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Karlson, Elizabeth

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Karlson, Elizabeth

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    Hematopoietic mosaic chromosomal alterations increase the risk for diverse types of infection
    (Springer Science and Business Media LLC, 2021-06) Zekavat, Seyedeh M.; Lin, Shu-Hong; Bick, Alexander G.; Liu, Aoxing; Paruchuri, Kaavya; Wang, Chen; Uddin, Md Mesbah; Ye, Yixuan; Yu, Zhaolong; Liu, Xiaoxi; Kamatani, Yoichiro; Bhattacharya, Romit; Pirruccello, James; Pampana, Akhil; Loh, Po-Ru; Kohli, Puja; McCarroll, Steven; Kiryluk, Krzysztof; Neale, Benjamin; Ionita-Laza, Iuliana; Engels, Eric; Brown, Derek W.; Smoller, Jordan; Green, Robert; Karlson, Elizabeth; Lebo, Matthew; Ellinor, Patrick; Weiss, Scott; Daly, Mark; Terao, Chikashi; Zhao, Hongyu; Ebert, Benjamin; Reilly, Muredach; Ganna, Andrea; Machiela, Mitchell; Genovese, Giulio; Natarajan, Pradeep
    The burden of mosaic chromosomal alterations in blood-derived DNA, a type of clonal hematopoiesis, is associated with an increased risk for diverse types of infections, including sepsis and pneumonia. Age is the dominant risk factor for infectious diseases, but the mechanisms linking age to infectious disease risk are incompletely understood. Age-related mosaic chromosomal alterations (mCAs) detected from genotyping of blood-derived DNA, are structural somatic variants indicative of clonal hematopoiesis, and are associated with aberrant leukocyte cell counts, hematological malignancy, and mortality. Here, we show that mCAs predispose to diverse types of infections. We analyzed mCAs from 768,762 individuals without hematological cancer at the time of DNA acquisition across five biobanks. Expanded autosomal mCAs were associated with diverse incident infections (hazard ratio (HR) 1.25; 95% confidence interval (CI) = 1.15-1.36; P = 1.8 x 10(-7)), including sepsis (HR 2.68; 95% CI = 2.25-3.19; P = 3.1 x 10(-28)), pneumonia (HR 1.76; 95% CI = 1.53-2.03; P = 2.3 x 10(-15)), digestive system infections (HR 1.51; 95% CI = 1.32-1.73; P = 2.2 x 10(-9)) and genitourinary infections (HR 1.25; 95% CI = 1.11-1.41; P = 3.7 x 10(-4)). A genome-wide association study of expanded mCAs identified 63 loci, which were enriched at transcriptional regulatory sites for immune cells. These results suggest that mCAs are a marker of impaired immunity and confer increased predisposition to infections.
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    Postmenopausal hormone therapy and the risk of rheumatoid arthritis: results from the Swedish EIRA population-based case-control study
    (Springer Netherlands, 2015) Orellana, Cecilia; Saevarsdottir, Saedis; Klareskog, Lars; Karlson, Elizabeth; Alfredsson, Lars; Bengtsson, Camilla
    To study the association between postmenopausal hormone therapy (PMH) use and the risk of rheumatoid arthritis (RA) stratifying the cases by the presence/absence of antibodies against citrullinated peptides (ACPA). A subset of the Epidemiological Investigation of RA (EIRA), a population-based case-control study, comprising postmenopausal women aged 50–70 living in Sweden, between 2006 and 2011 was analysed (523 cases and 1057 controls). All participants answered an extensive questionnaire, including questions regarding PMH use and potential confounders (education, smoking, BMI, oral contraceptives, reproductive factors). We calculated odds ratios (OR) of developing ACPA-positive/-negative RA, with 95 % confidence intervals (CI) and adjusted for age, residential area and smoking. Current users of PMH had a decreased risk of ACPA-positive RA compared with never users (OR 0.6, 95 % CI 0.3–0.9). The decreased risk was observed mainly in the age-group 50–59 years (OR 0.3, 95 % CI 0.1–0.8) but not in the age-group 60–70 years (OR 0.8, 95 % CI 0.4–1.4). Among current users of a combined therapy (estrogen plus progestogens) an OR of 0.3 (95 % CI 0.1–0.7) of ACPA-positive RA was observed, while no significant association was found among women who used estrogen only (OR 0.8, 95 % CI 0.5–1.6). No association between PMH use and ACPA-negative RA was found. PMH use might reduce the risk of ACPA-positive RA in post-menopausal women over 50 years of age, but not of ACPA-negative RA. The negative influence of this treatment on the risk of other chronic conditions cannot be overlooked.
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    Rationale and Design of the Brigham Cohort for psoriasis and psoriatic arthritis registry (COPPAR)
    (BioMed Central, 2017) Schneeweiss, Maria; Merola, Joseph; Karlson, Elizabeth; Solomon, Daniel
    Background: Psoriasis (PsO) and psoriatic arthritis (PsA) are related conditions with poorly defined transition among them, risk factors for progression, complex treatment algorithms, and biomarkers for treatment response and long-term outcomes. We describe the development of a PsO/PsA registry at an academic medical center. Methods: We developed a single-center PsO/PsA longitudinal disease registry including biorepository that captures relevant disease markers and treatment choices in a circumscribed population with a defined catchment area. We searched the electronic medical record for patients with visits in the last year for PsO or PsA. They formed the potentially eligible registry population. Baseline patient and provider questionnaires were developed using standardized measures, including demographics, comorbidities, medications, specific disease characteristics, functional status, quality of life, mental health, and resource use. An abbreviated set of items was collected every six month and at visits with treatment changes or disease flares. Biospecimens included blood (serum, plasma, DNA, RNA) and skin biopsy samples, with repeat collections of serum and plasma. Data from the EMR to augment the registry questionnaires are available on all patients. Discussion Searching the Brigham EMR system from 2013 through 2014, we found 1694 patients with PsO and 1028 with PsA. Their mean age was 55 years and 53% were female. Of these 17% had diabetes, 38% hyperlipidemia, and 45% hypertension. The median BMI was 29.6. PsA patients used more systemic prednisone, MTX, and TNF alpha inhibitors (47%, 60%, and 66%) compared to PsO patients (28%, 20% and 21%). We have collected plasma in 410 patients, DNA/RNA in 453 patients. In conclusion, we have developed a PsO/PsA registry to better define longitudinal disease characteristics, perform biomarker studies, and examine treatment trends.
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    Modeling Disease Severity in Multiple Sclerosis Using Electronic Health Records
    (Public Library of Science, 2013) Xia, Zongqi; Secor, Elizabeth; Chibnik, Lori; Bove, Riley; Cheng, Suchun; Chitnis, Tanuja; Cagan, Andrew; Gainer, Vivian S.; Chen, Pei J.; Liao, Katherine; Shaw, Stanley; Ananthakrishnan, Ashwin; Szolovits, Peter; Weiner, Howard; Karlson, Elizabeth; Murphy, Shawn; Savova, Guergana; Cai, Tianxi; Churchill, Susanne E.; Plenge, Robert M.; Kohane, Isaac; De Jager, Philip
    Objective: To optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings. Methods: In a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume). Results: The EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R2 = 0.38±0.05, and that between EHR-derived and true BPF has a mean R2 = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10−12). Conclusion: Incorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.
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    Automatic Prediction of Rheumatoid Arthritis Disease Activity from the Electronic Medical Records
    (Public Library of Science, 2013) Lin, Chen; Karlson, Elizabeth; Canhao, Helena; Miller, Timothy; Dligach, Dmitriy; Chen, Pei Jun; Perez, Raul Natanael Guzman; Shen, Yuanyan; Weinblatt, Michael; Shadick, Nancy; Plenge, Robert M.; Savova, Guergana
    Objective: We aimed to mine the data in the Electronic Medical Record to automatically discover patients' Rheumatoid Arthritis disease activity at discrete rheumatology clinic visits. We cast the problem as a document classification task where the feature space includes concepts from the clinical narrative and lab values as stored in the Electronic Medical Record. Materials and Methods The Training Set consisted of 2792 clinical notes and associated lab values. Test Set 1 included 1749 clinical notes and associated lab values. Test Set 2 included 344 clinical notes for which there were no associated lab values. The Apache clinical Text Analysis and Knowledge Extraction System was used to analyze the text and transform it into informative features to be combined with relevant lab values. Results: Experiments over a range of machine learning algorithms and features were conducted. The best performing combination was linear kernel Support Vector Machines with Unified Medical Language System Concept Unique Identifier features with feature selection and lab values. The Area Under the Receiver Operating Characteristic Curve (AUC) is 0.831 (σ = 0.0317), statistically significant as compared to two baselines (AUC = 0.758, σ = 0.0291). Algorithms demonstrated superior performance on cases clinically defined as extreme categories of disease activity (Remission and High) compared to those defined as intermediate categories (Moderate and Low) and included laboratory data on inflammatory markers. Conclusion: Automatic Rheumatoid Arthritis disease activity discovery from Electronic Medical Record data is a learnable task approximating human performance. As a result, this approach might have several research applications, such as the identification of patients for genome-wide pharmacogenetic studies that require large sample sizes with precise definitions of disease activity and response to therapies.
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    Anti-citrullinated peptide autoantibodies, human leukocyte antigen shared epitope and risk of future rheumatoid arthritis: a nested case–control study
    (BioMed Central, 2013) Arkema, Elizabeth V; Goldstein, Barbara L; Robinson, William; Sokolove, Jeremy; Wagner, Catriona A; Malspeis, Susan; Rosner, Bernard; Grodstein, Francine; Karlson, Elizabeth; Costenbader, Karen
    Introduction: The aim of this study was to characterize anti-citrullinated peptide antibody (ACPA) serostatus in pre-clinical rheumatoid arthritis (RA) with and without Human Leukocyte Antigen-Shared Epitope (HLA-SE) alleles. Methods: We identified 192 women in the Nurses’ Health Study cohorts with blood samples obtained 4 months to 17 years prior to medical record-confirmed RA diagnosis. Three controls were selected matched on age, cohort, menopausal status and post-menopausal hormone use. Reactivities to 18 ACPAs were measured using a custom BioPlex platform. We used conditional logistic regression to calculate the relative risk (RR) of RA for any ACPA-positive and peptide-specific ACPA-positive and examined RRs by time between blood draw and RA onset. Measures of multiplicative and additive interaction between any ACPA-positive and HLA-SE were calculated. Results: All ACPAs by peptide groups were significantly associated with RA risk, RRs ranged from 4.7 to 11.7. The association between ACPA and RA varied over time with the strongest association in those with blood draw less than 5 years before onset (RR 17.0 [95% CI 5.8 to 53.7]) and no association 10 or more years prior to onset (RR 1.4 [95% CI 0.5 to 4.3]). Individuals with both HLA-SE and any ACPA-positive had the highest risk of RA. HLA-SE-positive RA cases showed reactivity to more ACPA types than HLA-SE negative (χ2 test for trend, P = 0.01). Conclusions: There is increasing ACPA reactivity up to 10 years before RA onset with the strongest association within 5 years of RA onset. The magnitude of the response to ACPAs, in combination with the presence of HLA-SE, is most important for identifying those individuals with the highest risk of RA.
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    Joint Effects of Colorectal Cancer Susceptibility Loci, Circulating 25-Hydroxyvitamin D and Risk of Colorectal Cancer
    (Public Library of Science, 2014) Hiraki, Linda T.; Joshi, Amit; Ng, Kimmie; Fuchs, Charles; Ma, Jing; Hazra, Aditi; Peters, Ulrike; Karlson, Elizabeth; Giovannucci, Edward; Kraft, Phillip; Chan, Andrew
    Background: Genome wide association studies (GWAS) have identified several SNPs associated with colorectal cancer (CRC) susceptibility. Vitamin D is also inversely associated with CRC risk. Methods: We examined main and joint effects of previously GWAS identified genetic markers of CRC and plasma 25-hydroxyvitamin D (25(OH)D) on CRC risk in three prospective cohorts: the Nurses' Health Study (NHS), the Health Professionals Follow-up Study (HPFS), and the Physicians' Health Study (PHS). We included 1895 CRC cases and 2806 controls with genomic DNA. We calculated odds ratios and 95% confidence intervals for CRC associated with additive genetic risk scores (GRSs) comprised of all CRC SNPs and subsets of these SNPs based on proximity to regions of increased vitamin D receptor binding to vitamin D response elements (VDREs), based on published ChiP-seq data. Among a subset of subjects with additional prediagnostic 25(OH)D we tested multiplicative interactions between plasma 25(OH)D and GRS's. We used fixed effects models to meta-analyze the three cohorts. Results: The per allele multivariate OR was 1.12 (95% CI, 1.06–1.19) for GRS-proximalVDRE; and 1.10 (95% CI, 1.06–1.14) for GRS-nonproxVDRE. The lowest quartile of plasma 25(OH)D compared with the highest, had a multivariate OR of 0.63 (95% CI, 0.48–0.82) for CRC. We did not observe any significant interactions between any GRSs and plasma 25(OH)D. Conclusions: We did not observe evidence for the modification of genetic susceptibility for CRC according to vitamin D status, or evidence that the effect of common CRC risk alleles differed according to their proximity to putative VDR binding sites.
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    The Influence of Polygenic Risk Scores on Heritability of Anti-CCP Level in RA
    (2014) Cui, Jing; Taylor, Kimberly E.; Lee, Yvonne Claire; Källberg, Henrik; Weinblatt, Michael; Coblyn, Jonathan; Klareskog, Lars; Criswell, Lindsey A.; Gregersen, Peter K.; Shadick, Nancy; Plenge, Robert M.; Karlson, Elizabeth
    Objective: To study genetic factors that influence quantitative anti-cyclic citrullinated peptide (anti-CCP) antibody levels in RA patients. Methods: We carried out a genome wide association study (GWAS) meta-analysis using 1,975 anti-CCP+ RA patients from 3 large cohorts, the Brigham Rheumatoid Arthritis Sequential Study (BRASS), North American Rheumatoid Arthritis Consortium (NARAC), and the Epidemiological Investigation of RA (EIRA). We also carried out a genome-wide complex trait analysis (GCTA) to estimate the heritability of anti-CCP levels. Results: GWAS-meta analysis showed that anti-CCP levels were most strongly associated with the human leukocyte antigen (HLA) region with a p-value of 2×10−11 for rs1980493. There were 112 SNPs in this region that exceeded the genome-wide significance threshold of 5×10−8, and all were in linkage disequilibrium (LD) with the HLA- DRB1*03 allele with LD r2 in the range of 0.25-0.88. Suggestive novel associations outside of the HLA region were also observed for rs8063248 (near the GP2 gene) with a p-value of 3×10−7. None of the known RA risk alleles (~52 loci) were associated with anti-CCP level. Heritability analysis estimated that 44% of anti-CCP variation was attributable to genetic factors captured by GWAS variants. Conclusions: Anti-CCP level is a heritable trait. HLA-DR3 and GP2 are associated with lower anti-CCP levels.
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    Genetics of rheumatoid arthritis contributes to biology and drug discovery
    (2013) Okada, Yukinori; Wu, Di; Trynka, Gosia; Raj, Towfique; Terao, Chikashi; Ikari, Katsunori; Kochi, Yuta; Ohmura, Koichiro; Suzuki, Akari; Yoshida, Shinji; Graham, Robert R.; Manoharan, Arun; Ortmann, Ward; Bhangale, Tushar; Denny, Joshua C.; Carroll, Robert J.; Eyler, Anne E.; Greenberg, Jeffrey D.; Kremer, Joel M.; Pappas, Dimitrios A.; Jiang, Lei; Yin, Jian; Ye, Lingying; Su, Ding-Feng; Yang, Jian; Xie, Gang; Keystone, Ed; Westra, Harm-Jan; Esko, Tõnu; Metspalu, Andres; Zhou, Xuezhong; Gupta, Namrata; Mirel, Daniel; Stahl, Eli A.; Diogo, Dorothée; Cui, Jing; Liao, Katherine; Guo, Michael; Myouzen, Keiko; Kawaguchi, Takahisa; Coenen, Marieke J.H.; van Riel, Piet L.C.M.; van de Laar, Mart A.F.J.; Guchelaar, Henk-Jan; Huizinga, Tom W.J.; Dieudé, Philippe; Mariette, Xavier; Bridges, S. Louis; Zhernakova, Alexandra; Toes, Rene E.M.; Tak, Paul P.; Miceli-Richard, Corinne; Bang, So-Young; Lee, Hye-Soon; Martin, Javier; Gonzalez-Gay, Miguel A.; Rodriguez-Rodriguez, Luis; Rantapää-Dahlqvist, Solbritt; Ärlestig, Lisbeth; Choi, Hyon; Kamatani, Yoichiro; Galan, Pilar; Lathrop, Mark; Eyre, Steve; Bowes, John; Barton, Anne; de Vries, Niek; Moreland, Larry W.; Criswell, Lindsey A.; Karlson, Elizabeth; Taniguchi, Atsuo; Yamada, Ryo; Kubo, Michiaki; Liu, Jun; Bae, Sang-Cheol; Worthington, Jane; Padyukov, Leonid; Klareskog, Lars; Gregersen, Peter K.; Raychaudhuri, Soumya; Stranger, Barbara E.; De Jager, Philip; Franke, Lude; Visscher, Peter M.; Brown, Matthew A.; Yamanaka, Hisashi; Mimori, Tsuneyo; Takahashi, Atsushi; Xu, Huji; Behrens, Timothy W.; Siminovitch, Katherine A.; Momohara, Shigeki; Matsuda, Fumihiko; Yamamoto, Kazuhiko; Plenge, Robert M.
    A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological datasets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)1. Here, we performed a genome-wide association study (GWAS) meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ~10 million single nucleotide polymorphisms (SNPs). We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 1012–4. We devised an in-silico pipeline using established bioinformatics methods based on functional annotation5, cis-acting expression quantitative trait loci (cis-eQTL)6, and pathway analyses7–9 – as well as novel methods based on genetic overlap with human primary immunodeficiency (PID), hematological cancer somatic mutations and knock-out mouse phenotypes – to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.
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    Development of phenotype algorithms using electronic medical records and incorporating natural language processing
    (BMJ Publishing Group Ltd., 2015) Liao, Katherine; Cai, Tianxi; Savova, Guergana K; Murphy, Shawn; Karlson, Elizabeth; Ananthakrishnan, Ashwin; Gainer, Vivian S; Shaw, Stanley; Xia, Zongqi; Szolovits, Peter; Churchill, Susanne; Kohane, Isaac
    Electronic medical records are emerging as a major source of data for clinical and translational research studies, although phenotypes of interest need to be accurately defined first. This article provides an overview of how to develop a phenotype algorithm from electronic medical records, incorporating modern informatics and biostatistics methods.