data management and preliminary analysis of snps

DATA MANAGEMENT

AND PRELIMINARY ANALYSIS OF

SNPSSamantha Cravens

OVERVIEW AHB Data

Manage measured genotypic data (SNPs) Merge two batches Label SNPs

Preliminary analyses of SNPs Focus on dopaminergic system

DRD2

AHB (ALCOHOL HEALTH AND BEHAVIOR)

Prospective study of 489 college students enrolling as first-time freshman in 1987

Approximately half with positive history of paternal alcohol; half with no family history

Equal numbers of men and women Extensive assessments at years 1 (age 18), 2 (19), 3 (20), 4 (21), 7 (25), 11 (29), and 17 (35) Measured genotypic data collected in two batches

n = 203 n = 71

Thus, the first step was to merge genetic data on both batches and link to AHB measures!

DATA MANAGEMENT:BACKGROUND DNA

Double helix comprised of 2 anti-parallel strands

Four nucleotides: A, C, G, T A corresponds with T G corresponds with C

SNPsSingle Nucleotide PolymorphismsRefer to variability for a given nucleotide for

different people: Andrew: G A C T G Amelia: G A T T G

This would be considered a C/T SNP

DATA MANAGEMENT ISSUES

Non A/T, C/G SNPs

PROBLEM!!!

Batch 1 (with top and bottom strand for each participant): ID Top Strand Bottom

Strand Moe A T Larry C G


Strand Curly A T Shemp C G

Top Strand Used for Both Batches: Type Freq %

A 2 50 C 2 50

Top Strand Used for One Batch, Bottom Strand Used for the Other: Type Freq %

A 1 25 C 1 25 G 1 25 T 1 25

DATA MANAGEMENT ISSUES (CONTINUED)

A/T, C/G SNPs

(A NOT AS OBVIOUS) PROBLEM!!


Strand Moe A T Larry T A


Strand Curly A T Shemp T A

Top Strands Used for Both Batches: Type Freq %

A 2 50 T 2 50

Top Strand Used for One Batch, Bottom Strand Used for the Other: Type Freq %

A 2 50 T 2 50

MERGING AND RECODING SNPS

Of the total 1208 SNPs in both batches, we identified and recoded:494 problematic Non A/T, C/G SNPs (41%)185 potentially problematic A/T, C/G SNPs

(15%) 6 SNPs “too close to call”

OVERVIEW AHB Data

Manage measured genotypic data Merge two batches Label SNPs


DRD2

DATA MANAGEMENT:LABELING Why Label the SNPs?

Easier to find and use Ability to organize by gene, chromosome, etc.

How we labeled the SNPs: Used data from 3 different sites to identify:

Type of SNP (A/G, C/T, etc.) Chromosomal location Gene association (i.e., DRD2, GABRA, etc.) Function within gene (intron, untranslated region, etc.)

END RESULT: Accurate & Informative labels for all SNPs!

OVERVIEW AHB Data

Manage measured genotypic data Merge two batches Label SNPs


DRD2

DATA ANALYSIS:FIRST STEPS Literature Review

Identify SNPs in systems relevant to substance use and substance use disorders (SUDs) Focus on dopaminergic system

Relevant to SUDs and related phenotypes Coding genotypes

Recode alleles to AA (homozygous major allele) Aa (heterozygous allele) aa (homozygous minor allele)

Also recoded based on previous studies E.g., AA vs. a*

DATA ANALYSIS: INITIAL FINDINGS Examined relation between SNPs, SUD

phenotypesE.g., Tobacco Dependence, DUDs, AUDs

DSM-III diagnoses to maintain consistency across waves

Excluding non-Caucasians (n=459)Adjusting for sexDue to small n of AHB for genotype data—

focus on effect size

DATA ANALYSIS: INITIAL FINDINGS Numerous SNPs showed associations

with SUDs, related phenotypes Exemplar SNP finding for today’s talk:

rs4648317G>A SNP within DRD2

Other DRD2 SNPs linked to AUDs (Munafo, Matheson, & Flint, 2007)

Associated with nicotine dependence in adolescents (Laucht et al., 2008) Minor allele carriers had higher dependence

scores

ASSOCIATION BETWEEN RS4648317 (GG = 0, A* = 1)AND CATEGORICALTOBACCO/DRUG/ALCOHOL PHENOTYPES

Tobacco Dependence Drug Use Disorder Alcohol Use Disorder

AgeOdds Ratio 95% CI

Odds Ratio 95% CI

Odds Ratio 95% CI

18 0.85 0.29-

2.47 4.03 1.27-12.8 1.05 0.53-2.07

19 1.24 0.47-

3.24 2.31 0.78-6.85 1.41 0.73-2.71

20 2.76 1.23-

6.19 5.27 1.89-14.7 1.38 0.70-2.72

21 2.02 0.94-

4.35 3.05 1.05-8.89 1.18 0.58-2.40

25 1.43 0.64-

3.18 2.78 0.85-9.16 1.31 0.59-2.93

29 1.01 0.44-

2.34 1.20 0.43-3.38 2.59 1.06-6.37

35 0.74 0.28-

1.95 1.81 0.39-8.35 2.07 0.96-4.45

Tobacco Dependence Inconsistent evidence of higher rates

of dependence among minor allele carriers



Odds Ratio 95% CI

Odds Ratio 95% CI

18 0.85 0.29-

2.47 4.03 1.27-12.8 1.05 0.53-2.07

19 1.24 0.47-

3.24 2.31 0.78-6.85 1.41 0.73-2.71

20 2.76 1.23-

6.19 5.27 1.89-14.7 1.38 0.70-2.72

21 2.02 0.94-

4.35 3.05 1.05-8.89 1.18 0.58-2.40

25 1.43 0.64-

3.18 2.78 0.85-9.16 1.31 0.59-2.93

29 1.01 0.44-

2.34 1.20 0.43-3.38 2.59 1.06-6.37

35 0.74 0.28-

1.95 1.81 0.39-8.35 2.07 0.96-4.45

Drug Use Disorder Consistent evidence of higher rates of

dependence among minor allele carriers, esp. in early twenties

427% Higher Odds for DUD at age 20



Odds Ratio 95% CI

Odds Ratio 95% CI

18 0.85 0.29-

2.47 4.03 1.27-12.8 1.05 0.53-2.07

19 1.24 0.47-

3.24 2.31 0.78-6.85 1.41 0.73-2.71

20 2.76 1.23-

6.19 5.27 1.89-14.7 1.38 0.70-2.72

21 2.02 0.94-

4.35 3.05 1.05-8.89 1.18 0.58-2.40

25 1.43 0.64-

3.18 2.78 0.85-9.16 1.31 0.59-2.93

29 1.01 0.44-

2.34 1.20 0.43-3.38 2.59 1.06-6.37

35 0.74 0.28-

1.95 1.81 0.39-8.35 2.07 0.96-4.45

Alcohol Use Disorder Evidence of higher rates of AUD among

minor allele carriers, esp. during late twenties, thirties

200%+ Higher Odds of AUD at Age 29Tobacco Dependence Drug Use Disorder Alcohol Use Disorder


Odds Ratio 95% CI

Odds Ratio 95% CI

18 0.85 0.29-

2.47 4.03 1.27-12.8 1.05 0.53-2.07

19 1.24 0.47-

3.24 2.31 0.78-6.85 1.41 0.73-2.71

20 2.76 1.23-

6.19 5.27 1.89-14.7 1.38 0.70-2.72

21 2.02 0.94-

4.35 3.05 1.05-8.89 1.18 0.58-2.40

25 1.43 0.64-

3.18 2.78 0.85-9.16 1.31 0.59-2.93

29 1.01 0.44-

2.34 1.20 0.43-3.38 2.59 1.06-6.37

35 0.74 0.28-

1.95 1.81 0.39-8.35 2.07 0.96-4.45

A DEVELOPMENTAL PERSPECTIVE: GENOTYPE X TIME INTERACTION

18 19 20 21 25 29 35-5%

0%

5%

10%

15%

20%

25%

30% Genotype = 0 (GG) (n=166)Genotype = 1 (A*) (n=72)

%Diagnosing

AGE

At age 18,when AUDs are more common, no difference between GG, A* allele carriers


18 19 20 21 25 29 35-5%

0%

5%

10%

15%

20%

25%


%Diagnosing

AGE

During young adulthood, when AUDs are less common, sizeable difference in AUD prevalence by genotype


18 19 20 21 25 29 35-5%

0%

5%

10%

15%

20%

25%


%Diagnosing

AGE

Group with risk allele show shallower decreases in AUD from 18-35

FINDINGS HIGHLIGHT IMPORTANCE OF DEVELOPMENTAL PERSPECTIVE!

RS4648317 AND OTHER ALCOHOL PHENOTYPES No significant association between

genotype andMax quantity, max drinks in a 24 hour

periodAverage quantity of drinking across time

However, significant associations between genotype andMax frequency, max quantity/frequency Average frequency, quantity/frequency

across time

RS4648317 AND OTHER SUD PHENOTYPES: SUMMARY Carriers of minor allele demonstrated:

Inconsistently higher rates of tobacco dependence

Consistently higher rates of drug use disorders

Higher rates of AUDs in young adulthood, shallower decreases in AUDs across time

Higher maximum and average frequency, quantity/frequency

FUTURE PLANS Continue to examine relation between

SNPs, SUD phenotypes in AHB Further refine understanding of potential link

between rs4648317 and relevant phenotypes E.g., Is relationship moderated by environmental

influences? Examine SNPs relevant to other systems

Serotonin GABA

Also examine the interplay of genetic and environmental factors on alcohol-related phenotypes Personality Drinking motives

THANK YOU! MARC ARTSS administrators and

coordinators Genetic Experts

Arpana AgrawalSean Kristjansson Ian Gizer

Dr. Sher’s LabPhil KennyMargie NicholasAlvaro AmeliaRachel SimonAngi Gail

AND A SPECIAL THANKS TO ANDREW AND MILES!

data management and preliminary analysis of snps

Documents

strand curly

strand moe

t shemp t

t t gthis

genotypic data snps

t shemp c gtop strand

genotypic data merge

genetic data