**CONCEPT CLEARANCE FOR RFA**

**\
Rapid Investigation of Genetic Associations in Population Studies**

**NHGRI Advisory Council, May 2007**

**Purpose**

This RFA is proposed to support the investigation, in well-characterized
population samples, of genetic variants identified as potentially
causally associated with complex diseases in genome-wide association
(GWA) and other genetic studies. The goal of this initiative is to
utilize existing prospective cohort studies and clinical trials, and the
investigators most familiar with their intricacies, to: 1) determine the
population impact of putative risk variants, including prevalence,
disease risk, and associations with other health characteristics; 2)
identify modifiers of gene-trait associations, particularly those
related to lifestyle factors or medication use; and 3) identify
potential clues to gene function, by examining associations of putative
risk variants with related phenotypic characteristics such as laboratory
measures or imaging findings.

**Background**

Large-scale GWA studies are a crucial first step in the identification
of genetic variants related to complex diseases, but they are only a
first step. Several subsequent phases of investigation are needed to
eliminate potentially spurious associations, characterize the full
spectrum of variants in an associated region, determine the impact of
variants on gene function, and design appropriate intervention
strategies. Many, but not all, of these steps are already underway or in
planning to follow up current or proposed GWA studies. Most proposals
for initial GWA scans, for example, now include plans for repeating an
entire genome scan, or a subset of selected variants, in independent
replication studies. Fine mapping of associated regions, and identifying
and typing rarer variants found by targeted sequencing, are increasingly
being pursued in initial or replication population samples. Once a small
number of repeatedly associated, potentially causal variants is
isolated, however, investigation often shifts away from human
populations to the laboratory, to examine the impact of variants on gene
function experimentally in model systems.

Yet much remains to be learned from well-characterized human population
samples in which potentially causative variants have been, or could be,
assayed. Important clues to gene function could be identified by
examining associations with related or intermediate phenotypes such as
hormone levels or bone density. More importantly, the potential
population impact of variants of interest is often poorly described by
the often highly-selected or otherwise non-representative group of cases
in whom they were initially identified. The "epidemiologic architecture"
of these putative causal variants---their population prevalence;
prevalence in race/ethnic subgroups; relative risk of
rigorously-defined, incident disease; consistency of association across
subgroups defined by age, sex, race/ethnicity, or exposures; and
potential modifiability of associated risk---is rarely pursued in
population studies once a genotype-phenotype association is identified.
This information is critical, however, to determining the health
implications of a given variant and the priority it should receive for
identifying and testing interventions to reduce its associated risk.
This information may also be quite valuable in exploring gene function,
since the epidemiologic approach of genetic investigation, starting from
observed phenotypic characteristics and moving more proximally to gene
pathways and sequence variants, complements well the laboratory approach
of moving from DNA sequence to function to phenotype.

Reasons for failing to pursue population-based investigations of genetic
variants further may include lack of appreciation of their potential for
illuminating the population importance and physiologic effects of gene
variants, lack of information on available population studies and the
data they contain, lack of familiarity with the intricacies of these
databases and the requisite analytic methods, lack of access to the
data, and lack of patience with the potentially arduous process of
examining the full range of associations and interactions in population
samples. Although these obstacles might be addressed by cataloguing
potentially available studies and encouraging collaboration, the
complexity of these datasets is such that the epidemiologic
investigators who develop and routinely work with them remain uniquely
positioned to carry out these analyses. Rarely, however, are they
supported to do so. Although no single group of investigators can fully
mine the massive potential of these large-scale datasets, and
consequently the wide sharing of de-identified data continues to be
strongly encouraged, not all studies are suitable for widespread sharing
of genotype-phenotype data on individual participants. Widespread
sharing of association data should present far fewer obstacles.

More importantly, defining the risk associated with a specific genotype
is essentially an epidemiologic problem, similar to characterizing any
other risk factor, and requiring a detailed understanding of bias,
confounding, and interaction. Such investigations may best be undertaken
by experienced epidemiologists using data from the large-scale,
prospective, population-based studies they have designed and carried
out, because of the complexity of these datasets and the potential
biases involved in exposure assessment and follow-up. Clinical trials,
particularly primary prevention trials, may provide similarly
representative and well-characterized population samples, with the added
value of randomized interventions that may suggest potential avenues for
modifying genotype-phenotype associations.

**Research Scope and Objectives**

This RFA would support multiple investigative groups with
population-based epidemiologic and analytic expertise, and affiliated
with existing population studies, to investigate and disseminate
information on the epidemiologic architecture of putative
disease-associated genetic variants. These groups would work together to
develop criteria and methods for identifying highly-replicated, putative
causal variants suitable for epidemiologic pursuit within individual
studies. Vigorous and productive debate will likely ensue on the
evidence needed to justify extensive population-based characterization
of a variant (such as the number of independent replications, number of
subjects and consistency of associations, number or type of traits
associated), which could provide guidance to the studies participating
in this program and to population studies in general. It will be
important to balance the advantages of group consideration and
prioritization against the speed and flexibility of pursuit possible
within an individual study, so that research would neither be
unnecessarily slowed nor undesirably duplicated.

Subsequent investigation of variants identified as suitable for
epidemiologic characterization in individual studies supported by this
program might include: 1) identifying appropriate study participants to
be genotyped and relevant phenotypic and exposure data to be used in
analyses; 2) retrieving existing samples, performing whole genome
amplification and/or transformation of cryopreserved cells if needed,
and isolating, quantifying, and aliquoting DNA samples if not already
funded to do so; 3) conducting genotypic and other evolving genomic
assays (such as gene expression in transformed lymphoblasts) as needed
to characterize the reported association; 4) analyzing the resulting
data in detail, in relation to all or nearly all the relevant phenotypic
and covariate/exposure data available, and synthesizing the results into
a readily comprehensible form; and 5) disseminating the findings in
user-friendly formats through standard databases such as dbGaP. The most
rapid possible timeframe for these steps should be proposed, and should
continue to accelerate over the course of the program as efficiencies
and best practices evolve.

A key step in this process will be fully utilizing the available data
outside the primary genotype-phenotype association in ways that maximize
information on population impact and potential gene function, while
minimizing spurious findings and difficulty sifting through the
resulting analyses. Innovative bioinformatic approaches for identifying
and displaying potentially important findings will be encouraged to
increase the ease of use of the association results.. Sharing of
expertise and experience in this and other areas across investigative
groups will be a major goal of this collaborative program, with the
intent of developing best practices for rapid investigation of putative
genetic associations and making the resulting findings available and
understandable to investigators from a wide range of genomics-related
disciplines.

Early in the project period, investigative groups and a Coordinating
Center supported by this RFA would form a Steering Committee to examine
the phenotypic, covariate, and exposure data available in each
participating population study, including the variables collected,
methods used, and documentation available. Quality and quantity of DNA
and related genomic and other samples would be assessed and documented
in a standard format across all participating studies. The Steering
Committee would identify strengths and weaknesses of the various studies
in terms of traits and covariates available for investigation, and would
suggest best uses for specific studies, such as examining the impact of
a particular intervention on a given genotype-phenotype association, or
the risk of a specific trait associated with a given genetic variant.
The Coordinating Center would catalog all this information across
studies and disseminate it through a program website, standard
databases, and other means. Approaches to analysis would be shared and
reviewed in periodic investigators' meetings, using summary data derived
from the studies themselves. Investigators would be required to propose
a plan for rapid release of analytic results, in both a summary,
user-friendly form and a more detailed, comprehensive form, to be
compiled by the Coordinating Center and provided through databases such
as dbGaP. Appropriate embargoes on submission of publications from these
findings would be applied program-wide. Sharing of genotype or phenotype
data on individual study participants across studies or outside the
program would not be necessary to participate in the program, but would
be strongly encouraged in keeping with NHGRI and NIH policies.

Each applicant would be asked to propose a method for rapid
investigation and dissemination of genetic association findings in a
cohort study or clinical trial, or consortium of cohorts or trials,
involving large numbers (10,000 or more as a rough estimate) of
well-characterized participants in whom extensive phenotype, covariate,
and exposure data are available and high-quality DNA is isolated and
accessible, or could be isolated from stored specimens. Studies should
be of sufficient size and breadth to permit examination of a wide range
of variants and traits, and to identify associations and interactions
with sufficient power. Neither recruitment of participants nor
collection of phenotype, covariate or exposure data, or biospecimens
would be supported by this initiative. Whole-genome amplification and/or
isolation of DNA in a high-priority subset of subjects could be
supported as funding permits. Applicants would be encouraged to include
in each study a broad range of participants approximating the diversity
of the U.S. population on factors such as age, sex, race/ethnicity,
socioeconomic status, US geographic region, and urban/rural residence.
Studies involving under-represented racial/ethnic minorities,
particularly those suffering disproportionate burdens of disease, would
particularly be sought. Selection criteria for funding would include
studies that provide a diversity of trait and exposure information of
population health importance, and a diversity of population groups
(particularly U.S. racial/ethnic minorities), to the program as a whole.

**Mechanism of Support**

This initiative would use the NIH U01 (Cooperative Agreement) award
mechanism. Three to five investigative groups, each representing a
single population study or a consortium of studies, and each
investigative group involving roughly 10,000 or more total participants,
would be selected for award. A separate solicitation for a Coordinating
Center would also be released.

**Funds Available**

NHGRI will commit approximately \$32M over a four-year period to support
these cooperative agreements; roughly \$10M of these costs are for
genotyping (and whole genome amplification, cell transformation, and DNA
isolation as needed) on roughly 40,000 or more participants.