Associations of GWAS-Supported Non-MHC Genes with Autoimmune Thyroiditis in Patients with Type 1 Diabetes

Figures & data

Table 1 Baseline Characteristic of Total Population, T1D Patients with AITD (Cases), and T1D Patients without AITD (Controls)

Characteristic	Total (n = 489)	Cases with AITD (n = 159)	Controls (n = 330)	P ^a
Female, n (%)	310 (63.4)	114 (71.7)	196 (59.4)	0.011
Age, years	36.0 (24.0–53.0)	39.0 (27.0–58.0)	35.5 (22.0–50.0)	0.004
T1D duration, years	5.0 (1.6–12.0)	6.0 (1.6–10.5)	5.0 (2.0–12.0)	0.658
Late-onset T1D, n (%)	224 (45.8)	84 (52.8)	140 (42.4)	0.031
BMI, kg/m^2	20.69 (18.79–22.89)	20.55 (18.93–21.64)	20.94 (18.67–23.11)	0.098
Systolic blood pressure, mmHg	113 (104–125)	114 (103–127)	113 (104–125)	0.455
Diastolic blood pressure, mmHg	72 (64–79)	72 (64–79)	71 (63–78)	0.916
Fasting blood glucose, mmol/L	10.30 (6.79–14.17)	10.10 (6.46–14.24)	10.30 (6.81–13.97)	0.858
Fasting C-peptide, ng/mL	0.04 (0.00–0.29)	0.10 (0.00–0.31)	0.02 (0.00–0.27)	0.010
HbA1c, % (mmol/mol)	8.1 (6.9–10.2)	8.0 (6.8–10.6)	8.2 (7.1–10.0)	0.427
	65 (52–88)	64 (51–92)	66 (54–86)
GADA positive, n (%)	257 (52.6)	104 (65.4)	153 (46.4)	< 0.001
LDL-C, mmol/L	2.72 (2.21–3.30)	2.63 (2.13–3.09)	2.77 (2.26–3.37)	0.011
HDL-C, mmol/L	1.29 (1.20–1.40)	1.37 (1.26–1.50)	1.26 (1.17–1.36)	< 0.001
Triglyceride, mmol/L	0.79 (0.61–1.16)	0.80 (0.58–1.13)	0.77 (0.61–1.17)	0.715
Creatinine, μmol/L	59.0 (50.0–70.0)	59.0 (53.0–70.5)	59.0 (48.0–68.0)	0.131

Note: ^a P value for comparing T1D with AITD vs T1D without AITD.

Abbreviations: T1D, type 1 diabetes; AITD, autoimmune thyroid diseases; BMI, body mass index; HbA1c, glycated hemoglobin; GADA, glutamic acid decarboxylase autoantibody; LDL-C, low-density lipoprotein; HDL-C, high-density lipoprotein.

Table 2 Genotype and Allele Frequencies of the Five Selected SNPs in T1D Patients with and without AITD

Genetic Models	Cases with AITD	Controls	Model 1^a OR (95% CI)/ P	Model 2^b OR (95% CI)/ P	Model 3^c OR (95% CI)/ P
QRFPR (rs7679475)
Codominant
A/A	59 (37.1%)	154 (46.7%)	1	1	1
G/A	75 (47.2%)	153 (46.4%)	1.28 (0.85–1.92)/ 0.236	1.45 (0.95–2.22)/ 0.085	1.60 (1.01–2.53)/ 0.044
G/G	25 (15.7%)	23 (7.0%)	2.84 (1.49–5.39)/ 0.001	3.41 (1.75–6.64)/ < 0.001	2.93 (1.44–5.96)/ 0.003
Dominant
A/A	59 (37.1%)	154 (46.7%)	1	1	1
G/A-G/G	100 (62.9%)	176 (53.3%)	1.48 (1.01–2.19)/ 0.045	1.70 (1.13–2.54)/ 0.010	1.81 (1.17–2.79)/ 0.007
Recessive
A/A-G/A	134 (84.3%)	307 (93.0%)	1	1	1
G/G	25 (15.7%)	23 (7.0%)	2.49 (1.36–4.54)/ 0.003	2.78 (1.50–5.18)/ 0.001	2.28 (1.17–4.43)/ 0.015
PHTF1 (rs1111695)
Codominant
C/C	112 (70.4%)	224 (67.9%)	1	1	1
C/A	44 (27.7%)	93 (28.2%)	0.95 (0.62–1.45)/ 0.799	0.98 (0.63–1.52)/ 0.937	0.85 (0.53–1.36)/ 0.489
A/A	3 (1.9%)	13 (3.9%)	0.46 (0.13–1.65)/ 0.235	0.42 (0.12–1.56)/ 0.195	0.35 (0.08–1.59)/ 0.173
Dominant
C/C	112 (70.4%)	224 (67.9%)	1	1	1
C/A-A/A	47 (29.6%)	106 (32.1%)	0.89 (0.59–1.34)/ 0.566	0.91 (0.59–1.39)/ 0.654	0.79 (0.50–1.25)/ 0.322
Recessive
C/C-C/A	156 (98.1%)	317 (96.1%)	1	1	1
A/A	3 (1.9%)	13 (3.9%)	0.47 (0.13–1.67)/ 0.210	0.43 (0.12–1.55)/ 0.165	0.37 (0.08–1.65)/ 0.161
PTPN22 (rs1217407)
Codominant
A/A	44 (27.7%)	109 (33.0%)	1	1	1
G/A	91 (57.2%)	162 (49.1%)	1.39 (0.90–2.15)/ 0.136	1.24 (0.79–1.94)/ 0.347	1.30 (0.81–2.09)/ 0.281
G/G	24 (15.1%)	59 (17.9%)	1.01 (0.56–1.82)/ 0.980	0.94 (0.52–1.72)/ 0.849	0.70 (0.36–1.38)/ 0.301
Dominant
A/A	44 (27.7%)	109 (33.0%)	1	1	1
G/A-G/G	115 (72.3%)	221 (67.0%)	1.29 (0.85–1.95)/ 0.229	1.16 (0.76–1.78)/ 0.494	1.13 (0.72–1.79)/ 0.593
Recessive
A/A-G/A	135 (84.9%)	271 (82.1%)	1	1	1
G/G	24 (15.1%)	59 (17.9%)	0.82 (0.49–1.37)/ 0.439	0.82 (0.48–1.40)/ 0.464	0.59 (0.32–1.08)/ 0.082
MAGI3 (rs2153977)
Codominant
T/T	55 (34.6%)	131 (39.7%)	1	1	1
C/T	84 (52.8%)	150 (45.5%)	1.33 (0.88–2.02)/ 0.172	1.35 (0.89–2.07)/ 0.161	1.51 (0.95–2.39)/ 0.079
C/C	20 (12.6%)	49 (14.8%)	0.97 (0.53–1.79)/ 0.928	0.98 (0.52–1.82)/ 0.939	0.80 (0.40–1.61)/ 0.526
Dominant
T/T	55 (34.6%)	131 (39.7%)	1	1	1
C/T-C/C	104 (65.4%)	199 (60.3%)	1.24 (0.84–1.85)/ 0.274	1.26 (0.84–1.89)/ 0.261	1.33 (0.85–2.06)/ 0.211
Recessive
T/T-C/T	139 (87.4%)	281 (85.2%)	1	1	1
C/C	20 (12.6%)	49 (14.8%)	0.83 (0.47–1.44)/ 0.496	0.82 (0.46–1.46)/ 0.497	0.63 (0.33–1.19)/ 0.153
BCL2L15 (rs2358994)
Codominant
A/A	51 (32.1%)	119 (36.1%)	1	1	1
G/A	85 (53.5%)	159 (48.2%)	1.25 (0.82–1.90)/ 0.303	1.31 (0.85–2.02)/ 0.225	1.35 (0.85–2.14)/ 0.203
G/G	23 (14.5%)	52 (15.8%)	1.03 (0.57–1.86)/ 0.917	1.07 (0.59–1.97)/ 0.819	0.93 (0.47–1.86)/ 0.841
Dominant
A/A	51 (32.1%)	119 (36.1%)	1	1	1
G/A-G/G	108 (67.9%)	211 (63.9%)	1.19 (0.80–1.78)/ 0.384	1.25 (0.83–1.89)/ 0.289	1.26 (0.80–1.96)/ 0.314
Recessive
A/A-G/A	136 (85.5%)	278 (84.2%)	1	1	1
G/G	23 (14.5%)	52 (15.8%)	0.90 (0.53–1.54)/ 0.709	0.91 (0.53–1.58)/ 0.747	0.77 (0.42–1.44)/ 0.422

Notes: ^aModel 1 unadjusted. ^bModel 2 adjusted for age, sex, and BMI. ^cModel 3 adjusted for age, sex, BMI, proportion of late-onset T1D, fasting C-peptide, GADA positivity, LDL-C, and HDL-C.

Figure 1 Adjusted odd ratio (OR) of autoimmune thyroid diseases (AITD) in type 1 diabetes (T1D) patients according rs7679475 and sex, male with A/A genotype as a reference (adjusted for age, sex, BMI, proportion of late-onset T1D, fasting C-peptide, glutamic acid decarboxylase autoantibody, low-density lipoprotein, and high-density lipoprotein). The interaction between rs7679475 and sex was significant (P _interaction = 0.005).

Table 3 Regulatory Motifs Altered for Rs7679475 Based on HaploReg V4.1

PWM ID	PWM match score		A allele: CCATAGACGTAGGGCAAGACTAGGCACTGAGTGTCAAGGAGATCTGTGGTAGGTGGCCA
	A allele	G allele	G allele: CCATAGACGTAGGGCAAGACTAGGCACTGGGTGTCAAGGAGATCTGTGGTAGGTGGCCA
BDP1_disc3	11.6	12.1	SNNNDRGSWSDGRS
Brachyury_2	−5.5	6.5	STAGGTGTSAA
Esr2	11.9	10.0	VWRGGTCABNBTRHCCYN
PPAR_1	−15.3	−3.4	CWRAWCYAGGYCAWAGKTCA
SIX5_known1	6.9	12.8	BNVDNGGTRTCAWDYND

Abbreviation: PWM, position weight matrix (Library from Kheradpour and Kellis, 2013).

Figure 2 Evidence from UCSC data for regulatory function of rs7679475. The plot is presented on chromosome 4q27 (NCBI GRCh37). Tracks (from top to bottom) in the picture are genome base position, chromosome bands, UCSC genes, protein interactions from curated databases and text-mining, human mRNAs from GenBank, gene expression, H3K27Ac Mark, DNaseI Hypersensitivity Clusters, ENCODE transcription factor ChIP-seq clusters, ENCODE chromatin state segmentation by HMM, and simple nucleotide polymorphisms (dbSNP 151).