Literature DB >> 27761488

Data on common variants associated with coronary artery disease/myocardial infarction in ethnic Arabs.

Salma M Wakil1, Ramesh Ram2, Nzioka P Muiya1, Munish Mehta2, Editha Andres1, Nejat Mazhar1, Batoul Baz1, Samya Hagos1, Maie Alshahid3, Brian F Meyer1, Grant Morahan2, Nduna Dzimiri1.   

Abstract

The data shows results acquired in a large cohort of 5668 ethnic Arabs involved in a common variants association study for coronary artery disease (CAD) and myocardial infarction (MI) using the Affymetrix Axiom Genotyping platform ("A genome-wide association study reveals susceptibility loci for myocardial infarction/coronary artery disease in Saudi Arabs" Wakil et al. (2015) [1] ). Several loci were described that conferred risk for CAD or MI, some of which were validated in an independent set of samples. Principal Component (PCA) analysis suggested that the Saudi Cohort was close to the CEU and TSI populations, thus pointing to similarity with European populations.

Entities:  

Year:  2016        PMID: 27761488      PMCID: PMC5063810          DOI: 10.1016/j.dib.2016.02.010

Source DB:  PubMed          Journal:  Data Brief        ISSN: 2352-3409


Specifications table Value of the data Genomic distribution of risk variants for CAD/MI in ethnic Arabs. Comparative analysis of the genomic distribution of the associated loci for CAD/MI between the Arab population and other ethnic groups. Regional association plots demonstrate loci on 2q33, 8q13, 9p31 for CAD and on 21q22.11 for MI. Principal component analysis comparison with 11 other MApMAp3 populations shows variations and clustering of ethnic populations. The Saudi Arab cohort show similarities with the Caucasian populations.

Data

The summary puts together the clinical features of the studied cases versus controls, the genomic distribution of the implicated variants and the principal component analysis of the data, as well as comparison of the Saudi population with other ethnic groups(Fig. 1, Fig. 2, Fig. 3)(Table 1).
Fig. 1

Regional association plots for coronary artery disease and myocardial infarction.

Fig. 2

Principal Component Analysis.

Fig. 3

Principal component analysis.

Table 1

Important clinical features and demographics of the coronary artery disease cases (CAD) versus angiographed controls

CAD controls
CAD cases
AllMaleFemaleAllMaleFemale
Gender30001590(0.53)1410(0.47)26682028(0.76)640(0.24)
Age49.8±0.3050.3±0.4049.2±0.4059.8±0.2059.3±0.2561.6±0.40
BMI29.4±0.1228.3±0.1530.6±0.1929.3±0.1028.5±0.1031.9±0.23
MI933589(0.63)344(0.37)24951919(0.77)576(0.23)
T2DM1207646(0.54)561(0.46)18481354(0.73)494(0.27)
HTN1846963(0.52)883(0.48)21871631(0.75)556(0.25)
lHDLC907592(0.65)315(0.35)13531115(0.82)238(0.18)
hLDLC312166(0.53)146(0.47)346251(0.73)95(0.27)
hTG1846963(0.52)883(0.48)21871631(0.75)556(0.25)
hChol710365(0.51)345(0.49)1177876(0.74)301(0.26)
FH679378(0.56)301(0.44)500397(0.79)103(0.21)
OBS1228533(0.43)695(0.57)1087710(0.65)377(0.35)
Smokers882820(0.93)62(0.07)12511219(0.97)32(0.03)
VD
One000973718(0.74)255(0.26)
Two000529408(0.77)121(0.23)
>Two0001164910(0.78)254(0.22)

The numbers in brackets give the percentages of the total (all) values of the group. BMI, body mass index; FH, family history of CAD; MI, myocardial infarction; hLDLC, high low density lipoprotein-cholesterol level; lHDLC, low high density lipoprotein-cholesterol level; hTG, hypertriglyceridaemia; hChol, hypercholesterolaemia; HTN, hypertension; T2DM, type 2 diabetes mellitus; VD, number of diseased vessels.

The figure shows the loci on loci 2q33, 8q13, 9p31 associated with coronary artery disease and locus 21q22.11.associated with and myocardial infarction. The figure shows the first and second principal component plot for the Saudi Arab samples (4431 samples: 2165 cases and 2266 controls) with eleven other HapMap3 populations (ASW, African ancestry in Southwest USA; CEU, Utah residents of European ancestry; CHB, Han Chinese in Beijing, China; CHD, Chinese in Metropolitan Denver, Colorado; JPT, Japanese in Tokyo, Japan; GIH, Gujarati Indians in Houston, Texas; LWK, Luhya in Webuye, Kenya; MKK, Maasai in Kinyawa, Kenya; TSI, Tuscans in Italy; YRI, Yoruba in Ibadan, Nigeria; MEX, Mexicans). The figure displays the first and second principal component plot of the Saudi Arab samples (4431 samples: 2165 cases and 2266 controls) without Hapmap3 populations.

Experimental design, materials and methods

The discovery study involved 5668 Saudi Arabs who were subjected to genotyping using Affymetrix Axiom Genome-Wide ASI Array (Asian population). Genotyping data were generated using the Axiom GT1 algorithm and an IBS/IBD analysis in PLINK [2]. Analyses of the genome-wide association (GWA) were based on a linear mixed model method using FASTLMM-Select with Principal Component (PCs) as in Lippert et al. [3] and Widmer et al. [4]. Heritability estimation was performed according to Yang et al. [5] implemented in Genome-wide Complex Trait analysis (GCTA) software and extended in REACTA. The population substructure was examined by Principal Component Analysis (PCA) using the GCTA as described by Yang and colleagues [5] to eliminate the outliers that do not conform to the main cluster of samples that form the Saudi cohort, and may therefore lead to false positive results.
Subject areaGenetics
More specific subject areaGenetics of complex cardiovascular diseases
Type of dataTables and figures
How data was acquiredData table was acquired using statistical methods by SPSS,
Data formatRaw and analyzed data
Experimental factorsNone
Experimental featuresGenome-wide association experiments were performed using Affymetrix platform; analysis performed using PLINK, GTCA, FASTLMM and principal Component Analysis
Data source locationAll regions of Saudi Arabia
Data accessibilityData is with this article
  5 in total

1.  GCTA: a tool for genome-wide complex trait analysis.

Authors:  Jian Yang; S Hong Lee; Michael E Goddard; Peter M Visscher
Journal:  Am J Hum Genet       Date:  2010-12-17       Impact factor: 11.025

2.  A genome-wide association study reveals susceptibility loci for myocardial infarction/coronary artery disease in Saudi Arabs.

Authors:  Salma M Wakil; Ramesh Ram; Nzioka P Muiya; Munish Mehta; Editha Andres; Nejat Mazhar; Batoul Baz; Samya Hagos; Maie Alshahid; Brian F Meyer; Grant Morahan; Nduna Dzimiri
Journal:  Atherosclerosis       Date:  2015-11-22       Impact factor: 5.162

3.  PLINK: a tool set for whole-genome association and population-based linkage analyses.

Authors:  Shaun Purcell; Benjamin Neale; Kathe Todd-Brown; Lori Thomas; Manuel A R Ferreira; David Bender; Julian Maller; Pamela Sklar; Paul I W de Bakker; Mark J Daly; Pak C Sham
Journal:  Am J Hum Genet       Date:  2007-07-25       Impact factor: 11.025

4.  FaST linear mixed models for genome-wide association studies.

Authors:  Christoph Lippert; Jennifer Listgarten; Ying Liu; Carl M Kadie; Robert I Davidson; David Heckerman
Journal:  Nat Methods       Date:  2011-09-04       Impact factor: 28.547

5.  Further improvements to linear mixed models for genome-wide association studies.

Authors:  Christian Widmer; Christoph Lippert; Omer Weissbrod; Nicolo Fusi; Carl Kadie; Robert Davidson; Jennifer Listgarten; David Heckerman
Journal:  Sci Rep       Date:  2014-11-12       Impact factor: 4.379

  5 in total

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