Strategies for candidate gene x environment research and opportunities provided by intervention designs

H. Harrington ClevelandHuman Development Family

StudiesThe Pennsylvania State UniversityThanks 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 confoundsThe 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 StratificationA 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 casesAnalyses 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. 520-526.Cardon & Palmer (2003). Population stratification and spurious allelic association. Lancet, 361, 598-604

We suggest five domains in which candidate gene research should be evaluated:• Design• Measurement• Theory• Biological Role• Population Structure

Moving cGxE Research Forward

• 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 non-random 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.1

• 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?

Design.2

• Theory can direct researchers toward constructs to consider

• It can also help researchers avoid searching for findings and leveraging chance results

Theory

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).

Measurement

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.

Biological Role

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).

Population Structure

gPROSPER builds on the PROSPER intervention studyo 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 analyses2,000 genotyped for adolescent-only analyses550 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

Population Stratificationin gProsper

PC2

PC1

Population Stratificationin gProsper

PC2

PC1

1 sd from mean of pc 1 among self-identified non-euros

Common gPROSPER Analytic Framework

Analytic steps: 1. Run models on all available cases2. Repeat controlling for pop stratification3. Repeat dropping all non-euros based on PC classification

Results from gPROSPER analysesStudy 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

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

Study 1: Genetic moderation of adolescent alcohol use trajectories and intervention effects on genetic moderation

MeasuresAlcohol 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, ADH4Each 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 Later Adolescence Slope

9th Grade Intercept

1) Unconditional .308(.007)* .236(.009)*

1.405(.027)*

2) ADH Main Effects

ADH1C -.036(.013)* .046(.022)*

.007(.012)

ADH1B .046(.022)* -.008(.029).170(.082)*

ADH4 .007(.012) -.007(.015) .004(.042)

3) Multiple Group Control /

Intervention

ADH1C -.066(.020)*/ -.015(.026)/

-.218(.074)*/

.009(.018) -.015(.024)

.028(.065)

ADH1B .050(.033) / .005(.043) /

.200(.125)/

.046(.030) -.023(.039)

.158(.108)

ADH4 .020(.017)/ -.012(.022) /

.037(.062)/

-.006 (.016) .001(.021)

- .030(.058)

Table 3. Piecewise Growth Alcohol Model Results for Early vs. Later Adolescent Drinking

ModelEarly Adolescence Slope Later Adolescence Slope

9th Grade Intercept

1) Unconditional .308(.007)* .236(.009)*1.405(.027)*

2) ADH Main EffectsADH1C -.036(.013)* .046(.022)*

.007(.012)ADH1B .046(.022)* -.008(.029)

.170(.082)*ADH4 .007(.012) -.007(.015)

.004(.042)

3) Multiple Group Control / Intervention

ADH1C -.066(.020)*/ -.015(.026)/ -.218(.074)*/

.009(.018) -.015(.024) .028(.065)

ADH1B .050(.033) / .005(.043) / .200(.125)/

.046(.030) -.023(.039) .158(.108)

ADH4 .020(.017)/ -.012(.022) /.037(.062)/

-.006 (.016).001(.021) -.030(.058)

Alco

hol I

nitia

tion

W1 W5 W8W4W3W2 W7W6W5Time

-.015ns

ADH1C

-.218*-.066*

Control Group

Alco

hol I

nitia

tion

W1 W5 W8W4W3W2 W7W6W5Time

.028ns

ADH1C

.015ns

.009ns

Intervention Group

CHRNA5 GeneOne of several nicotinic genes related to smokingrs16969968 is known as “Mr. Big”Linked to Cigarettes-per-day, Nicotine Dependence (DSM4), and Craving

High School SmokingAverage “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

Study 2: The Nicotinic Receptor Subunit α5 Gene, Smoking, and Intervention

Measures

Controls show an expected increase in smoking with greater # of the risk allele (A)

G/G G/A A/A0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6Control

Control

Genotype at rs16969968 [increasing # of risk alleles (A) to the right]

Past Month Smoking in High School

Vandenbergh et al., N&TR (In press) doi: 10.1093/ntr/ntv095, PMID: 25941207

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)

G/G G/A A/A0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Control

Intervention

Genotype at rs16969968 [increasing # of risk alleles (A) to the right]

Past Month Smoking in High School

Vandenbergh et al., N&TR (In press) doi: 10.1093/ntr/ntv095, PMID: 25941207

Study 3: Interactions between DRD4, intervention, and maternal involvement in the prediction of alcohol useIntervention Experience:

1. Decrease contextual pressures and opportunities to use2. 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

Three-way Int*MCA*D4-.47(.21)*

Interaction

1. Significant main effect

Predicting 9th grade alcohol use by grade 6 Mother Activity and Intervention Status

Three-way Int*MCA*D4-.47(.21)*

Interaction

1. Significant main effect

2. Significant 3-way interaction

Intervention Effects of the Association between 6th grade Maternal Activities and Alcohol Use Initiation by 9th Grade among DRD4 7+ Adolescents

Thanks to Gabriel Schlomer

.06

-.06

-.24

-.23

Intervention Effects of the Association between 6th grade Maternal Activities and Alcohol Use Initiation by 9th Grade among DRD4 7+ Adolescents

Thanks to Gabriel Schlomer

.06

-.06

-.24

-.23

Intervention effect on DRD4 7+carriers withhigh maternal activity

Intervention Effects of the Association between 6th grade Maternal Activities and Alcohol Use Initiation by 9th Grade among DRD4 7+ Adolescents

Thanks to Gabriel Schlomer

.06

-.06

-.24

-.23

Intervention effect on DRD4 7+carriers withhigh maternal activityp < .05

DRD4 Results

Low Mean HighMomAct

00.2

0.40.6

0.81

1.2

1.41.61.8 Control

Intervention

Low Mean HighMomAct

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

DRD4 7R-

DRD4 7R+

Involvement

Involvement

DRD4 Results

Low Mean HighMomAct

00.2

0.40.6

0.81

1.2

1.41.61.8 Control

Intervention

Low Mean HighMomAct

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

DRD4 7R-

DRD4 7R+

Involvement

Involvement

DRD4 Results

Low Mean HighMomAct

00.2

0.40.6

0.81

1.2

1.41.61.8 Control

Intervention

Low Mean HighMomAct

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

DRD4 7R-

DRD4 7R+

Involvement

Involvement

DRD4 Results

Low Mean HighMomAct

00.2

0.40.6

0.81

1.2

1.41.61.8 Control

Intervention

Low Mean HighMomAct

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

DRD4 7R-

DRD4 7R+

Involvement

Involvement

2-way interaction

between DRD4 and maternal involvement in

interventiongroup

Intervention Effects of the Association between 6th grade Maternal Activities and Alcohol Use Initiation by 9th Grade among DRD4 7+ Adolescents

Thanks to Gabriel Schlomer

.06

-.06

-.24

-.23

Intervention effect on DRD4 7+carriers withhigh maternal activityp < .05

3-way 2-way Int*MA, 7+ 7+ intervention

Full (n = 545) -.469* -.289* -.228*

PC-control (n = 511) -.446* -.272* -.221*

PC-drop (n = 470) -.512* -.342* -.220*

Population stratification sensitivity analysis

• Bonus findings . . .

Control, 7- Control, 7+

Intervention, 7- Intervention, 7+

Schlomer, et al. (2015). Developmental differences in early adolescent aggression: A gene x environment x intervention analysis. Journal of Youth and Adolescence.

Interparental Conflict

Threat Appraisals

Externalizing

Internalizing

Well-being

Grych & Fincham, 1990Cummings & Cummings, 1988Davies, Cummings, & Winter, 2004Fosco & Grych, 2008

Investigating Genetic Moderation of Threat Appraisals

DRD4 7-.21

Interparental Conflict

Interparental Positivity

Adolescent Perception IPC

Adolescent Perception IPP

Threat Appraisals

Internalizing Problems

.33

.32.27

-.08

.28-.19 -.19

DRD4 7+Interparental

Conflict

Interparental Positivity

Adolescent Perception IPC

Adolescent Perception IPP

Threat Appraisals

Internalizing Problems

.20

.33

.36.05

-.22

.30-.06 -.24

Conclusions1. 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

Dopaminergic:Catechol-O-Methyl TransferaseDopamine TransporterDopamine ReceptorDopamine ReceptorDopamine ReceptorDopamine Beta-HydroxylaseDopamine ReceptorAmine oxidase (MAOB)

Cholinergic:Cholinergic receptor, muscarinic 2Cholinergic receptor, nicotinic, alpha 4 subunitCholinergic receptor, nicotinic, alpha 7 subunitNeuronal nicotinic acetylcholine receptor beta 2Cholinergic receptor, nicotinic, alpha 5Cholinergic receptor, nicotinic, alpha 3Cholinergic receptor, nicotinic, beta 4Cholinergic receptor, nicotinic, beta 3Cholinergic receptor, nicotinic, alpha 6Cytochrome P450 2A6

Serotonergic: Monoamine Oxidase A Serotonin Transporter 5-Hydroxytryptamine (serotonin) receptor 1B5-Hydroxytryptamine (serotonin) receptor 2A Neuronal tryptophan hydroxylase

Vasopressin/Oxytocin:Oxytocin receptorArginine vasopressin-neurophysin II Arginine vasopressin receptor 1AArginine vasopressin receptor 1BCD38 (cyclic ADP ribose hydrolase)

GABAergic:Gamma-aminobutyric acid A receptor, alpha 1 Gamma-aminobutyric acid A receptor, alpha 6Gamma-aminobutyric acid A receptor, beta Gamma-aminobutyric acid A receptor, beta 2 Gamma-aminobutyric acid A receptor, alpha 2

Alcohol Metabolism:Alcohol Dehydrogenase 1BAlcohol Dehydrogenase 1C Alcohol Dehydrogenase 2 Alcohol Dehydrogenase 4Alcohol Dehydrogenase 5

Opioid:Opioid receptor, kappa 1Opioid receptor mu 1, isoform MOR-1XBeta-neoendorphin-dynorphin preproteinProenkephalin (PENK)

Cannabinoid, other Genes, and AIMs:Cannabinoid Receptor 1 (brain)Fatty Acid amide hydrolase (FAAH)Brain Derived Neurotrophic FactorcAMP Resp Elem Binding Prot 1 (CREB)FBJ murine osteosarcoma viral oncogene homolog B (FOSB)DNA cytosine methyltransferase 3 alpha

Multi-locus Genetic Scores: Neurotransmitter System Approach

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