A combined genome-wide linkage and association approach to find susceptibility loci for platelet function phenotypes in European American and African American families with coronary artery disease
- Rasika A Mathias†1, 2Email author,
- Yoonhee Kim†1,
- Heejong Sung1,
- Lisa R Yanek2,
- VJ Mantese2,
- J Enrique Hererra-Galeano2,
- Ingo Ruczinski3,
- Alexander F Wilson1,
- Nauder Faraday2,
- Lewis C Becker2 and
- Diane M Becker2
© Mathias et al; licensee BioMed Central Ltd. 2010
Received: 8 September 2009
Accepted: 7 June 2010
Published: 7 June 2010
The inability of aspirin (ASA) to adequately suppress platelet aggregation is associated with future risk of coronary artery disease (CAD). Heritability studies of agonist-induced platelet function phenotypes suggest that genetic variation may be responsible for ASA responsiveness. In this study, we leverage independent information from genome-wide linkage and association data to determine loci controlling platelet phenotypes before and after treatment with ASA.
Clinical data on 37 agonist-induced platelet function phenotypes were evaluated before and after a 2-week trial of ASA (81 mg/day) in 1231 European American and 846 African American healthy subjects with a family history of premature CAD. Principal component analysis was performed to minimize the number of independent factors underlying the covariance of these various phenotypes. Multi-point sib-pair based linkage analysis was performed using a microsatellite marker set, and single-SNP association tests were performed using markers from the Illumina 1 M genotyping chip from deCODE Genetics, Inc. All analyses were performed separately within each ethnic group.
Several genomic regions appear to be linked to ASA response factors: a 10 cM region in African Americans on chromosome 5q11.2 had several STRs with suggestive (p-value < 7 × 10-4) and significant (p-value < 2 × 10-5) linkage to post aspirin platelet response to ADP, and ten additional factors had suggestive evidence for linkage (p-value < 7 × 10-4) to thirteen genomic regions. All but one of these factors were aspirin response variables. While the strength of genome-wide SNP association signals for factors showing evidence for linkage is limited, especially at the strict thresholds of genome-wide criteria (N = 9 SNPs for 11 factors), more signals were considered significant when the association signal was weighted by evidence for linkage (N = 30 SNPs).
Our study supports the hypothesis that platelet phenotypes in response to ASA likely have genetic control and the combined approach of linkage and association offers an alternative approach to prioritizing regions of interest for subsequent follow-up.
Platelet activation plays a critical role in atherothrombotic diseases such as acute myocardial infarction (MI) and stroke. Aspirin (acetylsalicylic acid [ASA]) is a mainstay of both primary and secondary prevention of MI and stroke . ASA inhibits cyclooxygenase-1 (COX-1) and thromboxane-dependent platelet activation, which decreases the probability of acute thrombosis related proximally to cardiac and stroke events [2–4]. Large aspirin chemoprophylaxis trials, however, demonstrate that many individuals fail to achieve the expected protective benefits of therapy, presumably related to failure of ASA to adequately suppress platelet activation [5–7]. Residual platelet activation, from pathways directly and indirectly related to COX-1, is reported to be related to greater risk of MI and stroke in persons on aspirin therapy [8, 9].
Previously, we have found evidence of moderate to high heritability of platelet function phenotypes, in both the presence  and absence of aspirin , suggesting that genetic variants contribute to differences among individuals in platelet activation and response to aspirin therapy. In this study, we take advantage of two alternative platforms of genotype data available, a linkage panel of microsatellite markers (STRs) and a genome-wide association study (GWAS) panel of single nucleotide polymorphisms (SNPs), in a combined linkage and association approach to search for loci controlling phenotypes in known platelet activation pathways.
The study protocol was approved by the Johns Hopkins Institutional Review Board, and all subjects gave written informed consent. Subjects were identified from European American and African American families with premature coronary artery disease (CAD) through a proband with documented CAD prior to 60 years of age. Apparently healthy siblings of the probands, the adult offspring of both the probands and their siblings, and the coparents of the offspring were recruited for the Genetic Study of Aspirin Responsiveness (GeneSTAR). GeneSTAR was designed to examine genetic and environmental determinants of platelet function in response to low-dose aspirin therapy. Eligible subjects were ≥ 21 years of age and had no history of any coronary heart disease, thrombotic event, peripheral vascular disease, stroke, transient ischemic attacks, known derangement in hematologic profiles (aplastic anemia, Sickle Cell Disease, von Willebrand's Disease, Factor V Leiden), renal or hepatic failure, autoimmune diseases, glucocorticosteroid use, hemorrhagic event, measured blood pressure ≥ 180/105 mmHg or current pregnancy. Subjects were also excluded if they had an allergy or intolerance to ASA, baseline platelet count ≤ 100,000 or ≥ 500,000 cells/μL, hematocrit ≤ 30%, or white blood cell count ≥ 20,000 cells/μL. Anticoagulants, ASA, nonsteroidal anti-inflammatory agents, illicit drugs, were proscribed for 14 days before the baseline visit and during the study interval. Consumption of tea and coffee and flavenol-rich foods (egg, wine, grape juice, chocolate) and other dietary items known to affect platelet function (fish rich in omega-3 fatty acids and garlic) were prohibited for 24 hours and smoking for 12 hours prior to assessments. Subjects with zero aggregation of platelets to arachidonic acid in both whole blood and platelet-rich plasma (PRP) at the baseline visit and questionable adherence to the list of proscribed substances, were omitted from the study. At the end of the first screening visit, eligible subjects were given a supply of 81-mg ASA tablets and instructed to take 1 pill each day for 14 days. Adherence to ASA use during the study was assessed at the post-ASA visit using pill counts and an adapted standardized medication adherence questionnaire .
Blood for the measurement of platelet function in whole blood (WB) and platelet rich plasma (PRP) was obtained by venipuncture at the same time of day at baseline and following 14 days of ASA therapy. Baseline and post-ASA measures included aggregation, slope, and lag time in response to selected doses of 4 known platelet agonists, and urinary excretion of the thromboxane B2 metabolite 11- dehydrothromboxane B2 (Tx-M). Optical aggregation was measured in PRP in a PAP-4 aggregometer (Horsham, PA) after samples were stimulated with arachidonic acid (1.6 mmol/L, collagen (1, 2, 5 μg/mL), ADP (2, 10 mmol/L), and epinephrine (2, 10 μmol/L). Whole blood impedance aggregometry was measured in a Chrono-Log dual-channel lumiaggregometer (Havertown, PA) after samples were stimulated with arachidonic acid (0.5 mmol/L), collagen (1, 5 μg/mL), and ADP (10 μmol/L). Platelet function under shear stress was determined by the platelet function analyzer (PFA) test (PFA-100, Dade- Behring, Newark, DE). Whole blood was loaded into standard proprietary cartridges (Dade-Behring) containing collagen and epinephrine, and aperture closure time was recorded in seconds (maximum of 300 seconds).
Plasma fibrinogen was assayed in the Johns Hopkins Clinical Coagulation Laboratory and von Willebrand factor (vWF) was measured using enzyme-linked immunosorbent assays (DiaPharma, West Chester, OH).
Urine was collected at the same time of day for pre- and post-ASA measurements of Tx-M using an enzyme-linked immunosorbent assay (Cayman Chemical Co., Ann Arbor, MI) and values were normalized to urinary creatinine.
Cardiac Risk Factor Evaluation
Blood pressure was measured according to methods of the American Heart Association and hypertension was defined as the average of 4 resting blood pressures ≥ 140/90 mm Hg and/or the taking of antihypertensive medications. Current smoking was defined as any reported cigarette smoking within the past 30 days and nonsmoking status was validated by exhaled CO levels < 8 ppm on the average of 2 measurements. Height and weight were measured and body mass index was calculated as the weight (kg)/height squared (m2). Plasma glucose, total cholesterol, triglyceride, and HDL-cholesterol levels were measured after patients had fasted for 12 hours overnight. LDL cholesterol was estimated using the Friedewald equation. Diabetes was defined as self-reported diabetes with the use of diabetes medications, or measured glucose levels ≥ 126 mg/dl.
Microsatellite genotyping was performed at deCODE Genetics, Inc. with the standard deCODE 550 STR marker set (average spacing = 8 cM). There were 574 successfully released STR markers with an overall duplicate error rate (3 CEPH sample duplicates on each of 18 plates) of 0.1% and an overall Mendelian error rate of 2% on 99.7% of samples. The STR genotyping data were checked for Mendelian inconsistencies using PEDCHECK , and all persons with inconsistencies were removed prior to analysis. The most likely relationship between pairs of relatives was inferred using RELCHECK , and these were used to verify self-reported relationships. SIBPAIR (v 0.99.9) was used to calculate allele frequencies where the contribution of each pedigree is weighted by the number of founders it contains.
SNP genotyping was performed at deCODE Genetics, Inc. using the Human 1Mv1_C array from Illumina, Inc. and 1,044,977 markers were released with an average call rate per sample of 99.65% and an overall missing data rate of 0.35%. Using 25 duplicate pairs of CEPH samples, the reproducibility rate was >99.95% for all duplicate pairs. Analyses at deCODE Genetics revealed Mendelian errors (> 5%) in 14 samples, which were eliminated from further analysis. Finally, 9 samples with gender discrepancies and an additional 3,427,500 inconsistency calls over all SNPs were eliminated from the final data prior to analyses. While all markers were analyzed, SNPs were flagged for closer examination where minor allele frequency was low (2%) and/or deviation from Hardy Weinberg Equilibrium was severe (p-value < 10-6).
Given the large number of potentially different biologically-related platelet function cascades examined , principal components analysis (PCA) was used to define a set of independent factors that explained a large proportion of the phenotypic co-variance. PCA was run separately for European Americans and African Americans within each of three major groups of outcome phenotypes: (1) baseline, representing native platelet function, (2) post-aspirin, representing platelet function measures after 2 weeks of daily aspirin, and (3) post-aspirin platelet function adjusted for pre-aspirin platelet function, representing the change attributable to aspirin, or aspirin responsiveness. Prior to PCA, all platelet variables were first adjusted for age and sex, levels of LDL cholesterol, fibrinogen, and body mass index, and for the presence of diabetes, hypertension, and current smoking using linear regression models. For PFA test only, von Willebrand's factor was included in the adjustment. Following adjustment for covariates, there were 37 pre-ASA platelet function variables, 32 post-ASA variables, and 27 post-adjusted for pre-ASA variables within each race with distributions that were adequate to enable calculation of accurate Z-scores. All Z-scored variables were then used for PCA in PROC Factor in SAS version 9.1 implementing orthogonal varimax rotations. Eight components with Eigenvalues > 1 were identified for the pre-ASA variables, post ASA and post-adjusted for pre-ASA variables within each race. The components were labeled by their primary platelet phenotypic variables, defined as loadings > 0.4. These final PCA components were used in the tests for linkage and additional association analyses described below.
Linkage analysis was performed with the Haseman-Elston regression approach  in SAGE (v 5.1.0) for each principal component and each STR for each ethnic group separately. In these analyses the traditional Haseman-Elston analysis for a quantitative trait (i.e. the regression of the squared traits difference on the IBD estimate) was performed on full sib relationships under a multipoint approach. This approach has been shown to be adequately robust and powered even for sample sizes with less than 300 sib-pairs . We used p-values defined by Lander and Kruglyak  to represent specific LOD score thresholds: (1) suggestive linkage with p value = 0.00074 and corresponding LOD = 2.2; (2) significant linkage with p-value = 0.00002 and corresponding LOD = 3.6; and (3) highly significant linkage with p- value = 0.0000003 and corresponding LOD = 5.4.
Associations between SNPs on the GWAS panel and factors that had evidence for linkage based on the analyses described above were tested using linear mixed effects models (LME). In the formulation of the LME model, SNP genotypes were included as fixed effects setting the genotypes to be additive in effect, and family identification number was included in the random effects (essentially treating the correlations between all pairs of individuals in the family as equal). We tested whether the additive effects of each SNP was different from zero. The LME in SAS (v. 9.1.3 for Linux OS) was applied with PROC MIXED using the option for EMPIRICAL variance.
In an attempt to offset the diminished power to detect association that arises as a result of multiple testing issues inherent in genomic searches and to determine the best set of loci to pursue in further follow-up studies (i.e. to control false positive association signals) in the absence of external replication data, we used the False Discovery Rate (FDR) approach proposed by Roeder et al . This new weighted FDR methodology involves weighting the association test p-values on the basis of prior data derived from linkage. A combined map was obtained; interpolating the STRs with the SNP map by assigning a physical location (in Mb) to the mid-point of the STR. Using p-values from the linkage scan at these assigned physical locations, continuous linkage traces were derived from the standard normal cumulative distribution and used to weight the association p-values prior to the calculation of the FDR threshold. In this analysis we used Storey's FDR approach  which can be more powerful than that proposed by Benjamini and Hochberg  under the same error rate (here, alpha = 0.05).
Clinical Characteristics of subjects.
European Americans (n = 1231)
African Americans (n = 846)
Female sex (%)
Current smoking (%)
Age (years, mean ± SD)
44.52 ± 13.2
43.29 ± 12.4
Total cholesterol (mg/dl, mean ± SD)
203.94 ± 41.0
196.68 ± 43.4
HDL cholesterol (mg/dl, mean ± SD)
50.91 ± 14.6
54.93 ± 16.1
Triglycerides (mg/dl, mean ± SD)
141.28 ± 83.5
107.36 ± 71.6
LDL cholesterol (mg/dl, mean ± SD)
125.15 ± 37.1
120.49 ± 38.5
Glucose (mg/dl, mean ± SD)
94.08 ± 20.7
98.29 ± 36.1
Fibrinogen (mg/dl, mean ± SD)
374.41 ± 111.4
416.67 ± 127.9
vWF (%normal, mean ± SD)
87.60 ± 58.6
87.24 ± 53.8
(mmHg systolic/diastolic, mean ± SD)
118.25 ± 14.9/75.69 ± 9.6
123.36 ± 18.4/79.32 ± 11.1
Heritability Estimates of factors in African American and European American pedigrees.
Post Aspirin Factors
2B: High Dose Collagen
3W: High Dose Collagen/TXM
4W: ADP PRP
5B: ADP PRP
6W: Low Dose Collagen
6B: Very Low Dose Collagen
7W: Very Low Dose Collagen
Post-adjusted-for-pre Aspirin Factors
1W: High Dose Collagen
1B: High Dose Collagen
4W: Low Dose Collagen
4B: Low Dose Collagen
6W: Low Dose Collagen
6B: Low Dose Collagen
7W: High Dose ADP
8B: High Dose ADP
Pre Aspirin Factors
1B: Low Dose Collagen
3W: Low Dose Collagen
4W: Collagen Lag
4B: Collagen Lag
6B: Collagen Lag
7W: ADP/Epinephrine Lag
8B: AA/ADP/Collagen Lag
Summary of multipoint linkage signals in European American pedigrees.
P < 0.00002
P < 0.00074
3W(POST):High Dose Collagen/TXM
3W(POST):High Dose Collagen/TXM
3W(POST):High Dose Collagen/TXM
7W(PP):High Dose ADP
7W(PP):High Dose ADP
Summary of multipoint linkage signals in African American pedigrees.
P < 0.00002
P < 0.00074
Significant genome wide association signals defined at Bonferroni thresholds for association tests (P) and false discovery rate controlled linkage-weighted P values (WP) for the eleven factors with linkage evidence.
To date only specific agonists or single pathway platelet function phenotypes have been examined offering little insight into the potentially complex interplay among platelet function cascades. The GeneSTAR study is the most comprehensive assessment of platelet response to aspirin done to date and given the cost, labor intensiveness, and methodologic difficulty in measuring platelet function across multiple agonists and doses in a large sample size, it is unlikely that another study of this magnitude will exist to support external validation of association signals. The PCA-derived traits represent novel variables that take into account the naturally occurring correlations among important platelet variables. While the PCA-derived phenotypic values are not in and of themselves intuitively informative, they enable the identification of possibly important loci for more integrated platelet phenotypes that represent baseline platelet function or true global responses to aspirin.
We noted linkage in fourteen regions of the genome for eleven factors; interestingly, only one single linkage signal was found for factors representing baseline platelet function. The reasons for this are uncertain, but there is likely to be a greater variability in phenotype in native platelets as compared with measurements following ASA administration. ASA inhibits thromboxane-related aggregation, a pathway which augments aggregation to a variable extent, in a positive feedback loop initiated by other agonists, such as collagen. By removing variability in platelet function associated with the thromboxane pathway, ASA treatment may increase the ability to detect genotype-phenotype association signals related to the remaining platelet activation pathways.
Genome-wide association studies rapidly emerged as the leading tool in the identification of disease susceptibility loci in the recent past  and have proved successful in mapping novel and previously not implicated loci for a multitude of diseases [22–24]. However, these studies have largely been successful in mapping trait/disease-associated SNPs that are common (with median risk allele frequency shown to be 36%) and having only modest effect sizes (median odds ratio OR 1.33)  that together only account for a small fraction of the total risk/variance of the diseases/traits . With much of the genetic variation in these traits as yet left to discover, attention has been focused on issues of increased sample size, incorporating more than main effects of SNPs (i.e. accounting for gene*environment and gene*gene interactions), capturing variation not assessed in commercial GWAS arrays (i.e. low frequency variants and copy number variants) and region/gene-based signals rather than pure SNP-SNP replication . Another approach would be to leverage prior information in the evaluation and prioritization of genome wide association signal . Here, the availability of linkage and association information from two independent methods of analysis provides this unique opportunity, allowing us to prioritize signals for future follow-up based on the combined signal from both approaches.
While many significant association signals are intergenic, several genes are identified in Table 5, some of which are related to pathways known to be involved with platelet function. One such example in European Americans is MME (a membrane metalloendopeptidase) on chromosome 3q21-27, associated with ASA response to collagen induced aggregation in PRP).
Given that GeneSTAR is the most comprehensive assessment of platelet response to aspirin done to date, this also leads to two general weaknesses: our limited sample size for linkage and the lack of external replication. In light of these two limitations, we have implemented several novel approaches to best search for susceptibility loci, implementing the principal component approach to first find factors that represent the underlying biological correlation between the multitude of measured phenotypes and the combined approaches of linkage and association with two sets of marker data. In conclusion, this study is unique in its ability to identify loci controlling platelet response to aspirin intervention in both European American and African American families identified to be at risk for CAD, and our combined novel approaches have yielded several loci that we believe worthy of further follow up.
coronary artery disease
genome-wide association study
single nucleotide polymorphism
von Willebrand factor
principal components analysis
linear mixed effects models
False Discovery Rate.
This work was supported in part by grants from the National Heart, Lung, and Blood Institute, (HL72518 and HL087698), and the National Center for Research Resources (M01-RR000052) and by the Intramural Research Program of the National Human Genome Research Institute, National Institutes of Health. McNeil Consumer and Specialty Pharmaceuticals supplied aspirin to the study.
- Hayden M, Pignone M, Phillips C, Mulrow C: Aspirin for the primary prevention of cardiovascular events: a summary of the evidence for the U.S. Preventive Services Task Force. Ann Intern Med. 2002, 136 (2): 161-172.View ArticlePubMedGoogle Scholar
- Abrams CS, Brass LF: Platelet signal transduction. Hemostasis and thrombosis Basic principles and clinical practice. Edited by: Colman RW, Hirsh J, Marder VJ, Clowes AW, George JN. 2001, Philadelphia: Lippincott Williams & Wilkins, 541-559. FourthGoogle Scholar
- Ashby B, Colman RW, Daniel JL, Kunapuli SP, Smith JB: Platelet stimulatory and inhibitory receptors. Hemostasis and Thrombosis Basic principles and clinical practice Fourth ed. Edited by: Colman RW, Hirsh J, Marder VJ, Clowes AW, George JN. 2001, Philadelphia: Lippincott Williams & Wilkins, 505-520.Google Scholar
- Marcus AJ, Safier LB: Thromboregulation: multicellular modulation of platelet reactivity in hemostasis and thrombosis. FASEB J. 1993, 7 (6): 516-522.PubMedGoogle Scholar
- Gum PA, Kottke-Marchant K, Poggio ED, Gurm H, Welsh PA, Brooks L, Sapp SK, Topol EJ: Profile and prevalence of aspirin resistance in patients with cardiovascular disease. Am J Cardiol. 2001, 88 (3): 230-235. 10.1016/S0002-9149(01)01631-9.View ArticlePubMedGoogle Scholar
- Helgason CM, Bolin KM, Hoff JA, Winkler SR, Mangat A, Tortorice KL, Brace LD: Development of aspirin resistance in persons with previous ischemic stroke. Stroke. 1994, 25 (12): 2331-2336.View ArticlePubMedGoogle Scholar
- Helgason CM, Tortorice KL, Winkler SR, Penney DW, Schuler JJ, McClelland TJ, Brace LD: Aspirin response and failure in cerebral infarction. Stroke. 1993, 24 (3): 345-350.View ArticlePubMedGoogle Scholar
- Krasopoulos G, Brister SJ, Beattie WS, Buchanan MR: Aspirin "resistance" and risk of cardiovascular morbidity: systematic review and meta-analysis. BMJ. 2008, 336 (7637): 195-198. 10.1136/bmj.39430.529549.BE.View ArticlePubMedPubMed CentralGoogle Scholar
- Snoep JD, Dekkers OM, Vandenbroucke JP: A possible overestimation of the effect of aspirin. Arch Intern Med. 2007, 167 (21): 2372-2373. 10.1001/archinte.167.21.2372-b. author reply 2373View ArticlePubMedGoogle Scholar
- Faraday N, Yanek LR, Mathias R, Herrera-Galeano JE, Vaidya D, Moy TF, Fallin MD, Wilson AF, Bray PF, Becker LC, et al: Heritability of platelet responsiveness to aspirin in activation pathways directly and indirectly related to cyclooxygenase-1. Circulation. 2007, 115 (19): 2490-2496. 10.1161/CIRCULATIONAHA.106.667584.View ArticlePubMedGoogle Scholar
- Bray PF, Mathias RA, Faraday N, Yanek LR, Fallin MD, Herrera-Galeano JE, Wilson AF, Becker LC, Becker DM: Heritability of platelet function in families with premature coronary artery disease. J Thromb Haemost. 2007, 5 (8): 1617-1623. 10.1111/j.1538-7836.2007.02618.x.View ArticlePubMedGoogle Scholar
- Kim MT, Hill MN, Bone LR, Levine DM: Development and testing of the Hill-Bone Compliance to High Blood Pressure Therapy Scale. Prog Cardiovasc Nurs. 2000, 15 (3): 90-96. 10.1111/j.1751-7117.2000.tb00211.x.View ArticlePubMedGoogle Scholar
- O'Connell JR, Weeks DE: PedCheck: a program for identification of genotype incompatibilities in linkage analysis. Am J Hum Genet. 1998, 63: 259-266. 10.1086/301904.View ArticlePubMedPubMed CentralGoogle Scholar
- Boehnke M, Cox NJ: Accurate inference of relationships in sib-pair linkage studies. Am J Hum Genet. 1997, 61 (2): 423-429. 10.1086/514862.View ArticlePubMedPubMed CentralGoogle Scholar
- Haseman JK, Elston RC: The investigation of linkage between a quantitative trait and a marker locus. Behav Genet. 1972, 2 (1): 3-19. 10.1007/BF01066731.View ArticlePubMedGoogle Scholar
- Blackwelder WC, Elston RC: Power and robustness of sib-pair linkage tests and extension to larger sibships. Commun Stat Theor Meth. 1982, 11: 449-484. 10.1080/03610928208828250.View ArticleGoogle Scholar
- Lander E, Kruglyak L: Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet. 1995, 11 (3): 241-247. 10.1038/ng1195-241.View ArticlePubMedGoogle Scholar
- Roeder K, Bacanu SA, Wasserman L, Devlin B: Using linkage genome scans to improve power of association in genome scans. Am J Hum Genet. 2006, 78 (2): 243-252. 10.1086/500026.View ArticlePubMedPubMed CentralGoogle Scholar
- Storey JD: A direct approach to false discovery rates. J R Stat Soc B. 2002, 64 (3): 479-498. 10.1111/1467-9868.00346.View ArticleGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the False Discovery Rate: a Practical and Powerful Approach to Multiple Testing. J R Stat Soc B. 1995, 57 (1): 289-300.Google Scholar
- McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP, Hirschhorn JN: Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet. 2008, 9 (5): 356-369. 10.1038/nrg2344.View ArticlePubMedGoogle Scholar
- Easton DF, Eeles RA: Genome-wide association studies in cancer. Hum Mol Genet. 2008, 17 (R2): R109-115. 10.1093/hmg/ddn287.View ArticlePubMedGoogle Scholar
- Lettre G, Rioux JD: Autoimmune diseases: insights from genome-wide association studies. Hum Mol Genet. 2008, 17 (R2): R116-121. 10.1093/hmg/ddn246.View ArticlePubMedPubMed CentralGoogle Scholar
- Mohlke KL, Boehnke M, Abecasis GR: Metabolic and cardiovascular traits: an abundance of recently identified common genetic variants. Hum Mol Genet. 2008, 17 (R2): R102-108. 10.1093/hmg/ddn275.View ArticlePubMedPubMed CentralGoogle Scholar
- Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA: Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA. 2009, 106 (23): 9362-9367. 10.1073/pnas.0903103106.View ArticlePubMedPubMed CentralGoogle Scholar
- Frazer KA, Murray SS, Schork NJ, Topol EJ: Human genetic variation and its contribution to complex traits. Nat Rev Genet. 2009, 10 (4): 241-251. 10.1038/nrg2554.View ArticlePubMedGoogle Scholar
- McCarthy MI, Hirschhorn JN: Genome-wide association studies: potential next steps on a genetic journey. Hum Mol Genet. 2008, 17 (R2): R156-165. 10.1093/hmg/ddn289.View ArticlePubMedPubMed CentralGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/3/22/prepub
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