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Strategies for candidate gene x environment research and opportunities provided by intervention designs H. Harrington Cleveland Human Development Family Studies The Pennsylvania State University Thanks to NIDA: PROSPER Pis: Mark Greenberg, Mark Feinberg, Richard Spoth, GPROSPER collaborators: David Vandenbergh (M-PI), and Gabriel Scholmer (Post Doctoral Scholar) Overview • • • • Critiques of candidate gene research Common difficulties in candidate gene research General guidelines for strengthening cGxE How gPROSPER leverages the PROSPER intervention study to investigate cG x E • Results from three or more analyses Candidate G X E Research Controversy • Candidate Gene Research – especially G x E research (e.g. Caspi et. al., 2003) – has been aggressively criticized. • Some (e.g. meta-analysis by Risch et.al., 2009) claim that findings are consistent with chance. • Others say that Risch et. al. (2009) is incorrect due to inclusion of weak studies (see Uher & McGuffin, 2010, and Rutter et al., 2010). • Common difficulties in candidate gene research – Poor measurement: few items, single reporters – Retrospective data: recall bias – Compound phenotyping – Passive large scale data sets are vulnerable to rGE: • reducing exposure to full range of environments across genotypes (reduces power) • undercutting interpretation of findings – Population structure confounds Compound Phenotyping • What it is: Combining subgroups of behaviors into one behavioral measure, either explicitly or because assessments are non-specific – e.g., ”Amount of monthly drinking” captures social drinking and coping drinking • If predictors are etiologically non-specific, such as parental drinking levels, associations could be due to several causal links (i.e., etiological paths). • However, if predictors are etiologically-specific, such as genes(?), correlations will be substantially attenuated. rGE confounds The problem: Families, peers, experiences are all genetically influenced The result: G x E “effects” might be due to genetic selection of environments/experiences Minimum controls: investigate associations between genes and presumptive “environmental moderator” Better solutions: Random assignment Quasi-experimental solutions - school level interventions (e.g., PROSPER) - regression discontinuity designs - state level differences in welfare policies Population Stratification A 3rd variable confound: 1. Samples may be made up of distinct genetic groups or they have been genetic mixing of groups in recent past (i.e., racial admixture). 2. Groups may differ in allelic distributions and outcomes 3. Creating spurious associations between alleles and outcomes 4. Classic Study: Knowler et al. General Finding – Genetic variant negatively associated with Type 2 diabetes in Native American /Euro sample. But: Genetic variant more common among European descent cases Analyses within Native American and Euro groups found no association Citations: Kwowler, et al. (1988).GM and Type 2 Diabetis Mellitus: An Association in American Indians with Racial Admixture. American Journal of Human Genetics. 43. 520526. Cardon & Palmer (2003). Population stratification and spurious allelic association. Lancet, 361, 598-604 Moving cGxE Research Forward We suggest five domains in which candidate gene research should be evaluated: • Design • Measurement • Theory • Biological Role • Population Structure Design.1 • In epidemiological studies, causality is difficult to determine since experience-outcome associations may equally reflect causal environmental influences or self-selection into those environmental experiences. • Prevention/intervention trials, via randomization, eliminate nonrandom selection to the environment (i.e. intervention vs. control) and create a unique opportunity to examine cGxE interactions without rGE confounds. • Randomized designs offer substantially more power to detect interactions in cGxE than other designs (see Bakermans-Kranenberg & van IJzendoorn, 2015; McClelland & Judd, 1993). Design.2 • Other quasi-experimental designs should also be leveraged – Do Regional or cohort differences modify the effect of genes? – Within-family sibling comparisons • Do discordant genes predict discordant outcomes? • Do these patterns vary across families with different environments? Theory • Theory can direct researchers toward constructs to consider • It can also help researchers avoid searching for findings and leveraging chance results Measurement The strongest critiques of cGxE research have emerged from research reviews (e.g. Duncan & Keller, 2011; Risch et al., 2009; Munafo et al., 2009) that include studies that vary substantially in measurement quality. Family and developmental researchers are in a unique position to capitalize on high quality measurement, which can increase statistical power to a greater extent than increasing sample size (Manchia, et al., 2013). Biological Role The “which gene” decision should be made on a range of levels that demonstrate a cogent role for the marker vis-à-vis both the phenotype and environment. From broad to narrow: • Association with behavioral outcomes – eg. , DRD4 linked to attention deficit disorder and novelty-seeking. • Associations with perceptual processes – eg., DRD4 encodes receptors in frontal cortex associated with recognizing and paying attention to salient information in the environment (reviewed by Bromberg-Martin et al., 2010). • Associations with neurocognitive functioning – eg., elevated levels of activity in the striatum (caudate nucleus). • Molecular level – eg., DRD4 7+ variant has been linked to less effective receptor signaling) and lower gene expression. Population Structure Because allele frequencies can vary across populations, inattention to population structure (e.g. genetic ancestry) can lead to spurious results (Knowler, et al., 1988). The best strategies to address population structure confounds will vary by sample size, sample diversity, genes examined, outcomes considered, and the combination of these that pose threats to internal validity. We use principle coordinate methods and subsample confirmation. Readings: Ziv et al. (2003) and Keller (2014). gPROSPER builds on the PROSPER intervention study o Randomized prevention trial design: *Community-level randomized of interventions removes person-level selection –both general and rGE– of intervention-related experiences *14 intervention communities and 14 control communities in Pennsylvania and Iowa *Random assignment increases G x E statistical power 5 – 10 fold o Detailed measurement of environment and outcomes: Developed to assess early adolescent interventions designed to operate through family and peer contexts, a primary focus of PROSPER is measuring processes within environmental domains o Prospective cohort longitudinal design: Reduces perception-related rGE by not using long-term retrospective report of environmental factors (Jaffe & Price, 2007). Allows for construction of complex longitudinal phenotypes o Adequate sample size for Gene x Intervention Research (PROSPER = 8,000) 1,000 genotyped for adolescent and young adult analyses 2,000 genotyped for adolescent-only analyses 550 of hyper-measured family process data genotyped with Affymetrix 300K SNP Exon array Conceptual model: How genes may influence substance use and transact with intervention, family, and peer factors Intervention Family context Peer context rGE GxE Genes Substance use Population Stratification in gProsper PC2 PC1 Population Stratification in gProsper 1 sd from mean of pc 1 among selfidentified noneuros PC2 PC1 Common gPROSPER Analytic Framework Analytic steps: 1. Run models on all available cases 2. Repeat controlling for pop stratification 3. Repeat dropping all non-euros based on PC classification Results from gPROSPER analyses Study 1: Genetic moderation of adolescent alcohol use trajectories and intervention effects on genetic moderation Study 2: The nicotinic receptor subunit α5 gene, smoking, and intervention status Study 3: Interactions between DRD4, intervention, and maternal involvement in the prediction of alcohol use Study 1: Genetic moderation of adolescent alcohol use trajectories and intervention effects on genetic moderation Adolescent Drinking is Linked to Array of Risks, from School Failure to Sexual Risks Early Adolescent Drinking is Linked to Problem Behaviors, Aggression, Family Dysfunction, and School Failure Later Adolescence: Common, Less Risky. . . Part of Autonomy Seeking Measures Alcohol Use: Assessed across 8 waves: 0 = No, 1 = Yes - Ever had a drink of alcohol? - Ever had more than a few sips of alcohol? - Ever been drunk from drinking alcohol? Intervention Status: Analytic Sample = 1,932 46.5% of adolescents in the control condition (coded = 0; n = 899) 53.5% in the intervention (coded = 1; n = 1,033) Alcohol Dehydrogenase (ADH) Genes:. Six SNPs in three ADH Genes – ADH1C, ADH1B, ADH4 Each marker was coded 0 = homozygous non-risk allele, 1 = heterozygous, and 2 = homozygous risk allele. The two markers for each gene were averaged to create three gene scores, range 0 to 2; where a score of 2 indicates an individual carried 4 risk alleles across the two SNPs in a given gene. Analyses: Piecewise Latent Growth Models: Evaluate alcohol use changes and levels across early and late adolescence Step 1: Models estimated: Growth in alcohol use during early adolescence (S1; waves 1 to 5) Growth in alcohol use during late adolescence (S2; waves 5 to 8) Intercept at 9th grade (wave 5; I) Step 2: Adding ADH Genes to determine their associations with Slope1, Slope 2, and Intercept Step 3: Adding Intervention by Genes Interactions to Investigate Whether Genes’ Associations with Slope1, Slope 2, and Intercept are Modified by Intervention Table 1. Piecewise Growth Alcohol Model Results for Early vs. Later Adolescent Drinking Model Early Adolescence Slope 1) Unconditional 2) ADH Main Effects ADH1C ADH1B ADH4 Later Adolescence Slope 9th Grade Intercept .308(.007)* .236(.009)* 1.405(.027)* -.036(.013)* .046(.022)* .007(.012) .046(.022)* .007(.012) -.008(.029) -.007(.015) .170(.082)* .004(.042) 3) Multiple Group Control / Intervention ADH1C -.066(.020)*/ .009(.018) -.015(.026)/ -.015(.024) -.218(.074)*/ .028(.065) ADH1B .050(.033) / .046(.030) .005(.043) / -.023(.039) .200(.125)/ .158(.108) ADH4 .020(.017)/ -.006 (.016) -.012(.022) / .001(.021) .037(.062)/ - .030(.058) Table 3. Piecewise Growth Alcohol Model Results for Early vs. Later Adolescent Drinking Model Early Adolescence Slope Later Adolescence Slope 9th Grade Intercept 1) Unconditional .308(.007)* .236(.009)* 1.405(.027)* 2) ADH Main Effects ADH1C ADH1B ADH4 -.036(.013)* .046(.022)* .007(.012) .046(.022)* -.008(.029) -.007(.015) .007(.012) .170(.082)* .004(.042) 3) Multiple Group Control / Intervention ADH1C -.066(.020)*/ .009(.018) -.015(.026)/ -.015(.024) -.218(.074)*/ .028(.065) ADH1B .050(.033) / .046(.030) .005(.043) / -.023(.039) .200(.125)/ .158(.108) ADH4 .020(.017)/ -.006 (.016) -.012(.022) / .001(.021) .037(.062)/ -.030(.058) ADH1C Control Group -.015ns Alcohol Initiation -.218* -.066* W1 W2 W3 W4 W5 Time W5 W6 W7 W8 Intervention Group ADH1C .028ns Alcohol Initiation .015ns .009ns W1 W2 W3 W4 W5 Time W5 W6 W7 W8 Study 2: The Nicotinic Receptor Subunit α5 Gene, Smoking, and Intervention Measures CHRNA5 Gene One of several nicotinic genes related to smoking rs16969968 is known as “Mr. Big” Linked to Cigarettes-per-day, Nicotine Dependence (DSM4), and Craving High School Smoking Average “Past Month Smoking” over 9th-12th grades of past month score where 0=never, 1=ever but not past month, 2=one to a few times, 3=once a week or more Controls show an expected increase in smoking with greater # of the risk allele (A) Control 1.6 1.4 1.2 Past Month Smoking in High School Control 1 0.8 0.6 0.4 0.2 0 G/G G/A Genotype at rs16969968 [increasing # of risk alleles (A) to the right] Vandenbergh et al., N&TR (In press) doi: 10.1093/ntr/ntv095, PMID: 25941207 A/A Controls show an expected increase in smoking with greater # of the risk alleles (A) But there is no increase in smoking in the intervention group (p < 0.05) 1.6 1.4 Control 1.2 Past Month Smoking in High School 1 Intervention 0.8 0.6 0.4 0.2 0 G/G G/A Genotype at rs16969968 [increasing # of risk alleles (A) to the right] Vandenbergh et al., N&TR (In press) doi: 10.1093/ntr/ntv095, PMID: 25941207 A/A Study 3: Interactions between DRD4, intervention, and maternal involvement in the prediction of alcohol use Intervention Experience: 1. Decrease contextual pressures and opportunities to use 2. Increase peer resistance skills Maternal Involvement: Index of parental investment DRD4: Provides index of genetic variability in susceptabilty to experience DRD4 findings: Interacting with both family factors and interventions 1. Maternal insensitivity at 10 months predicts externalizing at 39 months (r = .61) among DRD4 7+ children; but not among7- youth (Bakermans-Kranenberg & van IJzendoorn, 2006). 2. Greater relationship between overall parental quality and lab-measured sensation seeking behavior for DRD4 7+ youth (r = .58) than among DRD4 7- youth (r =.22; Sheese, Voelker, Rothbart, & Posner, 2007). 3. Maternal attachment predicted altruistic behaviors among seven year olds who were 7+ compared to those who were DRD4 7- (Bakermans-Kranenberg and van IJzendoorn , 2011) 4. Parenting program aimed at affecting child behavior through enhancing maternal sensitivity and positive discipline strategies reduced externalizing more for DRD4 7+ children than those who were 7- (Bakermans-Kranenburg et al., 2008). Predicting 9th grade alcohol use by grade 6 Mother Activity and Intervention Status 1. Significant main effect Three-way Interaction Int*MCA*D4 -.47(.21)* Predicting 9th grade alcohol use by grade 6 Mother Activity and Intervention Status 1. Significant main effect Three-way Interaction Int*MCA*D4 -.47(.21)* 2. Significant 3way interaction Intervention Effects of the Association between 6th grade Maternal Activities and Alcohol Use Initiation by 9th Grade among DRD4 7+ Adolescents .06 -.06 -.24 -.23 Thanks to Gabriel Schlomer Intervention Effects of the Association between 6th grade Maternal Activities and Alcohol Use Initiation by 9th Grade among DRD4 7+ Adolescents .06 -.06 -.24 -.23 Thanks to Gabriel Schlomer Intervention effect on DRD4 7+ carriers with high maternal activity Intervention Effects of the Association between 6th grade Maternal Activities and Alcohol Use Initiation by 9th Grade among DRD4 7+ Adolescents .06 -.06 -.24 -.23 Thanks to Gabriel Schlomer Intervention effect on DRD4 7+ carriers with high maternal activity p < .05 DRD4 Results 1.8 Control 1.6 Intervention DRD4 7R- 1.4 1.2 1 0.8 0.6 0.4 0.2 0 Low Mean High MomAct Involvement 1.8 DRD4 7R+ 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 Low Mean MomAct Involvement High DRD4 Results 1.8 Control 1.6 Intervention DRD4 7R- 1.4 1.2 1 0.8 0.6 0.4 0.2 0 Low Mean High MomAct Involvement 1.8 DRD4 7R+ 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 Low Mean MomAct Involvement High DRD4 Results 1.8 Control 1.6 Intervention DRD4 7R- 1.4 1.2 1 0.8 0.6 0.4 0.2 0 Low Mean High MomAct Involvement 1.8 DRD4 7R+ 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 Low Mean MomAct Involvement High DRD4 Results 1.8 Control 1.6 Intervention DRD4 7R- 1.4 2-way interaction between DRD4 and maternal involvement in intervention group 1.2 1 0.8 0.6 0.4 0.2 0 Low Mean High MomAct Involvement 1.8 1.6 DRD4 7R+ 1.4 1.2 1 0.8 0.6 0.4 0.2 0 Low Mean MomAct Involvement High Intervention Effects of the Association between 6th grade Maternal Activities and Alcohol Use Initiation by 9th Grade among DRD4 7+ Adolescents .06 -.06 -.24 -.23 Thanks to Gabriel Schlomer Intervention effect on DRD4 7+ carriers with high maternal activity p < .05 Population stratification sensitivity analysis 3-way 2-way Int*MA, 7+ 7+ intervention -.469* -.289* -.228* PC-control (n = 511) -.446* -.272* -.221* -.512* -.342* -.220* Full (n = 545) PC-drop (n = 470) • Bonus findings . . . Schlomer, et al. (2015). Developmental differences in early adolescent aggression: A gene x environment x intervention analysis. Journal of Youth and Adolescence. Control, 7- Control, 7+ Intervention, 7- Intervention, 7+ Investigating Genetic Moderation of Threat Appraisals Grych & Fincham, 1990 Cummings & Cummings, 1988 Davies, Cummings, & Winter, 2004 Fosco & Grych, 2008 Interparental Conflict Externalizing Threat Appraisals Internalizing Well-being .21 Interparental Conflict .32 -.19 Interparental Positivity Adolescent Perception IPC .27 Threat Appraisals -.19 .33 DRD4 7- Adolescent Perception IPP .28 -.08 .20 Interparental Conflict .36 -.24 -.06 Interparental Positivity Adolescent Perception IPC .33 Adolescent Perception IPP Internalizing Problems DRD4 7+ .05 Threat Appraisals -.22 .30 Internalizing Problems Conclusions 1. Intervention designs provide an important opportunity to examine GxE interactions. 2. Results confirm the co-active nature of both genes and environments. 3. Using well-characterized candidate genes provides an opportunity to understand for whom interventions work and perhaps why and how interventions work. Next Steps • Begin to characterize genetic variance in more biologically thoughtful ways – Using multi-locus gene scores – Using combinatorial approaches to examine gene networks • Examine how genes interact with social networks across adolescence to create risk and promote well-being Multi-locus Genetic Scores: Neurotransmitter System Approach Dopaminergic: Cholinergic: Catechol-O-Methyl Transferase Dopamine Transporter Dopamine Receptor Dopamine Receptor Dopamine Receptor Dopamine Beta-Hydroxylase Dopamine Receptor Amine oxidase (MAOB) Cholinergic receptor, muscarinic 2 Cholinergic receptor, nicotinic, alpha 4 subunit Cholinergic receptor, nicotinic, alpha 7 subunit Neuronal nicotinic acetylcholine receptor beta 2 Cholinergic receptor, nicotinic, alpha 5 Cholinergic receptor, nicotinic, alpha 3 Cholinergic receptor, nicotinic, beta 4 Cholinergic receptor, nicotinic, beta 3 Cholinergic receptor, nicotinic, alpha 6 Cytochrome P450 2A6 Serotonergic: Vasopressin/Oxytocin: Monoamine Oxidase A Serotonin Transporter 5-Hydroxytryptamine (serotonin) receptor 1B 5-Hydroxytryptamine (serotonin) receptor 2A Neuronal tryptophan hydroxylase Oxytocin receptor Arginine vasopressin-neurophysin II Arginine vasopressin receptor 1A Arginine vasopressin receptor 1B CD38 (cyclic ADP ribose hydrolase) GABAergic: Alcohol Metabolism: Gamma-aminobutyric acid A receptor, alpha 1 Gamma-aminobutyric acid A receptor, alpha 6 Gamma-aminobutyric acid A receptor, beta Gamma-aminobutyric acid A receptor, beta 2 Gamma-aminobutyric acid A receptor, alpha 2 Alcohol Dehydrogenase 1B Alcohol Dehydrogenase 1C Alcohol Dehydrogenase 2 Alcohol Dehydrogenase 4 Alcohol Dehydrogenase 5 Opioid: Cannabinoid, other Genes, and AIMs: Opioid receptor, kappa 1 Opioid receptor mu 1, isoform MOR-1X Beta-neoendorphin-dynorphin preprotein Proenkephalin (PENK) Cannabinoid Receptor 1 (brain) Fatty Acid amide hydrolase (FAAH) Brain Derived Neurotrophic Factor cAMP Resp Elem Binding Prot 1 (CREB) FBJ murine osteosarcoma viral oncogene homolog B (FOSB) DNA cytosine methyltransferase 3 alpha • Thank You