The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Jordan W. Smoller - One of the best experts on this subject based on the ideXlab platform.
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Phenome wide heritability analysis of the uk biobank
PLOS Genetics, 2017Co-Authors: Benjamin M. Neale, Tian Ge, Chiayen Chen, Jordan W. Smoller, Mert R SabuncuAbstract:Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive heritability attributable to common genetic variants (SNP heritability) across a broad phenotypic spectrum. Here, we present a computationally and memory efficient heritability estimation method that can handle large sample sizes, and report the SNP heritability for 551 complex traits derived from the interim data release (152,736 subjects) of the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes. We demonstrate that common genetic variation contributes to a broad array of quantitative traits and human diseases in the UK population, and identify phenotypes whose heritability is moderated by age (e.g., a majority of physical measures including height and body mass index), sex (e.g., blood pressure related traits) and socioeconomic status (education). Our study represents the first comprehensive Phenome-wide heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in interpreting heritability.
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Phenome wide heritability analysis of the uk biobank
bioRxiv, 2016Co-Authors: Tian Ge, Benjamin M. Neale, Chiayen Chen, Mert R Sabuncu, Jordan W. SmollerAbstract:Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive heritability attributable to common genetic variants (SNP heritability) across a broad phenotypic spectrum. However, assessing the comparative heritability of multiple traits estimated in different cohorts may be misleading due to the population-specific nature of heritability. Here we report the SNP heritability for 551 complex traits derived from the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes, and examine the moderating effect of three major demographic variables (age, sex and socioeconomic status) on the heritability estimates. Our study represents the first comprehensive Phenome-wide heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in comparing and interpreting heritability.
Tian Ge - One of the best experts on this subject based on the ideXlab platform.
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Phenome wide heritability analysis of the uk biobank
PLOS Genetics, 2017Co-Authors: Benjamin M. Neale, Tian Ge, Chiayen Chen, Jordan W. Smoller, Mert R SabuncuAbstract:Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive heritability attributable to common genetic variants (SNP heritability) across a broad phenotypic spectrum. Here, we present a computationally and memory efficient heritability estimation method that can handle large sample sizes, and report the SNP heritability for 551 complex traits derived from the interim data release (152,736 subjects) of the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes. We demonstrate that common genetic variation contributes to a broad array of quantitative traits and human diseases in the UK population, and identify phenotypes whose heritability is moderated by age (e.g., a majority of physical measures including height and body mass index), sex (e.g., blood pressure related traits) and socioeconomic status (education). Our study represents the first comprehensive Phenome-wide heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in interpreting heritability.
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Phenome wide heritability analysis of the uk biobank
bioRxiv, 2016Co-Authors: Tian Ge, Benjamin M. Neale, Chiayen Chen, Mert R Sabuncu, Jordan W. SmollerAbstract:Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive heritability attributable to common genetic variants (SNP heritability) across a broad phenotypic spectrum. However, assessing the comparative heritability of multiple traits estimated in different cohorts may be misleading due to the population-specific nature of heritability. Here we report the SNP heritability for 551 complex traits derived from the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes, and examine the moderating effect of three major demographic variables (age, sex and socioeconomic status) on the heritability estimates. Our study represents the first comprehensive Phenome-wide heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in comparing and interpreting heritability.
Lars G Fritsche - One of the best experts on this subject based on the ideXlab platform.
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a Phenome wide association study phewas of covid 19 outcomes by race using the electronic health records data in michigan medicine
Journal of Clinical Medicine, 2021Co-Authors: Maxwell Salvatore, Jasmine A Mack, Swaraaj Prabhu Sankar, Snehal Patil, Thomas S Valley, Karandeep Singh, Brahmajee K Nallamothu, Sachin Kheterpal, Lynda D Lisabeth, Lars G FritscheAbstract:Background: We performed a Phenome-wide association study to identify pre-existing conditions related to Coronavirus disease 2019 (COVID-19) prognosis across the medical Phenome and how they vary by race. Methods: The study is comprised of 53,853 patients who were tested/diagnosed for COVID-19 between 10 March and 2 September 2020 at a large academic medical center. Results: Pre-existing conditions strongly associated with hospitalization were renal failure, pulmonary heart disease, and respiratory failure. Hematopoietic conditions were associated with intensive care unit (ICU) admission/mortality and mental disorders were associated with mortality in non-Hispanic Whites. Circulatory system and genitourinary conditions were associated with ICU admission/mortality in non-Hispanic Blacks. Conclusions: Understanding pre-existing clinical diagnoses related to COVID-19 outcomes informs the need for targeted screening to support specific vulnerable populations to improve disease prevention and healthcare delivery.
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exploring various polygenic risk scores for basal cell carcinoma cutaneous squamous cell carcinoma and melanoma in the Phenomes of the michigan genomics initiative and the uk biobank
bioRxiv, 2018Co-Authors: Lars G Fritsche, Maxwell Salvatore, Matthew Zawistowski, Lauren J Beesley, Peter Vandehaar, Robert B Peng, Sarah A Gagliano, Sayantan Das, Jonathon Lefaive, Erin O KalebaAbstract:Polygenic risk scores (PRS) are designed to serve as a single summary measure condensing information from a large number of genetic variants associated with a disease. They have been used for stratification and prediction of disease risk. The construction of a PRS often depends on the purpose of the study, the available data/summary estimates and the underlying genetic architecture of a disease. In this paper, we consider several choices of constructing a PRS using summary data obtained from various publicly-available sources including the UK Biobank and evaluate them in predicting outcomes derived from electronic health records (EHR) that define the medical Phenome. We examine the three most common skin cancer subtypes in the USA: basal cell carcinoma, cutaneous squamous cell carcinoma, and melanoma, which may share elements of a common genetic risk profile across the subtypes. This study is conducted using data from 30,702 unrelated, genotyped patients of recent European descent who consented to be part of the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort within Michigan Medicine. Using these PRS for various skin cancer subtypes, we conduct a Phenome-wide association study (PheWAS) within the MGI data to evaluate their association with secondary traits. PheWAS results are then replicated using population-based UK Biobank data. We develop a web platform called PRSweb that provides detailed PheWAS results and allows users to directly compare different PRS construction methods. The results of this study can provide guidance regarding PRS construction in future PRS-PheWAS studies using EHR data involving disease subtypes.
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association of polygenic risk scores for multiple cancers in a Phenome wide study results from the michigan genomics initiative
American Journal of Human Genetics, 2018Co-Authors: Sachin Kheterpal, Lars G Fritsche, Stephen B Gruber, Ellen M Schmidt, Matthew Zawistowski, Stephanie E Moser, Victoria M Blanc, Chad M Brummett, Goncalo R AbecasisAbstract:Health systems are stewards of patient electronic health record (EHR) data with extraordinarily rich depth and breadth, reflecting thousands of diagnoses and exposures. Measures of genomic variation integrated with EHRs offer a potential strategy to accurately stratify patients for risk profiling and discover new relationships between diagnoses and genomes. The objective of this study was to evaluate whether polygenic risk scores (PRS) for common cancers are associated with multiple phenotypes in a Phenome-wide association study (PheWAS) conducted in 28,260 unrelated, genotyped patients of recent European ancestry who consented to participate in the Michigan Genomics Initiative, a longitudinal biorepository effort within Michigan Medicine. PRS for 12 cancer traits were calculated using summary statistics from the NHGRI-EBI catalog. A total of 1,711 synthetic case-control studies was used for PheWAS analyses. There were 13,490 (47.7%) patients with at least one cancer diagnosis in this study sample. PRS exhibited strong association for several cancer traits they were designed for, including female breast cancer, prostate cancer, melanoma, basal cell carcinoma, squamous cell carcinoma, and thyroid cancer. Phenome-wide significant associations were observed between PRS and many non-cancer diagnoses. To differentiate PRS associations driven by the primary trait from associations arising through shared genetic risk profiles, the idea of "exclusion PRS PheWAS" was introduced. Further analysis of temporal order of the diagnoses improved our understanding of these secondary associations. This comprehensive PheWAS used PRS instead of a single variant.
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association of polygenic risk scores for multiple cancers in a Phenome wide study results from the michigan genomics initiative
bioRxiv, 2017Co-Authors: Lars G Fritsche, Sachin Kheterpal, Stephen B Gruber, Ellen M Schmidt, Matthew Zawistowski, Stephanie E Moser, Victoria M Blanc, Chad M Brummett, Goncalo R Abecasis, Bhramar MukherjeeAbstract:Health systems are stewards of patient electronic health record (EHR) data with extraordinarily rich depth and breadth, reflecting thousands of diagnoses and exposures. Measures of genomic variation integrated with EHRs offer a potential strategy to accurately stratify patients for risk profiling and discover new relationships between diagnoses and genomes. The objective of this study was to evaluate whether Polygenic Risk Scores (PRS) for common cancers are associated with multiple phenotypes in a Phenome-wide Association Study (PheWAS) conducted in 28,260 unrelated, genotyped patients of recent European ancestry who consented to participate in the Michigan Genomics Initiative, a longitudinal biorepository effort within Michigan Medicine. PRS for 12 cancer traits were calculated using summary statistics from the NHGRI-EBI GWAS catalog. A total of 1,711 synthetic case-control studies was used for PheWAS analyses. Patients with at least one cancer diagnosis constituted 13,490 patients. PRSs exhibited strong association for several cancer traits they were designed for including female breast cancer, prostate cancer and melanomas of skin. Phenome-wide significant associations were observed between PRS and many non-cancer diagnoses. To differentiate primary trait-mediated PRS associations from associations that arise through shared genetic risk profiles, the idea of "exclusion PRS PheWAS" was introduced and uncovered Phenome-wide significant associations between a lower risk for hypothyroidism in patients with high thyroid cancer PRS and a higher risk for actinic keratosis in patients with high squamous cell carcinoma PRS after removing all cases of the primary cancer trait. This is the first PheWAS study using PRS instead of single variant.
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association of polygenic risk scores for multiple cancers in a Phenome wide study results from the michigan genomics initiative
bioRxiv, 2017Co-Authors: Lars G Fritsche, Sachin Kheterpal, Stephen B Gruber, Ellen M Schmidt, Matthew Zawistowski, Stephanie E Moser, Victoria M Blanc, Chad M Brummett, Zhenke Wu, Goncalo R AbecasisAbstract:Health systems are stewards of patient electronic health record (EHR) data with extraordinarily rich depth and breadth, reflecting thousands of diagnoses and exposures. Measures of genomic variation integrated with EHRs offer a potential strategy to accurately stratify patients for risk profiling and discover new relationships between diagnoses and genomes. The objective of this study was to evaluate whether Polygenic Risk Scores (PRS) for common cancers are associated with multiple phenotypes in a Phenome-wide Association Study (PheWAS) conducted in 28,260 unrelated, genotyped patients of recent European ancestry who consented to participate in the Michigan Genomics Initiative, a longitudinal biorepository effort within Michigan Medicine. PRS for 12 cancer traits were calculated using summary statistics from the NHGRI-EBI catalog. A total of 1,711 synthetic case-control studies was used for PheWAS analyses. There were 13,490 (47.7%) patients with at least one cancer diagnosis in this study sample. PRSs exhibited strong association for several cancer traits they were designed for including female breast cancer, prostate cancer, melanoma, basal cell carcinoma, squamous cell carcinoma and thyroid cancer. Phenome-wide significant associations were observed between PRS and many non-cancer diagnoses. To differentiate PRS associations driven by the primary trait from associations arising through shared genetic risk profiles, the idea of "exclusion PRS PheWAS" was introduced. This approach led to Phenome-wide significant associations between a lower risk for hypothyroidism in patients with high thyroid cancer PRS and a higher risk for actinic keratosis in patients with high squamous cell carcinoma PRS after removing all cases of the primary cancer trait. Further analysis of temporal order of the diagnoses improved our understanding of these secondary associations. This is the first comprehensive PheWAS study using PRS instead of a single variant.
Kathleen M. Brown - One of the best experts on this subject based on the ideXlab platform.
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New roots for agriculture: Exploiting the root Phenome
Philosophical Transactions of the Royal Society B: Biological Sciences, 2012Co-Authors: Jonathan P Lynch, Kathleen M. BrownAbstract:Recent advances in root biology are making it possible to genetically design root systems with enhanced soil exploration and resource capture. These cultivars would have substantial value for improving food security in developing nations, where yields are limited by drought and low soil fertility, and would enhance the sustainability of intensive agriculture. Many of the phenes controlling soil resource capture are related to root architecture. We propose that a better understanding of the root Phenome is needed to effectively translate genetic advances into improved crop cultivars. Elementary, unique root phenes need to be identified. We need to understand the 'fitness landscape' for these phenes: how they affect crop performance in an array of environments and phenotypes. Finally, we need to develop methods to measure phene expression rapidly and economically without artefacts. These challenges, especially mapping the fitness landscape, are non-trivial, and may warrant new research and training modalities.
Joshua C Denny - One of the best experts on this subject based on the ideXlab platform.
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a polygenic and phenotypic risk prediction for polycystic ovary syndrome evaluated by Phenome wide association studies
The Journal of Clinical Endocrinology and Metabolism, 2020Co-Authors: Yoonjung Yoonie Joo, Kyera Actkins, Jennifer A Pacheco, Anna O Basile, Robert Carroll, David R Crosslin, Felix R Day, Joshua C Denny, Digna Velez R Edwards, Hakon HakonarsonAbstract:Context As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated to be unidentified in clinical practice. Objective Utilizing polygenic risk prediction, we aim to identify the Phenome-wide comorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventive treatment. Design, patients, and methods Leveraging the electronic health records (EHRs) of 124 852 individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores (PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). We evaluated its predictive capability across different ancestries and perform a PRS-based Phenome-wide association study (PheWAS) to assess the phenomic expression of the heightened risk of PCOS. Results The integrated polygenic prediction improved the average performance (pseudo-R2) for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null model across European, African, and multi-ancestry participants respectively. The subsequent PRS-powered PheWAS identified a high level of shared biology between PCOS and a range of metabolic and endocrine outcomes, especially with obesity and diabetes: "morbid obesity", "type 2 diabetes", "hypercholesterolemia", "disorders of lipid metabolism", "hypertension", and "sleep apnea" reaching Phenome-wide significance. Conclusions Our study has expanded the methodological utility of PRS in patient stratification and risk prediction, especially in a multifactorial condition like PCOS, across different genetic origins. By utilizing the individual genome-Phenome data available from the EHR, our approach also demonstrates that polygenic prediction by PRS can provide valuable opportunities to discover the pleiotropic phenomic network associated with PCOS pathogenesis.
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a polygenic and phenotypic risk prediction for polycystic ovary syndrome evaluated by Phenome wide association studies
bioRxiv, 2019Co-Authors: Yoonjung Yoonie Joo, Kyera Actkins, Jennifer A Pacheco, Anna O Basile, Robert Carroll, David R Crosslin, Felix R Day, Joshua C Denny, Digna Velez R Edwards, Hakon HakonarsonAbstract:Abstract Purpose As many as 75% of patients with Polycystic ovary syndrome (PCOS) are estimated to be unidentified in clinical practice. Utilizing polygenic risk prediction, we aim to identify the Phenome-wide comorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventive treatment. Methods and Findings Leveraging the electronic health records (EHRs) of 124,852 individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores (PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). We evaluated its predictive capability across different ancestries and perform a PRS-based Phenome-wide association study (PheWAS) to assess the phenomic expression of the heightened risk of PCOS. The integrated polygenic prediction improved the average performance (pseudo-R2) for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null model across European, African, and multi-ancestry participants respectively. The subsequent PRS-powered PheWAS identified a high level of shared biology between PCOS and a range of metabolic and endocrine outcomes, especially with obesity and diabetes: ‘morbid obesity’, ‘type 2 diabetes’, ‘hypercholesterolemia’, ‘disorders of lipid metabolism’, ‘hypertension’ and ‘sleep apnea’ reaching Phenome-wide significance. Conclusions Our study has expanded the methodological utility of PRS in patient stratification and risk prediction, especially in a multifactorial condition like PCOS, across different genetic origins. By utilizing the individual genome-Phenome data available from the EHR, our approach also demonstrates that polygenic prediction by PRS can provide valuable opportunities to discover the pleiotropic phenomic network associated with PCOS pathogenesis.
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using topic modeling via non negative matrix factorization to identify relationships between genetic variants and disease phenotypes a case study of lipoprotein a lpa
PLOS ONE, 2019Co-Authors: Juan Zhao, Jeremy L Warner, Qiping Feng, Patrick Wu, Joshua C DennyAbstract:Genome-wide and Phenome-wide association studies are commonly used to identify important relationships between genetic variants and phenotypes. Most studies have treated diseases as independent variables and suffered from the burden of multiple adjustment due to the large number of genetic variants and disease phenotypes. In this study, we used topic modeling via non-negative matrix factorization (NMF) for identifying associations between disease phenotypes and genetic variants. Topic modeling is an unsupervised machine learning approach that can be used to learn patterns from electronic health record data. We chose the single nucleotide polymorphism (SNP) rs10455872 in LPA as the predictor since it has been shown to be associated with increased risk of hyperlipidemia and cardiovascular diseases (CVD). Using data of 12,759 individuals with electronic health records (EHR) and linked DNA samples at Vanderbilt University Medical Center, we trained a topic model using NMF from 1,853 distinct phenotypes and identified six topics. We tested their associations with rs10455872 in LPA. Topics enriched for CVD and hyperlipidemia had positive correlations with rs10455872 (P < 0.001), replicating a previous finding. We also identified a negative correlation between LPA and a topic enriched for lung cancer (P < 0.001) which was not previously identified via Phenome-wide scanning. We were able to replicate the top finding in a separate dataset. Our results demonstrate the applicability of topic modeling in exploring the relationship between genetic variants and clinical diseases.
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an integrative functional genomics framework for effective identification of novel regulatory variants in genome Phenome studies
Genome Medicine, 2018Co-Authors: Junfei Zhao, Joshua C Denny, Feixiong Cheng, Zhongming ZhaoAbstract:Genome–Phenome studies have identified thousands of variants that are statistically associated with disease or traits; however, their functional roles are largely unclear. A comprehensive investigation of regulatory mechanisms and the gene regulatory networks between Phenome-wide association study (PheWAS) and genome-wide association study (GWAS) is needed to identify novel regulatory variants contributing to risk for human diseases. In this study, we developed an integrative functional genomics framework that maps 215,107 significant single nucleotide polymorphism (SNP) traits generated from the PheWAS Catalog and 28,870 genome-wide significant SNP traits collected from the GWAS Catalog into a global human genome regulatory map via incorporating various functional annotation data, including transcription factor (TF)-based motifs, promoters, enhancers, and expression quantitative trait loci (eQTLs) generated from four major functional genomics databases: FANTOM5, ENCODE, NIH Roadmap, and Genotype-Tissue Expression (GTEx). In addition, we performed a tissue-specific regulatory circuit analysis through the integration of the identified regulatory variants and tissue-specific gene expression profiles in 7051 samples across 32 tissues from GTEx. We found that the disease-associated loci in both the PheWAS and GWAS Catalogs were significantly enriched with functional SNPs. The integration of functional annotations significantly improved the power of detecting novel associations in PheWAS, through which we found a number of functional associations with strong regulatory evidence in the PheWAS Catalog. Finally, we constructed tissue-specific regulatory circuits for several complex traits: mental diseases, autoimmune diseases, and cancer, via exploring tissue-specific TF-promoter/enhancer-target gene interaction networks. We uncovered several promising tissue-specific regulatory TFs or genes for Alzheimer’s disease (e.g. ZIC1 and STX1B) and asthma (e.g. CSF3 and IL1RL1). This study offers powerful tools for exploring the functional consequences of variants generated from genome–Phenome association studies in terms of their mechanisms on affecting multiple complex diseases and traits.
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Phenome wide association studies as a tool to advance precision medicine
Annual Review of Genomics and Human Genetics, 2016Co-Authors: Joshua C Denny, Lisa Bastarache, Dan M RodenAbstract:Beginning in the early 2000s, the accumulation of biospecimens linked to electronic health records (EHRs) made possible genome-Phenome studies (i.e., comparative analyses of genetic variants and phenotypes) using only data collected as a by-product of typical health care. In addition to disease and trait genetics, EHRs proved a valuable resource for analyzing pharmacogenetic traits and developing reverse genetics approaches such as Phenome-wide association studies (PheWASs). PheWASs are designed to survey which of many phenotypes may be associated with a given genetic variant. PheWAS methods have been validated through replication of hundreds of known genotype-phenotype associations, and their use has differentiated between true pleiotropy and clinical comorbidity, added context to genetic discoveries, and helped define disease subtypes, and may also help repurpose medications. PheWAS methods have also proven to be useful with research-collected data. Future efforts that integrate broad, robust collection of phenotype data (e.g., EHR data) with purpose-collected research data in combination with a greater understanding of EHR data will create a rich resource for increasingly more efficient and detailed genome-Phenome analysis to usher in new discoveries in precision medicine.