Open Access

Multiplex SNaPshot—a new simple and efficient CYP2D6 and ADRB1 genotyping method

  • Songtao Ben1,
  • Rhonda M. Cooper-DeHoff2,
  • Hanna K. Flaten3,
  • Oghenero Evero3,
  • Tracey M. Ferrara1,
  • Richard A. Spritz1 and
  • Andrew A. Monte3, 4Email author
Human Genomics201610:11

https://doi.org/10.1186/s40246-016-0073-3

Received: 11 March 2016

Accepted: 12 April 2016

Published: 23 April 2016

Abstract

Background

Reliable, inexpensive, high-throughput genotyping methods are required for clinical trials. Traditional assays require numerous enzyme digestions or are too expensive for large sample volumes. Our objective was to develop an inexpensive, efficient, and reliable assay for CYP2D6 and ADRB1 accounting for numerous polymorphisms including gene duplications.

Materials and methods

We utilized the multiplex SNaPshot® custom genotype method to genotype CYP2D6 and ADRB1. We compared the method to reference standards genotyped using the Taqman Copy Number Variant Assay followed by pyrosequencing quantification and determined assigned genotype concordance.

Results

We genotyped 119 subjects. Seven (5.9 %) were found to be CYP2D6 poor metabolizers (PMs), 18 (15.1 %) intermediate metabolizers (IMs), 89 (74.8 %) extensive metabolizers (EMs), and 5 (4.2 %) ultra-rapid metabolizers (UMs). We genotyped two variants in the β1-adrenoreceptor, rs1801253 (Gly389Arg) and rs1801252 (Ser49Gly). The Gly389Arg genotype is Gly/Gly 18 (15.1 %), Gly/Arg 58 (48.7 %), and Arg/Arg 43 (36.1 %). The Ser49Gly genotype is Ser/Ser 82 (68.9 %), Ser/Gly 32 (26.9), and Gly/Gly 5 (4.2 %). The multiplex SNaPshot method was concordant with genotypes in reference samples.

Conclusions

The multiplex SNaPshot method allows for specific and accurate detection of CYP2D6 genotypes and ADRB1 genotypes and haplotypes. This platform is simple and efficient and suited for high throughput.

Keywords

ADRB1 CYP2D6 Copy number variation Gene deletion Genotyping Personalized medicine Precision medicine

Background

Cytochrome P450 family 2 subfamily D member 6 (CYP2D6) is one of the most important drug-metabolizing enzymes expressed in humans. The enzyme metabolizes 20–30 % of all xenobiotics including numerous antidepressants, antipsychotics, anti-emetics, analgesics, and cardiovascular medications. The expression of CYP2D6 is highly polymorphic; more than 70 allelic variants have been identified, and deletion or duplication of the gene leads to variable enzyme function in individual patients [1, 2]. The CYP2D6 gene has been mapped to chromosome 22q13.1 and consists of nine exons with an open reading frame of 1491 base pairs coding for 497 amino acids [25]. Variability in enzyme expression has been associated with altered drug effectiveness and safety [2]. In fact, there are four functional genotype groups identified for the enzyme, poor metabolizers (PMs), intermediate metabolizers (IMs), extensive metabolizers (EMs), and ultra-rapid metabolizers (UMs) based upon the number and activity of the gene copies the patients express (Table 1) [1, 6]. Therefore, many researchers and clinicians have targeted genotyping CYP2D6 in efforts to improve drug therapy.
Table 1

Predicted CYP2D6 enzyme function based upon the activity score derived from SNV identification

Predicted CYP2D6 enzyme function

Activity score

Poor metabolizer (PM)

0

Intermediate metabolizer (IM)

0.5

Extensive metabolizer (EM)

1.0–2.0

Ultra-rapid metabolizer (UM)

>2.0

Some genes have partial functionality based upon in vitro enzyme activity for specific substrates. This table is adapted from Crews et al. and Gaedigk et al. [1, 6]

Expanding on previously published work [79], we sought to determine the effectiveness and safety of metoprolol succinate utilizing a systems biology approach, whereby we genotyped the drug-metabolizing enzyme CYP2D6 as well as the drug target, β1-adrenoreceptor (ADRB1). Additionally, we captured demographic and clinical factors that are necessary to understand the potential for individualized therapy in patients taking metoprolol succinate. Metoprolol is a B1 selective antagonist, known as a beta-blocker. Beta-blockers are first-line treatment for heart failure, hypertension (HTN), angina, and myocardial infarction [1014]. In 2011, 34.5 million prescriptions for metoprolol were written in the USA [15]. Thus, metoprolol is a first-line therapy for several of the most common chronic diseases and is one of the most commonly prescribed drugs in the USA. CYP2D6 is the only clinically pertinent pathway of metoprolol metabolism, and polymorphisms have been associated with altered levels of metoprolol [16, 17]. ADRB1 is the drug target, and polymorphisms in this receptor have been associated with variable drug response [7, 18]. Prediction of drug response with knowledge of one without knowledge of the other could be incomplete since both contribute to the ultimate clinical effect [19]. This highlights the importance of accounting for both factors. Prior investigators have identified imperfect associations with CYP2D6 [16, 17] and ADRB1 [7, 18] genotypes, but no study has been appropriately powered to account for variability in both genotypes [19]. No single assay accounts for variants in both genes. Given that multiple single-nucleotide variants (SNVs) and the presence of gene duplication can affect CYP2D6 and ADRB1, a simple, efficient, and inexpensive method that identifies both SNVs and gene duplication is required to allow for an adequately powered systems biology approach.

CYP2D6 and ADRB1 variants have been identified using long-range polymerase chain reaction (XL-PCR) with PCR restriction fragment length polymorphism (PCR-RFLP) or microarray analysis. Microarray analysis remains expensive for large-scale genotyping. Commercially available tests cost a minimum of $500 per patient. XL-PCR with PCR-RFLP accounts for allele variants, and multiplication is inexpensive, easy, and reliable in patients regardless of ethnicity or race [6]. However, XL-PCR with PCR-RFLP requires numerous enzymatic digestions and multiple amplification steps to identify more than one polymorphism in the CYP2D6 gene. In order to conduct a large-scale clinical trial to determine the effectiveness and safety of metoprolol in treating hypertension, we sought to develop a simple, rapid, yet less expensive assay for CYP2D6 and ADRB1 that accounts for numerous polymorphisms including gene duplications. Drug effectiveness data will be presented at a later date when the trial closes.

Material and methods

Study design and setting

This was a prospective observational clinical trial (NCT02293096) that enrolled patients with uncontrolled HTN from clinics, the emergency department, and across the community at the University of Colorado Hospital in Aurora, Colorado.

Subjects

In accordance with the University of Colorado IRB approval, we enrolled subjects with uncontrolled HTN between 30 and 80 years of age. Exclusion criteria included end-stage liver disease, glomerular filtration rate < 60 ml/min/1.73 m2, pregnancy, American Society of Anesthesiologists (ASA) classification of >3, prisoners or wards of the state, decisionally challenged, heart rate < 60 beats per minute, AV block > 240 ms, active reactive airway disease, illicit drug use in the preceding 30 days (excluding marijuana), allergy to metoprolol succinate, or severe peripheral arterial circulatory disorders.

Drug intervention

Patients were initially managed according to the Eighth Joint National Committee guidelines for management of HTN [20]. Patients were started on angiotensin-converting enzyme inhibitor or angiotensin receptor blockers as first-line therapy. If blood pressure remained uncontrolled, then metoprolol succinate was added. Patients were then followed up and metoprolol titrated weekly for 4 weeks. Drug effectiveness data will be presented at a later date.

DNA isolation

Genomic DNA was extracted from whole blood via the Puregene® Blood Core kit B (Qiagen) according to the manufacturer’s instructions.

Long-range PCR analyses to determine CYP2D6 gene multiplication

We analyzed CYP2D6 duplication using long-range PCR with the primers described by Lovlie et al. [21]. This primer combination amplifies a 5.2-kb PCR fragment from the CYP2D7-CYP2D6 intergenic regions in all individuals and a 3.6-kb PCR fragment from the CYP2D6-CYP2D6 region in individuals with a duplication of the gene [22]. The amplification was done using the Phusion High-Fidelity DNA Polymerase kit (New England Biolabs). The reaction components included 2 μl of 5× Phusion HF buffer, 0.2 μl of 25 mM dNTPs, 0.5 μl of 10 μM/μl forward primer, 0.5 μl of 10 μM/μl reverse primer, 1 μl of template DNA, 0.2 μl of Phusion DNA polymerase, 0.3 μl DMSO, 2.5 μl of BETAIN, and 2.8 μl of nuclease-free water. The total reaction volume was 10 μl. Thermal cycling conditions were as follows: 98 °C for 30 s followed by 35 cycles of 98 °C for 10 s, 67 °C for 30 s, and 72 °C for 2 min 30 s. After cycling, samples were stored at 4 °C. PCR products were analyzed by electrophoresis.

Long-range PCR analyses to determine CYP2D6 gene deletion

To detect deletion of the entire CYP2D6 gene, (CYP2D6 *5) we performed a multiplex longer-PCR reaction with the primers described by Okubo et al. [23]. The reaction components included 2 μl of 5× Phusion HF buffer, 0.2 μl of 25 mM dNTPs, 0.2 μl of 10 μM F1 primer, 0.2 μl of 10 μM R2 primer, 2 μl of 10 μM F3 primer, 1 μl of template DNA, 0.2 μl of Phusion DNA polymerase, 2.5 μl of BETAIN, and 1.7 μl of nuclease-free water. The PCR conditions were as follows: initiation at 98 °C for 3 min, 35 cycles of 98 °C for 10 s and 70 °C for 30 s, termination at 72 °C for 2 min 30 s, and a final elongation at 72 °C for 10 min. The PCR products were examined by electrophoresis and include the amplification of a 3.5-kb fragment, which indicates CYP2D6 *5, and a 4.7-kb fragment, which indicates CYP2D6 wild type.

SNV selection

We detected 20 variants associated with altered CYP2D6 enzyme activity. SNVs were chosen because they are representative of major haplotypes associated with altered enzyme function without providing redundancy of rsIDs leading to the same functional activity within the same allele variant (Table 2). If none of these SNVs were identified, the allele designation was defaulted to the reference allele, CYP2D6*1. Haplotype analysis for CYP2D6 was based upon predicted enzyme activity of the SNVs identified in the genotyping stage.
Table 2

CYP2D6 and ADRB1 variant alleles with rsIDs and subsequent effect on the gene sequence

Allele

Major nucleotide variation

SNV

Effect

*1

Presumed

NA

Wild type

*2

2850C>G

rs16947

Arg296Cys

*3

2549delA

rs35742686

Frameshift

*4

100C>T

rs1065852

Pro34Ser

1846G>A

s3892097

Splicing defect

*6

1707delT

rs5030655

Frameshift

*7

2935A>C

rs5030867

His324Pro

*9

2615_2617delAAG

rs5030656

Lys281del

*10

100C>T

rs1065852

Pro34Ser

*12

124G>A

rs5030862

Gly42Arg

*14

1758G>A

rs5030865

Gly169Arg

*17

1023C>T

rs28371706

Thr107Ile

2850C>T

rs16947

Arg296Cys

*19

2539_2542delAACT

rs72549353

255Frameshift

*20

1973_1974insG

rs72549354

211Frameshift

*38

2587_2590delGACT

rs72549351

271Frameshift

*40

1863_1864insTTTCGCCCCX2

rs72549356

174_175insFRP × 2

*41

2850C>T

rs16947

Arg296Cys

2988G>A

rs38371725

Splicing defect

*42

3259_3260insGT

rs72549346

363Frameshift

*49

100C>T

rs1065852

Pro34Ser

1611T>A

rs1135822

Phe120Ile

*69

100C>T

rs1065852

Pro34Ser

2850C>T

rs16947

Arg296Cys

2988G>A

rs38371725

Splicing defect

ADRB 1 Ser49Gly

A>G

rs1801252

Ser49Gly

ADRB 1 Gly389Arg

G>C

rs1801253

Gly389Arg

Additionally, two ADRB1 SNVs were genotyped (Table 2) because haplotypes of these alleles are known to be associated with altered clinical response to metoprolol treatment (Table 3). If neither of these SNVs were identified, the allele was defaulted to Ser49 and Gly389, the reference allele.
Table 3

ADRB1 haplotype and associated metoprolol clinical effect

ADRB1 haplotypes

Metoprolol clinical effect

49Ser389Arg/49Ser389Arg

Threefold greater diastolic blood pressure reduction [7, 12]

49Ser389Arg/49Gly389Arg

Good responder [12]

49Ser389Gly/49Gly389Arg and 49Ser389Gly/49Ser389Gly

Non-responders [7, 12]

Genotyping

Genotyping was performed using two separate multiplex reactions. First, we used PCR to amplify a purified template DNA fragment that included the target nucleotide. We designed a total of eight pairs of PCR primers (Table 4) that were divided into two separate pools. The PCR was performed with the AmpliTaq® Gold kit (Applied Biosystems) using a hot start/touchdown PCR assay. The 10 μl reaction included 1 μl of template DNA, 1.6 μl of 10× GeneAmp® PCR Buffer, 0.15 μl of 25 mM/each dNTPs, 0.4 μl of 1 μM/each primer mix, 2.15 μl of 50 mM MgCl2, 0.15 μl of 5 U/μl AmpliTaq Gold, and 4.55 μl of nuclease-free water. Thermal cycling conditions were as follows: 94 °C for 5 min followed by 15 cycles of 94 °C for 30 s; annealing temperature steps down every cycle by 0.5 °C (from 63 to 56.5 °C) every 30 s and then 72 °C for 1 min. The annealing temperature for the final 25 cycles was 56 °C with a denaturation temperature of 94 °C for 30 s and extension temperature of 72 °C for 1 min. After PCR, the products were first treated with shrimp alkaline phosphatase (SAP) and Exonuclease Ι (Exo Ι) to remove excess primers and dNTPs. Two units of SAP and 1 unit of Exo Ι were added to 5 μl of PCR product and incubated at 37 °C for 30 min and then 80 °C for 15 min. Second, we performed multiplex single-base extraction (SBE) reactions. We designed 19 SBE primers and divided the reactions into two pools. See Table 5. Each SBE reaction was carried out in a 10 μl final volume containing 5 μl of SNaPshot Multiplex Ready Reaction Mix, 2 μl of pooled PCR products, 1 μl of pooled SNaPshot primers (0.3 μmol/each μl), and 2 μl of deionized water. Extension was performed for 25 cycles under the following conditions: 96 °C for 10 s, 50 °C for 5 s, and 60 °C for 30 s. To remove ddNTPs, the SBE reactions were then treated with 1.0 unit of calf-intestinal phosphatase (CIP) and incubated at 37 °C for 30 min. The enzyme was deactivated by incubating at 80 °C for 15 min. The samples were run by electrophoresis on the 3130 Genetic Analyzer (Applied Biosystems). The subsequent data were analyzed with GeneScan software and GeneScan-120 LIZ size standard.
Table 4

The oligonucleotide PCR primers used for amplification of genomic DNA to obtain template

Multiplex pool

Fragment name

Sequence 5′ → 3′

Length (bases)

Product size (bp)

SNV

1

P1PCR1

agcccggtaacctgtcgt

18

162

rs1801252

ccatcagcagacccatgc

18

  

P1PCR2

tggaggaggtcaggcttaca

20

342

rs16947

ggtgcagaattggaggtcat

20

 

rs5030867

   

rs28371725

P1PCR3

gtgtggtggcattgaggact

20

332

rs72549346

gtggggacgcatgtctgt

18

  

P1PCR4

gatgcactggtccaaccttt

20

223

rs35742686

ctggtgtaggtgctgaatgc

20

 

rs5030656

   

rs72549353

   

rs72549351

2

P2PCR1

gccttcaaccccatcatcta

20

328

rs1801253

ggccctacaccttggattc

19

  

P2PCR2

ctcacctggtcgaagcagta

20

145

rs1065852

ccatcttcctgctcctggt

19

 

rs5030862

P2PCR3

cagctcggactacggtcatc

20

272

rs28371706

cttgacaagaggccctgacc

20

  

P2PCR4

gtcctttcccaaacccatct

20

562

rs3892097

gtggggctaatgccttcat

19

 

rs5030655

   

rs5030865

   

rs72549354

   

rs72549356

   

rs1135822

Table 5

The oligonucleotide primers for SNaPshot primer extension reactions

Multiplex pool

SNV

Primer direction

Peak to SNV correspondence

Primer length

Primer sequence (5′ → 3′)

1

rs28371725C/T

F(C/T)

C=C T=T

16

CCCCGCCTGTACCCTT

rs1801252A/G

F(A/G)

A=A G=G

18

GACTCTCCCGCCAGCGAA

rs72549346-/AC

R(T/G)

T=AC C=-

30

GACTGACTGACTGCCGTGATTCATGAGGTG

rs16947A/G

F(A/G)

A=A G=G

40

GACTGACTGACTGACTGACTGAGGTCAGCCACCACTATGC

rs5030656-/CTT

F(T/C)

T=CTT C=-

40

GACTGACTGACTGACTGACTGATGGCAGCCACTCTCACCT

rs72549351-/AGTC

R(C/G)

C=AGTC G=-

46

GACTGACTGACTGACTGACTGACTGACTGACCCCCCCGAGACCTGA

rs72549353-/AGTT

F(T/A)

T=AGTT A=-

46

GACTGACTGACTGACTGACTGACTGACCAGGTCATCCTGTGCTCAG

rs35742686-/T

F(T/G)

T=T G=-

52

GACTGACTGACTGACTGACTGACTGACTGACTGACTGGGTCCCAGGTCATCC

rs5030867T/G

R(A/C)

A=T C=G

52

GACTGACTGACTGACTGACTGACTGACTGACTCCTCCTGCTCATGATCCTAC

2

rs1801253C/G

F(C/G)

C=C G=G

15

CGCAAGGCCTTCCAG

rs72549356-/GGGGCGAAAGGGGCGAAA

R(T/A)

T=GGGGCGAAAGGGGCGAAA A=-

18

GACTGCCCCTTTCGCCCC

rs1065852G/A

R(C/T)

C=G T=A

36

GACTGACTGACTGACTGACTCTGGGCTGCACGCTAC

rs3892097T/C

R(A/G)

A=T G=C

36

GACTGACTGACTGACTGACTGCATCTCCCACCCCCA

rs28371706G/A

R(C/T)

C=G T=A

43

GACTGACTGACTGACTGACTGACTGACTCGCCTGTGCCCATCA

rs5030862T/C

R(A/G)

A=T G=C

43

GACTGACTGACTGACTGACTGACTGACTCCCCTGCCACTGCCC

rs72549354-/C

F(T/C)

T=- C=C

47

GACTGACTGACTGACTGACTGACTGACTCGACTCCTCCTTCAGTCCC

rs5030655-/A

R(T/G)

T=T G=-

51

GACTGACTGACTGACTGACTGACTGACTGACTCAAGAAGTCGCTGGAGCAG

rs1135822A/T

F(A/T)

A=A T=T

55

GACTGACTGACTGACTGACTGACTGACTGACTGACTGACTCATAGCGCGCCAGGA

rs5030865A/C/T

F(A/C/T)

A=A C=C T=T

59

TGACTGACTGACTGACTGACTGACTGACTGACTGACTGACTCTTCTGCCCATCACCCAC

CYP2D6 multiple-copy allele determination

The SNaPshot Multiplex System (Applied Biosystems) is a primer extension-based method developed for the analysis of SNVs. Theoretically, with the same SNV peak in the same reacting system with the same reaction conditions, a comparatively higher density means more DNA copies, and the ratios of any two peak densities of the same sample are relatively inflexible. We identified the copy numbers and duplicated/multiplicated allele by the ratio of the peak densities. In each pool, we designed one primer to identify an ADRB1 gene’s SNV. ADRB1 is a single-copy gene which allowed us to use the peaks of ADRB1 gene’s SNVs as the inner standard peaks. We calculated the ratios of the peak density of CYP2D6 SNVs to the peak density of the ADRB1 SNV. We set the reliability value range of the ratios of each SNV based on the values of the ratio of single-copy control samples with the same SNVs. If a ratio of a SNV of a known duplicated sample was greater than the maximum of the reference reliability range, this SNV was designated a duplication. We identified the copy number based upon that value. Based on published results and the values of the ratios, we assume all duplicated samples have the total copy number of three since this was the highest number of copy number variants (CNVs) in our controls.

Activity scoring and predicted phenotype assignment

Each identified CYP2D6 SNV was assigned a predicted enzyme activity score [1, 6]. Gene deletions were designated as an activity score of zero. The predicted enzyme phenotype was determined by addition of the individual gene activity scores, accounting for gene copies yielding decreased enzyme activity and gene duplications in each patient. A score of 0 was predicted to be a PM, 0.5 was predicted to be IM, 1–2 was predicted to be an EM, and 2.5 or greater was predicted to have a UM phenotype.

Assay verification

Genotypes were confirmed with known reference genotype samples from 5 PMs, 4 IMs, and 24 EMs [8, 24]. Copy number variations were determined by Taqman Copy Number Assay (Life Technologies, CA) and then by pyrosequencing allele quantification in the known samples [24].

Results

Subjects

The demographics on the initial 79 subjects with unknown haplotypes in this cohort were as follows: the median age was 52 (IQR 45, 60), 46 (58.2 %) were males, 14 (19.9 %) were Hispanic/Latino, 37 (46.8 %) were Black or African American, 3 (3.8 %) were American Indian or Alaskan Native, 3 (3.8 %) were of Asian decent, 1 (1.3 %) was Native Hawaiian or Pacific Islander, and 38 (48.1 %) were Caucasian.

Genotypes

Our genotyping method demonstrated consistent results with all 30 reference standards. The CYP2D6 haplotype analysis revealed 7 (5.88 %) CYP2D6 PMs, 18 (15.1 %) IMs, 89 (74.78 %) EMs, and 5 (4.2 %) UMs (Table 6). We also genotyped two variants of ADRB1, rs1801253 (Gly389Arg) and rs1801252 (Ser49Gly). The Gly389Arg genotype is Gly/Gly 18 (15.1 %), Gly/Arg 58 (48.7 %), and Arg/Arg 43 (36.1 %). The Ser49Gly genotype is Ser/Ser 82 (68.9 %), Ser/Gly 32 (26.9), and Gly/Gly 5 (4.2 %). See Table 7. CYP2D6 allele frequencies are shown in Table 8. All genotypes were in the Hardy-Weinberg equilibrium (HWE) after allele designation by SNV identification and CNV determination.

Copy number variants

We identified 13 subjects with CNVs with predicted enzyme activities scores ranging from 0 to 3 (Table 9). An example CNV determination is as follows. One sample had a genotype of *4/*41 and the total gene copy number of three. In this sample, we detected four heterozygous SNVs: rs1065852, rs3892097, rs28371725, and rs16947. The ratio of rs1065852 was 0.92, higher than the maximum of the reference reliability range (0.7–0.85). The ratio of rs3892097 was 0.98, higher than the maximum of the reference reliability range (0.6–0.9). The ratio of rs28371725 was 1.97, lower than the minimum of the reference reliability range (2.4–2.9). The ratio of rs16947 was 1.09, lower than minimum of the reference reliability range (1.28–1.38). All these ratios suggest that the variant allele, *4 was duplicated in this sample. Thus, the genotype is *4xN/*41. Since *4 is a non-functional allele, the activity score is assigned 0, and *41 is a reduced function allele, assigned an activity score of 0.5, even if the *4 is duplicated, the activity score remains 0.5. Hence, the genotype for this sample was designated IM (Table 6).
Table 6

Distribution of CYP2D6 genotypes and phenotypes (n = 119, including 79 unknown subjects and 30 reference subjects)

Genotype

Number of subjects

Frequency (%)

Active score

Predicted phenotype

Phenotype frequency (%)

*1/*2xN

1

0.84

3.0

UM

4.2

*1xN/*2

1

0.84

3.0

UM

4.2

*1/*1xN

1

0.84

3.0

UM

4.2

*2/*2xN

1

0.84

3.0

UM

4.2

*1xN/*2

1

0.84

3.0

UM

4.2

*1/*1

15

12.61

2.0

EM

74.8

*1/*2

15

12.61

2.0

EM

74.8

*1/*3

4

3.36

1.0

EM

74.8

*1/*4

7

5.88

1.0

EM

74.8

*1/*6

2

1.68

1.0

EM

74.8

*1/*10

4

3.36

1.5

EM

74.8

*1/*17

8

6.72

1.5

EM

74.8

*1/*41

7

5.88

1.5

EM

74.8

*2/*2

4

3.36

2.0

EM

74.8

*2/*3

1

0.84

1.0

EM

74.8

*2/*4

2

1.68

1.0

EM

74.8

*2/*5

2

1.68

1.0

EM

74.8

*2/*10

2

1.68

1.5

EM

74.8

*2/*17

6

5.04

1.5

EM

74.8

*2/*40

1

0.84

1.0

EM

74.8

*2/*41

3

3.52

1.5

EM

74.8

*17/*17

1

0.84

1.0

EM

74.8

*1/*4xN

1

0.84

1.0

EM

74.8

*2xN/*12

1

0.84

2.0

EM

74.8

*2/*17xN

2

1.68

2.0

EM

74.8

*1/*5

1

0.84

1.0

EM

74.8

*4/*9

1

0.84

0.5

IM

16

*4/*17

2

1.68

0.5

IM

16

*5/*17

2

1.68

0.5

IM

16

*4/*41

4

3.36

0.5

IM

16

*5/*41

2

1.68

0.5

IM

16

*6/*17

1

0.84

0.5

IM

16

*6/*41

1

0.84

0.5

IM

16

*10/*40

1

0.84

0.5

IM

16

*17/*40

2

1.68

0.5

IM

16

*4xN/*10

1

0.84

0.5

IM

16

*4xN/*41

1

0.84

0.5

IM

16

*3/*4

1

0.84

0.0

PM

5.9

*4/*4

4

3.36

0.0

PM

5.9

*4xN/*5

2

1.68

0.0

PM

5.9

PM poor metabolizer, IM intermediate metabolizer, EM extensive metabolizer, UM ultra-rapid metabolizer

Table 7

ADRB1 genotype

ADRB1 haplotype

Number of subjects

Frequencies (%)

Expected metoprolol clinical effect

49Ser389Arg/49Ser389Arg

30

25.2

Threefold greater diastolic blood pressure reduction [7, 12]

49Ser389Arg/49Gly389Arg

8

6.7

Good responder [12]

49Ser389Gly/49Gly389Arg

22

18.5

Non-responders [7, 12]

49Ser389Gly/49Ser389Gly

16

13.4

Non-responders [7, 12]

49Ser389Gly/49Gly389Gly

2

1.7

Unknown, designated good responders by prior investigators [7, 12]

49Ser389Gly/49Ser389Arg

36

30.3

Good responder [12]

49Gly389Arg/49Gly389Arg

5

4.2

Unknown, designated good responders by prior investigators [7, 12]

Table 8

CYP2D6 Allele frequencies

CYP2D6 allele

Number of subjects

Frequency (%)

*1

81

34

*1xN

3

1.3

*2

45

18.9

*2xN

3

1.3

*3

6

2.5

*4

25

10.5

*4xN

5

2.1

*5

9

3.8

*6

4

1.7

*9

1

0.4

*10

8

3.4

*12

1

0.4

*17

23

9.7

*17xN

2

0.8

*41

18

7.6 %

Table 9

Genotypes, activity score and predicted phenotypes for samples with gene duplications

Genotype (xN) before revision

Activity score

Predicted phenotype before revision

Genotype after revision

Activity score

Predicted phenotype after revision

*1/*2

3

UM

*1/*2xN

3

UM

*4/*41

0.5–1.0

IM OR EM

*4xN/*41

0.5

IM

*1/*2

3

UM

*1xN/*2

3

UM

*1/*1

3

UM

*1/*1xNa

3

UM

*1/*4

1.0–2.0

EM

*1/*4xN

1

EM

*4/*5

0

PM

*4xN/*5

0

PM

*2/*2

3

UM

*2/*2xNa

3

UM

*2/*17

2.0–2.5

UM OR EM

*2/*17xN

2

EM

*4/*10

0.5–1.0

IM

*4xN/*10b

0.5

IM

*1/*2

3

UM

*1xN/*2

3

UM

*2/*17

2.0–2.5

UM OR EM

*2/*17xN

2

EM

*4/*5

0

PM

*4xN/*5

0

PM

*2/*12

1.0–2.0

EM

*2xN/*12

2

EM

aThe number of duplication alleles is far less than single-copy alleles. We assumed the subject was heterozygous for a single-copy allele and the duplicated/multiplicated allele

bNo reference

*4 had a higher distribution or copy number in comparison with *10

Assay verification

Genotyping results for the UM alleles showed a high degree of concordance between the Taqman Copy Number Assay paired with pyrosequencing quantification [24] and our SNaPshot methods. In fact, the only difference between the two assays was the identification of an additional CYP2D6*5 gene deletion with our multiplex longer-PCR method. Therefore, this assay is reliable for CYP2D6 genotyping dependent upon the polymorphisms listed.

Limitations

This method should be validated in an additional cohort with known genotypes to ensure concordance with other haplotype designations. This assay has not been validated to determine more than three CNVs because the control samples did not contain subjects with more than three. However, more than three CNVs of the identified SNVs are universally considered UMs. If additional CNVs of SNVs with associated lower activity scores are found, this may become important for haplotype distinction, depending upon the genotype at the second loci. Additional SNVs with altered predicted enzyme activity will not be captured unless the additional primers are added to the reaction pool. This flexibility of the method is an advantage though the assay is limited by 10 SNVs per pool and identification of allele duplications requires samples to be tested in batches in order to establish reference data. Oversaturation of the assay with additional primers in each pool may lead inconsistent detection of SNVs. Therefore, significantly increasing the number of SNVs identified will require larger volumes of DNA. With the presented assay, less than 60 ng of DNA was necessary to genotype 10 SNVs in these two genes in each sample.

Discussion

We have demonstrated that the SNaPshot method of genotyping CYP2D6 and ADRB1 is reliable and efficient for rapidly identifying numerous SNVs. We demonstrate concordance with known reference standards using this method that requires no enzymatic digestion and can be performed at high volumes. The only difference was an additional identification of a CYP2D6*5 gene deletion not identified by the Taqman method. Less than 60-ng genomic DNA was used for each sample in our assay. The assay is flexible; it would be easy to add additional primers to cover more CYP2D6 SNVs should this be necessary. This customizable assay has advantages given the speed of discovery of CYP SNV identification. As demonstrated by the success with CYP2D6, the multiplex SNaPshot method can be used for designing genotype assays for complex genotyping circumstances rapidly and inexpensively.

This method allows genotyping patients for two genes at only $40 per sample compared to commercially available microarray assays that cost in excess of $500 per sample. While the assay is limited by 10 SNVs per pool, this provided flexibility of the assay to add additional SNVs should they be clinically important. Additional SNVs would require additional DNA, but only 30 ng is required per pool allowing for significant up-scaling of the assay. This method is well suited for pharmacogenes with well-established clinical associations and a finite number of SNVs.

Subjects in our cohort had predicted phenotype frequencies similar to European populations.

Genotypes for CYP2D6 and ADRB1 demonstrate that 25 % may have a gene-gene interaction affecting their metoprolol therapy. Results in our cohort demonstrate a non-responder haplotype in 31.9 % of subjects. Liu et al. demonstrated a non-responder phenotype based upon these same haplotypes in 45.9 % of subjects in that trial [18]. Variability in ethnic proportions may explain this discrepancy though the frequencies are similar. These genes do not co-segregate, requiring knowledge of both genotypes to determine this interaction potential. Paired with clinical outcomes, these gene-gene interactions can be further clarified as the cohort matures. Overall, the multiplex SNaPshot represents a valuable tool for systems biology studies in need of flexible genotyping methods.

Conclusion

Our multiplex SNaPshot protocol allows for specific and accurate detection of CYP2D6 and ADRB1 genotypes. This platform is flexible, simple, efficient, and suited for high throughput.

Declarations

Acknowledgements

None

Disclosures and funding

Dr. Monte receives support from NIH 1 K23 GM110516 and NIH CTSI UL1 TR001082. Dr. Cooper-DeHoff receives funding from NIH U01 GM074492 and U01 HG007269.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Human Medical Genetics Program, University of Colorado
(2)
Center for Pharmacogenomics, College of Pharmacy, University of Florida
(3)
Department of Emergency Medicine, School of Medicine, University of Colorado
(4)
Rocky Mountain Poison & Drug Center, Denver Health and Hospital Authority

References

  1. Crews KR, Gaedigk A, Dunnenberger HM, Klein TE, Shen DD, Callaghan JT, Kharasch ED, Skaar TC, Clinical Pharmacogenetics Implementation C. Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for codeine therapy in the context of cytochrome P450 2D6 (CYP2D6) genotype. Clin Pharmacol Ther. 2012;91:321–6.View ArticlePubMedPubMed CentralGoogle Scholar
  2. Zhou SF. Polymorphism of human cytochrome P450 2D6 and its clinical significance: part I. Clin Pharmacokinet. 2009;48:689–723.View ArticlePubMedGoogle Scholar
  3. Gough AC, Smith CA, Howell SM, Wolf CR, Bryant SP, Spurr NK. Localization of the CYP2D gene locus to human chromosome 22q13.1 by polymerase chain reaction, in situ hybridization, and linkage analysis. Genomics. 1993;15:430–2.View ArticlePubMedGoogle Scholar
  4. Kimura S, Umeno M, Skoda RC, Meyer UA, Gonzalez FJ. The human debrisoquine 4-hydroxylase (CYP2D) locus: sequence and identification of the polymorphic CYP2D6 gene, a related gene, and a pseudogene. Am J Hum Genet. 1989;45:889–904.PubMedPubMed CentralGoogle Scholar
  5. Eichelbaum M, Baur MP, Dengler HJ, Osikowska-Evers BO, Tieves G, Zekorn C, Rittner C. Chromosomal assignment of human cytochrome P-450 (debrisoquine/sparteine type) to chromosome 22. Br J Clin Pharmacol. 1987;23:455–8.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Gaedigk A, Simon SD, Pearce RE, Bradford LD, Kennedy MJ, Leeder JS. The CYP2D6 activity score: translating genotype information into a qualitative measure of phenotype. Clin Pharmacol Ther. 2008;83:234–42.View ArticlePubMedGoogle Scholar
  7. Johnson JA, Zineh I, Puckett BJ, McGorray SP, Yarandi HN, Pauly DF. Beta 1-adrenergic receptor polymorphisms and antihypertensive response to metoprolol. Clin Pharmacol Ther. 2003;74:44–52.View ArticlePubMedGoogle Scholar
  8. Hamadeh IS, Langaee TY, Dwivedi R, Garcia S, Burkley BM, Skaar TC, Chapman AB, Gums JG, Turner ST, Gong Y, Cooper-DeHoff RM, Johnson JA. Impact of CYP2D6 polymorphisms on clinical efficacy and tolerability of metoprolol tartrate. Clin Pharmacol Ther. 2014;96:175–81.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Terra SG, Pauly DF, Lee CR, Patterson JH, Adams KF, Schofield RS, Belgado BS, Hamilton KK, Aranda JM, Hill JA, Yarandi HN, Walker JR, Phillips MS, Gelfand CA, Johnson JA. beta-Adrenergic receptor polymorphisms and responses during titration of metoprolol controlled release/extended release in heart failure. Clin Pharmacol Ther. 2005;77:127–37.View ArticlePubMedGoogle Scholar
  10. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo Jr JL, Jones DW, Materson BJ, Oparil S, Wright Jr JT, Roccella EJ. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. Jama. 2003;289:2560–72.View ArticlePubMedGoogle Scholar
  11. Pfeffer MA, Braunwald E, Moye LA, Basta L, Brown Jr EJ, Cuddy TE, Davis BR, Geltman EM, Goldman S, Flaker GC, et al. Effect of captopril on mortality and morbidity in patients with left ventricular dysfunction after myocardial infarction. Results of the survival and ventricular enlargement trial. The SAVE Investigators. N Engl J Med. 1992;327:669–77.View ArticlePubMedGoogle Scholar
  12. Hunt SA, Baker DW, Chin MH, Cinquegrani MP, Feldman AM, Francis GS, Ganiats TG, Goldstein S, Gregoratos G, Jessup ML, Noble RJ, Packer M, Silver MA, Stevenson LW, Gibbons RJ, Antman EM, Alpert JS, Faxon DP, Fuster V, Jacobs AK, Hiratzka LF, Russell RO, Smith Jr SC. ACC/AHA guidelines for the evaluation and management of chronic heart failure in the adult: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines (Committee to Revise the 1995 Guidelines for the Evaluation and Management of Heart Failure): developed in collaboration with the International Society for Heart and Lung Transplantation; endorsed by the Heart Failure Society of America. Circulation. 2001;104:2996–3007.View ArticlePubMedGoogle Scholar
  13. Gibbons RJ, Abrams J, Chatterjee K, Daley J, Deedwania PC, Douglas JS, Ferguson Jr TB, Fihn SD, Fraker Jr TD, Gardin JM, O'Rourke RA, Pasternak RC, Williams SV. ACC/AHA 2002 guideline update for the management of patients with chronic stable angina—summary article: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines (Committee on the Management of Patients with Chronic Stable Angina). J Am Coll Cardiol. 2003;41:159–68.View ArticlePubMedGoogle Scholar
  14. Shin J, Johnson JA. Pharmacogenetics of beta-blockers. Pharmacotherapy. 2007;27:874–87.View ArticlePubMedPubMed CentralGoogle Scholar
  15. The use of medicines in the United States: review of 2011. IMS Institute for Healthcare Informatics. [https://www.imshealth.com/files/web/IMSH%20Institute/Reports/The%20Use%20of%20Medicines%20in%20the%20United%20States%202011/IHII_Medicines_in_U.S_Report_2011].
  16. Goryachkina K, Burbello A, Boldueva S, Babak S, Bergman U, Bertilsson L. CYP2D6 is a major determinant of metoprolol disposition and effects in hospitalized Russian patients treated for acute myocardial infarction. Eur J Clin Pharmacol. 2008;64:1163–73.View ArticlePubMedGoogle Scholar
  17. Ismail R, Teh LK. The relevance of CYP2D6 genetic polymorphism on chronic metoprolol therapy in cardiovascular patients. J Clin Pharm Ther. 2006;31:99–109.View ArticlePubMedGoogle Scholar
  18. Liu J, Liu ZQ, Yu BN, Xu FH, Mo W, Zhou G, Liu YZ, Li Q, Zhou HH. beta1-Adrenergic receptor polymorphisms influence the response to metoprolol monotherapy in patients with essential hypertension. Clin Pharmacol Ther. 2006;80:23–32.View ArticlePubMedGoogle Scholar
  19. Beitelshees AL, Zineh I, Yarandi HN, Pauly DF, Johnson JA. Influence of phenotype and pharmacokinetics on beta-blocker drug target pharmacogenetics. Pharmacogenomics J. 2006;6:174–8.View ArticlePubMedGoogle Scholar
  20. James PA, Oparil S, Carter BL, Cushman WC, Dennison-Himmelfarb C, Handler J, Lackland DT, LeFevre ML, MacKenzie TD, Ogedegbe O, Smith Jr SC, Svetkey LP, Taler SJ, Townsend RR, Wright Jr JT, Narva AS, Ortiz E. 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). Jama. 2014;311:507–20.View ArticlePubMedGoogle Scholar
  21. Lovlie R, Daly AK, Molven A, Idle JR, Steen VM. Ultrarapid metabolizers of debrisoquine: characterization and PCR-based detection of alleles with duplication of the CYP2D6 gene. FEBS Lett. 1996;392:30–4.View ArticlePubMedGoogle Scholar
  22. Bathum L, Johansson I, Ingelman-Sundberg M, Horder M, Brosen K. Ultrarapid metabolism of sparteine: frequency of alleles with duplicated CYP2D6 genes in a Danish population as determined by restriction fragment length polymorphism and long polymerase chain reaction. Pharmacogenetics. 1998;8:119–23.View ArticlePubMedGoogle Scholar
  23. Okubo M, Murayama N, Miura J, Shimizu M, Yamazaki H. A rapid multiplex PCR assay that can reliably discriminate the cytochrome P450 2D6 whole-gene deletion allele from 2D6*10 alleles. Clin Chim Acta. 2012;413:1675–7.View ArticlePubMedGoogle Scholar
  24. Langaee T, Hamadeh I, Chapman AB, Gums JG, Johnson JA. A novel simple method for determining CYP2D6 gene copy number and identifying allele(s) with duplication/multiplication. PLoS One. 2015;10:e0113808.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© Ben et al. 2016

Advertisement