Platelets are enucleated cell fragments derived from megakaryocytes that play key roles in hemostasis and in the pathogenesis of atherothrombosis and cancer. Platelet traits are highly heritable and identification of genetic variants associated with platelet traits and assessing their pleiotropic effects may help to understand the role of underlying biological pathways. We conducted an electronic medical record (EMR)-based study to identify common variants that influence inter-individual variation in the number of circulating platelets (PLT) and mean platelet volume (MPV), by performing a genome-wide association study (GWAS). We characterized association of variants influencing MPV and PLT using functional, pathway and disease enrichment analysis assess pleiotropic effects of such variants by performing a phenome-wide association study (PheWAS) with a wide range of EMR-derived phenotypes. A total of 13,582 participants in the electronic MEdical Records and GEnomic (eMERGE) network had data for PLT and 6,291 participants had data for MPV. We identified 5 chromosomal regions associated with PLT and 8 associated with MPV at genome-wide significance (P<5E-8). In addition, we replicated 20 SNPs (out of 56 SNPs (α: 0.05/56=9E-4)) influencing PLT and 22 SNPs (out of 29 SNPs (α: 0.05/29=2E-3)) influencing MPV in a meta-analysis of GWAS of PLT and MPV. While our GWAS did not reveal any novel associations, our functional analyses revealed that genes in these regions influence thrombopoiesis and encode kinases, membrane proteins, proteins involved in cellular trafficking, transcription factors, proteasome complex subunits, proteins of signal transduction pathways, proteins involved in megakaryocyte development and platelet production and hemostasis. PheWAS using a single-SNP Bonferroni correction for 1368 diagnoses (0.05/1368=3.6E-5) revealed that several variants in these genes have pleiotropic associations with myocardial infarction, autoimmune and hematologic disorders. We conclude that multiple genetic loci influence interindividual variation in platelet traits and also have significant pleiotropic effects; the related genes are in multiple functional pathways including those relevant to thrombopoiesis.

Background Whether knowledge of genetic risk for coronary heart disease (CHD) affects health-related outcomes is unknown. We investigated whether incorporating a genetic risk score (GRS) in CHD risk estimates lowers low-density lipoprotein cholesterol (LDL-C) levels. Methods and Results Participants (n=203, 45–65 years old, at intermediate risk for CHD, and not on statins) were randomized to receive their 10-year probability of CHD based either on a conventional risk score (CRS) or CRS + GRS (+GRS). Participants in the +GRS group were stratified as having high (+H-GRS) or average/low (+L-GRS) GRS. Risk was disclosed by a genetic counselor followed by shared decision-making regarding statin therapy with a physician. We compared the primary endpoint of LDL-C levels at 6 months and assessed whether any differences were due to changes in dietary fat intake, physical activity levels or statin use. Participants (mean age 59.4±5 years, 48% men, mean 10-year CHD risk 8.5±4.1%) were allocated to receive either CRS (n=100) or +GRS (n=103). At the end of the study period, the +GRS group had a lower LDL-C than the CRS group (96.5±32.7 vs. 105.9±33.3 mg/dL; P=0.04). +H-GRS participants had lower LDL-C levels (92.3±32.9 mg/dL) than CRS participants (P=0.02) but not +L-GRS participants (100.9±32.2 mg/dL; P=0.18). Statins were initiated more often in the +GRS group than in the CRS group (39% vs. 22%, P<0.01). No significant differences in dietary fat intake and physical activity levels were noted. Conclusions Disclosure of CHD risk estimates that incorporated genetic risk information led to lower LDL-C levels than disclosure of CHD risk based on conventional risk factors alone. Clinical Trial Registration Information ClinicalTrials.gov. Identifier: NCT01936675.

BackgroundThe Electronic Medical Record (EMR) is a potential source for high throughput phenotyping to conduct genome-wide association studies (GWAS), including those of medically relevant quantitative traits. We describe use of the Mayo Clinic EMR to conduct a GWAS of red blood cell (RBC) traits in a cohort of patients with peripheral arterial disease (PAD) and controls without PAD.Methodology and Principal FindingsResults for hemoglobin level, hematocrit, RBC count, mean corpuscular volume, mean corpuscular hemoglobin, and mean corpuscular hemoglobin concentration were extracted from the EMR from January 1994 to September 2009. Out of 35,159 RBC trait values in 3,411 patients, we excluded 12,864 values in 1,165 patients that had been measured during hospitalization or in the setting of hematological disease, malignancy, or use of drugs that affect RBC traits, leaving a final genotyped sample of 3,012, 80% of whom had ≥2 measurements. The median of each RBC trait was used in the genetic analyses, which were conducted using an additive model that adjusted for age, sex, and PAD status. We identified four genomic loci that were associated (P<5×10−8) with one or more of the RBC traits (HBLS1/MYB on 6q23.3, TMPRSS6 on 22q12.3, HFE on 6p22.1, and SLC17A1 on 6p22.2). Three of these loci (HBLS1/MYB, TMPRSS6, and HFE) had been identified in recent GWAS and the allele frequencies, effect sizes, and the directions of effects of the replicated SNPs were similar to the prior studies.ConclusionsOur results demonstrate feasibility of using the EMR to conduct high throughput genomic studies of medically relevant quantitative traits.

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The most common side effect of angiotensin converting enzyme inhibitor drugs (ACEi) is a cough. We conducted a genome wide association study (GWAS) of ACEi-induced cough among 7,080 subjects of diverse ancestries in the eMERGE network. Cases were subjects diagnosed with ACEi-induced cough. Controls were subjects with at least 6 months of ACEi use and no cough. A GWAS (1,595 cases and 5,485 controls) identified associations on chromosome 4 in an intron of KCNIP4. The strongest association was at rs145489027 (MAF=0.33, OR=1.3 [95%CI: 1.2–1.4], p=1.0×10−8). Replication for six SNPs in KCNIP4 was tested in a second eMERGE population (n=926) and in the GoDARTS cohort (n=4,309). Replication was observed at rs7675300 (OR=1.32 [1.01–1.70], p=0.04) in eMERGE and rs16870989 and rs1495509 (OR=1.15 [1.01–1.30], p=0.03 for both) in GoDARTS. The combined association at rs1495509 was significant (OR=1.23 [1.15–1.32], p=1.9×10−9). These results indicate that SNPs in KCNIP4 may modulate ACEi-induced cough risk.

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Whether disclosure of genetic risk for coronary heart disease (CHD) influences shared decision-making (SDM) regarding use of statins to reduce CHD risk is unknown. We randomized 207 patients, age 45– 65 years, at intermediate CHD risk, and not on statins, to receive the 10-year risk of CHD based on conventional risk factors alone (n=103) or in combination with a genetic risk score (n=104). A genetic counselor disclosed this information followed by a physician visit for SDM regarding statin therapy. A novel decision aid was used in both encounters to disclose the CHD risk estimates and facilitate SDM regarding statin use. Patients reported their decision quality and physician visit satisfaction using validated surveys. There were no statistically significant differences between the two groups in the SDM score, satisfaction with the clinical encounter, perception of the quality of the discussion or of participation in decision-making and physician visit satisfaction scores. Quantitative analyses of a random subset of 80 video-recorded encounters using the OPTION5 scale also showed no significant difference in SDM between the two groups. Disclosure of CHD genetic risk using an electronic health record-linked decision aid did not adversely affect SDM or patients' satisfaction with the clinical encounter. Trial registration number NCT01936675; Results.

Objective: To identify common genetic variants influencing red blood cell (RBC) traits. Patients and Methods: We performed a genomewide association study from June 2008 through July 2011 of hemoglobin, hematocrit, RBC count, mean corpuscular volume, mean corpuscular hemoglobin, and mean corpuscular hemoglobin concentration in 12,486 patients of European ancestry from the electronic MEdical Records and Genomics (eMERGE) network. We developed an electronic medical record-based algorithm that included individuals who had RBC measurements obtained for clinical care and excluded values measured in the setting of hematopoietic disorders, comorbid conditions, or medications known to affect RBC production or a recent history of blood loss. Results: We identified 4 new genetic loci and replicated 11 loci previously reported to be associated with one or more RBC traits in individuals of European ancestry. Notably, genes present in 3 of the 4 newly identified loci (THRB, PTPLAD1, CDT1) and in 6 of the 11 replicated loci (KLF1, ALDH8A1, CCND3, SPTA1, FBXO7, TFR2/EPO) are implicated in erythroid differentiation and regulation of cell cycle in hematopoietic stem cells. Conclusion: Genes in the erythroid differentiation and cell cycle regulation pathways influence interindividual variation in RBC indices. Our results provide insights into the molecular basis underlying variation in RBC traits.