Transcript The application of genome-wide association studies

The application of genome-wide association studies of
aging in a patient-driven clinical trial outline
Melanie Swan, Aaron Vollrath, Cindy Chen & Raymond McCauley, DIYgenomics, Palo Alto, CA USA
[email protected] +1.415.505.4426 www.DIYgenomics.org/aging_poster.ppt
Background
The rapidly decreasing cost of whole genome sequencing could
soon make the Personal Genome a reality for large numbers of
individuals wanting access to and interpretation of their genomic
information. Already the accessibility of this information is providing
an impetus for patient-driven research. Though the advent of the
truly personal genome, whereby everyone has access to their
entire genomic data at an affordable price is not yet here, a number
of options exist for individuals to obtain genotyping data from
consumer genomic services. Costs range from $400-$2,000 for
genotyping services to $20,000 for whole human genome
sequencing for consumers. As proof of principle of a patient-driven
clinical trial using personal genomic data in the form of identified
variants, this study utilizes published data from genome-wide
association studies (GWAS) to link genes and variants to a variety
of biomarkers associated with human aging.
The study outline takes into consideration GWAS results for critical
aspects of aging including the inability to adequately regulate
glucose levels, the decline of the immune system, ineffective
catabolism, shortening of telomeres, and defects in lipoprotein
metabolism. Genotyping data for a group of twenty citizen scientists
is reviewed and can be further integrated with phenotypic measures
of aging (including blood pressure, cholesterol, BMI, VO2 max,
erythrocyte glycosylation, LDL particle size, telomere length, and
lymphocyte growth rate), and used as the basis for proposed
personalized interventions. Citizen-science contributed biobanks
and databases are examined as a resource for the immediate, costeffective, and large-scale application of research studies.
1. Aging-specific GWAS
Figure 1
Figure 3
Genotype
Condition
Aging GWAS
1.1
1.1.1 Aging GWAS - BU
References
1
Aging GWAS - BU
2
Aging GWAS - Meta
3
Aging GWAS - Meta
4
Insulin/IGF-1 signaling
5
Insulin/IGF-1 signaling
6
RNA editing
7
DNA damage repair
8
Cell cycle
9
Telomere length
10
Telomere length
11
Telomere length
12
Immune system
13
Immune system
The five categories of aging-related GWAS are presented in
Figures 1-5: aging-specific GWAS, diabetes, lipids, and metabolic
disease, catabolism (waste break-down) and other, heart disease
and blood operations, and cancer.
DIY
Total
23andMe
Ref
Ref
3.1
PMID
20595579
17903295
20304771
18765803
19901535
20011587
11691784
20351400
20139977
20157543
20016137
18852199
19038835
0
150
150
143-147
1
0
35
35
17
2
Multi-condition
0
288
288
61
3
Multi-condition
1
15
16
7
4,5
0
0
0
18
1
0
18
1
0
18
1
0
6
7
8
0
0
4
1
4
1
1
1
9-10
12
Protein quality (custom assay)
Transcription & translation assay
Cell turnover (custom assay)
Telomere length
IgA, IgG, IgM (mg/dL); CD levels (CD4,
CD16, CD56); lymphocyte growth rate
11
13
Citation
Sebastiani Science 2010
Lunetta BMC Med Genet 2007
Newman J Gerontol A Biol Sci Med Sci 2010
Willcox PNAS 2008
Narasimhan Cell Cycle 2009
Sebastiani PLoS One 2009
Menoyo Cancer Res 2001
Blagosklonny Aging 2010
Codd Nat Gen 2010
Lou Aging 2009
Andrews Gerontology 2010
Lettre Hum Mol Genet 2008
Willcox J Gerontol A Biol Sci Med Sci 2008
Gene(s)
DTC
TCF7L2,
CDKN2A-CDKN2B, 59
FTO,
THADA,
IGF2BP2,
KCNJ11, PPARG, CDKAL1,
HHEX-IDE,
NOTCH2,
SLC30A8, CDC123-CAMK1D,
JAZF1,
TSPAN8-LGR5,
ADAMTS9, ZFAND6, CHCHD9,
MTNR1B, HMGA2, CENTD2,
KCNQ1,
BCL11A,
ZBED3,
DUSP9, IRS1, HNF1A, PRC1,
TP53INP1, KLF14
Fasting glucose homeostasis ADCY5, ADRA2A, C2CD4B,
CRY2,
DGKB-TMEM195,
FADS1, G6PC2, GCK, GCKR,
GLIS3, IGF1, MADD, MTNR1B,
PROX1, SLC2A2, SLC30A8,
TCF7L2
Glucose & metabolism
G6PC2, SLC2A9
2.2
Obesity
NEGR1, SEC16B, TMEM18,
ETV5, PCSK1_2, AIF1/NCR3,
BDNF, FAIM2, SH2B1, FT0,
MC4R, KCTD15
2.3
BMI
FTO, MC4R
2.4
Adiposity & fat distribution
CTNNBL1, NOS1AP, TFAP2B,
MSRA, LYPLAL1
2.5
Total Cholesterol
LDLRAP1,
EVI5,
MOSC1,
IRF2BP2, RAB3GAP1, RAF1,
HMGCR, TIMD4, HLA, FRK,
DNAH11, NPC1L1, CYP7A1,
GPAM, SPTY2D1, UBASH3B,
BRAP, HNF1A, HPR, CILP2,
FLJ36070, ERGIC3, MAFB
2.5.1 HDL
CETP, LPL, LIPC, LIPG,
GALNT2, ABCA1, MMAB-MVK,
PABPC4, ZNF648, GALNT2,
IRS1,
SLC39A8,
ARL15,
C6orf106, CITED2, KLF14,
PPP1R3B, TRPS1, TTC39B,
ABCA1,
AMPD3,
LRP4,
PDE3A,
MVK,
SBNO1,
ZNF664,
SCARB1,
LIPC,
LACTB, LCAT, CMIP, STARD3,
ABCA8, PGS1, LIPG, MC4R,
ANGPTL4, LOC55908, LILRA3,
HNF4A, PLTP, UBE2L3
2.5.2
LDL
Triglycerides
References
1
Type 2 diabetes
2
Type 2 diabetes
3
Type 2 diabetes
4
Type 2 diabetes
5
Type 2 diabetes
6
Adiposity & fat distribution
7
Cholesterol
LDLR, APOE-C1-C4, CELSR2PSRC1-SORT1, APOB, NCANCILP2,
PCSK9,
HMGCR,
SORT1,
APOB,
ABCG5/8,
MYLIP, HFE, LPA, PLEC1,
ABO,
ST3GAL4,
NYNRIN,
OSBPL7, LDLR, APOE, TOP1
APOA5-A4-C3-A1, LPL, GCKR,
MLXIPL, ANGPTL3, TRIB1,
NCAN-CILP2, LIPC, GALNT2,
ANGPTL3, GCKR, COBLL1,
MSL2L1, KLHL8, MAP3K1,
TYW1B,
MLXIPL,
PINX1,
NAT2, LPL, TRIB1, JMJD1C,
CYP26A1, FADS1-2-3, APOA1,
LRP1, CAPN3, FRMD5, CTF1,
PLA2G6
PMID
18852197
20581827
19038835
20200384
20081858
19557161
20686565
0
DIY
15
Total
74
3.2
Macular degeneration
3.3
Rheumatoid arthritis
3.4
Osteoarthritis
3.5
Osteoporosis
3.6
Sarcopenia
3.7
Liver function
3.8
Kidney function
3.9
Lung capacity
References
1
Alzheimer's disease
2
Alzheimer's disease
3
Alzheimer's disease
4
Alzheimer's disease
5
Alzheimer's disease
6
Alzheimer's disease
7
Alzheimer's disease
8
Alzheimer's disease
9
Alzheimer's disease
10
Alzheimer's disease
11
Macular degeneration
12
Macular degeneration
13
Rheumatoid arthritis
14
Osteoarthritis
15
Osteoporosis
16
Osteoporosis
17
Sarcopenia
18
Sarcopenia
19
Sarcopenia
20
Sarcopenia
21
Sarcopenia
22
Sarcopenia
23
Sarcopenia
24
Sarcopenia
25
Sarcopenia
26
Liver function
27
Liver function
28
Kidney function
29
Kidney function
30
Lung capacity
31
Lung capacity
32
Lung capacity
33
Lung capacity
34
Lung capacity
Insulin signaling
Genotype
Condition
Type 2 diabetes
23andMe
45
Ref
1-2
Total
31
23andMe
17
1
10
7
0
33
12
PTPN22,
PADI4,
MMEL1,
STAT4, IL2/IL21, MHC, HLADRB1,
TNFAIP3/OLIG3,
TRAF1/C5
EDG2,
PTGS2,
PLA2G4A,
DVWA
PPAP2B, GPR177, HECW2,
CASR, MMRN1, IRX2, PDZD2,
TGFBI,
CACNB2,
DOCK1,
SOX6,
PDGFD,
PDGFD,
RAD51L1, SALL1, FBXO31,
CDH2, RANKL, RANK, OPG
IGF-1, ACE, ACTN3, IL-1B, IL1RN,
IL6,
CKMM/CKM,
MSTN/GDF8, PEPCK-C/PCK1,
VDR
33
0
17
17
10
ABCG8, HLA-DPA/B, CPN1ERLIN1-CHUK,
PNPLA3SAMM50, ALPL, GPLD, ABO,
JMJD1C,
REEP3,
HNF1A,
IL12A, IL12RB2, HLA-B*5701,
IL28B
UMOD, APOE, ABCA1, PTGS1,
TNF, CPB2, AGTR1, OR13G1,
GNB3
NRF1,
ADRB1,
APOE,
PPARGC1A
0
22
22
5
PMID
20029386
20457951
20298972
20236449
20460622
19608551
20595579
17761686
20697045
19648749
17884985
18724980
19460157
18325907
20548944
20205168
20175886
20459474
19237423
19630564
20490824
19724965
20005538
20148371
20157530
19916168
19038835
20686651
19056482
16421173
19666693
20044476
20459474
19713012
2
3
5
2
0
17
17
7
0
2
2
1
0
6
6
4
Ref
1-8
11
14
Ref
Spinal fluid protein signature of three 9,10
proteins (CSF beta-amyloid protein 1-42
(CSF Aß1-42), total CSF tau protein, and
CSF
phosphorylated
tau181P
(PTau181P)); mid-life cholesterol levels
Vision impairment (blurry or white-spot 12
area in center of vision), early detection of
choroidal neovascularization
Blood assay to determine pre-clinical 13
autoantibody and cytokine profiles
Joint function
15, 16 Bone mineral density
17-24 Muscle mass baseline, degeneration &
replacement; quality of proteolytic systems
activation (calpain, ubiquitin-proteasome,
and caspases), and alteration in muscle
growth factors
26
Gallstones, fatty liver, primary cholestatic
liver diseases, chronic hepatitis C virus
(HCV) infection, and progressive liver
fibrosis. Serum levels of hepatic enzymes:
GOT (AST), GPT (ALT), ALP, GGT (IU/L)
25
28-29 Measure: creatinine (mg/dL); eGFR (mL)
30
19,20, VO2 max
31-33
Fasting glucose, nonfasting glucose, total
protein, albumin, uric acid (mg/dl);
glycosylated hemoglobin (HbA1c)
34
Friedman Am J Ophthalmol 2008
Hueber Arthritis Res & Therapy 2009
Mototani Hum Mol Gen 2008
Hsu PLoS Genet 2010
Roshandel J Bone Miner Res 2010
Cauci BMC Med Genet 2010
Buxens Scand J Med Sci Sports 2010
Ruiz J Physiol 2009
Ostrander Annu Rev Genomics Hum Genet 2009
Kostek Eur J Appl Physiol 2010
Wackerhage J Sports Sci 2009
Eynon Metabolism 2010
Bustamante-Ara Int J Sports Med 2010
Scicchitan Aging 2009
Melum World J Gastroenterol 2009
Willcox J Gerontol A Biol Sci Med Sci 2008
Gudbjartsson PLoS Genetics 2010
Yoshida Genomics 2009
Defoor Eur Heart J 2006
Enyon Exp Physiol 2009
Tsianos J Appl Physiol 2010
Scand J Med Sci Sports 2010 Buxens
Sergi Clin Nutr 2010
Ref
3,4
4. Heart disease and blood operations
Condition
16
16
5
5
2
12
1
0
0
2
5
2
5
1
4
1
1,6
0
25
25
10
7
Fasting & nonfasting glucose (mg/dl)
3
Fasting & nonfasting glucose (mg/dl)
BMI
3
3
BMI, BMI/waist circumference
BMI, caliper
3
Cholesterol (mg/dL)
3
4.1
4.3
44
44
18
1,7
HDL (mg/dL); TC/HDL (ratio)
3
4.4
4.5
20
32
20
32
13
13
1,7
1,7
LDL (mg/dL); LDL particle size
Triglycerides (mg/dL)
3
DIY
Total
23andMe
Ref
CETP
0
1
1
1
1-2
APOA4, APOC3
0
1
1
1
3
AB002360,
AB116074,
AK092739,
AK123267,
AK127723, APG4C, ATP2B1,
BC036771, CR603372, CSK,
CYP17A1, F7, F10, HSPC159,
LRRC7,
MCF2L,
NEGR1,
PLEKHA7, PROZ, SLC9A10,
SMYD3, VAV3, ZNF165
0
25
25
11
4
& ATP2B1,
CACNB2,
CSK,
CYP17A1, ITGA9, MTHFR,
PLEKHA7, PMS1, TBX3-TBX5,
ULK4, ZNF652
Coronary artery disease & CDKN2A-CDKN2B, CELSR2atherosclerosis
PSRC1-SORT1,
MTHFD1L,
NPY
Myocardial infarction
CDKN2A/CDKN2B,
CELSR2/PSRC1,
CXCL12,
MIA3,
MRAS,
MTHFD1L,
PHACTR1,
SH2B3,
SLC5A3/MRPS6/KCNE2,
WDR12
Atrial fibrillation
CAV1, PITX2/ENPEP, TBX5,
ZFHX3
C-reactive protein (CRP) APOE, CRP, LEPR, HNF1A,
GCKR, IL6R, 12q23
0
27
27
13
5
12
25
37
4
7-8
18
0
18
8
5
0
5
5
1
7
8
6
Blood
pressure
essential hypertension
4.2
Phenotype
DTC
Cardiovascular disease
Plasma
lipid
transfer
protein
Serum lipoprotein levels
and particle size
Hemostatic
factors
(Fibrinogen, FVII, PAI1,
tPA, vWF)
Gene(s)
References
1
Cardiovascular disease
2
Cardiovascular disease
3
Cardiovascular disease
4
Cardiovascular disease
5
Cardiovascular disease
6
Cardiovascular disease
7
Coronary artery disease
8
Atherosclerosis
PMID
20068209
17190939
16602826
17903294
20414254
19038835
18852197
19119412
Citation
Sanders JAMA 2010
Barzilai Neurology 2006
Atzmon PLoS 2006
Yang BMC Med Gen 2007
Hong J Hum Genet 2010
7
Ref
Systolic & diastolic Blood Pressure (mm
Hg); Hematocrit (%); Hemoglobin (g/dL);
RBC, WBC, platelets (x 104/uL)
C-reactive protein (CRP) (mg/L)
5.01
Condition
Basal Cell Carcinoma
5.02
5.03
Bladder
Brain (Glioma)
5.04
Breast
5.05
5.06
Breast BRCA mutations
Colorectal
5.07
5.08
5.09
5.1
Esophageal
Larynx
Leukemia
Lung
5.11
Melanoma
5.12
5.13
5.14
5.15
5.16
Neuroblastoma
Oral and Throat
Ovarian
Pancreatic
Prostate
5.17
5.18
5.19
Stomach
Testicular
Thyroid
27
Citation
Roses Pharmacogenomics 2009
Roses Arch Neurol 2010
Lutz Alz Dement 2010
Erekin-Taner Alz Res & Therapy 2010
Seshadri JAMA 2010
Kim Hum Mol Genet 2009
Sebastiani Science 2010
Miyashita Hum Mol Genet 2007
De Meyer Arch Neurol 2010
Solomon Dement Geriatr Cogn Disord 2009
Kanda PNAS 2007
Genotype
2
18
0
DIY
28
Genotype
Figure 4
2
0
0
Gene(s)
DTC
APOE,
TOMM40,
CLU,
3
ADAM10, CTNNA3, GALP,
APOC1,
GAB2,
GOLPH2,
LRAT,
LMNA,
TRPC4AP,
PCDH11X, PICALM, CR1
ARMS2/HTRA1, CFH, C2/CFB,
9
C3-R80G
Phenotype
Phenotype
0
18
0
Condition
Alzheimer's disease
Multi-condition
Figure 2
2.5.3
As illustrated in Figure 1, the best-known processes of aging are
not yet extensively covered in human GWAS, for example
insulin/IGF-1 signaling pathways, inflammation, mitochondrial
dysfunction, reactive oxygen species generation, cell cycle, stem
cell generation, and immune response. Other areas such as
diabetes and adiposity (Figure 2) have broader coverage and
continue to be the focus of recent significant findings of novel loci.
New cancer studies (Figure 5) have been more rare, and it is not
necessary to supplement DTC curation.
DTC
Figure 5
Genotype
Phenotype
2. Diabetes, lipids, and metabolic disease
Methodology
The DIYgenomics study design methodology has three steps:
identify strongly associated genomic variants for specific conditions,
find corresponding phenotypic biomarkers, and establish
corresponding interventions to the extent possible. Direct-toconsumer (DTC) genomic services cover some aging conditions.
These data were included and supplemented with a more recent
and exhaustive self-curation of GWAS. Self-curated GWAS
references are cited; DTC references are available at
DIYgenomics.org under ‘Health Risk.’ Approximately half of the
SNPs identified in DTC and DIYgenomics curation are available for
analysis in 23andme genotyping files.
Gene(s)
ANK2,
APOE/TOMM40,
ARMC2,
ATF6,
ATP6V0E,
BACH2,
BTNL2,
C18orf1,
C19orf36,
CDKN2B,
CEACAM16, CHAF1B, CHGA,
CLSTN2, CRTAC1, CTBP2,
CTNNA3,
DBC1,
DCPS,
DEFB1,
DKFZp762E1312,
EIF4E3,
ELL2,
FAM98B,
FDFT1,
FGD5,
FGFR1,
FLJ21103, FLJ46010, GIP,
GORASP2,
GPC5,
GPC6,
GPR27,
GRM8,
GUCA1A,
IGF2BP1, IL7, KSR2, LDHD,
LHFP,
LOC202459,
LOC388882,
LOC400499,
LOC441108, LUC7L, MATN2,
MGC35295, MORN1, MSI2,
NAV2,
NTN1,
PARP10,
PHYHIP, POU5F1, POU6F2,
RASGRF2, SH3BP4, SLC6A7,
SMYD3, SORCS1, SORCS2,
SPIRE1,
STK32A,
STX8,
SULT1C3,
SUSD3,
TEK,
TNFRSF11A, TPPP, TTC6,
TTC6, VISA, ZC3H3
1.1.2 Aging GWAS - Meta
FOXO1A, GAPDH, KL, LEPR,
PON1, PSEN1, SOD2, WRN
1.1.3 Aging GWAS - Meta
ACCN1, ANKRD46, BMP4,
C3orf21, CASP7, CRISPLD1,
DIRAS2,
GRIK2,
IL20RB,
LASS3, LOC196913, MINPP1,
OR2W3, OTUD3, PAPPA2,
PIK3C3,
REM2,
RGS7,
SC4MOL, SCN7A, SPRY2,
TMPRSS5, ZNF19
1.2
Insulin/IGF-1
signaling ADIPOQ, FOXO1A, FOXO3A,
(IIS) pathway
SIRT1, COQ7
1.3
RNA editing
ADARB1/2
1.4
DNA damage repair
CHK1/CHEK1
1.5
Cell cycle, apoptosis, and ATR
transcription
Telomere length
TERC, 9p21.1, 11q22.3, ISG15
1.6
1.7
Immune system
ATG16L1,
IRGM,
PTPN2,
PTPN22
2.1
5. Cancer
3. Catabolism (waste break-down) and other
Gene(s)
DTC
CDKN2A/B,
KRT5,
PADI6,
6
TERT
MYC, PSCA, TERT, TP63
4
CCDC26, CDKN2A/B, RTEL1,
4
TERT
CASP8, COX11, FGFR2, LSP1, 17
MAP3K1, MRPS30, NEK10,
RAD51L1, TNRC9/TOX3
CHEK2, FGFR2, 16q12 region
BMP4,
C11orf92,
CRAC1,
EIF3H, POU5F1P1, SMAD7
PSCA
ADH7
ACOXL, IRF4, PKRD2, SP140
CHRNA3/A5/B4,
CHRNA3/LOC123688, TERT
ASIP, MC1R, PIGU , SLC45A2,
TYR , TYRP1
6p22
ADH7
BNC2, LOC648570, CNTLN
ABO, CLPTM1L/TERT, NR5A2
CTBP2,
EHBP1,
IGF2/IGF2AS/INS/TH, JAZF1,
KLK2/KLK3, LMTK2, MSMB,
NKX3.1, NUDT11, POU5F1P1,
SLC22A3, TCF2, TERT, TET2
PSCA
BAK1, KITLG, SPRY4
FOXE1, NKX2-1
DIY
0
Total
6
23andMe
0
0
4
4
4
4
0
17
16
3
9
0
0
3
9
3
8
1
1
6
9
0
0
0
0
1
1
6
9
1
1
6
3
2
0
2
2
1
1
1
4
43
0
0
0
0
0
1
1
1
4
43
1
1
1
4
27
1
3
3
0
0
0
1
3
3
1
3
3
6
Discussion
These ~thousand variants associated with aging and their
corresponding phenotypic measures provide the start of a
comprehensive program for personal aging measurement and
intervention. There are two kinds of intervention, traditional
solutions and novel solutions. Traditional solutions consist of the
usual condition management through diet, exercise, vitamins, and
pharmaceuticals. Novel interventions consist of a variety of
emerging solutions, many of which are speculative. Some of these
include a crosslink breakers supplement to improve systolic
hypertension (Zieman Journal of Hypertension 2007), brain fitness
programs and mid-life cholesterol management for Alzheimer’s
disease, TA-65 telomerase activation (TA Sciences) for telomere
length management, resistance weight lifting for sarcopenia,
interval training and aerobic exercise for VO2 max improvement,
and blood-based assays for early detection and response to
rheumatoid arthritis (Swanson Nat Rev Rheumatol 2009), macular
degeneration (MacuCLEAR), and kidney and liver disease.
6
Conclusion
An urgent contemporary objective in public health is to implement
preventive medicine. Tools are needed for personalized genome
interpretation, and the meaningful integration of multiple health data
streams: various levels of genomic and phenotypic data, and as
they become available, environmental, microbiome, and other data.
The large-scale open platforms of citizen science biobanks could
be ideal for crowdsourced longitudinal data collection, targeted
patient recruitment, and the conduct of a new generation of health
research studies.
Willcox J Gerontol A Biol Sci Med Sci 2008
Mohlke Hum Mol Genet 2008
Shah PLoS Genetics 2009
Acknowledgements
3
We would like to acknowledge review feedback from Lorenzo
Albanello, Mark Hamalainen, and Anne and Gary Hudson.
Citation
Mohlke Hum Mol Genet 2008
Voight Nat Genet 2010
Willcox J Gerontol A Biol Sci Med Sci 2008
Selvin N Engl J Med 2010
Dupuis Nat Genet 2010
Lindgren PLoS Genet. 2009
Teslovich Nature 2010
DIYgenomics is a non-profit research organization coordinating
patient-driven clinical trials and providing personal genome data
applications for 20 health conditions and 250 pharmaceutical drugs
at http://www.DIYgenomics.org.