Exome sequencing-driven discovery of coding polymorphisms associated with common metabolic phenotypes
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- 作者：A. Albrechtsen (1)
N. Grarup (2)
Y. Li (3)
T. Spars? (2)
G. Tian (4)
H. Cao (3)
T. Jiang (3)
S. Y. Kim (5)
T. Korneliussen (1)
Q. Li (3)
C. Nie (3)
R. Wu (3)
L. Skotte (1)
A. P. Morris (6)
C. Ladenvall (7)
S. Cauchi (8)
A. Stan?áková (9)
G. Andersen (2)
A. Astrup (10)
K. Banasik (2)
A. J. Bennett (11)
L. Bolund (12)
G. Charpentier (13)
Y. Chen (3)
J. M. Dekker (14)
A. S. F. Doney (15) (16)
M. Dorkhan (7)
T. Forsen (17) (18)
T. M. Frayling (19) (20)
C. J. Groves (11)
Y. Gui (3)
G. Hallmans (21)
A. T. Hattersley (19) (20)
K. He (22)
G. A. Hitman (23)
J. Holmkvist (2) (24)
S. Huang (25) (3)
H. Jiang (3)
X. Jin (3)
J. M. Justesen (2)
K. Kristiansen (26)
J. Kuusisto (9)
M. Lajer (27)
O. Lantieri (28)
W. Li (3)
H. Liang (3)
Q. Liao (3)
X. Liu (3)
T. Ma (3)
X. Ma (3)
M. P. Manijak (2)
M. Marre (29) (30)
J. Mokrosiński (2) (31)
A. D. Morris (15) (16)
B. Mu (3)
A. A. Nielsen (32)
G. Nijpels (14)
P. Nilsson (33)
C. N. A. Palmer (15) (16)
N. W. Rayner (11) (6)
F. Renstr?m (34)
R. Ribel-Madsen (2)
N. Robertson (11) (6)
O. Rolandsson (21)
P. Rossing (27)
T. W. Schwartz (2) (31)
P. E. Slagboom (35) (36)
M. Sterner (7)
M. Tang (3)
L. Tarnow (27)
T. Tuomi (37) (38)
E. van’t Riet (14)
N. van Leeuwen (39)
T. V. Varga (34)
M. A. Vestmar (2) (31)
M. Walker (40)
B. Wang (3)
Y. Wang (3)
H. Wu (3)
F. Xi (3)
L. Yengo (8)
C. Yu (3)
X. Zhang (3)
J. Zhang (3)
Q. Zhang (3)
W. Zhang (3)
H. Zheng (3)
Y. Zhou (3)
D. Altshuler (41) (42)
L. M. ‘t Hart (35) (39)
P. W. Franks (21) (34) (43)
B. Balkau (44)
P. Froguel (45) (8)
M. I. McCarthy (11) (46) (6)
M. Laakso (9)
L. Groop (7)
C. Christensen (47)
I. Brandslund (32) (48)
T. Lauritzen (49)
D. R. Witte (27)
A. Linneberg (50)
T. J?rgensen (50) (51) (52)
T. Hansen (2) (53)
J. Wang (2) (26) (3)
R. Nielsen (1) (5) (54)
O. Pedersen (2) (55) (56) (57) - 关键词：Exome sequencing ; Genetic epidemiology ; Genetics ; Lipids ; Next ; generation sequencing ; Obesity ; Type 2 diabetes
- 刊名：Diabetologia
- 出版年：2013
- 出版时间：February 2013
- 年：2013
- 卷：56
- 期：2
- 页码：298-310
- 全文大小：392KB
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32. Bansal V, Libiger O, Torkamani A, Schork NJ (2010) Statistical analysis strategies for association studies involving rare variants. Nat Rev Genet 11:773-85 CrossRef - 作者单位：A. Albrechtsen (1)
N. Grarup (2)
Y. Li (3)
T. Spars? (2)
G. Tian (4)
H. Cao (3)
T. Jiang (3)
S. Y. Kim (5)
T. Korneliussen (1)
Q. Li (3)
C. Nie (3)
R. Wu (3)
L. Skotte (1)
A. P. Morris (6)
C. Ladenvall (7)
S. Cauchi (8)
A. Stan?áková (9)
G. Andersen (2)
A. Astrup (10)
K. Banasik (2)
A. J. Bennett (11)
L. Bolund (12)
G. Charpentier (13)
Y. Chen (3)
J. M. Dekker (14)
A. S. F. Doney (15) (16)
M. Dorkhan (7)
T. Forsen (17) (18)
T. M. Frayling (19) (20)
C. J. Groves (11)
Y. Gui (3)
G. Hallmans (21)
A. T. Hattersley (19) (20)
K. He (22)
G. A. Hitman (23)
J. Holmkvist (2) (24)
S. Huang (25) (3)
H. Jiang (3)
X. Jin (3)
J. M. Justesen (2)
K. Kristiansen (26)
J. Kuusisto (9)
M. Lajer (27)
O. Lantieri (28)
W. Li (3)
H. Liang (3)
Q. Liao (3)
X. Liu (3)
T. Ma (3)
X. Ma (3)
M. P. Manijak (2)
M. Marre (29) (30)
J. Mokrosiński (2) (31)
A. D. Morris (15) (16)
B. Mu (3)
A. A. Nielsen (32)
G. Nijpels (14)
P. Nilsson (33)
C. N. A. Palmer (15) (16)
N. W. Rayner (11) (6)
F. Renstr?m (34)
R. Ribel-Madsen (2)
N. Robertson (11) (6)
O. Rolandsson (21)
P. Rossing (27)
T. W. Schwartz (2) (31)
P. E. Slagboom (35) (36)
M. Sterner (7)
M. Tang (3)
L. Tarnow (27)
T. Tuomi (37) (38)
E. van’t Riet (14)
N. van Leeuwen (39)
T. V. Varga (34)
M. A. Vestmar (2) (31)
M. Walker (40)
B. Wang (3)
Y. Wang (3)
H. Wu (3)
F. Xi (3)
L. Yengo (8)
C. Yu (3)
X. Zhang (3)
J. Zhang (3)
Q. Zhang (3)
W. Zhang (3)
H. Zheng (3)
Y. Zhou (3)
D. Altshuler (41) (42)
L. M. ‘t Hart (35) (39)
P. W. Franks (21) (34) (43)
B. Balkau (44)
P. Froguel (45) (8)
M. I. McCarthy (11) (46) (6)
M. Laakso (9)
L. Groop (7)
C. Christensen (47)
I. Brandslund (32) (48)
T. Lauritzen (49)
D. R. Witte (27)
A. Linneberg (50)
T. J?rgensen (50) (51) (52)
T. Hansen (2) (53)
J. Wang (2) (26) (3)
R. Nielsen (1) (5) (54)
O. Pedersen (2) (55) (56) (57)
1. Centre of Bioinformatics, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
2. The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, DIKU Building, Universitetsparken 1, 2100, Copenhagen ?, Denmark
3. BGI-Shenzhen, Beishan Industrial Zone, Yantian District, 518083, Shenzhen, China
4. BGI-Tianjin, Tianjin, China
5. Department of Integrative Biology, University of California, 3060 Valley Life Sciences, Bldg #3140, Berkeley, CA, 94720-3140, USA
6. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
7. Department of Clinical Sciences, Diabetes and Endocrinology, Lund University and Lund University Diabetes Centre, Malm?, Sweden
8. UMR CNRS 8199, Genomic and Metabolic Disease, Lille, France
9. Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
10. Department of Human Nutrition, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
11. Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
12. Institute of Human Genetics, Aarhus University, Aarhus, Denmark
13. Department of Endocrinology-Diabetology, Corbeil-Essonnes Hospital, Corbeil-Essonnes, France
14. EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands
15. Diabetes Research Centre, Biomedical Research Institute, University of Dundee, Ninewells Hospital, Dundee, UK
16. Pharmacogenomics Centre, Biomedical Research Institute, University of Dundee, Ninewells Hospital, Dundee, UK
17. Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland
18. Vasa Health Care Center, Vaasa, Finland
19. Genetics of Complex Traits, Institute of Biomedical and Clinical Science, Peninsula Medical School, University of Exeter, Exeter, UK
20. Diabetes Genetics, Institute of Biomedical and Clinical Science, Peninsula Medical School, University of Exeter, Exeter, UK
21. Department of Public Health and Clinical Medicine, Ume? University, Ume?, Sweden
22. Chinese PLA General Hospital, Beijing, China
23. Centre for Diabetes, Blizard Institute, Queen Mary University of London, London, UK
24. Vipergen Aps, Copenhagen, Denmark
25. School of Bioscience and Biotechnology, South China University of Technology, Guangzhou, China
26. Department of Biology, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
27. Steno Diabetes Center, Gentofte, Denmark
28. Institut inter Regional pour la Santé (IRSA), La Riche, France
29. Department of Endocrinology, Diabetology and Nutrition, Bichat-Claude Bernard University Hospital, Assistance Publique des H?pitaux de Paris, Paris, France
30. Inserm U695, Université Denis Diderot Paris 7, Paris, France
31. Laboratory for Molecular Pharmacology, Department of Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
32. Department of Clinical Biochemistry, Vejle Hospital, Vejle, Denmark
33. Department of Clinical Sciences, Medicine, Lund University, Malm?, Sweden
34. Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Sk?na University Hospital, Lund University, Malm?, Sweden
35. Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
36. Netherlands Center for Healthy Ageing, Leiden, the Netherlands
37. Department of Medicine, Helsinki University Hospital, Helsinki, Finland
38. Folkh?lsan Research Center, Helsinki, Finland
39. Department of Molecular Cell Biology, Leiden University Medical Center, Leiden, the Netherlands
40. Diabetes Research Group, School of Clinical Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
41. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
42. Broad Institute of Harvard and MIT, Cambridge, MA, USA
43. Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
44. Inserm CESP U1018, Villejuif, France
45. Genomic Medicine, Hammersmith Hospital, Imperial College London, London, UK
46. Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Oxford, UK
47. Department of Internal Medicine and Endocrinology, Vejle Hospital, Vejle, Denmark
48. Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
49. Department of General Practice, Aarhus University, Aarhus, Denmark
50. Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark
51. Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
52. Faculty of Medicine, University of Aalborg, Aalborg, Denmark
53. Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
54. Department of Statistics, University of California, Berkeley, CA, USA
55. Faculty of Health Sciences, Aarhus University, Aarhus, Denmark
56. Hagedorn Research Institute, Gentofte, Denmark
57. Institute of Biomedical Science, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark - ISSN：1432-0428
文摘
Aims/hypothesis Human complex metabolic traits are in part regulated by genetic determinants. Here we applied exome sequencing to identify novel associations of coding polymorphisms at minor allele frequencies (MAFs) >1% with common metabolic phenotypes. Methods The study comprised three stages. We performed medium-depth (8×) whole exome sequencing in 1,000 cases with type 2 diabetes, BMI >27.5?kg/m2 and hypertension and in 1,000 controls (stage 1). We selected 16,192 polymorphisms nominally associated (p-lt;-.05) with case–control status, from four selected annotation categories or from loci reported to associate with metabolic traits. These variants were genotyped in 15,989 Danes to search for association with 12 metabolic phenotypes (stage 2). In stage 3, polymorphisms showing potential associations were genotyped in a further 63,896 Europeans. Results Exome sequencing identified 70,182 polymorphisms with MAF >1%. In stage 2 we identified 51 potential associations with one or more of eight metabolic phenotypes covered by 45 unique polymorphisms. In meta-analyses of stage 2 and stage 3 results, we demonstrated robust associations for coding polymorphisms in CD300LG (fasting HDL-cholesterol: MAF 3.5%, p--.5?×-0?4), COBLL1 (type 2 diabetes: MAF 12.5%, OR 0.88, p--.2?×-0?1) and MACF1 (type 2 diabetes: MAF 23.4%, OR 1.10, p--.2?×-0?0). Conclusions/interpretation We applied exome sequencing as a basis for finding genetic determinants of metabolic traits and show the existence of low-frequency and common coding polymorphisms with impact on common metabolic traits. Based on our study, coding polymorphisms with MAF above 1% do not seem to have particularly high effect sizes on the measured metabolic traits.