Integrating Genetic and Biomarker
Data with Social Science Research:
Genetics
Jason Fletcher
Assistant Professor
Health Policy and Administration
Yale University
RWJ Health and Society Scholar
Columbia University
Goals

Introduce some terminology


Requires multiple exposures
Focus

Limitations


What findings from genetics should you believe?
Opportunities
How might social scientists use genetic data?
 Advances in both genetics and social science

Data Opportunities

Currently Available—DNA data

Add Health


Fragile Families


1957 HS grads and sibs, long follow up
Framingham Heart Study

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National longitudinal sample, 5K, Mothers and
children, lower income/immigrant samples
Wisconsin Longitudinal Study


National longitudinal sample, 15K, Age 12-30,
siblings, school friends, focus on health
Medical focus, multigenerational study
Many international datasets
Eventually available(?)

Health and Retirement Study


Panel study of income dynamics


National longitudinal study, ages 50+,
spouses, health and aging
National longitudinal study, multigenerational
families/all ages, income and labor market,
health
National Longitudinal Survey of Youth

National longitudinal study, labor market
focus, multigenerational, siblings
Outline

Background



Behavioral genetics (non-molecular)
Molecular genetics
Integration with Social Science


Gene X Environment interactions
Instrumental variables
Behavioral genetics


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Family based/twin studies
No DNA data
Decomposition of variance of outcomes
into three components



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A=Heritability
C=Shared/Common environment
E=Unshared/Unique environment
Heritability estimates (h2)

Comparison of correlation of MZ twins with DZ
twins
The basic BG model

Variation in phenotype
(outcome/observable characteristic) is a
function of variation in additive genetic
(genotype) and environmental
contributions (shared and unshared)
Classic twin design
y  AC E
y 1  A1  C 1  E 1
y 2  A2  C 2  E 2

A=genotype; C=common environment;
E=unique environment



Identical/Monozygotic (MZ) twins share 100%
of genetic makeup
Fraternal/Dizygotic (DZ) twins share ~50% of
genetic makeup
Equal environments assumption
Classic twin design
rmz  A  C
rdz 
1
AC
2
A  2 ( rmz  rdz )
C  rmz  A  2 rdz  rmz
E  1  rmz
Typical estimates
Example from literature
Credibility Test?
Schonemann 1997
Example from literature:
Boardman et al (2008)

Regression based approach
y1 j   0   1 y 2 j   2 g j   3 g j y 2 j  
j
g is zygosity (0.5 for MZ, 0 for DZ),  3 is coefficient
of interest
 Equal environments issue

y1 j   0   1 y 2 j   2 g j   3 g j y 2 j   X   j

Dressed the same, same room, same playmates
Findings


Positive affect is highly heritable (~.6)
Controls decrease heritability estimate for
women to 0.3 but men to 0.52

Socioeconomic Xs, emotional support, stress
Some key assumptions/issues

Random mating of parents generation


Equal environments assumption

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Violations tend to inflate h2
External validity to non-twins
Gene-environment correlation


Assortative mating tends to deflate h2
Inflates h2
Gene-environment interaction

Subsumed in h2
2nd Design: Adoptions

Correlation between two adopted siblings


Correlation between two non-adopted
siblings



C
1/2A+C
Assumes equal environments
What about gene-environment
correlations? Selection of adoptees
Recent Innovation:
Adoption Studies

Sacerdote (2007)

Quasi-Random assignment of adoptees


Gene-environment correlation
h2
41% for college graduation
 44% for education attainment
 33% for income
 5% for alcohol use
 27% for tobacco use
 17% for overweight status

Innovations: Twin Studies

Random mating

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Equal environments assumption

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Mating parameter in robustness checks
Genotype siblings in order to estimate
assortative mating parameter
Use survey questions that measure shared
environments
Richer family level data

Cousins, siblings, parents, etc.
A Puzzle: Heritability vs.
Measured Genetic Variation


Large heritability estimates (~.3)
Small measured variation using genetic
On the other hand…
Additional new directions

Variation of h2 by study population

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Gender, Race, Country, Time Period
Can this tell us anything about gene x environment
interactions?
Boardman and Blaylock in press
Quiz from Collegeboard.com

If a person has a disorder with h2=1, then the person will
suffer from the disorder


The heritability of having fingers on each hand is 1 or close
to 1

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False, it is close to zero because the source is often environmental
Heritability and inherited are nearly the opposite in meaning


False, phenylketonuria (PKU) = 1 but mental retardation can be
prevented through diet
True; equalizing school environments will increase heritability of
achievement
The heritability of behaviors of identical twins is 1


False, it is zero
http://apcentral.collegeboard.com/apc/members/homepage/45829.ht
ml
Discussion/Questions


What do we learn from h2 estimates?
What are the policy implications of estimates?




Heritability estimates set no upper limit on the potential effect of
reducing or eliminating variation in environmental factors that
currently vary in response to genotype, as many do. Nor do they
set an upper limit on the effect of creating new environments.
Heritability estimates do set an upper limit on the effect of reducing
or eliminating environmental variations that are independent of
genotype, but other statistics usually provide even better estimates
of these effects.
There is no evidence that genetically based inequalities are harder
to eliminate than other inequalities.
Until we know how genes affect specific forms of behavior,
heritability estimates will tell us almost nothing of importance
(Jenks).
Molecular Genetics



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Describe a few concepts
How do scientists/biologists/geneticists
use genetic data?
Sources:
http://www.psych.umn.edu/courses/fall09
/mcguem/psy5137/lectures.htm
Properties of Genetic
Material


Specify a code for protein synthesis
(i.e., code for an the sequence of amino
acids in a polypeptide chain.)
Duplicate or replicate during both
mitosis and meiosis
Deoxyribonucleic Acid
(DNA)


Double stranded
Strands are held together by (hydrogen)
bonds that form between the nucleotide
bases of the DNA molecule
Adenine (A) <====> Thymine (T)
Guanine (G) <====> Cytosine (C)
DNA
Length of Human Genome

~ 3,000,000,000 bases of DNA



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1 kilo base (kb) = 1000 bases
1 mega base (Mb) = 1,000,000 bases
1 giga base (Gb) = 1,000,000,000 bases
Average protein has ~ 400 amino acids,
requiring 1200 DNA bases or 1200bp
Translation
The basic informational unit is 3 nucleotide
bases (called a codon). Each codon
specifies a single amino acid.
There are 4*4*4=64 possible sequences but
only 20 possible amino acids.
Gene
A sequence of DNA (a locus on a
chromosome) that is involved in (“codes
for”) the synthesis of a functional
polypeptide (proteins consist of one or more
polypeptides).
“Modern Definition” (circa 2006): A locatable region of
genomic sequence, corresponding to a unit of inheritan
which is associated with regulatory regions, transcribed
regions and/or other functional sequence regions
Relative Genome Size
Non-coding DNA

~98% of human DNA does not code
directly for protein


Pseudogenes (evolutionary relics)
Repetitive DNA
Interspersed
 Minisatellite repeats (10-30 bp)
 Microsatellite repeats (< 10 bp)


Regulatory regions
VNTR
Gene Structure

Typical gene is composed of multiple


exons – Expressed sequences of DNA that are
translated into protein
introns - Intervening DNA sequences that are
not translated
Genetic Variation
Genetic variation between individuals refers to
differences in the DNA sequence
1.
Originally arose through (gametic)
mutation.
2.
An estimated 99.8% - 99.9% of our DNA is
common
3.
But then .1% of 3,000,000,000 = 3 million
differences
The Genetic Basis for Human
Variation
Class of variation
Designation/Description
Example
Frequency
Single Nucleotide
Polymorphism (SNP)
Single base substitution involving
A,T,C, or G
A/T
COMT
11,883,685
(6,262,709)
Deletion/Insertion
Polymorphisms
(DIPs; In/Del)
Designated using the full sequence
of the insertion as one allele, and
either a fully defined string for the
variant allele or a “-” character to
specify the deleted allele.
T/-CCTA/G
431,319
Microsatellite or short
tandem repeat (STR)
Alleles are designated by providing
the repeat motif and the copy
number for each allele.
DRD4
MAO-A
Copy Number Variants
Duplication, deletion of 1000 up to
1 Mb of DNA
Gross Chromosomal
Variation
Contiguous gene deletion
syndromes (> 1 Mb)
Aneuploidy
Derived from dbSNP release 128
5-HTTLPR
~10,000
TBD
5% of
genome
VCFS
small
Sources of Genetic
Variation
Types of Genetic Variation:
Chromosomal
Sickle-cell Mutation
Types of Genetic Variation


Chromosomal/Structural: Variations
(or rearrangements) in the amount of
genetic material inherited
Polymorphisms: – Variations in the
DNA sequence


SNPs (~10,000,000)
VNTR (STR, SSR)
Types of Genetic Variation:
Variable Number of Tandem
Repeats (VNTR)

Microsatellite: Small number of bases
(<10) repeated a variable number of
times (usually < 100)(>100,000)
Huntington’s
disease is an
example of a
microsatellite
triplet repeat
in a coding
region
How do researchers link genetic
variation to outcomes?

Candidate gene examinations


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Sometimes from animal models
Specifically examine a small number of
polymorphisms and an outcome
Sometimes use family based designs
Replication
Gene association studies/Genome wide
association studies (GWAS)

Gene-finding exercise (atheoretical)
Ex: Corder et al. Science 1993,
p. 921-923

Alzheimer and APOE
Ex 2: BRCA1 and Breast Cancer

Mutations thought to account for 45% of
GWAS Example
GWAS Example: Beauchamp et
al. 2007



9,000 observations from Framingham data
Phenotype: educational attainment
500,000 SNPS



350,000 after cleaning
Run 350,000 regressions
Find several SNPS with very low p-values


MAPKAP2—inflammatory responses/neuronal death
IER3—regulation of cell death/impact on cognitive
development
GWAS vs. Candidate Gene

More powerful for low penetrance
variants
Better resolution, reduce region of interest
Do not need to specify particular variants

But…


Problems with studies


Data mining
False positives

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Gene-environmental interactions
Population Stratification


(500K+ tests)
Use adjusted/tiny p-values
Replication
Linkage disequilibrium
Hamer (2002)—markers for chopstick use
Gene-gene interactions
Integrating Genetics and Social
Science

Improve theory/empirics

Gene X Environment Interactions

New sources of variation
Why would social scientists care?

GxE



Help understanding of individual level
heterogeneity
Knowledge of biological pathways may aid the
development of interventions
Empirics

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Reduce omitted variable bias in causal models
Additional source of (“natural”) variation

Instrumental variables analysis
Shanahan and Hofer:
GxE typology

Social context (E) may


serve as a trigger for those predisposed
compensate for genetic predisposition

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act as a control to prevent behaviors for which there
is a genetic predisposition

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Avoidance of low levels of functioning through the absence
of a stressor or provision of enriched setting
Focus on social norms and structural constraints that are
placed on people to limit choice
enhance adaptation through proximal processes
Shanahan and Hofer:
Promise of GxE


Conclusive evidence from animal models
Genetic main effects have been elusive
and small



But we think that “genes matter”
Gene-environment correlations likely will
not completely explain variation in
outcomes
Emerging human evidence
Gene x Environment

Caspi et al., Science 2002


Why do some maltreated children grow up to
develop antisocial behaviors and others do
not?
MAOA gene (encodes neurotransmittermetabolizing enzyme)


Animal model evidence
Found to moderate the effect of childhood
maltreatment
Example: Guo et al. 2008 AJS


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Outcome: number of sexual partners
Gene: DAT (dopamine transporter)
Environment:


school-level norms: % of kids who have sex
early
Cognitive ability

Forthcoming, Journal of Politics
Issues with GxE studies


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Non-replication
Theory
Measurement of Environment

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Endogenous vs. Exogenous
Power
Data

Environmental variation
Other issues:
Shanahan and Hofer

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Static vs. dynamics
Multifaceted nature of E
Mediating mechanisms
Simple statistical models
New Example:
Conley and Rauscher 2010

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Focus on exogenous “E” in GxE
Outcome: academic outcomes
G: several markers in Add Health Data
E: birth weight variation
Key: within MZ twins, birth weight
differences are “random”

Examine effects of “types” of twin pairs based
on genetic marker variation
Extending GxE studies

Most GxE studies use measures of environment
that are non-randomly distributed (e.g. parental
divorce)




Potential for gene-environment correlation (rGE)
Does G affect response to E or affect risk of exposure
Possibility of GxG interactions
Instead use variation in public policies (e.g.
tobacco taxes) in order to:


increase understanding of the biological factors affecting
decisions and
increase understanding of the heterogeneity of
responses to environmental (e.g. policy) factors
Example of idea:
(Fletcher, unpublished data, 2010)
High Tax
Number of Cigarettes
Low Tax
6.34
High Tax/Homozygous
Number of Cigarettes



6.40
Homozygous
8.08
High Tax/Heterozygous
7.28
Heterozygous
6.98
Low Tax/Homozygous
9.38
Low Tax/Heterozygous
7.44
Sample: White males in the Add Health data
Environmental risk: Low tobacco tax vs. high tobacco tax state
Genetic risk variant: Homozygous (60%) or heterozygous (40%)
for the CHRNA6 SNP


Codes for the α6 subunit found in certain types of nicotinic acetylcholine
receptors found primarily in the brain.
Neural nicotinic acetylcholine receptors containing α6 subunits are expressed on
dopamine-releasing neurons in the midbrain, and dopamine release following
activation of these neurons is thought to be involved in the addictive properties
of nicotine.
10.80
Discussion/Questions

Emerging national representative with
DNA data allows substantial variation in
“E” (Add Health, Fragile Families…)


Leverage empirical strategies from the social
sciences for plausibly exogenous “E”
Need to team up with biological sciences
for measurement of “G”

They need us, too
Genetics and Social Science II:
New Variation for Causal Inference
Genetic Lotteries within Families
Jason M. Fletcher
Yale University
Steven F. Lehrer
Queen’s University
Motivation


Tremendous advances in research that links
molecular genetic markers to health outcomes
How might social scientists (economists)
leverage new knowledge to advance our own
research?



Example: links between health and schooling
Or: income, socioeconomic status, occupation, labor
force participation, marital status…
Idea: use sibling differences in genetic
inheritance as an “experiment in nature” in order
to trace through the effects of poor health on
schooling
Empirical Example
Testscore

  Health   X  
Issues:
 Health and the error term may be correlated
 Reverse causality
Health   S   X  
• Would like experimental variation in health, uncorrelated with
epsilon
• Instrumental variable
A Start:
Mendelian Randomization

Definition

Random assortment of genes from parents to
offspring that occurs during gamete formation
and conception (Smith and Ebrahim IJE 2003)

Used in a growing number of studies

Strengths


Not generally susceptible to reverse causation
Scientific basic for link
Instrumental variables
Testscore
  Health   X  
Health   SNP   X  
• Requirements for instruments
• Related to the potentially problematic variable
(theta is not zero)
• Unrelated to the error in the main equation
(epsilon)
Example: Von Hinke et al. 2010



Examines causal effects of child fat mass
on academic achievement
ALSPAC data—Avon, England; 12,000
kids followed from birth
Instrument: FTO gene


1-4 pound increase
R-square is <1%
FTO

Findings: OLS, 1 SD increase in fat mass
reduces achievement by 0.1 points
Findings
Building on Mendelian
Randomization

Limitations


Confounding by polymorphisms in linkage
disequilibrium
Multiple phenotypic effects


Dynastic effects—not only does the child inherit the
gene, but also the consequences of the gene from the
previous generation


May invalidate the instrument, if correlated with epsilon
Risk of inheriting the polymorphism may not be random
New Approach: “Genetic Lottery”

Eliminates dynastic effects
Genetic Lottery Approach
Testscore
Health

if
if
  Health
  Gene
if
if
  X if  
  X if  
  if
f
f
  if
Combines a sibling fixed effect and instrumental
variables strategy
 All common family factors are controlled
 Source of variation in health is from “experiment in
nature”
Application: estimating causal effects of poor
health on achievement





National Longitudinal Study of Adolescent Health
(Add Health)
School-based study of the health-related
behaviors of 12 to 18 year old adolescents who
were in grades 7 to 12 in 1994/5
We use a specific subsample of the respondents
for which DNA measures were collected, all of
which have multiple family members in the
survey.
In total we have information on 1684 individuals.
The data contains information a score on a
common verbal test (Peabody picture vocabulary
test).
Health Summary Statistics
Variable
Full Sample
Sibling Sample Twin Sample
AD
0.052
(0.221)
0.049
(0.215)
0.056
(0.229)
HD
0.049
(0.216)
0.052
(0.223)
0.043
(0.203)
ADHD
0.078
(0.268)
0.077
(0.266)
0.078
(0.268)
Depression
0.063
(0.243)
0.067
(0.251)
0.052
(0.223)
Obese
0.13
(0.338)
0.14
(0.349)
0.12
(0.319)
While the AD and HD subscale averages fell within standard ranges,
roughly 8% of the sample has ADHD which exceeds the 6% national
average.
Genetic markers







Our dataset contains information on six markers
Dopamine Receptor D2 (DRD2)
The dopamine D4 receptor (DRD4) gene.
The dopamine transporter (DAT) and serotonin transporter
(5HTT) code for proteins that lead to the reuptake of
dopamine and serotonin respectively.
Monoamine oxidase A (MAOA) gene responsible for the
degradation of dopamine, serotonin, and norepinephrine.
CYP2A6 gene metabolizes tobacco, drugs and other toxins,
it is primarily operative in the liver
Evidence that interactions of markers could have powerful
effects. (Gene-gene interactions)
Results
Additional analyses

Tests support that sibling fixed effects are necessary


Tests support that instrumental variable is necessary


Endogeneity
Instrument invalidity (genes correlated with epsilon)



Confounding
Over-identification tests
Sensitivity to small to moderate violations of instrument validity
(Conley et al. 2007)
Examination based on different samples
Limitations

Incomplete understanding of links found
in genetic research

Potential for multiple pathways


May invalidate instruments
Data



Large samples of genotyped siblings
Limited variation with families
Weak instruments
Genetic lottery idea




Genetic lotteries is an interesting research
design for causal inference
The impact of poor mental health outcomes on
academic achievement is large (one s.d.). Why?
Accounting for family fixed effects is needed but
does not fully solve problems due to
endogeneity of health.
The presence of high comorbidity of adverse
health outcomes is striking, which presents a
challenge to properly control them all.
Discussion/Questions
Technical take-home points:
Strengths & weaknesses of alternative approaches
1. Candidate gene studies: focus on 1 candidate
Advantages
- Scientifically tractable: incremental & cross-validatable
- Maximal statistical power (focused hypothesis)
Disadvantages
- Can only “discover” what we already know (i.e., biased)
2. Genome-wide association studies: focus on all candidates
Advantages
- Unbiased de novo discovery
Disadvantages
- Minimal statistical power, particularly for interactions
3. The bioinformatic “middle road”: focus on a small set of causally
plausible candidates (unbiased search of regulatory and coding SNPs)
Advantages
- Scientifically tractable: “short leap of inference” & cross-validatable
- Relatively high statistical power (focus on 1-10% of plausible SNPs)
Disadvantages
- Likely missing some true causal genetic influences
- Bioinformatically intensive – thought (and programming) required
Take-home points for this group:
1. Gene-Environment interactions are likely far more…
- ubiquitous
- large in effect size
- clinically/socially meaningful
…than current genetic analyses presume.
There is plenty left for you to find.
2. If you have the study you have (i.e., can’t alter sampling design),
your major opportunities for increasing power/discovery involve:
- focusing on substantive effects that are true/big
(e.g., GxE, not G, given antagonistic pleiotropy; E, ExE, GxG, etc.)
- modeling biological mechanisms to focus power/impose constraints
(e.g., candidate systems, functional themes, regulatory themes)
- combinatorial data-mining (e.g., machine learning in discovery sample)
- sequential testing designs
(low stringency discovery, med stringency test, high stringency confirm)
Your advantage is smart data analysis.
Follow-up references
Overview of genetics / biology
Attia, J., et al. (2009) How to use an article about genetic association: A: Background
concepts. JAMA, 301, 74-81
Genetic association studies
Hirschhorn, J., & Daly, M. (2005) Genome-wide association studies for common
diseases and complex traits. Nature Reviews Genetics, 6, 95-108.
Attia, J., et al. (2009) How to use an article about genetic association: B: Are the results
of the study valid? JAMA, 301, 191-197.
Cordell, H, & Clayton, D. (2005) Genetic epidemiology 3: Genetic association studies.
Lancet, 366, 1121-1131
Basic statistical modeling for genetics
Siegmund, D., & Yakir, B. (2007) The statistics of gene mapping. New York, Springer
Sampling & statistical approaches for GxE discovery
Thomas, D., (2010) Gene-environment-wide association studies: emerging approaches.
Nature Reviews Genetics, 11, 259-272
Statistical strategies for combinatorial discovery
Hastie, T., Tibshirani, R. & Friedman, J. (2001) The elements of statistical learning.
New York, Springer.
Acknowledgements

BG Model slides

http://www.psych.umn.edu/courses/fall09/mc
guem/psy5137/lectures.htm