Abstract
Background:
Prospective pain genetics research is hindered by a lack of data on the prevalence of polymorphisms in pain-relevant genes for patients with sickle cell disease (SCD). For African–Americans in general, limited information is available in public databases.
Methods:
We prioritized and examined the genotype and allele frequencies of 115 SNPs from 49 candidate pain genes in 199 adult African–Americans and pediatric patients of African origin with SCD. Analyses were performed and compared with available data from public databases.
Results:
Genotype and allele frequencies of a number of SNPs were found to be different between our cohort and those from the databases and between adult and pediatric subjects.
Conclusion:
As pain therapy is inadequate in a significant percentage of patients with SCD, candidate pain genetic studies may aid in designing precision pain medicine. We provide prevalence data as a reference for prospective genetic studies in this population.
Keywords: : African–American, genotype, pain, pharmacogenomics, polymorphisms, population, sickle cell disease, SNPs
Pain is pervasive throughout the life of those with sickle cell disease (SCD) and it significantly impacts their quality of life [1–3]. SCD is characterized by both acute and chronic pain, that is highly variable in frequency and severity [4–6]. Moreover, it was found that one in three patients were not satisfied with their level of pain relief from analgesics including opioids [4]. Understanding individual differences in pain and pain relief will not only help to elucidate underlying pain mechanisms, but could have a potential to guide precision pain medicine for those with SCD [7].
There have been several genetic studies focusing on SCD, with few small studies dedicated to sickle cell pain [7–12]. Identifying genetic polymorphisms and their influence on pain phenotypes may explain some of the pain variation seen in SCD [7]. The lack of data on polymorphism frequencies for pain-relevant SNPs in SCD patients significantly hinders the proper design of prospective studies. For African–Americans, only a small number of samples with genotype and allele frequency information are found in public databases. Because of the significant genetic admixture that is found in African–Americans, many genetic studies are typically not generalizable to this population. SCD occurs in 90,000–100,000 Americans and in about 1 out of every 500 African–American births [13]. Sickle cell trait, where a person inherits one sickle hemoglobin gene and one normal hemoglobin gene, occurs in 1 in 12 African–Americans. In this study, we are able to identify and prioritize candidate genes that may contribute to pain in SCD. We have investigated 115 SNPs in a total of 49 genes that were chosen based on their relevance for pain.
Methods
Subjects
The Institutional Review Boards of the University of Illinois at Chicago (UI) and Northwestern University (NU) approved the study. All participants gave written informed consent and provided blood or buccal swab samples. Patients were recruited during routine outpatient clinic visits (i.e., patients were not seeking immediate or urgent medical care). Eligibility criteria for patient recruitment is described in detail in another article [14]. For this study, 199 self-reported people of African background were included.
Candidate gene identification
Literature searches were performed to identify SNPs that had been associated with pain in previous studies in PubMed, using the key words: pain, polymorphism, association, genetic, pharmacogenetic and other related words including substance abuse and psychiatric disorders that are known comorbidities [15,16]. A total of 115 SNPs from 49 genes were prioritized for this exploratory study, which is not an exclusive list of all pain-related SNPs.
Genotyping
Genotyping was performed for all but four SNPs by the MassARRAY iPLEX Platform (Sequenom, CA, USA) [7]. PCR-RFLP was used to genotype the remaining four SNPs: rs1799971 [17], rs2075572 [17], rs222747 [18] and rs224534 [18], as published. Genotyping success rate was >98%.
Statistical analysis
Hardy–Weinberg equilibrium was calculated using χ2 goodness-of-fit test with a significance level α = 10–3 [19]. The χ2 test for independence or Fisher’s exact test was used where appropriate to compare genotype or allele frequencies of different samples. Haplotypes are not analyzed for this study because of the lack of clinical relevance it would have, based on the limited number of SNPs per gene.
Results
Subject demographic data including age, gender and sickle cell genotypes are provided in Table 1 for 127 adult patients from the UI and 72 pediatric patients from NU. Combined demographics for all subjects are also provided in Table 1. The average age in the pediatric group was 14 years and 17 patients were 18 years or older. There are a disproportional number of females enrolled in this study, especially for the adult group; however, SCD itself is not known to be gender biased. Only a few studies have reported gender differences: the mortality was higher in males than females among 20–49 year old patients with SCD [20], and males were more frequently admitted for acute pain episodes whereas females had significantly longer lengths of stay in another study [21].
Table 1. . Patient demographics.
| UI (n = 127) | NU (n = 72) | Total (n = 199) | ||
|---|---|---|---|---|
| Age (years) | Mean ± SD | 34.9 ± 11.4 | 14.3 ± 4.1 | 27.5 ± 13.7 |
| Minimum | 19 | 8 | 8 | |
| |
Maximum |
70 |
24 |
70 |
| Gender, n (%) | Female | 84 (66.1) | 40 (55.6) | 124 (62.3) |
| |
Male |
43 (33.9) |
32 (44.4) |
75 (37.7) |
| Sickle cell type, n (%) | SCD-SS | 97 (76.4) | 56 (77.8) | 153 (76.9) |
| SCD-SC | 15 (11.8) | 14 (19.4) | 29 (14.6) | |
| SCD-β+ thal | 7 (5.5) | 1 (1.4) | 8 (4.0) | |
| SCD-β° thal | 7 (5.5) | 1 (1.4) | 8 (4.0) | |
| SCD-α thal | 1 (0.8) | 0 (0.0) | 1 (0.5) |
NU: Northwestern University; SC: Sickle hemoglobin C (SCD-SC, hemoglobin S and hemoglobin C); SCD: Sickle cell disease; SD: Standard deviation; SS: Homozygous hemoglobin S (SCD-SS, sickle cell anemia); thal: Thalassemia; UI: University of Illinois at Chicago
The genotype frequencies and allele frequencies are shown in Table 2 and Supplementary Table 1, respectively. For comparison, we also provide genotype and allele frequencies from HapMap [22] and 1000 Genomes [23] for African–Americans.
Table 2. . Genotype frequencies of 115 single nucleotide polymorphisms in 49 pain-candidate genes.
| Gene | dbSNP ID |
Alleles |
Current study |
Literature frequencies† |
|||||
|---|---|---|---|---|---|---|---|---|---|
| Major | Minor | Homozygote (major allele), n (%) | Heterozygote, n (%) | Homozygote (minor allele), n (%) | Homozygote (major allele), n (%) | Heterozygote, n (%) | Homozygote (minor allele), n (%) | ||
| ABCB1 | 1045642 | C | T | 87 (63.5) | 41 (29.9) | 9 (6.6) | 35 (62.5) | 19 (33.9) | 2 (3.6) |
| |
2032582 |
G |
A |
132 (75.0) |
41 (23.3) |
3 (1.7) |
46 (80.7) |
10 (17.5) |
1 (1.8) |
|
ADORA2A |
3761422 |
C |
T |
54 (39.4) |
68 (49.6) |
15 (10.9) |
20 (35.1) |
26 (45.6) |
11 (19.3) |
|
ADRA1A |
1048101‡ |
C |
T |
79 (58.1) |
53 (39.0) |
4 (2.9) |
25 (43.9) |
22 (38.6) |
10 (17.5) |
| ADRA2A | 521674 | T | A | 99 (55.9) | 67 (37.9) | 11 (6.2) | 28 (45.9) | 28 (45.9) | 5 (8.2) |
| 602618 | C | A | 79 (44.6) | 76 (42.9) | 22 (12.4) | 19 (33.3) | 31 (54.4) | 7 (12.3) | |
| 1800544 | G | C | 79 (58.1) | 53 (39.0) | 4 (2.9) | 20 (32.8) | 30 (49.2) | 11 (18.0) | |
| |
1800545 |
G |
A |
79 (57.7) |
49 (35.8) |
9 (6.6) |
38 (62.3) |
21 (34.4) |
2 (3.3) |
|
ADRB1 |
1801253 |
C |
G |
63 (35.4) |
84 (47.2) |
31 (17.4) |
22 (36.1) |
27 (44.3) |
12 (19.7) |
| ADRB2 | 1042713 | G | A | 47 (26.6) | 88 (49.7) | 42 (23.7) | 10 (17.5) | 29 (50.9) | 18 (31.6) |
| 1042717 | G | A | 57 (41.6) | 58 (42.3) | 22 (16.1) | 26 (45.6) | 27 (47.4) | 4 (7.0) | |
| 1432622 | C | T | 47 (34.3) | 64 (46.7) | 26 (19.0) | 16 (26.2) | 37 (60.7) | 8 (13.1) | |
| 1432623 | T | C | 49(36.0) | 61 (44.9) | 26 (19.1) | 16 (26.2) | 37 (60.7) | 8 (13.1) | |
| 2400707‡ | G | A | 48 (35.3) | 57 (41.9) | 31 (22.8) | 15 (26.3) | 34 (59.6) | 8 (14.0) | |
| |
11958940 |
T |
A |
47 (34.3) |
64 (46.7) |
26 (19.0) |
16 (26.2) |
37 (60.7) |
8 (13.1) |
|
ANKK1 |
4938012 |
G |
A |
112 (62.9) |
60 (33.7) |
6 (3.4) |
44 (72.1) |
15 (24.6) |
2 (3.3) |
| ARRB2 | 1045280 | C | T | 38 (27.7) | 77 (56.2) | 22 (16.1) | 18 (32.1) | 29 (51.8) | 9 (16.1) |
| |
2271167 |
A |
G |
51 (28.7) |
97 (54.5) |
30 (16.9) |
19 (33.3) |
29 (50.9) |
9 (15.8) |
|
AVPR1A |
10877969 |
C |
T |
54 (30.7) |
74 (42.0) |
48 (27.3) |
16 (28.1) |
29 (50.9) |
12 (21.1) |
|
BDKRB2 |
1799722‡ |
C |
T |
68 (49.6) |
60 (43.8) |
9 (6.6) |
26 (45.6) |
23 (40.4) |
8 (14.0) |
|
BDNF |
6265 |
G |
A |
165(93.2) |
12 (6.8) |
0 (0.0) |
52 (91.2) |
5 (8.8) |
0 (0.0) |
| CACNA2D3 | 1851048 | G | A | 95(69.9) | 38 (27.9) | 3 (2.2) | 35 (63.6) | 19 (34.5) | 1 (1.8) |
| |
6777055 |
A |
C |
90 (65.7) |
40 (29.2) |
7 (5.1) |
43 (75.4) |
12 (21.1) |
2 (3.5) |
| CHRNA4 | 1044396 | C | T | 132 (74.6) | 41 (23.2) | 4 (2.3) | 35 (62.5) | 21 (37.5) | 0 (0.0) |
| 2236196 | G | A | 81 (45.8) | 76 (42.9) | 20 (11.3) | 20 (32.8) | 29 (47.5) | 12 (19.7) | |
| 4522666 | G | A | 134 (75.7) | 39 (22.0) | 4 (2.3) | 36 (63.2) | 21 (36.8) | 0 (0.0) | |
| |
4603829 |
C |
T |
60 (33.7) |
78 (43.8) |
40 (22.5) |
21 (36.8) |
30 (52.6) |
6 (10.5) |
| CNR1 | 806379 | T | A | 39 (28.5) | 64 (46.7) | 34 (24.8) | 18 (29.5) | 25 (41.0) | 18 (29.5) |
| |
1049353 |
G |
A |
160 (89.9) |
18 (10.1) |
0 (0.0) |
47 (82.5) |
10 (17.5) |
0 (0.0) |
| COMT | 4633 | C | T | 46 (33.6) | 78 (56.9) | 13 (9.5) | 30 (52.6) | 18 (31.6) | 9 (15.8) |
| 6269‡ | A | G | 60 (43.8) | 64 (46.7) | 13 (9.5) | 27 (44.3) | 29 (47.5) | 5 (8.2) | |
| 737865 | A | G | 110 (80.3) | 25 (18.2) | 2 (1.5) | 43 (75.4) | 13 (22.8) | 1 (1.8) | |
| |
4646312 |
T |
C |
98 (71.5) |
36 (26.3) |
3 (2.2) |
42 (73.7) |
15 (26.3) |
0 (0.0) |
|
DRD1 |
4532 |
T |
C |
109 (79.6) |
25 (18.2) |
3 (2.2) |
44 (77.2) |
12 (21.1) |
1 (1.8) |
|
DRD2 |
4274224 |
G |
A |
68 (50.0) |
57 (41.9) |
11 (8.1) |
24 (42.1) |
26 (45.6) |
7 (12.3) |
| DRD3 | 167770‡ | G | A | 68 (38.6) | 81 (46.0) | 27 (15.3) | 24 (42.1) | 25 (43.9) | 8 (14.0) |
| 167771‡ | G | A | 100 (56.5) | 70 (39.5) | 7 (4.0) | 22 (38.6) | 28 (49.1) | 7 (12.3) | |
| 324023‡ | A | G | 92 (52.0) | 75 (42.4) | 10 (5.6) | 34 (55.7) | 22 (36.1) | 5 (8.2) | |
| 324026‡ | C | T | 91 (51.4) | 76 (42.9) | 10 (5.6) | 32 (56.1) | 21 (36.8) | 4 (7.0) | |
| 324029‡ | A | G | 80 (45.2) | 73 (41.2) | 24 (13.6) | 27 (47.4) | 24 (42.1) | 6 (10.5) | |
| 905568 | G | C | 110 (61.8) | 60 (33.7) | 8 (4.5) | 38 (62.3) | 20 (32.8) | 3 (4.9) | |
| 1394016 | A | G | 132 (74.2) | 41 (23.0) | 5 (2.8) | 34 (59.6) | 21 (36.8) | 2 (3.5) | |
| 1800828 | G | C | 128 (71.9) | 48 (27.0) | 2 (1.1) | 39 (63.9) | 19 (31.1) | 3 (4.9) | |
| 2087017 | G | A | 51 (28.7) | 86 (48.3) | 41 (23.0) | 16 (28.1) | 22 (38.6) | 19 (33.3) | |
| 2134655 | G | A | 159 (89.3) | 19 (10.7) | 0 (0.0) | 50 (87.7) | 7 (12.3) | 0 (0.0) | |
| 2399504 | G | A | 151 (84.8) | 25 (14.0) | 2 (1.1) | 44 (72.1) | 16 (26.2) | 1 (1.6) | |
| 3773679 | G | A | 142 (79.8) | 36 (20.2) | 0 (0.0) | 42 (68.9) | 17 (27.9) | 2 (3.3) | |
| |
7611535 |
C |
T |
131 (73.6) |
43 (24.2) |
4 (2.2) |
33 (57.9) |
22 (38.6) |
2 (3.5) |
| FAAH | 324419 | G | A | 126(70.8) | 49 (27.5) | 3 (1.7) | 37 (64.9) | 19 (33.3) | 1 (1.8) |
| 932816 | G | A | 140 (78.7) | 37 (20.8) | 1 (0.6) | 45 (73.8) | 14 (23.0) | 2 (3.3) | |
| 2295632 | A | C | 90 (50.6) | 73 (41.0) | 15 (8.4) | 21 (34.4) | 30 (49.2) | 10 (16.4) | |
| |
4141964 |
A |
G |
93 (52.2) |
69 (38.8) |
16 (9.0) |
21 (36.8) |
29 (50.9) |
7 (12.3) |
| GCH1 | 752688 | C | T | 117 (65.7) | 54 (30.3) | 7 (3.9) | 36 (59.0) | 23 (37.7) | 2 (3.3) |
| 3783641 | T | A | 63 (46.0) | 57 (41.6) | 17 (12.4) | 30 (49.2) | 23 (37.7) | 8 (13.1) | |
| 4411417 | T | C | 116 (65.5) | 55 (31.1) | 6 (3.4) | 36 (59.0) | 23 (37.7) | 2 (3.3) | |
| |
8007267 |
A |
G |
53 (38.7) |
63 (46.0) |
21 (15.3) |
19 (33.3) |
28 (49.1) |
10 (17.5) |
|
GRIK3 |
6691840 |
C |
A |
41 (29.9) |
70 (51.1) |
26 (19.0) |
21 (36.8) |
25 (43.9) |
11 (19.3) |
| GRIN2B | 2284411 | C | T | 85 (47.8) | 72 (40.4) | 21 (11.8) | 26 (45.6) | 28 (49.1) | 3 (5.3) |
| |
10845840 |
T |
C |
55 (30.9) |
84 (47.2) |
39 (21.9) |
12 (21.1) |
23 (40.4) |
22 (38.6) |
| HBS1L-MYB | 4895441 | A | G | 142 (79.8) | 35 (19.7) | 1 (0.6) | 41 (71.9) | 15 (26.3) | 1 (1.8) |
| 9399137 | T | C | 157 (88.7) | 20 (11.3) | 0 (0.0) | 48 (84.2) | 9 (15.8) | 0 (0.0) | |
| |
28384513 |
A |
C |
111 (62.7) |
58 (32.8) |
8 (4.5) |
36 (59) |
23 (37.7) |
2 (3.3) |
|
HTR2A |
6313 |
C |
T |
53 (39.0) |
68 (50.0) |
15 (11.0) |
17 (30.4) |
28 (50.0) |
11 (19.6) |
| HTR3B | 7943062 | G | A | 96(70.1) | 39 (28.5) | 2 (1.5) | 37 (66.1) | 18 (32.1) | 1 (1.8) |
| |
11214763 |
G |
A |
96(70.1) |
40 (29.2) |
1 (0.7) |
40 (65.6) |
19 (31.1) |
2 (3.3) |
| HTR3E | 4912522 | G | C | 85 (62.1) | 48 (35.0) | 4 (2.9) | 31 (54.4) | 24 (42.1) | 2 (3.5) |
| |
7432211 |
T |
C |
62 (45.3) |
57 (41.6) |
18 (13.1) |
26 (45.6) |
28 (49.1) |
3 (5.3) |
|
IL10 |
1800896 |
A |
G |
64 (46.7) |
60 (43.8) |
13 (9.5) |
25 (43.9) |
24 (42.1) |
8 (14.0) |
|
IL13 |
20541 |
C |
T |
98 (71.5) |
38 (27.7) |
1 (0.7) |
35 (61.4) |
21 (36.8) |
1 (1.8) |
|
IL16 |
4778889 |
T |
C |
73 (41.0) |
78 (43.8) |
27 (15.2) |
19 (33.3) |
33 (57.9) |
5 (8.8) |
|
IL1A |
1800587 |
C |
T |
62 (34.8) |
88 (49.4) |
28 (15.7) |
20 (35.7) |
33 (58.9) |
3 (5.4) |
| IL1B | 16944 | A | G | 42 (30.7) | 69 (50.4) | 26 (19.0) | 20 (35.1) | 26 (45.6) | 11 (19.3) |
| |
1143634 |
C |
T |
136 (76.8) |
40 (22.6) |
1 (0.6) |
45 (78.9) |
12 (21.1) |
0 (0.0) |
|
IL1RN |
2234677 |
G |
A |
150 (84.3) |
27 (15.2) |
1 (0.6) |
52 (85.2) |
9 (14.8) |
0 (0.0) |
|
IL6 |
1800796 |
G |
C |
113 (82.5) |
24 (17.5) |
0 (0.0) |
51 (83.6) |
10 (16.4) |
0 (0.0) |
|
KCNS1 |
734784 |
G |
A |
54 (30.5) |
86 (48.6) |
37 (20.9) |
21 (36.8) |
29 (50.9) |
7 (12.3) |
|
MTHFR |
1801133 |
C |
T |
115 (83.9) |
22 (16.1) |
0 (0.0) |
47 (82.5) |
6 (10.5) |
4 (7.0) |
| OPRD1 | 569356 | T | C | 160 (90.4) | 17 (9.6) | 0 (0.0) | 54 (88.5) | 7 (11.5) | 0 (0.0) |
| 1042114 | T | G | 161 (90.4) | 16 (9.0) | 1 (0.6) | 52 (91.2) | 5 (8.8) | 0 (0.0) | |
| |
2234918 |
C |
T |
75 (42.4) |
86 (48.6) |
16 (9.0) |
27 (44.3) |
28 (45.9) |
6 (9.8) |
| OPRM1 | 1799971 | A | G | 191 (96.0) | 8 (4.0) | 0 (0.0) | 53 (93.0) | 4 (7.0) | 0 (0.0) |
| 2075572 | G | C | 56 (28.1) | 108 (54.3) | 35 (17.6) | 13 (21.3) | 33 (54.1) | 15 (24.6) | |
| |
6912029 |
G |
T |
106 (77.9) |
29 (21.3) |
1 (0.7) |
49 (86.0) |
8 (14.0) |
0 (0.0) |
|
P2RX7 |
2230912 |
A |
G |
160 (89.9) |
17 (9.6) |
1 (0.6) |
54 (94.7) |
3 (5.3) |
0 (0.0) |
|
PTGES2 |
13283456 |
C |
T |
175 (98.3) |
3 (1.7) |
0 (0.0) |
52 (91.2) |
5 (8.8) |
0 (0.0) |
|
RET |
1799939 |
G |
A |
101 (73.7) |
35 (25.5) |
1 (0.7) |
45 (81.8) |
9 (16.4) |
1 (1.8) |
| SCN1A | 2298771 | A | G | 101 (73.7) | 32 (23.4) | 4 (2.9) | 43 (70.5) | 16 (26.2) | 2 (3.3) |
| |
121918628 |
C |
A |
178 (100.0) |
0 (0.0) |
0 (0.0) |
NA |
NA |
NA |
| SCN9A | 6746030 | G | A | 150 (84.7) | 27 (15.3) | 0 (0.0) | 42 (73.7) | 15 (26.3) | 0 (0.0) |
| |
41268673 |
G |
T |
178 (100.0) |
0 (0.0) |
0 (0.0) |
59 (96.7) |
2 (3.3) |
0 (0.0) |
| SLC6A2 | 36020 | G | A | 160 (89.9) | 18 (10.1) | 0 (0.0) | 50 (87.7) | 6 (10.5) | 1 (1.8) |
| 36029 | T | C | 86 (48.3) | 79 (44.4) | 13 (7.3) | 31 (50.8) | 22 (36.1) | 8 (13.1) | |
| 40147 | C | T | 107 (60.5) | 62 (35.0) | 8 (4.5) | 30 (52.6) | 20 (35.1) | 7 (12.3) | |
| |
187715 |
A |
G |
172 (96.6) |
6 (3.4) |
0 (0.0) |
55 (96.5) |
2 (3.5) |
0 (0.0) |
|
SLC6A4 |
3813034‡ |
A |
C |
74 (54.0) |
55 (40.1) |
8 (5.8) |
32 (56.1) |
24 (42.1) |
1 (1.8) |
| STAT6 | 167769 | C | T | 105 (76.6) | 27 (19.7) | 5 (3.6) | 41 (71.9) | 14 (24.6) | 2 (3.5) |
| 841718‡ | C | T | 69 (50.4) | 54 (39.4) | 14 (10.2) | 16 (28.1) | 34 (59.6) | 7 (12.3) | |
| |
3024971 |
A |
C |
123 (90.4) |
13 (9.6) |
0 (0.0) |
45 (78.9) |
12 (21.1) |
0 (0.0) |
| TH | 6356 | G | A | 99 (72.3) | 34 (24.8) | 4 (2.9) | 43 (75.4) | 13 (22.8) | 1 (1.8) |
| 6357 | G | A | 146 (82.0) | 32 (18.0) | 0 (0.0) | 44 (72.1) | 14 (23.0) | 3 (4.9) | |
| 2070762 | T | C | 109 (61.6) | 60 (33.9) | 8 (4.5) | 33 (58.9) | 20 (35.7) | 3 (5.4) | |
| |
10770141 |
A |
G |
56 (31.6) |
75 (42.4) |
46 (26) |
22 (38.6) |
24 (42.1) |
11 (19.3) |
| TRPA1 | 920829 | G | A | 86 (48.3) | 80 (44.9) | 12 (6.7) | 28 (49.1) | 25 (43.9) | 4 (7.0) |
| 1025928 | C | T | 75 (42.1) | 81 (45.5) | 22 (12.4) | 24 (42.1) | 26 (45.6) | 7 (12.3) | |
| 1443952 | G | A | 73 (41.0) | 76 (42.7) | 29 (16.3) | 30 (52.6) | 21 (36.8) | 6 (10.5) | |
| 3735942 | G | A | 70 (39.3) | 74 (41.6) | 34 (19.1) | 29 (50.9) | 22 (38.6) | 6 (10.5) | |
| 13255063 | T | A | 145 (81.5) | 33 (18.5) | 0 (0.0) | 46 (80.7) | 9 (15.8) | 2 (3.5) | |
| 3735943 | G | A | 49 (27.7) | 80 (45.2) | 48 (27.1) | 20 (35.1) | 24 (42.1) | 13 (22.8) | |
| |
13279503 |
G |
C |
119 (66.9) |
54 (30.3) |
5 (2.8) |
37 (60.7) |
22 (36.1) |
2 (3.3) |
| TRPV1 | 222747 | C | G | 154 (77.4) | 40 (20.1) | 5 (2.5) | 46 (75.4) | 14 (23.0) | 1 (1.6) |
| 224534 | C | T | 171 (85.9) | 26 (13.1) | 2 (1.0) | 51 (89.5) | 6 (10.5) | 0 (0.0) | |
| |
8065080 |
T |
C |
102 (74.5) |
34 (24.8) |
1 (0.7) |
39 (69.6) |
17 (30.4) |
0 (0.0) |
| WWC1 | 17070145‡ | T | C | 56 (31.5) | 85 (47.8) | 37 (20.8) | 16 (28.1) | 31 (54.4) | 10 (17.5) |
†HapMap population frequencies for African ancestry in Southwest USA (ASW) from the data release 28 Phase II + III are used for comparison. In the case ASW from HapMap was not available, 1000 Genomes population frequencies (ASW) were obtained from the Ensembl Genome Browser release 72 (Flicek et al. 2013).
‡SNPs showing p < 0.05 between University of Illinois at Chicago and Northwestern University samples by the χ2 or Fisher’s exact test.
NA: Not applicable.
Two SNPs (rs2075507 and rs1800871) had significant deviations from Hardy–Weinberg equilibrium (p < 0.001) for UI and NU samples analyzed separately or combined. Two other SNPs (rs41268673 and rs121918628) were found to be monogenotypes in both UI and NU samples. These four SNPs were excluded from further statistical analyses.
We further compared our data with the literature for both allele and genotype frequencies using the χ2 and Fisher’s exact test. SNPs exhibiting significant deviation from the literature reported allele or genotype frequencies are listed in Table 3, with statistically significant p-values (p < 0.05) shown in bold. SNPs that are significantly different for both genotype and allele frequencies include rs1800544 in ADRA2A, rs1048101 in ADRA1A, rs167771 and rs3773679 in DRD3, rs10845840 in GRIN2B, rs13283456 in PTGES2, rs841718 and rs3024971 in STAT6 and rs6357 in TH.
Table 3. . SNPs differ in genotype and/or allele frequencies between the current study and those from literature.
| Gene | dbSNP ID | Major alleles | Minor alleles | Genotype comparisons p-value | Allele comparisons p-value |
|---|---|---|---|---|---|
|
ADRA1A |
1048101 |
C |
T |
0.002 |
0.003 |
|
ADRA2A |
1800544 |
G |
C |
0.000† |
0.000‡ |
|
CHRNA4 |
2236196 |
G |
A |
0.112 |
0.034 |
|
COMT |
4633 |
C |
T |
0.006 |
0.233 |
| DRD3 | 167771 | G | A | 0.014 | 0.006 |
| 2399504 | G | A | 0.065 | 0.034 | |
| 3773679 | G | A | 0.028 | 0.037 | |
| |
7611535 |
C |
T |
0.071 |
0.033 |
|
FAAH |
2295632 |
A |
C |
0.050 |
0.014 |
|
GRIN2B |
10845840 |
T |
C |
0.038 |
0.014 |
|
MTHFR |
1801133 |
C |
T |
0.010 |
0.189 |
|
PTGES2 |
13283456 |
C |
T |
0.022 |
0.011 |
|
SCN9A |
41268673 |
G |
T |
0.064 |
0.015 |
| STAT6 | 841718 | C | T | 0.015 | 0.021 |
| |
3024971 |
A |
C |
0.036 |
0.036 |
| TH | 6357 | G | A | 0.012 | 0.023 |
p < 0.05 is indicated in bold.
†p = 0.000092.
‡p = 0.000042.
A previous study reported major opiate-related polymorphisms in an SCD cohort [24]. The survey includes genotype and allele frequency data for genes including OPRM1 (rs1799971) and ABCB1 (rs1045642) in an SCD cohort [24]. We found no differences between our study population and their two cohorts for these two SNPs for either genotype or allele frequencies. Between the UI and NU subjects, the genotype frequencies of 12 SNPs and allele frequencies of 12 SNPs were statistically different (p < 0.05) (Table 4, also noted in Supplementary Table 1). Out of these, seven SNPs differ significantly in both genotype and allele frequencies.
Table 4. . SNPs with significantly different genotype and/or allele frequencies between University of Illinois at Chicago and Northwestern University samples.
| Gene | dbSNP ID | Major | Minor alleles |
UI frequencies |
NU frequencies |
Genotype comparisons p-value | Allele comparisons p-value | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Homozygote (major allele), n (%) | Heterozygote, n (%) | Homozygote (minor allele), n (%) | Homozygote (major allele), n (%) | Heterozygote, n (%) | Homozygote (minor allele), n (%) | ||||||
|
ADRA1A |
1048101 |
C |
T |
43 (67.2) |
21 (32.8) |
0 (0.0) |
36 (50.0) |
32 (44.4) |
4 (5.6) |
0.040† |
0.025† |
| ADRB2 | 1432622 | C | T | 25 (38.5) | 33 (50.8) | 7 (10.7) | 22 (30.6) | 31 (43.1) | 19 (26.4) | 0.066 | 0.049† |
| 1432623 | T | C | 27 (42.2) | 30 (46.9) | 7 (10.9) | 22 (30.6) | 31 (43.1) | 19 (26.4) | 0.060 | 0.024† | |
| |
2400707 |
G |
A |
26 (40.0) |
31 (47.7) |
8 (12.3) |
22 (31.0) |
26 (36.6) |
23 (32.4) |
0.020† |
0.016† |
|
BDKRB2 |
1799722 |
C |
T |
24 (36.9) |
39 (60.0) |
2 (3.1) |
44 (61.1) |
21 (29.2) |
7 (9.7) |
0.001† |
0.108 |
|
COMT |
6269 |
A |
G |
20 (30.8) |
36 (55.4) |
9 (13.8) |
40 (55.6) |
28 (38.9) |
4 (5.6) |
0.010† |
0.004† |
|
DRD1 |
4532 |
T |
C |
48 (73.8) |
14 (21.5) |
3 (4.6) |
61 (84.7) |
11 (15.3) |
0 (0.0) |
0.102 |
0.043† |
| DRD3 | 167770 | G | A | 32 (30.5) | 57 (54.3) | 16 (15.2) | 36 (50.7) | 24 (33.8) | 11 (15.5) | 0.015† | 0.059 |
| 167771 | G | A | 52 (49.5) | 50 (47.6) | 3 (2.9) | 48 (66.7) | 20 (27.8) | 4 (5.6) | 0.027† | 0.117 | |
| 324023 | A | G | 45 (42.9) | 55 (52.4) | 5 (4.8) | 47 (65.3) | 20 (27.8) | 5 (6.9) | 0.005† | 0.035† | |
| 324026 | C | T | 44 (41.9) | 56 (53.3) | 5 (4.8) | 47 (65.3) | 20 (27.8) | 5 (6.9) | 0.003† | 0.028† | |
| |
324029 |
A |
G |
38 (36.2) |
52 (49.5) |
15 (14.3) |
42 (58.3) |
21 (29.2) |
9 (12.5) |
0.011† |
0.020† |
|
FAAH |
932816 |
G |
A |
89 (84.0) |
17 (16.0) |
0 (0.0) |
51 (70.8) |
20 (27.8) |
1 (1.4) |
0.072 |
0.031† |
|
SLC6A4 |
3813034 |
A |
C |
41 (63.1) |
18 (27.7) |
6 (9.2) |
33 (45.8) |
37 (51.4) |
2 (2.8) |
0.011† |
0.309 |
|
STAT6 |
841718 |
C |
T |
25 (38.5) |
31 (47.7) |
9 (13.8) |
44 (61.1) |
23 (31.9) |
5 (6.9) |
0.027† |
0.008† |
| WWC1 | 17070145 | T | C | 25 (23.6) | 57 (53.8) | 24 (22.6) | 31 (43.1) | 28 (38.9) | 13 (18.1) | 0.022† | 0.025† |
†Statistically significant SNPs showing genotype or allele frequency differences between UI and NU samples.
NU: Northwestern University; UI: University of Illinois at Chicago.
Discussion
Surveying 115 SNPs in 49 pain-relevant genes, we report here genotype and allele frequencies from a cohort of 199 patients with SCD who self-identified as of African origin. We found that genotype and/or allele frequencies for a number of SNPs were statistically different between our SCD cohort and literature data for African–Americans and also between our UI and NU data. The differences in the frequencies could be due to population stratification and/or sample size, among other variables. The admixture in the African–American population is about 10–20% from European genetic ancestry and the frequency differences observed between any two populations that have high admixture has been observed previously [25,26]. Allele frequency differences in admixed populations must be considered in studies performed in populations even within the same country [26]. In our study, the NU cohort includes more subjects who immigrated directly from Africa. Case control studies with admixed populations may choose to control for population stratification by methods such as genomic control or structured association to avoid spurious associations [27]. A review on association studies in structured populations has been published [28].
Several SNPs reported in this study show potential for spurious associations in an African–American population. For example, the rs1800544 SNP has been associated with various disorders in many different populations. It has been associated with tobacco smoking in Brazilians [29], antipsychotic-induced weight gain in European–Americans and Asian populations [30], and schizophrenia in a Czech population [31]. Antipsychotic-induced weight gain has been studied in an African–American population but was not significant, possibly due to small sample size [30]. These studies have accounted for genetic admixture, and it would be important to consider this for future studies that include rs1800544 to avoid any spurious associations. Our study shows that there is a significant difference in frequencies between our study population and what is reported in the literature for rs1800544.
Fifteen of our 49 genes and 49 out of 115 SNPs are related to the monoamine neurotransmitter system as a receptor, enzyme or transporter. The monoamine neurotransmitter system has previously been implicated in pain [32,33]. It is an important parameter to consider when looking for newer therapeutic strategies for sickle cell pain. As there are studies of therapies targeting the monoamine neurotransmitter system for pain [34], it will be interesting to examine the role of these polymorphisms in SCD pain. Transient receptor potential (TRP) channels, which show therapeutic promise in pain relief [35] and inflammation in SCD also play, a prominent role [6]. There also has been a study reporting frequency data on major opiate-related polymorphisms in genes including OPRM1, COMT, CYP2D6, CYP3A, UGT2B7 and ABCB1 in an SCD cohort [24]. The survey includes genotype and allele frequency data for genes including OPRM1 (rs1799971) and ABCB1 (rs1045642). We found no differences between this study cohort and our cohort for these two SNPs for either genotype or allele frequencies.
A limitation of the study is that the number and selection of SNPs included is not an exhaustive list, due to the nature of the candidate gene approach. We believe many more SNPs and genes are relevant for SCD pain as it is a complex phenotype. However, we have provided a list of pain-related SNPs in SCD to start assisting in understanding SCD pain mechanisms.
Conclusion
We have reported genotype and allele frequencies data for 115 SNPs in 49 selected pain-relevant genes from a cohort of 199 patients with SCD. These data were obtained from two different academic medical centers that serve adult and pediatric patients with SCD. These findings will facilitate prospective genetics studies for pain in SCD, including the importance of considering polymorphism frequencies in admixed populations to avoid spurious associations.
Future perspective
Sickle cell pain is the most common and debilitating symptom in SCD that is highly variable in frequency and severity. Little is known about the underlying causes, variability and proper therapy of pain in SCD [36]. This study opens an avenue to study pain genetic mechanisms and consider precision medicine strategies for SCD pain. Genetic polymorphisms in the monoamine neurotransmitter system, TRP channels and other pain-relevant systems remain potentially fruitful areas to focus pain genetics studies. Our study has provided polymorphism prevalence data that are essential to properly design future prospective studies.
Executive summary.
Only a small number of African–American samples with genotype and allele frequency information are found in public databases.
The data are even rarer for pain-relevant SNPs in sickle cell disease patients.
We identified 115 SNPs in a total of 49 genes based on their relevance to sickle cell or nonsickle cell pain.
Several SNP frequencies were statistically different between our cohort and data derived from the literature, including nine SNPs that differ in both genotype and allele frequencies.
The genotype frequencies of 12 SNPs and allele frequencies of 12 SNPs were statistically different (p < 0.05) between the University of Illinois at Chicago adult and Northwestern Univeristy pediatric samples.
Allele frequency differences in admixed populations must be considered in studies to avoid spurious associations.
Several SNPs reported in this study show potential for spurious associations in an African–American population.
Findings reported here can help design large prospective pain genetic studies that may ultimately lead to precision therapies for pain in sickle cell disease.
Supplementary Material
Footnotes
Disclaimer
The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the IDPH, NIH, NHLBI, NIDCR or Veteran’s Administration.
Financial & competing interests disclosure
The study was supported in part by funds from the Illinois Department of Public Health (IDPH) and grants R01HL124945, R01HL098141 and R01HL078536 from the National Heart, Lung, and Blood Institute (NHLBI). EH Jhun is funded by a predoctoral fellowship award from NIDCR (T32DE018381). Y He is a Sickle Cell Scholar supported by U01 HL117658 from NHLBI. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of IDPH, NHLBI, or NIDCR. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
No writing assistance was utilized in the production of this manuscript.
Ethical conduct of research
The authors state that they have obtained appropriate institutional review board approval or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved.
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