DCC-harmonized phenotypes

This page contains information on the DCC phenotype harmonization strategy and available DCC-harmonized phenotypes as of November 5, 2019 at 11:20:28 AM PST.

Available datasets

The following is a list of available datasets. Clicking on the dataset name will take you to more information about which phenotypes are included and the number of participants with non-missing information by study.

These datasets are split by study and uploaded to each TOPMed study’s exchange area. They can be found under the “Provisional Files” tab and within the Phenotype/DCC/official folder. An example for one study is shown below:

topmed-dcc
  exchange
    phs000956_TOPMed_WGS_Amish
      Phenotype
        DCC
          official
            topmed_dcc_baseline_common_covariates_v1_phs000956.tar
            topmed_dcc_demographic_v1_phs000956.tar

The study-specific data files downloaded from the exchange areas can then be combined for cross-study analysis. Phenotypes for studies without a TOPMed exchange area will be uploaded once the exchange area is created at dbGaP.

The remainder of the information on this page is about phenotypes that have been harmonized using the DCC’s official system. The DCC has also occasionally assisted with phenotype harmonization for phenotypes that are not tracked in our system. Other files containing phenotypes not shown on this page, or for the same phenotypes for studies not yet released on dbGaP, can be found in the Phenotype/DCC/unofficial folder.

Authorship guidelines

If you have used phenotypes that the DCC has harmonized in your analysis, please see authorship guidelines for DCC-harmonized phenotypes for information about including DCC authors from the phenotype harmonization team.

Available phenotypes by dataset

For each phenotype, an associated age at measurement variable is also provided. For example, “weight_baseline_1” is body weight at the baseline exam and “age_at_weight_baseline_1” is the age of the participant at which that weight measurement was made. These age variables are not shown in the available phenotypes below but are a part of the datasets. The exception is for demographic phenotypes (e.g., sex, race, etc.), which do not have an associated age; they were derived primarily from baseline information, although later exams were used in some cases.

Atherosclerosis events incident

Number of non-missing measurements by study

Note that NOT all of these participants have been sequenced in TOPMed.

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Atherosclerosis events prior

Number of non-missing measurements by study

Note that NOT all of these participants have been sequenced in TOPMed.

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Demographic

Number of non-missing measurements by study

Note that NOT all of these participants have been sequenced in TOPMed.

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Baseline Common Covariates

Number of non-missing measurements by study

Note that NOT all of these participants have been sequenced in TOPMed.

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Sleep

Number of non-missing measurements by study

Note that NOT all of these participants have been sequenced in TOPMed.

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Inflammation

Number of non-missing measurements by study

Note that NOT all of these participants have been sequenced in TOPMed.

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Lipids

Number of non-missing measurements by study

Note that NOT all of these participants have been sequenced in TOPMed.

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VTE

Number of non-missing measurements by study

Note that NOT all of these participants have been sequenced in TOPMed.

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Blood Cell Count

Number of non-missing measurements by study

Note that NOT all of these participants have been sequenced in TOPMed.

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Blood Pressure

Number of non-missing measurements by study

Note that NOT all of these participants have been sequenced in TOPMed.

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Atherosclerosis

Number of non-missing measurements by study

Note that NOT all of these participants have been sequenced in TOPMed.

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Study abbreviations

DCC harmonization strategy

The TOPMed DCC is conducting phenotype harmonization to enable cross-study analyses. The main goals of this process are to provide harmonized phenotypes that are well-documented, reproducible, and as homogeneous across studies as possible. In harmonized datasets and documents, the DCC typically uses “phenotype” to refer to the general concept of a measurement and “variable” to refer to the specific data vector values of a given phenotype. The underlying database assigns a “trait_id” to uniquely identify a given variable, which appears in some of the documentation.

Collaboration between working groups, studies, and DCC analysts is essential for rigorous phenotype harmonization. Working group members provide domain expertise in their phenotype area, and liaisons from each study provide guidance about which study variables are appropriate to use. Harmonized phenotypes are constructed from “observed” study variables whenever possible, as opposed to using “derived” variables, unless otherwise specified. The DCC relies on the working groups to provide both the initial harmonization algorithm and the component variables to use, with the study liaisons assisting if necessary.

The DCC is acquiring phenotype data for all studies from dbGaP. In addition to being a stable repository, dbGaP has already curated and processed the data into a consistent format, which allows for automated processing, and all phenotypic variables have been assigned accession numbers, for tracking provenance. Using the data on dbGaP, the DCC provides harmonized phenotypes for all available study participants instead of just those being sequenced in TOPMed, which allows for non-TOPMed participants to be included in analyses after imputation or future sequencing.

Both the original study phenotypes and the final, harmonized phenotypes are stored in a relational database at the DCC. This setup allows DCC analysts to work with the study data in a consistent format instead of referencing a large number of files. It also provides a mechanism for tracking the provenance of each harmonized phenotype. In addition to storing metadata, the database also tracks the definition of the algorithms used to calculate each harmonized phenotype as well as the exact study phenotypes used in the calculation. Using this information, harmonized phenotypes can automatically be recomputed when updated study data are acquired from dbGaP. It also creates a lasting resource for the broader scientific community, as this detailed information will allow external investigators to augment the phenotypes harmonized in TOPMed with additional, non-TOPMed studies and will likely facilitate additional harmonization in future cross-study projects.

DCC analysts perform QC of component phenotypes, and consult with the relevant Working Group and study liaisons to resolve issues. This work is focused on finding inconsistencies and large batch effects in the study data. The harmonized traits are also QCed to detect possible errors in the harmonization process; this consists of checking whether most values are within the expected range and evaluating differences among studies and sample sets within study that may have been handled differently during harmonization. For each phenotype, the DCC analysts provide comments on the harmonization and QC process. The information in these comments should be considered before including a harmonized phenotype in any analysis. The analysts note outliers, but they are not removed from the harmonized data set for two reasons: (1) they may represent extreme effects of rare loss-of-function variants and (2) the definition of an outlier may vary according to the intended use, so users of the data should be able to make their own decisions about exclusionary criteria. Unless otherwise specified, the precision of phenotypic measurements is not harmonized, and they are not rounded to significant digits because the necessary information is generally not available.

For each phenotypic value for a given subject, an associated age at measurement is provided. These age values may have been winsorized in some studies and, if so, that winsorization carries through to the harmonized phenotype. For example, a study may give an age value as “>89” or “90+” instead of specific ages for subjects greater than 89 years of age; in this case, that text string was converted to a numeric value of 90. Otherwise, if the age measurements have not been winsorized by the study, we provide those age measurements with no winsorization. Analysts should also take care when working with multiple phenotypic variables at the same time, as variables across datasets or even within the same dataset are not necessarily measured at the same time for each subject.

The DCC also provides detailed documentation about which study phenotypes were used and the code that was run to produce a harmonized phenotype. The data values for each subject can be linked to the documentation using harmonization “unit” variables in each dataset. For each harmonized variable, a paired “unit_at_variable” is provided, whose value indicates where in the documentation to look to find the set of component variables and the algorithm used to harmonize those variables.

The DCC recommends that any phenotype variable be carefully inspected before use in analysis. We recommend caution in use of categorical variables as covariates in genetic association tests without first checking for categories with low counts, which might cause model-fitting problems. Analysts are advised to view plots of the data distribution to identify potential outliers they might want to exclude from analysis.