Exploring and visualizing large-scale genetic associations by using PheWeb

Taliun, Sarah A. Gagliano; VandeHaar, Peter; Boughton, Andrew P.; Welch, Ryan; Taliun, Daniel; Schmidt, Ellen M.; Zhou, Wei; Jb, Nielsen; Willer, Cristen J.; Lee, Seunggeun; Fritsche, Lars G.; Boehnke, Michael; Abecasis, Gonçalo R.

doi:10.1038/s41588-020-0622-5

Cited by 139 publications

(138 citation statements)

References 12 publications

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“…We collected GWAS analysis data within the TMM project and host summary statistics data in a uniform format similar to statistical files provided from the GWAS Catalog ( 37 ) database for reusability. The summary statistics files can be easily visualized on the jMorp website as Manhattan plots and QQ plots with the power of PheWeb ( 38 ) application. Currently, several GWAS statistics files, including MGWAS results described in Metabolome section above, are registered and linked to the corresponding entries in genome-omics layers in jMorp.…”

Section: Overview Of Jmorp: Available Data and Functionalitiesmentioning

confidence: 99%

jMorp updates in 2020: large enhancement of multi-omics data resources on the general Japanese population

Tadaka

Hishinuma

Komaki

et al. 2020

Nucleic Acids Research

113

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In the Tohoku Medical Megabank project, genome and omics analyses of participants in two cohort studies were performed. A part of the data is available at the Japanese Multi Omics Reference Panel (jMorp; https://jmorp.megabank.tohoku.ac.jp) as a web-based database, as reported in our previous manuscript published in Nucleic Acid Research in 2018. At that time, jMorp mainly consisted of metabolome data; however, now genome, methylome, and transcriptome data have been integrated in addition to the enhancement of the number of samples for the metabolome data. For genomic data, jMorp provides a Japanese reference sequence obtained using de novo assembly of sequences from three Japanese individuals and allele frequencies obtained using whole-genome sequencing of 8,380 Japanese individuals. In addition, the omics data include methylome and transcriptome data from ∼300 samples and distribution of concentrations of more than 755 metabolites obtained using high-throughput nuclear magnetic resonance and high-sensitivity mass spectrometry. In summary, jMorp now provides four different kinds of omics data (genome, methylome, transcriptome, and metabolome), with a user-friendly web interface. This will be a useful scientific data resource on the general population for the discovery of disease biomarkers and personalized disease prevention and early diagnosis.

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Section: Overview Of Jmorp: Available Data and Functionalitiesmentioning

confidence: 99%

jMorp updates in 2020: large enhancement of multi-omics data resources on the general Japanese population

Tadaka

Hishinuma

Komaki

et al. 2020

Nucleic Acids Research

113

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“…Introduced by Denny et al in 2010, a phenome-wide association study (PheWAS) is an omnibus scan to identify genedisease associations across the medical phenome. 1 Due to computational advances and development of widely available analytic frameworks, [2][3][4][5][6] PheWAS are now relatively easy to implement. The main goal of a PheWAS is to replicate known gene-disease relationships and to search for hidden and unanticipated associations.…”

Section: Introductionmentioning

confidence: 99%

A phenome-wide association study (PheWAS) of COVID-19 outcomes by race using the electronic health records data in Michigan Medicine

Salvatore

Mack

et al. 2020

Preprint

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Blacks/African Americans are overrepresented in the number of hospitalizations and deaths from COVID-19 in the United States, which could be explained through differences in the prevalence of existing comorbidities. We performed a disease-disease phenome-wide association study (PheWAS) using data representing 5,698 COVID-19 patients from a large academic medical center, stratified by race. We explore the association of 1,043 pre-occurring conditions with several COVID-19 outcomes: testing positive, hospitalization, ICU admission, and mortality. Obesity, iron deficiency anemia and type II diabetes were associated with susceptibility in the full cohort, while ill-defined descriptions/complications of heart disease and stage III chronic kidney disease were associated among non-Hispanic White (NHW) and non-Hispanic Black/African American (NHAA) patients, respectively. The top phenotype hits in the full, NHW, and NHAA cohorts for hospitalization were acute renal failure, hypertension, and insufficiency/arrest respiratory failure, respectively. Suggestive relationships between respiratory issues and COVID-19-related ICU admission and mortality were observed, while circulatory system diseases showed stronger association in NHAA patients. We were able to replicate some known comorbidities related to COVID-19 outcomes while discovering potentially unknown associations, such as endocrine/metabolic conditions related to hospitalization and mental disorders related to mortality, for future validation. We provide interactive PheWAS visualization for broader exploration.

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“…Large-scale biobanks with hundreds of thousands of genotyped and deeply phenotyped subjects are valuable resources to identify genetic components of complex phenotypes. 1,2 In biobanks, ordinal categorical data is a common type of phenotype, which is often collected from surveys, questionnaires, and testing to measure human behaviors, satisfaction, and preferences. 3,4 For example, a web questionnaire was used for 182,219 UK Biobank participants to collect 150 food and other health behavior related preferences, all of which are ordinal categorical phenotypes based on a 9-point hedonic scale of liking from 1 (extremely dislike) to 9 (extremely like).…”

Section: Mainmentioning

confidence: 99%

“…The web interface provides intuitive visualizations at three levels of granularity: genome-wide summaries at the trait level, and regional (LocusZoom) 15 and phenome-wide summaries at the variant level. 2 To compare BOLT-LMM and FastPOLMM in ordinal categorical data analysis, we selected four food preferences with different sample size distribution as phenotypes ( Figure S12). The preferences were encoded from 1 (extremely dislike) to 9 (extremely like).…”

Section: Overviewmentioning

confidence: 99%

Efficient mixed model approach for large-scale genome-wide association studies of ordinal categorical phenotypes

Zhou

Day

et al. 2020

Preprint

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In genome-wide association studies (GWAS), ordinal categorical phenotypes are widely used to measure human behaviors, satisfaction, and preferences. However, due to the lack of analysis tools, methods designed for binary and quantitative traits have often been used inappropriately to analyze categorical phenotypes, which produces inflated type I error rates or is less powerful. To accurately model the dependence of an ordinal categorical phenotype on covariates, we propose an efficient mixed model association test, Proportional Odds Logistic Mixed Model (POLMM). POLMM is demonstrated to be computationally efficient to analyze large datasets with hundreds of thousands of genetic related samples, can control type I error rates at a stringent significance level regardless of the phenotypic distribution, and is more powerful than other alternative methods. We applied POLMM to 258 ordinal categorical phenotypes on array-genotypes and imputed samples from 408,961 individuals in UK Biobank. In total, we identified 5,885 genome-wide significant variants, of which 424 variants (7.2%) are rare variants with MAF < 0.01.

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Exploring and visualizing large-scale genetic associations by using PheWeb

Cited by 139 publications

References 12 publications

jMorp updates in 2020: large enhancement of multi-omics data resources on the general Japanese population

jMorp updates in 2020: large enhancement of multi-omics data resources on the general Japanese population

A phenome-wide association study (PheWAS) of COVID-19 outcomes by race using the electronic health records data in Michigan Medicine

Efficient mixed model approach for large-scale genome-wide association studies of ordinal categorical phenotypes

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