**CONCEPT CLEARANCE FOR RFA**

**\
High-Priority Phenotype and Exposure Measures for Cross-Study Analysis
in Genome-Wide Association Studies**

**NHGRI Advisory Council, February 2007**

**Purpose**

Staff seeks Council clearance for an RFA to support the identification,
development, and dissemination of readily standardized and implemented
phenotypic and environmental exposure measures suitable for addition to
ongoing genome-wide association (GWA) studies. The goal of this RFA is
to enhance the value of costly GWA genotyping performed in large numbers
of individuals for the investigation of a specific disease or trait, by
facilitating collection of selected additional standardized phenotypic
and exposure measures. These additional measures can then be linked to
GWA data in these subjects for: 1) identification of genetic variants
related to these newly-collected traits; 2) investigation of
environmental modifiers of putative genetic associations (GxE
interactions); and 3) if appropriately standardized, replication of
associations within and across GWA studies.

**Background**

Genome-wide association studies, in which hundreds of thousands of
single nucleotide polymorphisms (SNPs) are measured and related to
common diseases and traits, are designed to characterize the vast
majority of inter-individual variation across the human genome. Once an
individual's genome has been comprehensively characterized in this way,
it can potentially be related to any properly defined and collected
trait, not just the primary trait initially proposed for GWA study, so
long as these additional uses are consistent with the informed consent
provided by study participants.

Despite the vast potential of such multi-faceted studies, very few, such
as the Framingham SHARe project, actually include phenotypic and
exposure data on a wide variety of traits and diseases. Instead, most
current GWA studies supported by NIH utilize a case-control design
focusing on a single disease or group of related traits, such as the
Age-Related Eye Disease Study (AREDS), the Cancer Genetic Markers of
Susceptibility (CGEMS), the Finland United States Investigation of NIDDM
(FUSION), the Genetic Association Information Network (GAIN), and the
NINDS Parkinson's Disease study. Similar efforts internationally also
tend to emphasize the case-control design, such as the German National
Genome Research Network and the Wellcome Trust Case-Control Consortium.

The potential for leveraging such studies, particularly those with
appropriately-consented control or comparison subjects selected
essentially at random, for other research uses is considerable but is
often limited by the paucity of available phenotypic and environmental
information. More importantly, the potential for cross-study comparisons
is severely restricted by lack of standardization or comparability of
phenotypic and environmental measures across studies, despite many risk
factors (obesity, smoking, low socioeconomic status) being common to
multiple diseases. At present, the only data elements in common across
multiple studies proposed for GWA genotyping tend to be age, sex, and
self-reported race/ethnicity (which are often defined differently across
studies) and, less commonly, height or weight.

Standardization or harmonization of data elements and associated
controlled vocabularies is the focus of many current and planned efforts
in bioinformatics, including the American Health Information Community
(AHIC), Biomedical Informatics Research Network (BIRN), Cancer
Biomedical Informatics Grid (caBIG), Human Phenome Project, Inventory
and Evaluation of Clinical Research Networks (IECRN) of the NIH Roadmap,
National Electronic Clinical Trials and Research (NECTAR), and the
National Health Information Network (NHIN). Clinical groups have also
developed standardized measures in specific conditions, such as
childhood acute lymphoblastic leukemia, tuberculosis treatment outcomes,
and trauma.

While these efforts are crucial for developing future large-scale
ontologies and databases, or for promoting in-depth research in specific
conditions, most are not yet ready for application to the GWA studies
being conducted now, and application of all of them in a single study
would be overwhelmingly complex. Far better would be to select a
relatively small subset of easily standardized and implemented measures
in domains related to the most common complex diseases, as a "basic tool
set" for phenotypic and environmental exposure assessment. These
measures could then be applied to appropriately consented subjects
proposed for GWA studies. Where similar measures have already been
collected, the availability of this basic tool set could facilitate
converting or transforming existing measures to be comparable across
studies (harmonization).

Identification and application of such a "uniform" or "preferred" set of
data elements for GWA studies could substantially enhance the value of
such studies for research in a variety of common, complex diseases. It
could also identify a key subset of measures to be collected in
large-scale prospective cohorts of genes and environment and in ongoing
"biobanks" such as those underway at the Marshfield Clinic, Northwestern
University, and the UK. Development of such a tool set was a
high-priority recommendation of the June 2006 NIH-wide Multi-IC
Symposium on Application of Genomic Technologies to Population-Based
Studies and has been a recurrent recommendation received in response to
the Request for Information (NOT-OD-06-094) on Proposed Policy for
Sharing of Data Obtained in NIH Supported or Conducted Genome-Wide
Association Studies.

**Research Scope and Objectives**

A single awardee would be selected to consult with ongoing bioinformatic
efforts, review the current literature, survey expert opinion, and
convene appropriate working groups to define 15-20 highest priority
phenotypic and exposure domains for GWA studies, such as addiction,
cancer, pulmonary disease, anthropometrics or lifestyle factors. For
each domain, a similar process (consultation, literature review, expert
opinion, consensus groups) would be used to identify standardized
measures available or under development and to recommend 10-15 highest
priority measures for consideration for inclusion in GWA studies.
Although this could conceivably lead to identification of 150-300 high
priority measures (15-20 domains X 10-15 measures), no GWA study would
be expected to include all measures in all domains, but rather to select
the subset that are most appropriate for the additional GWA hypotheses
that can be tested in that study.

The awardee would be responsible for establishing an over-arching
working group to define criteria for prioritizing domains, such as
evidence for strong genetic influence on the domain, evidence for
environmental modification of genetic influence, and public health
significance. The group would also define additional criteria for
inclusion of specific phenotypic or exposure measures in GWA studies,
such as validity, reproducibility, cost, and feasibility, recognizing
that these criteria may need to vary somewhat across domains. Where
appropriate, the applicants should leverage ongoing activities in each
specific domain by engaging the respective communities and reusing
established data elements where possible. Separate working groups with
appropriate expertise would be established for each domain to refine
prioritization criteria, review available measures, develop standard
instruments for collection of new data and approaches for harmonization
of existing data.

The initial over-arching working group could be reconvened as needed to
assess progress and promote consistency of approach across domains where
appropriate. Careful attention would be needed to keeping working groups
focused and on task, encouraging the adoption of a selected, streamlined
tool set of existing measures and recognizing that compromises will be
needed to develop a feasible, cost-effective set of measures that may be
less than perfect in comprehensively characterizing any specific domain.
A standardized format would be developed that can be easily adopted by
all users to facilitate data exchange. Proposed elements would be vetted
by the primary awardee with relevant professional organizations and
other experts and disseminated for use in GWA studies.

**Mechanism of Support**

This RFA would use the NIH U01 (Cooperative Agreement) award mechanism.
Although one award is anticipated, multiple awards could be made to
complete the definition process more quickly. Anticipated duration of
the program is three years, with five to eight domains addressed per
year.

**Funds Available**

NHGRI would commit approximately \$8.5M over a three-year period for up
to 20 phenotypic and exposure domains. Each domain is expected to cost
an average of \$400K to prioritize, review, develop, vet, and
disseminate recommended measures. Support would be sought from other NIH
Institutes, and consideration would be given to elevating priority for
moderate-priority domains where co-funding is proposed.