Literature DB >> 23672255

Polymorphisms of PTPN11 gene could influence serum lipid levels in a sex-specific pattern.

Zhi-Fang Jia1, Xue-Yuan Cao, Dong-Hui Cao, Fei Kong, Punyaram Kharbuja, Jing Jiang.   

Abstract

BACKGROUND: Previous studies have reported that different genotypes of PTPN11 gene (protein tyrosine phosphatase, non-receptor 11) were associated with different levels of serum lipids. The aim of this study was to explore the relationship between single nucleotide polymorphisms (SNPs) of PTPN11 and serum lipids in Northeast Chinese.
METHODS: A total of 1003 subjects, 584 males and 419 females, were included in the study and their serum lipids were determined. Five htSNPs (rs2301756, rs12423190, rs12229892, rs7958372 and rs4767860) of PTPN11 gene were genotyped using TaqMan assay method.
RESULTS: All of the five SNPs were in Hardy-Weinberg equilibrium. The male subjects had higher triglyceride (TG), higher low-density lipoprotein cholesterol (LDL-C) and lower high-density lipoprotein cholesterol (HDL-C) level than females. In males, rs4767860 was found to be associated with serum TG and total cholesterol (TC) levels and rs12229892 was associated with TC level. However, these significant associations could not be observed in females. In females, rs2301756 was found to be associated with TG and rs7958372 was associated with LDL-C level. Haplotype analysis showed that the GCGTG haplotype was associated with slightly higher TG level and ATGCG with higher TC level.
CONCLUSIONS: SNPs of PTPN11 may play a role in serum lipids in a sex-specific pattern. However, more studies are needed to confirm the conclusion and explore the underlying mechanism.

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Year:  2013        PMID: 23672255      PMCID: PMC3685535          DOI: 10.1186/1476-511X-12-72

Source DB:  PubMed          Journal:  Lipids Health Dis        ISSN: 1476-511X            Impact factor:   3.876


Introduction

Dyslipidemia such as the increased levels of total cholesterol (TC), triglyceride (TG) or the decreased level of high-density lipoprotein cholesterol (HDL-C) has been concluded to be involved in the higher risk of cardiac-cerebral vascular disease [1]–[3] and has become a serious public health problem [4]. It is a complex trait that many factors, environmental and genetic [5,6], have been reported to be associated with it. However, these factors could only explain part of the total variance, and more factors need to be identified. Src homology-2 domain-containing protein tyrosine phosphatase 2 (SHP2), a ubiquitously expressed protein tyrosine phosphatase, plays an essential role in many cell signaling events such as metabolic control and transcription regulation [7,8]. SHP2 could regulate the apoB (apolipoprotein B) secretion in insulin-dependent pattern via phosphatidylinositol 3′-kinase [9,10]. SHP2 activity was associated with the expression of the fatty acid-metabolizing enzyme Acyl-CoA synthetase 4 (ACSL4) [11] and the synthesis of steroid [12]. SHP2 deletion mice could develop a profile of higher serum levels of cholesterol, TG, and low-density lipoprotein [8]. Single nucleotide polymorphisms (SNPs) of protein tyrosine phosphatase, non-receptor 11 (PTPN11) gene, which encodes SHP2, may be associated with serum lipid levels via changing the activity of SHP2 on lipometabolism. Jamshidi et al. first reported that one of the tagging SNPs of the PTPN11 gene, rs11066320, was associated with serum low-density lipoprotein cholesterol (LDL-C) level in normal Caucasian female twins [13] and Lu et al. reported that rs11066322 was associated with plasma HDL-C level. The data from Hapmap database show that variants of PTPN11 gene present great varieties in different ethnicities. The role of PTPN11 gene on lipid profile has not been described in Chinese so far. The aim of this study was to explore the association of tagging SNPs of PTPN11 gene and lipid levels in Chinese normal people.

Methods

Subjects

From January to December 2009, people who attended the physical examination center of the First Hospital of Jilin University were invited to the study. A total of 1080 persons signed the informed consent and agreed to participate in this study. Subjects who had been taking lipid-lowing medication were excluded from the analysis (n = 73). At last, 1003 subjects, 584 males and 419 females, were included in the analysis. The range of age was from 35 to 79 years, with a median of 49 years. This study protocol was approved by the ethics committee of the First Hospital of Jilin University. Venous blood samples were obtained from all subjects after overnight fasting. The levels of serum TC, TG, HDL-C and LDL-C were determined by enzymatic methods in an autoanalyzer (Type 7600; Hitachi Ltd., Japan) in our Clinical Laboratory Center. The inter-day coefficient variations (CV) of the two distinct analyte levels (Bio-Rad, USA) of the lab were 3.17% and 3.90% for TC, 2.74% and 2.64% for TG, 3.85% and 4.08% for HDL-C, 3.72% and 3.37% for LDL-C during the researching period.

Tagging SNPs selection and genotyping

SNP tagging was to identify a set of SNPs that efficiently tags all known SNPs. Haplotype tagging SNPs (htSNPs) were selected from the Han Chinese data in the HapMap Project (06-02-2009 HapMap) using the SNPbrowser™ Software v4.0 to capture SNPs with a minimum minor allele frequency (MAF) of 0.05 with a pair-wise r square of 0.8 or greater [14]. There were nine SNPs at MAF > 0.05 in the PTPN11 gene in Chinese on HapMap, all of which were located in non-coding regions. Five SNPs, rs2301756, rs12423190, rs12229892, rs7958372 and rs4767860, were selected as htSNPs for further study. Genomic DNA was extracted from whole blood following the protocols provided by the manufacturer (Axygen, USA). Genotypes of each SNP were determined using TaqMan SNP genotying assays (Applied Biosystems, USA) and the detailed process of polymerase chain reaction (PCR) was described elsewhere [15]. The amplified products of PCR were read on ABI PRISM 7900 Sequence Detector in the end-point mode and genotypes were identified using the Allelic Discrimination Sequence Detector Software V2.3.

Statistical analysis

Categorical data were described as frequency and percentage and compared using χ2 test or Fisher exact test when appropriate. Continuous variables were summarized as median (25th to 75th percentiles) and compared by Kraskal-Wallis test among groups. The frequencies of genotypes of each SNP were determined via direct counting and deviation from Hardy-Weinberg equilibrium was assessed by a goodness-of-fit χ2 test. Levels of TC, TG, HDL-C and LDL-C were transformed to their logarithms to improve the normality of distribution. Associations of the SNPs and lipid levels were assessed using analysis of covariance within each gender type, adjusted for age, body mass index (BMI) and waist circumference. The above analyses were performed in SAS 9.1.3 software (SAS Institute Inc, USA). For haplotypes with frequencies >1%, their associations with lipids were assessed compared to the most common haplotype using the linear regression model with the HAPSTAT software 3.0 [16]. The statistical significance was P < 0.05.

Results

The baseline characteristics of the subjects are shown in Table 1. The body mass index (BMI) was higher than 24.0 Kg/m2 in half of the subjects (the median value of BMI was 24.0 Kg/m2, with a quartile range from 21.9 to 26.1 Kg/m2). No difference was observed between males and females in terms of age, but BMI and waist circumference were higher in males than in females.
Table 1

Characteristics of subjects included

 All (n= 1003)Male (n= 584)Female (n= 419)P
Age (year)
49 (45–55)
49 (45–54)
48 (44–56)
0.640
Waist (cm)
85 (77–92)
90 (84–94)
77 (72–83)
<0.001
BMI (Kg/m2)
24.0 (21.9–26.2)
24.9 (23.0–26.7)
22.6 (20.7–24.9)
<0.001
TG (mmol/L)
1.44 (0.98–2.12)
1.61 (1.16–2.44)
1.21 (0.84–1.73)
<0.001
TC (mmol/L)
5.04 (4.49–5.66)
5.03 (4.48–5.68)
5.09 (4.52–5.64)
0.669
HDL-C (mmol/L)
1.33 (1.15–1.56)
1.27 (1.10–1.45)
1.48 (1.27–1.70)
<0.001
LDL –C (mmol/L)
3.09 (2.63–3.60)
3.10 (2.69–3.63)
3.00 (2.53–3.54)
<0.001
rs2301756
 GG
750 (74.8%)
439 (75.2%)
311 (74.2%)
0.625
 GA
232 (23.1%)
131 (22.4%)
101 (24.1%)
 AA
21 (2.1%)
14 (2.4%)
7 (1.7%)
rs12423190
 TT
515 (51.3%)
304 (52.0%)
211 (50.4%)
0.782
 TC
399 (39.8%)
227 (38.9%)
172 (41.0%)
 CC
89 (8.9%)
53 (9.1%)
36 (8.6%)
rs12229892
 GG
342 (34.1%)
189 (32.4%)
153 (36.5%)
0.271
 GA
485 (48.4%)
285 (48.8%)
200 (47.7%)
 AA
176 (17.5%)
110 (18.8%)
66 (15.8%)
rs7958372
 TT
751 (74.9%)
439 (75.2%)
312 (74.5%)
0.962
 TC
235 (23.4%)
135 (23.1%)
100 (23.9%)
 CC
17 (1.7%)
10 (1.7%)
7 (1.7%)
rs4767860
 AA
335 (33.4%)
198 (33.9%)
137 (32.7%)
0.666
 GA
480 (47.9%)
282 (48.3%)
198 (47.3%)
 GG188 (18.7%)104 (17.8%)84 (20.0%)

Unless indicated, data were described as median (Q1–Q3).

Characteristics of subjects included Unless indicated, data were described as median (Q1–Q3). The linkage disequilibrium structure of the five SNPs studied, rs2301756, rs12423190, rs12229892, rs7958372 and rs4767860 is presented in Table 2. They were all in linkage disequilibrium, though to different extents. All of the five SNPs were in Hardy-Weinberg equilibrium (P = 0.540, 0.354, 0.778, 0.858, 0.489, respectively). There were no significant differences in the distribution of genotypes between males and females (Table 1). And no differences were observed among genotypes of each SNP in terms of age, sex, BMI and waist circumference (data were not shown).
Table 2

The linkage disequilibrium coefficient (Lewontin’sD’ and r) between SNPs of PTPN11

 
rs2301756
rs12423190
rs12229892
rs7958372
rs4767860
rs2301756
--
0.038
0.103
0.871
0.194
rs12423190
0.774
--
0.289
0.055
0.509
rs12229892
0.953
1.000
--
0.107
0.512
rs7958372
0.944
0.937
0.984
--
0.201
rs47678600.9560.9680.9800.982--

Values on the left of “--” were Lewontin’s D’ coefficients and on the right were r2.

The linkage disequilibrium coefficient (Lewontin’sD’ and r) between SNPs of PTPN11 Values on the left of “--” were Lewontin’s D’ coefficients and on the right were r2. As lipid levels of males were different from those of females, except for cholesterol (Table 1), separate analyses were performed on the association of lipid levels and SNPs. In males, the median serum level of TG was 1.61 mmol/L, with a quartile range 1.16–2.44 mmol/L. Rs4767860 and rs12229892 were observed to be associated with TG level after controlling for the effects of age, waist circumference and BMI in male subjects. The genotype GG or GA of rs4767860 was found to be with higher TG level compared to the most common genotype AA (P = 0.028, 0.024, respectively), and genotype AA of rs12229892 was associated with lower TG level compared to genotype GG (P = 0.009, Table 3). The median level of TC was 5.03 mmol/L, and subjects bearing GG genotype of rs4767860, were found to have slightly higher serum TC compared to subjects with genotype AA (5.13 v.s. 4.98 mmol/L, P = 0.021) in males. The median levels of HDL-C and LDL-C were 1.27 mmol/L and 3.10 mmol/L, respectively, and no SNP was found to be related to them.
Table 3

Associations between SNPs of and lipid levels stratified by gender

 Frequency (%)TG
TC
HDL-C
LDL-C
Median (Q1–Q3)PMedian (Q1–Q3)PMedian (Q1–Q3)PMedian (Q1–Q3)P
Male (n = 584)
rs2301756
GG
439 (75.2)
1.59 (1.09–2.37)

5.02 (4.45–5.63)

1.25 (1.10–1.43)

3.10 (2.68–3.61)

GA
131 (22.4)
1.63 (1.20–2.47)
0.319
5.07 (4.52–5.84)
0.161
1.30 (1.10–1.47)
0.164
3.10 (2.68–3.74)
0.236
AA
14 (2.4)
1.74 (1.19–2.85)
0.516
5.03 (4.76–5.32)
0.877
1.30 (1.12–1.60)
0.572
2.98 (2.75–3.65)
0.969
rs12423190
TT
304 (52.0)
1.57 (1.08–2.44)

4.99 (4.46–5.57)

1.27 (1.10–1.47)

3.10 (2.65–3.62)

TC
227 (38.9)
1.67 (1.26–2.33)
0.184
5.10 (4.49–5.74)
0.411
1.26 (1.09–1.42)
0.309
3.14 (2.70–3.63)
0.380
CC
53 (9.1)
1.64 (1.08–2.68)
0.120
5.07 (4.49–5.76)
0.219
1.30 (1.07–1.51)
0.203
3.00 (2.72–3.70)
0.595
 rs12229892
GG
189 (32.4)
1.67 (1.21–2.53)

5.13 (4.53–5.72)

1.30 (1.10–1.47)

3.11 (2.70–3.67)

GA
285 (48.8)
1.61 (1.20–2.45)
0.405
5.02 (4.49–5.64)
0.106
1.23 (1.10–1.41)
0.988
3.14 (2.67–3.66)
0.386
AA
110 (18.8)
1.41 (0.96–2.16)
0.009
4.96 (4.35–5.50)
0.118
1.29 (1.14–1.49)
0.562
3.08 (2.64–3.57)
0.354
rs7958372
TT
439 (75.2)
1.59 (1.10–2.37)

5.03 (4.46–5.64)

1.25 (1.10–1.43)

3.10 (2.69–3.61)

TC
135 (23.1)
1.64 (1.20–2.47)
0.351
5.05 (4.54–5.77)
0.200
1.30 (1.10–1.47)
0.197
3.10 (2.68–3.74)
0.260
CC
10 (1.7)
1.89 (1.22–2.85)
0.619
4.86 (4.56–5.28)
0.648
1.30 (1.12–1.45)
0.615
2.94 (2.61–3.65)
0.535
rs4767860
AA
198 (33.9)
1.53 (1.02–2.37)

4.98 (4.42–5.51)

1.25 (1.10–1.45)

3.10 (2.60–3.60)

GA
282 (48.3)
1.63 (1.20–2.41)
0.024
5.03 (4.48–5.68)
0.185
1.28 (1.09–1.43)
0.973
3.10 (2.67–3.60)
0.252
GG
104 (17.8)
1.64 (1.22–2.64)
0.028
5.13 (4.56–5.80)
0.021
1.30 (1.10–1.47)
0.598
3.15 (2.75–3.71)
0.084
Female (n = 419)
rs2301756
GG
311 (74.2)
1.19 (0.83–1.71)

4.94 (4.49–5.60)

1.48 (1.27–1.69)

2.98 (2.50–3.43)

GA
101 (24.1)
1.25 (0.85–1.85)
0.780
5.23 (4.72–5.87)
0.071
1.51 (1.27–1.76)
0.283
3.12 (2.66–3.69)
0.083
AA
7 (1.7)
2.33 (1.14–3.20)
0.005
5.02 (4.53–6.20)
0.300
1.37 (1.21–1.59)
0.605
3.16 (2.42–3.84)
0.555
rs12423190
TT
211 (50.4)
1.25 (0.82–1.75)

5.03 (4.53–5.67)

1.50 (1.27–1.71)

3.04 (2.57–3.56)

TC
172 (41.0)
1.18 (0.87–1.64)
0.592
5.12 (4.47–5.68)
0.709
1.46 (1.26–1.66)
0.149
3.00 (2.47–3.56)
0.759
CC
36 (8.6)
1.10 (0.80–1.99)
0.388
5.00 (4.64–5.32)
0.877
1.40 (1.22–1.74)
0.184
2.97 (2.52–3.35)
0.895
rs12229892
GG
153 (36.5)
1.21 (0.85–1.71)

5.11 (4.57–5.64)

1.48 (1.26–1.70)

3.00 (2.65–3.48)

GA
200 (47.7)
1.23 (0.86–1.74)
0.497
5.04 (4.45–5.67)
0.599
1.45 (1.27–1.68)
0.880
3.00 (2.51–3.60)
0.873
AA
66 (15.8)
1.19 (0.80–1.68)
0.232
5.07 (4.60–5.61)
0.463
1.53 (1.30–1.77)
0.128
3.07 (2.67–3.40)
0.526
rs7958372
TT
312 (74.5)
1.19 (0.83–1.71)

4.95 (4.50–5.60)

1.48 (1.27–1.69)

2.98 (2.51–3.44)

TC
100 (23.9)
1.25 (0.87–1.88)
0.787
5.22 (4.67–5.93)
0.090
1.52 (1.26–1.76)
0.319
3.10 (2.62–3.63)
0.278
CC
7 (1.7)
1.92 (0.85–2.33)
0.201
5.02 (4.68–6.20)
0.183
1.31 (1.21–1.59)
0.418
3.48 (3.16–4.06)
0.019
rs4767861
AA
137 (32.7)
1.23 (0.81–1.68)

4.94 (4.53–5.61)

1.51 (1.30–1.70)

3.00 (2.57–3.40)

GA
198 (47.3)
1.21 (0.86–1.79)
0.474
5.11 (4.45–5.64)
0.515
1.45 (1.24–1.70)
0.113
2.96 (2.46–3.54)
0.600
GG84 (20.0)1.21 (0.84–1.91)0.1345.16 (4.68–5.87)0.1231.41 (1.26–1.67)0.2363.13 (2.68–3.58)0.132

Differences between genotype groups were determined using analysis of covariance within each gender type, adjusted for age, BMI and waist circumference. P value in bold indicated the difference was significant comparing to the reference group (P<0.05).

Associations between SNPs of and lipid levels stratified by gender Differences between genotype groups were determined using analysis of covariance within each gender type, adjusted for age, BMI and waist circumference. P value in bold indicated the difference was significant comparing to the reference group (P<0.05). In females, however, the results were different. Female subjects had lower TG (1.21 v.s. 1.61 mmol/l), lower LDL-C (3.00 v.s. 3.10 mmol/L) and higher HDL-C (1.48 v.s. 1.27 mmol/L) level than males. The SNPs which were found to be significantly associated with TC or TG level in males could not be repeated in females. However, two other SNPs, rs2301756 and rs7958372, were found to be significantly associated with lipid level in females. The AA genotype of rs2301756 (P = 0.005) was found to be associated with higher serum TG level and the CC genotype of rs7958372 (P = 0.019) was associated with higher LDL-C level when compared to their most common genotype (Table 3). None of the five SNPs was observed to be associated with TC or HDL-C level. Because of the linkage disequilibrium, 18 haplotypes were observed using HAPSTAT software which estimated haplotype frequencies based on an EM algorithm and only four of them had the frequencies greater than 1% (Table 4). The GCGTG haplotype, with an estimated frequency of 27.75%, was found to be significantly associated with the increased level of serum TG compared to the most common haplotype GTATA (41.17%) after adjusting for age, sex, BMI and waist circumference (The slope of the linear regression is 0.054, P = 0.042). The ATGCG haplotype (12.71%) was found to be associated with slightly higher TC level (The slope of the linear regression is 0.027, P = 0.030). None of the haplotypes was found to be associated with HDL-C or LDL-C.
Table 4

Haplotype analysis of SNPs of on the lipid levels

HaplotypeFrequencyTG
TC
HDL-C
LDL-C
bPbPbPbP
GTATA
41.17%
Reference

Reference

Reference

Reference

GCGTG
27.75%
0.054
0.042
0.013
0.157
−0.018
0.099
0.006
0.624
GTGTA
15.26%
0.028
0.389
−0.006
0.619
−0.002
0.841
−0.017
0.265
ATGCG12.71%0.0500.1650.0270.0300.0140.3430.0280.082

Differences between haplotype groups were assessed using the linear regression model adjusted for age, sex, BMI and waist circumference. P value in bold indicated the difference was significant comparing to the most common haplotype group (P<0.05).

SNPs were aligned as rs2301756, rs12423190, rs12229892, rs7958372 and rs4767860.

Haplotype analysis of SNPs of on the lipid levels Differences between haplotype groups were assessed using the linear regression model adjusted for age, sex, BMI and waist circumference. P value in bold indicated the difference was significant comparing to the most common haplotype group (P<0.05). SNPs were aligned as rs2301756, rs12423190, rs12229892, rs7958372 and rs4767860.

Discussion

The results of our study showed that lipid profile was different between males and females that the serum TG and LDL-C levels were higher and HDL-C lower in males than in females. But no difference was observed in the level of TC. These results were similar to those of previous reports [17,18]. The associations between SNPs of PTPN11 gene and serum lipid levels in 1003 Chinese people presented a sex-specific pattern though the distribution of genotypes had no differences between the two sexes. Rs4767860 and rs12229892 were associated with TG level in males, but these significant associations could not be observed in females. In females, the genotype AA of rs2301756 was found to be associated with higher TG compared to the most common genotype GG. The SNP of rs4767860 was associated with TC in males but no SNP was related to TC in females. Genotypes of SNPs of PTPN11 varied in different ethnicities. In our study, the genotypes of GG, GA and AA of rs2301756 were 75.2%, 22.4% and 2.4%, respectively. They were similar to those of Japanese (62.1%, 32.9% and 5.0%, respectively) [19] but absolutely different from those of Caucasian (0.5%, 13.2% and 86.3%, respectively) [13]. The data from Hapmap show that rs12229892 and rs4767860 are very rare or do not exist in Caucasian and African Americans while in Chinese and Japanese these two SNPs are very common. The A allele of rs12229892 was 41.7% and G allele of rs4767860 was 42.7% in our study. The C allele of rs7958372 in HapMap database is the dominant allele in Caucasian while in Asian it is the minor allele (13.4% in our study). Considering the diversity of variants of PTPN11 in different ethnicities, the positive associations observed in our study might not be repeated in other ethnic populations. The PTPN11 gene, which encodes SHP2, has been reported to be associated with helicobacter pylori-related gastric atrophy [15,20] and gastric cancer [21]. Jamshidi et al. [13] first selected three htSNPs of PTPN11 gene (rs2301756, rs11066320 and rs11066322) and assessed their associations with serum lipid levels in a Caucasian female population. They found that subjects with AA genotype of rs11066320 had lower LDL-C by 2.6% compared to subjects with GG genotype. They also observed a non-significant increasing trend of TG level from 1.26 mmol/L in rs11066322 GG genotype carriers to 1.47 mmol/L of AA genotype carriers. Lu et al. [22] reported that genotype AA of rs11066322 of PTPN11 was associated with the higher plasma HDL-C levels. However, the htSNPs were different in Chinese population. One of the SNPs, rs11066320, which had MAF > 0.05 in Caucasian, did not exist in Chinese and Japanese [19]. Rs2301756 and rs11066322 were in complete disequilibrium that rs11066322 could not be chosen as htSNP. Okada et al. [19] reported that the HDL-C levels were different in the non-smokers and the current smokers within the same rs2301756 genotype, however, the role of rs2301756 was not assessed. In our study, rs2301756 was associated with TG level in females that subjects of AA genotype had higher TG than subjects of GG genotype. The mechanism underlying these associations was still in the stage of hypothesis which stated that the SNPs of PTPN11 might change the expression of the gene and consequently influenced the protein encoded, SHP2, which could regulate lipometabolism [9,10]. Two limitations should be noted in our study. The first one was only htSNPs with MAF > 5% were studied. We could not rule out the possibility that other SNPs, especially the rare SNPs, were associated with the lipid levels, as SNPs with low minor frequency had been reported to be associated with lipid profile [23]–[26]. Sequencing of the whole gene might be the solution. The another limitation was that the influence of life style on lipid levels could not be assessed because of the design, as previous studies had reported that lifestyle factors such as cigarette or alcohol consuming could affect lipid profile [27,28]. More rigorous design would be performed in the future study.

Conclusions

In summary, we found that SNPs of PTPN11 gene were associated with serum lipid levels in a sex-specific pattern. Rs12229892 and rs4767860 may play an important role in lipid profile in males, and rs2301756 and rs7958372 may be related to TG and LDL-C levels in females. Further studies are needed to explore the mechanism on how PTPN11 SNPs exert their effects on lipid profile.

Abbreviations

SNPs: Single nucleotide polymorphisms; PTPN11: Protein tyrosine phosphatase, non-receptor 11; TG: Triglyceride; TC: Total cholesterol; HDL-C: High-density lipoprotein cholesterol; LDL-C: Low-density lipoprotein cholesterol; SHP2: Src homology-2 domain-containing protein tyrosine phosphatase 2; BMI: Body mass index; htSNPs: Haplotype tagging SNPs; MAF: Minor allele frequency; PCR: Polymerase chain reaction.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

JJ and XYC designed the study. ZFJ, XYC, DHC and FK performed the experiments. ZFJ and JJ analyzed the data and wrote the first draft of manuscript. JJ and PK revised the manuscript. All authors read and approved the final manuscript.
  28 in total

1.  Selecting single-nucleotide polymorphisms for association studies with SNPbrowser software.

Authors:  Francisco M De La Vega
Journal:  Methods Mol Biol       Date:  2007

2.  SHP-2 and PI3-kinase genes PTPN11 and PIK3R1 may influence serum apoB and LDL cholesterol levels in normal women.

Authors:  Y Jamshidi; S B Gooljar; H Snieder; X Wang; D Ge; R Swaminathan; T D Spector; S D O'Dell
Journal:  Atherosclerosis       Date:  2007-01-09       Impact factor: 5.162

3.  Prevalence of the metabolic syndrome and overweight among adults in China.

Authors:  Dongfeng Gu; Kristi Reynolds; Xigui Wu; Jing Chen; Xiufang Duan; Robert F Reynolds; Paul K Whelton; Jiang He
Journal:  Lancet       Date:  2005 Apr 16-22       Impact factor: 79.321

Review 4.  High-density lipoprotein cholesterol as an independent risk factor in cardiovascular disease: assessing the data from Framingham to the Veterans Affairs High--Density Lipoprotein Intervention Trial.

Authors:  W E Boden
Journal:  Am J Cardiol       Date:  2000-12-21       Impact factor: 2.778

5.  Dietary and plasma lipid, lipoprotein, and apolipoprotein profiles among elderly Hispanics and non-Hispanics and their association with diabetes.

Authors:  Odilia I Bermudez; Wanda Velez-Carrasco; Ernst J Schaefer; Katherine L Tucker
Journal:  Am J Clin Nutr       Date:  2002-12       Impact factor: 7.045

6.  Multiple genetic variants along candidate pathways influence plasma high-density lipoprotein cholesterol concentrations.

Authors:  Yingchang Lu; Martijn E T Dollé; Sandra Imholz; Ruben van 't Slot; W M Monique Verschuren; Cisca Wijmenga; Edith J M Feskens; Jolanda M A Boer
Journal:  J Lipid Res       Date:  2008-07-25       Impact factor: 5.922

7.  Expression of dominant negative mutant SHPTP2 attenuates phosphatidylinositol 3'-kinase activity via modulation of phosphorylation of insulin receptor substrate-1.

Authors:  S Ugi; H Maegawa; A Kashiwagi; M Adachi; J M Olefsky; R Kikkawa
Journal:  J Biol Chem       Date:  1996-05-24       Impact factor: 5.157

8.  Lipids and risk of coronary heart disease. The Framingham Study.

Authors:  W P Castelli; K Anderson; P W Wilson; D Levy
Journal:  Ann Epidemiol       Date:  1992 Jan-Mar       Impact factor: 3.797

9.  APOA5 Ala315>Val, identified in patients with severe hypertriglyceridemia, is a common mutation with no major effects on plasma lipid levels.

Authors:  Jaroslav A Hubacek; Wu-Wei Wang; Zdena Skodová; Vera Adámková; Michal Vráblík; Ales Horínek; Tomás Stulc; Richard Ceska; Philippa J Talmud
Journal:  Clin Chem Lab Med       Date:  2008       Impact factor: 3.694

10.  Prevalence and determinants of metabolic syndrome among women in Chinese rural areas.

Authors:  Hui Cai; Jianping Huang; Guangfei Xu; Zili Yang; Ming Liu; Yaoping Mi; Weisheng Liu; Hongjun Wang; Derong Qian
Journal:  PLoS One       Date:  2012-05-10       Impact factor: 3.240

View more
  4 in total

1.  Association between phosphatase related gene variants and coronary artery disease: case-control study and meta-analysis.

Authors:  Xia Han; Lijun Zhang; Zhiqiang Zhang; Zengtang Zhang; Jianchun Wang; Jun Yang; Jiamin Niu
Journal:  Int J Mol Sci       Date:  2014-08-13       Impact factor: 5.923

Review 2.  New and Unexpected Biological Functions for the Src-Homology 2 Domain-Containing Phosphatase SHP-2 in the Gastrointestinal Tract.

Authors:  Geneviève Coulombe; Nathalie Rivard
Journal:  Cell Mol Gastroenterol Hepatol       Date:  2015-11-14

3.  Genetic Polymorphisms Associated with the Neutrophil⁻Lymphocyte Ratio and Their Clinical Implications for Metabolic Risk Factors.

Authors:  Boram Park; Eun Kyung Choe; Hae Yeon Kang; Eunsoon Shin; Sangwoo Lee; Sungho Won
Journal:  J Clin Med       Date:  2018-08-08       Impact factor: 4.241

4.  Genome wide association study identifies novel potential candidate genes for bovine milk cholesterol content.

Authors:  Duy N Do; Flavio S Schenkel; Filippo Miglior; Xin Zhao; Eveline M Ibeagha-Awemu
Journal:  Sci Rep       Date:  2018-09-05       Impact factor: 4.379

  4 in total

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