Abstract
Context: Acid phosphatase locus 1 (ACP1) is a low molecular weight tyrosine phosphatase that has been shown to be an important regulator of insulin receptor signaling.
Objective: We tested whether variation in ACP1 is associated with type 2 diabetes-related traits in 1035 individuals in 339 Mexican-American families of probands with or without a previous diagnosis of gestational diabetes mellitus (GDM).
Design: Study participants were phenotyped by oral glucose tolerance test (for glucose and insulin level) and iv glucose tolerance test (for insulin sensitivity and acute insulin response) and had dual-energy x-ray absorptiometry scans to assess body composition. Six tag single nucleotide polymorphisms (SNPs) were identified from among 15 SNPs genotyped across the ACP1 region. SNPs were tested for association with phenotypes using a likelihood ratio test under a variance components framework.
Results: After Bonferroni correction, none of the SNPs were associated with type 2 diabetes mellitus-related phenotypes. However, we observed a significant sex-specific effect of rs3828329. Among males, rs3828329 was significantly associated with fasting insulin (Bonferroni P = 0.007) and insulin sensitivity (Bonferroni P = 0.019) and marginally associated with 2-h insulin (Bonferroni P = 0.058) and percentage body fat (Bonferroni P = 0.09).
Conclusions: There were no significant associations in females. We conclude that variation in ACP1 is associated with fasting insulin and insulin sensitivity in a sex-specific manner.
Variation in acid phosphatase locus 1 is associated with fasting insulin and insulin sensitivity in Mexican American males, but not Mexican American females.
Protein tyrosine phosphatases (PTPs) regulate insulin signaling and therefore are functional candidate genes for type 2 diabetes mellitus (T2DM) and adiposity. Genetic studies thus far have focused on PTP1B, which is encoded by PTPN1 (1,2,3), a gene shown to be associated with T2DM and T2DM-related traits (1,2,4). However, the low molecular weight PTP (LMPTP) is emerging as a second important negative regulator of insulin signaling (5,6,7,8,9). LMPTP, a class II PTP (10) encoded by acid phosphatase locus 1 (ACP1), was originally isolated as an acid phosphatase in red blood cells (5) and was later independently isolated from adipocytes where it is highly expressed (7,8,11). ACP1 efficiently inhibits both metabolic and growth signaling through the insulin receptor (12,13). Recently, Pandey et al. (14) showed correction of metabolic abnormalities associated with adiposity in mice by knocking down ACP1 using antisense oligonucleotides in liver and adipose tissue. The ACP1 knock-down resulted in a phenotype similar to the PTP1B−/− mutation (15), suggesting that ACP1 also directly dephosphorylates the insulin receptor. Overall, the evidence suggests that ACP1 could be a critical regulator of insulin signaling in adipose tissue and a promising drug target to treat obesity and obesity-associated metabolic abnormalities.
The BetaGene Study is a family-based study with the goal of identifying genes influencing variation in T2DM-related quantitative traits. In BetaGene, we are performing detailed phenotyping of Mexican-American probands with recent gestational diabetes mellitus (GDM) and their family members to obtain quantitative estimates of body composition, insulin sensitivity (SI), acute insulin response (AIR) to glucose, and β-cell compensation (disposition index). Variability in these traits is involved in the pathogenesis of T2DM (cf. Refs. 16 and 17) and is heritable in our Mexican-American families (18). Based on the evidence that ACP1 variation may regulate insulin signaling in adipose tissue (10,19,20,21), we examined genetic variation in the ACP1 region and tested the variants for association with T2DM-related quantitative traits and adiposity in our sample of Mexican-Americans.
Subjects and Methods
Subject recruitment
At the time of these analyses, subject recruitment for BetaGene was ongoing, and for the purpose of this report we describe only those subjects, clinical protocols, and assays related to the results presented herein. Subjects are Mexican-American (both parents and at least three grandparents Mexican or of Mexican descent) who are either probands with GDM diagnosed within the previous 5 yr and their family members or probands with normal glucose levels in pregnancy in the past 5 yr. All probands are identified from the patient populations at Los Angeles County/University of Southern California Medical Center, the Kaiser Permanente Southern California health plan membership, and obstetrical/gynecological clinics at local hospitals. Details regarding family recruitment have been described previously (22). Briefly, GDM probands are recruited for phenotyping if they: 1) have a confirmed diagnosis of GDM within the previous 5 yr; 2) have glucose levels associated with poor pancreatic β-cell function and a high risk of diabetes when not pregnant (23); 3) have no evidence of β-cell autoimmunity by glutamic acid decarboxylase-65 testing; and 4) have available for study at least two siblings. Non-GDM probands are recruited if they had a 1-h 50-g glucose screening result below 130 mg/dl (7.2 mm) during their most recent pregnancy, have no family history of diabetes, and have normal glucose tolerance. The non-GDM probands are frequency-matched to GDM probands by age, BMI, and parity categories.
All protocols for BetaGene have been approved by the Institutional Review Boards of participating institutions, and all participants provided written informed consent before participation.
Clinical protocols
Phenotyping was performed on two separate visits to the University of Southern California General Clinical Research Center. The first visit consisted of a physical examination, DNA collection, and a 75-g 2-h oral glucose tolerance test with blood samples obtained before and 30, 60, 90, and 120 min after glucose ingestion. Participants in GDM families with fasting glucose below 126 mg/ml and non-GDM probands with normal fasting and 2-h glucose levels (24) were invited for a second visit, which consisted of a dual-energy x-ray absorptiometry scan for body composition and an insulin-modified iv glucose tolerance test analyzed by minimal model (MINMOD Millennium V5.18; Richard N. Bergman, Los Angeles, CA) to quantify glucose effectiveness (SG), SI, AIR, and the disposition index (DI = SI × AIR) (25).
Assays
Plasma glucose is measured on an autoanalyzer using the glucose oxidase method (YSI Model 2300; Yellow Springs Instruments, Yellow Springs, OH). Insulin is measured by two-site immunoenzymometric assay (TOSOH Corp., San Francisco, CA) that has less than 0.1% cross-reactivity with proinsulin and intermediate split products.
Molecular analysis
We screened the ACP1 region by selecting polymorphic single nucleotide polymorphisms (SNPs) from HapMap (http://hapmap.org/) and dbSNP (http://www.ncbi.nlm.nih.gov/SNP/) at approximately 2.5-Kb intervals across the coding region of the gene, 40 Kb upstream and 10 Kb downstream from the gene. The extra 50 Kb beyond the coding regions were examined to ensure that we did not miss potential nearby regulatory elements. We preferentially selected SNPs that had minor allele frequency greater than 5% in all four HapMap populations. In addition to the 13 SNPs selected from HapMap and dbSNP, we also include two SNPs believed to have functional consequence (rs11553742 and rs7576247) (7). All SNPs were genotyped using the TaqMan system of Applied Biosystems, Inc. (Foster City, CA) (ABI) (26,27). Genotyping assays were either selected through ABI’s “Assays on Demand” database (https://products.appliedbiosystems.com/ab/en/Us/adirect/ab?cmd=ABGTKeywordSearch&catID=600769) or custom designed using ABI’s “Assays by Design” service.
Data analysis
Genotype data were tested for deviation from Hardy-Weinberg equilibrium and for non-Mendelian inheritance using PEDSTATS V0.6.4 (28). Allele frequencies were estimated by maximum likelihood using all available data, taking into account relatedness using SOLAR V2.1.4 (29). Haplotype frequencies were estimated using FUGUE (http://www.sph.umich.edu/csg/abecasis/FUGUE/), which uses MERLIN to enumerate possible haplotypes for founders and then uses the E-M algorithm to estimate haplotype frequencies. Pair-wise linkage disequilibrium (LD) and haplotype block structure were assessed using Haploview V4.1 (30). Haplotype block structure was assessed using the method of Gabriel et al. (31). Tag SNPs were selected from among the genotyped SNPs using aggressive tagging with two- and three-marker haplotypes in TAGGER as implemented in Haploview (30). We used the default settings of pair-wise r2 ≥ 0.8 and LOD threshold of 3.0.
Quantitative trait data were statistically transformed to approximate univariate normality before analyses. Each tag SNP was then tested individually for associations with T2DM-related traits using likelihood ratio testing under a variance components framework using SOLAR (29). Because families were ascertained on probands with or without previous GDM, we corrected for ascertainment bias by conditioning on the proband’s phenotype. SNPs were coded by the number of minor alleles under an additive genetic model, and the univariate association between tag SNPs and traits was adjusted for age and sex. Any result under the additive model that showed a trend for association, defined as P = 0.1 Bonferroni corrected for the number of SNPs examined (corrected P = 0.017), was subsequently tested for association under dominant and/or recessive genetic models.
Given prior evidence that variation in ACP1 may be associated with adiposity (7,11,32), we performed an independent series of tests to examine whether adiposity might alter the association between variation in ACP1 and T2DM-related quantitative traits. Specifically, we compared a model with age, sex, and percentage body fat as main effects with a second model that included a main effect for the SNP and the multiplicative interaction between the SNP and percentage body fat. We tested for the overall gene effect using a 2-df test and also tested for a significant interaction effect using a 1-df test.
We also considered the possibility that sex-specific effects may exist, given previous observations on serum triglyceride levels in females (33). These analyses were similar to that performed for percentage body fat described above. We compared a model with main effects for covariates and sex to a second model that included a SNP main effect and the multiplicative interaction between the SNP and sex. A 2-df likelihood ratio test for the association was performed by jointly testing for the main effect of the SNP and the SNP-sex interaction. A 1-df test of the interaction term alone was also performed. We repeated the test of association between variation in ACP1 and T2DM-related quantitative traits stratifying by sex for significant interactions.
For the interaction and sex-stratified analyses, we applied a Bonferroni correction to account for the multiple SNPs and traits tested (6 SNPs × 14 traits = 84 for the adiposity and sex interactions and 3 SNPs × 14 traits = 42 for sex-stratified). However, Bonferroni assumes independence among tests, and there exists correlation both among the SNPs and among the quantitative traits we examined. Therefore, the Bonferroni-corrected P values we report should be overly conservative. We also applied the PACT approach (34) to adjust P values for our most significant results. PACT adjusts significance levels accounting for the correlation among both SNPs and traits across all tests.
All results are reported as age- and sex-adjusted means and sd values, unless otherwise specified.
Results
We report results from 1035 individuals in 339 families for whom both phenotype and genotype data were available. Subject characteristics are shown in Table 1. In general, GDM probands, siblings, and cousins were similar in median age, BMI, and percentage body fat, although these characteristics tended to be highest in the GDM probands and lowest in the cousins. The median BMI exceeded the threshold for overweight (25 kg/m2) in all three groups and exceeded the threshold for obese (30 kg/m2) only in the GDM probands. The non-GDM probands were of similar age as GDM probands but were less likely to be obese, reflecting the fact that BetaGene participant accrual was ongoing at the time of analysis and recruitment of non-GDM probands was lagging behind GDM probands to allow for matching as described above.
Table 1.
Subject characteristics
| GDM probands | Siblings | Cousins | Non-GDM probands | |
|---|---|---|---|---|
| Females/males (n) | 225/0 | 272/184 | 147/116 | 91/0 |
| Age (yr) | 35.3 (6.5) | 34.3 (12.3) | 32.9 (12.9) | 34.0 (5.8) |
| BMI (kg/m2) | 30.4 (7.8) | 29.1 (6.8) | 27.8 (7.4) | 28.5 (7.0) |
| Body fat (%) | 38.7 (7.7) | 33.4 (13.0) | 31.2 (13.2) | 36.6 (7.2) |
| Fasting glucose (mm) | 5.2 (0.7) | 5.1 (0.6) | 5.1 (0.6) | 4.9 (0.6) |
| 2-h glucose (mm) | 8.0 (3.3) | 7.3 (2.7) | 6.6 (2.4) | 6.6 (2.1) |
| Fasting insulin (pm) | 54 (48) | 42 (42) | 36 (36) | 36 (30) |
| 2-h insulin (pm) | 450 (429) | 354 (372) | 306 (342) | 252 (306) |
| 30-min ΔInsulin (pm) | 363 (282) | 384 (282) | 408 (342) | 384 (300) |
| SG (×10−2 per min−1) | 1.47 (0.69) | 1.64 (0.80) | 1.79 (0.85) | 1.90 (0.92) |
| SI (×10−3 per min−1 per pm) | 2.62 (1.75) | 2.61 (1.99) | 2.89 (1.87) | 3.31 (1.89) |
| AIR (pm × 10 min) | 3,456 (4,275) | 4,310 (5,047) | 4,830 (5,340) | 5,530 (4,307) |
| Disposition index | 8,786 (9,492) | 11,944 (11,566) | 13,264 (10,251) | 17,431 (10,440) |
| Cholesterol (mm) | 4.37 (1.16) | 4.50 (1.19) | 4.42 (1.06) | 4.01 (1.03) |
| HDL (mm) | 1.24 (0.36) | 1.14 (0.36) | 1.22 (0.41) | 1.22 (0.34) |
| Triglyceride (mm) | 2.51 (2.07) | 2.77 (2.48) | 2.30 (1.84) | 1.73 (1.47) |
Data are reported as unadjusted median and interquartile range. HDL, High-density lipoprotein.
Among the 15 SNPs genotyped, the assay for rs10171111 failed and rs11691572 was monomorphic in our sample. The LD and haplotype block structure for the remaining 13 SNPs are shown in Fig. 1. In Mexican-Americans, these 13 SNPs form a single 30.3 kb haplotype block that encompasses and extends beyond the ACP1 coding region. This block structure is similar to what is observed in the HapMap CEU population (www.hapmap.org). The SNPs in this region formed 33 total haplotypes, with five having frequencies greater than 5% (Supplemental Table 1, published as supplemental data on The Endocrine Society’s Journals Online web site at http://jcem.endojournals.org). The three most frequent haplotypes had similar frequencies in our sample of Mexican-Americans (21.3–27.4%), followed by one haplotype with frequency of approximately 11% and another with a frequency of approximately 8%.
Figure 1.
ACP1 pairwise LD and haplotype block structure. Pairwise LD and haplotype block structure as determined by Gabriel method as implemented in Haploview V4.0 for the 13 SNPs (one monomorphic SNP at position 254985, rs11691572, is excluded) genotyped in our Mexican-American families. The 12 SNPs form a single 30.3-kb haplotype block. LD is displayed as pair-wise r2 values (values within boxes), where white indicates r2 = 0, varying shades of gray indicate 0 < r2 < 1, and black indicates r2 = 1.
Six SNPs were selected as tags for the ACP1 region (Supplemental Table 2) and were tested for association with T2DM-related quantitative traits. Multimarker tags were not required to capture the variation in the region. Among the six tag SNPs, three (rs11553742, rs3828329, and rs10167992) showed nominal evidence for association with T2DM-related quantitative traits. The strongest associations were observed between rs10167992 and BMI (P = 0.009), rs3828329 and SI (P = 0.008), and rs11553742 and triglycerides (P = 0.005) under an additive genetic model. However, none of these associations remained significant after correction for multiple testing. Complete results for all 13 SNPs are presented in Supplemental Table 3. Similarly, when we tested whether the association between tag SNPs and T2DM-related quantitative traits was modified by percentage body fat, we observed nominal associations that became nonsignificant when corrected for multiple testing (data not shown).
We used a 2-df test to assess whether tag SNPs were associated with T2DM-related quantitative traits in the presence of an interaction with sex. The interaction between sex and rs11553742, rs3828329, and rs10167992 all showed nominal evidence for association with one or more traits (Table 2). We further explored the association between these three tag SNPs and T2DM-related quantitative traits by stratifying on sex and observed a striking difference between males and females (Tables 3–5 and Fig. 2).
Table 2.
Results of associations between T2DM-related quantitative traits and the interaction between SNPs in ACP1 and sex
| Trait | rs11553742a (T/0.02)b
|
rs3828329a (T/0.24)b
|
rs10167992a (T/0.10)b
|
|||
|---|---|---|---|---|---|---|
| 2-df | 1-df | 2-df | 1-df | 2-df | 1-df | |
| BMI | 0.1940 | 0.2292 | 0.0443 | 0.0473 | 0.0259 | 0.5025 |
| Body fat | 0.0314 | 0.0401 | 0.0068 | 0.0181 | 0.1441 | 0.9597 |
| Fasting glucose | 0.5532 | 0.3722 | 0.0248 | 0.0066 | 0.1918 | 0.1034 |
| 2-h glucose | 0.4221 | 0.3451 | 0.0426 | 0.0167 | 0.0589 | 0.0176 |
| Fasting insulin | 0.1273 | 0.0447 | 0.0001 | 0.0002 | 0.0163 | 0.0054 |
| 2-h insulin | 0.7363 | 0.5732 | 0.0048 | 0.0100 | 0.0365 | 0.0123 |
| 30-min ΔInsulin | 0.1638 | 0.0574 | 0.0066 | 0.0194 | 0.9011 | 0.8307 |
| SG | 0.1854 | 0.8010 | 0.9943 | 0.9710 | 0.6725 | 0.3876 |
| SI | 0.2492 | 0.0957 | 0.0025 | 0.0247 | 0.0067 | 0.0143 |
| AIR | 0.2442 | 0.2601 | 0.6179 | 0.7619 | 0.6997 | 0.4080 |
| Disposition index | 0.4630 | 0.8048 | 0.0744 | 0.0871 | 0.0837 | 0.2527 |
| Cholesterol | 0.7024 | 0.4062 | 0.8400 | 0.7316 | 0.0207 | 0.0087 |
| HDL | 0.0827 | 0.5263 | 0.8029 | 0.5163 | 0.1492 | 0.5988 |
| Triglyceride | 0.0027 | 0.0472 | 0.3865 | 0.1699 | 0.0123 | 0.0032 |
HDL, High-density lipoprotein.
Nominal P value not corrected for multiple testing.
Minor allele/minor allele frequency.
Table 3.
Results of single marker tests of association between tag SNPs in ACP1 and T2DM-related quantitative traits in females only
| rs11553742 (T/0.02)a
|
rs3828329 (T/0.24)a
|
rs10167992 (T/0.10)a
|
|||||||
|---|---|---|---|---|---|---|---|---|---|
| Effectb | P value | Bonferroni P valuec | Effectb | P value | Bonferroni P valuec | Effectb | P value | Bonferroni P valuec | |
| BMI | − | 0.1240 | 1.0 | − | 0.9847 | 1.0 | − | 0.1334 | 1.0 |
| Body fat | − | 0.0377 | 1.0 | + | 0.4858 | 1.0 | − | 0.1786 | 1.0 |
| Fasting glucose | − | 0.3782 | 1.0 | − | 0.0878 | 1.0 | + | 0.1458 | 1.0 |
| 2-h glucose | − | 0.1492 | 1.0 | − | 0.1733 | 1.0 | + | 0.2379 | 1.0 |
| Fasting insulin | − | 0.1385 | 1.0 | − | 0.4767 | 1.0 | + | 0.2959 | 1.0 |
| 2-h insulin | + | 0.9217 | 1.0 | − | 0.6981 | 1.0 | + | 0.3530 | 1.0 |
| 30-min ΔInsulin | − | 0.4207 | 1.0 | + | 0.6925 | 1.0 | − | 0.7982 | 1.0 |
| SG | + | 0.1297 | 1.0 | − | 0.9615 | 1.0 | − | 0.7020 | 1.0 |
| SI | + | 0.3371 | 1.0 | − | 0.3440 | 1.0 | + | 0.8353 | 1.0 |
| AIR | − | 0.6524 | 1.0 | − | 0.3721 | 1.0 | − | 0.6205 | 1.0 |
| Disposition index | + | 0.2215 | 1.0 | − | 0.9916 | 1.0 | + | 0.4496 | 1.0 |
| Cholesterol | + | 0.3998 | 1.0 | + | 0.7952 | 1.0 | + | 0.0270 | 1.0 |
| HDL | + | 0.0470 | 1.0 | + | 0.5765 | 1.0 | + | 0.3233 | 1.0 |
| Triglyceride | − | 0.0039 | 0.164 | − | 0.3176 | 1.0 | + | 0.0579 | 1.0 |
HDL, High-density lipoprotein.
Minor allele/minor allele frequency.
Refers to the directional effect of the minor allele on the quantitative trait based on the regression coefficient and assuming an additive genetic model.
Bonferroni corrected P value. Correction is made for three SNPs and 14 quantitative traits tested.
Table 4.
Results of single marker tests of association between tag SNPs in ACP1 and T2DM-related quantitative traits in males only
| rs11553742 (T/0.02)a
|
rs3828329 (T/0.24)a
|
rs10167992 (T/0.10)a
|
|||||||
|---|---|---|---|---|---|---|---|---|---|
| Effectb | P value | Bonferroni P valuec | Effectb | P value | Bonferroni P valuec | Effectb | P value | Bonferroni P valuec | |
| BMI | − | 0.7898 | 1.0 | + | 0.0362 | 1.0 | − | 0.0431 | 1.0 |
| Body fat | − | 0.9585 | 1.0 | + | 0.0022 | 0.094 | − | 0.1627 | 1.0 |
| Fasting glucose | − | 0.9702 | 1.0 | + | 0.0253 | 1.0 | − | 0.4516 | 1.0 |
| 2-h glucose | + | 0.9736 | 1.0 | + | 0.0136 | 0.572 | − | 0.0488 | 1.0 |
| Fasting insulin | + | 0.3148 | 1.0 | + | 0.0002 | 0.007 | − | 0.0266 | 1.0 |
| 2-h insulin | + | 0.5497 | 1.0 | + | 0.0014 | 0.058 | − | 0.0688 | 1.0 |
| 30-min ΔInsulin | + | 0.1120 | 1.0 | + | 0.0034 | 0.142 | + | 0.4763 | 1.0 |
| SG | + | 0.2804 | 1.0 | − | 0.9183 | 1.0 | + | 0.3884 | 1.0 |
| SI | − | 0.1742 | 1.0 | − | 0.0005 | 0.019 | + | 0.0036 | 0.150 |
| AIR | − | 0.0611 | 1.0 | − | 0.2987 | 1.0 | + | 0.8421 | 1.0 |
| Disposition index | + | 0.5292 | 1.0 | − | 0.0323 | 1.0 | + | 0.0322 | 1.0 |
| Cholesterol | − | 0.7921 | 1.0 | + | 0.6129 | 1.0 | − | 0.2098 | 1.0 |
| HDL | + | 0.2061 | 1.0 | − | 0.5666 | 1.0 | + | 0.2277 | 1.0 |
| Triglyceride | − | 0.5746 | 1.0 | + | 0.3594 | 1.0 | − | 0.0610 | 1.0 |
HDL, High-density lipoprotein.
Minor allele/minor allele frequency.
Refers to the directional effect of the minor allele on the quantitative trait based on the regression coefficient and assuming an additive genetic model.
Bonferroni corrected P value. Correction is made for three SNPs and 14 quantitative traits tested.
Table 5.
Association between rs3828329 and fasting insulin and SI in males and females
| Trait | Sex | Genotypic mean (sd)a
|
Per allele effect | Standardized effect size | Bonferroni P value | ||
|---|---|---|---|---|---|---|---|
| C/C | C/T | T/T | |||||
| Fasting insulin (pm) | Males | 47.40 (3.23) | 60.52 (3.50) | 73.70 (6.21) | 13.149 | 3.753 | 0.007 |
| Females | 55.35 (3.35) | 53.32 (3.74) | 50.41 (6.90) | 2.472 | 0.673 | 1.0 | |
| SI(×10−3 per min−1 per pm) | Males Females | 3.16 (0.17) 3.08 (0.13) | 2.76 (0.18) 2.95 (0.15) | 2.37 (0.30) 2.82 (0.26) | 0.393 0.130 | 2.165 0.891 | 0.019 1.0 |
Values are adjusted for age.
Figure 2.
Interaction between rs3828329 and sex on fasting insulin and SI. Top, Age-adjusted mean fasting insulin and sd stratified by sex and rs3828329 genotype. There was a strong association between fasting insulin and genotype among males (Bonferroni P = 0.007), but not among females (Bonferroni P = 1.0). Bottom, Age-adjusted mean SI and sd stratified by sex and rs3828329 genotype. Like fasting insulin, there was a strong association between SI and genotype among males (Bonferroni P = 0.019), but not among females (Bonferroni P = 1.0).
Among females, we observed nominal associations between rs11553742 and triglycerides (P = 0.004), but this did not remain significant after Bonferroni correction for multiple testing (Table 3). Thus, variation in ACP1 did not appear to have any detectable effect on T2DM-related quantitative traits in females (Table 3). However, among males rs3828329 showed nominal evidence for association with BMI (P = 0.036), percentage body fat (P = 0.002), fasting and 2-h glucose (P = 0.025 and P = 0.014, respectively), fasting and 2-h insulin (P = 0.0002 and P = 0.001, respectively), 30-min ΔInsulin (P = 0.003), SI (P = 0.0005), and disposition index (P = 0.032) (Table 4). rs11553742 only showed marginal association with AIR (P = 0.061), whereas rs10167992 showed weak association with several T2DM-realted traits (P < 0.049), with the strongest association being with SI (P = 0.004) (Table 4).
After correction for multiple testing, rs3828329 remained significantly associated with fasting insulin (Bonferroni P = 0.007, PACT = 0.013) and SI (Bonferroni P = 0.019, PACT = 0.034) and marginally associated with 2-h insulin (Bonferroni P = 0.058) and percentage body fat (Bonferroni P = 0.09) (Table 4). Each copy of the rs3828329 T allele increased fasting insulin by approximately 13.1 pm (∼24.7%/allele) in males but modestly decreased fasting insulin by approximately 2.5 pm (∼4.6%/allele) in females (Fig. 2, top, and Table 5). The increase in fasting insulin with number of T alleles in males could reflect compensation for increasing insulin resistance as demonstrated by the decrease in SI (0.39 SI units per allele or ∼13.3%/allele) that is conferred by the T allele (Fig. 2, bottom, and Table 5). The association between rs3828319 and both fasting insulin and SI in males remained significant even after the additional adjustment for percentage body fat (P = 0.005 and P = 0.020, respectively).
Discussion
In the present study, we examined whether variation in ACP1, an acid phosphatase involved in insulin signaling, was associated with T2DM-related quantitative traits. To our knowledge, this is the first report in which variation at the ACP1 was comprehensively tested for association with such traits in humans. Previous reports have been restricted to analysis of either biochemical genotypes or single SNPs (7,8,9,11,19,21,33,35,36). Our analysis reveals that rs3828329, which is located in the 3′ untranslated region of ACP1, is associated with fasting insulin and SI in a sex-specific manner. The T allele of rs3828329 appears to increase fasting insulin and decrease SI in males, but not females.
Sex-specific effects of ACP1 have been reported for other phenotypes (37,38,39). For example, Bottini et al. (37) showed that variation in ACP1 was associated with increased age at onset of type 1 diabetes in male children and decreased age at onset in female children. In a separate study, they also showed that variation in ACP1 was associated with triglycerides in obese females (33). We also found nominal evidence for association between rs11553742 and triglycerides in female participants in BetaGene (cf. Table 3), although the association became nonsignificant after correction for multiple testing. Gloria-Bottini et al. (39) showed that the ACP1 biochemical genotype ACP1*A was associated with increased susceptibility to allergy in females compared with males. Mechanisms underlying the various sex-specific effects of ACP1 remain to be determined.
rs11553742 and rs7576247, which are reported to be correlated with the levels of ACP1-F (also called LMPTP-A) and ACP1-S (also called LMPTP-B) isoenzymes (20), respectively, did not show any evidence for association with T2DM-related quantitative traits. It is also noteworthy that LD between rs3828329 and both rs11553742 (r2 = 0.073) and rs7576247 (r2 = 0.105) is very weak. Additionally, rs11691572, which results in a Asn→Lys substitution at codon 7, was monomorphic in our sample, consistent with observations from the HapMap populations (www.hapmap.org). Thus, our analysis suggests that the functionally relevant variant(s) associated with metabolic phenotypes in Mexican-Americans is distinct from the previously described biochemical alleles.
Given our study population, it is possible that population stratification may confound our analyses. However, two lines of evidence suggest that this is not the case. First, our recruitment strategy was such that both parents and three of four grandparents of probands were required to have been born in Mexico. Although such selection does not guarantee a sample devoid of population substructure, it should minimize the level of admixture in our sample. Second, when we reanalyze the association for rs3828329 with fasting insulin and SI in males using the family-based association test (40), which is protected from population stratification, we still see evidence for association (fasting insulin P = 0.013 and SI P = 0.066). The reduction in the magnitude of the association is expected, given that the family-based association test is based on allelic transmissions, which reduces the efficiency and power of the test relative to the variance components approach. Thus, these results suggest that our results are not confounded by population substructure and may represent true associations between variation in ACP1 and both fasting insulin and SI.
Mechanistic studies suggest that ACP1 is a negative regulator of insulin receptor phosphorylation (12,13,14,15) and may alter insulin receptor signaling. For example, Pandey et al. performed a series of in vitro and in vivo experiments in which ACP1 expression was suppressed using an antisense oligonucleotide (14). Their results show that both in culture mouse hepatocytes and in liver and fat tissue isolated from diet-induced obese and ob/ob mice, the presence of the antisense oligonucleotide resulted in a reduction of ACP1, a concomitant decrease in insulin receptor phosphorylation, and significant changes in activities of key components of the insulin receptor signaling pathway (e.g. phosphatidylinositol-3-kinase, Akt) (14). Furthermore, diet-induced obese and ob/ob mice treated with the antisense oligonucleotide exhibited hyperphosphorylation of the insulin receptor, subsequent improvement in insulin sensitivity, and reductions in fasting glucose and insulin. Treatment by antisense oligonucleotide did not appear to change body weight or increase metabolic rate assessed by indirect calorimetry (14). These phenotypic changes are similar to our observations in Mexican-Americans where variation in rs3828329 appears to alter fasting insulin and SI, at least in males. Pandey et al. (14) did not report sex-specific effects.
Unfortunately, the location of rs3828329 does not provide information about the potential location of the putative functional variant, given the LD structure in this region (cf. Fig. 1). For example, rs3828329 is in strong LD with rs4455191 (D′ = 0.99; r2 = 0.96) and rs4255987 (D′ = 0.99; r2 = 0.99), which are 8.6 and 5.6 kb 5′ from exon 1, respectively. LD is also strong with rs6755722 (D′ = 1.0; r2 = 1.0), located in intron 1, and rs4452185 (D′ = 0.98; r2 = 0.96) located in intron 4. We therefore performed haplotype-based tests of association to determine whether a specific haplotype might be associated with T2DM-related quantitative traits and provide insights into the region that may harbor the putative functional variant. We assigned most probable haplotypes to each individual and performed additional tests of association. However, our analyses, which showed that the CGCCACGCAATTT haplotype (haplotype 3 in Supplemental Table 1) was associated with our phenotypes of interest (data not shown), did not yield information beyond that observed from our single SNP analyses. Therefore, additional studies will be required to identify the putative functional variant(s).
In summary, we screened genetic variation across the coding region of ACP1 and for association with type 2 diabetes-related quantitative traits in a large sample of Mexican-Americans from families of probands with a previous diagnosis of GDM. In univariate analyses, ACP1 variation was not associated with type 2 diabetes-related quantitative traits, nor did we detect an effect of body fat to modify the association between variation in ACP1 and these traits. However, we did observe a significant association between fasting insulin and SI and the interaction between rs3828329 and sex. Specifically, SI decreased and fasting insulin increased with each copy of the T allele for rs3828329 in males, whereas there was no such effect in females. Our results, combined with the known biology of ACP1, suggest that there is a sex-specific effect of variation in ACP1 to alter insulin signaling. The specific mechanism underlying this effect will require additional study.
Supplementary Material
Acknowledgments
We thank the families who participated in the BetaGene study and are grateful for the support of the University of Southern California (USC) General Clinical Research Center. We also acknowledge the efforts of our recruiting and technical staff.
Footnotes
This work was supported by National Institutes of Health Grant DK-61628; an American Diabetes Association Distinguished Clinical Scientist Award (to T.A.B.); and the University of Southern California General Clinical Research Center (M01-RR-00043). A portion of this work was conducted in a facility constructed with support from Research Facilities Improvement Program, Grant C06 (RR10600-01, CA62528-01, and RR14514-01), from the National Center for Research Resources.
Disclosure Summary: Y-H.S., J.H., A.H.X., E.T., J.M.L., H.A., T.A.B., and R.M.W. have nothing to declare. N.B. is an inventor on U.S. Patents 2005/0055732 and 2008/0241928.
First Published Online July 21, 2009
Abbreviations: ACP1, Acid phosphatase locus 1; AIR, acute insulin response; GDM, gestational diabetes mellitus; LD, linkage disequilibrium; LMPTP, low molecular weight PTP; PTP, protein tyrosine phosphatase; SG, glucose effectiveness; SI, insulin sensitivity; SNP, single nucleotide polymorphism; T2DM, type 2 diabetes mellitus.
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