Release Date:  February 11, 1998

RFA:  MH-98-017


National Institute of Mental Health
National Human Genome Research Institute
National Institute of General Medical Sciences
National Institute of Neurological Disorders and Stroke
National Institute of Dental Research
National Institute of Diabetes and Digestive and Kidney Diseases
National Institute on Aging
National Institute of Environmental Health Sciences
National Institute of Alcohol Abuse and Alcoholism
National Eye Institute
National Institute on Drug Abuse

Letter of Intent Receipt Date: April 6, 1998
Application Receipt Date: May 6, 1998


The purpose of this Request for Applications (RFA) is to solicit applications for
innovative research projects that will develop new quantitative methods for
mapping sets of genes that influence susceptibility to complex human diseases.
The specific objectives to be accomplished are: (1) the development of powerful
and flexible statistical methods that will enable researchers to localize and map
multiple disease susceptibility genes; and (2) the implementation of these new,
sophisticated methods in highly efficient and well-documented computer programs
widely disseminated for use by a broad range of biomedical researchers.

Such methods will permit the rapid and efficient analysis of information derived
from new maps of polymorphisms, in order to better correlate disease phenotypes
with underlying genotypes. The widespread availability of innovative quantitative
methods for genetic analysis will greatly foster gene discovery and stimulate
many areas of biological research on complex diseases, particularly the ultimate
identification of new treatments and preventive interventions. These new
statistical tools are essential for obtaining the maximal yield of information
from the avalanche of genomic information being generated from new and evolving
molecular technologies.


The Public Health Service (PHS) is committed to achieving the health promotion
and disease prevention objectives of "Healthy People 2000," a PHS-led national
activity for setting priority areas. This RFA, New Quantitative Methods to Map
Genes for Complex Diseases, is related to many priority areas.  Potential
applicants may obtain a copy of "Healthy People 2000" (Full Report: Stock No.
017-001-00474-0 or Summary Report: Stock No. 017-001-00473-1) through the
Superintendent of Documents, Government Printing Office, Washington, DC
20402-9325 (telephone 202-512-1800).


Applications may be submitted by domestic and foreign, for-profit and non-profit
organizations, public and private organizations, such as universities, colleges,
hospitals, laboratories, units of State and local governments, and eligible
agencies of the Federal Government. Racial/ethnic minority individuals, women,
and persons with disabilities are encouraged to apply as principal investigators.


All of the Institutes participating in this RFA will use the National Institutes
of Health (NIH) individual research project grant (R01). The total project period
for an application submitted in response to this RFA is not generally expected
to exceed 3 years. Applicants requesting direct costs of $500,000 or more for any
one year must obtain written agreement from National Institute of Mental Health
(NIMH) staff that the application will be accepted for consideration of award,
in accordance with NIH policy. The earliest anticipated award date is September
30, 1998.

Responsibility for the planning, direction, and execution of the proposed project
will be solely that of the applicant. Awards will be administered under PHS
grants policy as stated in the PHS Grants Policy Statement. Future unsolicited
competing continuation applications will compete with all investigator-initiated
applications and will be reviewed according to the customary peer review

For administrative reasons all applications received in response to this
solicitation will be assigned initially to NIMH. After discussions among the
participating Institutes and before review, applications will be reassigned to
the Institute(s) that are programmatically most appropriate. Because the scope
of the research proposed in response to this RFA encompasses the interests of
several NIH Institutes, applications may receive dual assignments based on the
established PHS guidelines. Awards will be made and managed by NIMH and/or the
other participating Institutes.


This RFA is a one-time solicitation. Approximately $4 million (including direct
and indirect costs) per year will be available for this initiative. It is
anticipated that 16 to 20 awards will be made. Awards pursuant to this RFA are
contingent upon the availability of funds for this purpose. The amount of funding
for these projects may be increased if a large number of highly meritorious
applications are received and if funds are available. Only applications that are
found to be of the highest scientific merit will be considered for funding and
not all of the funding will be spent if there are not enough highly meritorious
applications. Funding in future years will be subject to the availability of



Genetic factors contribute to virtually every human disease by conferring
susceptibility or resistance, affecting the severity or progression of disease,
and interacting with environmental factors that modify disease course and
expression. Much of current biomedical research, in both the public and private
sectors, is based upon the expectation that understanding the genetic basis of
disease will revolutionize diagnosis, treatment, and prevention. Defining and
understanding the role of genetic factors in disease will also allow the non-
genetic, environmental contributions to disease to be more clearly identified and

Tremendous advances have occurred in mapping and cloning genes for diseases that
follow Mendelian patterns in families. The three major analytic methods that have
proved successful include: localization to a chromosomal region of several
megabases by linkage analysis of family data, followed by fine genetic mapping;
analysis of haplotypes for linkage disequilibrium mapping; and direct detection
of an increased prevalence of a functional variant in affected individuals,
through association analysis. These methods have led to the mapping of over 500
rare Mendelian diseases to specific chromosomal regions, with nearly 100 being
cloned based on their position. Consequently, human genetics has sparked a
revolution in medical science on the basis of this seemingly improbable notion
that the systematic discovery of disease genes can occur without any prior
biological clue as to how they function. These breakthroughs are steadily
reshaping biological thinking and medical practice.

In contrast, the discovery of genes that influence susceptibility to more common
human diseases has proceeded slowly. The etiologies of these disorders are highly
complex, with disease susceptibility likely influenced by multiple genes of small
relative effect (frequently referred to as quantitative trait loci, or QTLs) and
environmental factors. These complexities present a considerable challenge to
geneticists, and account for the much more modest success of existing analytic
methods in teasing apart multifactorial causes to dissect the genetic basis of
common diseases.

Traditional linkage analysis required specification of the mode of disease
transmission. Denser maps led to methodologic improvements, by which several
marker loci could be simultaneously analyzed to increase the information content
of pedigree data. Given that the modes of transmission of common, complex
diseases are unknown, there has been a recent focus on mode-of-inheritance-free
linkage methods that use smaller subsets of family members (usually relative
pairs). A powerful new approach for conducting nonparametric multipoint linkage
analysis in moderately sized pedigrees has recently been implemented. Another new
method for linkage analysis in nuclear families has been developed, which is
parameterized in terms of the recurrence risks for different relative classes.
Other newly developed variance component methods for analyzing extended pedigree
data make no assumptions regarding the mode of inheritance, but make
distributional assumptions and can be computationally intensive.

Taken together, these new linkage methods offer significant computational and
analytic advantages for the mapping of genes that underlie complex diseases:
robustness to uncertainty about the mode of inheritance, given that specification
of the mode of inheritance is not required; capacity for multipoint analysis,
potentially involving dozens of markers; convenient computations in partially
informative families; ability to infer missing parental genotypes; and the
relatively low loss of power compared to parametric methods. However, limitations
exist: the full information content of large extended pedigrees and of unaffected
individuals cannot be taken into account, environmental or other factors that
modify susceptibility to illness cannot be easily incorporated, locus
heterogeneity cannot be specified, their robustness to violation of underlying
assumptions is untested, or involvement of more than two disease loci cannot be
explicitly modeled รพ this is especially problematic, given that complex human
diseases likely involve several genes in interaction.

Association studies provide a complementary strategy for detection of complex
disease susceptibility genes. Linkage disequilibrium, the nonrandom association
of alleles at linked loci, occurs when haplotype combinations of alleles at
different loci occur more frequently than would be expected by chance.
Disequilibrium between closely linked loci will dissipate slowly over successive
generations. Thus, it is hoped that detection of  association between a marker
and a disease will lead to the identification of a susceptibility locus near the

Fine-scale gene mapping by linkage disequilibrium works best with data from
genetic isolates (e.g., North American Hutterites) because it is likely that
there is only one or few ancestral haplotypes on which a disease mutation arose.
A small founding population increases the likelihood that a disease mutation was
introduced by a single individual, producing a strong association between the
disease and a specific haplotype in the current generation. These methods were
use to fine map disease genes for rare Mendelian disorders like diastrophic
dysplasia, progressive myoclonus epilepsy, congenital nephrotic syndrome, and
cartilage-hair hypoplasia. Recent extensions of linkage disequilibrium methods
allow for consideration of multiple, multiallelic markers. However, limitations
arise in the face of factors such as marker mutation, recurrent disease
mutations, an unknown population growth rate, increased genetic drift, increased
background kinship, reduced availability of kindreds, locus heterogeneity, and
the involvement of multiple disease susceptibility loci.

Another approach to association analysis is searching for an increased prevalence
of a particular functional variant in affected individuals within coding regions
of genes. In this fashion, a gene is directly implicated as influencing disease
susceptibility. This is accomplished by searching for differences in the
frequency of a marker allele between a sample of patients and controls. These two
groups must be carefully matched for ethnicity or other factors that may
contribute to genetic differences. Spurious association evidence can occur if the
population from which these groups are drawn from is assumed to be homogeneous,
but in fact is made up of different subpopulations with differing allele
frequencies. Such population stratification is the major confounding factor in
association studies and can be due to recent admixture of different populations
or inappropriate matching of patients and controls. These problems are
circumvented with family-based association designs, in which controls are
constructed from the set of nontransmitted parental haplotypes.

Large-scale testing by direct association analysis, even if one needs to test
every human gene, offers a powerful methodology recently proposed as the method
of choice for identifying the genes of modest effect that are likely operative
in the transmission of complex diseases. Recent technological developments have
enabled the identification of individual variations in DNA and a large increase
in the number of available polymorphisms for use in genome-wide association
studies to identify disease susceptibility genes. This approach should be
particularly efficient for identifying genes with relatively common mutations
that confer a modest or small effect on disease susceptibility. Simple, bi-
allelic polymorphisms throughout the genome are being discovered, and systematic
cataloging of this sequence variation will soon begin. Semi- or fully automated
methods for the discovery and scoring of thousands of such single nucleotide
polymorphisms (SNPs) currently under development will greatly facilitate disease
gene detection through whole-genome association analysis. However, there are
limitations in applying current direct association-based analytic approaches to
search for genes that underlie complex diseases: the involvement of multiple
susceptibility loci, potentially in interaction, cannot be modeled; information
provided by environmental risk factors or other covariates that modulate disease
susceptibility is not incorporated; and genetic heterogeneity cannot be modeled.

Promising new methods and technologies are being developed to provide for rapid
and efficient cataloging of human DNA sequence variation. The maximum yield from
such discoveries will require more powerful and sophisticated quantitative
methods of analysis. New methods are required to extract in a highly organized
and efficient way the maximum yield of information from the avalanche of
biological data to be generated, in order to: (1) localize and map genes of
varying effect sizes; (2) better correlate the relationship between disease
phenotype and underlying genotype; and (3) model highly complex modes of disease
transmission. This RFA is intended to solicit applications that specifically
address the development of highly practical and feasible quantitative analytic
methods that can immediately be put into practice by a broad range of biomedical
researchers in rapid and efficient genome-wide analyses.

Objective and Scope

Tremendous advances have been made in developing new statistical methods and
computer software for quantitative genetic analysis. Current analytic methods
have been successfully applied to map Mendelian disease genes, but are not well
suited for the genetic analysis of complex human diseases. Human geneticists are
now beginning to explore a new genetic frontier, driven by the inconvenient
reality that most diseases of medical relevance have irregular familial patterns
and lack a simple one-to-one correspondence between genotype and phenotype. The
greatest challenge will be to produce innovative and novel analytic methods that
can incorporate the full range of information being generated from new genetic
and physical maps. Sophisticated quantitative methods for performing extensive
multipoint analysis with large numbers of SNPs from a new genetic map of the
human genome will greatly facilitate gene identification and elucidation of
complex patterns of disease transmission.

The inherent biologic complexity of complex diseases necessitates a critical need
for new statistical tools for dissecting their genetic basis. As with any
scientific endeavor, discoveries and insights into the biological underpinnings
of complex diseases will only occur with the concomitant development of the tools
and analytic methods adopted by its practitioners.

The field of complex genetics is still at an early stage, but is ready to explode
much as it has done with the analysis of rare Mendelian diseases. The primary
goal of this RFA is to attract investigators from a variety of scientific
disciplines with the promise of shedding new light on old problems, i.e., the
spawning of novel and innovative statistical analytic techniques needed to
provide the necessary methodologic scaffolding for significant biological
discoveries in complex human diseases. This will be accomplished through
solicitation of applications to develop innovative and powerful statistical
methods for mapping complex disease susceptibility genes and understanding the
mode of inheritance. These new, sophisticated methods will be made available to
a broad range of biomedical researchers studying complex diseases, in the form
of highly efficient, powerful, exportable, and well-documented computer programs
released to the scientific community in a timely fashion. In order to achieve
this goal, statistical geneticists are encouraged to collaborate with computer
scientists in the development of software and thorough documentation that can be
provided through a transparent graphical user interface.

Applications are solicited to develop novel and creative quantitative methods
that will facilitate: (1) discovery of genes, including estimation of their
relative effect size; (2) analysis of large amounts of map and sequence data, to
better correlate disease phenotypes with underlying genotypes; and (3)
specification of complex modes of disease transmission, including the interplay
of multiple genetic and environmental factors that act independently or
interactively.  It is expected that the methods to be developed will be
implemented in rapid computational algorithms that will permit large-scale
genomic analysis of sequence and map data. Areas of interest for new methodologic
development include, but are not limited to, the following topics:

o Resolution of locus or allelic heterogeneity;

o Analysis of multiple disease loci that act independently or interactively;

o Full extraction of information from multiple bi- or multiallelic markers;

o Detection of disease susceptibility genes with a high population frequency;

o Analysis of haplotype sharing across the genome;

o Simultaneous consideration of multiple polymorphisms in a putative disease

o Rapid computation of multipoint likelihoods in extended pedigrees;

o Rapid computations of identity-by-descent matrices in pedigrees with missing

o Incorporation of information from multiple affected family members;

o Incorporation of covariates (e.g., environmental risk factors, sex, age);

o Gene-environment interaction;

o Simultaneous analysis of information extracted from array technologies;

o Integration of data from independent studies.

The major focus is to develop robust and practical methods that can be applied
by a broad range of biomedical scientists in data sets that currently exist, are
being gathered, or will be gathered. The operating characteristics of the methods
developed should be extensively explored through analysis of real or simulated
data sets. If assumptions are made (e.g., regarding multivariate normality, mode
of inheritance, population growth rate), it is expected that the robustness of
the method will be examined in regard to violation of these assumptions. Special
emphasis shall be placed on demonstrating the computational feasibility and
robustness of the methods as applied in data sets of the size expected in


Wide Distribution of Well-Documented Computer Programs

It is expected that well-documented, efficient, and exportable computer programs
will be developed to implement the new statistical genetic methods developed in
projects funded under this RFA. The distribution of such well-documented software
has been an essential element in the rapid progress made to map Mendelian disease
genes. To address the joint interests of the government in the availability of,
and access to, the results of publicly funded research and in the opportunity for
economic development based on these results, NIH requires applicants who respond
to this RFA to develop and propose specific plans for sharing the software
generated through the grant. Applications submitted in response to this RFA must
include a detailed plan and timetable for rapidly developing and widely
distributing computer programs that implement the proposed methods to the
scientific community of biomedical researchers studying the genetics of complex
diseases. The preferred mode of distribution is through a well-publicized World
Wide Web site. It is strongly encouraged that this site provides an opportunity
for users to direct questions to the developer about the analytic methods and
software. Successful applicants whose methods become widely distributed and
utilized may apply in the future for funding to train users.

The initial review group will comment on the proposed plan for making software
and documentation widely available. The adequacy of the plan will also be
considered by NIH staff as one of the criteria for award. The proposed sharing
plan, after negotiation with the applicant when necessary, will be made a
condition of the award. The evaluation of renewal applications will include an
assessment of the effectiveness of software release, distribution, and


Prospective applicants are encouraged to discuss their research objectives with
NIMH staff early in their planning process.  Prospective applicants are asked to
submit, by April 6, 1998, a letter of intent that includes a descriptive title
of the proposed research, the name, address, and telephone number of the
principal investigator, the identities of other key personnel and participating
institutions, and the number and title of the RFA in response to which the
application may be submitted.  Although a letter of intent is not required, is
not binding, and does not enter into the review of a subsequent application, the
information that it contains allows NIMH staff to estimate the potential review
workload and avoid conflict of interest in the review.

Any application that requests direct costs of $500,000 or more for any one year
must follow the guidelines for acceptance for review provided in the NIH Guide
for Grants and Contracts dated May 3, 1996. This may be accessed on the NIH Guide
World Wide Web site at 

The letter of intent is to be sent to:

Dr. Steven O. Moldin
Division of Basic and Clinical Neuroscience Research
National Institute of Mental Health
5600 Fishers Lane, Room 10C-26
Rockville, MD  20857
Telephone:  (301) 443-2037
FAX:  (301) 443-9890
Email:  smoldin@nih.gov


The research grant application form PHS 398 (rev. 5/95) is to be used in applying
for these grants.  These forms are available at most institutional offices of
sponsored research and from the Division of Extramural Outreach and Information
Resources, NIH, 6701 Rockledge Drive, MSC 7910, Bethesda, MD 20892-7910;
telephone 301-710-0267; fax 301-480-0525; Email: asknih@od.nih.gov.

The RFA label available in the PHS 398 (rev. 5/95) application form must be
affixed to the bottom of the face page of the application.  Failure to use this
label could result in delayed processing of the application such that it may not
reach the review committee in time for review. In addition, the RFA title and
number, "New Quantitative Methods to Map Genes for Complex Diseases: MH-98-017",
must be typed in section 2 of the face page of the application form and the YES
box must be marked.

Submit a signed, typewritten original of the application, including the
checklist, and four photocopies, in one package to:

6701 ROCKLEDGE DRIVE, ROOM 1040 - MSC 7710
BETHESDA, MD  20892-7710
BETHESDA, MD  20817 (for courier/express service)

At the time of submission, one additional copy of the application must be sent

Dr. Steven O. Moldin
Division of Basic and Clinical Neuroscience Research
National Institute of Mental Health
5600 Fishers Lane, Room 10C-26
Rockville, MD  20857

Applications must be received by May 6, 1998. If an application is received after
that date, it will be returned to the applicant without review. The Center for
Scientific Review (CSR) will not accept any application in response to this RFA
that is essentially the same as one currently pending initial review, unless the
applicant withdraws the pending application. CSR will not accept any application
that is essentially the same as one already reviewed. This does not preclude the
submission of substantial revisions of applications already reviewed, but such
applications must include an introduction addressing the previous critique.


Upon receipt, applications will be reviewed for completeness by CSR and for
responsiveness by NIMH staff. Incomplete applications will be returned to the
applicant without further consideration. If the application is not responsive to
the RFA, NIH staff will contact the applicant to determine whether to return the
application to the applicant or submit it for review in competition with
unsolicited applications at the next review cycle.

Those applications that are complete and responsive to this RFA will be evaluated
for scientific and technical merit in accordance with the criteria stated below
by an appropriate peer review group. As part of the initial merit review, all
applications will receive a written critique and may undergo a process in which
only those applications deemed to have the highest scientific merit will be
discussed and assigned a priority score. All applications will receive a second
level of review by the appropriate National Advisory Council.

Review Criteria

o  Significance: Does this study address the development of important
quantitative methods for mapping genes for complex human diseases? If the
specific aims of the application are achieved, how will this facilitate ultimate
detection, isolation, and cloning of these genes? Do the new methods enhance
understanding of modes of complex disease transmission, including the interplay
of multiple genetic and environmental factors? What will be the effect of these
methods on future genetic study designs, in which better correlations can be made
between disease phenotype and genotype?

o  Approach: Are the conceptual framework, design, techniques, and approaches
adequate, appropriate, and feasible to accomplish the specific aims of the
project? Does the applicant acknowledge potential problem areas and consider
alternate approaches? Is the scientific and technical merit of the proposed
research sufficient to advance the objectives of the RFA?  Can the methods be
applied to the sample sizes expected in practice?

o  Innovation and originality: Does the project employ novel and creative
concepts and approaches? Does the project challenge existing paradigms and foster
new analytic approaches in developing computationally feasible methods?  Are
highly rapid computational algorithms employed? If the aims of the application
are achieved, will the methods provide new analytic approaches to the discovery
of genes that underlie complex diseases above and beyond what is currently
available to the biomedical research community?

o Investigator: Are the principal investigator and staff appropriately trained
and well suited to carry out this work? Is the work proposed appropriate to the
experience level of the principal  investigator and other researchers (if any)?

o Scalability: What is the likelihood that the methods will be rapidly available
for efficient use by a broad range of biomedical investigators?

o Integration with other resources: Are the plans adequate to integrate the
methods with other mathematical (e.g., pedigree processing programs) and
biological (e.g., SNPs) resources?

o Exportability and accessibility: Will the methods developed in the research
project be implemented in software that runs efficiently in a variety of computer
environments and operating systems? Are the plans adequate for development of
documentation? Will resulting computer programs be usable by, and accessible to,
the broad scientific community of biomedical researchers studying the genetics
of complex diseases?  Will software and documentation be accessible via a
transparent graphical user interface?

o  Environment: Does the scientific environment in which the work will be done
contribute to the probability of success? Do the proposed methods take advantage
of unique features of the scientific environment or employ useful collaborative
arrangements? Is there evidence of institutional support?

o  Budget and duration: Are the proposed budget and duration appropriate in
relation to the proposed research?

The availability of special opportunities for furthering methods development
through the use of unusual talent resources or environmental conditions in other
countries which are not readily available in the United States, or which provide
augmentation of existing U.S. resources, will be considered in the review.


The earliest anticipated date of award is September 30, 1998. Subject to the
availability of funds, and consonant with the priorities of this RFA, the
participating Institutes will provides funds for a project period not generally
expected to exceed 3-4 years. Factors that will be used to make award decisions
are as follows:

o  Quality of the proposed project as determined by rigorous scientific peer

o  Cost effectiveness of the proposed strategy;

o  Promise of the proposed methods to accomplish the goals of this RFA, by
facilitating the identification of genes that underlie complex human diseases and
enhancing the understanding of the mode of  complex disease inheritance;

o  Promise of the proposed methods to extend and improve upon pre-existing gene
mapping methods;

o  Adequacy of plans to make exportable, rapid, and efficient computer programs
and accompanying documentation that implement the methods developed as a result
of this RFA highly accessible to the biomedical research community;

o Availability of funds.


Written, telephone, and e-mail inquiries concerning this RFA are encouraged. The
opportunity to clarify any issues or questions from potential applicants is
welcome. Direct inquiries regarding programmatic issues to:

Dr. Steven O. Moldin
Division of Basic and Clinical Neuroscience Research
National Institute of Mental Health
5600 Fishers Lane, Room 10C-26
Rockville, MD  20857
Telephone:  301-443-2037
FAX:  301-443-9890
E-mail: smoldin@nih.gov

Programmatic contacts for the other participating Institutes are:

Dr. Lisa D. Brooks
Genetic Variation and Genome Informatics Programs
National Human Genome Research Institute
Building 38A, Room 614, MSC 6050
Bethesda, MD  20892-6050
Telephone:  (301) 496-7531
FAX:  (301) 480-2770
Email:  lisa_brooks@nih.gov

Dr. Irene Eckstrand
Division of Genetics and Developmental Biology
National Institute of General Medical Sciences
Building 45, Room 2AS-25K, MSC 6200
Bethesda, MD  20892-6200
Telephone:  (301) 594-0943
FAX:  (301) 480-2228
Email:  irene_eckstrand@nih.gov

Dr. Joan Harmon
Division of Diabetes, Endocrinology, and Metabolic Diseases
National Institute of Diabetes and Digestive and Kidney Diseases
Building 45, 5AN-18G, MSC 6600
Bethesda, MD  20892-6600
Telephone:  (301) 594-8813
FAX:  (301) 480-3503
Email:  jh90u@nih.gov

Dr. Linda A. Thomas
Program Development Branch
National Institute of Dental Research
Building 45, Room 4AN-24J
Bethesda, MD  20892
Telephone:  (301) 594-2425
FAX:  (301) 480-8318
Email:  lt16r@nih.gov

Dr. Robert W. Karp
Division of Basic Research
National Institute on Alcohol Abuse and Alcoholism
6000 Executive Boulevard, Suite 402, MSC 7003
Bethesda, MD  20892-7003
Telephone:  (301) 443-4223
FAX:  (301) 594-0673
Email:  rkarp@willco.niaaa.nih.gov

Dr. Maria Y. Giovanni
Vision Research Program
National Eye Institute
Executive Plaza, Suite 350, MSC 7164
Bethesda, MD  20892-7164
Telephone:  (301) 496-0484
FAX:  (301) 402-0528
Email:  myg@nei.nih.gov

Dr. Harold Gordon
Division of Clinical and Services Research
National Institute on Drug Abuse
5600 Fishers Lane, Room 10A-46
Rockville, MD  20857
Telephone:  (301) 443-4877
FAX:  (301) 443-6814
Email:  hg23r@nih.gov

Dr. Jose Velazquez
Division of Extramural Research and Training
National Institute of Environmental Health Sciences
P.O.Box 12233
Research Triangle Park, NC  27709
Telephone:  (919) 541-4998
FAX:  (919) 541-4937
Email:  velazqu1@niehs.nih.gov

Dr. Anna M. McCormick
Biology of Aging Program
National Institute on Aging
7201 Wisconsin Avenue, Suite 2C231
Bethesda, MD  20892-9205
Telephone:  (301) 496-6402
FAX:  (301) 492-0010
Email:  am38k@nih.gov

Dr. Judy A. Small
Division of Fundamental Neuroscience and Developmental Disorders
National Institute of Neurological Disorders and Stroke
7550 Wisconsin Avenue, Room 8C04
Bethesda, MD  20892-9170
Telephone:  (301) 496-5821
FAX:  (301) 402-0887
Email:  js134h@nih.gov

Direct inquiries regarding fiscal matters to:

Ms. Diana S. Trunnell
Grants Management Branch
National Institute of Mental Health
5600 Fishers Lane, Room 7C-08
Rockville, MD  20857
Telephone: (301) 443-2805
FAX:  (301) 443-6885
Email:  Diana_Trunnell@nih.gov

Fiscal and administrative contacts for the other participating Institutes are:

Ms. Jean Cahill
Grants Management Officer
National Human Genome Research Institute
Building 38A, Room 613, MSC 6050
Bethesda, MD  20892-6050
Telephone:  (301) 402-0733
FAX:  (301) 402-1951
Email:  Jean_Cahill@nih.gov

Mr. Gary Fleming
Grants Management Branch
National Institute on Drug Abuse
5600 Fishers Lane
Rockville, MD  20857
Telephone:  (301) 443-6710
FAX: (301) 594-6847
Email:  gf6s@nih.gov

Mr. David L. Mineo
Grants Management Officer
Division of Extramural Research and Training
P.O. Box 12233
Research Triangle Park, NC  27709
Telephone:  (919) 541-7628
FAX:  (919) 541-2860
Email:  mineo@niehs.nih.gov

Ms. Nancy C. Dixon
Division of Extramural Activities
National Institute of Diabetes and Digestive and Kidney Diseases
45 Center Drive, MSC 6600
Bethesda, MD  20892-6600
Telephone:  (301) 594-8854
FAX:  (301) 480-4237
Email:  dixonn@extra.niddk.nih.gov

Mr. Martin R. Rubenstein
Division of Extramural Research
National Institute of Dental Research
Building 45, Room 4AN43, MSC 6402
Bethesda, MD  20892-6402
Telephone:  (301) 594-4800
FAX:  (301) 480-8301
Email:  Martin.Rubenstein@nih.gov

Ms. Marcia Cohn
Grants Management Officer
National Institute of General Medical Sciences
45 Center Drive, Room 2AN-44E, MSC 6200
Bethesda, MD  20892-6200
Telephone:  (301) 594-3918
FAX:  (301) 480-2554
Email:  cohnm@nigms.nih.gov

Ms. Carolyn E. Grimes
Grants Management Branch
National Eye Institute
Executive Plaza South, Suite 350, MSC 7164
Bethesda, MD  20892-7164
Telephone:  (301) 496-5884
FAX:  (301) 496-9997
Email:  cegrimes@nei.nih.gov

Ms. Linda Hilley
Office of Planning and Resource Management
National Institute on Alcohol Abuse and Alcoholism
6000 Executive Boulevard, Suite 504, MSC 7003
Bethesda, MD  20892-7003
Telephone:  (301) 443-4703
FAX:  (301) 443-3891
Email:  lhilley@willco.niaaa.nih.gov

Mr. Joseph Ellis
Grants and Contracts Management Office
National Institute on Aging
7201 Wisconsin Avenue, Suite 2C231
Bethesda, MD  20892-9205
Telephone:  (301) 496-1472
FAX:  (301) 402-0066
Email:  ellisj@exmur.nia.nih.gov

Ms. Tina Carlisle
Grants Management Branch
National Institute of Neurological Disorders and Stroke
7550 Wisconsin Avenue, Room 1004
Bethesda, MD  20892-9190
Telephone:  (301) 496-9231
FAX:  (301) 402-0218
Email:  tc48k@nih.gov


This program is described in the Catalog of Federal Domestic Assistance No.
93.242 (NIMH), 93.279 (NIDA), 93.172 (NHGRI), 93.114 (NIEHS), 93.273 (NIAAA),
93.121 (NIDR), 93.847 (NIDDK), 93.862 (NIGMS), 93.866 (NIA), 93.853 (NINDS), and
93.867 (NEI). Awards are made under authorization of the Public Health Service
Act, Title IV, Part A (Public Law 78-410, as amended by Public Law 99-158, 42 USC
241 and 285) and administered under PHS grants policies and Federal Regulations
42 CFR 52 and 45 CFR Part 74. This program is not subject to the
intergovernmental review requirements of Executive Order 12372 or Health Systems
Agency review. Awards will be administered under PHS grants policy as stated in
the Public Health Service Grants Policy Statement (April 1, 1994).

PHS strongly encourages all grant and contract recipients to provide a smoke-free
workplace and promote the nonuse of all tobacco products.  In addition, Public
Law 103-227, the  Pro-Children Act of 1994, prohibits smoking in certain
facilities (or in some cases, any portion of a facility) in which regular or
routine education, library, day care, health care or early childhood development
services are provided to children. This is consistent with the PHS mission to
protect and advance the physical and mental health of the American people.

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