aug2013 mike snyder the genomics revolution and human health

The Genomics Revolution and Human Health

Michael Snyder

August 15, 2013

Conflicts: Personalis, Genapsys, Illumina

Health Is a Product of Genome + Environment

Exposome

Health

Genome

Health Is a Product of Genome + Environment

Exposome

Health

Genome

• Understand and Treat Disease – Cancer– Mystery diseases

• Pharmacogenomics – Determining which drug side effects and doses

• Managing Health Care in Healthy Individuals

Impact of Genomics on Medicine

Personalized Omics Profiling: Combine Genomic and Other Omic Information

Genomic Transcriptomic, Proteomic, Metabolomic

1. Predict risk2. Diagnose3. Monitor4. Treat &5. UnderstandDisease States

GGTTCCAAAAGTTTATTGGATGCCGTTTCAGTACATTTATCGTTTGCTTTGGATGCCCTAATTAAAAGTGACCCTTTCAAACTGAAATTCATGATACACCAATGGATATCCTTAGTCGATAAAATTTGCGAGTACTTTCAAAGCCAAATGAAATTATCTATGGTAGACAAAACATTGACCAATTTCATATCGATCCTCCTGAATTTATTGGCGTTAGACACAGTTGGTATATTTCAAGTGACAAGGACAATTACTTGGACCGTAATAGATTTTTTGAGGCTCAGCAAAAAAGAAAATGGAAATTAATTTTGAAGTGCCATTGA….

Genome

Transcriptome(mRNA, miRNA, isoforms, edits)

Proteome

Metabolome

PersonalOmicsProfile

Autoantibody-ome

Microbiome

Personal “Omics” Profiling (POP)

Cytokines

Epigenome

Genome

Transcriptome(mRNA, miRNA, isoforms, edits)

Proteome

Metabolome

PersonalOmicsProfile

Autoantibody-ome

Microbiome

Personal “Omics” Profiling (POP)

Cytokines

Epigenome

Initially 40K

Molecules/Measure-

ments

Now Billions!

Personal Omics Profile40 months; 61 Timepoints; 6 Viral Infections

/

/

Chen et al., Cell 2012

Accurate Genome Sequencing

3.3 M Hi conf. SNVs, 217K Indels and 3K SVs2 or more Platforms

(Plus low confidence)

Whole Genome Sequencing• Complete Genomics: 35 b paired ends (150X)• Illumina: 100 b paired ends (120X)

Exome Sequencing• Nimblegen• Illumina• Aglilent

3.30M89%

100K2%

345K9%

CGIllumina

Local phasing + population data= highly phased blocks

Genome Phasing: Assign Variants to Parental Chromosomes

Moleculo Technology: ~6-10 kb Sequences, 6X coverage

Moleculo: Volodymyr Kuleshov, Michael Kertesz

Percent SNPs phased 98.2%

Switch accuracy 99.9%+

CodingNon-Coding

miRNA Splice UTR

miRNA targets

Seedsequence SIFT PP2

OMIM/Curated Mendelian disease

(51)

Nonsynonymous(1320)

Synonymous

mRNA stability

tRNA rate

I. Highly Penetrant Variants:

Mendelian Disease Risk Pipeline

Rick Dewey & Euan Ashley

Damaging(234)

All variants~3.5M

Rare/novel variants (<5%)

Missense• ALAD, ABCC2, ACADVL, ADAMTS13, AGRN, BAAT, CDS1,

CHD7, COL4A3, CTSD, DGCR2, DLD, DYSF, EPCAM, FGFR1OP, FKRP, GAA, GNAI2, HSPB1, IGKC, ITPR1, MED12, MKS1, NTRK1, PCM1, PKD1, PLEKHG5, PMS2, PRSS1, PTCH2, SERPINA1, SETX, SYNE1, TERT, TTN, VWF, ZFPM2, PNPLA2.

Nonsense• PRAMEF2, PLCXD2, NUP54, RP1L1, PIK3C2G,

NDE1, GGN, CYP2A7, IGKC

Not Rare But Important• KCNJ11 , KLF4, GCKR …

High Cholesterol

Aplastic Anemia

Rare Variants in Disease Genes (51 Total)

Integrate Over Many Markers:Complex Disease

0% 100%

Predict Type 2 Diabetes

GLUCOSE LEVELS

HRV INFECTION(DAY 0-21)

RSV INFECTION(DAY 289-311)

LIFESTYLE CHANGE(DAY 380-

CURRENT)14

HbA1c (%): 6.4 6.7 4.9 5.4 5.3 4.7 (Day Number) (329) (369) (476) (532) (546) (602)

Dynamical Outcomes for Integrated Analysis of Proteome, Transcriptome, Metabolome

george mias RSV 18 days

Platelet Plug Formation

Glucose Regulation of Insulin Secretion

The Future?

Genomic Sequencing

1. Predict risk2. Early Diagnose3. Monitor4. Treat

Omes and Other Information: Home Sensors

http://www.baby-connect.com/

GGTTCCAAAAGTTTATTGGATGCCGTTTCAGTACATTTATCGTTTGCTTTGGATGCCCTAATTAAAAGTGACCCTTTCAAACTGAAATTCATGATACACCAATGGATATCCTTAGTCGATAAAATTTGCGAGTACTTTCAAAGCCAAATGAAATTATCTATGGTAGACAAAACATTGACCAATTTCATATCGATCCTCCTGAATTTATTGGCGTTAGACACAGTTGGTATATTTA….

Study of 10 Healthy People5 Asian, 5 European

Dewey, Grove, Pan, Ashley, Quertermous et al

- Median 5 reportable disease risk associations (ACMG) per individual (range 2-6)

- 3 followup diagnostic tests (range 0-10)- Cost $362-$1427 per individual

- 54 minutes per variant

Many Unaddressed Challenges

1) Accuracy and coverage

2) Interpretation

3) Interpreting non-protein coding regions

4) DNA Methylation

5) Sample size

6) Exposome

1) Accurate Genome Sequences and Coverage

Whole Genome Sequencing• Complete Genomics: 35 b paired ends (150X)• Illumina: 100 b paired ends (120X)

3.30M89%

100K2%

345K9%

CGIllumina

Single Nucleotide Variants Getting Better.

Indels and Structural Variants Need Work!

SNV Comparison

• Complete Genomics: 35 b paired ends (150X)• Illumina: 100 b paired ends (120X)

3.30M89%

100K2%

345K9%

Complete Genomics

Illumina

Hugo Lam, Michael Clark, Rui Chen

Ti/Tv = 1.6817/18 Sanger

Ti/Tv = 2.1420/20 Sanger

Ti/Tv = 1.402/15 Sanger

31 DiseaseAssociated SNP

3 DiseaseAssociated SNP

Sequencing AccuracySequencing the Same Genome Twice

Personalis

146,100 SNPs (3.7%)

Exome-seq and WGS-specific detection45X WGS vs 80X Exome

Clark et al. 2011 Nature Biotech

Overall Statistics for Finishing Medically Interesting Genes- ACE

ACE v1 = Thick LinesTruSeq Exome (10G) = Thin Lines

Personalis

Normal Exome ~2,000

Custom Exome (ACE) ~2,000

Exons Covered by ACE, Missed by Standard Exome

Personalis

1) Search for disease causing mutations (highly penetrant)

GCKR (high lipids); TERT (aplastic anemia)

2) Sum over multiple common risk allele to predict risk

2. Genome Interpretation

Missense VariantsALAD, ABCC2, ACADVL, ADAMTS13, AGRN, BAAT, CDS1, CHD7, COL4A3, CTSD, DGCR2, DLD, DYSF, EPCAM, FGFR1OP, FKRP, GAA, GNAI2, HSPB1, IGKC, ITPR1, MED12, MKS1, NTRK1, PCM1, PKD1, PLEKHG5, PMS2, PRSS1, PTCH2, SERPINA1, SETX, SYNE1, TERT, TTN, VWF, ZFPM2, PNPLA2.

0% 100% Ashley, Butte et al.

Missing Regulatory Variation

88% of Disease Variants Lie Outside of Genes!

26

X

Two approaches:

1) Mapping transcription factor binding in different people.

2) RegulomeDB: Assembling regulatory information from the ENCODE Project and other sources.

Damaging Variation in an Individual

Gene Regulatory region

Protein Coding Non-coding

and

CAPN1: Protective against Alzheimer’s

Coding Variants

Regulatory Variants

3. Incorporate Methylation Data

Possible Phenotypic Consequences of Differentially Methylated Regions?

4. Sample Size—Need to Reduce

AliveCor Measures ECG

5. Other Data Types: Sensors

71

Moves App

Conclusions1) Personal genome sequencing is here. The

medical interpretation is difficult.

2) Genome sequencing can predict disease risk that can be monitored with other omics information.

3) Integrated analysis can provide a detailed physiological perspective for what is occurring.

4) Every person’s complex disease profile is different and following many components longitudinally may provide valuable information.

5) You are responsible for your own health

Data at: snyderome.stanford.edu

The Personal Omics Profiling Project

Rui Chen, George Mias, Hugo Lam, Jennifer Li-Pook-Than, Lihua Jiang, Konrad Karczewski, Michael

Clark, Maeve O’Huallachain, Manoj Hariharan,Yong Cheng, Suganthi Bali, Sara Hillemenyer, Rajini

Haraksingh, Elana Miriami, Lukas Habegger, Rong Chen, Joel Dudley, Frederick Dewey, Shin Lin, Teri Klein, Russ Altman, Atul Butte, Euan Ashley, Tom

Quetermous, Mark Gerstein, Kari Nadeau, Hua Tang, Phyllis Snyder

Acknowledgements

34

Human Regulatory Variation:Maya Kasowski, Fabian Grubert, Alex Urban, Alexej A, Chris Heffelfinger, Manoj Harihanan, Akwasi Asbere, Lukas Habegger, Joel Rozowsky, Mark Gerstein, Sebastian Waszak, Jan Korbel (EMBL, Heidelberg)

Regulome DB:Alan Boyle, Manoj Hariharan, Yong Cheng, Eurie Hong, Mike Cherry

Methylome:Dan Xie, Volodymyr Kuleshov, Rui Chen, Dmitry Pushkarev, Konrad Karczewski, Alan Boyle, Tim Blauwkamp, Michael Kertesz

Genome (1TB)

Transcriptome (0.7TB)(mRNA, miRNA, isoforms, edits)

Proteome (0.02 TB)

Metabolome (0.02 TB)

PersonalOmicsProfileTotal =5.74TB/

Sample + 1 TB

GenomeAutoantibody-ome

Microbiome (3TB)

6. Big Data Handling and Storage

Cytokines

Epigenome (2TB)