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

T C A G T TTT Phe (F) TCT Ser (S) TAT Tyr (Y) TGT Cys (C) TTC TCC TAC TGC TTA Leu (L) TCA TAA Ter TGA Ter TTG TCG TAG Ter TGG Trp (W) C CTT Leu (L) CCT Pro (P) CAT His (H) CGT Arg (R) CTC CCC CAC CGC CTA CCA CAA Gln (Q) CGA CTG CCG CAG CGG A ATT Ile (I) ACT Thr (T) AAT Asn (N) AGT Ser (S) ATC ACC AAC AGC ATA ACA AAA Lys (K) AGA Arg (R) ATG Met (M) ACG AAG AGG G GTT Val (V) GCT Ala (A) GAT Asp (D) GGT Gly (G) GTC GCC GAC GGC GTA GCA GAA Glu (E) GGA GTG GCG GAG GGG

Double Helix Standard Codon Table Nucleotides Overlapping Protein Sequences

ataaatttgagtcagcaccagcgacagctctgcagtcctc tctacagaacaagacgacctttaagtttcccagagaaaa

G A C T A T A A A T G A Asn'(N) Ile'(I) Val'(V) Ile'(I) Ser'(S) Ter Ter Met'(M) Leu'(L) Lys'(K) Ser'(S) Tyr'(Y) Gln'(Q) Tyr'(Y) Lys'(K) Ter Asp'(D) Ile'(I) Asn'(N) Thr'(T)

Codons (3 nucleotides) Molecules Whole Genome: Functions utilize information from multiple locations Epigenetic Switching: Turns genes on and off

TATA Box

Seeing the Layers Together

Molecules

• Atoms
• Nucleotides

Double Helix

• Groups of

Molecules

• C, G, T, A

Codons

• Made of 3

Nucleotides

• Six reading

frames

Standard Codon Table

• Common

Coding Alphabet

Overlapping Protein Sequences

• Bi-directional

Whole Genome Function

• Access Whole

Genome

Epigenetic Switching

• Localized
• By Function

How can I be informed by my faith to better understand this holistically?

Genome Assembly

Processing Parts Assembled Human Genome by Chromosome Double Helix Structure

Overlap to piece parts together Break down biological sample

How Do We Access the Information?

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
• bserved sample results (a test

statistic) relative to a statistical model, which measures how extreme the observation is

• It is the probability that the
• bserved 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

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

✓ ✓

rs1260326

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

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

✓

UK Biobank Alcohol Dependency

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

• Correlating data is challenging
• How well do we understand the

process mechanisms? UK Biobank Alcohol Dependency

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

Histone, RNA, other

DNA Methylation – Environment Change

Coding Information Non-Coding Information Genome Gene methylate demethylate

Switch – turn on, off, or modify function

DNA Methylation

(Epi)Genetic Variation: What may happen

• Environmental adaptation
• Multi-generation inheritable reprogramming
• Damage from the fall - Aging
• Program for a person – animate a body?
• Spirit – soul interface in brain?

Methylation - Flexible cellular dynamic adaption

• A type of cellular programmable memory
• Possible analogies: BIOS, PROM, RAM, FPGA

Characteristics of the cellular system

• Biomechanical computational mechanism
• Biophysical system for cellular function

https://pubs.niaaa.nih.gov/publications/arcr351/6-16.htm

Other forms of methylation

Methylation

More speculative

Epigenetic Mods - Location by Location

• The NIH Roadmap

Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues.

• Epigenomic

information is key for understanding gene regulation, cellular differentiation and human disease

111 Reference Human Epigenomes

https://www.nature.com/articles/nature14248

Positive Therapeutic Effects of Intercessory Prayer in a Coronary Care Unit Population

“In this study I have attempted to determine whether intercessory prayer to the Judeo- Christian God has any effect on the medical condition and recovery of hospitalized patients. I further have attempted to measure any effects, if present, of those prayers. Based on these data there seemed to be an effect, and that effect was presumed to be beneficial.”

Randolph Byrd, MD. July 1988 • Southern Medical Journal • Vol. 81, No. 7 Epigenetic Process?

Prayer, Process, & the Future of Medicine

“The wall of separation between spirituality and medicine is crumbling. Physicians are discovering the importance of prayer, spirituality, and religious participation in enhancing physical and mental health and responding to stressful life circumstances.”

Bruce Epperly, Journal of Religion and Health, Vol. 39, No. 1, Spring 2000 Epigenetic Process?

Relationship Between Parent & Student Religious Coping & College Alcohol Use

“Religious and spiritual coping practices are associated with less frequent drinking and less heavy drinking among college

• students. Our results offer evidence that there is positive

relationship between students’ use of religious coping strategies, such as prayer and meditation, and their parents’ use of religious coping. Moreover, our findings support that the protective relationship between dimensions of religiousness and college drinking may be accounted for by more conservative norms about the appropriateness of alcohol use.”

Zaje Harrell, Kandace Powell, J Relig Health (2014) 53:895–903 DOI 10.1007/s10943-013-9683-4 Epigenetic Process?

Exercise Changed DNA Expression in Fat Cells

• Lund University in Sweden conducted a six

month study showing how exercise changed the DNA expression in fat cells via DNA Methylation

• Monitored 23 slightly overweight healthy men

who were about 35 years old who had not previously engaged in any physical activity

• Regularly attended spinning & aerobic classes,

attending on average 1.8 times per week

• Examined 480,000 locations in 7,000 genes
• Found changes in genes expression - Switching

in gene function

https://www.sciencedaily.com/releases/2013/07/130703101344.htm https://www.med.lu.se/english/news_archive/130704_epigenetic_changes

Conclusions

• We see examples of how we are fearfully and wonderful made

– Our body systems work together at various levels – Cells have multiple layers of complex functions

• The more evidence that is found the more complexity that is uncovered

– There is a connection and interdependency between various systems – Examining these relationships is an interesting endeavor

• Examination of GWAS and EWAS sources were summarized

– UK Biobank GWAS findings are based on preliminary assessments

• Other data source assessments will be explored

– Epigenetics show how our bodies can adapt to many factors – physical, emotional, spiritual

• Adaptation can be localized
• A methodological approach has been defined and being used to

consider the relationship between biology and spiritual activity

– Preliminary thoughts are outlined how genomic and epigenomic processes can be influenced by spiritual factors

Back Up More Assessment Detail

Examining a SNP Coupled with Alcohol Use Having P-Value Showing High Correlation

Never Drank

rs1229984 (N=336,965)

Key 20 SNPs Never Drank

CHR:BP:A1:A2 SNP AC ytx beta se tstat pval 1 4:100239319:T:C rs1229984 6.59E+05 2.02E+04

• 1.30E-02

1.42E-03

• 9.11E+00

7.98E-20 2 4:100248642:G:C rs145452708 6.82E+03 3.01E+02 1.36E-02 2.12E-03 6.40E+00 1.59E-10 3 4:100249726:G:A rs138495951 6.88E+03 3.02E+02 1.35E-02 2.12E-03 6.39E+00 1.71E-10 4 6:121690163:C:T rs145783810 2.65E+03 1.20E+02 1.75E-02 3.69E-03 4.75E+00 2.08E-06 5 15:41288239:G:C rs11854924 2.04E+05 6.63E+03 2.15E-03 4.58E-04 4.69E+00 2.73E-06 6 6:121572207:T:A rs185742437 1.58E+03 7.97E+01 2.14E-02 4.61E-03 4.65E+00 3.27E-06 7 4:100474179:A:C rs147825380 4.25E+03 1.83E+02 1.24E-02 2.68E-03 4.64E+00 3.41E-06 8 4:100451781:C:T rs188726265 4.26E+03 1.83E+02 1.24E-02 2.68E-03 4.64E+00 3.57E-06 9 4:100588827:G:A rs148960385 4.16E+03 1.80E+02 1.25E-02 2.70E-03 4.62E+00 3.85E-06 10 15:41312368:G:A rs28550294 2.04E+05 6.63E+03 2.11E-03 4.58E-04 4.61E+00 4.00E-06 11 15:41409821:G:A rs28539674 2.10E+05 6.81E+03 2.09E-03 4.54E-04 4.61E+00 4.04E-06 12 15:41399492:A:C rs28896021 2.10E+05 6.81E+03 2.09E-03 4.54E-04 4.60E+00 4.22E-06 13 10:87822636:C:T rs575652338 1.08E+03 5.82E+01 2.57E-02 5.59E-03 4.59E+00 4.50E-06 14 4:100465573:T:G rs543669349 4.25E+03 1.82E+02 1.23E-02 2.68E-03 4.58E+00 4.74E-06 15 4:100608799:G:A rs150848708 4.11E+03 1.77E+02 1.23E-02 2.71E-03 4.55E+00 5.34E-06 16 11:39096977:T:C rs10466397 1.56E+03 7.98E+01 2.00E-02 4.44E-03 4.50E+00 6.95E-06 17 15:41416276:T:C rs11858278 2.09E+05 6.78E+03 2.04E-03 4.55E-04 4.49E+00 7.26E-06 18 4:77190011:C:T rs140609514 1.11E+03 5.80E+01 2.41E-02 5.52E-03 4.37E+00 1.25E-05 19 4:46093713:T:A rs1497577 3.50E+05 1.05E+04

• 1.84E-03

4.25E-04

• 4.33E+00

1.49E-05 20 7:49084556:G:A rs141202752 1.78E+03 8.53E+01 1.86E-02 4.30E-03 4.32E+00 1.59E-05

(N=336,965)

Former Alcoholic

rs1229984 (N=21,894)

Key 20 SNPs Former Alcoholic

CHR:BP:A1:A2 rsid AC ytx beta se tstat pval 1 4:100239319:T:C rs1229984 4.26E+04 2.25E+04 8.22E-02 1.46E-02 5.65E+00 1.61E-08 2 5:15977695:T:C rs115043397 4.44E+02 2.78E+02 1.11E-01 2.39E-02 4.64E+00 3.45E-06 3 9:106101691:T:C rs7037370 1.80E+04 9.70E+03 2.13E-02 4.79E-03 4.45E+00 8.52E-06 4 5:21721388:G:T rs113610964 1.11E+03 5.13E+02

• 6.62E-02

1.50E-02

• 4.41E+00

1.06E-05 5 5:21722005:T:C rs111817046 1.11E+03 5.14E+02

• 6.60E-02

1.50E-02

• 4.39E+00

1.15E-05 6 5:21722043:C:G rs111350442 1.11E+03 5.14E+02

• 6.60E-02

1.50E-02

• 4.39E+00

1.15E-05 7 5:21722064:A:T rs111399799 1.11E+03 5.14E+02

• 6.59E-02

1.50E-02

• 4.38E+00

1.20E-05 8 5:21723247:A:G rs112116090 1.11E+03 5.15E+02

• 6.61E-02

1.51E-02

• 4.37E+00

1.23E-05 9 6:71859846:C:T rs12202150 9.66E+02 4.47E+02

• 7.08E-02

1.62E-02

• 4.36E+00

1.29E-05 10 5:21723100:G:A rs17839334 1.11E+03 5.15E+02

• 6.58E-02

1.51E-02

• 4.36E+00

1.33E-05 11 5:21722186:T:G rs16888553 1.11E+03 5.14E+02

• 6.55E-02

1.50E-02

• 4.35E+00

1.35E-05 12 8:29033406:G:T rs193119386 6.97E+01 5.27E+01 2.82E-01 6.48E-02 4.35E+00 1.35E-05 13 5:21723548:G:C rs6876234 1.11E+03 5.15E+02

• 6.58E-02

1.52E-02

• 4.34E+00

1.43E-05 14 5:21723373:G:A rs6875917 1.11E+03 5.15E+02

• 6.56E-02

1.51E-02

• 4.34E+00

1.45E-05 15 5:21721814:A:T rs113146804 1.11E+03 5.15E+02

• 6.51E-02

1.50E-02

• 4.33E+00

1.49E-05 16 2:64224977:T:C rs147971440 1.76E+02 1.20E+02 1.67E-01 3.87E-02 4.32E+00 1.54E-05 17 5:21723311:A:G rs112089188 1.11E+03 5.15E+02

• 6.54E-02

1.51E-02

• 4.32E+00

1.56E-05 18 5:21723495:C:T rs6877295 1.11E+03 5.15E+02

• 6.55E-02

1.52E-02

• 4.32E+00

1.56E-05 19 2:64112337:C:T rs114190015 1.45E+02 1.01E+02 1.86E-01 4.31E-02 4.31E+00 1.63E-05 20 1:195322984:T:A rs1348101 1.32E+04 7.11E+03 2.19E-02 5.12E-03 4.28E+00 1.86E-05

(N=21,894)

Chr 4, SNP RS1229984 (ADH1B Gene)

• RS 1229984 encodes a form of the alcohol dehydrogenase ADH1B gene that reduces the

clearance rate of alcohol from the liver. This SNP is also known as Arg48His, with the (G) allele corresponding to Arg & (A) to His.

• Known as ADH2*2 or ADH1B*2, the allele with increased activity (more rapid oxidation
• f ethanol to acetaldehyde) is His48, encoded by rs1229984(A). Individuals with one or

especially two ADH2*2 alleles, i.e. genotypes rs1229984(A;G) or rs1229984(A;A) are more likely to find drinking unpleasant & have reduced risk for alcoholism.

https://www.snpedia.com/index.php/Rs1229984 https://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=1229984

GrCh38

Possible Genetic & Epigenetic Process

ADH1B ADH1B

DNA Methylation

ADH1B

DNA Demethaltion

Stop Drinking Spiritual Event DNA Predisposition Fetal Alcohol Exposure Assessing SNP RS1229984 (ADH1B Gene) GWAS Data Results

rs1229984 Would not like drinking Susceptible to drinking Reverted to drinking aversion

Other Alcohol Use Related Data Showing Multiple Factors in Results

Drank Before

(N=336,965)

Stopped: Illness

(N=11,101)

Pruned data

Stopped: Doctor’s Advice

(N=11,101)

Pruned data

Stopped: Health

(N=11,101)

Pruned data

Stopped: Finances

(N=11,101)

Pruned data

PRS Exploration Example

• Look at the whole genome for a group with a

specific phenotype

Jack Euesden, et. al., PRSice: Polygenic Risk Score software, Bioinformatics, 31(9), 2015, 1466–1468, doi: 10.1093/bioinformatics/btu848

References

Clarke, T-K., 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. Comstock, G. W., Paktridge, K. B. “Church Attendance and Health.” American Journal of Epidemiology Vol. 168, No. 7, Johns Hopkins Bloomberg School of Public Health 2008. DOI: 10.1093/aje/kwn326. Dick, D. M., Nasim, A., Edwards, A. C., Salvatore, J. E., Cho, S. B., Adkins, A., et al. (2014). “Spit for Science: launching a longitudinal study of genetic and environmental influences on substance use and emotional health at a large US university.” Front. Genet. 5:47. doi: 10.3389/fgene.2014.00047. El-Sayed, A. M. Haloossim, M. Galea R. Koenen. K. C. “Epigenetic modifications associated with suicide and common mood and anxiety disorders: a systematic review of the literature.” Biology of Mood & Anxiety Disorders 2012, 2:10 http://www.biolmoodanxietydisord.com/content/2/1/10. Epperly, B. G. “Prayer, Process, and the Future of Medicine.” Journal of Religion & Health, Vol. 39, No. 1, Spring 2000. Harrell, Z. A. T., Powell K. “The Relationship Between Parent and Student Religious Coping and College Alcohol Use.” J Relig Health (2014) 53:895–903. DOI 10.1007/s10943-013-9683-4. Hingson, R.W., Zha, W., and Weitzman, E.R. 2009. “Magnitude of and trends in alcohol-related mortality and morbidity among U.S. college students ages.” 18-24, 1998-2005. J. Stud. Alcohol Drugs Suppl. 16, 12–20. Jankowski, Peter J., Hardy, Sam A., Zamboanga, Byron L., Ham, Lindsay S. (2013), “Religiousness and hazardous alcohol use: A conditional indirect effects model.” Journal of Adolescence 36 (2013) 747–758. Johansen, J. D. “Applied Theology: Exploring the Utility of Theological Method in Scientific Research with Genomic Research as an Example.” Naturalism and Its Alternatives in Scientific Methodologies. Blythe Institute, 2016. Wang, J-C. et. al., A genome-wide association study of alcohol-dependence symptom counts in extended pedigrees identifies C15orf53, Molecular Psychiatry 2012, 1–7.