Genetic studies of body mass index yield new insights for obesity biologyDiverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factorsNephrocystin-conserved domains involved in targeting to epithelial cell-cell junctions, interaction with filamins, and establishing cell polarityIdentification of genomic predictors of atrioventricular conduction: using electronic medical records as a tool for genome scienceEffect of CYP2B6, ABCB1, and CYP3A5 polymorphisms on efavirenz pharmacokinetics and treatment response: an AIDS Clinical Trials Group studyInteraction between interleukin 3 and dystrobrevin-binding protein 1 in schizophreniaOptimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseasesAn application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene interactions on risk of myocardial infarction: the importance of model validationGPNN: power studies and applications of a neural network method for detecting gene-gene interactions in studies of human diseasePhenome-Wide Association Studies: Embracing Complexity for DiscoveryGenetic risk factors for BMI and obesity in an ethnically diverse population: results from the population architecture using genomics and epidemiology (PAGE) studyGenetic determinants of age-related macular degeneration in diverse populations from the PAGE studyGenome wide analysis of drug-induced torsades de pointes: lack of common variants with large effect sizesThe phenotypic legacy of admixture between modern humans and NeandertalsGenetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarizationReturn of individual research results from genome-wide association studies: experience of the Electronic Medical Records and Genomics (eMERGE) NetworkThe Electronic Medical Records and Genomics (eMERGE) Network: past, present, and futureEnabling high-throughput genotype-phenotype associations in the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) project as part of the Population Architecture using Genomics and Epidemiology (PAGE) studySystems and genome-wide approaches unite to provide a route to personalized medicineThe eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studiesEmbracing Complex Associations in Common Traits: Critical Considerations for Precision Medicine.Electronic medical records and genomics (eMERGE) network exploration in cataract: several new potential susceptibility loci.eMERGE Phenome-Wide Association Study (PheWAS) identifies clinical associations and pleiotropy for stop-gain variantsATHENA: a tool for meta-dimensional analysis applied to genotypes and gene expression data to predict HDL cholesterol levelsThe central role of biological data mining in connecting diverse disciplinesINTEGRATING CLINICAL LABORATORY MEASURES AND ICD-9 CODE DIAGNOSES IN PHENOME-WIDE ASSOCIATION STUDIES.Mechanistic phenotypes: an aggregative phenotyping strategy to identify disease mechanisms using GWAS dataIncorporating inter-relationships between different levels of genomic data into cancer clinical outcome predictionKnowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction.Detection of pleiotropy through a Phenome-wide association study (PheWAS) of epidemiologic data as part of the Environmental Architecture for Genes Linked to Environment (EAGLE) study.Methods of integrating data to uncover genotype-phenotype interactions.Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer.Knowledge-driven multi-locus analysis reveals gene-gene interactions influencing HDL cholesterol level in two independent EMR-linked biobanks.PHENOME-WIDE INTERACTION STUDY (PheWIS) IN AIDS CLINICAL TRIALS GROUP DATA (ACTG).Discovery of gene-gene interactions across multiple independent data sets of late onset Alzheimer disease from the Alzheimer Disease Genetics ConsortiumIDENTIFYING GENETIC ASSOCIATIONS WITH VARIABILITY IN METABOLIC HEALTH AND BLOOD COUNT LABORATORY VALUES: DIVING INTO THE QUANTITATIVE TRAITS BY LEVERAGING LONGITUDINAL DATA FROM AN EHR.Using knowledge-driven genomic interactions for multi-omics data analysis: metadimensional models for predicting clinical outcomes in ovarian carcinoma.Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity.Exploring epistasis in candidate genes for rheumatoid arthritis.Alternative contingency table measures improve the power and detection of multifactor dimensionality reduction
P50
Q22305005-8BEE2BB1-7629-463A-9BD2-5F30237AFD6BQ24288911-742DF30A-B34F-4A77-BD30-865F95FD34EFQ24297187-B3229853-8746-41AE-800E-4819A4C0770DQ24305201-24AE97AB-5B10-4F29-B954-903C6B2B3ECAQ24633579-25BE05C6-72FE-4679-BBC7-1703F70CEA60Q24651143-99131471-E794-4761-81F9-6C9A537E0D1AQ24794014-D4279013-EFFC-4F0E-8047-8C46B06B14C6Q24804221-FA7334FF-FD4F-471A-8638-168C80EADF2BQ25255473-CC27CF14-6FCD-4F34-AB0D-B5ECE9023D49Q26800817-8C680E21-B387-493C-8481-FC0AC431B292Q28390241-A5B38C7E-58E0-45B4-898C-D4E428C46145Q28396822-6C7D5483-6764-4871-8265-CAD836E8002CQ28534878-BDD3FEAD-7B9A-4768-8572-3733E3853224Q28603615-1993F319-9208-4C45-A473-4FB247299A2FQ28654771-F6A25D40-3C27-44FF-A511-6B48F7D3FB3BQ28673413-D006E5FE-F16B-4216-9ABB-F1B33642EF20Q28674604-C2EF36CC-454E-4B00-B0E7-F07047B67331Q28707630-8ADA86AA-CE8F-49E5-BDAB-A1AD71B8996FQ28727714-36CC59AC-D2FD-4B2D-BAF5-491A4A7C2285Q28742793-E3CDBF20-F88E-4611-924C-68330AD13FA6Q30249101-509787E4-F9A0-48A4-8ABE-72B504E2C809Q30303045-8F17368A-9D80-4F5E-93AC-3E5CB99CF143Q30391938-048D570E-9FF6-423C-A97F-5DA9ED253581Q30591243-3FACB662-D787-4C4B-B718-EAB00EC2F84AQ30661070-CDC34896-6F65-4AD3-8D2F-C553362A73B7Q30699615-2967CAE4-5229-474D-B25B-869CCA55A9F3Q30717783-2E98C00A-667E-4E3B-92E2-2AE77D11B19FQ30762166-4FE6F18B-BBE5-4075-B070-ED39D0C490C8Q30835541-2CB36B7F-7677-4CF1-AFB9-8EC7D3CAAD75Q30874310-1975C272-1028-4427-9FEC-B1FC049A5934Q30884207-BAEE0A69-DB85-47CE-92FC-BFC793E71A71Q30966966-29EABE67-CBEC-4ADE-8169-CF0788590E8BQ31010852-CD022493-F7A1-4131-A953-C13D405BACC1Q31037300-3FE9F8F1-3640-4210-8F71-E30E0E3504E3Q31041407-FB88783A-5C36-4DB6-B8C5-68DAF61525FAQ31145333-56C73F82-CF32-42E7-AA6F-61D36B1FACE7Q31152236-6C47A042-495F-449D-B435-929FFA1ED961Q33185819-46299536-6AAE-4004-AD05-61BCE03ACA5EQ33333053-9550072C-9DAC-4448-BDC4-733A59D7DB60Q33335357-6C5A80DB-30DB-4048-BA1B-5C7EDCEA7B15
P50
description
Professor of Genetics
@en
biochemicus
@nl
name
Marylyn D. Ritchie
@ast
Marylyn D. Ritchie
@en
Marylyn D. Ritchie
@es
type
label
Marylyn D. Ritchie
@ast
Marylyn D. Ritchie
@en
Marylyn D. Ritchie
@es
prefLabel
Marylyn D. Ritchie
@ast
Marylyn D. Ritchie
@en
Marylyn D. Ritchie
@es
P106
P1960
cfuoZagAAAAJ