Qian Wang1,2, Andrew T Grainger3,4, Ani Manichaikul5, Emily Farber6, Suna Onengut-Gumuscu7, Weibin Shi8,9. 1. Department of Radiology & Medical Imaging, University of Virginia, Snyder Bldg Rm 266, 480 Ray C. Hunt Dr., P.O. Box 801339, Fontaine Research Park, Charlottesville, VA, 22908, USA. qw3q@Virginia.EDU. 2. University of Virginia, Snyder Bldg Rm 266, 480 Ray C. Hunt Dr., P.O. Box 801339, Fontaine Research Park, Charlottesville, VA, 22908, USA. qw3q@Virginia.EDU. 3. Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA, USA. atg3qz@Virginia.EDU. 4. University of Virginia, Charlottesville, VA, USA. atg3qz@Virginia.EDU. 5. Center for Public Health and Genomics, University of Virginia, Charlottesville, VA, USA. am3xa@Virginia.EDU. 6. Center for Public Health and Genomics, University of Virginia, Charlottesville, VA, USA. ef8j@Virginia.EDU. 7. Center for Public Health and Genomics, University of Virginia, Charlottesville, VA, USA. so4g@Virginia.EDU. 8. Department of Radiology & Medical Imaging, University of Virginia, Snyder Bldg Rm 266, 480 Ray C. Hunt Dr., P.O. Box 801339, Fontaine Research Park, Charlottesville, VA, 22908, USA. ws4v@Virginia.EDU. 9. University of Virginia, Snyder Bldg Rm 266, 480 Ray C. Hunt Dr., P.O. Box 801339, Fontaine Research Park, Charlottesville, VA, 22908, USA. ws4v@Virginia.EDU.
Abstract
BACKGROUND: Individuals with dyslipidemia often develop type 2 diabetes, and diabetic patients often have dyslipidemia. It remains to be determined whether there are genetic connections between the 2 disorders. METHODS: A female F2 cohort, generated from BALB/cJ (BALB) and SM/J (SM) Apoe-deficient (Apoe(-/-)) strains, was started on a Western diet at 6 weeks of age and maintained on the diet for 12 weeks. Fasting plasma glucose and lipid levels were measured before and after 12 weeks of Western diet. 144 genetic markers across the entire genome were used for quantitative trait locus (QTL) analysis. RESULTS: One significant QTL on chromosome 9, named Bglu17 [26.4 cM, logarithm of odds ratio (LOD): 5.4], and 3 suggestive QTLs were identified for fasting glucose levels. The suggestive QTL near the proximal end of chromosome 9 (2.4 cM, LOD: 3.12) was replicated at both time points and named Bglu16. Bglu17 coincided with a significant QTL for HDL (high-density lipoprotein) and a suggestive QTL for non-HDL cholesterol levels. Plasma glucose levels were inversely correlated with HDL but positively correlated with non-HDL cholesterol levels in F2 mice on either chow or Western diet. A significant correlation between fasting glucose and triglyceride levels was also observed on the Western diet. Haplotype analysis revealed that "lipid genes" Sik3, Apoa1, and Apoc3 were probable candidates for Bglu17. CONCLUSIONS: We have identified multiple QTLs for fasting glucose and lipid levels. The colocalization of QTLs for both phenotypes and the sharing of potential candidate genes demonstrate genetic connections between dyslipidemia and type 2 diabetes.
BACKGROUND: Individuals with dyslipidemia often develop type 2 diabetes, and diabeticpatients often have dyslipidemia. It remains to be determined whether there are genetic connections between the 2 disorders. METHODS: A female F2 cohort, generated from BALB/cJ (BALB) and SM/J (SM) Apoe-deficient (Apoe(-/-)) strains, was started on a Western diet at 6 weeks of age and maintained on the diet for 12 weeks. Fasting plasma glucose and lipid levels were measured before and after 12 weeks of Western diet. 144 genetic markers across the entire genome were used for quantitative trait locus (QTL) analysis. RESULTS: One significant QTL on chromosome 9, named Bglu17 [26.4 cM, logarithm of odds ratio (LOD): 5.4], and 3 suggestive QTLs were identified for fasting glucose levels. The suggestive QTL near the proximal end of chromosome 9 (2.4 cM, LOD: 3.12) was replicated at both time points and named Bglu16. Bglu17 coincided with a significant QTL for HDL (high-density lipoprotein) and a suggestive QTL for non-HDLcholesterol levels. Plasma glucose levels were inversely correlated with HDL but positively correlated with non-HDLcholesterol levels in F2 mice on either chow or Western diet. A significant correlation between fasting glucose and triglyceride levels was also observed on the Western diet. Haplotype analysis revealed that "lipid genes" Sik3, Apoa1, and Apoc3 were probable candidates for Bglu17. CONCLUSIONS: We have identified multiple QTLs for fasting glucose and lipid levels. The colocalization of QTLs for both phenotypes and the sharing of potential candidate genes demonstrate genetic connections between dyslipidemia and type 2 diabetes.
Individuals with dyslipidemia have an increased risk of developing type 2 diabetes (T2D), and diabeticpatients often have dyslipidemia, which includes elevations in plasma triglyceride and low-density lipoprotein (LDL) cholesterol levels and reductions in high-density lipoprotein (HDL) cholesterol levels [1]. Part of the increased diabetic risk associated with dyslipidemia is due to genetic variations that influence both lipoprotein homeostasis and the development of T2D. Indeed, a few rare gene mutations result in both dyslipidemia and T2D, which include ABCA1 [2], LIPE [3], LPL [4], and LRP6 [5]. Genome-wide association studies (GWAS) have identified >150 loci associated to variation in plasma lipids [6, 7] and >70 loci associated with T2D, fasting plasma glucose, glycated hemoglobin (HbA1c), or insulin resistance [8-10]. Nearly a dozen of the loci detected are associated with both lipid and T2D-related traits at the genome-wide significance level, including GCKR, FADS1, IRS1, KLF14, and HFE (http://www.genome.gov/GWAStudies/). Surprisingly, half of them have shown opposite allelic effect on dyslipidemia and glucose levels [11], and this is in contrary to the positive correlations observed at the clinical level. Furthermore, it is challenging to establish causality between genetic variants and complex traits in humans due to small gene effects, complex genetic structure, and environmental influences.A complementary approach to finding genetic components in human disease is to use animal models. Apolipoprotein E-deficient (Apoe−/−) mice are a commonly used mouse model of dyslipidemia, with elevations in non-HDLcholesterol levels and reductions in HDL levels, even when fed a low fat chow diet [12, 13]. High fat diet feeding aggravates dyslipidemia. Moreover, these mice develop all phases of atherosclerotic lesions seen in humans [14] and are extensively used for atherosclerosis research [15-18]. We have found that Apoe−/− mice with certain genetic backgrounds develop significant hyperglycemia and T2D when fed a Western-type diet but become resistant with some other genetic backgrounds [16, 19, 20]. BALB/cJ (BALB) and SM/J (SM) Apoe−/− mice exhibit differences in dyslipidemia and T2D-related phenotypes [16]. The objective of the present study was to explore potential genetic connections between dyslipidemia and T2D through quantitative trait locus (QTL) analysis of a female cohort derived from an intercross between BALB-Apoe−/− and SM-Apoe−/− mice.
Methods
Ethics statement
All procedures were in accordance with current National Institutes of Health guidelines (https://grants.nih.gov/grants/olaw/Guide-for-the-Care-and-use-of-laboratory-animals.pdf) and approved by the institutional Animal Care and Use Committee (protocol #: 3109). Blood was drawn from the retro-orbital plexus of overnight fasted mice with the animals under isoflurane anesthesia.
Animals, experimental design and procedures
BALB and SM Apoe−/− mice were created using the classic congenic breeding strategy, as described [16]. BALB-Apoe−/− mice were crossed with SM-Apoe−/− mice to generate F1s, which were intercrossed by brother-sister mating to generate a female F2 cohort. Mice were weaned at 3 weeks of age onto a rodent chow diet. At 6 weeks of age, female F2 mice were started on a Western diet containing 21 % fat, 34.1 % sucrose, 0.15 % cholesterol, and 19.5 % casein by weight (Harlan Laboratories, TD 88137) and maintained on the diet for 12 weeks. Mice were bled twice: once before initiation of the Western diet and once at the end of the 12-week feeding period. Overnight fasted mice were bled into tubes containing 8 μL of 0.5 mol/L ethylenediaminetetraacetic acid. Plasma was prepared and stored at −80 °C before use.
Housing and husbandry
Breeding pairs were housed in a cage of 1 adult male and 2 females, and litters were weaned at 3 weeks of age onto a rodent chow diet in a cage of 5 or less. At 6 weeks of age, F2 mice were switched onto the Western diet and maintained on the diet for 12 weeks. All mice were housed under a 12-h light/dark cycle at an ambient temperature of 23 °C and allowed free access to water and drinking food. Mice were fasted overnight before blood samples were collected.
Measurements of plasma glucose and lipid levels
Plasma glucose was measured with a Sigma glucose (HK) assay kit, as reported with modification to a longer incubation time [21]. Briefly, 6 μl of plasma samples were incubated with 150 μl of assay reagent in a 96-well plate for 30 min at 30 °C. The absorbance at 340 nm was read on a Molecular Devices (Menlo Park, CA) plate reader. The measurements of total cholesterol, HDLcholesterol, and triglyceride were performed as reported previously [13]. Non-HDLcholesterol was calculated as the difference between total and HDLcholesterol.
Genotyping
Genomic DNA was isolated from the tails of mice by using the phenol/chloroform extraction and ethanol precipitation method. The Illumina LD linkage panel consisting of 377 SNP loci was used to genotype the F2 cohort. Microsatellite markers were typed for chromosome 8 where SNP markers were uninformative in distinguishing the parental origin of alleles. DNA samples from the two parental strains and their F1s served as controls. Uninformative SNPs were excluded from QTL analysis. SNP markers were also filtered based on the expected pattern in the control samples, and F2 mice were filtered based on 95 % call rates in genotype calls. After filtration, 228 F2s and 144 markers were included in genome-wide QTL analysis.
Statistical analysis
QTL analysis was performed using J/qtl and Map Manager QTX software as previously reported [19, 22, 23]. One thousand permutations of trait values were run to define the genome-wide LOD (logarithm of odds) score threshold needed for significant or suggestive linkage of each trait. Loci that exceeded the 95th percentile of the permutation distribution were defined as significant (P < 0.05) and those exceeding the 37th percentile were suggestive (P < 0.63).
Prioritization of positional candidate genes
The Sanger SNP database (http://www.sanger.ac.uk/sanger/Mouse_SnpViewer/rel-1410) was used to prioritize candidate genes for overlapping QTLs affecting plasma glucose and HDLcholesterol levels on chromosome (Chr) 9, which were mapped in two or more crosses derived from different parental strains for either phenotype. We converted the original mapping positions in cM for the confidence interval to physical positions in Mb and then examined SNPs within the confidence interval. Probable candidate genes were defined as those with one or more SNPs in coding or upstream promoter regions that were shared by the parental strains carrying the “high” allele but were different from the parental strains carrying the “low” allele at a QTL, as previously reported [24].
Results
Trait value distributions
Fasting plasma glucose and lipid levels of F2 mice were measured before and after 12-weeks of Western diet. Values of fasting plasma glucose, non-HDLcholesterol and triglyceride levels of F2 mice on both chow and Western diets and of HDLcholesterol level on the chow diet were normally or approximately normally distributed (Fig. 1). Values of square root-transformed HDLcholesterol levels on the Western diet showed a normal distribution. These data were then analyzed to search for QTLs affecting the traits. Loci with a genome-wide suggestive or significant P value are presented in Table 1.
Fig. 1
The distributions of trait values for fasting plasma glucose, HDL, non-HDL cholesterol and triglyceride of 228 female F2 mice derived from an intercross between BALB-Apoe
−/− and SM-Apoe
−/− mice. Fasting blood was collected once before initiation of the Western diet (left panel) and once after 12 weeks on the Western diet (right panel). Graphs were created using a plotting function of J/qtl software
Table 1
Significant and suggestive QTLs for plasma glucose and lipid levels in female F2 mice derived from BALB-Apoe
−/− and SM-Apoe
−/− mice
Locus
Chr
Trait
LODa
p-valueb
Peak (cM)
95 % CIc
High allele
Mode of inheritenced
Bglu16
9
Glucose-C
2.214
0.549
2.37
0.37–30.37
B
Additive
Bglu13
5
Glucose-W
2.1.8
<0.63
67.4
45.4–80.03
S
Recessive
-
5
Glucose-W
3.198
0.097
101.24
29.40–101.24
S
Additive
Bglu16
9
Glucose-W
3.12
<0.63
2.37
0–10.37
B
Additive
Bglu17
9
Glucose-W
5.425
0.001
26.37
16.37–40.37
B
Additive
Hdlq5
1
HDL-C
8.64
0.000
93.52
87.52–97.02
S
Additive
Hdlcl1
7
HDL-C
2.668
0.321
61.33
35.57–89.57
S
Dominant
Hdlq17
9
HDL-C
4.614
0.014
30.37
16.37–32.37
S
Additive
Hdlq26
10
HDL-C
2.181
0.591
61.22
25.03–61.22
S
Dominant
Hdlq5
1
HDL-W
13.944
0.000
87.52
83.52–93.52
S
Additive
Hdlcl1
7
HDL-W
3.658
0.034
85.57
77.57–89.67
S
Additive
Hdlq17
9
HDL-W
10.625
0.000
30.42
24.37–30.53
S
Additive
Chol7
1
non-HDL-C
2.093
0.626
66.95
9.52–74.56
B
Recessive
Nhdlq15
2
non-HDL-C
2.56
0.321
23.86
8.73–38.73
B
Additive
Hdlq34
5
non-HDL-C
2.106
0.614
19.4
19.4–30.5
S
Additive
Pnhdlc1
6
non-HDL-C
2.489
0.362
57.53
1.53–77.53
B
Recessive
Nhdlq1
8
non-HDL-C
2.221
0.537
44.14
10.14–60.14
B
Additive
Nhdlq12
12
non-HDL-C
2.73
0.245
39.41
15.41–59.41
B
Additive
Nhdlq15
2
non-HDL-W
4.79
0.002
31.80
22.73–40.73
B
Dominant
Nhdlq11
9
non-HDL-W
2.136
0.585
32.37
0.37–75.33
B
Additive
-
11
non-HDL-W
2.332
0.436
1.99
1.99–17.99
B
Dominant
Nhdlq16
16
non-HDL-W
3.99
0.011
46.66
35.43–46.66
S
Dominant
Tgq11
2
Triglyceride-C
2.952
0.169
26.73
12.73–60.83
B
Additive
-
5
Triglyceride-C
2.759
0.234
80.03
73.40–93.40
S
Heterosis
Trglyd
1
Triglyceride-W
3.291
0.091
97.02
79.24–97.02
S
Additive
aLOD scores were obtained from genome-wide QTL analysis using J/qtl software. The significant LOD scores were highlighted in bold. The suggestive and significant LOD score thresholds were determined by 1,000 permutation tests for each trait. Suggestive and significant LOD scores were 2.116 and 3.429, respectively, for glucose on the chow diet; 2.056 and 3.569 for glucose on the Western diet; 2.127 and 3.725 for HDL cholesterol, 2.09 and 3.662 for non-HDL cholesterol, and 2.102 and 3.522 for triglyceride on the chow diet; 2.10 and 3.486 for HDL, 2.123 and 3.628 for non-HDL, and 2.123 and 3.628 for triglyceride on the Western diet
bThe p-values reported represent the level of genome-wide significance
c95 % Confidence interval in cM defined by a whole genome QTL scan
dMode of inheritance was defined according to allelic effect at the nearest marker of a QTL
The distributions of trait values for fasting plasma glucose, HDL, non-HDLcholesterol and triglyceride of 228 female F2 mice derived from an intercross between BALB-Apoe
−/− and SM-Apoe
−/− mice. Fasting blood was collected once before initiation of the Western diet (left panel) and once after 12 weeks on the Western diet (right panel). Graphs were created using a plotting function of J/qtl softwareSignificant and suggestive QTLs for plasma glucose and lipid levels in female F2 mice derived from BALB-Apoe
−/− and SM-Apoe
−/− miceaLOD scores were obtained from genome-wide QTL analysis using J/qtl software. The significant LOD scores were highlighted in bold. The suggestive and significant LOD score thresholds were determined by 1,000 permutation tests for each trait. Suggestive and significant LOD scores were 2.116 and 3.429, respectively, for glucose on the chow diet; 2.056 and 3.569 for glucose on the Western diet; 2.127 and 3.725 for HDLcholesterol, 2.09 and 3.662 for non-HDLcholesterol, and 2.102 and 3.522 for triglyceride on the chow diet; 2.10 and 3.486 for HDL, 2.123 and 3.628 for non-HDL, and 2.123 and 3.628 for triglyceride on the Western dietbThe p-values reported represent the level of genome-wide significancec95 % Confidence interval in cM defined by a whole genome QTL scandMode of inheritance was defined according to allelic effect at the nearest marker of a QTL
Fasting glucose levels
A genome-wide scan for main effect QTL revealed a suggestive QTL near the proximal end of Chr9 for fasting glucose when mice were fed the chow diet (2.37 cM, LOD: 2.21) (Fig. 2 and Table 1). As this QTL was replicated on the Western diet, it was named Bglu16. For fasting glucose levels on the Western diet, a significant QTL on Chr9 and 3 suggestive QTLs, including Bglu16 on Chr9, were identified. The significant QTL on Chr9 peaked at 26.37 cM and had a LOD score of 5.425. It was named Bglu17. The suggestive QTL near the middle portion of Chr5 (67.4 cM, LOD 2.18) replicated Bglu13, initially mapped in a B6 x BALB Apoe−/− intercross [21]. The suggestive QTL on distal Chr5 (101.24 cM, LOD 3.198) was novel. The BALB allele conferred an increased glucose level for both of the Chr9 QTLs while the SM allele conferred increased glucose levels for the 2 Chr5 QTLs (Table 2).
Fig. 2
Genome-wide scans to search for main effect loci influencing fasting plasma glucose levels of female F2 mice when fed a chow (a) or Western diet (b). Chromosomes 1 through X are represented numerically on the X-axis. The Y-axis represents the LOD score. Two horizontal dashed lines denote genome-wide empirical thresholds for suggestive (P = 0.63) and significant (P = 0.05) linkage
Table 2
Allelic effects in different QTLs on plasma glucose and lipids of female F2 mice derived from BALB and SM Apoe
−/− mice
Locus name
Chr
Trait
LOD
Peak (cM)
Closest marker
BB
SS
SB
Bglu16
9
Glucose-C
2.214
2.37
rs13480073
109.0 ± 28.7 (n = 44)
93.9 ± 22.9 (n = 43)
97.4 ± 22.6 (n = 141)
Bglu13
5
Glucose-W
2.1.8
67.4
rs3726547
144.4 ± 30.6 (n = 43)
153.5 ± 40.4 (n = 88)
142.9 ± 35.3 (n = 97)
-
5
Glucose-W
3.198
101.24
rs13478578
132.7 ± 31.3 (n = 51)
158.8 ± 41.8 (n = 63)
147.5 ± 34.0 (n = 113)
Bglu16
9
Glucose-W
3.12
2.37
rs13480073
165.3 ± 40.9 (n = 54)
138.3 ± 30.0 (n = 43)
144.4 ± 35.7 (n = 141)
Bglu17
9
Glucose-W
5.425
26.37
CEL.9_49183636
168.0 ± 39.8 (n = 42)
134.7 ± 26.5 (n = 62)
146.1 ± 37.1 (n = 124)
Hdlq5
1
HDL-C
8.64
93.52
rs13476259
49.5 ± 20.9 (n = 60)
73.2 ± 26.5 (n = 62)
55.1 ± 19.4 (n = 106)
Hdlcl1
7
HDL-C
2.668
61.33
rs3724711
49.0 ± 20.0 (n = 50)
58.3 ± 21.0 (n = 63)
62.9 ± 25.5 (n = 115)
Hdlq17
9
HDL-C
4.614
30.37
CEL.9_49183636
49.5 ± 15.9 (n = 42)
69.4 ± 26.8 (n = 62)
56.2 ± 22.5 (n = 124)
Hdlq26
10
HDL-C
2.181
61.22
rs3688351
50.7 ± 19.2 (n = 60)
59.4 ± 21.9 (n = 53)
62.4 ± 25.8 (n = 114)
Hdlq5
1
sqrtHDL-W
13.944
87.52
rs3685643
66.6 ± 50.2 (n = 57)
201.1 ± 118.8 (n = 62)
117.0 ± 97.1 (n = 109)
Hdlcl1
7
sqrtHDL-W
3.658
85.57
rs6216320
95.5 ± 87.6 (n = 63)
173.3 ± 126.7 (n = 55)
122.4 ± 98.4 (n = 110)
Hdlq17
9
sqrtHDL-W
10.625
30.42
CEL.9_49183636
57.6 ± 49.3 (n = 42)
183.3 ± 115.0 (n = 62)
122.8 ± 101.7 (n = 124)
Chol7
1
non-HDL-C
2.093
66.95
rs6354736
279.5 ± 62.8 (n = 56)
257.8 ± 56.9 (n = 57)
251.2 ± 52.1 (n = 114)
Nhdlq15
2
non-HDL-C
2.56
23.86
mCV23209429
273.9 ± 56.1 (n = 55)
238.0 ± 47.0 (n = 53)
262.7 ± 59.0 (n = 120)
Hdlq34
5
non-HDL-C
2.106
19.4
rs3658401
244.5 ± 54.7 (n = 63)
276.0 ± 53.2 (n = 61)
259.3 ± 58.3 (n = 104)
Pnhdlc1
6
non-HDL-C
2.489
57.53
rs13478909
279.6 ± 51.3 (n = 51)
252.0 ± 65.2 (n = 57)
254.8 ± 53.5 (n = 120)
Nhdlq1
8
non-HDL-C
2.221
44.14
D8Mit50
275.0 ± 54.5 (n = 60)
242.4 ± 57.4 (n = 57)
262.9 ± 55.6 (n = 96)
Nhdlq12
12
non-HDL-C
2.73
39.41
rs6195664
278.6 ± 52.3 (n = 62)
243.8 ± 57.8 (n = 59)
257.4 ± 56.5 (n = 107)
Nhdlq15
2
non-HDL-W
4.79
31.8
rs13476507
954.1 ± 156.0 (n = 56)
806.9 ± 158.2 (n = 47)
915.6 ± 166.1 (n = 125)
Nhdlq11
9
non-HDL-W
2.136
32.37
rs3709825
958.4 ± 211.4 (n = 42)
856.8 ± 165.3 (n = 62)
906.6 ± 149.5 (n = 124)
-
11
non-HDL-W
2.332
1.99
rs4222040
927.3 ± 149.8 (n = 67)
849.0 ± 165.1 (n = 69)
917.0 ± 170.5 (n = 85)
Nhdlq16
16
non-HDL-W
3.99
46.66
rs3721202
820.2 ± 152.7 (n = 56)
931.9 ± 146.4 (n = 52)
928.4 ± 174.6 (n = 120)
Tgq11
2
Triglyceride-C
2.952
26.73
mCV23209429
123.7 ± 35.7 (n = 55)
101.9 ± 34.6 (n = 53)
107.3 ± 31.8 (n = 120)
-
5
Triglyceride-C
2.759
80.03
gnf05.120.578
110.2 ± 33.2 (n = 43)
119.3 ± 35.8 (n = 88)
101.6 ± 31.3 (n = 97)
Trglyd
1
Triglyceride-W
3.291
97.02
rs13476259
94.0 ± 28.6 (n = 59)
115.3 ± 33.0 (n = 62)
100.7 ± 30.8 (n = 106)
Bglu16
9
Glucose-C
2.214
2.37
rs13480073
109.0 ± 28.7 (n = 44)
93.9 ± 22.9 (n = 43)
97.4 ± 22.6 (n = 141)
Bglu13
5
Glucose-W
2.1.8
67.4
rs3726547
144.4 ± 30.6 (n = 43)
153.5 ± 40.4 (n = 88)
142.9 ± 35.3 (n = 97)
-
5
Glucose-W
3.198
101.24
rs13478578
132.7 ± 31.3 (n = 51)
158.8 ± 41.8 (n = 63)
147.5 ± 34.0 (n = 113)
Bglu16
9
Glucose-W
3.12
2.37
rs13480073
165.3 ± 40.9 (n = 54)
138.3 ± 30.0 (n = 43)
144.4 ± 35.7 (n = 141)
Bglu17
9
Glucose-W
5.425
26.37
CEL.9_49183636
168.0 ± 39.8 (n = 42)
134.7 ± 26.5 (n = 62)
146.1 ± 37.1 (n = 124)
Hdlq5
1
HDL-C
8.64
93.52
rs13476259
49.5 ± 20.9 (n = 60)
73.2 ± 26.5 (n = 62)
55.1 ± 19.4 (n = 106)
Hdlcl1
7
HDL-C
2.668
61.33
rs3724711
49.0 ± 20.0 (n = 50)
58.3 ± 21.0 (n = 63)
62.9 ± 25.5 (n = 115)
Hdlq17
9
HDL-C
4.614
30.37
CEL.9_49183636
49.5 ± 15.9 (n = 42)
69.4 ± 26.8 (n = 62)
56.2 ± 22.5 (n = 124)
Hdlq26
10
HDL-C
2.181
61.22
rs3688351
50.7 ± 19.2 (n = 60)
59.4 ± 21.9 (n = 53)
62.4 ± 25.8 (n = 114)
Hdlq5
1
sqrtHDL-W
13.944
87.52
r[55]s3685643
66.6 ± 50.2 (n = 57)
201.1 ± 118.8 (n = 62)
117.0 ± 97.1 (n = 109)
Hdlcl1
7
sqrtHDL-W
3.658
85.57
rs6216320
95.5 ± 87.6 (n = 63)
173.3 ± 126.7 (n = 55)
122.4 ± 98.4 (n = 110)
Hdlq17
9
sqrtHDL-W
10.625
30.42
CEL.9_49183636
57.6 ± 49.3 (n = 42)
183.3 ± 115.0 (n = 62)
122.8 ± 101.7 (n = 124)
Chol7
1
non-HDL-C
2.093
66.95
rs6354736
279.5 ± 62.8 (n = 56)
257.8 ± 56.9 (n = 57)
251.2 ± 52.1 (n = 114)
Nhdlq15
2
non-HDL-C
2.56
23.86
mCV23209429
273.9 ± 56.1 (n = 55)
238.0 ± 47.0 (n = 53)
262.7 ± 59.0 (n = 120)
Hdlq34
5
non-HDL-C
2.106
19.4
rs3658401
244.5 ± 54.7 (n = 63)
276.0 ± 53.2 (n = 61)
259.3 ± 58.3 (n = 104)
Pnhdlc1
6
non-HDL-C
2.489
57.53
rs13478909
279.6 ± 51.3 (n = 51)
252.0 ± 65.2 (n = 57)
254.8 ± 53.5 (n = 120)
Nhdlq1
8
non-HDL-C
2.221
44.14
D8Mit50
275.0 ± 54.5 (n = 60)
242.4 ± 57.4 (n = 57)
262.9 ± 55.6 (n = 96)
Nhdlq12
12
non-HDL-C
2.73
39.41
rs6195664
278.6 ± 52.3 (n = 62)
243.8 ± 57.8 (n = 59)
257.4 ± 56.5 (n = 107)
Nhdlq15
2
non-HDL-W
4.79
31.8
rs13476507
954.1 ± 156.0 (n = 56)
806.9 ± 158.2 (n = 47)
915.6 ± 166.1 (n = 125)
Nhdlq11
9
non-HDL-W
2.136
32.37
rs3709825
958.4 ± 211.4 (n = 42)
856.8 ± 165.3 (n = 62)
906.6 ± 149.5 (n = 124)
-
11
non-HDL-W
2.332
1.99
rs4222040
927.3 ± 149.8 (n = 67)
849.0 ± 165.1 (n = 69)
917.0 ± 170.5 (n = 85)
Nhdlq16
16
non-HDL-W
3.99
46.66
rs3721202
820.2 ± 152.7 (n = 56)
931.9 ± 146.4 (n = 52)
928.4 ± 174.6 (n = 120)
Tgq11
2
Triglyceride-C
2.952
26.73
mCV23209429
123.7 ± 35.7 (n = 55)
101.9 ± 34.6 (n = 53)
107.3 ± 31.8 (n = 120)
-
5
Triglyceride-C
2.759
80.03
gnf05.120.578
110.2 ± 33.2 (n = 43)
119.3 ± 35.8 (n = 88)
101.6 ± 31.3 (n = 97)
Trglyd
1
Triglyceride-W
3.291
97.02
rs13476259
94.0 ± 28.6 (n = 59)
115.3 ± 33.0 (n = 62)
100.7 ± 30.8 (n = 106)
Chr chromosome, LOD logarithm of odds, C chow diet, W Western diet, BB homozygous BALB allele, SS homozygous SM allele, SM heterozygous allele
Data are mean ± SD. The units for these measurements are mg/dL for plasma glucose or lipid levels. The number in the brackets represents the number of progeny with a specific genotype at a peak marker. The significant QTLs and their LOD scores were highlighted in bold
Genome-wide scans to search for main effect loci influencing fasting plasma glucose levels of female F2 mice when fed a chow (a) or Western diet (b). Chromosomes 1 through X are represented numerically on the X-axis. The Y-axis represents the LOD score. Two horizontal dashed lines denote genome-wide empirical thresholds for suggestive (P = 0.63) and significant (P = 0.05) linkageAllelic effects in different QTLs on plasma glucose and lipids of female F2 mice derived from BALB and SM Apoe
−/− miceChr chromosome, LOD logarithm of odds, C chow diet, W Western diet, BB homozygous BALB allele, SS homozygous SM allele, SM heterozygous alleleData are mean ± SD. The units for these measurements are mg/dL for plasma glucose or lipid levels. The number in the brackets represents the number of progeny with a specific genotype at a peak marker. The significant QTLs and their LOD scores were highlighted in bold
Fasting lipid levels
Genome-wide scans for main effect QTLs showed that HDL, non-HDLcholesterol, and triglyceride levels were each controlled by multiple QTLs (Figs. 3, 4 and 5; Table 1). For HDL, 3 significant QTLs, located on Chr1, Chr7 and Chr9, and 1 suggestive QTL on Chr10, were identified. All 3 significant QTLs for HDL were detected when mice were fed either chow or Western diet, while the suggestive QTL on Chr10 was found when mice were on the chow diet. The significant QTL on Chr1 replicated Hdlq5, which had been mapped in numerous crosses [25]. The Chr7 QTL replicated Hdlcl1, initially mapped in (PERA/EiJ x B6-Ldlr)) x B6-Ldlr backcross [26]. The Chr9 QTL replicated Hdlq17, previously mapped in B6 x 129S1/SvImJ F2 mice [27]. The suggestive QTL on Chr10 overlapped with Hdlq26 mapped in a SM/J x NZB/BlNJ intercross [28]. For all 4 HDL QTLs, F2 mice homozygous for the SS allele had higher HDL levels than those homozygous for the BB allele (Table 2).
Fig. 3
Genome-wide scans to search for loci influencing HDL cholesterol levels of female F2 mice when fed a chow (a) or Western diet (b). Three significant loci on chromosomes 1, 7, and 9 and one suggestive locus on chromosome 10 were detected to affect HDL cholesterol levels of mice
Fig. 4
Genome-wide scans to search for loci influencing non-HDL cholesterol levels of female F2 mice fed a chow (a) or Western diet (b). Two significant loci on chromosomes 2 and 16 were identified to affect non-HDL cholesterol levels of mice fed the Western diet
Fig. 5
Genome-wide scans to search for loci influencing triglyceride levels of female F2 mice fed a chow (a) or Western diet (b). Three suggestive loci were identified for triglyceride levels
Genome-wide scans to search for loci influencing HDLcholesterol levels of female F2 mice when fed a chow (a) or Western diet (b). Three significant loci on chromosomes 1, 7, and 9 and one suggestive locus on chromosome 10 were detected to affect HDLcholesterol levels of miceGenome-wide scans to search for loci influencing non-HDLcholesterol levels of female F2 mice fed a chow (a) or Western diet (b). Two significant loci on chromosomes 2 and 16 were identified to affect non-HDLcholesterol levels of mice fed the Western dietGenome-wide scans to search for loci influencing triglyceride levels of female F2 mice fed a chow (a) or Western diet (b). Three suggestive loci were identified for triglyceride levelsFor non-HDLcholesterol levels, 6 suggestive QTLs were detected when F2 mice were fed the chow diet, and 2 significant and 2 suggestive QTLs were detected on the Western diet (Fig. 4). The 2 significant QTLs on Chr2 and Chr16 and the suggestive QTL on Chr11 were novel. The former 2 QTLs were named Nhdlq15 and Nhdlq16, respectively. Nhdlq15 peaked at 31.8 cM on Chr2 and affected non-HDL levels in a dominant mode from the BB allele while Nhdlq16 peaked at 46.66 cM on Chr16 and affected non-HDL levels in a dominant mode from the SS allele. The rest replicated previously identified ones in other mouse crosses: The Chr1 QTL peaked at 66.95 cM, overlapping with Chol7 mapped in an intercross of 129S1/SvImJ and CAST/Ei mice [29]. The Chr5 QTL overlapped with Hdlq34 mapped in PERA/EiJ × I/LnJ and PERA/EiJ × DBA/2 J intercrosses [30]. The Chr6 QTL overlapped with Pnhdlc1, initially mapped in a B6 x CASA/Rk intercross and then replicated in B6 x C3H Apoe−/− F2 mice [31, 32]. The Chr8 QTL replicated Nhdlq1, initially mapped in B6 x 129S1/SvImJ F2 mice [33]. The Chr9 QTL replicated Nhdlq11, initially mapped in B6 x C3H Apoe−/− F2 mice [32]. The Chr12 QTL peaked at 44.14 cM, overlapping with Nhdlq12 mapped in a B6 x C3H Apoe−/− F2 intercross [32].For triglyceride levels, 3 suggestive QTLs, located on Chr1, 2, and 5, respectively, were identified (Fig. 5). The Chr1 QTL peaked at 97 cM, 17 cM distal the Apoa2 gene (80 cM). The Chr2 QTL replicated Tgq11, mapped in an intercross between DBA/1J and DBA/2J [34]. The Chr5 QTL was novel.
Coincident QTLs for fasting glucose and lipids
LOD score plots for Chr9 showed that the QTL for fasting glucose (Bglu17) coincided precisely with the QTLs for HDL (Hdlq17) and non-HDL (Nhdlq11) in the confidence interval (Fig. 6). F2 mice homozygous for the BB allele exhibited elevated levels of fasting glucose and non-HDL but decreased levels of HDL, compared to those homozygous for the SS allele (Table 2). These QTLs affected their respective trait values in an additive manner.
Fig. 6
LOD score plots for fasting glucose, HDL, and non-HDL cholesterol of F2 mice fed the Western diet on chromosome 9. Plots were created with the interval mapping function of Map Manager QTX. The histogram in the plot estimates the confidence interval for a QTL. Two green vertical lines represent genome-wide significance thresholds for suggestive or significant linkage (P = 0.63 and P = 0.05, respectively). Black plots reflect the LOD score calculated at 1-cM intervals, the red plot represents the effect of the BALB allele, and the blue plot represents the effect of the SM allele. If BALB represents the high allele, then the red plot will be to the right of the graph; otherwise, it will be to the left
LOD score plots for fasting glucose, HDL, and non-HDLcholesterol of F2 mice fed the Western diet on chromosome 9. Plots were created with the interval mapping function of Map Manager QTX. The histogram in the plot estimates the confidence interval for a QTL. Two green vertical lines represent genome-wide significance thresholds for suggestive or significant linkage (P = 0.63 and P = 0.05, respectively). Black plots reflect the LOD score calculated at 1-cM intervals, the red plot represents the effect of the BALB allele, and the blue plot represents the effect of the SM allele. If BALB represents the high allele, then the red plot will be to the right of the graph; otherwise, it will be to the left
Correlations between plasma glucose and lipid levels
The correlations of fasting glucose levels with plasma levels of HDL, non-HDLcholesterol, or triglyceride were analyzed with the F2 population (Fig. 7). A significant inverse correlation between fasting glucose and HDLcholesterol levels was observed when the mice were fed a chow (R = −0.220; P = 8.1E-4) or Western diet (R = −0.257; P = 8.5E-5). F2 mice with higher HDLcholesterol levels had lower fasting glucose levels. Conversely, significant positive correlations between fasting glucose and non-HDLcholesterol levels were observed when mice were fed either chow (R = 0.194; P = 3.31E-3) or Western diet (R = 0.558; P = 4.7E-20). F2 mice with higher non-HDLcholesterol levels also had higher fasting glucose levels, especially on the Western diet. A significant positive correlation between plasma levels of fasting glucose and triglyceride was observed when mice were fed the Western diet (R = 0.377; P = 3.9E-9) but not the chow diet (R = 0.065; P = 0.330).
Fig. 7
Correlations of fasting plasma glucose levels with plasma levels of HDL, non-HDL cholesterol and triglyceride in the F2 population fed a chow (top row: a, b, c) or Western diet (bottom row: d, e, f). Each point represents values of an individual F2 mouse. The correlation coefficient (R) and significance (P) are shown
Correlations of fasting plasma glucose levels with plasma levels of HDL, non-HDLcholesterol and triglyceride in the F2 population fed a chow (top row: a, b, c) or Western diet (bottom row: d, e, f). Each point represents values of an individual F2 mouse. The correlation coefficient (R) and significance (P) are shown
Prioritization of positional candidate genes for Chr9 coincident QTLs
Bglu17 on Chr9 has been mapped in 3 separate intercrosses, including previously reported C57BLKS x DBA/2 [35] and B6-Apoe−/− x BALB-Apoe−/− crosses [21]. Hdlq17 on Chr9 has been mapped in multiple crosses, including B6 x 129, B6 x CAST/EiJ, B6-Apoe−/− x C3H-Apoe−/−, and B6-Apoe−/− x BALB-Apoe−/− crosses [24, 27, 31, 32, 36–38]. We conducted haplotype analyses using Sanger SNP database to prioritize positional candidate genes for both QTLs. Prioritized candidate genes for Hdlq17 are shown in Additional file 1: Table S1, and candidate genes for Bglu17 are shown in Additional file 2: Table S2. Most candidates for Hdlq17 are also candidate genes for Bglu17. These candidates contain one or more non-synonymous SNPs in the coding regions or SNPs in the upstream regulatory region that are shared by the high allele strains but are different from the low allele strains at the QTL. All candidate genes were further examined for associations with relevant human diseases using the NIH GWAS database (http://www.genome.gov/GWAStudies/). Sik3, Apoa1, and Apoc3 have been shown to be associated with variations in total, HDL, LDL-cholesterol or triglyceride levels [6, 7, 39], and Cadm1 with obesity-related traits [40].
Discussion
BALB and SM are two mouse strains that exhibit distinct differences in HDL, non-HDLcholesterol, and type 2 diabetes-related traits when deficient in Apoe [16]. BALB-Apoe−/− mice have higher HDL, lower non-HDLcholesterol, and lower glucose levels than SM-Apoe−/− mice when they are fed a Western diet. To identify the genetic factors responsible for these differences, we performed QTL analysis on a female cohort derived from an intercross between the two Apoe−/− strains. We have identified four loci contributing to fasting glucose levels, four loci contributing to HDLcholesterol levels, nine loci for non-HDLcholesterol levels, and three loci for triglyceride levels. Moreover, we have observed genetic connections between dyslipidemia and type 2 diabetes in that the QTL for fasting glucose is colocalized with the QTLs for HDL and non-HDLcholesterol on chromosome 9 and these coincident QTLs share a large fraction of potential candidate genes.We identified a significant QTL on chromosome 9, peaked at 26 cM, which affected fasting plasma glucose levels when mice were fed a chow or Western diet. We named it Bglu17 to represent a novel locus regulating fasting glucose levels in the mouse. This locus is overlapping with a significant QTL (not named) for blood glucose levels on the intraperitoneal glucose tolerance test identified in a BKS-Cg-Leprdb+/+ x DBA/2 intercross and a suggested QTL identified in a B6-Apoe−/− x BALB-Apoe−/− intercross [21, 35]. Interestingly, we found that Bglu17 coincided precisely with Hdlq17, a QTL for HDLcholesterol levels, and Nhdlq11, a QTL for non-HDLcholesterol levels. The colocalization of two or more QTLs for different traits suggests that these traits are controlled either by the same gene(s) or closely linked but different individual genes. Hdlq17 has been mapped in multiple crosses derived from inbred mouse strains whose genomes have been resequenced by Sanger, including B6, 129, BALB, C3H/HeJ, and CAST/EiJ [24, 27, 31, 32, 36–38]. Nhdlq11 was previously mapped in a NZB/BINJ x SM/JF2 cross and a B6-Apoe−/− x C3H-Apoe−/− intercross [32, 41]. To determine whether Bglu17 and Hdlq17 share the same underlying candidate genes, we performed haplotype analyses on those crosses that led to the identification of the QTLs. The number of shared genetic variants between Bglu17 and Hdlq17 was surprisingly high. Of them, Sik3, Apoa1, and Apoc3 are located precisely underneath the linkage peak of Bglu17 and Hdlq17, and they are also functional candidate genes of Hdlq17. Indeed, recent GWAS studies have associated these three genes with dyslipidemia or variations in HDL, LDL cholesterol, and triglyceride levels [6, 39, 42]. The finding in this study strongly suggests that one or more of these “lipid genes” might be the causal gene(s) of Bglu17, contributing to variation in fasting glucose levels. Although it is unknown how they affect glucose homeostasis, one probable effect path is through the influence on plasma lipid levels, which then predispose variation in glucose-related traits. The current observation on the significant correlations of fasting glucose levels with HDL, non-HDLcholesterol, and triglyceride levels in this cross supports this speculation. Plasma lipid levels, especially non-HDLcholesterol, of the F2 mice were significantly elevated on the Western diet, so were the fasting glucose levels. When fed the Western diet, Apoe−/− mice display a rapid rise in non-HDLcholesterol levels, often reaching their peak within a couple of weeks (unpublished data), whereas their blood glucose levels rise more slowly and gradually within 12 weeks [43, 44]. This difference in onset suggests a causal role for plasma lipids in the rise of blood glucose in the Apoe−/− mouse model.A significant reverse correlation was observed between plasma HDLcholesterol levels and fasting glucose levels in this cross on either chow or Western diet. This result is consistent with the findings of prospective human studies that low HDL levels can predict the future risk of developing T2D and low HDL levels are more prevalent in diabeticpatients than in the normal population [45, 46]. HDL can increase insulin secretion from β-cells, improve insulin sensitivity of the target tissues, and accelerate glucose uptake by muscle via the AMP-activated protein kinase [47]. A significant correlation of non-HDLcholesterol levels with fasting glucose levels was also observed in this cross, and the correlation was extremely high when mice were fed the Western diet. Emerging human studies have also revealed associations of non-HDLcholesterol and ApoB with fasting glucose levels and incident type 2 diabetes [48-50]. We previously observed that the elevation of non-HDLcholesterol levels in Apoe−/− mice during the consumption of a Western diet induces a chronic, low-grade inflammation state characterized by rises in circulating cytokines and infiltration of monocytes/macrophages in various organs or tissues [13, 17, 20, 43]. Inflammation in the islets impairs β-cell function [20]. LDL can also directly affect function and survival of β-cells [51]. In addition, high levels of LDL can induce insulin resistance due to its lipotoxicity and effect on endoplasmic reticulum stress [1].Plasma triglyceride levels were strongly correlated with fasting glucose levels in this cross on the Western diet, although no significant correlation was found when mice were fed the chow diet. Despite the strong correlation, no overlapping QTLs were observed for fasting glucose and triglyceride. The reason for the discrepancy between non-HDLcholesterol and triglyceride in terms of the presence or absence of colocalized QTLs is unclear.A suggestive QTL for fasting glucose near the proximal end of chromosome 9 (2.37 cM) was detected in this cross, initially on the chow diet and then replicated on the Western diet. The LOD score plot for chromosome 9 has shown 2 distinct peaks, one with a suggestive LOD score at the proximal end and one with a significant LOD score at a more distal region, suggesting the existence of two loci for fasting glucose on the chromosome. The bootstrap test, a statistical method for defining the confidence interval of QTLs using simulation [52], also indicated the existence of two QTLs for the trait on chromosome 9. We named the proximal one Bglu16 to represent a new QTL for fasting glucose in the mouse. Naming a suggestive locus is considered appropriate if it is repeatedly observed [53].Two suggestive QTLs for fasting glucose on chromosome 5 were identified when mice were fed the Western diet. The proximal one replicated Bglu13, recently mapped in the B6-Apoe−/− x BALB-Apoe−/− cross [21]. One probable candidate gene for this QTL is Hnf1a, which encodes hepatocyte nuclear factor 1α. In humans, Hnf1a mutations are the most common cause of maturity-onset diabetes of the young (MODY) [54]. The suggestive QTL in the distal region was novel.Most of the QTLs identified for plasma lipids confirm those identified in previous studies, whereas two QTLs for non-HDL are new and named Nhdlq15 and Nhdlq16, respectively. The QTLs on distal chromosome 1 for HDL and triglyceride has been mapped in a number of mouse crosses, and Apoa2 has been identified as the underlying causal gene [55]. However, the QTL (~90 cM) mapped in this study showed that it was more distal to the Apoa2 gene (80 cM), thus suggesting a different underlying causal gene.
Conclusion
We have identified multiple QTLs contributing to dyslipidemia and hyperglycemia in a segregating F2 population. The finding on the colocalization of QTLs for fasting glucose, HDL and non-HDLcholesterol levels and the sharing of probable candidate genes has demonstrated genetic connections between dyslipidemia and type 2 diabetes. The close correlations of fasting glucose with HDL, non-HDLcholesterol, and triglyceride support the hypothesis that dyslipidemia plays a causal role in the development of type 2 diabetes [1]. The haplotype analysis has prioritized candidates for either chromosome 9 QTL down to a handful of genes. Nevertheless, functional studies need to be performed to prove causality.
Availability of supporting data
Data are accessible through this link: https://mynotebook.labarchives.com/doi/MTc2Mzk0LjR8MTM1Njg4LzEzNTY4OC9Ob3RlYm9vay80MTAyOTgxMTQ4fDQ0Nzc3MC40/10.6070/H4D50K0X.
Authors: Carole Sztalryd; Coleen M Damcott; Jessica S Albert; Laura M Yerges-Armstrong; Richard B Horenstein; Toni I Pollin; Urmila T Sreenivasan; Sumbul Chai; William S Blaner; Soren Snitker; Jeffrey R O'Connell; Da-Wei Gong; Richard J Breyer; Alice S Ryan; John C McLenithan; Alan R Shuldiner Journal: N Engl J Med Date: 2014-05-21 Impact factor: 91.245
Authors: Naishi Li; Jingyuan Fu; Debby P Koonen; Jan Albert Kuivenhoven; Harold Snieder; Marten H Hofker Journal: Atherosclerosis Date: 2014-01-07 Impact factor: 5.162
Authors: Naishi Li; Marijke R van der Sijde; Stephan J L Bakker; Robin P F Dullaart; Pim van der Harst; Ron T Gansevoort; Clara C Elbers; Cisca Wijmenga; Harold Snieder; Marten H Hofker; Jingyuan Fu Journal: Diabetes Date: 2014-04-10 Impact factor: 9.461
Authors: Alisa K Manning; Marie-France Hivert; Robert A Scott; Jonna L Grimsby; Nabila Bouatia-Naji; Han Chen; Denis Rybin; Ching-Ti Liu; Lawrence F Bielak; Inga Prokopenko; Najaf Amin; Daniel Barnes; Gemma Cadby; Jouke-Jan Hottenga; Erik Ingelsson; Anne U Jackson; Toby Johnson; Stavroula Kanoni; Claes Ladenvall; Vasiliki Lagou; Jari Lahti; Cecile Lecoeur; Yongmei Liu; Maria Teresa Martinez-Larrad; May E Montasser; Pau Navarro; John R B Perry; Laura J Rasmussen-Torvik; Perttu Salo; Naveed Sattar; Dmitry Shungin; Rona J Strawbridge; Toshiko Tanaka; Cornelia M van Duijn; Ping An; Mariza de Andrade; Jeanette S Andrews; Thor Aspelund; Mustafa Atalay; Yurii Aulchenko; Beverley Balkau; Stefania Bandinelli; Jacques S Beckmann; John P Beilby; Claire Bellis; Richard N Bergman; John Blangero; Mladen Boban; Michael Boehnke; Eric Boerwinkle; Lori L Bonnycastle; Dorret I Boomsma; Ingrid B Borecki; Yvonne Böttcher; Claude Bouchard; Eric Brunner; Danijela Budimir; Harry Campbell; Olga Carlson; Peter S Chines; Robert Clarke; Francis S Collins; Arturo Corbatón-Anchuelo; David Couper; Ulf de Faire; George V Dedoussis; Panos Deloukas; Maria Dimitriou; Josephine M Egan; Gudny Eiriksdottir; Michael R Erdos; Johan G Eriksson; Elodie Eury; Luigi Ferrucci; Ian Ford; Nita G Forouhi; Caroline S Fox; Maria Grazia Franzosi; Paul W Franks; Timothy M Frayling; Philippe Froguel; Pilar Galan; Eco de Geus; Bruna Gigante; Nicole L Glazer; Anuj Goel; Leif Groop; Vilmundur Gudnason; Göran Hallmans; Anders Hamsten; Ola Hansson; Tamara B Harris; Caroline Hayward; Simon Heath; Serge Hercberg; Andrew A Hicks; Aroon Hingorani; Albert Hofman; Jennie Hui; Joseph Hung; Marjo-Riitta Jarvelin; Min A Jhun; Paul C D Johnson; J Wouter Jukema; Antti Jula; W H Kao; Jaakko Kaprio; Sharon L R Kardia; Sirkka Keinanen-Kiukaanniemi; Mika Kivimaki; Ivana Kolcic; Peter Kovacs; Meena Kumari; Johanna Kuusisto; Kirsten Ohm Kyvik; Markku Laakso; Timo Lakka; Lars Lannfelt; G Mark Lathrop; Lenore J Launer; Karin Leander; Guo Li; Lars Lind; Jaana Lindstrom; Stéphane Lobbens; Ruth J F Loos; Jian'an Luan; Valeriya Lyssenko; Reedik Mägi; Patrik K E Magnusson; Michael Marmot; Pierre Meneton; Karen L Mohlke; Vincent Mooser; Mario A Morken; Iva Miljkovic; Narisu Narisu; Jeff O'Connell; Ken K Ong; Ben A Oostra; Lyle J Palmer; Aarno Palotie; James S Pankow; John F Peden; Nancy L Pedersen; Marina Pehlic; Leena Peltonen; Brenda Penninx; Marijana Pericic; Markus Perola; Louis Perusse; Patricia A Peyser; Ozren Polasek; Peter P Pramstaller; Michael A Province; Katri Räikkönen; Rainer Rauramaa; Emil Rehnberg; Ken Rice; Jerome I Rotter; Igor Rudan; Aimo Ruokonen; Timo Saaristo; Maria Sabater-Lleal; Veikko Salomaa; David B Savage; Richa Saxena; Peter Schwarz; Udo Seedorf; Bengt Sennblad; Manuel Serrano-Rios; Alan R Shuldiner; Eric J G Sijbrands; David S Siscovick; Johannes H Smit; Kerrin S Small; Nicholas L Smith; Albert Vernon Smith; Alena Stančáková; Kathleen Stirrups; Michael Stumvoll; Yan V Sun; Amy J Swift; Anke Tönjes; Jaakko Tuomilehto; Stella Trompet; Andre G Uitterlinden; Matti Uusitupa; Max Vikström; Veronique Vitart; Marie-Claude Vohl; Benjamin F Voight; Peter Vollenweider; Gerard Waeber; Dawn M Waterworth; Hugh Watkins; Eleanor Wheeler; Elisabeth Widen; Sarah H Wild; Sara M Willems; Gonneke Willemsen; James F Wilson; Jacqueline C M Witteman; Alan F Wright; Hanieh Yaghootkar; Diana Zelenika; Tatijana Zemunik; Lina Zgaga; Nicholas J Wareham; Mark I McCarthy; Ines Barroso; Richard M Watanabe; Jose C Florez; Josée Dupuis; James B Meigs; Claudia Langenberg Journal: Nat Genet Date: 2012-05-13 Impact factor: 38.330
Authors: Anthony G Comuzzie; Shelley A Cole; Sandra L Laston; V Saroja Voruganti; Karin Haack; Richard A Gibbs; Nancy F Butte Journal: PLoS One Date: 2012-12-14 Impact factor: 3.240
Authors: Cristen J Willer; Ellen M Schmidt; Sebanti Sengupta; Michael Boehnke; Panos Deloukas; Sekar Kathiresan; Karen L Mohlke; Erik Ingelsson; Gonçalo R Abecasis; Gina M Peloso; Stefan Gustafsson; Stavroula Kanoni; Andrea Ganna; Jin Chen; Martin L Buchkovich; Samia Mora; Jacques S Beckmann; Jennifer L Bragg-Gresham; Hsing-Yi Chang; Ayşe Demirkan; Heleen M Den Hertog; Ron Do; Louise A Donnelly; Georg B Ehret; Tõnu Esko; Mary F Feitosa; Teresa Ferreira; Krista Fischer; Pierre Fontanillas; Ross M Fraser; Daniel F Freitag; Deepti Gurdasani; Kauko Heikkilä; Elina Hyppönen; Aaron Isaacs; Anne U Jackson; Åsa Johansson; Toby Johnson; Marika Kaakinen; Johannes Kettunen; Marcus E Kleber; Xiaohui Li; Jian'an Luan; Leo-Pekka Lyytikäinen; Patrik K E Magnusson; Massimo Mangino; Evelin Mihailov; May E Montasser; Martina Müller-Nurasyid; Ilja M Nolte; Jeffrey R O'Connell; Cameron D Palmer; Markus Perola; Ann-Kristin Petersen; Serena Sanna; Richa Saxena; Susan K Service; Sonia Shah; Dmitry Shungin; Carlo Sidore; Ci Song; Rona J Strawbridge; Ida Surakka; Toshiko Tanaka; Tanya M Teslovich; Gudmar Thorleifsson; Evita G Van den Herik; Benjamin F Voight; Kelly A Volcik; Lindsay L Waite; Andrew Wong; Ying Wu; Weihua Zhang; Devin Absher; Gershim Asiki; Inês Barroso; Latonya F Been; Jennifer L Bolton; Lori L Bonnycastle; Paolo Brambilla; Mary S Burnett; Giancarlo Cesana; Maria Dimitriou; Alex S F Doney; Angela Döring; Paul Elliott; Stephen E Epstein; Gudmundur Ingi Eyjolfsson; Bruna Gigante; Mark O Goodarzi; Harald Grallert; Martha L Gravito; Christopher J Groves; Göran Hallmans; Anna-Liisa Hartikainen; Caroline Hayward; Dena Hernandez; Andrew A Hicks; Hilma Holm; Yi-Jen Hung; Thomas Illig; Michelle R Jones; Pontiano Kaleebu; John J P Kastelein; Kay-Tee Khaw; Eric Kim; Norman Klopp; Pirjo Komulainen; Meena Kumari; Claudia Langenberg; Terho Lehtimäki; Shih-Yi Lin; Jaana Lindström; Ruth J F Loos; François Mach; Wendy L McArdle; Christa Meisinger; Braxton D Mitchell; Gabrielle Müller; Ramaiah Nagaraja; Narisu Narisu; Tuomo V M Nieminen; Rebecca N Nsubuga; Isleifur Olafsson; Ken K Ong; Aarno Palotie; Theodore Papamarkou; Cristina Pomilla; Anneli Pouta; Daniel J Rader; Muredach P Reilly; Paul M Ridker; Fernando Rivadeneira; Igor Rudan; Aimo Ruokonen; Nilesh Samani; Hubert Scharnagl; Janet Seeley; Kaisa Silander; Alena Stančáková; Kathleen Stirrups; Amy J Swift; Laurence Tiret; Andre G Uitterlinden; L Joost van Pelt; Sailaja Vedantam; Nicholas Wainwright; Cisca Wijmenga; Sarah H Wild; Gonneke Willemsen; Tom Wilsgaard; James F Wilson; Elizabeth H Young; Jing Hua Zhao; Linda S Adair; Dominique Arveiler; Themistocles L Assimes; Stefania Bandinelli; Franklyn Bennett; Murielle Bochud; Bernhard O Boehm; Dorret I Boomsma; Ingrid B Borecki; Stefan R Bornstein; Pascal Bovet; Michel Burnier; Harry Campbell; Aravinda Chakravarti; John C Chambers; Yii-Der Ida Chen; Francis S Collins; Richard S Cooper; John Danesh; George Dedoussis; Ulf de Faire; Alan B Feranil; Jean Ferrières; Luigi Ferrucci; Nelson B Freimer; Christian Gieger; Leif C Groop; Vilmundur Gudnason; Ulf Gyllensten; Anders Hamsten; Tamara B Harris; Aroon Hingorani; Joel N Hirschhorn; Albert Hofman; G Kees Hovingh; Chao Agnes Hsiung; Steve E Humphries; Steven C Hunt; Kristian Hveem; Carlos Iribarren; Marjo-Riitta Järvelin; Antti Jula; Mika Kähönen; Jaakko Kaprio; Antero Kesäniemi; Mika Kivimaki; Jaspal S Kooner; Peter J Koudstaal; Ronald M Krauss; Diana Kuh; Johanna Kuusisto; Kirsten O Kyvik; Markku Laakso; Timo A Lakka; Lars Lind; Cecilia M Lindgren; Nicholas G Martin; Winfried März; Mark I McCarthy; Colin A McKenzie; Pierre Meneton; Andres Metspalu; Leena Moilanen; Andrew D Morris; Patricia B Munroe; Inger Njølstad; Nancy L Pedersen; Chris Power; Peter P Pramstaller; Jackie F Price; Bruce M Psaty; Thomas Quertermous; Rainer Rauramaa; Danish Saleheen; Veikko Salomaa; Dharambir K Sanghera; Jouko Saramies; Peter E H Schwarz; Wayne H-H Sheu; Alan R Shuldiner; Agneta Siegbahn; Tim D Spector; Kari Stefansson; David P Strachan; Bamidele O Tayo; Elena Tremoli; Jaakko Tuomilehto; Matti Uusitupa; Cornelia M van Duijn; Peter Vollenweider; Lars Wallentin; Nicholas J Wareham; John B Whitfield; Bruce H R Wolffenbuttel; Jose M Ordovas; Eric Boerwinkle; Colin N A Palmer; Unnur Thorsteinsdottir; Daniel I Chasman; Jerome I Rotter; Paul W Franks; Samuli Ripatti; L Adrienne Cupples; Manjinder S Sandhu; Stephen S Rich Journal: Nat Genet Date: 2013-10-06 Impact factor: 38.330
Authors: Jessica S Rowlan; Qiongzhen Li; Ani Manichaikul; Qian Wang; Alan H Matsumoto; Weibin Shi Journal: J Am Heart Assoc Date: 2013-08-12 Impact factor: 5.501
Authors: Arthur Ko; Rita M Cantor; Daphna Weissglas-Volkov; Elina Nikkola; Prasad M V Linga Reddy; Janet S Sinsheimer; Bogdan Pasaniuc; Robert Brown; Marcus Alvarez; Alejandra Rodriguez; Rosario Rodriguez-Guillen; Ivette C Bautista; Olimpia Arellano-Campos; Linda L Muñoz-Hernández; Veikko Salomaa; Jaakko Kaprio; Antti Jula; Matti Jauhiainen; Markku Heliövaara; Olli Raitakari; Terho Lehtimäki; Johan G Eriksson; Markus Perola; Kirk E Lohmueller; Niina Matikainen; Marja-Riitta Taskinen; Maribel Rodriguez-Torres; Laura Riba; Teresa Tusie-Luna; Carlos A Aguilar-Salinas; Päivi Pajukanta Journal: Nat Commun Date: 2014-06-02 Impact factor: 14.919
Authors: Andrew T Grainger; Michael B Jones; Jing Li; Mei-Hua Chen; Ani Manichaikul; Weibin Shi Journal: Atherosclerosis Date: 2016-10-06 Impact factor: 5.162
Authors: Andrew T Grainger; Nathanael Pilar; Jun Li; Mei-Hua Chen; Ashley M Abramson; Christoph Becker-Pauly; Weibin Shi Journal: Genetics Date: 2021-12-10 Impact factor: 4.402
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