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LEE SE et al.

REVIEW

Circulation Journal Official Journal of the Japanese Circulation Society http://www. j-circ.or.jp

Unraveling New Therapeutic Targets of Coronary Artery Disease by Genetic Approaches Sang Eun Lee, MD, PhD; Hyo-Soo Kim, MD, PhD

Coronary artery disease (CAD) is the most common cause of death and physical disabilities in developed countries, even though efforts to identify and target causal factors such as hypertension and dyslipidemia have brought tremendous improvements in prevention and treatment. A rapid advance in technology has unraveled new genetic variants associated with CAD and also provided great opportunities to identify novel pathogenic mechanisms and to develop new drugs with higher specificity. Whole-genome sequencing and whole-exome sequencing has made it possible to find rare alleles that are responsible for CAD in small, affected families and case-control studies in a very efficient manner. At present, genome-wide association studies have identified more than 50 loci that explain approximately 10% of the heritability of CAD, most of which is unrelated to traditional risk factors. Mendelian randomization studies enable identification of causal factors among numerous biomarkers and to narrow down promising therapeutic targets. This review highlights new genetic approaches and demonstrates the extent to which the outcome contributes to the finding of new therapeutic targets.   (Circ J 2015; 79: 8 – 14) Key Words: Coronary artery disease; Genetics; Genome-wide association studies; Mendelian randomization; Whole-genome sequencing

C

oronary artery disease (CAD) is the leading cause of mortality and morbidity worldwide. It is a complex disease resulting from the interplay between multiple genetic variants and environmental factors, and genetic variability explains only a small part of the whole pathogenic mechanism of the disease. Nevertheless, in the goal of detecting new therapeutic targets, reliable identification and thorough understanding of potential causal genetic variants can be crucial. For example, in the case of low-density lipoprotein (LDL) cholesterol and statins, the discovery of mutations affecting the LDL receptor and causing hypercholesterolemia and early-onset myocardial infarction (MI) enabled the use of LDL cholesterol-lowering therapies that remarkably reduce the risk of cardiovascular events.1 In 2003, a finding that gain-of-function mutations in the proprotein, converase stubilisin/kexin9 (PCSK9) is a causal event in 2 families with autosomal dominant hypercholesterolemia turned out to have a positive correlation with the incidence of CAD.2 PCSK9 was then identified as binding the LDL receptor for degradation and reducing the capacity of the liver to bind and remove LDL cholesterol.3,4 Subsequent studies revealed that some patients with PCSK9 loss-of-function mutations had a low LDL cholesterol level and a reduced incidence of CAD.5–8 These study results raised the possibility that pharmacologic inhibition of PCSK9 might lower the LDL cholesterol level in patients with hypercholesterolemia. In fact, recent clinical trials proved that a human monoclonal antibody to PCSK9 resulted in a significant reduction of LDL cholesterol levels in patients.9–11

The results from genetic studies can be applied to further research to improve disease risk prediction, pharmacogenomics and identifying new therapeutic targets. This review will mainly deal with the therapeutic aspect: to be more specific, how recent technical advances in the study of genetics help identify novel causative biochemical pathways and therapeutic targets in CAD.

Whole-Genome and Whole-Exome Sequencing in Linkage Analyses According to the pattern of inheritance, any genetic disease can be divided into 2 classes. A monogenic or Mendelian disease is caused by mutations in 1 gene and follows Mendel’s law of inheritance, whereas a polygenic or complex disease is characterized by complex patterns of inheritance caused by interaction between multiple genetic variants and environmental factors. The latter type of CAD is common; however, there are a few cases of CAD showing a monogenic inheritance pattern and identification of genes associated with this monogenic disease has led to breakthrough understanding of the mechanism of the disease. A representative example is the Nobel Prize-winning discovery that mutations affecting the LDL receptor also cause hypercholesterolemia and early-onset ischemic heart disease (IHD).1 Traditionally, linkage analysis (parametric) was adopted for Mendelian disease and gave researchers a powerful technique for mapping the location of disease-causing loci by identifying genetic markers that are co-inherited with a phenotype of inter-

Received September 10, 2014; revised manuscript received November 11, 2014; accepted November 12, 2014; released online December 12, 2014 Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea Mailing address:  Sang Eun Lee, MD, PhD, Assistant Professor, Department of Internal Medicine/Cardiology, Seoul National University College of Medicine, 101 Daehak-ro, Chongno-gu, Seoul 110-744, Korea.   E-mail: [email protected] ISSN-1346-9843  doi: 10.1253/circj.CJ-14-0985 All rights are reserved to the Japanese Circulation Society. For permissions, please e-mail: [email protected] Circulation Journal  Vol.79, January 2015

Advances in Genetic Therapy for CAD

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est (segregation).12 First, a particular region within the genome responsible for a disease of interest must be identified using DNA markers, then a chromosomal region that segregates with the disease can be pinpointed to identify the causal gene and its variant. This type of study also successfully identified the association between ALOX5AP and coronary and cerebrovascular disease,13 between MEF2A and CAD,14 and between genetic variation of PCSK9 and cholesterol metabolism.15 Although previous linkage analyses in affected families followed the sequence of finding the regions first and then identifying the causal gene and the mutation by fine-mapping (positional cloning), so-called next-generation sequencing (NGS) now allows direct screening of the candidate variants and performance of linkage analyses. The NGS technologies are based on parallel sequencing of millions of DNA strands simultaneously. It dramatically decreases the cost and time to sequence the entire human genome. Less than $1,000 and 1 week is all that is needed to sequence a whole genome. Compared with wholegenome sequencing, whole-exome sequencing focuses on an alignment of exons, collectively called ‘the exome’, which constitutes only approximately 1% of the human genome but harbors 85% of occurring mutations that affect disease-related traits.16,17 Detailed technical review of the existing platforms can be found elsewhere.18 A major advantage of these approaches is that less family members are required to have their genome sequenced to identify a particular genetic variation linked to a

disease phenotype compared with conventional linkage analysis with positional cloning.19–21 Using whole-exome sequencing and linkage analysis, Erdmann et al22 identified 2 heterozygous mutations in 2 functionally related genes, GUCY1A3 and CCT7, which impair nitric oxide signaling in an extended MI family. Only 3 distantly related members of the affected family underwent whole-exome sequencing and it was enough to screen out 4 candidate variants. Another study by Keramati et al also used the same method to identify a founder mutation of DYRK1B in 3 large families with coinheritance of early-onset CAD, central obesity, hypertension, and diabetes.23 These pioneering studies highlight the opportunities of applying NGS to severely affected families to identify new mechanism of the disease and thereby new therapeutic targets.

Genome-Wide Association Studies and WholeExome Sequencing in Association Study Performing a genome-wide association study (GWAS) is a genetic approach to a common disease based on a ‘common disease-common variant hypothesis’ that complex diseases result from cumulative and interactive effects of a large number of loci, each imparting a modest marginal effect to the expression of a phenotype.21 It compares the frequency of repeatedly appearing genetic variants in case-control studies and if sig-

Table 1.  Genetic Loci Associated With Coronary Artery Disease in Genome-Wide Association Studies SNP

Related gene(s)

Risk allele

OR (95% CI)

1p13.3

Chromosomal location

rs599839

SORT1

A

1.29 (1.18–1.40)

P value 4×10−9

1p13.3

rs602633

SORT1

T

1.11 (1.08–1.15)

1×10−8

1p13.3

rs646776

CELSR2, PSRC1, SORT1

T

1.19 (1.13–1.26)

8×10−12

1p32.2

rs17114036

PPAP2B

A

1.17 (1.13–1.22)

4×10−19

1p32.3

rs11206510

PCSK9

T

1.15 (1.10–1.21)

1×10−8 4×10−8

1q21.3

rs4845625

IL6R

T

1.04 (1.02–1.07)

1q41

rs17465637

MIA3

C

1.14 (1.10–1.19)

1×10−9

2p11.2

rs1561198

VAMP5-VAMP8-GGCX

A

1.05 (1.03–1.07)

4×10−9

2p21

rs6544713

ABCG5-ABCG8

T

1.06 (1.04–1.09)

9×10−10

2p24.1

rs2123536

TTC32-WDR35

T

1.12 (1.08–1.16)

7×10−11

2p24.1

rs515135

APOB

G

1.08 (1.05–1.11)

5×10−10

2q22.3

rs2252641

ZEB2-AC074093.1

G

1.04 (1.02–1.06)

4×10−8

2q33.2

rs6725887

WDR12

C

1.17 (1.11–1.23)

1×10−8

3q22.3

rs2306374

MRAS

C

1.12 (1.07–1.16)

3×10−8

4q31.22

rs1878406

EDNRA

T

1.06 (1.02–1.11)

3×10−8

4q32.1

rs7692387

GUCY1A3

G

1.06 (1.03–1.09)

5×10−9

4q32.1

rs1842896

GUCY1A3

T

1.14 (1.10–1.19)

1×10−11

5p15.33

rs11748327

IRX1-LINC01020

N/A

1.25 (1.18–1.33)

5×10−13

5q31.1

rs273909

SLC22A4-SLC22A5

C

1.09 (1.05–1.12)

1×10−8

6p21.2

rs10947789

KCNK5

T

1.06 (1.03–1.08)

2×10−8

6p21.31

rs17609940

ANKS1A

G

1.07 (1.05–1.10)

1×10−8

6p21.32

rs9268402

C6orf10-BTNL2

G

1.16 (1.12–1.20)

3×10−15

6p21.33

rs3869109

HLA-C, HLA-B, HCG27

G

1.14 (N/A)

1×10−9

6p24.1

rs12526453

PHACTR1

C

1.12 (1.08–1.17)

1×10−9

6p24.1

rs9369640

PHACTR1

C

1.10 (1.08–1.14)

3×10−11

6p24.1

rs9349379

PHACTR1

G

1.15 (1.10–1.21)

2×10−9

6p24.1

rs6903956

C6orf105

A

1.65 (1.44–1.90)

3×10−13

6q23.2

rs12190287

TCF21

C

1.08 (1.06–1.10)

1×10−12

6q25.3

rs3798220

LPA

C

1.51 (1.33–1.70)

3×10−11

6q26

rs4252120

PLG

T

1.06 (1.03–1.09)

5×10−9

(Table 1 continued the next page.) Circulation Journal  Vol.79, January 2015

10

LEE SE et al. SNP

Related gene(s)

Risk allele

OR (95% CI)

P value

7p21.1

Chromosomal location

rs2023938

HDAC9

G

1.07 (1.04–1.11)

5×10−8

7q22.3

rs10953541

BCAP29

C

1.08 (1.05–1.11)

3×10−8

7q32.2

rs11556924

ZC3HC1

C

1.09 (1.07–1.12)

9×10−18 5×10−9

8p21.3

rs264

LPL

G

1.05 (1.02–1.08)

8q24.13

rs2954029

TRIB1

A

1.04 (1.02–1.06)

5×10−8

9p21.3

rs1333049

CDKN2A, CDKN2B

C

1.37 (1.26–1.48)

2×10−14

9p21.3

rs10757274

CDKN2A, CDKN2B

G

1.37 (1.31–1.43)

8×10−45

9q34.2

rs579459

ABO

C

1.10 (1.07–1.13)

4×10−14

10p11.23

rs2505083

KIAA1462

C

1.07 (1.04–1.09)

4×10−8

10q11.21

rs1746048

CXCL12

C

1.17 (1.11–1.24)

7×10−9

10q23.31

rs1412444

LIPA

T

1.09 (1.07–1.12)

3×10−13

10q24.32

rs12413409

CYP17A1, CNNM2, NT5C2

G

1.12 (1.08–1.16)

1×10−9

11q22.3

rs974819

PDGFD

T

1.07 (1.04–1.09)

2×10−9

11q23.3

rs964184

ZNF259, APOA5-A4-C3-A1

G

1.13 (1.10–1.16)

1×10−17

12q21.33

rs7136259

ATP2B1

T

1.11 (1.08–1.15)

6×10−10

12q24.11

rs3782889

MYL2

C

1.26 (1.19–1.34)

4×10−14

12q24.12

rs3184504

SH2B3

T

1.13 (1.08–1.18)

9×10−8

12q24.12

rs11066015

ACAD10, ALDH2, C12orf51, RPL6-PTPN11

A

1.41 (1.27–1.56)

5×10−11

12q24.13

rs11066280

C12orf51

1.19 (1.13–1.25)

2×10−11

13q12.3

rs9319428

FLT1

A

1.05 (1.03–1.08)

1×10−8

13q34

rs4773144

COL4A1, COL4A2

G

1.07 (1.05–1.09)

4×10−9

14q32.2

rs2895811

HHIPL1

C

1.07 (1.05–1.10)

1×10−10

15q25.1

rs3825807

ADAMTS7

A

1.08 (1.06–1.10)

1×10−12

15q26.1

rs17514846

FURIN-FES

A

1.05 (1.03–1.08)

4×10−10

17p11.2

rs12936587

RASD1, SMCR3, PEMT

G

1.07 (1.05–1.09)

4×10−10

17p13.3

rs216172

SMG6, SRR

C

1.07 (1.05–1.09)

1×10−9

17q21.32

rs46522

UBE2Z, GIP, ATP5G1, SNF8

T

1.06 (1.04–1.08)

2×10−8

19p13.2

rs1122608

LDLR, SMARCA4

G

1.15 (1.10–1.20)

2×10−9

19q13.32

rs2075650

APOE/TOMM40

G

1.14 (1.09–1.19)

3×10−8

21q22.11

rs9982601

KCNE2

T

1.20 (1.14–1.27)

6×10−11

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Circulation Journal  Vol.79, January 2015