GRASP Python API Documentation

A Simple GRASP (grasp.nhlbi.nih.gov) API based on SQLAlchemy and Pandas.

Author	Michael D Dacre <mike.dacre@gmail.com>
License	MIT License, made at Stanford, use as you wish.
Version	0.4.0b1

For an introduction see the github readme

For a community discussion of the data itself see the wiki. This contains some important disclaimers regarding the quality of the data in GRASP itself, that fundamentally limit the utility of this package.

Basic Usage

This module contains a Python 3 API to work with the GRASP database. The database must be downloaded and initialized locally. The default is to use an sqlite backend, but postgresql or mysql may be used also; these two are slower to initialize (longer write times), but they may be faster on indexed reads.

The GRASP project is a SNP-level index of over 2000 GWAS datasets. It is very useful, but difficult to batch query as study descriptions are heterogeneous and there are more than 9 million rows. By putting this information into a relational database, it is easy to pull out bite-sized chunks of data to analyze with pandas. Be aware that GRASP does not include all of the SNPs that they should (see the wiki for info), so use this software with caution.

Commonly queried columns are indexed within the database for fast retrieval. A typical query for a single phenotype category returns several million SNPs in about 10 seconds, which can then be analyzed with pandas.

To read more about GRASP, visit the official page.

For a community discussion of the data itself see the wiki. This contains some important disclaimers regarding the quality of the data in GRASP itself, that fundamentally limit the utility of this package.

For complete API documentation, go to the documentation site

For a nice little example of usage, see the BMI Jupyter Notebook

Contents

• Basic Usage
  • Installation
  • Usage
    • Tables
    • Querying
  • Example Workflow
  • ToDo

Installation

The best way to install is with pip:

pip install https://github.com/MikeDacre/grasp/archive/v0.4.0b1.tar.gz

When this code reaches version 0.4.1, it will be placed on pypi.

Alternatively, you can clone the repo and install with setuptools:

git clone https://github.com/MikeDacre/grasp.git
cd grasp
python ./setup.py install --user

This code requires a grasp database. Currently sqlite/postgresql/mysql are supported. Mysql and postgresql can be remote (but must be set up with this tool), sqlite is local.

Database configuration is stored in a config file that lives by default in ~/.grasp. This path is set in config.py and can be changed there is needed.

A script, grasp, is provided in bin and should automatically be installed to your PATH. It contains functions to set up your database config and to initialize the grasp database easily, making the initial steps trivial.

To set up your database configuration, run:

grasp config --init

This will prompt you for your database config options and create a file at ~/.grasp with those options saved.

You can now initialize the grasp database:

grasp init study_file grasp_file

The study file is available in this repository (grasp2_studies.txt.gz) It is just a copy of the official GRASP List of Studies converted to text and with an additional index that provides a numeric index for the non pubmed indexed studies.

Both files can be gzipped or bzipped.

The grasp file is the raw unzipped file from the project page: GRASP2fullDataset

The database takes about 90 minutes to build on a desktop machine and uses about 3GB of space. The majority of the build time is spent parsing dates, but because the dates are encoded in the SNP table, and the formatting varies, this step is required.

Usage

The code is based on SQLAlchemy, so you should read their ORM Query tutorial to know how to use this well.

It is important to note that the point of this software is to make bulk data access from the GRASP DB easy, SQLAlchemy makes this very easy indeed. However, to do complex comparisons, SQLAlchemy is very slow. As such, the best way to use this software is to use SQLAlchemy functions to bulk retrieve study lists, and then to directly get a pandas dataframe of SNPs from those lists.

Tables are defined in grasp.tables Database setup functions are in grasp.db Query tools for easy data manipulation are in grasp.query.

Tables

This module provides 6 tables:

Study, Phenotype, PhenoCats, Platform, Population, and SNP (as well as several association tables)

Querying

The functions in grasp.query are very helpful in automating common queries.

The simplest way to get a dataframe from SQLAlchemy is like this:

df = pandas.read_sql(session.query(SNP).statement)

Note that if you use this exact query, the dataframe will be too big to be useful. To get a much more useful dataframe:

studies = grasp.query.get_studies(pheno_cats='t2d', primary_pop='European')
df = grasp.query.get_snps(studies)

It is important to note that there are three ways of getting phenotype information: - The Phenotype table, which lists the primary phenotype for every study - The PhenoCats table, which lists the GRASP curated phenotype categories,

each Study has several of these.

• The phenotype_desc column in the SNP table, this is a poorly curated column directly from the full dataset, it roughly corresponds to the information in the Phenotype table, but the correspondance is not exact due to an abundance of typos and slightly differently typed information.

Example Workflow

from grasp import db
from grasp import tables as t
from grasp import query as q
s, e = db.get_session()

# Print a list of all phenotypes (also use with populations, but not with SNPs (too many to display))
s.query(t.Phenotype).all()

# Filter the list
s.query(t.Phenotype).filter(t.Phenotype.phenotype.like('%diabetes%').all()

# Get a dictionary of studies to review
eur_t2d = get_studies(only_disc_pop='eur', primary_phenotype='Type II Diabetes Mellitus', dictionary=True)

# Filter those by using eur.pop() to remove unwanted studies, and then get the SNPs as a dataframe
eur_snps_df = get_snps(eur, pandas=True)

# Do the same thing for the african population
afr_t2d = get_studies(only_disc_pop='afr', primary_phenotype='Type II Diabetes Mellitus', dictionary=True)
afr.pop('Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study.')
afr_snps_df = get_snps(afr, pandas=True)

# Collapse the matrices (take median of pvalue) and filter by resulting pvalue
eur_snps_df = q.collapse_dataframe(eur_snps_df, mechanism='median', pvalue_filter=5e-8)
afr_snps_df = q.collapse_dataframe(afr_snps_df, mechanism='median', pvalue_filter=5e-8)

# The new dataframes are indexed by 'chr:pos'

# Plot the overlapping SNPs
snps = q.intersect_overlapping_series(eur_snps_df.pval_median, afr_snps_df.pval_median)
snps.plot()

ToDo

• Add more functions to grasp script, including lookup by position or range of positions

GRASP Console Script

A Simple GRASP (grasp.nhlbi.nih.gov) API based on SQLAlchemy and Pandas

Author	Michael D Dacre <mike.dacre@gmail.com>
Organization	Stanford University
License	MIT License, use as you wish
Created	2016-10-08
Version	0.4.0b1

Last modified: 2016-10-18 00:38

This is the front-end to a python grasp api, intended to allow easy database creation and simple querying. For most of the functions of this module, you will need to call the module directly.

usage: grasp [-h] {search,conf,info,init} ...

Sub-commands:

search (s, lookup)

Query database for variants by location or id

Query for SNPs in the database. By default returns a tab-delimeted list of SNPs with the following columns: ‘id’, ‘snpid’, ‘study_snpid’, ‘chrom’, ‘pos’, ‘phenotype’, ‘pval’ The –extra flag adds these columns: ‘InGene’, ‘InMiRNA’, ‘inLincRNa’, ‘LSSNP’ The –study-info flag adds these columns: ‘study_id (PMID)’, ‘title’ The –db-snp flag uses the myvariant API to pull additional data from db_snp.

usage: grasp search [-h] [--extra] [--study-info] [--db-snp] [--pandas] [-o]
                    [--path]
                    query

Positional arguments:

query

rsID, chrom:loc or chrom:start-end

Options:

--extra	Add some extra columns to output
--study-info	Include study title and PMID
--db-snp	Add dbSNP info to output
--pandas	Write output as a pandas dataframe
-o, --out	File to write to, default STDOUT.
--path	PATH to write files to

conf (config)

Manage local config

usage: grasp conf [-h]
                  [--db {sqlite,postgresql,mysql} | --get-path | --set-path PATH | --init]

Options:

--db	Set the current database platform. Possible choices: sqlite, postgresql, mysql
--get-path	Change the sqlite file path
--set-path	Change the sqlite file path
--init	Initialize the config with default settings. Will ERASE your old config!

info

Display database info

Write data summaries (also found on the wiki) to a file or the console. Choices: all: Will write everything to separate rst files, ignores all other flags except `–path` phenotypes: All primary phenotypes. phenotype_categories: All phenotype categories. populations: All primary populations. population_flags: All population flags. snp_columns: All SNP columns. study_columns: All Study columns.

usage: grasp info [-h] [-o] [--path]
                  {population_flags,phenotypes,all,populations,phenotype_categories,study_columns,snp_columns}

Positional arguments:

display

Choice of item to display, if all, results are written to independant rst files, and optional args are ignored Possible choices: population_flags, phenotypes, all, populations, phenotype_categories, study_columns, snp_columns

Options:

-o, --out	File to write to, default STDOUT.
--path	PATH to write files to

init

Initialize the database

usage: grasp init [-h] [-n] study_file grasp_file

Positional arguments:

study_file	GRASP study file from: github.com/MikeDacre/grasp/blob/master/grasp2_studies.txt
grasp_file	GRASP tab delimeted file

Options:

-n, --no-progress
	Do not display a progress bar

Library (API Documentation)

This code is intended to be primarily used as a library, and works best when used in an interactive python session (e.g. with jupyter) alongside pandas. Many of the query functions in this library returns pandas dataframes.

Below is a complete documentation of the API for this library. The functions in grasp.query will be the most interesting for most users wanting to do common db queries.

Tables are defined in grasp.tables, functions for connecting to and building the database are in grasp.db. grasp.info contains simple documentation for all of the tables and phenotypes (used to build this documentation).

grasp.config handles the static database configuration at ~/.grasp, and grasp.ref is used to define module wide static objects, like dictionaries and the PopFlags class.

grasp.query

A mix of functions to make querying the database and analyzing the results faster.

Primary query functions:

get_studies():

Allows querying the Study table by a combination of population and phenotype variables.

get_snps():

Take a study list (possibly from get_studies) and return a SNP list or dataframe.

Helpful additional functions:

intersecting_phenos():

Return a list of phenotypes or phenotype categories present in all queried populations.

write_study_dict():

Write the dictionary returned from get_studies(dictionary=True) to a tab delimited file with extra data from the database.

Lookup functions:

lookup_rsid(), lookup_location() and lookup_studies() allow the querying of the database for specific SNPs and can return customized information on them.

MyVariant:

get_variant_info():

Use myvariant to get variant info for a list of SNPs.

DataFrame Manipulation:

collapse_dataframe():

Collapse a dataframe (such as that returned by get_snps()) to include only a single entry per SNP (collapsing multiple studies into one).

intersect_overlapping_series():

Merge two sets of pvalues (such as those from collapse_dataframe()) into a single merged dataframe with the original index and one column for each pvalue. Good for plotting.

get_studies

grasp.query.get_studies(primary_phenotype=None, pheno_cats=None, pheno_cats_alias=None, primary_pop=None, has_pop=None, only_pop=None, has_disc_pop=None, has_rep_pop=None, only_disc_pop=None, only_rep_pop=None, query=False, count=False, dictionary=False, pandas=False)

Return a list of studies filtered by phenotype and population.

There are two ways to query both phenotype and population.

Phenotype:

GRASP provides a ‘primary phenotype’ for each study, which are fairly poorly curated. They also provide a list of phenotype categories, which are well curated. The problem with the categories is that there are multiple per study and some are to general to be useful. If using categories be sure to post filter the study list.

Note: I have made a list of aliases for the phenotype categories to make them easier to type. Use pheno_cats_alias for that.

Population:

Each study has a primary population (list available with ‘get_populations’) but some studies also have other populations in the cohort. GRASP indexes all population counts, so those can be used to query also. To query these use has_ or only_ (exclusive) parameters, you can query either discovery populations or replication populations. Note that you cannot provide both has_ and only_ parameters for the same population type.

For doing population specific analyses most of the time you will want the excl_disc_pop query.

Argument Description:

Phenotype Arguments are ‘primary_phenotype’, ‘pheno_cats’, and ‘pheno_cats_alias’.

Only provide one of pheno_cats or pheno_cats_alias

Population Arguments are primary_pop, has_pop, only_pop, has_disc_pop, has_rep_pop, only_disc_pop, only_rep_pop.

primary_pop is a simple argument, the others use bitwise flags for lookup.

has_pop and only_pop simpply combine both the discovery and replication population lookups.

The easiest way to use the has_ and only_ parameters is with the PopFlag object. It uses py-flags. For example:

pops = PopFlag.eur | PopFlag.afr

In addition you can provide a list of strings corresponding to PopFlag attributes.

Note: the only_ parameters work as ANDs, not ORs. So only_disc_pop=’eur|afr’ will return those studies that have BOTH european and african discovery populations, but no other discovery populations. On the other hand, has_ works as an OR, and will return any study with any of the specified populations.

Parameters:

• primary_phenotype – Phenotype of interest, string or list of strings.
• pheno_cats – Phenotype category of interest.
• pheno_cats_alias – Phenotype category of interest.
• primary_pop – Query the primary population, string or list of strings.
• has_pop – Return all studies with these populations
• only_pop – Return all studies with these populations
• has_disc_pop – Return all studies with these discovery populations
• has_rep_pop – Return all studies with these replication populations
• only_disc_pop – Return all studies with ONLY these discovery populations
• only_rep_pop – Return all studies with ONLY these replication populations
• query – Return the query instead of the list of study objects.
• count – Return a count of the number of studies.
• dictionary – Return a dictionary of title->id for filtering.
• pandas – Return a dataframe of study information instead of the list.

Returns:

A list of study objects, a query, or a dataframe.

get_snps

grasp.query.get_snps(studies, pandas=True)

Return a list of SNPs in a single population in a single phenotype.

Studies:A list of studies. Pandas:Return a dataframe instead of a list of SNP objects. Returns:Either a DataFrame or list of SNP objects.

lookup_rsid

grasp.query.lookup_rsid(rsid, study=False, columns=None, pandas=False)

Query database by rsID.

Parameters:

• rsID (str) – An rsID or list of rsIDs
• study (bool) – Include study info in the output.
• columns (list) – A list of columns to include in the query. Default is all. List must be made up of column objects, e.g. [t.SNP.snpid, t.Study.id]
• pandas (bool) – Return a dataframe instead of a list of SNPs

Returns:

List of SNP objects

Return type:

list

lookup_location

grasp.query.lookup_location(chrom, position, study=False, columns=None, pandas=False)

Query database by location.

Parameters:

• chrom (str) – The chromosome as an int or string (e.g. 1 or chr1)
• position (int) – Either one location, a list of locations, or a range of locations, range can be expressed as a tuple of two ints, a range object, or a string of ‘int-int’
• study (bool) – Include study info in the output.
• columns (list) – A list of columns to include in the query. Default is all. List must be made up of column objects, e.g. [t.SNP.snpid, t.Study.id]
• pandas (bool) – Return a dataframe instead of a list of SNPs

Returns:

List of SNP objects

Return type:

list

lookup_studies

grasp.query.lookup_studies(title=None, study_id=None, columns=None, pandas=False)

Find all studies matching either title or id.

Parameters:

• title (str) – The study title, string or list of strings.
• study_id (int) – The row ID, usually the PMID, int or list of ints.
• columns (list) – A list of columns to include in the query. Default is all. List must be made up of column objects, e.g. [t.SNP.snpid, t.Study.id]
• pandas (bool) – Return a dataframe instead of a list of SNPs

Returns:

All matching studies as either a dataframe or a list

Return type:

DataFrame or list

get_variant_info

grasp.query.get_variant_info(snp_list, fields='dbsnp', pandas=True)

Get variant info for a list of SNPs.

Parameters:

• snp_list – A list of SNP objects or SNP rsIDs
• fields – Choose fields to display from: docs.myvariant.info/en/latest/doc/data.html#available-fields Good choices are ‘dbsnp’, ‘clinvar’, or ‘gwassnps’ Can also use ‘grasp’ to get a different version of this info.
• pandas – Return a dataframe instead of dictionary.

Returns:

A dictionary or a dataframe.

get_collapse_dataframe

grasp.query.collapse_dataframe(df, mechanism='median', pvalue_filter=None, protected_columns=None)

Collapse a dataframe by chrom:location from get_snps.

Will use the mechanism defined by ‘mechanism’ to collapse a dataframe to one indexed by ‘chrom:location’ with pvalue and count only.

This function is agnostic to all dataframe columns other than:

['chrom', 'pos', 'snpid', 'pval']

All other columns are collapsed into a comma separated list, a string. ‘chrom’ and ‘pos’ are merged to become the new colon-separated index, snpid is maintained, and pval is merged using the function in ‘mechanism’.

Parameters:

• df – A pandas dataframe, must have ‘chrom’, ‘pos’, ‘snpid’, and ‘pval’ columns.
• mechanism – A numpy statistical function to use to collapse the pvalue, median or mean are the common ones.
• pvalue_filter – After collapsing the dataframe, filter to only include pvalues less than this cutoff.
• protected_columns – A list of column names that will be maintained as is, although all duplicates will be dropped (randomly). Only makes sense for columns that are identical for all studies of the same SNP.

Returns:

Indexed by chr:pos, contains flattened pvalue column, and

all original columns as a comma-separated list. Additionally contains a count and stddev (of pvalues) column. stddev is nan if count is 1.

Return type:

DataFrame

intersect_overlapping_series

grasp.query.intersect_overlapping_series(series1, series2, names=None, stats=True, plot=None, name=None)

Plot all SNPs that overlap between two pvalue series.

Parameters:

• series{1,2} (Series) – A pandas series object
• names (list) – A list of two names to use for the resultant dataframes
• stats (bool) – Print some stats on the intersection
• plot (str) – Plot the resulting intersection, path to save figure to (must end in .pdf/.png). Numpy and Matplotlib are required.
• name (str) – A name for the plot, optional.

Returns:

with the two series as columns

Return type:

DataFrame

write_study_dict

grasp.query.write_study_dict(study_dict, outfile)

Write a study dictionary from get_studies(dictionary=True) to file.

Looks up studies in the Study table first.

Parameters:

• study_dict (dict) – A dictionary of title=>id from the Study table.
• outfile – A valid path to a file with write permission.

Outputs:

A tab delimited file of ID, Title, Author, Journal, Discovery Population, Replication Population, SNP_Count

Returns:None

grasp.tables

GRASP table descriptions in SQLAlchemy ORM.

These tables do not exist in the GRASP data, which is a single flat file. By separating the data into these tables querying is much more efficient.

This submodule should only be used for querying.

SNP

class grasp.tables.SNP(**kwargs)

Bases: sqlalchemy.ext.declarative.api.Base

An SQLAlchemy Talble for GRASP SNPs.

Study and phenotype information are pushed to other tables to minimize table size and make querying easier.

Table Name:

snps

Columns:

Described in the columns attribute

int

The ID number of the SNP, usually the NHLBIkey

str

SNP loction expressed as ‘chr:pos’

hvgs_ids

A list of HGVS IDs for this SNP

columns

A dictionary of all columns ‘column_name’=>(‘type’, ‘desc’)

columns = OrderedDict([('id', ('BigInteger', 'NHLBIkey')), ('snpid', ('String', 'SNPid')), ('chrom', ('String', 'chr')), ('pos', ('Integer', 'pos')), ('pval', ('Float', 'Pvalue')), ('NHLBIkey', ('String', 'NHLBIkey')), ('HUPfield', ('String', 'HUPfield')), ('LastCurationDate', ('Date', 'LastCurationDate')), ('CreationDate', ('Date', 'CreationDate')), ('population_id', ('Integer', 'Primary')), ('population', ('relationship', 'Link')), ('study_id', ('Integer', 'Primary')), ('study', ('relationship', 'Link')), ('study_snpid', ('String', 'SNPid')), ('paper_loc', ('String', 'LocationWithinPaper')), ('phenotype_desc', ('String', 'Phenotype')), ('phenotype_cats', ('relationship', 'Link')), ('InGene', ('String', 'InGene')), ('NearestGene', ('String', 'NearestGene')), ('InLincRNA', ('String', 'InLincRNA')), ('InMiRNA', ('String', 'InMiRNA')), ('InMiRNABS', ('String', 'InMiRNABS')), ('dbSNPfxn', ('String', 'dbSNPfxn')), ('dbSNPMAF', ('String', 'dbSNPMAF')), ('dbSNPinfo', ('String', 'dbSNPalleles')), ('dbSNPvalidation', ('String', 'dbSNPvalidation')), ('dbSNPClinStatus', ('String', 'dbSNPClinStatus')), ('ORegAnno', ('String', 'ORegAnno')), ('ConservPredTFBS', ('String', 'ConservPredTFBS')), ('HumanEnhancer', ('String', 'HumanEnhancer')), ('RNAedit', ('String', 'RNAedit')), ('PolyPhen2', ('String', 'PolyPhen2')), ('SIFT', ('String', 'SIFT')), ('LSSNP', ('String', 'LS')), ('UniProt', ('String', 'UniProt')), ('EqtlMethMetabStudy', ('String', 'EqtlMethMetabStudy'))])

A description of all columns in this table.

display_columns(display_as='table', write=False)

Return all columns in the table nicely formatted.

Display choices:

table: A formatted grid-like table tab: A tab delimited non-formatted version of table list: A string list of column names

Parameters:

• display_as – {table,tab,list}
• write – If true, print output to console, otherwise return string.

Returns:

A formatted string or None

get_columns(return_as='list')

Return all columns in the table nicely formatted.

Display choices:

list: A python list of column names dictionary: A python dictionary of name=>desc long_dict: A python dictionary of name=>(type, desc)

Parameters:return_as – {table,tab,list,dictionary,long_dict,id_dict} Returns:A list or dictionary

get_variant_info(fields='dbsnp', pandas=True)

Use the myvariant API to get info about this SNP.

Note that this service can be very slow. It will be faster to query multiple SNPs.

Parameters:

• fields – Choose fields to display from: docs.myvariant.info/en/latest/doc/data.html#available-fields Good choices are ‘dbsnp’, ‘clinvar’, or ‘gwassnps’ Can also use ‘grasp’ to get a different version of this info.
• pandas – Return a dataframe instead of dictionary.

Returns:

A dictionary or a dataframe.

hvgs_ids

The HVGS ID from myvariant.

snp_loc

Return a simple string containing the SNP location.

Study

class grasp.tables.Study(**kwargs)

Bases: sqlalchemy.ext.declarative.api.Base

An SQLAlchemy table to store study information.

This table provides easy ways to query for SNPs by study information, including population and phenotype.

Note: disc_pop_flag and rep_pop_flag are integer representations of a bitwise flag describing population, defined in ref.PopFlag. To see the string representation of this property, lookup disc_pops or rep_pops.

Table Name:

studies

Columns:

Described in the columns attribute.

int

The integer ID number, usually the PMID, unless not indexed.

str

Summary data on this study.

len

The number of individuals in this study.

disc_pops

A string displaying the number of discovery poplations.

rep_pops

A string displaying the number of replication poplations.

columns

A dictionary of all columns ‘column_name’=>(‘type’, ‘desc’)

population_information

A multi-line string describing the populations in this study.

columns = OrderedDict([('id', ('Integer', 'id')), ('pmid', ('String', 'PubmedID')), ('title', ('String', 'Study')), ('journal', ('String', 'Journal')), ('author', ('String', '1st_author')), ('grasp_ver', ('Integer', 'GRASPversion?')), ('noresults', ('Boolean', 'No results flag')), ('results', ('Integer', '#results')), ('qtl', ('Boolean', 'IsEqtl/meQTL/pQTL/gQTL/Metabolmics?')), ('snps', ('relationship', 'Link to all SNPs in this study')), ('phenotype_id', ('Integer', 'ID of primary phenotype in Phenotype table')), ('phenotype', ('relationship', 'A link to the primary phenotype in the Phenotype table')), ('phenotype_cats', ('relationship', 'A link to all phenotype categories assigned in the PhenoCats table')), ('datepub', ('Date', 'DatePub')), ('in_nhgri', ('Boolean', 'In NHGRI GWAS catalog (8/26/14)?')), ('locations', ('String', 'Specific place(s) mentioned for samples')), ('mf', ('Boolean', 'Includes male/female only analyses in discovery and/or replication?')), ('mf_only', ('Boolean', 'Exclusively male or female study?')), ('platforms', ('relationship', 'Link to platforms in the Platform table. Platform [SNPs passing QC]')), ('snp_count', ('String', 'From "Platform [SNPs passing QC]"')), ('imputed', ('Boolean', 'From "Platform [SNPs passing QC]"')), ('population_id', ('Integer', 'Primary key of population table')), ('population', ('relationship', 'GWAS description, link to table')), ('total', ('Integer', 'Total Discovery + Replication sample size')), ('total_disc', ('Integer', 'Total discovery samples')), ('pop_flag', ('Integer', 'A bitwise flag that shows presence/absence of all populations (discovery and replication)')), ('disc_pop_flag', ('Integer', 'A bitwise flag that shows presence/absence of discovery populations')), ('european', ('Integer', 'European')), ('african', ('Integer', 'African ancestry')), ('east_asian', ('Integer', 'East Asian')), ('south_asian', ('Integer', 'Indian/South Asian')), ('hispanic', ('Integer', 'Hispanic')), ('native', ('Integer', 'Native')), ('micronesian', ('Integer', 'Micronesian')), ('arab', ('Integer', 'Arab/ME')), ('mixed', ('Integer', 'Mixed')), ('unpecified', ('Integer', 'Unspec')), ('filipino', ('Integer', 'Filipino')), ('indonesian', ('Integer', 'Indonesian')), ('total_rep', ('Integer', 'Total replication samples')), ('rep_pop_flag', ('Integer', 'A bitwise flag that shows presence/absence of replication populations')), ('rep_european', ('Integer', 'European.1')), ('rep_african', ('Integer', 'African ancestry.1')), ('rep_east_asian', ('Integer', 'East Asian.1')), ('rep_south_asian', ('Integer', 'Indian/South Asian.1')), ('rep_hispanic', ('Integer', 'Hispanic.1')), ('rep_native', ('Integer', 'Native.1')), ('rep_micronesian', ('Integer', 'Micronesian.1')), ('rep_arab', ('Integer', 'Arab/ME.1')), ('rep_mixed', ('Integer', 'Mixed.1')), ('rep_unpecified', ('Integer', 'Unspec.1')), ('rep_filipino', ('Integer', 'Filipino.1')), ('rep_indonesian', ('Integer', 'Indonesian.1')), ('sample_size', ('String', 'Initial Sample Size, string description of integer population counts above.')), ('replication_size', ('String', 'Replication Sample Size, string description of integer population counts above.'))])

A description of all columns in this table.

disc_pops

Convert disc_pop_flag to PopFlag.

display_columns(display_as='table', write=False)

Return all columns in the table nicely formatted.

Display choices:

table: A formatted grid-like table tab: A tab delimited non-formatted version of table list: A string list of column names

Parameters:

• display_as – {table,tab,list}
• write – If true, print output to console, otherwise return string.

Returns:

A formatted string or None

get_columns(return_as='list')

Return all columns in the table nicely formatted.

Display choices:

list: A python list of column names dictionary: A python dictionary of name=>desc long_dict: A python dictionary of name=>(type, desc)

Parameters:return_as – {table,tab,list,dictionary,long_dict,id_dict} Returns:A list or dictionary

pops

Convert rep_pop_flag to PopFlag.

population_information

Display a summary of population data.

rep_pops

Convert rep_pop_flag to PopFlag.

Phenotype

class grasp.tables.Phenotype(**kwargs)

Bases: sqlalchemy.ext.declarative.api.Base

An SQLAlchemy table to store the primary phenotype.

Table Name:

phenos

Columns:

phenotype: The string phenotype from the GRASP DB, unique. alias: A short representation of the phenotype, not unique. studies: A link to the studies table.

int

The ID number.

str

The name of the phenotype.

PhenoCats

class grasp.tables.Phenotype(**kwargs)

Bases: sqlalchemy.ext.declarative.api.Base

An SQLAlchemy table to store the primary phenotype.

Table Name:

phenos

Columns:

phenotype: The string phenotype from the GRASP DB, unique. alias: A short representation of the phenotype, not unique. studies: A link to the studies table.

int

The ID number.

str

The name of the phenotype.

Population

class grasp.tables.Population(population)

Bases: sqlalchemy.ext.declarative.api.Base

An SQLAlchemy table to store the platform information.

Table Name:

populations

Columns:

population: The name of the population. studies: A link to all studies in this population. snps: A link to all SNPs in this populations.

int

Population ID number

str

The name of the population

Platform

class grasp.tables.Platform(platform)

Bases: sqlalchemy.ext.declarative.api.Base

An SQLAlchemy table to store the platform information.

Table Name:

platforms

Columns:

platform: The name of the platform from GRASP. studies: A link to all studies using this platform.

int

The ID number of this platform

str

The name of the platform

grasp.db

Functions for managing the GRASP database.

get_session() is used everywhere in the module to create a connection to the database. initialize_database() is used to build the database from the GRASP file. It takes about an hour 90 minutes to run and will overwrite any existing database.

grasp.db.get_session(echo=False)

Return a session and engine, uses config file.

Parameters:echo – Echo all SQL to the console. Returns:

A SQLAlchemy session and engine object corresponding

to the grasp database for use in querying.

Return type:session, engine

grasp.db.initialize_database(study_file, grasp_file, commit_every=250000, progress=False)

Create the database quickly.

Study_file:Tab delimited GRASP study file, available here: github.com/MikeDacre/grasp/blob/master/grasp_studies.txt Grasp_file:Tab delimited GRASP file. Commit_every:How many rows to go through before commiting to disk. Progress:Display a progress bar (db length hard coded).

grasp.config

Manage a persistent configuration for the database.

grasp.config.config = <configparser.ConfigParser object>

A globally accessible ConfigParger object, initialized with CONFIG_FILE.

grasp.config.CONFIG_FILE = '/home/docs/.grasp'

The PATH to the config file.

grasp.config.init_config(db_type, db_file='', db_host='', db_user='', db_pass='')

Create an initial config file.

Parameters:

• db_type – ‘sqlite/mysql/postgresql’
• db_file – PATH to sqlite database file
• db_host – Hostname for mysql or postgresql server
• db_user – Username for mysql or postgresql server
• db_pass – Password for mysql or postgresql server (not secure)

Returns:

NoneType

Return type:

None

grasp.config.init_config_interactive()

Interact with the user to create a new config.

Uses readline autocompletion to make setup easier.

grasp.config.write_config()

Write the current config to CONFIG_FILE.

grasp.info

Little functions to pretty print column lists and category info.

get_{phenotypes,phenotype_categories,popululations} all display a dump of the whole database.

get_population_flags displays available flags from PopFlag.

display_{study,snp}_columns displays a list of available columns in those two tables as a formatted string.

get_{study,snp}_columns return a list of available columns in those two tables as python objects.

grasp.info.display_snp_columns(display_as='table', write=False)

Return all columns in the SNP table as a string.

Display choices:

table: A formatted grid-like table tab: A tab delimited non-formatted version of table list: A string list of column names

Parameters:

• display_as – {table,tab,list}
• write – If true, print output to console, otherwise return string.

Returns:

A formatted string or None

grasp.info.display_study_columns(display_as='table', write=False)

Return all columns in the Study table as a string.

Display choices:

table: A formatted grid-like table tab: A tab delimited non-formatted version of table list: A string list of column names

Parameters:

• display_as – {table,tab,list}
• write – If true, print output to console, otherwise return string.

Returns:

A formatted string or None

grasp.info.get_phenotype_categories(list_only=False, dictionary=False, table=False)

Return all phenotype categories from the PhenoCats table.

List_only:Return a simple text list instead of a list of Phenotype objects. Dictionary:Return a dictionary of phenotype=>ID Table:Return a pretty table for printing.

grasp.info.get_phenotypes(list_only=False, dictionary=False, table=False)

Return all phenotypes from the Phenotype table.

List_only:Return a simple text list instead of a list of Phenotype objects. Dictionary:Return a dictionary of phenotype=>ID Table:Return a pretty table for printing.

grasp.info.get_population_flags(list_only=False, dictionary=False, table=False)

Return all population flags available in the PopFlags class.

List_only:Return a simple text list instead of a list of Phenotype objects. Dictionary:Return a dictionary of population=>ID Table:Return a pretty table for printing.

grasp.info.get_populations(list_only=False, dictionary=False, table=False)

Return all populatons from the Population table.

List_only:Return a simple text list instead of a list of Phenotype objects. Dictionary:Return a dictionary of population=>ID Table:Return a pretty table for printing.

grasp.info.get_snp_columns(return_as='list')

Return all columns in the SNP table.

Display choices:

list: A python list of column names dictionary: A python dictionary of name=>desc long_dict: A python dictionary of name=>(type, desc)

Parameters:return_as – {table,tab,list,dictionary,long_dict,id_dict} Returns:A list or dictionary

grasp.info.get_study_columns(return_as='list')

Return all columns in the SNP table.

Display choices:

list: A python list of column names dictionary: A python dictionary of name=>desc long_dict: A python dictionary of name=>(type, desc)

Parameters:return_as – {table,tab,list,dictionary,long_dict,id_dict} Returns:A list or dictionary

grasp.ref

ref.py holds some simple lookups and the PopFlags classes that don’t really go anywhere else.

Holds reference objects for use elsewhere in the module.

class grasp.ref.PopFlag

Bases: flags.Flags

A simplified bitwise flag system for tracking populations.

Based on py-flags

eur

1 # European

afr

2 # African ancestry

east_asian

4 # East Asian

south_asian

8 # Indian/South Asian

his

16 # Hispanic

native

32 # Native

micro

64 # Micronesian

arab

128 # Arab/ME

mix

256 # Mixed

uns

512 # Unspec

filipino

1024 # Filipino

indonesian

2048 # Indonesian

Example::

eur = PopFlag.eur afr = PopFlag(2) his_micro = PopFlag.from_simple_string(‘his|micro’) four_pops = eur | afr four_pops |= his_micro assert four_pops == PopFlag.from_simple_string(‘his|micro|afr|eur’) PopFlag.eur & four_pops > 0 # Returns True PopFlag.eur i== four_pops # Returns False PopFlag.arab & four_pops > 0 # Returns False

Table Columns

The two important tables with the majority of the data are Study and SNP. In addition, phenotype data is stored in Phenotype and PhenoCats, population data is in Population, and platforms are in Platform.

Contents

• Table Columns
  • Study
  • SNP
  • Phenotype
  • PhenoCats
  • Population
  • Platform

Study

To query studies, it is recommended to use the query.get_studies() function.

Column	Description	Type
id	id	Integer
pmid	PubmedID	String
title	Study	String
journal	Journal	String
author	1st_author	String
grasp_ver	GRASPversion?	Integer
noresults	No results flag	Boolean
results	#results	Integer
qtl	IsEqtl/meQTL/pQTL/gQTL/Metabolmics?	Boolean
snps	Link to all SNPs in this study	relationship
phenotype_id	ID of primary phenotype in Phenotype table	Integer
phenotype	A link to the primary phenotype in the Phenotype table	relationship
phenotype_cats	A link to all phenotype categories assigned in the PhenoCats table	relationship
datepub	DatePub	Date
in_nhgri	In NHGRI GWAS catalog (8/26/14)?	Boolean
locations	Specific place(s) mentioned for samples	String
mf	Includes male/female only analyses in discovery and/or replication?	Boolean
mf_only	Exclusively male or female study?	Boolean
platforms	Link to platforms in the Platform table. Platform [SNPs passing QC]	relationship
snp_count	From “Platform [SNPs passing QC]”	String
imputed	From “Platform [SNPs passing QC]”	Boolean
population_id	Primary key of population table	Integer
population	GWAS description, link to table	relationship
total	Total Discovery + Replication sample size	Integer
total_disc	Total discovery samples	Integer
pop_flag	A bitwise flag that shows presence/absence of all populations (discovery and replication)	Integer
disc_pop_flag	A bitwise flag that shows presence/absence of discovery populations	Integer
european	European	Integer
african	African ancestry	Integer
east_asian	East Asian	Integer
south_asian	Indian/South Asian	Integer
hispanic	Hispanic	Integer
native	Native	Integer
micronesian	Micronesian	Integer
arab	Arab/ME	Integer
mixed	Mixed	Integer
unpecified	Unspec	Integer
filipino	Filipino	Integer
indonesian	Indonesian	Integer
total_rep	Total replication samples	Integer
rep_pop_flag	A bitwise flag that shows presence/absence of replication populations	Integer
rep_european	European.1	Integer
rep_african	African ancestry.1	Integer
rep_east_asian	East Asian.1	Integer
rep_south_asian	Indian/South Asian.1	Integer
rep_hispanic	Hispanic.1	Integer
rep_native	Native.1	Integer
rep_micronesian	Micronesian.1	Integer
rep_arab	Arab/ME.1	Integer
rep_mixed	Mixed.1	Integer
rep_unpecified	Unspec.1	Integer
rep_filipino	Filipino.1	Integer
rep_indonesian	Indonesian.1	Integer
sample_size	Initial Sample Size, string description of integer population counts above.	String
replication_size	Replication Sample Size, string description of integer population counts above.	String

SNP

Column	Description	Type
id	NHLBIkey	BigInteger
snpid	SNPid	String
chrom	chr	String
pos	pos	Integer
pval	Pvalue	Float
NHLBIkey	NHLBIkey	String
HUPfield	HUPfield	String
LastCurationDate	LastCurationDate	Date
CreationDate	CreationDate	Date
population_id	Primary	Integer
population	Link	relationship
study_id	Primary	Integer
study	Link	relationship
study_snpid	SNPid	String
paper_loc	LocationWithinPaper	String
phenotype_desc	Phenotype	String
phenotype_cats	Link	relationship
InGene	InGene	String
NearestGene	NearestGene	String
InLincRNA	InLincRNA	String
InMiRNA	InMiRNA	String
InMiRNABS	InMiRNABS	String
dbSNPfxn	dbSNPfxn	String
dbSNPMAF	dbSNPMAF	String
dbSNPinfo	dbSNPalleles	String
dbSNPvalidation	dbSNPvalidation	String
dbSNPClinStatus	dbSNPClinStatus	String
ORegAnno	ORegAnno	String
ConservPredTFBS	ConservPredTFBS	String
HumanEnhancer	HumanEnhancer	String
RNAedit	RNAedit	String
PolyPhen2	PolyPhen2	String
SIFT	SIFT	String
LSSNP	LS	String
UniProt	UniProt	String
EqtlMethMetabStudy	EqtlMethMetabStudy	String

Phenotype

All available phenotypes are available on the Phenotypes wiki page

• id
• phenotype
• studies (link to Study table)
• snps (link to SNP table)

PhenoCats

All phenotype categories are available on the Phenotype Categories wiki page

• id
• population
• alias
• studies (link to Study table)
• snps (link to SNP table)

Population

• id
• population
• studies (link to Study table)
• snps (link to SNP table)

All population entries are available on the Populations wiki page

Platform

• id
• platform
• studies (link to Study table)
• snps (link to SNP table)

GRASP Table Query Reference

Phenotype

The following phenotypes are stored in the GRASP database (there are 1,209 of them):

Phenotype	ID
5-HTT serotonin transporter levels, in brain	999
93 circulating phenotypes	557
ADD	1021
ADHD	566
ADHD and conduct disorder symptoms	663
ADHD in adults	662
ADHD, inattention and hyperactivity-impulsivity tests	496
ADHD, methylphenidate treatment response in	660
Abdominal aortic aneurysm	714
Abdominal subcutaneous and visceral adipose depots	160
Acetaminophen toxicity in blood cell lines	965
Activated partial thromboplastin time (aPTT) and activated protein C resistance	377
Activated partial thromboplastin time (aPTT) and prothrombin time	202
Activated partial thromboplastin time (aPTT), in blood	811
Acute lung injury	983
Acute lung injury following major trauma	32
Acute respiratory distress syndrome	463
Adaptation of normoxic and mild-hypoxic inhabitants	169
Addiction phenotypes (alcohol, nicotine, marijuana, cocaine, opiates, other drugs)	989
Adipocyte fatty acid-binding protein concentration, in serum	1113
Adiponectin levels in women	905
Adiponectin levels, in plasma	679
Adiponectin, high molecular weight, in serum	1073
Adiponectin, in plasma	1167
Adiponectin, in serum	105
Adiposity traits	211
Adolescent idiopathic scoliosis	546
Adolescent idiopathic scoliosis, in women	1152
Advanced age-related macular degeneration subtypes	205
Age at menarche	528
Age at menarche and age at natural menopause	458
Age at menarche, age at menopause	1186
Age at menopause	20
Age at menopause (early menopause)	415
Age at natural menopause	709
Age-related cataract (ARC) and Alzheimer disease (AD)	300
Age-related macular degeneration	197
Age-related macular degeneration (AMD), wet neovascular	580
Age-related macular degeneration, advanced	822
Age-related macular degeneration, exudative	1122
Aging successfully (longevity and low disease burden)	221
Airflow obstruction	242
Albumin and total protein, serum	313
Albumin:globin ratio, serum	414
Albuminuria, in urine	1002
Alcohol and nicotine co-dependence	112
Alcohol consumption	166
Alcohol consumption (heavy)	454
Alcohol craving	109
Alcohol dependence	338
Alcohol dependence in bipolar cases and non-bipolar controls	1115
Alcohol withdrawal symptoms	1169
Alcohol-related and sporadic pancreatitis	356
Allele-specific FAIRE regulation in lymphoblastoid cell lines	273
Allergic rhinitis and grass sensitization	1157
Alopecia (early-onset androgenetic alopecia)	196
Alopecia areata	858
Alpha-tocopherol after vitamin E supplementation in men, serum	86
Alzheimer’s disease	83
Alzheimer’s disease with psychotic symptoms	1146
Alzheimer’s disease, MRI atrophy as a QTL for	948
Alzheimer’s disease, age at onset	1147
Alzheimer’s disease, late onset	223
Amygdala activation, in youths, with and without bipolar disorder	807
Amygdala reactivity	249
Amyloid A levels, in serum	952
Amyotrophic Lateral Sclerosis (ALS)	290
Amyotrophic Lateral Sclerosis (ALS), sporadic	100
Androgen levels in men, serum	279
Androgenic alopecia (Male pattern baldness)	664
Anemia, ribavirin-induced in hepatitis C treatment	864
Angiotensin-converting enzyme (ACE) activity, in serum	779
Ankle-brachial index (ABI)	50
Ankylosing spondylitis	778
Anorexia nervosa	940
Anthropometric traits (body mass index, waist to hip ratio, height, obesity grade)	513
Anti-cyclic citrullinated peptide titer	695
Anti-cytomegalovirus antibody response	1141
Antibody response to anthrax vaccine adsorbed	176
Antibody response to smallpox vaccine	137
Antibody titer to hepatitis B vaccination	1088
Antidepressant efficacy in major depressive disorder	339
Antidepressant response in major depressive disorder	159
Antihypertensive response to an Angiotensin II Receptor Blocker	150
Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis	234
Antiphospholipid antibodies	490
Antipsychotic drug-induced weight gain, severe	151
Antipsychotic treatment outcomes	392
Antipsychotic-induced tardive dyskinesia	1103
Antisocial behavior	814
Antisocial behavior (adult)	336
Antithrombin, in plasma	562
Anxiety (childhood anxiety)	515
Anxiety spectrum disorders	820
Aortic root diameter	975
Arsenic metabolism and toxicity	63
Arterial stiffness	765
Arthritis (juvenile idiopathic arthritis)	48
Arthritis (juvenile rheumatoid arthritis)	530
Asparaginase sensitivity in blood cell lines	936
Aspartate aminotransferase (AAT) levels, in serum	1120
Asperger disorder	966
Aspirin hydrolytic activity, in plasma	489
Aspirin-exacerbated respiratory disease	371
Asthma	1
Asthma (acute bronchodilator response)	487
Asthma (adult)	373
Asthma (childhood allergic asthma)	1004
Asthma (childhood asthma)	594
Asthma (childhood asthma, age at onset)	146
Asthma (severe asthma)	149
Asthma exacerbation	563
Asthma response to bronchodilators	228
Asthma response to inhaled corticosteroids	135
Asthma, aspirin-intolerant	937
Asthma, childhood	1069
Asthma, severe exacerbations	1076
Asthma, toluene diisocynate induced	682
Astigmatism	420
Atherosclerosis and myocardial infarction	978
Atopic dermatitis	321
Atopy	942
Atopy and allergic rhinitis	1053
Atopy, with and without asthma	757
Atrial fibrillation	139
Atrial fibrillation, atrial flutter	592
Atypical cytochrome P450 3A4 (CYP3A4) enzyme activity	283
Autism	277
Autism like traits	759
Autism spectrum disorders	74
Autism spectrum disorders with language delay	1034
Autism, gender differences	961
Autism, monoallelic expression in blood cell lines	1063
Autoimmune thyroid disease (Grave’s disease and Hashimoto’s thyroiditis)	274
Azoospermia and oligozoospermia	711
B-vitamin level (Vitamin B6, Vitamin B12) concentrations, folate and homocysteine, in serum	697
Barrett’s esophagus	292
Behavior, childhood	1091
Behcet’s disease	707
Behet’s disease	307
Beta-2 microglobulin, in plasma	451
Beta-thalassemia/hemoglobin E disease	801
Beta-trace protein levels, in plasma	422
Bicuspid aortic valve	784
Biliary atresia	835
Bilirubin levels	431
Bilirubin levels (total bilirubin)	539
Bilirubin levels, in serum	703
Bilirubin levels, in serum, unconjugated	1183
Biomarkers (liver function, butrylycholinesterase, CRP, ferritin, glucose, HDL cholesterol, insulin, LDL cholesterol, triglycerides, uric acid), body mass index (BMI)	1132
Biomarkers (natriuretic peptides, vitamin K, vitamin D, CD40L, osteoprotegerin, P-selectin, TNFR2, TNFa, liver function, osteocalcin, CRP, IL6, sICAM, MCP1, myelperoxidase), in plasma or serum	605
Bipolar disorder	334
Bipolar disorder (mood-incongruent psychotic bipolar disorder)	340
Bipolar disorder and schizophrenia	909
Bipolar disorder and white matter integrity	386
Bipolar disorder with irritable or elated mania in severe episodes	421
Bipolar disorder with negative mood delusions	310
Bipolar disorder with seasonal pattern mania	276
Bipolar disorder, affective	845
Bipolar disorder, age of onset in	993
Bipolar disorder, personality traits within	1006
Bipolar disorder, schizoaffective	719
Birth weight	380
Birth weight, length, head circumference and fat mass	519
Bisphosphonate-related osteonecrosis of the jaw	21
Bivariate analysis of femoral neck bone mineral density and age at menarche	525
Bladder cancer	725
Bleomycin sensitivity, in blood samples	945
Blood biomarkers in chronic obstructive pulmonary disease	358
Blood cell count (lymphocyte count) and gene expression	23
Blood cell count (monocyte count)	416
Blood cell count (neutrophil count)	799
Blood cell counts and other traits (platelet count (PLT), red cell count, white cell count, hemoglobin, urate, GGT, alkaline phosphatase, AST, ALT, creatinine kinase, total protein, albumin, blood urea nitrogen, serum creatinine, HDL cholesterol, triglycerides)	787
Blood cell counts and traits, in red and white blood cells	402
Blood cell counts and traits, in red blood cells	555
Blood cell counts, in white cells	1080
Blood cell counts, in white cells in leukemia patients in remission	1033
Blood cell traits (red blood cell count, hemoglobin, hematocrit)	1209
Blood cell traits, in red blood cells	147
Blood group types in women	165
Blood phenotypes and cell counts (fibrinogen, FVII, PAI1, vWF, tPA, D-dimer, platelet aggregation, viscosity, hemoglobin, red blood cell counts)	606
Blood pressure	930
Blood pressure and arterial stiffness	612
Blood pressure and/or hypertension	673
Blood pressure lowering with thiazide-diuretic treatment	793
Blood pressure, CVD RF and other traits (body mass index (BMI), height, waist circumference, weight, leptin, percent body fat, HDL cholesterol, LDL cholesterol, total cholesterol, triglycerides, fasting glucose, thyroid stimulating hormone, C-reactive protein (CRP))	685
Blood pressure, CVD RF and other traits (body mass index (BMI), waist:hip ratio, pulse rate, bone mineral density (BMD))	704
Blood pressure, CVD RF and other traits (body mass index (BMI), waist:hip ratio, renin activity in plasma, aldosterone concentration in plasma, BNP levels in plasma, alcohol consumption)	907
Blood pressure, early onset hypertension	705
Body fat percentage	1075
Body mass index (BMI)	47
Body mass index (BMI) and asthma	493
Body mass index (BMI) in adolescents and young adults	548
Body mass index (BMI), height, weight, waist circumference	918
Bone geometry (femoral neck)	800
Bone geometry (femoral neck), and appendicular lean mass	1175
Bone mass and geometry	608
Bone mineral density (BMD)	194
Bone mineral density (BMD) (wrist)	943
Bone mineral density (BMD) and osteoporosis-related phenotypes	1170
Bone mineral density (BMD), cortical density, in men	951
Bone mineral density (BMD), in women	794
Bone mineral density (hip), in women	1036
Bone mineral density and fat mass	460
Bone mineral density in premenopausal women	335
Bone mineral density of forearm	518
Bone mineral density, low-trauma fracture	123
Bone mineral traits	970
Bone mineral traits, uni and bivariate analyses, in men	1014
Bone size	658
Bone size and body lean mass	345
Bone thickness, bone strength, osteoporotic fracture risk	227
Bone-related traits (pleiotropy in bone mineral density (BMD), bone geometry, muscle mass, bone quantitative ultrasound)	1049
Brachial circumference	106
Brain A_ levels	452
Brain activation patterns in response to human facial expressions	238
Brain aging, MRI and cognition phenotypes	609
Brain derived neurotrophic factor levels, in serum	1162
Brain imaging phenotypes	785
Brain infarcts, covert MRI-defined	774
Brain microstructure; intellectual performance	208
Brain neural connectivity	265
Brain size	1192
Brain structure	798
Brain white matter hyperintensity	1064
Brain white matter integrity	81
Breast and ovarian cancer risk in BRCA1 carriers	506
Breast cancer	8
Breast cancer (ER-positive) and post-menopausal estradiol concentrations, in plasma	494
Breast cancer and prostate cancer	510
Breast cancer in males	305
Breast cancer meta	903
Breast cancer risk	855
Breast cancer risk in Ashkenazi Jewish women without BRCA1/2 mutations	427
Breast cancer risk related to menopausal hormone therapy	457
Breast cancer survival	12
Breast cancer survival (early-onset breast cancer)	418
Breast cancer, BRCA1-positive	901
Breast cancer, BRCA2-positive	931
Breast cancer, ER negative	500
Breast cancer, adverse effects to aromatase inhibitors	904
Breast cancer, clinical outcomes of adjuvant tamoxifen therapy	1199
Breast cancer, early onset	640
Breast cancer, estrogen receptor-negative	1159
Breast cancer, hormonal receptor-positive	285
Breast cancer, lapatinib-induced hepatotoxicity in	981
Breast cancer, prostate cancer	576
Breast cancer, sporadic post-menopausal	589
Breast size	215
Bronchopulmonary dysplasia	1109
Butyrylcholinesterase activity, in serum	1112
C-reactive protein (CRP)	115
C-reactive protein (CRP) and white blood cell (WBC)	224
C-reactive protein (CRP) levels, in plasma, in women	635
C-reactive protein (CRP) levels, in serum	636
CD4:CD8 T cell ratios	775
CVD outcomes (CVD, MI, stroke, CHD death, atrial fibrillation, heart failure)	614
CVD risk factors and quantitative traits (blood pressure, heart rate, LDL cholesterol, HDL cholesterol, total cholesterol, triglycerides, glucose, insulin, height, weight, waist circumference)	899
Cachexia	924
Caffeine-induced insomnia	216
Calcium intake levels and metabolic syndrome	1193
Calcium levels, in serum	871
Cannabis dependence	1060
Cannabis use initiation	237
Capecitabine sensitivity	253
Carbamazepine ADRs	960
Carboplatin cytotoxicity and gene expression, in blood cell lines	656
Cardiac structure and function measurements (LV mass, internal dimensions, wall size, systolic dysfunction, aortic root size, left atrial size)	721
Cardiac structure and systolic function	405
Cardiovascular disease adverse events in renal patients treated with calcineurin inhibitors	577
Cardiovascular disease events in migraineurs	1090
Cardiovascular disease risk	1155
Carotenoid and tocopherol levels, in plasma	681
Carotid artery intimal-media thickness	481
Carotid atherosclerosis in HIV-infected men	758
Carotid intima-media thickness	365
Carotid intima-media thickness and plaque	1124
Carotid-femoral pulse wave velocity	1168
Cataracts (diabetic cataract)	352
Cataracts in T2D	873
Caudate volume	1031
Celiac disease	591
Celiac disease and Rheumatoid arthritis	1008
Cell-Free DNA, serum	127
Central cornea thickness	837
Central corneal thickness	235
Central corneal thickness and keratoconus	411
Cerebrospinal fluid tau	512
Ceruloplasmin levels, in serum	1171
Cervical cancer	479
Chemerin levels, in plasma	810
Chemotherapeutic response (cytabarine, 5’deoxyfluorouridine, carboplatin, cisplatin), in blood cell lines	1084
Chewing tobacco associated oral cancers	119
Childhood dental caries	1131
Chronic fatigue syndrome	1127
Chronic hepatitis B	699
Chronic hepatitis B progression	1145
Chronic kidney disease (CKD)	104
Chronic kidney disease (CKD) and kidney stones	877
Chronic kidney disease (CKD) and renal traits	232
Chronic obstructive pulmonary disease (COPD)	696
Chronic obstructive pulmonary disease (COPD), smoking behavior in	1065
Chronic widespread pain	289
Circulating 25-hydroxyvitamin D	351
Circulating PCSK9 Levels	97
Circulating estradiol, testosterone, and sex hormone-binding globulin in postmenopausal women	185
Circulating galectin-3 levels	331
Circulating haptoglobin levels	73
Circulating levels of plasminogen activator inhibitor-1 (PAI-1)	303
Circulating phospho- and sphingolipid concentrations	49
Circulating resistin levels	246
Circulating vitamin D levels in children with asthma	184
Cisplatin and carboplatin cytotoxicity, in blood cell lines	1110
Cisplatin cytotoxicity and gene expression, in blood cell lines	601
Cisplatin cytotoxicity, in blood cell lines	1089
Cisplatin-induced apoptosis and gene expression in blood cell lines	1054
Cleft lip (nonsyndromic cleft lip with or without cleft palate)	693
Cleft lip (nonsyndromic cleft lip)	251
Cleft lip, with or without cleft palate	830
Cleft palate (nonsyndromic cleft palate)	1050
Coagulation factor levels (FVII, FVIII, vWF), in plasma	809
Coagulation factors and fibrin factor levels and ischemic stroke	439
Coffee consumption	1003
Cognition (childhood intelligence)	428
Cognition (information processing speed)	955
Cognition (intelligence)	1102
Cognition (mathematical ability)	771
Cognition with anti-psychotic treatment	946
Cognition, early reading ability	599
Cognition, in schizophrenia	1062
Cognition, memory (episodic memory)	587
Cognition, memory (memory task performance)	581
Cognition, memory (short term memory)	770
Cognitive ability	621
Cognitive decline	701
Cognitive decline (nonpathological)	382
Cognitive decline (rate in Alzheimer’s disease)	499
Cognitive decline, age-related rate of	1163
Cognitive function, normal and in bipolar disorder and schizophrenia	1185
Cognitive impairment induced by topiramate	1176
Cognitive impairment without dementia	322
Cognitive performance	737
Colorectal adenomas	554
Colorectal and prostate cancer risk	134
Colorectal cancer	172
Colorectal cancer (drug response in metastatic colorectal cancer)	979
Colorectal cancer, efficacy of capecitabine, oxaliplatin and bevacizumab in metastatic colorectal cancer	1207
Colorectal cancer, severe oxaliplatin-induced chronic peripheral neuropathy in	1153
Common variable immunodeficiency	1029
Comorbid depressive syndrome and alcohol dependence	1166
Complement c3 and c4, serum	315
Compressive strength index (CSI) and appendicular lean mass (ALM)	291
Concentrations of cancer antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA) and _ fetoprotein (AFP)	413
Conduct Disorder	854
Confectionary intake	449
Congenital heart malformations (sporadic non-syndromic)	564
Congenital heart malformations (with septal, obstructive and cyanotic defects)	565
Copper, selenium and zinc levels	569
Corneal astigmatism	1191
Corneal curvature	296
Coronary artery and aortic artery calcification	511
Coronary artery calcification	445
Coronary artery disease	38
Coronary artery lesions in Kawasaki disease	553
Coronary artery stenosis	214
Coronary heart disease	126
Coronary heart disease (incident CHD)	1106
Coronary heart disease and related risk factors (LDL cholesterol, HDL cholesterol, hypertension, smoking, T2D)	1000
Coronary spasm	622
Cortical thickness, in brain	1098
Cortisol secretion, in saliva	997
Creatinine level, in serum	808
Creutzfeldt-Jakob disease	1187
Creutzfeldt-Jakob disease and other prion disease variants	1204
Creutzfeldt-Jakob disease variant	670
Crohn’s disease	75
Crohn’s disease (earlier required surgery)	545
Crohn’s disease and Celiac disease	988
Crohn’s disease and Psoriasis	110
Crohn’s disease and ulcerative colitis	349
Cystic fibrosis with meconium ileus	98
Cystic fibrosis, lung disease in	690
Cystic fibrosis, severity of	1048
Cytabarine toxicity in blood cell lines	503
Cytokine responses to Pam(3)CSK(4) (N-palmitoyl-S-dipalmitoylglyceryl Cys-Ser-(Lys)(4)) in blood	363
D-dimer levels, in plasma	1030
DNA methylation (allele-specific methylation), in blood cell lines	792
DNA methylation in blood	571
DNA methylation, in blood cell lines	982
Dabigatran plasma levels	470
Daunorubicin cytotoxicity and gene expression, in blood cell lines	637
Dehydroepiandrosterone sulphate (DHEAS) levels, in serum	1038
Dengue shock syndrome	1143
Dental caries	397
Dental caries in permanent dentition	332
Depressive affect	889
Diabetes in cystic fibrosis	549
Diabetic retinopathy	902
Differential cardiovascular event reduction by pravastatin therapy	181
Dilated cardiomyopathy	920
Disordered eating	517
Disordered gambling	222
Down’s Syndrome & Alzheimer’s disease	529
Drug response to interferon-beta therapy in multiple sclerosis (MS)	1032
Drug-induced liver injury (>200 drugs included)	295
Drug-induced liver injury with amoxicillin-clavulanate treatment	1043
Drug-induced liver injury with flucloxacillin treatment	713
Duodenal ulcer	66
Dupuytren’s disease	919
Dyslexia	378
Dyslexia (and mathematical ability)	456
ECG (Electrocardiogram measurements), PR interval	353
ECG (Electrocardiogram measurements), PR interval, QRS duration	702
ECG (Electrocardiogram measurements), PR interval, QRS interval, QTc interval	777
ECG (Electrocardiogram measurements), QRS duration	469
ECG (Electrocardiogram measurements), QRS interval	938
ECG (Electrocardiogram measurements), QT interval	209
ECG (Electrocardiogram measurements), QT interval change with iloperidone treatment in schizophrenia	647
ECG (Electrocardiogram measurements), QT interval prolongation with antipsychotic treatment	910
ECG (Electrocardiogram measurements), QT interval, PR interval, QRS duration, Heart rate variability	498
ECG (Electrocardiogram measurements), QT interval, PR interval, RR interval, Heart rate variability	615
ECG (Electrocardiogram measurements), RR interval	767
ECG (Electrocardiogram measurements), T-Peak to T-End interval	45
ECG (Electrocardiogram measurements), early repolarization pattern	190
ECG dimensions, brachial artery endothelial function, treadmill exercise responses	611
EEG measurements, in brain	828
Eating disorders	270
Economic and political preferences	153
Educational attainment	570
Effectiveness of iloperidone treatment in schizophrenia	646
Emphysema	885
End-stage renal disease	754
End-stage renal disease (ESRD), non-diabetic	848
Endometrial cancer	3
Endometriosis	343
Epilepsy	1181
Epilepsy (genetic generalized epilepsies)	284
Epilepsy (partial epilepsy)	843
Epirubicin-induced leukopenia in cancer patients	1095
Equol producers	103
Erectile dysfunction	913
Erectile dysfunction (ED) among prostate cancer patients treated with radiation therapy	312
Erectile dysfunction in Type 1 Diabetes	203
Eruption of permanent teeth	1130
Erythrocyte sedimentation rate	1071
Esophageal cancer (esophageal squamous cell carcinoma)	41
Esophageal cancer (esophageal squamous cell carcinoma) survival	540
Essential hypersomnia	541
Essential tremor	220
Etoposide cytotoxicity and gene expression in blood cell lines	590
Ewing sarcoma	42
Exercise participation	735
Eye color	480
FSH levels, anti-Mullerian hormone levels, in serum	1182
FVII levels	1061
FVIII levels, vWF levels	1097
Facial morphology	317
Facial photoaging	387
Factor XI Level and activated partial thromboplastin time (aPTT), in plasma	201
Familial hypercholesterolemia	294
Family chaos	633
Fasting glucose	7
Fasting glucose and insulin, and response to glucose in plasma	256
Fasting glucose, in plasma	638
Fasting glycemic traits and insulin resistance	158
Fasting insulin	1121
Fasting insulin processing and secretion in non-diabetics	401
Fasting insulin; insulin resistance	225
Fasting proinsulin levels in non-diabetics	1114
Fasting triglycerides, in plasma	668
Fatty acid levels, in plasma	430
Fatty liver and alanine aminotransferase levels (ALT)	476
Fenofibrate effects on circulating adiponectin	360
Ferritin and soluble transferrin receptor levels, in serum	959
Ferritin levels, in serum	671
Fetal growth and birth weight	821
Fetal hemoglobin levels	280
Fibrinogen (gamma fibrinogen)	1086
Fibrinogen levels	922
Fibrinogen levels, in plasma	762
Fibrinogen levels, in plasma, in women	763
Frontal cortex theta oscillations in alcoholism	142
Frontotemporal lobar degeneration with TDP-43 inclusions, in brain	790
Fuch’s corneal dystrophy	893
GABA concentration in the occipital cortex in children	977
Gains in maximal O(2) uptake (VO(2max)) after exposure to a standardized 20-wk exercise program	4
Gallbladder cancer	37
Gallstone disease	595
Gamma-glutamyl transferase (GGT) levels, in serum	1150
Gastric adenocarcinoma and esophageal squamous cell carcinoma	888
Gastric cancer (diffuse-type gastric cancer)	644
Gastric cancer (non-cardia gastric cancer)	1158
Gastric cancer, chemosensitivity to oxaliplatin, docetaxel and paclitaxel	1202
Gaucher disease	67
Gender	51
Gene expression and DNA methylation in 4 brain regions (pons, cerebellum, frontal cortex, temporal cortex)	838
Gene expression and DNA methylation in brain cerebellum	6
Gene expression in 3 blood cell types, in blood cell lines	723
Gene expression in CD4+ T cells in HIV-1 infected individuals	803
Gene expression in CD4+ lymphocytes	896
Gene expression in adipose and blood cells	632
Gene expression in basal cell carcinomas	1149
Gene expression in blood cell lines	603
Gene expression in blood cell lines (indel eQTLs)	477
Gene expression in blood cell lines (parent of origin effects)	271
Gene expression in blood cells	193
Gene expression in blood cells and fibroblasts	550
Gene expression in blood dendritic cells before and after exposure to Mycobacterium tuberculosis	14
Gene expression in brain (cerebellum and temporal cortex)	191
Gene expression in brain cortex	619
Gene expression in brain prefrontal cortex	816
Gene expression in breast tumors	130
Gene expression in cortex and peripheral blood mononuclear cells	688
Gene expression in cortex in Alzheimer’s disease and controls	700
Gene expression in cultured endothelial cells	797
Gene expression in endometrial cancer tumors	976
Gene expression in intestine	474
Gene expression in introns and nonsense-mediated decay in blood cell lines	869
Gene expression in leukemia cells and normal leukocytes	643
Gene expression in leukocytes	676
Gene expression in liver	93
Gene expression in monocytes	840
Gene expression in muscle	990
Gene expression in osteoblasts	726
Gene expression in osteoblasts and blood cell lines	727
Gene expression in skin cells	953
Gene expression in skin cells, adipose and blood cell lines, in women	992
Gene expression in sputum	1134
Gene expression in stomach, liver and adipose	1047
Gene expression in treated osteoblasts	987
Gene expression networks in adipose and blood	62
Gene expression of microRNA (miRNA) in abdominal and gluteal adipose	1177
Gene expression of microRNA (miRNA) in blood cell lines	1067
Gene expression of microRNA (miRNA) in fibroblasts	958
Gene expression of microRNAs (miRNA) and other small RNAs in adipose tissue	161
Gene expression of microRNAs (miRNA) in blood cell lines	174
Gene expression splicing and processing in glioblastoma cell lines	99
Glaucoma	600
Glaucoma (central corneal thickness in primary open-angle glaucoma)	178
Glaucoma (intraocular pressure and primary open-angle glaucoma)	156
Glaucoma (normal tension glaucoma)	229
Glaucoma (optic nerve degeneration in glaucoma)	82
Glaucoma, normal tension	819
Glaucoma, open angle	1035
Glaucoma, open-angle	898
Glaucoma, primary angle closure	275
Glaucoma, primary open-angle	78
Glaucoma, primary open-angle and age-related macular degeneration	502
Glaucoma-related traits	1164
Glioblastoma	101
Glioma	258
Glomerulosclerosis	874
Glucose homeostasis traits (fasting glucose, fasting insulin, HOMA-B, HOMA-IR)	783
Gout	128
Grave’s disease	531
HDL cholesterol	318
HDL cholesterol (high/low extremes)	1206
HDL cholesterol and triglyceride levels, in plasma	626
HDL particle features	986
HIV-1 (efavirenz pharmacokinetics)	337
HIV-1 acquisition	52
HIV-1 acquisition and viral load at set point	1198
HIV-1 control and progression	928
HIV-1 infection (development of cross-reacting neutralizing antibodies)	434
HIV-1 non-progression	883
HIV-1 non-progression (untreated, long term)	15
HIV-1 progression to AIDS and death	1100
HIV-1 replication	1005
HIV-1 resistance in highly exposed individuals with hemophilia A	433
HIV-1 susceptibility	963
HIV-1 viral load at set point	772
HIV-1, mother to child transmission	839
HIV-1/AIDS progression	597
HIV-associated neurocognitive disorders	167
Hair color	143
Hair color (red)	1189
Hair morphology	750
Hair, eye and skin pigmentation	348
Handedness	535
Health and aging, CVD and cancer age of onset	1196
Hearing function	1028
Hearing impairment, age related	655
Hearing impairment, age-related	780
Heart failure (incident risk)	833
Heart failure mortality among adults	826
Heart failure risk and mortality	1001
Heart failure with dilated cardiomyopathy	1018
Heart rate	521
Heart rate response to exercise training	1197
Heart rate, resting	374
Height	466
Height (in Pygmies)	155
Height (pubertal growth)	462
Height and body mass index	824
Height and body mass index (BMI)	299
Height, pubertal growth in	825
Helicobacter pylori serologic status	543
Hematological toxicities in cancer patients receiving gemcitabine therapy	30
Hemoglobin (HbA1c, glycated hemoglobin levels)	27
Hemoglobin A2 (HbA2) levels	323
Hemoglobin concentration	849
Hemoglobin levels	744
Hemoglobin levels, fetal hemoglobin levels in adults (HbF) by F cell levels	602
Hemoglobin levels, in serum	745
Hepatic adverse events with thrombin inhibitor ximelagatran	588
Hepatitis B virus (HBV) clearance	210
Hepatitis C infection	776
Hepatitis C virus-induced liver fibrosis	245
Hepatitis C-induced liver cirrhosis	419
Hepcidin, in serum	1092
Heroin addiction vulnerability	842
Hippocampal and intracranial volumes	120
Hippocampal volume	124
Hippocampal volume, total cerebral volume, white matter hyperintensities in Alzheimer disease patients	213
Hirschsprung’s disease	684
Hoarding behavior	991
Homocysteine levels, in plasma	717
Homocysteine levels, in plasma, in women	764
Human intelligence or general cognitive ability in ADHD families	94
Human papilloma virus seropositivity	1117
Huntington’s disease	65
Hypertension	107
Hypertension (essential hypertension)	1200
Hypertension and blood pressure traits	64
Hypertension with short sleep duration	40
Hypertension, salt-sensitive	1045
Hypertriglyceridemia	867
Hypertropic cardiomyopathy	396
Hypospadias	947
Hypothyroidism	116
Hypothyroidism and thyroid conditions	1137
IFN_ response to smallpox vaccine	177
IL-6 levels	486
IL-6, erythrocyte sedimentation rate, MCP-1, C-reactive protein (CRP)	29
Idiopathic premature ovarian failure	1138
Idiopathic pulmonary fibrosis	661
IgA deficiency (selective IgA deficiency)	879
IgA levels, in serum	252
IgE (total IgE) concentrations, plasma	1172
IgE levels, in serum	359
IgG index in multiple sclerosis	388
IgG level, in serum	183
IgM, in serum	347
Ileal carcinoids	957
Implantable cardioverter-defibrillator activation with life-threatening arrhythmias	18
Infant head circumference	122
Infantile hypertrophic pyloric stenosis	34
Inflammatory demyelinating disease	746
Insulin like growth factor levels	973
Insulin response	706
Insulin traits (Insulin sensitivity index (ISI), Insulin disposition index (IDI))	753
Inter-adventitial common carotid artery diameter	393
Interferon-related cytopenia in hepatitis C	1074
Interleukin levels (IL10, IL1Ra, IL6), in plasma	1203
Interleukin levels (IL18 levels)	789
Intracranial aneurysm	24
Intracranial aneurysm, sporadic	293
Intracranial volume	121
Irinotecan-related severe toxicities in patients with advanced non-small-cell lung cancer	179
Iris color	630
Iris patterns	1108
Iron deficiency	1026
Iron levels, in serum	749
Job-related exhaustion	533
Joint damage in rheumatoid arthritis	556
Kawasaki disease	91
Keloid	886
Keratoconus	1135
Kidney function and endocrine traits (urinary albumin, creatinine, cystatin-C, thyroid stimulating hormone), in serum and in urine	604
Kidney stone disease	718
LDL cholesterol	333
LDL cholesterol in genotype-1 chronic hepatitis C	118
LDL cholesterol, coronary artery calcification	929
Lactate dehydrogenase, in serum	923
Lamotrigine- and phenytoin-induced hypersensitivity reactions	61
Late rectal bleeding following chemotherapy for prostate cancer	568
Lean body mass	692
Lean body mass; age at menarche	212
Left ventricular (LV) wall thickness	971
Left ventricular hypertrophy by electrocardiogram (ECG)	1104
Left ventricular mass	710
Lentiform nucleus volume	266
Leprosy	760
Leukemia (B-cell chronic lymphocytic leukemia)	654
Leukemia (acute lymphoblastic leukemia) (ALL)	1173
Leukemia (childhood acute lymphoblastic leukemia relapse)	309
Leukemia (childhood acute lymphoblastic leukemia)	492
Leukemia (childhood acute lymphoblastic leukemia) (ALL)	875
Leukemia (chronic lymphocytic leukemia)	200
Leukemia (chronic myeloid leukemia)	1039
Leukemia (pediatric acute lymphoblastic leukemia)	730
Leukemia, T-cell recognition in patients	934
Lipid level measurements	170
Lipid level measurements, blood pressure, albumin, CRP levels, fibrinogen, uric acid, white cell count, FII, FIII, vWF, glucose, insulin, waist circumference	1154
Lipid level measurements, in plasma	743
Lipid measurements and other quantitative traits (in serum: sodium, potassium, chloride, urea, creatinine, calcium, albumin, GGT, glucose, urate, total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides; in urine: sodium, potassium, creatinine, albumin)	625
Lipoprotein A [Lp(a)] levels and coronary artery disease	769
Lipoprotein A [Lp(a)] levels in plasma	1046
Lipoprotein A [Lp(a)] levels in plasma, cardiovascular disease and mortality	1119
Lipoprotein A [Lp(a)] levels, in plasma	675
Lipoprotein-associated phospholipase A2 mass and activity; coronary heart disease	1144
Liver cancer (hepatocellular carcinoma)	944
Liver cancer (hepatocellular carcinoma) in patients with chronic hepatitis B virus infection	233
Liver cancer (hepatocellular carcinoma), progresstion to with chronic viral hepatitis	1077
Liver enzyme concentrations (alanine aminotransaminase, alkaline phosphatase, gamme-glutamyl transferase), in plasma	666
Long chain n-3 polyunsaturated fatty acid levels, in plasma	1105
Longevity	22
Longevity and age-related phenotypes (age at menopause, walking speed, biological age)	607
Longevity, exceptional	857
Low thyroid-stimulation hormone (TSH) levels and thyroid cancer	19
Lp-PLA(2) mass and activity at baseline and after 12 months of rosuvastatin therapy	346
Lp-PLA2 activity and mass	832
Lumbar disc degeneration	304
Lumiracoxib-related liver injury	866
Lung adenocarcinoma	231
Lung cancer	264
Lung cancer (DNA-repair capacity in lung cancer)	344
Lung cancer (interstitial lung disease in gefitinib-treated non-small-cell lung cancer)	1093
Lung cancer (lung adenocarcinoma stage)	980
Lung cancer (lung adenocarcinoma)	653
Lung cancer (non-small cell lung cancer)	906
Lung cancer (non-small cell lung cancer), hypertriglyceridemia with bexarotene treatment of	1079
Lung cancer (squamous cell carcinoma)	424
Lung cancer in never smokers	812
Lung cancer in never-smoking women	355
Lung cancer, non-small cell lung cancer prognosis	357
Lung cancer, prognosis in advanced non-small cell lung carcinoma with platinum-based chemotherapy	255
Lung cancer, small-cell	949
Lung cancer, smokers with versus smokers without	579
Lung cancer, survival in advanced non-small cell lung cancer with carboplatin and paclitaxel treatment	939
Lung cancer, survival in non-small cell lung carcinoma in never smokers	560
Lung cancer, survival in non-small cell lung carcinoma with platinum-based chemotherapy	1024
Lung cancer, survival in small cell lung cancer treated with irinitecan and cisplatin	478
Lung function	408
Lung function decline in adults with and without asthma	80
Lung function decline in mild to moderate chronic obstructive pulmonary disease	301
Lung function in textile workers with endotoxin exposure	495
Lung function phenotypes	616
Lung function with asthma	504
Lupus (neonatal lupus)	872
Lymphoma (Hodgkin’s lymphoma and Epstein-Barr virus status-defined subgroups)	25
Lymphoma (Hodgkin’s lymphoma)	925
Lymphoma (diffuse large B-cell lymphoma)	1020
Lymphoma (follicular lymphoma)	314
Lymphoma (non-Hodgkin lymphoma)	722
Lymphoma subtypes	426
Lypmhoma (nodular sclerosis Hodgkin lymphoma)	1174
Macronutrient intake	432
Magnesium levels, in serum	882
Major depression	102
Major depression (age of onset)	272
Major depression (suicidal thoughts and behavior)	1083
Major depression, gender differences	1051
Major depression, recurrent	841
Major depression, recurrent early onset	786
Major depression, side-effects of antidepressant treatment	1160
Major depressive disorder	361
Major mood disorder	781
Malaria	567
Malaria (severe malaria)	263
Malarial infection	868
Male fertility (family size, birth rate)	171
Malignant pleural mesothelioma	534
Mammographic density	985
Maternally-mediated genetic effects and parent-of-origin effects on risk of orofacial clefting	77
Matrix metalloproteinase (MMP-1) levels, in serum	768
Meningioma	1096
Meningococcal disease	880
Mercaptopurine toxicity in acute lymphoblastic leukemia	248
Metabolic response to hydrochlorothiazide	447
Metabolic side effects to antipsychotic drugs	802
Metabolic syndrome (HDL cholesterol, plasma glucose, T2D, waist to hip ratio, diastolic blood pressure)	881
Metabolic syndrome (HDL cholesterol, triglycerides, plasma glucose, waist circumference, systolic and diastolic blood pressure)	72
Metabolic syndrome (waist circumference, fasting glucose, HDL cholesterol, triglycerides, blood pressure)	1009
Metabolic traits (triglycerides, HDL cholesterol, LDL cholesterol, fasting plasma glucose, albumin, blood urea nitrogen, gamma-glutaryl transpeptidase, alanine aminotransferase, aspartate aminotransferase)	1125
Metabolite concentrations, gender-specific, in serum	1111
Metabolites and sphingolipids, circulating concentrations	742
Metabolites related insulin sensitivity in non-diabetics, in plasma	437
Metabolites, in plasma	1129
Metabolites, in serum	26
Metabolites, in serum in men	667
Metabolites, in serum in prostate cancer	407
Metabolites, in urine	1044
Methamphetamine dependence	631
Methamphetamine-induced psychosis and schizophrenia	527
Methotrexate clearance in acute lymphoblastic leukemia	390
Midregional-proadrenomedullin and C-terminal-pro-endothelin-1, in plasma	438
Migraine	187
Migraine without aura	189
Minor histocompatibility antigenicity in blood cell lines	624
Monoamine metabolite levels in cerebrospinal fluid	417
Monocyte chemoattractant protein-1 (MCP-1) in obese children	311
Monocyte colony-forming units (CFUs)	1027
Moyamoya disease	927
Multiple cancer types (lung cancer, noncardia gastric cancer, esophageal squamous-cell carcinoma)	342
Multiple myeloma	1184
Multiple sclerosis	157
Multiple sclerosis (brain lesion distribution in multiple sclerosis)	450
Multiple sclerosis (oligoclonal bands in multiple sclerosis)	473
Multiple sclerosis, glutamate concentrations in brains in	891
Multiple sclerosis, progressive	96
Multiple sclerosis, severity	1056
Multiple traits (Alzheimer’s disease, progressive supranuclear palsy, sudden infant death with dysgenesis of the testes syndrome)	583
Multiple traits (bipolar disorder, coronary artery disease, Crohn’s disease, rheumatoid arthritis, T1D, T2D, hypertension)	31
Multiple traits (coronary heart disease, T2D, LDL cholesterol, HDL cholesterol)	1040
Multiple traits (eye color, freckles, hair color, hair curl, asparagus anosmia, photic sneeze reflex, handedness, footedness, attached earlobes, dental work, myopia, taste preference, motion sickness, astigmatism)	856
Multiple traits (lipids, glucose, obesity, blood pressure)	586
Myasthenia gravis	330
Myeloperoxidase levels	532
Myeloproliferative neoplasm	694
Myocardial infarction	573
Myopia	471
Myopia (high myopia)	325
Myopia and refractive errors	446
Myopia, pathological	740
N-glycan levels, in plasma	969
N-glycosylation of IgG in plasma	440
NT-proBNP levels	984
Narcolepsy	467
Narcolepsy with cataplexy	483
Nasion Position	44
Nasopharyngeal carcinoma	384
Natural anticoagulant inhibitors and protein C anticoagulant pathway in venous thrombosis, in plasma	89
Nephrolithiasis	70
Nephropathy	1012
Nephropathy (Immunoglobulin A (IgA) nephropathy)	574
Nephropathy (diabetic nephropathy)	207
Nephropathy, idiopathic membranous	998
Nephrotic syndrome, acquired	1017
Neuroblastoma	282
Neuroblastoma (in low-risk cases)	1015
Neurodevelopmental phenotypes at four-year follow-up following cardiac surgery in infancy	327
Neurofibrillary tangles in non-demented elderly subjects, in brain	834
Neuropsychological treatments and metabolic and cardiovascular risk factors (HDL cholesterol, BMI)	76
Neuroticism	389
Neutropenia or leukopenia in response to chemotherapeutic agents	542
Nevirapine-induced rash	1099
Nevus count	859
Nicotine and alcohol dependence	791
Nicotine dependence	59
Nicotine dependence and smoking initiation	1148
Nicotine dependence, cigarettes per day	131
Nicotine dependence, relapse	505
Nicotine smoking	683
Nicotine, smoking behavior	241
Nicotine, smoking cessation	645
Nicotine, smoking quantity	326
Non-Albumin, albumin and total protein, serum	144
Non-obstructive azoospermia	1201
Nonalcoholic fatty liver disease	206
Nonobstructive Azoospermia	136
Nonsyndromic sagittal craniosynostosis	367
Nonsyndromic striae distensae	536
Obesity traits (body mass index (BMI), total fat mass), blood pressure	1151
Obesity traits in postmenopausal women	379
Obesity, childhood	111
Obesity, early onset	514
Obesity, early onset extreme	623
Obesity, early onset in children and adolescents	829
Obesity, extreme	584
Obesity, menopause	1013
Obesity-related traits	678
Obesity-related traits (body mass index (BMI), waist circumference, weight change, height, adiposity)	610
Obesity-related traits (body mass index (BMI), weight)	669
Obesity-related traits (body mass index (BMI), weight, hip circumference)	598
Obesity-related traits (body mass index (BMI), weight, hip circumference, waist circumference, brachial circumference, height)	691
Obesity-related traits, body mass index (BMI), blood pressure	1208
Obsessive-compulsive disorder	259
Ocular axial length and high myopia	192
Opiates addiction	497
Opioid sensitivity in healthy subjects	375
Optic disc area	994
Optic disc parameters	851
Optic nerve assessment	823
Osteoarthritis	218
Osteoarthritis (hand)	715
Osteoarthritis (hip) and joint-space width (cartilage thickness)	152
Osteoarthritis (knee and hip)	964
Osteoarthritis (knee)	578
Osteoarthritis (knee), in women	642
Osteonecrosis of the jaw	650
Osteoporosis	732
Osteoporotic fracture	425
Osteoporotic fractures (hip)	1087
Otitis media	350
Otosclerosis	689
Ovarian cancer	230
Ovarian cancer survival	262
Ovarian failure (premature ovarian failure)	716
Ovarian follicle number and menopause	198
Ovarian response to FSH stimulation in IVF	996
Oxidized LDL cholesterol levels	395
Paclitaxel sensitivity in NCI60 cancer cell lines	995
Paclitaxel-induced sensory peripheral neuropathy	247
Paget’s disease of bone	831
Pain relief with opioid treatment	1052
Pancreatic adenocarcinoma	372
Pancreatic cancer	724
Pancreatic cancer and survival	180
Pancreatic cancer, survival with gemcitabine treatment	1190
Panic disorder	362
Paraoxonase activity	368
Paraoxonase and arylesterase activities in serum	298
Parkinson’s disease	87
Parkinson’s disease (early onset)	269
Parkinson’s disease motor and cognitive outcomes	175
Parkinson’s disease, age at onset	739
Parkinsonism in schizophrenia patients, antipsychotic-induced	728
Pediatric eosinophilic esophagitis	806
Pelvic organ prolapse	1178
Pemphigus vulgaris	85
Percent mammographic density	133
Perception of the odorants androstenone and galaxolide	53
Pericardial fat	162
Periodontal pathogen colonization	199
Periodontitis	751
Periodontitis (chronic periodontitis)	468
Peripartum cardiomyopathy	1058
Peripheral artery disease (PAD)	860
Personality (temperament scales)	878
Personality disorders and adult ADHD	297
Personality traits	544
Personality traits and mood states	168
Personality traits and mood states (depressive affects)	409
Phosphorous concentrations, in serum	852
Physical activity	1094
Phytosterol levels, in serum	847
Pit-and-fissure- and smooth-surface carries	472
Placental abruption	381
Plasma uric acid level in obese and never-overweight individuals	559
Plasminogen activator inhibitor-1 (PAI1) levels, in plasma	853
Platelet CD36 surface expression	1022
Platelet aggregation	844
Platelet aggregation, pre-aspirin and post-aspirin	846
Platelet count (PLT) and platelet volume (MPV)	79
Platelet reactivity in patients with type 2 diabetes during acetylsalicylic acid (ASA) treatment	329
Platelet response, antiplatelets and cardiovascular outcomes	443
Platelet thrombus formation under high shear stress	140
Platelet volume (MPV)	672
Podoconiosis	95
Polycystic ovary syndrome	257
Polycystic ovary syndrome through obesity-related condition	286
Polysubstance addiction	582
Polyunsaturated fatty acid levels, in plasma	677
Post-operative nausea and vomiting	1068
Post-traumatic stress disorder (PTSD)	254
Pre-eclampsia	84
Premature ovarian failure	39
Preoperative chemoradiation therapy response in rectal cancer	482
Primary biliary cirrhosis	278
Primary nonsyndromic vesicoureteric reflex	756
Primary rhegmatogenous retinal detachment	523
Primary sclerosing cholangitis	129
Primary sclerosing cholangitis and ulcerative colitis	236
Primary tooth development during infancy	804
Primary tooth eruption	561
Progranulin levels, in plasma	941
Progressive supranuclear palsy	585
Prostate cancer	55
Prostate cancer and Type II Diabetes Mellitus	593
Prostate cancer gene 3 (PCA3) mRNA levels	508
Prostate cancer mortality	921
Prostate cancer, advanced	1082
Prostate cancer, aggressive	674
Prostate-specific antigen	404
Prostate-specific antigen (free-to-total, %fPSA), in serum	429
Prostate-specific antigen levels in men, in serum	962
Protein C levels and protein S levels, in plasma	1205
Protein expression in blood cell lines	2
Protein expression in blood plasma	163
Protein quantitative traits (42 protein levels in fasting serum and plasma: including C-reactive protein (CRP), IL6R, IL18, Lipoprotein A (LPA), GGT, IL1RN, TNFa, adiponectin, albumin, alkaline phosphatase, fibrinogen, ferritin, hemoglobin, insulin, leptin, SHBG, transferrin, thyroid stimulating hormone, MCP1)	641
Protein-C levels, in plasma	890
Pseudoexfoliation syndrome	892
Psoriasis	354
Psoriasis and psoriatic arthritis	917
Psoriasis risk prediction	972
Psoriatic arthritis	1194
Psychiatric disorders (Autism, ADHD, Bipolar disorder, Schizophrenia, Depression)	464
Pulmonary arterial hypertension	484
Pulmonary fibrosis (fibrotic idiopathic interstitial pneumonias)	522
Pulse pressure	219
Pulse pressure and mean arterial pressure	1126
Radiation-induced damage on blood cell lines	911
Recipient kidney allograft function	459
Recombination rate	629
Refractive errors	475
Renal cancer	376
Renal cell carcinoma	956
Renal cell carcinoma in stem cell transplantation	1081
Renal sinus fat accumulation	1161
Response to TNF_ inhibitors in patients with rheumatoid arthritis	154
Response to anti-TNF treatment in arthritis	509
Response to antidepressants	738
Response to antidepressants (citalopram-induced side effects)	217
Response to antidepressants (escitaloprim, nortriptyline)	817
Response to antidepressants (selective serotonin reuptake inhibitors (SSRIs))	268
Response to antidepressants (sustained antidepressant response)	572
Response to antipsychotics	748
Response to antipsychotics (olanzapine, quetiapine, risperidone, ziprasidone, perhpenazine)	733
Response to antipsychotics in schizophrenia (treatment refractory)	108
Response to atorvastatin	57
Response to cholinesterase inhibitors in Alzheimer’s disease	435
Response to citalopram in major depressive disorder	861
Response to clopidogrel (anti-platelet), variation in	731
Response to fenofibrate	261
Response to fenofibrate treatment on inflammation biomarkers	9
Response to gemcitabine or arabinosylcytosin in blood cell lines	752
Response to glucocorticoid therapy in asthma	1140
Response to glucose and GLP-1-infusion on insulin secretion	552
Response to glucose and insulin	782
Response to glucose in plasma	520
Response to influenza vaccination	113
Response to interferon beta in multiple sclerosis	627
Response to lithium treatment in bipolar disorder	708
Response to metformin	968
Response to methylphenidate treatment	954
Response to pegylated interferon-alpha and ribavirin treatment in chronic hepatitis C	729
Response to platinum-based chemotherapy in small-cell lung cancer	836
Response to simvastatin	341
Response to statin treatment (atorvastatin), change in cholesterol levels	766
Response to statin treatment (simvastatin, pravastatin, atorvastatin), change in cholesterol levels	813
Response to statins	43
Response to statins (simvastatin, lovastatin) in NCI60 cancer cell lines	1025
Response to thiazide diuretic (hydrochlorothiazide)	649
Response to tocilizumab for the treatment of rheumatoid arthritis	114
Response to treatment (fludarabine, chlorambucil, combination) in chronic lymphocytic leukemia	1057
Response to treatment and survival on dialysis in T2D patients	1041
Response to treatment in pediatric acute lymphoblastic leukemia	680
Response to treatment with analgesics (midazolam, lidocaine)	687
Restenosis after percutaneous coronary intervention (PCI)	741
Resting heart rate	865
Restless leg syndrome	596
Retinal vascular caliber	932
Retinopathy (diabetic retinopathy)	5
Retinopathy (non-diabetic)	444
Rheumatoid arthritis	92
Rheumatoid arthritis, anti-TNF response in	933
Rheumatoid arthritis, cyclic citrullinated peptide (CCP) positive	618
SSRI/SNRI-induced sexual dysfunction in depression	90
Salmonella induced pyroptosis in B-lymphoblastoid cell lines	244
Sarcoidosis	243
Sasang constitution	69
Schizophrenia	58
Schizophrenia and bipolar disorder	370
Schizophrenia and bipolar disorder (joint pleiotropy)	538
Schizophrenia and brain fMRI during sensorimotor tasks	88
Schizophrenia symptoms (positive, negative/disorganized, mood)	385
Schizophrenia with formal thought disorder (disorganized speech)	173
Schizophrenia with negative symptoms	441
Schizophrenia, age at onset	1066
Schizophrenia, bipolar disorder and depression	887
Schizophrenia, treatment response to risperidone	747
Second to fourth digit length ratio	399
Selenium concentration, serum	558
Selenium resistance in NCI60 cancer cell lines	895
Self-employment	526
Self-rated health	884
Severity of response to H1N1 infection	195
Sex hormone-binding globulin (SHBG) concentrations	239
Sexual dysfunction (female)	125
Sick sinus syndrome	1007
Sickle cell anemia (haemolytic anemia)	448
Sickle cell anemia total bilirubin and cholelithiasis risk	145
Sickle cell anemia with elevated tricuspid regurgitation velocity	188
Sickle cell anemia, severity	761
Skin cancer (basal cell carcinoma)	665
Skin cancer (cutaneous basal cell carcinoma and squamous cell carcinoma)	1072
Skin cancer (cutaneous basal cell carcinoma)	1133
Skin cancer (cutaneous melanoma)	1128
Skin cancer (cutaneous nevi and melanoma risk)	1023
Skin cancer (malignant melanoma)	575
Skin cancer (melanoma)	465
Skin naphthyl-keratin adduct levels in workers exposed to naphthalene	68
Skin pigmentation	620
Skin pigmentation and skin cancer	507
Sleep and circadian phenotypes	617
Sleep apnea	366
Sleep duration	1179
Smallpox vaccine cytokine responses	164
Soluble CD14	369
Soluble E-selectin levels, in plasma, in women	788
Soluble E-selectin levels, in serum	736
Soluble ICAM-1 levels, in women	1037
Soluble ICAM1 (sICAM) levels, in plasma, in women	651
Soluble P-selectin levels and soluble ICAM-1 levles	796
Soluble leptin receptor (sOB-R) levels, in plasma	795
Spine bone size	383
Spontaneous resolution of hepatitis C virus	453
Statin-Induced reductions in C-Reactive Protein	11
Statin-induced (cerivastatin) rhabdomyolysis	1010
Statin-induced myopathy	652
Stevens-Johnson syndrome	916
Stevens-Johnson syndrome and toxic epidermal necrolysis	974
Stressful life events	391
Stroke	551
Stroke in sickle cell anemia patients	455
Stroke, atherothrombotic	773
Stroke, ischemic	320
Stroke, ischemic stroke and large artery atherosclerosis	281
Stroke, large vessel ischemic	33
Stroke, pediatric	302
Subclinical atherosclerosis (coronary artery calcium, abdominal artery calcium, ankle-brachial index, carotid intimal media thickness)	613
Subcutaneous adipose tissue volume in HIV-infected men	1118
Subjective response to d-amphetamine in healthy subjects	288
Substance dependence	1101
Sudden cardiac arrest	818
Sudden cardiac death	524
Suicidal ideation with antidepressant (escitaloprim, nortriptyline) treatment	908
Suicidal ideation with antidepressant treatment	1156
Suicidal ideation with citalopram treatment	734
Suicide attempts in bipolar disorder patients	926
Suicide, with and without major depression	1165
Systemic lupus erythematosus	28
Systemic lupus erythematosus, in women	628
Systemic lupus erythematosus, rheumatoid arthritis	328
Systemic lupus erythematosus, serologic and cytokine (interferon gamma) profiles in serum in	870
Systemic sclerosis	755
Tamoxifen sensitivity and gene expression in blood cell lines	488
Tamsulosin hydrochloride pharmacokinetics in benign prostatic hyperplasia	364
Tanning ability	698
Tardive dyskinesia	915
Taste perception	876
Tau biomarkers (Ab1-42, t-tau, p-tau181p), in cerebrospinal fluid (CSF)	950
Tau protein levels, in cerebrospinal fluid (CSF)	912
Taxane response in lymphoblastoid cell lines and taxane-treated lung cancer survival	308
Telomere length	306
Telomere length (mean telomere length)	501
Temozolomide response in lymphoblastoid cell lines	324
Temperament	240
Temperament in bipolar disorder	54
Temporal lobe volumes	805
Testicular dysgenesis syndrome	1188
Testicular germ cell carcinoma	712
Testicular germ cell tumor	547
Testosterone concentration in men, in serum	1142
Tetralogy of Fallot	412
Theta band oscillations and alcohol dependence	967
Thiazolidinedione-induced edema	863
Thoracic aortic aneurysms and aortic dissections	1123
Thyroid cancer	686
Thyroid cancer, radiation-related	815
Thyroid function	117
Thyroid volume and goiter risk	1042
Thyrotoxic hypokalemic periodic paralysis	71
Thyrotoxic periodic paralysis	250
Thyrotropin and thyroid function, in serum	894
Total body bone mineral density	226
Tourette’s syndrome	260
Toxicity after 5-fluorouracil or FOLFOX administration for colorectal cancer	35
Trabecular and cortical volumetric BMD, bone microstructure	461
Transferrin glycosylation, in serum	1059
Transmission distortion	60
Treatment responses in severe sepsis	36
Tremors, antipsychotic-induced	1139
Trichophyton tonsurans susceptibility	204
Triglycerides levels, in serum, in men	1195
Troponin levels (highly sensitive cardiac troponin-T levels), in plasma	394
Tuberculosis	17
Tuberculosis (early age of onset)	141
Type I Diabetes	316
Type I Diabetes, autoantibody positivity in	1107
Type I Diabetes, serum ZnT8 autoantibody levels in	132
Type II Diabetes Mellitus	16
Type II Diabetes Mellitus (gestational diabetes)	13
Type II Diabetes Mellitus and gene expression in muscle and adipose tissue	1055
Type II Diabetes Mellitus and prostate cancer	516
Ulcerative colitis	491
Ulcerative colitis and Crohn’s disease	1070
Ulcerative colitis, refractory	900
Upper airway tract cancer	1016
Urate (in serum), gout	400
Uric acid levels, in serum	10
Urinary bladder cancer	659
Urinary symptoms following radiotherapy for prostate cancer	436
Uterine fibroids (benign tumors)	1019
Uterine leiomyomata	319
VLDL, LDL and HDL cholesterol particle size	398
Valvular calcification and aortic stenosis	442
Vascular dementia	1180
Vascular endothelial growth factor (VEGF) levels, in serum	1085
Venous thromboembolism	182
Venous thrombosis	186
Ventricular dysfunction after primary coronary artery bypass graft surgery	1136
Ventricular fibrillation in acute MI	862
Visceral adipose tissue-derived serine protease inhibitor (vaspin)	267
Visceral leishmaniasis	410
Visual cortical surface area	46
Vitamin A (retinol), circulating levels	1116
Vitamin B12 levels, in plasma, in women	657
Vitamin B12, in serum	56
Vitamin D (25(OH)D), circulating levels	827
Vitamin D concentrations	850
Vitamin E, circulating levels	1078
Vitiligo	287
Vitiligo, generalized	148
Waist circumference	639
Waist:hip ratio	914
Warfarin dose	648
Warfarin dose (acenocoumarol)	720
Warfarin responsiveness	897
Warfarin responsiveness (phenprocoumon)	935
Weight loss after Roux-en-Y gastric bypass surgery	537
Wheezing after influenza vaccination in children	1011
Wilms tumor	138
YKL-40 levels, in serum	634
_(2) -GPI levels, in plasma	406
_2-adrenoceptor-mediated vasoconstriction	423
t(11;14)(q13;q32) translocation in multiple myeloma	485
vWF levels, in plasma	403

PhenoCat

The following 179 phenotype categories can be searched by alias or category:

Category	ID	Alias
Addiction	81	addiction
Adipose-related	84	adipose
Adverse drug reaction (ADR)	49	adr
Age-related macular degeneration (ARMD)	133	armd
Aging	44	aging
Alcohol	115	alcohol
Allergy	145	allergy
Alzheimer’s disease	99	alzheimers
Amyotrophic lateral sclerosis (ALS)	112	als
Anemia	127	anemia
Aneurysm	105	aneurysm
Anthrax	131	anthrax
Anthropometric	72	anthropometric
Arterial	106	arterial
Arthritis	61	arthritis
Asthma	1	asthma
Atrial fibrillation	123	afib
Attention-deficit/hyperactivity disorder (ADHD)	108	adhd
Autism	95	autism
Basal cell cancer	176	basal_cell_cancer
Behavioral	23	behavioral
Bipolar disorder	24	bipolar
Bladder cancer	165	bladder_cancer
Bleeding disorder	153	bleeding_disorder
Blood	177	blood
Blood cancer	52	blood_cancer
Blood pressure	59	bp
Blood-related	8	blood
Body mass index	75	bmi
Bone cancer	160	bone_cancer
Bone-related	47	bone
Brain cancer	111	brain_cancer
Breast cancer	25	breast_cancer
C-reactive protein (CRP)	29	crp
CVD	178	cvd
CVD risk factor (CVD RF)	27	cvd_risk
Calcium	155	calcium
Cancer	10	cancer
Cancer-related	86	cancer_related
Cardiomyopathy	152	cardiomyopathy
Cardiovascular disease (CVD)	38	cvd
Celiac disease	162	celiac
Cell line	9	cell_line
Cervical cancer	158	cervical_cancer
Chronic kidney disease	113	chronic_kidney_disease
Chronic lung disease	3	chronic_lung_disease
Chronic obstructive pulmonary disease (COPD)	140	copd
Cognition	107	cognition
Colorectal cancer	66	colorectal_cancer
Congenital	96	congenital
Coronary heart disease (CHD)	120	chd
Crohn’s disease	63	crohns
Cystic fibrosis	110	cf
Cytotoxicity	169	cytotoxicity
Dental	48	dental
Depression	104	depression
Developmental	43	developmental
Diet-related	100	diet
Drug response	26	drug_response
Drug treatment	179	drug_treatment
Emphysema	168	emphysema
Endometrial cancer	11	endometrial_cancer
Environment	87	environment
Epigenetics	22	epigenetics
Epilepsy	83	epilepsy
Esophageal cancer	70	espohageal_cancer
Eye-related	17	eye
Female	14	female
Gallbladder cancer	67	gallbladder_cancer
Gallstones	125	gallstones
Gastric cancer	147	gastric_cancer
Gastrointestinal	65	gi
Gender	13	gender
Gene expression (RNA)	19	rna_expression
Gene expression (protein)	6	gene_protein_expression
General health	167	health
Glaucoma	97	glaucoma
Graft-versus-host	164	graft_v_host
Grave’s disease	94	graves
HIV/AIDS	35	hiv
Hair	124	hair
Hearing	148	hearing
Heart	37	heart
Heart failure	163	heart_failure
Heart rate	149	heart_rate
Height	128	height
Hemophilia	154	hemophilia
Hepatic	89	hepatic
Hepatitis	118	hepatitis
Hormonal	42	hormonal
Huntington’s disease	88	huntingtons
Imaging	73	imaging
Immune-related	50	immune
Infection	33	infection
Inflammation	4	inflammation
Influenza	117	flu
Kidney cancer	150	kidney_cancer
Leukemia	53	leukemia
Lipids	28	lipids
Liver cancer	136	liver_cancer
Lung cancer	58	lung_cancer
Lymphoma	54	lyphoma
Male	78	male
Manic depression	175	manic
Melanoma	157	melanoma
Menarche	134	menarche
Menopause	45	menopause
Methylation	21	methylation
Mood disorder	166	mood_disorder
Mortality	31	mortality
Movement-related	130	movement
Multiple sclerosis (MS)	109	ms
Muscle-related	93	muscle
Musculoskeletal	114	msk
Myasthenia gravis	146	myasthenia
Myocardial infarction (MI)	39	mi
Narcotics	139	narc
Nasal	71	nasal
Nasal cancer	151	nasal_cancer
Neuro	20	neuro
Obsessive-compulsive disorder (OCD)	142	ocd
Oral cancer	119	oral_cancer
Oral-related	46	oral
Ovarian cancer	135	ovarian_cancer
Pain	143	pain
Pancreas	57	pancreas
Pancreatic cancer	56	pancreatic_cancer
Parkinson’s disease	101	parkinsons
Physical activity	16	activity
Plasma	76	plasma
Platelet	98	platlet
Pregnancy-related	32	pregnancy
Prostate cancer	79	prostate_cancer
Protein expression	7	protein_expression
Pulmonary	2	pulmonary
Quantitative trait(s)	5	quantitative
Radiation	144	radiation
Rectal cancer	159	rectal_cancer
Renal	91	renal
Renal cancer	122	renal_cancer
Reproductive	12	reproductive
Rheumatoid arthritis	62	rheumatoid
Salmonella	141	salmonella
Schizophrenia	80	schizophrenia
Serum	30	serum
Sickle cell anemia	126	sickle_cell
Skin cancer	156	skin_cancer
Skin-related	69	skin
Sleep	68	sleep
Smallpox	121	smallpox
Smoking	82	smoking
Social	90	social
Stone	92	stone
Stroke	51	stroke
Subclinical CVD	77	subclin_cvd
Surgery	36	surgery
Systemic lupus erythematosus (SLE)	55	sle
T2D-related	170	t2d_related
Testicular cancer	161	testicular
Thrombosis	103	thrombosis
Thyroid	41	thyroid
Thyroid cancer	40	thyroid_cancer
Treatment response	15	treatment_response
Treatment-related	171	treatment
Tuberculosis	34	tb
Type 1 diabetes (T1D)	60	t1d
Type 2 diabetes (T2D)	18	t2d
Ulcerative colitis	138	ulc_colitis
Upper airway tract cancer	172	upper_airway_cancer
Urinary	85	urinary
Uterine cancer	173	uterine_cancer
Uterine fibroids	174	uterine_fibroids
Vaccine	116	vaccine
Valve	132	valve
Vasculitis	137	vasculitis
Venous	102	venous
Weight	74	weight
Wound	64	wound
miRNA	129	mirna

Population

The following populations are available in the Population table:

Population	ID
Hispanic	1
European	2
Mixed	3
African	4
Asian	5
Unspecified	6
Indian/South Asian	7
Micronesian	8
Arab/ME	9
Native	10
Filipino	11
Indonesian	12

PopFlags

The following populations are available in the Population table:

FLAG	Label
1	eur
2	afr
4	east_asian
8	south_asian
16	his
32	native
64	micro
128	arab
256	mix
512	uns
1024	filipino
2048	indonesian

Indices and tables

• Index
• Module Index
• Search Page