Dataset Information

A System for Phenotype Harmonization in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Program.

ABSTRACT: Genotype-phenotype association studies often combine phenotype data from multiple studies to increase statistical power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data-set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data-sharing mechanisms. This system was developed for the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other -omics data for more than 80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants (recruited in 1948-2012) from up to 17 studies per phenotype. Here we discuss challenges in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include 1) the software code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify, or extend these harmonizations to additional studies, and 2) the results of labeling thousands of phenotype variables with controlled vocabulary terms.

SUBMITTER: Stilp AM

PROVIDER: S-EPMC8485147 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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A System for Phenotype Harmonization in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Program.

Stilp Adrienne M AM Emery Leslie S LS Broome Jai G JG Buth Erin J EJ Khan Alyna T AT Laurie Cecelia A CA Wang Fei Fei FF Wong Quenna Q Chen Dongquan D D'Augustine Catherine M CM Heard-Costa Nancy L NL Hohensee Chancellor R CR Johnson William Craig WC Juarez Lucia D LD Liu Jingmin J Mutalik Karen M KM Raffield Laura M LM Wiggins Kerri L KL de Vries Paul S PS Kelly Tanika N TN Kooperberg Charles C Natarajan Pradeep P Peloso Gina M GM Peyser Patricia A PA Reiner Alex P AP Arnett Donna K DK Aslibekyan Stella S Barnes Kathleen C KC Bielak Lawrence F LF Bis Joshua C JC Cade Brian E BE Chen Ming-Huei MH Correa Adolfo A Cupples L Adrienne LA de Andrade Mariza M Ellinor Patrick T PT Fornage Myriam M Franceschini Nora N Gan Weiniu W Ganesh Santhi K SK Graffelman Jan J Grove Megan L ML Guo Xiuqing X Hawley Nicola L NL Hsu Wan-Ling WL Jackson Rebecca D RD Jaquish Cashell E CE Johnson Andrew D AD Kardia Sharon L R SLR Kelly Shannon S Lee Jiwon J Mathias Rasika A RA McGarvey Stephen T ST Mitchell Braxton D BD Montasser May E ME Morrison Alanna C AC North Kari E KE Nouraie Seyed Mehdi SM Oelsner Elizabeth C EC Pankratz Nathan N Rich Stephen S SS Rotter Jerome I JI Smith Jennifer A JA Taylor Kent D KD Vasan Ramachandran S RS Weeks Daniel E DE Weiss Scott T ST Wilson Carla G CG Yanek Lisa R LR Psaty Bruce M BM Heckbert Susan R SR Laurie Cathy C CC

American journal of epidemiology 20211001 10

Genotype-phenotype association studies often combine phenotype data from multiple studies to increase statistical power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data-set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data-sharing mec ...[more]

PMID: 33861317

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Project description:BackgroundMitochondria support critical cellular functions, such as energy production through oxidative phosphorylation, regulation of reactive oxygen species, apoptosis, and calcium homeostasis.ObjectiveGiven the heightened level of cellular activity in patients with asthma, we sought to determine whether mitochondrial DNA (mtDNA) copy number measured in peripheral blood differed between individuals with and without asthma.MethodsWhole genome sequence data was generated as part of the Trans-Omics for Precision Medicine (TOPMed) Program on participants from the Study of Asthma Phenotypes and Pharmacogenomic Interactions by Race-ethnicity (SAPPHIRE) and the Study of African Americans, Asthma, Genes, & Environment II (SAGE II). We restricted our analysis to individuals who self-identified as African American (3,651 asthma cases and 1,344 controls). Mitochondrial copy number was estimated using the sequencing read depth ratio for the mitochondrial and nuclear genomes. Respiratory complex expression was assessed using RNA-sequencing.ResultsAverage mitochondrial copy number was significantly higher among individuals with asthma when compared with controls (SAPPHIRE: 218.60 vs. 200.47, P<0.001; SAGE II: 235.99 vs. 223.07, P<0.001). Asthma status was significantly associated with mitochondrial copy number after accounting for potential explanatory variables, such as participant age, sex, leukocyte counts, and mitochondrial haplogroup. Despite the consistent relationship between asthma status and mitochondrial copy number, the latter was not associated with time-to-exacerbation or patient-reported asthma control. Mitochondrial respiratory complex gene expression was disproportionately lower in individuals with asthma when compared with individuals without asthma and other protein-encoding genes.ConclusionsWe observed a robust association between asthma and higher mitochondrial copy number. Asthma having an effect on mitochondria function was also supported by lower respiratory complex gene expression in this group.

Project description:BackgroundRisk for venous thromboembolism has a strong genetic component. Whole genome sequencing from the TOPMed program (Trans-Omics for Precision Medicine) allowed us to look for new associations, particularly rare variants missed by standard genome-wide association studies.MethodsThe 3793 cases and 7834 controls (11.6% of cases were individuals of African, Hispanic/Latino, or Asian ancestry) were analyzed using a single variant approach and an aggregate gene-based approach using our primary filter (included only loss-of-function and missense variants predicted to be deleterious) and our secondary filter (included all missense variants).ResultsSingle variant analyses identified associations at 5 known loci. Aggregate gene-based analyses identified only PROC (odds ratio, 6.2 for carriers of rare variants; P=7.4×10-14) when using our primary filter. Employing our secondary variant filter led to a smaller effect size at PROC (odds ratio, 3.8; P=1.6×10-14), while excluding variants found only in rare isoforms led to a larger one (odds ratio, 7.5). Different filtering strategies improved the signal for 2 other known genes: PROS1 became significant (minimum P=1.8×10-6 with the secondary filter), while SERPINC1 did not (minimum P=4.4×10-5 with minor allele frequency <0.0005). Results were largely the same when restricting the analyses to include only unprovoked cases; however, one novel gene, MS4A1, became significant (P=4.4×10-7 using all missense variants with minor allele frequency <0.0005).ConclusionsHere, we have demonstrated the importance of using multiple variant filtering strategies, as we detected additional genes when filtering variants based on their predicted deleteriousness, frequency, and presence on the most expressed isoforms. Our primary analyses did not identify new candidate loci; thus larger follow-up studies are needed to replicate the novel MS4A1 locus and to identify additional rare variation associated with venous thromboembolism.

Dataset Information

A System for Phenotype Harmonization in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Program.

Publications

A System for Phenotype Harmonization in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Program.

Similar Datasets

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