baseline genetics and fenofibrate response in the accord clinical trial daniel rotroff phd, msph...
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Baseline genetics and fenofibrateresponse in the ACCORD clinical
trial
Daniel Rotroff PhD, MSPH
September 18, 2015
Postdoctoral Research Fellow, Bioinformatics Research Center, North Carolina State University
2Background on Dyslipidemia
o ~31.7% of adults in the U.S. have high LDL, and less than ½ are receiving cholesterol treatment.
o Individuals with high cholesterol are at ~2x risk for heart disease.
o People with type 2 diabetes are 2-3x more likely to develop cardiovascular disease, placing individuals with high cholesterol and type 2 diabetes at especially high risk.
3Rationale for the Action to Control Cardiovascular Risk in Diabetes (ACCORD) Clinical Trial
o All individuals enrolled have type 2 diabetes.
o Conclusion was that these treatments do not reduce cardiovascular events over standard treatments--one trial arm actually increased mortality.
o However, variability in response was observed.
“The purpose of this study is to prevent major cardiovascular events (heart attack, stroke, or cardiovascular death) in adults with type 2 diabetes mellitus using intensive glycemic control, intensive blood pressure control, and multiple lipid management.”-https://clinicaltrials.gov/ct2/show/study/NCT00000620
4Our goals with the ACCORD data
o Start with baseline genetic mapping of lipid phenotypes
o Characterize background etiology of dyslipidemia in individuals with type II diabetes
o Refine rare-variant methods
o Interrogate drug response associations for major trial drugso Fenofibrateo Rosiglitazoneo Metformino Sulfonylureao Statino Insulin
o Collaborators: Alessandro Doria’s group at Joslin Diabetes Center and Harvard Medical School are investigating genetic associations with adverse cardiovascular outcomes
5Genotyping Array and Study Design
o Genotyped using the Affymetrix Axiom Biobank Genotyping Array-exome chip.
o This platform became available on November 1, 2012 and this project was one of the first to use the array.
o The array contains several marker sets:
o The GWA markers were selected to maximize genome-wide coverage of imputed variants in European, Asian, African and Latino populations
~250k genome-wide association (GWA) markers, ~250k exome markers focusing on rare variants, ~70k novel loss-of-function markers, ~20k eQTLs and ~2k pharmacogenomic markers.
6Genotyping Array and Study Design
o New platform-significant efforts to create a sound QC pipeline
o Duplicate samples and HapMap samples were included on each plate
o Considerations were made for:o Hardy-Weinberg equilibriumo Genomic inflationo Sample/probe concordanceo Plate effectso Predicted vs Recorded Gender o Autosomal heterozygosityo Cryptic relatednesso Population stratificationo Others…
o 583,613 variants after QC (from 628,679 total). o 89,212 of these were monomorphic
o Imputed using 1000 genomes reference panelo 26,862,499 imputed variants after QC (71.7% of total imputed
variants)
7Genotyping Array and Study Design
o Population stratification across the ACCORD Trial (n=7929)
8Genetic Associations with Baseline Lipids
o Common Variant Analysis (MAF ≥ 3%) and n=7929
o 292,816 genotyped and 7,812,348 imputed variants had MAF ≥ 3%
o Phenotypes: HDL, LDL, Total Cholesterol, Triglycerides
o Study parameters and covariates were incorporated into the model via expert opinion and backwards selection
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Genetic Associations with Baseline Lipids
Total Cholesterol LDL
HDL Triglycerides
10
Genetic Associations with Baseline Lipids Total Cholesterol
LDL
11
Genetic Associations with Baseline Lipids
HDL
Triglycerides
12
Common Genetic Associations with Baseline Lipids
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Genetic Associations with Baseline Lipids
o Rare Variant Analysis (MAF 3%) and n=7929
o We used a suite of 5 commonly used methods
o All methods rely on collapsing of SNPs into geneso 16,480 genes that contained >1 SNP with MAF < 3%o 146,689 genotyped and 73,295 imputed SNPs were included
(median 9 SNPs/gene)
o 5 tests were combined using a correlated Lancaster approach*
o Methods were then adjusted for multiple comparisons using an FDR approach
*Hongying Dai, J., and Yuehua Cui. "A modified generalized Fisher method for combining probabilities from dependent tests." Frontiers in genetics 5 (2014).
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Rare Genetic Associations with Baseline Lipids Total Cholesterol LDL
HDL Triglycerides
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Rare Genetic Associations with Baseline Lipids
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Baseline Common and Rare Variant Conclusions
o Most statistically significant SNPs are in genes previously identified in large meta-analyses
o Indicates that the QC and analysis pipeline for this exome chip are working as expected
o Suggests that the underlying genetic factors contributing to dyslipidemia are similar in patients with type 2 diabetes
o Patients in ACCORD were prescribed many different diabetes, blood pressure, and cholesterol lowering drugs
o We can modify the analysis pipeline used for the baseline analysis to interrogate variants associated with drug response
o Fenofibrate was the first drug chosen due to the ACCORD lipid arm trial design
Next Steps
17
Background on Fibrateso Used to treat high cholesterol (↑HDL, ↓LDL), usually in
combination with statins.
o Use of fibrates alone has been shown to reduce the number of non-fatal myocardial infarctions, but not all-cause mortality.
o Have also been shown to reduce insulin resistance for individuals with type 2 diabetes.
o Activates PPARα, which regulates lipid metabolism in the liver.
18
Study Designo All individuals were on statin and started fibrate at the beginning of
the trial
o Must maintain fibrate compliance for 90 (+/- 10) consecutive days
o Phenotypes: HDL, LDL, Triglycerides, Cholesterol
o Concomitant medications were a major challenge, with over 71 different drugs accounted for in the trial (with a varying degree of detailed records)o Created the following scoring metric:Not on medication during the time-frame0
On medication prior to pre-treatment time point, dropped medication prior to post-treatment time point
1Started medication at or after pre-treatment time point, but dropped prior to post-treatment time point
2Started medication at or after pre-treatment time point and complied through post-treatment time point
3
On medication prior to pre-treatment time point and complied to post-treatment time point4
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Variation in Fibrate Response
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Fibrate Common Variant Analysis
FCRL5
PRRX1SYN2
PTPRDOSTF1,NMRK1
LHFP
FGF14LOC102724939
MRPL2
SLC25A10LDLR GMIP, ATP13A1,
SNF101,ZNF101
LDLAll Races Combined
(n=1261)
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Fibrate Common Variant Analysis
IGSF21 NRXN1
MYT1L
GPR75-ASB3, ERLEC1
FOXP1
ROBO1
NLGN1, PEX5L
SORCS2
IPO11, IPO11-LRRC70
GRIA1OFCC1
ARHGEF5
CLN8
FRMD3
BICC1, LOC102724768, FAM13C
TTC5
RIN3 SMAD3
LOC102724716, DLGAP1 NFIC
LDLOnly Black Subjects
(n=137)
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Fibrate Common Variant Analysis
MAU2, GATAD2A, PBX4, GMIP, ATP13A1
LDLOnly White Subjects
(n=779)
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Rare Variant Analysis
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TMEM210
DUSP3DCUN1D4
RAB27B
EVA1C
HES2
LOC101927763
LOC101928772
IDE
NHLRC1
CREG2MICU1
CDH6
TriglyceridesAll Races Combined
(n=1261)
24
Functional Validation
Tested for significant changes in gene expression in mice treated with fenofibrate for 14 days vs vehicle.
Tested overlapping common variants (p < 1x10-6) and rare variants (q< .05).
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Functional Validation
Analysis
Entrez IDGene
SymbolGeneName
log2(fold change)
P value Q value
Common Variant
17127 s24166 Smad3
SMAD family member 3 (Mothers against
decapentaplegic homolog 3)
-2.231320.00288
40.10383
Rare Variant
100737 s13991 Dcun1d4
DCN1, defective in cullin neddylation 1, domain
containing 4 (S. cerevisiae)
1.4682730.01335
20.19161
1
Common Variant
170759 s02971 Atp13a1 ATPase type 13A1 -1.57890.01596
80.19161
1Common Variant
76582 s20442 Ipo11 importin 11 -1.8763 0.022410.20169
3Rare
Variant80718 s01094 Rab27b
RAB27b, member RAS oncogene family
-1.364060.03306
60.23807
2
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SMAD3
o Smad3-KO mice had lower plasma free fatty acid and glycerol, reduced adiposity.
o Was shown to alter regulation PPARγ and PPARβ.
Receptor-regulated SMAD that is an intracellular signal transducer and transcriptional modulator activated by TGF-beta (transforming growth factor) and activin type 1 receptor kinases. Binds the TRE element in the promoter region of many genes that are regulated by TGF-beta and, on formation of the SMAD3/SMAD4 complex, activates transcription. http://www.uniprot.org/uniprot/P84022
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SMAD3
o PPARα inhibits TGF-β
o TGF-β has been shown to regulate Smad 2, 3, and 4 transcription factors
o These pathways are clearly convoluted and have not yet been fully elucidated.
o There does appear to be the potential for an indirect interaction between fenofibrate and SMAD3.
o Downstream activity from PPARα may be altered due to rare variants in the SMAD3 gene, but much more work is needed to draw conclusions.
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Next Steps
o Additional validation in mice fed high-fat diets
o Collaborating with other lipid consortiums to investigate meta-analysis of RV
o Additional functional follow-up will probably be necessary (e.g. knockouts, forced expression)
o We are also exploring many other drugs related to diabetes and dyslipidemia.
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AcknowledgmentsNorth Carolina State UniversityAlison Motsinger-ReifSkylar MarvelChris SmithHillary Graham
UNC-Chapel HillMichael WagnerJohn BuseTammy HavenerSantica Marcovina
Moffitt Cancer CenterHoward McLeod
University of Kentucky-LexingtonGreg GrafSonja PijutXiaoxi LiuJingjing LiuShuang Liang
The ACCORD/ACCORDION Investigators
Joslin Diabetes Center and Harvard Medical SchoolAlessandro DoriaHetal ShahHe GaoJan SkupienMario-Luca MorieriChristine Mendonca
University of VirginiaJosyf Mychaleckyi
Harvard School of Public HealthPeter Kraft
Funding: This research was supported by an NHLBI grant to the University of North Carolina at Chapel Hill (5R01HL110380-04).
Bellvitge Biomedical Research Institute, Barcelona, SpainAgatha SchlüterStephane FourcadeAurora Pujol
University of Alabama-BirminghamStella Aslibekyan
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32
Genetic Associations with Baseline Lipids
o The following covariates were forced into the linear model:o Trial armo Ageo Biguanideo Cardiovascular diseaseo Fibrate useo Gendero Insulino PC-1-3o Statino Years diabeteso Others…
o The following covariates were selected into the linear model:o Systolic blood pressureo HbA1co GFRo Smokingo Others…
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Genetic Associations with Baseline Lipids
o Covariate correlation matrix. Screat, dbp and waist_cm were removed from the lists of covariates due to correlations with gfr, sbp and bmi, respectively.
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Genetic Associations with Baseline Lipids
o Residuals from linear regressions of the phenotypes against the pruned covariates.
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Fibrate-HDLFibrate
Placebo
N=1261
N=1183
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Fibrate-HDLFibrate Placebo
N=1261 N=1183
37
Fibrate-HDL N=1261
LOC101928059, DENND4B
CAMTA1 ARPC2, LOC101928487
CSN1S2AP
AFAP1ZDHHC14
DPP6LOH12CR1, LOH12CR2
LINC00457
NBEA CYP4F22
CYP4F12
38
Placebo-HDL N=1183
RPS6KA2
STK33 AQRSV2B
ACACA
LOC284294
Fibrate-all races
Placebo-all races
Rare Variant Analysis-HDLRHOQ
LRRC20
IMPACT
SMAD6
PTGER4
DMRT1
SYMPK
LURAP1
MRPL18
MTHFD2
CHRNE
TMEM9B
NUF2
ITIH1
MB21D2
NUDT8
40
Fibrate-LDL Fibrate
Placebo
N=1261
N=1183
41
Fibrate-LDLFibrate Placebo
N=1261 N=1183
42
Placebo-LDL N=1183
TOMM40
43
Fibrate-all races
Placebo-all races
Rare Variant Analysis-LDL
ASIC5
E2F6POM121LS
OR5P2HSPB8
HMX1
MUC3A
44
Fibrate-Triglycerides
Fibrate
Placebo
N=1261
N=1183
45
Fibrate-Triglycerides
Fibrate Placebo
N=1261 N=1183
46
Fibrate-Triglycerides
N=1261
XPR1
ATAD2BTOX CEP55,
FFAR4 BEST3 PGAM1P5ITGBL1
GALNT16RPGRIP1L, FTO
47
Placebo-Triglycerides N=1183
HCN2TTC28
48
Fibrate-all races
Placebo-all races
Rare Variant Analysis-Triglycerides
TMEM210
SNRNP40
ATG16L1
ZBTB5
THG1L
HHIPL1
ANKRD1
DHH
LOC101928218SMIM13
DUSP3DCUN1D4
RAB27B
EVA1C
HES2
LOC101927763
LOC101928772
IDE
NHLRC1
CREG2MICU1
CDH6
C8orf82
MND1
CX3CL1
SPAG9
HHIPL1C16ORF70
RNF111
CAMK2D
49
Fibrate-Cholesterol Fibrate
Placebo
N=1261
N=1183
50
Fibrate-Cholesterol
Fibrate Placebo
N=1261 N=1183
51
Fibrate-Cholesterol
N=1261
PRRX1
IL12RB2
FBXL7OFCC1 ZNF775
OSTF1, NMRK1
LINC00539,MIPEPP3
FGF14
SERPINA13P
MIR3185, HOXB13, PRAC2
MRPL12SLC25A10
GMIP, ATP13A1
ZNF101
52
Placebo-CholesterolN=1183
DDX46
53
Fibrate-all races
Placebo-all races
Rare Variant Analysis-Cholesterol
ASIC5
NMUFAM110B
KRTAP10-1
ALDH1A2
NKAIN4
MUC3A
TMEM210
ZNF469
PAQR8
RBM15B
54
Fibrate-HDLBlack
White
N=779
N=137
55
Fibrate-HDLBlack White
N=779
N=137
56
Fibrate-HDLBlack
N=137
MSH3
LOC101927640
LOC102723892, CARD11
KMT2C
LOC101928077
SORBS1
STX8
LOC101927369, CCL3
CELSR1
CARD10
57
Fibrate-HDLWhite
N=779
ASTN2
White-Fibrate
Black-Fibrate
Rare Variant Analysis by Race-HDL HSD17B13
RAE1PTPN3
HARS2
59
Fibrate-LDLBlack
White
N=779
N=137
60
Fibrate-LDLBlack White
N=779
N=137
White-Fibrate
Black-Fibrate
Rare Variant Analysis by Race-LDL
62
Fibrate-Triglycerides Black
White
N=779
N=137
63
Fibrate-Triglycerides
Black White
N=779
N=137
64
Fibrate-Triglycerides
Black
N=137
PARVB
ZYG11BLINC00624
SYT14
BCL9
LOC100507073
HECW2
ADAMTS9, ROBO2
LINC01182CCDC149
LOC101927145, ADGRL3-AS1
ARHGAP26
BLOC1S5-TXNDC5, EEF1E1-BLOC1S5, BLOC1S5
LRFN2
GLIS3LINGO2
BPIFA1
SNHG17, LOC101928356
65
Fibrate-TriglyceridesWhite
N=779
CSMD3
RPGRIP1L
White-Fibrate
Black-Fibrate
Rare Variant Analysis by Race-Triglycerides
HSD17B13
MARCH3
OCSTAMP
POGZ
HARS2
67
Fibrate-Cholesterol Black
White
N=137
N=779
68
Fibrate-Cholesterol
Black White
N=137
N=779
69
Fibrate-Cholesterol
Black
N=137
NRXN1LRRFIP1
NLGN1, PEX5L
SCGN
CLN8 BICC1
RBM19 SMAD3
70
Fibrate-Cholesterol
White
N=779
PBX4
White-Fibrate
Black-Fibrate
Rare Variant Analysis by Race-Cholesterol
C9orf92DNAJB9
COL14A1
AKR7A3