Add Health) Attract & Introduce Model Social Space Model Fitness Model Exp. Random Graph Model Erdos Renyi Model 4 motifs Network Size (Number of Nodes) Network Size (Number of Nodes)

38 Discussion Evidence rejects homogenous node models AJract & Introduce model simple, effeceve We should search for specific genes - 5 HT2A associated with popularity (Burt 2008) We should search for specific intermediaeng causal mechanisms - Personality (extraversion)

39 Discussion Consider coevolueon of networks & cooperaeon in early human society - Heritability of network structure means either networks have no impact on fitness (drit) networks affect fitness via frequency dependent seleceon - Natural seleceon may ensure diversity of network types Networks might be a link from genes to behavior - What impact does this causal pathway have on fitness?

40 Genes NeurotransmiJers Storage vesicle MAOA Serotonin 5HTT Serotonin transporter Brains Behavior Serotonin receptors

41 Neuron Network Protein Network

42 Before the 17th Century The Solar System

43 The Solar System Galileo Galilei

44 The Solar System Galileo Galilei

45 Biological Systems Anton van Leeuwenhoek

46 Biological Systems Anton van Leeuwenhoek

47 Human Social Systems 18th Century

48 Human Social Systems Proletariat Bourgeoisie 19th Century Nobility

49 Human Social Systems 20th Century

50 Human Social Systems 21st Century

52 Acknowledgements Chris Dawes Nicholas Christakis Come see us at h:p://jhfowler.ucsd.edu

A Model of Genetic Variation in Human Social Networks

A Model of Genetic Variation in Human Social Networks The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters. Citation Published Version