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

9

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

13

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).

14

Rare Genetic Associations with Baseline Lipids Total Cholesterol LDL

HDL Triglycerides

15

Rare Genetic Associations with Baseline Lipids

16

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

20

Fibrate Common Variant Analysis

FCRL5

PRRX1SYN2

PTPRDOSTF1,NMRK1

LHFP

FGF14LOC102724939

MRPL2

SLC25A10LDLR GMIP, ATP13A1,

SNF101,ZNF101

LDLAll Races Combined

(n=1261)

21

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)

22

Fibrate Common Variant Analysis

MAU2, GATAD2A, PBX4, GMIP, ATP13A1

LDLOnly White Subjects

(n=779)

23

Rare Variant Analysis

23

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).

25

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

26

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

27

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.

29

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

30

Questions?

daniel.rotroff@ncsu.edu

31

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…

33

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.

34

Genetic Associations with Baseline Lipids

o Residuals from linear regressions of the phenotypes against the pruned covariates.

35

Fibrate-HDLFibrate

Placebo

N=1261

N=1183

36

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