Insights from Sample Human Genome GWAS and Epigenome EWAS Projects - PowerPoint PPT Presentation

Insights from Sample Human Genome GWAS and Epigenome EWAS Projects Jim Johansen MSEE, MASR, MACA, PhD (candidate) 29 July 2018 jdjohansen@liberty.edu

Overview (1 of 2) This presentation examines sample findings from recent genome- • wide association studies ( GWAS ) and epigenome wide association studies ( EWAS ) projects and examines insights that can be explored when considering them from a faith and science point of view Genome research is advancing from DNA sequencing to advanced • techniques that map trait and disease relationships with the genome With epigenetic adaptation to environmental changes, there are • interesting epigenomic results that are being uncovered, showing examples of gene over-riding behavior (e.g. methylation switching ) GWAS projects are doing genotype imputation that shows alcohol • and substance abuse relationships (including my dissertation) GWAS projects are mapping replicable genetic associations with • behavioral traits

Overview (2 of 2) EWAS projects have shown epigenetic evidence for such things • as anxiety disorders, tendencies for suicide, & issues with anger – Several studies have shown statistically significant health impacts from individuals who have active experience with religiosity factors like, faith, prayer, and church attendance – There is room for more research in these interdisciplinary areas and are key in the author’s ongoing research After summarizing these sample projects, a discussion of • proposed insights will be given – Faith impact in health and behavior, even overriding genes There is fascinating cellular function and interrelationships we • are now gaining understanding about with its robustness and multi-layered complexity that can be appreciated more when including perspectives from faith – Including “religiosity factors” as developed in the literature

Layers Molecules T C A G TTT TCT TAT TGT Phe (F) Tyr (Y) Cys (C) TTC TCC TAC TGC TATA Box T Ser (S) TTA TCA TAA Ter TGA Ter Leu (L) TTG TCG TAG Ter TGG Trp (W) G A C T A T A A A T G A CTT CCT CAT CGT His (H) Asp'(D) Tyr'(Y) Lys'(K) Ter CTC CCC CAC CGC C Leu (L) Pro (P) Arg (R) CTA CCA CAA CGA Thr'(T) Ile'(I) Asn'(N) Gln (Q) CTG CCG CAG CGG Leu'(L) Ter Met'(M) ATT ACT AAT AGT Asn (N) Ser (S) Gln'(Q) Tyr'(Y) Lys'(K) Ser'(S) ATC Ile (I) ACC AAC AGC A Thr (T) ATA ACA AAA AGA Ile'(I) Asn'(N) Val'(V) Lys (K) Arg (R) ATG Met (M) ACG AAG AGG Ser'(S) Ile'(I) Ter GTT GCT GAT GGT Asp (D) GTC GCC GAC GGC G Val (V) Ala (A) Gly (G) GTA GCA GAA GGA Glu (E) GTG GCG GAG GGG Codons Nucleotides Double Helix Standard Codon Table (3 nucleotides) Overlapping Protein Epigenetic Switching: Whole Genome: Turns genes on and off Sequences Functions utilize information from ataaatttgagtcagcaccagcgacagctctgcagtcctc multiple locations tctacagaacaagacgacctttaagtttcccagagaaaa

Seeing the Layers Together Molecules Double Codons Standard Overlapping Whole Epigenetic Helix Codon Table Protein Genome Switching •Atoms •Made of 3 Sequences Function Nucleotides •Nucleotides •Groups of •Common •Localized •Six reading Molecules Coding •By Function •Bi-directional •Access Whole frames Alphabet •C, G, T, A Genome How can I be informed by my faith to better understand this holistically?

Genome Assembly How Do We Access the Information? Processing Parts Assembled Human Genome by Double Chromosome Helix Structure Break down biological sample Overlap to piece parts together

What is GWAS • Genome-wide association studies (GWAS) examine common genetic variants in different individuals to determine if any variant is associated with a trait • GWAS studies typically focus on associations between single-nucleotide polymorphisms (SNPs) and traits like major diseases • SNPs are nucleotides that show variation (different alleles) between A & T or G & C in a small set

Linear Regression P-value P-value is a function of the • observed sample results (a test statistic) relative to a statistical model, which measures how extreme the observation is It is the probability that the • observed result has nothing to do with what one is actually testing for Smaller means there likely is a • correlation relationship https://en.wikipedia.org/wiki/P-value

UK Biobank Alcohol Consumption • Manhattan plot of GWAS data filtered for alcohol consumption • Plus two SNP (single nucleotide polymorphism) evaluations ✓ ✓ rs1260326 T-K Clarke, et. al., “Genome-wide association study of alcohol consumption and genetic overlap with other health- related traits in UK Biobank (N=112117),” Molecular Psychiatry (2017) 22, 1376–1384

Alcohol Dependency Preliminary Assessment of One Data Source • Goal: Examine genome data and the role of alcohol dependency • Sample set: Filter for those self identifying as having alcohol dependency • Available phenotype (“class”): non-cancer disease data taken with patient describing details, with nurse or doctor entering the proper clinical associations in the UK Biobank survey results

UK Biobank Alcohol Dependency Alcohol Dependency ✓ Phenotype: Non-cancer • illness Self reported alcohol • dependency Unique individuals in • sample N = 337,159 One possible common • alcohol consumption locale in CHR 2 may correspond with T-K Clarke’s work Manhattan plot generated with R and qqman library https://www.theoj.org/joss- papers/joss.00731/10.21105.joss.00731.pdf

Key 20 SNPs Associated with Alcohol Dependency Smallest p-val CHR:BP:A1:A2 SNP AC ytx beta se tstat pval -log(p) 1 11:42212601:G:T rs536162651 6.89E+02 7.07E+00 1.05E-02 1.57E-03 6.74E+00 1.59E-11 10.7998584 2 6:37931122:C:T rs375944322 2.33E+03 1.49E+01 5.13E-03 7.86E-04 6.53E+00 6.77E-11 10.1691253 3 19:52416150:G:A rs73934702 6.90E+02 6.94E+00 9.16E-03 1.48E-03 6.20E+00 5.51E-10 9.25866164 4 10:88091447:G:A rs146456009 2.11E+03 1.26E+01 5.45E-03 8.94E-04 6.09E+00 1.12E-09 8.94948104 5* 2:32562620:C:A rs192306272 6.93E+03 2.73E+01 2.92E-03 4.89E-04 5.98E+00 2.24E-09 8.65000216 6 2:32566261:C:T rs191444614 6.70E+03 2.65E+01 3.00E-03 5.03E-04 5.96E+00 2.46E-09 8.60917613 7 6:37845618:G:T rs56100008 1.87E+03 1.18E+01 5.05E-03 8.75E-04 5.77E+00 7.78E-09 8.10897184 8 4:180660418:A:G rs185393533 7.29E+02 6.25E+00 8.43E-03 1.50E-03 5.61E+00 2.07E-08 7.68318287 9 9:97632419:C:G rs573242084 1.60E+03 1.01E+01 5.58E-03 9.98E-04 5.59E+00 2.26E-08 7.64554964 10 12:107218409:G:A rs572148082 2.69E+03 1.44E+01 4.13E-03 7.51E-04 5.50E+00 3.90E-08 7.40851689 11 6:155624995:C:T rs181401718 1.39E+03 9.09E+00 5.92E-03 1.09E-03 5.42E+00 5.84E-08 7.23394351 12 2:32851160:T:C rs115405419 6.79E+03 2.50E+01 2.64E-03 4.94E-04 5.34E+00 9.43E-08 7.02538009 13 13:76501591:C:T rs192506791 1.97E+03 1.06E+01 4.97E-03 9.33E-04 5.32E+00 1.02E-07 6.99353396 14 3:15764003:A:G rs551069073 6.38E+02 5.51E+00 8.49E-03 1.60E-03 5.31E+00 1.11E-07 6.95629592 15 8:51397014:A:G rs117465326 3.21E+03 1.57E+01 3.72E-03 7.03E-04 5.29E+00 1.21E-07 6.91578131 16 3:99741230:T:C rs113097300 8.74E+02 6.98E+00 7.05E-03 1.33E-03 5.29E+00 1.22E-07 6.91244572 17 7:142612522:C:T rs150345829 1.18E+03 8.44E+00 6.06E-03 1.15E-03 5.26E+00 1.46E-07 6.83510016 18 8:51417816:T:A rs75873830 3.10E+03 1.50E+01 3.82E-03 7.27E-04 5.25E+00 1.49E-07 6.82809519 19 1:217190677:C:G rs182717155 1.32E+03 8.81E+00 5.69E-03 1.08E-03 5.25E+00 1.56E-07 6.80754127 20 3:17559724:G:T rs765174844 1.02E+03 8.01E+00 6.22E-03 1.20E-03 5.21E+00 1.91E-07 6.71837585 AD 20002 - Sample size of 337,159 * May correlates with Clarke’s UK Biobank Alcohol Consumption CHR 2 Region CHR 2 RS1260326 is not included in the Neale Lab processed data

UK Biobank Plot of P Values (Q-Q Plot) • Plot shows all of the p values • Sample size of N=337,159 Q-Q plot generated with R and qqman library https://www.theoj.org/joss- papers/joss.00731/10.21105.joss.00731.pdf

Data Comparison UK Biobank Alcohol Dependency Correlating data is challenging • How well do we understand the • process mechanisms? COGA DSM-IV Alcohol Dependency J-C Wang, et. al., A genome-wide association study of alcohol-dependence symptom counts in extended pedigrees identifies C15orf53, Molecular Psychiatry (2012), 1–7

Polygenic Risk Score (PRS) • Polygenic risk score combines associations at multiple locations in the genome and their associated weights. It serves as a predictor for a trait that can be made when taking into account variation in multiple genetic variants. – Usually, polygenic risk scores include SNPs from locations in the genome that are not in high linkage disequilibrium (LD, non-random association of alleles at different loci, where there are statistical associations between alleles at different loci), such as when they are on different chromosomes • Looks at the whole genome • With this information one can compare the result to similar characteristics for a population that has a given condition

What is EWAS • Epigenome-wide association studies (EWAS) examine genome-wide set of quantifiable epigenetic marks, such as DNA methylation , in different individuals to derive associations between epigenetic variation and a particular identifiable phenotype or trait