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Data management and preliminary analysis of SNPS

Data management and preliminary analysis of SNPS. Samantha 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).

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Data management and preliminary analysis of SNPS

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  1. Data management and preliminary analysis of SNPS Samantha Cravens

  2. Overview • AHB Data • Manage measured genotypic data (SNPs) • Merge two batches • Label SNPs • Preliminary analyses of SNPs • Focus on dopaminergic system • DRD2

  3. 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!

  4. 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 • SNPs • Single Nucleotide Polymorphisms • Refer 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

  5. Data Management Issues • Batch 1 (with top and bottom strand for each participant): • ID Top Strand Bottom Strand Moe A T Larry C G • Batch 2 (with top and bottom strand for each participant): • ID Top Strand Bottom 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 Non A/T, C/G SNPs PROBLEM!!!

  6. Data Management Issues (continued) • Batch 1 (with top and bottom strand for each participant): • ID Top Strand Bottom Strand Moe A T Larry T A • Batch 2 (with top and bottom strand for each participant): • ID Top Strand Bottom 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 A/T, C/G SNPs (A NOT AS OBVIOUS) PROBLEM!!

  7. 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”

  8. Overview • AHB Data • Manage measured genotypic data • Merge two batches  • Label SNPs • Preliminary analyses of SNPs • Focus on dopaminergic system • DRD2

  9. 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!

  10. Overview • AHB Data • Manage measured genotypic data • Merge two batches  • Label SNPs • Preliminary analyses of SNPs • Focus on dopaminergic system • DRD2

  11. 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*

  12. Data analysis: initial findings • Examined relation between SNPs, SUD phenotypes • E.g., Tobacco Dependence, DUDs, AUDs • DSM-III diagnoses to maintain consistency across waves • Excluding non-Caucasians (n=459) • Adjusting for sex • Due to small n of AHB for genotype data—focus on effect size

  13. Data analysis: initial findings • Numerous SNPs showed associations with SUDs, related phenotypes • Exemplar SNP finding for today’s talk: rs4648317 • G>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

  14. association between rs4648317 (GG = 0, A* = 1)and categoricaltobacco/drug/alcohol phenotypes

  15. Tobacco Dependence • Inconsistent evidence of higher rates of dependence among minor allele carriers

  16. 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

  17. 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 29

  18. A developmental perspective: Genotype X Time Interaction % Diagnosing At age 18,when AUDs are more common, no difference between GG, A* allele carriers AGE

  19. A developmental perspective: Genotype X Time Interaction % Diagnosing During young adulthood, when AUDs are less common, sizeable difference in AUD prevalence by genotype AGE

  20. A developmental perspective: Genotype X Time Interaction % Diagnosing Group with risk allele show shallower decreases in AUD from 18-35 FINDINGS HIGHLIGHT IMPORTANCE OF DEVELOPMENTAL PERSPECTIVE! AGE

  21. Rs4648317 and other alcohol phenotypes • No significant association between genotype and • Max quantity, max drinks in a 24 hour period • Average quantity of drinking across time • However, significant associations between genotype and • Max frequency, max quantity/frequency • Average frequency, quantity/frequency across time

  22. 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

  23. 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

  24. Thank you! • MARC ARTSS administrators and coordinators • Genetic Experts • ArpanaAgrawal • Sean Kristjansson • Ian Gizer • Dr. Sher’s Lab Phil Kenny Margie Nicholas Alvaro Amelia Rachel Simon Angi Gail

  25. And a special thanks to andrew and miles! 

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