slide1 l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
NIDA Genetics: An Update PowerPoint Presentation
Download Presentation
NIDA Genetics: An Update

Loading in 2 Seconds...

play fullscreen
1 / 45

NIDA Genetics: An Update - PowerPoint PPT Presentation


  • 161 Views
  • Uploaded on

NIDA. NATIONAL INSTITUTE ON DRUG ABUSE. NIDA Genetics: An Update . Jonathan Pollock, PhD Branch Chief, Genetics and Molecular Neurobiology Research Branch DBNBR And Joni L Rutter, PhD Associate Director for Population and Applied Genetics DBNBR.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'NIDA Genetics: An Update' - aislinn


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

NIDA

NATIONAL INSTITUTE

ON DRUG ABUSE

NIDA Genetics: An Update

Jonathan Pollock, PhD

Branch Chief, Genetics and Molecular Neurobiology Research Branch

DBNBR

And

Joni L Rutter, PhD

Associate Director for Population and Applied Genetics

DBNBR

individual differences in vulnerability to addiction
Individual Differences in Vulnerability to Addiction
  • Not everyone who takes drugs becomes addicted
    • “has characteristics of chronic disease” Piazza Science 13 August 2004
    • There are individual differences
    • Chronic exposure to drugs combined with a vulnerability phenotype leads to addiction
    • Genetics contributes to some of these individual differences
drug addiction is influenced by interactions of genes and environment
Drug Addiction Is Influenced by Interactions of Genes and Environment

Twin studies consistently show that there is a heritable component to drug abuse and addiction.

Now we are able to examine genetic variants, or single nucleotide polymorphisms that contribute to addiction vulnerability

goal identify genetic contributions to drug abuse

Genome

Chromosome

Microband Region

50-100 Genes

Single Gene

(exons/introns)

Single Nucleotide

Polymorphism

(SNPs)

Goal: Identify Genetic Contributions to Drug Abuse

Genome-wide Association Studies (GWAS) or

Linkage

Low-density

Markers

Fine-

mapping

Genotyping/

Resequencing

nida genetics identify genetic contributions to drug abuse
NIDA Genetics:Identify Genetic Contributions to Drug Abuse
  • 2 Main Programmatic Themes
    • Trans-NIH initiatives
      • Leverage ongoing programs
      • Roadmap, GAIN, GEI, PGRN, etc
    • Trans-NIDA programs
      • Coordination among divisions
      • Multidisciplinary/Interdisciplinary
        • Data
human genetics trans nih involvement
Human GeneticsTrans-NIH Involvement
  • Roadmap 1.0
    • National Centers for Biomedical Computing (NCBC) - NIDA Lead, Karen Skinner
      • i2b2 (informatics for integrating biology and the bedside)
        • Zak Kohane, PI--evaluate smoking data in medical records
        • forge collaborations on genetics and bioinformatics of smoking
  • Roadmap 1.5 – Epigenomics
      • Nora Volkow, John Satterlee, Christine Colvis, Genevieve de-Almeida-Morris, Jonathan Pollock, David Shurtleff, Joni Rutter

http://nihroadmap.nih.gov/epigenomics/

human genetics trans nih involvement7

Gene variant in CYP2B6 is associated with greater effectiveness of bupropion

Human GeneticsTrans-NIH Involvement
  • Pharmacogenetics Research Network (PGRN)
    • A collaboration studying the effects of genes on people’s responses to medicines
    • Supported by 9 NIH ICs
  • Pharmacogenetics of Nicotine Addiction and Treatment (PNAT) – Neal Benowitz, PI
    • Addresses pharmacogenetics of nicotine addiction
      • NRTs
      • Bupropion
      • Varenicline
      • Rimonabant
human genetics trans nih involvement8
Human GeneticsTrans-NIH Involvement
  • Genetic Association Information Network (GAIN)
    • Public-private partnership led by Foundation for NIH
    • Genome-wide association study policy development for NIH-- Jonathan Pollock
  • Genes, Environment and Health Initiative (GEI)
    • Genetics Program- Pipeline for analyzing genetic variation in groups of patients with specific illnesses
      • Genetics of Addiction – Laura Bierut, PI
    • Exposure Biology Program- Measuring environmental exposures
      • NIDA RFA: Technologies measuring exposure to psychosocial stress and addictive substances--Kay Wanke, Kevin Conway
nida genetics identify genetic contributions to drug abuse9
NIDA Genetics:Identify Genetic Contributions to Drug Abuse
  • 2 Main Programmatic Themes
    • Trans-NIH initiatives
      • Leverage ongoing programs
      • Roadmap, GAIN, GEI, PGRN, etc
    • Trans-NIDA programs
      • Coordination among divisions
      • Multidisciplinary/Interdisciplinary
        • Data
nida genetics a coordinated effort

Model Organisms

NIDA Genetics Workgroup

Members

NGCC members

Christine Colvis

Jim Glass

Hal Gordon

Mark Green

Jag Khalsa

Diane Lawrence

Mary Ellen Michel

Ivan Montoya

Jonathan Pollock, Chair

John Satterlee

David Shurtleff

Karen Skinner

Mark Swieter

Yonette Thomas

George Uhl (IRP)

Susan Volman

Da-Yu Wu

Scientific Goals & Services

  • Develop avenues of research through FOAs
  • Sponsor seminars to inform NIDA of new areas of genetics research
  • Develop staff knowledge in areas of genetics research
NIDA Genetics: A Coordinated Effort

Human

NIDA Genetic Coordinating Committee (NGCC)

Members

Beth Babecki

Kevin Conway

Ahmed Elkashef

Steve Grant

Rita Liu

Raul Mandler

Cindy Miner

JJ Pan

Amrat Patel

Jonathan Pollock

Joni Rutter, Chair

Larry Stanford

Kay Wanke

Naimah Weinberg

Scientific Goals & Services

  • Written ARA, supplement, and funding recommendations to the Director
  • Recommend genetic applications assignment
  • Provide standards for data collection, data sharing, and informed consent
  • Evaluate human genetics program
  • Serve as the NGC Steering Committee
human genetics trans nida genetics
Human GeneticsTrans-NIDA Genetics
  • NIDA Genetics Consortium (NGC)
    • NIDA Division Reps (+1 NCI rep)
    • >20 PIs; 24 studies; 1 contract
      • http://zork.wustl.edu/nida/
    • ~30,000 samples in Repository
      • DNA and clinical information (DSMIIIR or IV)
      • Nicotine, Opioids, Cocaine, Polysubstance
      • Data to be publicly available through controlled access
  • NIDA Phenotyping Consortium (NPC)
      • DESPR-led initiative (Kevin Conway); 5 PIs
      • Produce precise and specific phenotypes for drug abuse

Addiction “Bioseverity”

Develop a research instrument measuring addiction severity

Validated with biological processes (ex. Genetic, imaging, etc)

Michael Vanyukov (NGC), Gary Swan (NGC), Michael Neale (NPC)

Kevin Conway (DESPR), Janet Levy (DESPR), Joni Rutter (DBNBR)

human genetics trans nida genetics12
Human GeneticsTrans-NIDA Genetics
  • Collaboration between NGC, CTN, DPMCDA
    • “START” Study – Starting Treatment on Agonist Replacement Therapies –added pharmacogenetics
    • Targeting ~600 opioid dependent persons
    • Randomized, open-labeled CTN multi-site design to either buprenorphine or methadone
    • Primary outcomes are:
      • Liver toxicity (parent trial)
      • Individual genetic variation in treatment effectiveness (Pharmacogenetics)
        • Wade Berrettini and Lindsay DeVane
human genetics trans nida genetics13
Human GeneticsTrans-NIDA Genetics
  • Collaboration among NIDA Divisions
    • Genes, Environment, and Development Initiative (GEDI I & II) (DBNBR, DCNBR, DESPR)
      • Support research that investigates interplay among genetic, environmental, and developmental factors in the etiology of substance abuse and related phenotypes
      • Notice in NIH Guide for re-issue – Naimah Weinberg
converging genetics findings nida irp ngc data on nicotine

Saccone – Nicotine dependence HMG, 2007

Gelernter-Nicotine dependence HMG, 2006

Li - Nicotine dependence

AJHG, 2006

Areas of converging results

Madden – Maximum cigarettes

in 24-hrs AJHG, 2007

Converging Genetics Findings:NIDA IRP & NGC Data on Nicotine

Uhl GR, et al., Biochem Pharmacol (2007)

Smoking-related phenotypes

smoking phenotype
Smoking Phenotype

Controls(lifetime >100 cigarettes; FTND=0)

Cases (FTND 4 or more)

Number of Subjects

0

4

5

6

7

8

9

10

Nicotine Dependence Score

(Fagerstrom Test – FTND, scale 0-10)

slide16

Top 40,000 SNPs

2.2 Million Perlegen SNPs

Phase 2 Screen

Phase 1 Screen

Whole Genome-wide Association Study Targeting Nicotine Dependence

6 Million Common SNPs in Genome

71

Bullseye

71 SNPs show the most difference among the cases and controls

slide17

Several SNPS Found to be Different Between Cases and Control

  • 71 SNPS different between case and control
  • Many were not the “usual suspects”
  • Several nicotinic receptors implicated :
      • a3 nicotinic receptor
      • β3 nicotinic receptor
      • a5 nicotinic receptor SNP is highly associated with nicotine dependence

**CHRNA5 non-synonymous SNP D398N

Individuals with this SNP have 2-fold increase risk of developing nicotine dependence OR=1.9 (1.4-2.6)

Saccone et al. HMG 2007

Bierut et al. HMG 2007

the human chrna5 d398n polymorphism alters nicotinic receptor function in vitro
The human CHRNA5 D398N polymorphism alters nicotinic receptor function in vitro

Common allele

P < 0.01

**

Variant allele

Courtesy of Jerry Stitzel, PhD

summary
Summary
  • Trans-NIH involvement
    • Roadmap, GAIN, GEI, PGRN, others
    • NIDA staff play leadership roles
    • NIDA PIs positioned to take advantage of programs
  • NIDA Genetics
    • Divisional coordination, programmatic synergy
    • Phenotype, environment, development, family, clinical, animal models
    • Results are converging Functional information  Mechanism
    • Inform treatment, prevention, intervention approaches

Molecular Genetics of Drug Addiction and Related Co-Morbidities (R01)PA-07-073 (R01) - November 20, 2006

Functional Genetics and Genomics of Drug AddictionPA-07-166 (R01), PA-07-167 (R21), PA-07-168 (R03) - December 14, 2006

Genetic Epidemiology of Substance Use DisordersPA-07-413 (R01), PA-07-415 (R21), PA-07-414 (R03) - July 27, 2007

slide22

Genes

Development

Family/

Clinical

Drug Abuse

Genetics

Environment/

Epidemiology

Model

organisms

  • Define genetic contribution
  • Define environmental and developmental factors
  • Understand neurobiological mechanisms
  • Improve treatment and prevention/intervention approaches
slide23

Genetic model organisms, such as the mouse, can provide clues to the genetic basis of complex diseases

three different methods to identify gene function in mice
Three Different Methods to Identify Gene Function in Mice
  • Haplotype Associated Mapping of Natural Variation in Inbred Strains
  • Selective Breeding
  • Induced Mutations, e.g. Knockouts
three different methods to identify gene function in mice25
Three Different Methods to Identify Gene Function in Mice
  • Haplotype Associated Mapping of Natural Variation in Inbred Strains
  • Selective Breeding
  • Induced Mutations, e.g. Knockouts
power of inbred strains
Power of Inbred Strains
  • Inbred strains are a way to determine environmental effects through development across defined genotypes under controlled conditions
  • Confidence in the underlying genotype is strengthened when phenotypes map to homologous chromosomal loci in mice and humans
slide27

I/LnJ

A/J

C3H/HeJ

C57L/J

C57BL/6J

DBA/2J

LP/J

C57BR/cdJ

CE/J

haplotype associated mapping of natural variation in inbred strains
Haplotype Associated Mapping of Natural Variation in Inbred Strains
  • Many mouse genomes have been sequenced e.g. C57BL6/J, AJ, 129
  • Genetic variation in over 15 strains of mice have been identified
  • Use variation in gene expression profiling as a “signature” for a trait
slide29

Strain Distribution of Percent Time Spent in Center of Open Field

Time Spent in Center

Mouse Strain

Courtesy of Tim Wilshire

differential expression of grm7 in 4 brain regions
Differential expression of Grm7 in 4 brain regions

High “Anxiety”

Low “Anxiety”

Gene Expression

Brain Region

Low Anxiety -decreased expression of Grm7- increase in percent time spent in center of open field

three different methods to identify gene function in mice31
Three Different Methods to Identify Gene Function in Mice
  • Haplotype Associated Mapping of Natural Variation in Inbred Strains
  • Selective Breeding
  • Induced Mutations, e.g. Knockouts
mapping genes using selective breeding
Mapping Genes Using Selective Breeding
  • Takes advantage of the mouse genome and gene variants (SNPs) among different mouse strains
  • NIH proposed project will increase genetic diversity in inbred recombinant mice making it possible to map any trait in the mouse (i.e., The “Collaborative Cross”)
selection for locomotor stimulant response to methamphetamine in mice
Selection for Locomotor Stimulant Response to Methamphetamine in Mice

Locomotor response to MA (2 mg/kg)

Kamens et al. Genes, Brain & Behavior, 2005

csnk1e gene expressed more in methamphetamine sensitive mice
Csnk1e Gene Expressed More in Methamphetamine Sensitive Mice

The most differentially expressed gene was Csnk1e (10 fold different)

There is a massive eQTL for Csnk1e

Palmer et al. Mammalian Genome, 2005

slide35

Csnk1e Variant Enhances Dopamine Signaling Through Darpp-32 Signaling Protein

Palmer et al. Mammalian Genome, 2005

translational genetic models of methamphetamine sensitivity
Translational Genetic Models of Methamphetamine Sensitivity

Humans vary in methamphetamine sensitivity

Differences in sensitivity are partially under genetic control in both mice and humans

Differences in initial effects of drugs linked to future use

An predictor or intermediate phenotype to addiction in humans

effect of csnk1e polymorphisms on response to methamphetamine
Effect of CSNK1E Polymorphisms on Response to Methamphetamine
  • 3 polymorphisms in CSNK1E in humans
    • All 3 SNPs occur at high frequency
  • Analyzed their relationship with 3 primary outcome measures:
    • DEQ “Feel Drug”
    • ACRI “Euphoria”
    • POMS “Anxiety”

Veenstra-VanderWeele et al. Neuropsychopharm., 2005

slide38

ACRI “Euphoria” is Also Significantly Influenced by SNPs in CSNK1E

*

*

*

Veenstra-VanderWeele et al. Neuropsychopharmacology, 2005

chromosomal regions are conserved for drug response in mice and humans
Chromosomal Regions are Conserved for Drug Response in Mice and Humans
  • Formin
  • LRP2
  • Alpha 6 integrin
  • NFI/B
  • NPAS2
  • NIMA-related 7 Kinase

Uhl et al, 2007 Biochemical Pharmacology

three different methods to identify gene function in mice40
Three Different Methods to Identify Gene Function in Mice
  • Haplotype Associated Mapping of Natural Variation in Inbred Strains
  • Selective Breeding
  • Induced Mutations, e.g. Knockouts
nih knockout mouse project komp
NIH Knockout Mouse Project (KOMP)
  • KOMP is a trans-NIH project involving 18 Institutes
  • The NIH has committed ~ $52 M over 5 years
  • KOMP is cooperating with other international efforts to minimize overlap
why are knockouts and transgenic animals useful
Why are Knockouts and Transgenic Animals Useful?
  • Help identify essential molecules in reward pathways mediating drug abuse
  • Help separate cellular signaling pathways that mediate different phenomena associated with addiction (e.g., withdrawal, tolerance)
mice lacking the 2 nicotinic receptor do not exhibit nicotine place preference
Mice Lacking the Β2 Nicotinic Receptor Do Not Exhibit Nicotine Place Preference.

Walters CL, et al Psychopharmacology (Berl). 2006 Mar;184(3-4):339-44

slide45

Model

Organism

Genetics

Genes

Development

knockout

Drug Abuse

Genetics

Environment

Epigenetics

  • Define genetic contribution
  • Define environmental and developmental factors
  • Understand neurobiological mechanisms
  • Provide genetic resources to community
  • Test putative medications