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Meta-Analysis
. 2015 Oct;47(10):1121-1130.
doi: 10.1038/ng.3396. Epub 2015 Sep 7.

A Comprehensive 1,000 Genomes-based Genome-Wide Association Meta-Analysis of Coronary Artery Disease

Majid Nikpay #  1 Anuj Goel #  2   3 Hong-Hee Won #  4   5   6   7 Leanne M Hall #  8 Christina Willenborg #  9   10 Stavroula Kanoni #  11 Danish Saleheen #  12   13 Theodosios Kyriakou  2   3 Christopher P Nelson  8   14 Jemma C Hopewell  15 Thomas R Webb  8   14 Lingyao Zeng  16   17 Abbas Dehghan  18 Maris Alver  19   20 Sebastian M Armasu  21 Kirsi Auro  22   23   24 Andrew Bjonnes  4   6 Daniel I Chasman  25   26 Shufeng Chen  27 Ian Ford  28 Nora Franceschini  29 Christian Gieger  17   30   31 Christopher Grace  2   3 Stefan Gustafsson  32   33 Jie Huang  34 Shih-Jen Hwang  35   36 Yun Kyoung Kim  37 Marcus E Kleber  38 King Wai Lau  15 Xiangfeng Lu  27 Yingchang Lu  39   40 Leo-Pekka Lyytikäinen  41   42 Evelin Mihailov  19 Alanna C Morrison  43 Natalia Pervjakova  19   22   23   24 Liming Qu  44 Lynda M Rose  25 Elias Salfati  45 Richa Saxena  4   6   46 Markus Scholz  47   48 Albert V Smith  49   50 Emmi Tikkanen  51   52 Andre Uitterlinden  18 Xueli Yang  27 Weihua Zhang  53   54 Wei Zhao  12 Mariza de Andrade  21 Paul S de Vries  18 Natalie R van Zuydam  3   55 Sonia S Anand  56 Lars Bertram  57   58 Frank Beutner  48   59 George Dedoussis  60 Philippe Frossard  13 Dominique Gauguier  61 Alison H Goodall  14   62 Omri Gottesman  39 Marc Haber  63 Bok-Ghee Han  37 Jianfeng Huang  64 Shapour Jalilzadeh  2   3 Thorsten Kessler  16   65 Inke R König  10   66 Lars Lannfelt  67 Wolfgang Lieb  68 Lars Lind  69 Cecilia M Lindgren  3   4 Marja-Liisa Lokki  70 Patrik K Magnusson  71 Nadeem H Mallick  72 Narinder Mehra  73 Thomas Meitinger  17   74   75 Fazal-Ur-Rehman Memon  76 Andrew P Morris  3   77 Markku S Nieminen  78 Nancy L Pedersen  71 Annette Peters  17   30 Loukianos S Rallidis  79 Asif Rasheed  13   76 Maria Samuel  13 Svati H Shah  80 Juha Sinisalo  78 Kathleen E Stirrups  11   81 Stella Trompet  82   83 Laiyuan Wang  27   84 Khan S Zaman  85 Diego Ardissino  86   87 Eric Boerwinkle  43   88 Ingrid B Borecki  89 Erwin P Bottinger  39 Julie E Buring  25 John C Chambers  53   54   90 Rory Collins  15 L Adrienne Cupples  35   36 John Danesh  34   91 Ilja Demuth  92   93 Roberto Elosua  94 Stephen E Epstein  95 Tõnu Esko  4   19   96   97 Mary F Feitosa  89 Oscar H Franco  18 Maria Grazia Franzosi  98 Christopher B Granger  80 Dongfeng Gu  27 Vilmundur Gudnason  49   50 Alistair S Hall  99 Anders Hamsten  100 Tamara B Harris  101 Stanley L Hazen  102 Christian Hengstenberg  16   17 Albert Hofman  18 Erik Ingelsson  3   32   33   103 Carlos Iribarren  104 J Wouter Jukema  82   105   106 Pekka J Karhunen  41   107 Bong-Jo Kim  37 Jaspal S Kooner  54   90   108 Iftikhar J Kullo  109 Terho Lehtimäki  41   42 Ruth J F Loos  39   40   110 Olle Melander  111 Andres Metspalu  19   20 Winfried März  38   112   113 Colin N Palmer  55 Markus Perola  19   22   23   24 Thomas Quertermous  45   114 Daniel J Rader  115   116 Paul M Ridker  25   26 Samuli Ripatti  34   51   52 Robert Roberts  117 Veikko Salomaa  118 Dharambir K Sanghera  119   120   121 Stephen M Schwartz  122   123 Udo Seedorf  124 Alexandre F Stewart  1 David J Stott  125 Joachim Thiery  48   126 Pierre A Zalloua  63   127 Christopher J O'Donnell  35   128   129 Muredach P Reilly  116 Themistocles L Assimes  45   114 John R Thompson  130 Jeanette Erdmann  9   10 Robert Clarke  15 Hugh Watkins  2   3 Sekar Kathiresan  4   5   6   7 Ruth McPherson  1 Panos Deloukas  11   131 Heribert Schunkert  16   17 Nilesh J Samani  8   14 Martin Farrall  2   3
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Free PMC article
Meta-Analysis

A Comprehensive 1,000 Genomes-based Genome-Wide Association Meta-Analysis of Coronary Artery Disease

Majid Nikpay et al. Nat Genet. .
Free PMC article

Abstract

Existing knowledge of genetic variants affecting risk of coronary artery disease (CAD) is largely based on genome-wide association study (GWAS) analysis of common SNPs. Leveraging phased haplotypes from the 1000 Genomes Project, we report a GWAS meta-analysis of ∼185,000 CAD cases and controls, interrogating 6.7 million common (minor allele frequency (MAF) > 0.05) and 2.7 million low-frequency (0.005 < MAF < 0.05) variants. In addition to confirming most known CAD-associated loci, we identified ten new loci (eight additive and two recessive) that contain candidate causal genes newly implicating biological processes in vessel walls. We observed intralocus allelic heterogeneity but little evidence of low-frequency variants with larger effects and no evidence of synthetic association. Our analysis provides a comprehensive survey of the fine genetic architecture of CAD, showing that genetic susceptibility to this common disease is largely determined by common SNPs of small effect size.

Figures

Figure 1
Figure 1
Spectrum of minor allele frequencies (MAF) and median imputation quality (MEDIAN INFO) showing the number (N) of variants in each bin (a) shows the distribution for the 9.4M 1000 Genomes phase1v3 variants (b) shows the distribution for 2.5M HapMap2 SNPs. Imputation quality was calculated as the median of the respective values in up to 48 contributing studies; the imputation quality for genotyped variants was set equal to 1.0. The 1000 Genomes training set included more low frequency variants, many of which have imputation qualities > 0.9.
Figure 2
Figure 2
A circular Manhattan plot summarizing the 1000 Genomes CAD association results. The meta-analysis statistics have been adjusted for over-dispersion (before double genomic control, lambda = 1.18); over-dispersion is predicted to be a regular feature in GWAS under the polygenic inheritance model. The association statistics have been capped to P = 1 × 10−20. Genome-wide significant variants (P < 5 × 10−8) are indicated by red triangles. Novel CAD loci are named in red (Table 1). Previously reported loci showing genome-wide significance are shown in black and those showing nominal significance (P < 0.05) in our meta-analysis in blue (Supplementary Table 2). The inner track (see inset) shows the imputation quality score of the lead variants of the novel loci. The middle track shows numbered chromosome ideograms with the centromeres indicated by pink bars.
Figure 3
Figure 3
Imputation quality and effect size of lead variants at 46 genome-wide significant loci. (a) Imputation quality and minor allele frequency (MAF) for lead variants at 46 genome-wide significant susceptibility loci. Blue circles indicate novel additive loci, red squares - novel recessive loci, black triangles - previously mapped additive loci, black diamonds - key SNPs in LPA and APOE. Imputation quality and MAF were calculated as the median of the respective values in up to 48 contributing studies; the imputation quality for studies with genotype data was fixed at 1.0. (b) Odds ratios and effect allele frequency (EAF) for lead variants at 46 genome-wide significant loci. Blue circles indicate novel additive loci; red squares - novel recessive loci, black triangles - previously mapped additive loci. SNPs rs55730499 and rs2891168 are lead variants in the LPA and chromosome 9p21 susceptibility loci. EAF was calculated as the median value in up to 48 contributing studies.
Figure 4
Figure 4
Regional association plots of the eight additive (a–h) and two recessive (i–j) novel CAD loci. The association statistics have been adjusted for over-dispersion following meta-analysis (genomic control parameter 1.18 for the additive and 1.05 for the recessive models). Linkage disequilibrium (r2) calculations were based on the combined 1000 Genomes phase 1 v3 training dataset. Genomic coordinates in mega-base pairs (Mb) refer to the hg19 sequence assembly.

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