Teri A Manolio

Publications (91)949.21 Total impact

• Article: A Mechanism for Controlled Access to GWAS Data: Experience of the GAIN Data Access Committee.

  Erin M Ramos, Corina Din-Lovinescu, Ebony B Bookman, Lisa J McNeil, Carl C Baker, Georgy Godynskiy, Emily L Harris, Thomas Lehner, Catherine McKeon, Joel Moss, Vaurice L Starks, Stephen T Sherry, Teri A Manolio, Laura Lyman Rodriguez
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  ABSTRACT: The Genetic Association Information Network (GAIN) Data Access Committee was established in June 2007 to provide prompt and fair access to data from six genome-wide association studies through the database of Genotypes and Phenotypes (dbGaP). Of 945 project requests received through 2011, 749 (79%) have been approved; median receipt-to-approval time decreased from 14 days in 2007 to 8 days in 2011. Over half (54%) of the proposed research uses were for GAIN-specific phenotypes; other uses were for method development (26%) and adding controls to other studies (17%). Eight data-management incidents, defined as compromises of any of the data-use conditions, occurred among nine approved users; most were procedural violations, and none violated participant confidentiality. Over 5 years of experience with GAIN data access has demonstrated substantial use of GAIN data by investigators from academic, nonprofit, and for-profit institutions with relatively few and contained policy violations. The availability of GAIN data has allowed for advances in both the understanding of the genetic underpinnings of mental-health disorders, diabetes, and psoriasis and the development and refinement of statistical methods for identifying genetic and environmental factors related to complex common diseases.
  The American Journal of Human Genetics 04/2013; 92(4):479-88. · 10.60 Impact Factor
• Article: Genome- and Phenome-Wide Analysis of Cardiac Conduction Identifies Markers of Arrhythmia Risk.

  Marylyn D Ritchie, Joshua C Denny, Rebecca L Zuvich, Dana C Crawford, Jonathan S Schildcrout, Lisa Bastarache, Andrea H Ramirez, Jonathan D Mosely, Jill M Pulley, Melissa A Basford, [......], Abel N Kho, Christopher S Carlson, Eric B Larson, Gail P Jarvik, Nona Sotoodehnia, Teri A Manolio, Rongling Li, Daniel R Masys, Jonathan L Haines, Dan M Roden
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  ABSTRACT: BACKGROUND: Electrocardiographic QRS duration, a measure of cardiac intraventricular conduction, varies ~2-fold in individuals without cardiac disease. Slow conduction may promote reentrant arrhythmias. METHODS AND RESULTS: We performed a genome-wide association study (GWAS) to identify genomic markers of QRS duration in 5,272 individuals without cardiac disease selected from electronic medical record (EMR) algorithms at five sites in the Electronic Medical Records and Genomics (eMERGE) network. The most significant loci were evaluated within the CHARGE consortium QRS GWAS meta-analysis. Twenty-three single nucleotide polymorphisms in 5 loci, previously described by CHARGE, were replicated in the eMERGE samples; 18 SNPs were in the chromosome 3 SCN5A and SCN10A loci, where the most significant SNPs were rs1805126 in SCN5A with p=1.2x10-8 (eMERGE) and p=2.5x10-20 (CHARGE) and rs6795970 in SCN10A with p=6x10-6 (eMERGE) and p=5x10-27 (CHARGE). The other loci were in NFIA, near CDKN1A, and near C6orf204. We then performed phenome-wide association studies (PheWAS) on variants in these five loci in 13,859 European Americans to search for diagnoses associated with these markers. PheWAS identified atrial fibrillation and cardiac arrhythmias as the most common associated diagnoses with SCN10A and SCN5A variants. SCN10A variants were also associated with subsequent development of atrial fibrillation and arrhythmia in the original 5,272 "heart-healthy" study population. CONCLUSIONS: We conclude that DNA biobanks coupled to EMRs provide a platform not only for GWAS but may also allow broad interrogation of the longitudinal incidence of disease associated with genetic variants. The PheWAS approach implicated sodium channel variants modulating QRS duration in subjects without cardiac disease as predictors of subsequent arrhythmias.
  Circulation 03/2013; · 14.74 Impact Factor
• Article: Incidental genetic findings in randomized clinical trials: recommendations from the Genomics and Randomized Trials Network (GARNET).

  Ebony B Bookman, Corina Din-Lovinescu, Bradford B Worrall, Teri A Manolio, Siiri N Bennett, Cathy Laurie, Daniel B Mirel, Kimberly F Doheny, Garnet L Anderson, Kate Wehr, Richard Weinshilboum, Donna T Chen
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  ABSTRACT: Recommendations and guidance on how to handle the return of genetic results to patients have offered limited insight into how to approach incidental genetic findings in the context of clinical trials. This paper provides the Genomics and Randomized Trials Network (GARNET) recommendations on incidental genetic findings in the context of clinical trials, and discusses the ethical and practical issues considered in formulating our recommendations. There are arguments in support of as well as against returning incidental genetic findings in clinical trials. For instance, reporting incidental findings in clinical trials may improve the investigator-participant relationship and the satisfaction of participation, but it may also blur the line between clinical care and research. The issues of whether and how to return incidental genetic findings, including the costs of doing so, should be considered when developing clinical trial protocols. Once decided, plans related to sharing individual results from the aim(s) of the trial, as well as incidental findings, should be discussed explicitly in the consent form. Institutional Review Boards (IRBs) and other study-specific governing bodies should be part of the decision as to if, when, and how to return incidental findings, including when plans in this regard are being reconsidered.
  Genome Medicine 01/2013; 5(1):7.
• Article: High Density GWAS for LDL Cholesterol in African Americans Using Electronic Medical Records Reveals a Strong Protective Variant in APOE.

  Laura J Rasmussen-Torvik, Jennifer A Pacheco, Russell A Wilke, William K Thompson, Marylyn D Ritchie, Abel N Kho, Arun Muthalagu, M Geoff Hayes, Loren L Armstrong, Douglas A Scheftner, [......], Joshua C Denny, Gail P Jarvik, Christopher S Carlson, Iftikhar J Kullo, Suzette J Bielinski, Catherine A McCarty, Rongling Li, Teri A Manolio, Dana C Crawford, Rex L Chisholm
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  ABSTRACT: Only one low-density lipoprotein cholesterol (LDL-C) genome-wide association study (GWAS) has been previously reported in -African Americans. We performed a GWAS of LDL-C in African Americans using data extracted from electronic medical records (EMR) in the eMERGE network. African Americans were genotyped on the Illumina 1M chip. All LDL-C measurements, prescriptions, and diagnoses of concomitant disease were extracted from EMR. We created two analytic datasets; one dataset having median LDL-C calculated after the exclusion of some lab values based on comorbidities and medication (n= 618) and another dataset having median LDL-C calculated without any exclusions (n= 1,249). SNP rs7412 in APOE was strongly associated with LDL-C in both datasets (p < 5 × 10(-8) ). In the dataset with exclusions, a decrease of 20.0 mg/dL per minor allele was observed. The effect size was attenuated (12.3 mg/dL) in the dataset without any lab values excluded. Although other signals in APOE have been detected in previous GWAS, this large and important SNP association has not been well detected in large GWAS because rs7412 was not included on many genotyping arrays. Use of median LDL-C extracted from EMR after exclusions for medications and comorbidities increased the percentage of trait variance explained by genetic variation. Clin Trans Sci 2012; Volume 5: 394-399.
  Clinical and Translational Science 10/2012; 5(5):394-399. · 1.13 Impact Factor
• Article: Leveraging the electronic health record to implement genomic medicine.

  Iftikhar J Kullo, Gail P Jarvik, Teri A Manolio, Marc S Williams, Dan M Roden
  Genetics in medicine: official journal of the American College of Medical Genetics 09/2012; · 3.92 Impact Factor
• Article: Genetic Loci implicated in erythroid differentiation and cell cycle regulation are associated with red blood cell traits.

  Keyue Ding, Khader Shameer, Hayan Jouni, Daniel R Masys, Gail P Jarvik, Abel N Kho, Marylyn D Ritchie, Catherine A McCarty, Christopher G Chute, Teri A Manolio, Iftikhar J Kullo
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  ABSTRACT: To identify common genetic variants influencing red blood cell (RBC) traits. 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. 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. 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.
  Mayo Clinic Proceedings 05/2012; 87(5):461-74. · 5.70 Impact Factor
• Article: CHRNB3 is more strongly associated with Fagerström Test for Cigarette Dependence-based nicotine dependence than cigarettes per day: phenotype definition changes genome-wide association studies results.

  John P Rice, Sarah M Hartz, Arpana Agrawal, Laura Almasy, Siiri Bennett, Naomi Breslau, Kathleen K Bucholz, Kimberly F Doheny, Howard J Edenberg, Alison M Goate, [......], Teri A Manolio, Rosalind J Neuman, John I Nurnberger, Bernice Porjesz, Elizabeth Pugh, Erin M Ramos, Nancy Saccone, Scott Saccone, Marc Schuckit, Laura J Bierut
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  ABSTRACT: AIMS: Nicotine dependence is a highly heritable disorder associated with severe medical morbidity and mortality. Recent meta-analyses have found novel genetic loci associated with cigarettes per day (CPD), a proxy for nicotine dependence. The aim of this paper is to evaluate the importance of phenotype definition (i.e. CPD versus Fagerström Test for Cigarette Dependence (FTCD) score as a measure of nicotine dependence) on genome-wide association studies of nicotine dependence. DESIGN: Genome-wide association study. SETTING: Community sample. PARTICIPANTS: A total of 3365 subjects who had smoked at least one cigarette were selected from the Study of Addiction: Genetics and Environment (SAGE). Of the participants, 2267 were European Americans, 999 were African Americans. MEASUREMENTS: Nicotine dependence defined by FTCD score ≥4, CPD. FINDINGS: The genetic locus most strongly associated with nicotine dependence was rs1451240 on chromosome 8 in the region of CHRNB3 [odds ratio (OR) = 0.65, P = 2.4 × 10(-8) ]. This association was further strengthened in a meta-analysis with a previously published data set (combined P = 6.7 × 10(-16) , total n = 4200). When CPD was used as an alternate phenotype, the association no longer reached genome-wide significance (β = -0.08, P = 0.0004). CONCLUSIONS: Daily cigarette consumption and the Fagerstrom Test for Cigarette Dependence show different associations with polymorphisms in genetic loci.
  Addiction 04/2012; 107(11):2019-2028. · 4.31 Impact Factor
• Article: Genotype imputation of Metabochip SNPs using a study-specific reference panel of ~4,000 haplotypes in African Americans from the Women's Health Initiative.

  Eric Yi Liu, Steven Buyske, Aaron K Aragaki, Ulrike Peters, Eric Boerwinkle, Chris Carlson, Cara Carty, Dana C Crawford, Jeff Haessler, Lucia A Hindorff, Loic Le Marchand, Teri A Manolio, Tara Matise, Wei Wang, Charles Kooperberg, Kari E North, Yun Li
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  ABSTRACT: Genetic imputation has become standard practice in modern genetic studies. However, several important issues have not been adequately addressed including the utility of study-specific reference, performance in admixed populations, and quality for less common (minor allele frequency [MAF] 0.005-0.05) and rare (MAF < 0.005) variants. These issues only recently became addressable with genome-wide association studies (GWAS) follow-up studies using dense genotyping or sequencing in large samples of non-European individuals. In this work, we constructed a study-specific reference panel of 3,924 haplotypes using African Americans in the Women's Health Initiative (WHI) genotyped on both the Metabochip and the Affymetrix 6.0 GWAS platform. We used this reference panel to impute into 6,459 WHI SNP Health Association Resource (SHARe) study subjects with only GWAS genotypes. Our analysis confirmed the imputation quality metric Rsq (estimated r(2) , specific to each SNP) as an effective post-imputation filter. We recommend different Rsq thresholds for different MAF categories such that the average (across SNPs) Rsq is above the desired dosage r(2) (squared Pearson correlation between imputed and experimental genotypes). With a desired dosage r(2) of 80%, 99.9% (97.5%, 83.6%, 52.0%, 20.5%) of SNPs with MAF > 0.05 (0.03-0.05, 0.01-0.03, 0.005-0.01, and 0.001-0.005) passed the post-imputation filter. The average dosage r(2) for these SNPs is 94.7%, 92.1%, 89.0%, 83.1%, and 79.7%, respectively. These results suggest that for African Americans imputation of Metabochip SNPs from GWAS data, including low frequency SNPs with MAF 0.005-0.05, is feasible and worthwhile for power increase in downstream association analysis provided a sizable reference panel is available.
  Genetic Epidemiology 02/2012; 36(2):107-17. · 3.44 Impact Factor
• Source

  Available from: Petra Buzková

  Article: Evaluation of the metabochip genotyping array in African Americans and implications for fine mapping of GWAS-identified loci: the PAGE study.

  Steven Buyske, Ying Wu, Cara L Carty, Iona Cheng, Themistocles L Assimes, Logan Dumitrescu, Lucia A Hindorff, Sabrina Mitchell, Jose Luis Ambite, Eric Boerwinkle, [......], Carlos Rodriguez, Fredrick R Schumacher, Benjamin F Voight, Alicia Young, Teri A Manolio, Karen L Mohlke, Christopher A Haiman, Ulrike Peters, Dana C Crawford, Kari E North
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  ABSTRACT: The Metabochip is a custom genotyping array designed for replication and fine mapping of metabolic, cardiovascular, and anthropometric trait loci and includes low frequency variation content identified from the 1000 Genomes Project. It has 196,725 SNPs concentrated in 257 genomic regions. We evaluated the Metabochip in 5,863 African Americans; 89% of all SNPs passed rigorous quality control with a call rate of 99.9%. Two examples illustrate the value of fine mapping with the Metabochip in African-ancestry populations. At CELSR2/PSRC1/SORT1, we found the strongest associated SNP for LDL-C to be rs12740374 (p = 3.5 × 10(-11)), a SNP indistinguishable from multiple SNPs in European ancestry samples due to high correlation. Its distinct signal supports functional studies elsewhere suggesting a causal role in LDL-C. At CETP we found rs17231520, with risk allele frequency 0.07 in African Americans, to be associated with HDL-C (p = 7.2 × 10(-36)). This variant is very rare in Europeans and not tagged in common GWAS arrays, but was identified as associated with HDL-C in African Americans in a single-gene study. Our results, one narrowing the risk interval and the other revealing an associated variant not found in Europeans, demonstrate the advantages of high-density genotyping of common and rare variation for fine mapping of trait loci in African American samples.
  PLoS ONE 01/2012; 7(4):e35651. · 4.09 Impact Factor
• Article: Pitfalls of merging GWAS data: lessons learned in the eMERGE network and quality control procedures to maintain high data quality.

  Rebecca L Zuvich, Loren L Armstrong, Suzette J Bielinski, Yuki Bradford, Christopher S Carlson, Dana C Crawford, Andrew T Crenshaw, Mariza de Andrade, Kimberly F Doheny, Jonathan L Haines, [......], Andrew N McDavid, Daniel B Mirel, Lana M Olson, Justin E Paschall, Elizabeth W Pugh, Luke V Rasmussen, Laura J Rasmussen-Torvik, Stephen D Turner, Russell A Wilke, Marylyn D Ritchie
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  ABSTRACT: Genome-wide association studies (GWAS) are a useful approach in the study of the genetic components of complex phenotypes. Aside from large cohorts, GWAS have generally been limited to the study of one or a few diseases or traits. The emergence of biobanks linked to electronic medical records (EMRs) allows the efficient reuse of genetic data to yield meaningful genotype-phenotype associations for multiple phenotypes or traits. Phase I of the electronic MEdical Records and GEnomics (eMERGE-I) Network is a National Human Genome Research Institute-supported consortium composed of five sites to perform various genetic association studies using DNA repositories and EMR systems. Each eMERGE site has developed EMR-based algorithms to comprise a core set of 14 phenotypes for extraction of study samples from each site's DNA repository. Each eMERGE site selected samples for a specific phenotype, and these samples were genotyped at either the Broad Institute or at the Center for Inherited Disease Research using the Illumina Infinium BeadChip technology. In all, approximately 17,000 samples from across the five sites were genotyped. A unified quality control (QC) pipeline was developed by the eMERGE Genomics Working Group and used to ensure thorough cleaning of the data. This process includes examination of sample and marker quality and various batch effects. Upon completion of the genotyping and QC analyses for each site's primary study, eMERGE Coordinating Center merged the datasets from all five sites. This larger merged dataset reentered the established eMERGE QC pipeline. Based on lessons learned during the process, additional analyses and QC checkpoints were added to the pipeline to ensure proper merging. Here, we explore the challenges associated with combining datasets from different genotyping centers and describe the expansion to eMERGE QC pipeline for merged datasets. These additional steps will be useful as the eMERGE project expands to include additional sites in eMERGE-II, and also serve as a starting point for investigators merging multiple genotype datasets accessible through the National Center for Biotechnology Information in the database of Genotypes and Phenotypes. Our experience demonstrates that merging multiple datasets after additional QC can be an efficient use of genotype data despite new challenges that appear in the process.
  Genetic Epidemiology 12/2011; 35(8):887-98. · 3.44 Impact Factor
• Article: Variants near FOXE1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome- and phenome-wide studies.

  Joshua C Denny, Dana C Crawford, Marylyn D Ritchie, Suzette J Bielinski, Melissa A Basford, Yuki Bradford, High Seng Chai, Lisa Bastarache, Rebecca Zuvich, Peggy Peissig, [......], Rongling Li, Teri A Manolio, Iftikhar J Kullo, Christopher G Chute, Rex L Chisholm, Eric B Larson, Catherine A McCarty, Daniel R Masys, Dan M Roden, Mariza de Andrade
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  ABSTRACT: We repurposed existing genotypes in DNA biobanks across the Electronic Medical Records and Genomics network to perform a genome-wide association study for primary hypothyroidism, the most common thyroid disease. Electronic selection algorithms incorporating billing codes, laboratory values, text queries, and medication records identified 1317 cases and 5053 controls of European ancestry within five electronic medical records (EMRs); the algorithms' positive predictive values were 92.4% and 98.5% for cases and controls, respectively. Four single-nucleotide polymorphisms (SNPs) in linkage disequilibrium at 9q22 near FOXE1 were associated with hypothyroidism at genome-wide significance, the strongest being rs7850258 (odds ratio [OR] 0.74, p = 3.96 × 10(-9)). This association was replicated in a set of 263 cases and 1616 controls (OR = 0.60, p = 5.7 × 10(-6)). A phenome-wide association study (PheWAS) that was performed on this locus with 13,617 individuals and more than 200,000 patient-years of billing data identified associations with additional phenotypes: thyroiditis (OR = 0.58, p = 1.4 × 10(-5)), nodular (OR = 0.76, p = 3.1 × 10(-5)) and multinodular (OR = 0.69, p = 3.9 × 10(-5)) goiters, and thyrotoxicosis (OR = 0.76, p = 1.5 × 10(-3)), but not Graves disease (OR = 1.03, p = 0.82). Thyroid cancer, previously associated with this locus, was not significantly associated in the PheWAS (OR = 1.29, p = 0.09). The strongest association in the PheWAS was hypothyroidism (OR = 0.76, p = 2.7 × 10(-13)), which had an odds ratio that was nearly identical to that of the curated case-control population in the primary analysis, providing further validation of the PheWAS method. Our findings indicate that EMR-linked genomic data could allow discovery of genes associated with many diseases without additional genotyping cost.
  The American Journal of Human Genetics 10/2011; 89(4):529-42. · 10.60 Impact Factor
• Article: Genomics reaches the clinic: from basic discoveries to clinical impact.

  Teri A Manolio, Eric D Green
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  ABSTRACT: Today, more than ever, basic science research provides significant opportunities to advance our understanding about the genetic basis of human disease. Close interactions among laboratory, computational, and clinical research communities will be crucial to ensure that genomic discoveries advance medical science and, ultimately, improve human health.
  Cell 09/2011; 147(1):14-6. · 32.40 Impact Factor
• Source

  Available from: PubMed Central

  Article: The Next PAGE in understanding complex traits: design for the analysis of Population Architecture Using Genetics and Epidemiology (PAGE) Study.

  Tara C Matise, Jose Luis Ambite, Steven Buyske, Christopher S Carlson, Shelley A Cole, Dana C Crawford, Christopher A Haiman, Gerardo Heiss, Charles Kooperberg, Loic Le Marchand, Teri A Manolio, Kari E North, Ulrike Peters, Marylyn D Ritchie, Lucia A Hindorff, Jonathan L Haines
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  ABSTRACT: Genetic studies have identified thousands of variants associated with complex traits. However, most association studies are limited to populations of European descent and a single phenotype. The Population Architecture using Genomics and Epidemiology (PAGE) Study was initiated in 2008 by the National Human Genome Research Institute to investigate the epidemiologic architecture of well-replicated genetic variants associated with complex diseases in several large, ethnically diverse population-based studies. Combining DNA samples and hundreds of phenotypes from multiple cohorts, PAGE is well-suited to address generalization of associations and variability of effects in diverse populations; identify genetic and environmental modifiers; evaluate disease subtypes, intermediate phenotypes, and biomarkers; and investigate associations with novel phenotypes. PAGE investigators harmonize phenotypes across studies where possible and perform coordinated cohort-specific analyses and meta-analyses. PAGE researchers are genotyping thousands of genetic variants in up to 121,000 DNA samples from African-American, white, Hispanic/Latino, Asian/Pacific Islander, and American Indian participants. Initial analyses will focus on single nucleotide polymorphisms (SNPs) associated with obesity, lipids, cardiovascular disease, type 2 diabetes, inflammation, various cancers, and related biomarkers. PAGE SNPs are also assessed for pleiotropy using the "phenome-wide association study" approach, testing each SNP for associations with hundreds of phenotypes. PAGE data will be deposited into the National Center for Biotechnology Information's Database of Genotypes and Phenotypes and made available via a custom browser.
  American journal of epidemiology 08/2011; 174(7):849-59. · 5.59 Impact Factor
• Article: Progression of magnetic resonance imaging-defined brain vascular disease predicts vascular events in elderly: the Cardiovascular Health Study.

  W T Longstreth, Alice M Arnold, Lewis H Kuller, Charles Bernick, David S Lefkowitz, Norman J Beauchamp, Teri A Manolio
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  ABSTRACT: To determine whether progression of MRI-defined vascular disease predicts subsequent vascular events in the elderly. The Cardiovascular Health Study, a longitudinal cohort study of vascular disease in the elderly, allows us to address this question because its participants had 2 MRI scans≈5 years apart and have been followed for ≈9 years since the follow-up scan for incident vascular events. Both MRI-defined incident infarcts and worsened white matter grade were significantly associated with heart failure, stroke, and death, but not transient ischemic attacks, angina, or myocardial infarction. Strongest associations occurred when both incident infarcts and worsened white matter grade were present for heart failure (hazard ratio, 1.79; 95% confidence interval, 1.18-2.73), stroke (hazard ratio, 2.58; 95% confidence interval, 1.53-4.36), death (hazard ratio, 1.69; 95% confidence interval, 1.28-2.24), and cardiovascular death (hazard ratio, 1.97; 95% confidence interval, 1.24-3.14). Progression of MRI-defined vascular disease identifies elderly people at increased risk for subsequent heart failure, stroke, and death. Whether aggressive risk factor management would reduce risk is unknown.
  Stroke 08/2011; 42(10):2970-2. · 5.73 Impact Factor
• Article: Complement receptor 1 gene variants are associated with erythrocyte sedimentation rate.

  Iftikhar J Kullo, Keyue Ding, Khader Shameer, Catherine A McCarty, Gail P Jarvik, Joshua C Denny, Marylyn D Ritchie, Zi Ye, David R Crosslin, Rex L Chisholm, Teri A Manolio, Christopher G Chute
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  ABSTRACT: The erythrocyte sedimentation rate (ESR), a commonly performed test of the acute phase response, is the rate at which erythrocytes sediment in vitro in 1 hr. The molecular basis of erythrocyte sedimentation is unknown. To identify genetic variants associated with ESR, we carried out a genome-wide association study of 7607 patients in the Electronic Medical Records and Genomics (eMERGE) network. The discovery cohort consisted of 1979 individuals from the Mayo Clinic, and the replication cohort consisted of 5628 individuals from the remaining four eMERGE sites. A nonsynonymous SNP, rs6691117 (Val→IIe), in the complement receptor 1 gene (CR1) was associated with ESR (discovery cohort p = 7 × 10(-12), replication cohort p = 3 × 10(-14), combined cohort p = 9 × 10(-24)). We imputed 61 SNPs in CR1, and a "possibly damaging" SNP (rs2274567, His→Arg) in linkage disequilibrium (r(2) = 0.74) with rs6691117 was also associated with ESR (discovery p = 5 × 10(-11), replication p = 7 × 10(-17), and combined cohort p = 2 × 10(-25)). The two nonsynonymous SNPs in CR1 are near the C3b/C4b binding site, suggesting a possible mechanism by which the variants may influence ESR. In conclusion, genetic variation in CR1, which encodes a protein that clears complement-tagged inflammatory particles from the circulation, influences interindividual variation in ESR, highlighting an association between the innate immunity pathway and erythrocyte interactions.
  The American Journal of Human Genetics 06/2011; 89(1):131-8. · 10.60 Impact Factor
• Source

  Available from: Jean Maccluer

  Article: Genetic determinants of lipid traits in diverse populations from the population architecture using genomics and epidemiology (PAGE) study.

  Logan Dumitrescu, Cara L Carty, Kira Taylor, Fredrick R Schumacher, Lucia A Hindorff, José L Ambite, Garnet Anderson, Lyle G Best, Kristin Brown-Gentry, Petra Bůžková, [......], Sarah A Pendergrass, Miguel Quibrera, Ralph V Shohet, Lynne R Wilkens, Christopher A Haiman, Loïc Le Marchand, Steven Buyske, Charles Kooperberg, Kari E North, Dana C Crawford
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  ABSTRACT: For the past five years, genome-wide association studies (GWAS) have identified hundreds of common variants associated with human diseases and traits, including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) levels. Approximately 95 loci associated with lipid levels have been identified primarily among populations of European ancestry. The Population Architecture using Genomics and Epidemiology (PAGE) study was established in 2008 to characterize GWAS-identified variants in diverse population-based studies. We genotyped 49 GWAS-identified SNPs associated with one or more lipid traits in at least two PAGE studies and across six racial/ethnic groups. We performed a meta-analysis testing for SNP associations with fasting HDL-C, LDL-C, and ln(TG) levels in self-identified European American (~20,000), African American (~9,000), American Indian (~6,000), Mexican American/Hispanic (~2,500), Japanese/East Asian (~690), and Pacific Islander/Native Hawaiian (~175) adults, regardless of lipid-lowering medication use. We replicated 55 of 60 (92%) SNP associations tested in European Americans at p<0.05. Despite sufficient power, we were unable to replicate ABCA1 rs4149268 and rs1883025, CETP rs1864163, and TTC39B rs471364 previously associated with HDL-C and MAFB rs6102059 previously associated with LDL-C. Based on significance (p<0.05) and consistent direction of effect, a majority of replicated genotype-phentoype associations for HDL-C, LDL-C, and ln(TG) in European Americans generalized to African Americans (48%, 61%, and 57%), American Indians (45%, 64%, and 77%), and Mexican Americans/Hispanics (57%, 56%, and 86%). Overall, 16 associations generalized across all three populations. For the associations that did not generalize, differences in effect sizes, allele frequencies, and linkage disequilibrium offer clues to the next generation of association studies for these traits.
  PLoS Genetics 06/2011; 7(6):e1002138. · 8.69 Impact Factor
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  Available from: Alvaro Alonso

  Article: Genome partitioning of genetic variation for complex traits using common SNPs.

  Jian Yang, Teri A Manolio, Louis R Pasquale, Eric Boerwinkle, Neil Caporaso, Julie M Cunningham, Mariza de Andrade, Bjarke Feenstra, Eleanor Feingold, M Geoffrey Hayes, [......], Peng Lin, Hua Ling, William Lowe, Rasika A Mathias, Mads Melbye, Elizabeth Pugh, Marilyn C Cornelis, Bruce S Weir, Michael E Goddard, Peter M Visscher
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  ABSTRACT: We estimate and partition genetic variation for height, body mass index (BMI), von Willebrand factor and QT interval (QTi) using 586,898 SNPs genotyped on 11,586 unrelated individuals. We estimate that ∼45%, ∼17%, ∼25% and ∼21% of the variance in height, BMI, von Willebrand factor and QTi, respectively, can be explained by all autosomal SNPs and a further ∼0.5-1% can be explained by X chromosome SNPs. We show that the variance explained by each chromosome is proportional to its length, and that SNPs in or near genes explain more variation than SNPs between genes. We propose a new approach to estimate variation due to cryptic relatedness and population stratification. Our results provide further evidence that a substantial proportion of heritability is captured by common SNPs, that height, BMI and QTi are highly polygenic traits, and that the additive variation explained by a part of the genome is approximately proportional to the total length of DNA contained within genes therein.
  Nature Genetics 06/2011; 43(6):519-25. · 35.53 Impact Factor
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  Available from: Joshua C Denny

  Article: Electronic medical records for genetic research: results of the eMERGE consortium.

  Abel N Kho, Jennifer A Pacheco, Peggy L Peissig, Luke Rasmussen, Katherine M Newton, Noah Weston, Paul K Crane, Jyotishman Pathak, Christopher G Chute, Suzette J Bielinski, Iftikhar J Kullo, Rongling Li, Teri A Manolio, Rex L Chisholm, Joshua C Denny
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  ABSTRACT: Clinical data in electronic medical records (EMRs) are a potential source of longitudinal clinical data for research. The Electronic Medical Records and Genomics Network (eMERGE) investigates whether data captured through routine clinical care using EMRs can identify disease phenotypes with sufficient positive and negative predictive values for use in genome-wide association studies (GWAS). Using data from five different sets of EMRs, we have identified five disease phenotypes with positive predictive values of 73 to 98% and negative predictive values of 98 to 100%. Most EMRs captured key information (diagnoses, medications, laboratory tests) used to define phenotypes in a structured format. We identified natural language processing as an important tool to improve case identification rates. Efforts and incentives to increase the implementation of interoperable EMRs will markedly improve the availability of clinical data for genomics research.
  Science translational medicine 04/2011; 3(79):79re1. · 7.80 Impact Factor
• Article: Genetic architecture of cancer and other complex diseases: lessons learned and future directions.

  Lucia A Hindorff, Elizabeth M Gillanders, Teri A Manolio
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  ABSTRACT: Genome-wide association studies have broadened our understanding of the genetic architecture of cancer to include common variants, in addition to the rare variants previously identified by linkage analysis. We review current knowledge on the genetic architecture of four cancers--breast, lung, prostate and colorectal--for which the balance of common and rare alleles identified ranges from fewer common alleles (lung cancer) to more common alleles (prostate cancer). Although most variants are cancer specific, pleiotropy has been observed for several variants, for example, variants at the 8q24 locus and breast, ovarian and prostate cancers or variants in KITLG in relation to hair color and testicular cancer. Although few studies have been adequately powered to investigate heterogeneity among ancestry groups, effect sizes associated with common variants have been reported to be fairly homogenous among ethnic groups. Some associations appear to be ancestry specific, such as HNF1B, which is associated with prostate cancer in European Americans and Latinos but not in African-Americans. Studies of cancer and other complex diseases suggest that a simple dichotomy between rare and common allelic architectures may be too simplistic and that future research is needed to characterize a fuller spectrum of allele frequency (common (>5%), uncommon (1-5%) and rare (<1%) alleles) and effect size. In addition, a broadening of the concept of genetic architecture to encompass both population architecture, which reflects differences in exposures, genetic factors and population level risk among diverse groups of people, and genomic architecture, which includes structural, epigenomic and somatic variation, is envisioned.
  Carcinogenesis 03/2011; 32(7):945-54. · 5.70 Impact Factor
• Article: Comment on "Multidimensional results reporting to participants in genomic studies: getting it right".

  Ebony B Bookman, Aleisha A Langehorne, John H Eckfeldt, Kathleen C Glass, Gail P Jarvik, Michael Klag, Greg Koski, Arno Motulsky, Benjamin Wilfond, Teri A Manolio, Richard R Fabsitz, Russell V Luepker
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  ABSTRACT: Bookman et al. write to correct the impression given in the Commentary by Kohane and Taylor that the recommendations of the National Heart, Lung, and Blood Institute (NHLBI) Working Group "Reporting Genetic Results in Research Studies" included advice to return genetic information to research subjects only in cases where there is a proven or preventative intervention for the identified disorder. In fact, the report does recommend that genetic information be returned to subjects when there is an intervention available, but it does not recommend against giving this kind of information to subjects if there is no available intervention.
  Science translational medicine 02/2011; 3(70):70le1. · 7.80 Impact Factor