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American Journal of Medical Genetics Part C (Semin. Med. Genet.) 121C:32 – 37 (2003)
A R T I C L E
Anabaptist Genealogy Database
RICHA AGARWALA, LESLIE G. BIESECKER,
AND
ALEJANDRO A. SCHÄFFER*
In late 1996 we set out to build a computer-searchable genealogy of the Old Order Amish of Lancaster County,
Pennsylvania, for use by geneticists. The goals of the project included: 1) using the genealogy to expedite the mapping
of genes mutated in three rare recessive disorders under study at the National Institutes of Health (NIH); 2) building a
freely available software package, PedHunter, to answer genetically relevant queries on our database and other similar
databases; and 3) providing genealogy assistance to researchers outside NIH. All of these scientific goals had to be
accomplished while maintaining the confidentiality of the persons in the database and the confidentiality of preliminary research results. We expanded the project to include complementary data sources that contained many
individuals who were Anabaptist, but not Amish, and many individuals who never lived in Lancaster County. For this
reason, the project was renamed Anabaptist Genealogy Database (AGDB). All of the initial goals of the project have
been accomplished, and we recently marked the 5-year anniversary of answering the first of over 100 queries by
researchers outside NIH. Thus, it is an opportune time to review the construction of AGDB, summarize its usage to
date, and speculate on future projects it might stimulate and facilitate. Published 2003 Wiley-Liss, Inc.{
KEY WORDS: Amish; Mennonite; Anabaptist; consanguinity; inbreeding; genealogy; Steiner trees
INTRODUCTION
Amish and Mennonite communities are
fascinated with their ancestry, which is
one of many factors that make them
attractive study populations for medical
geneticists [McKusick, 1978a]. There
are thousands of Anabaptist genealogy
books, several libraries that collect them,
and social groups, such as the Lancaster
Mennonite Historical Society, which
meet regularly to discuss them. Genealogy books are a valuable resource in
constructing large pedigrees, useful for
genetic linkage analysis and disease gene
hunting [Angius et al., 2001; Ewald et al.,
2002]. Genealogy books tend to be more
reliable sources of relationship data than
interviews with family members [Zlotogora et al., 1998]. Leafing through
genealogy books can be enjoyable, but
medical geneticists can spend their time
more productively in the clinic and the
laboratory. Therefore, we set out to
construct a digital genealogy database
and query software that would solve
pedigree-related problems automatically
and systematically.
Dr. Richa Agarwala received her Ph.D. in Computer Science from lowa State University in 1994.
She was a Postdoctoral Fellow for ‘‘Special Year(s) in Mathematical Support for Molecular
Biology’’ at Center for Discrete Mathematics and Theoretical Computer Science (DIMACS),
Rutgers University from 1994 to 1996. She is currently a Staff Scientist at National Center for
Biotechnology Information (NCBI), National Institutes of Health (NIH). Her interest is to research
and develop algorithmic tools for better understanding of the human genome.
Dr. Leslie Biesecker received his M.D. from the University of Illinois. He received pediatrics
training at the University of Wisconsin and Medical and Molecular Genetics training at the
University of Michigan. He is a senior investigator at the National Human Genome Research
Institute at the NIH in Bethesda, MD. He directs a clinical and laboratory research program in the
molecular genetics of birth defects.
Alejandro Schäffer was born July 1, 1963 in Montevideo, Uruguay. He received his Ph.D. in
Computer Science from Stanford University in 1988, focusing on theoretical computer science. In
1992, he switched his research focus to software for genetics. Dr. Schäffer is best known for
leading the development of the genetic linkage analysis software package FASTLINK and for
implementing the PSI-BLAST module of the sequence analysis software package BLAST. Since
1998, he has been a Staff Scientist at the National Center for Biotechnology Information (NCBI),
National Institutes of Health (NIH).
*Correspondence to: Alejandro A. Schäffer, DHHS/NIH/NLM/NCBI, Building 38A, Room 8N805,
8600 Rockville Pike, Bethesda, MD 20894. E-mail: schaffer@helix.nih.gov
DOI 10.1002/ajmg.c.20004
Published 2003 Wiley-Liss, Inc.
{
This article is a U.S. Government work and, as such, is in the public domain in the
United States of America.
We set out to construct a digital
genealogy database and query
software that would
solve pedigree-related
problems automatically
and systematically.
We have heard anecdotally that
medical geneticists often consider a
pedigree construction successful if all
affected individuals are connected to a
common ancestor or a common ancestor couple, who serve as founder(s),
which we call the connectedness criterion. For disorders inherited in an autosomal recessive pattern, one seeks to
connect each obligate carrier to a founder and to explain how two copies of the
mutated gene may have been passed to
each affected individual. However, it
was rarely determined that 1) the
putative founder was the most recent
common ancestor, 2) all relevant parentchild links were included, or 3) the
connected pedigree reflected the most
likely paths of transmission of the diseaseassociated allele(s) by any criterion.
Checking these conditions by eye is
ARTICLE
tedious and difficult, especially since the
genealogy sources may contain errors or
inconsistencies that are not apparent by
checking a few entries at a time [Ewald
et al., 2002]. Even by the simple connectedness criterion, medical geneticists’ genealogy tracing and pedigree
construction efforts often fail. This can
be seen by the numerous papers that
announce the discovery of a founder
effect based on the discovery of the
identical causative mutation in supposedly unrelated individuals, only after
the disease gene has been identified by
positional cloning. For example, in the
case of sitosterolemia (OMIM 210250),
Lee et al. [2001] extended studies of
Berge et al. [2000] to multiple communities and did a ‘‘concerted genealogical
search’’ to connect two obligate carrier
Amish couples who carried a shared
haplotype across the sitosterolemia locus
and were not known to be related
initially. We suggest that for many populations, such as Anabaptists, it is more
efficient to find putative founders at
the start of the disease gene hunt and use
more genealogy information to make
larger pedigrees, thereby increasing
the power to detect linkage and reducing
the number of individuals who need to
be tested for mutations when a candidate
gene is identified.
We initially set out to construct an
Amish Genealogy Database (AGDB) of
the Old Order Amish of Lancaster
County, Pennsylvania, with the goal of
constructing large pedigrees to use in
mapping the causative genes for three
rare disorders inherited in an autosomal
recessive pattern under investigation by
researchers at the National Institutes of
Health (NIH). These disorders included
McKusick-Kaufman syndrome, Amish
nemaline myopathy, and Amish microcephaly. We subsequently expanded the
database and included numerous nonOld Order Amish and Mennonites from
North America. Therefore, we renamed
the database Anabaptist Genealogy
Database, retaining the acronym AGDB.
We hoped that our database would be
useful to researchers outside NIH studying the Amish and Mennonites, and that
our query software, PedHunter, would be
useful for searching other genealogies.
AMERICAN JOURNAL OF MEDICAL GENETICS (SEMIN. MED. GENET.)
All of these goals have been met. We
used pedigrees constructed from AGDB
toward identifying the causative genes
for McKusick-Kaufman syndrome
(OMIM 236700) [Stone et al., 1998],
Amish nemaline myopathy (OMIM
605355) [Johnston et al., 2000],
and most recently Amish microcephaly
(OMIM 607196) [Rosenberg et al.,
2002]. We have answered over 100
queries from other researchers. The
AGDB genealogy information has been
We used pedigrees constructed
from AGDB toward identifying
the causative genes for
McKusick-Kaufman
syndrome, Amish nemaline
myopathy, and most recently
Amish microcephaly.
used in studies on blood pressure [Hsueh
et al., 2000a], diabetes [Hsueh et al.,
2000b], obesity [Hsueh et al., 2001;
Steinle et al., 2002], aging [Mitchell
et al., 2001], family size [Pollin et al.,
2001], and osteogenesis imperfecta [McBride et al., 2002]. We used the database
ourselves for a large-scale study of the
effects of inbreeding [Agarwala et al.,
2001], reexamining and extending the
pioneering studies of Khoury et al.
[1987a, 1987b, 1987c, 1987d] based on
an earlier, smaller Amish genealogy
[Egeland, 1972]. Our query software
has been used by a few other groups and
in one published study that we know of
[Greenwood et al., 2001]. In this review
article we summarize what is in AGDB,
how AGDB can be accessed, and how we
have used PedHunter to query AGDB.
We conclude with some speculations
about benefits of our genealogy project
and how this resource might be used in
the future.
Amish and Mennonite
Genealogy Sources
We merged three genealogy sources to
construct AGDB. In the process we
33
corrected numerous errors and inconsistencies. Our gene mapping research
efforts have been focused on the Old
Order Amish of southeastern Pennsylvania, so we began with the Fisher Family
History (henceforth denoted FFH) [Beiler, 1988], which is the most complete
book for the contemporary Amish in
Lancaster, PA. Thanks to the editor of
that book, we have an updated version of
the published book that now has 55,636
individuals organized by family units,
with an informal syntax.
While using the first version of
AGDB (which contained only FFH
data), it became apparent that nearly all
individuals in the book were descendants of an Amish pioneer immigrant
named Christian Fisher. Therefore,
pedigree construction efforts would be
highly biased toward making him and his
two spouses the predicted mutationcarrying founders for any large pedigree
of a disorder inherited in an autosomal
recessive pattern. We therefore chose to
merge into the database a second source
book entitled Amish and Amish Mennonite Genealogies (AAMG) [Gingerich
and Kreider, 1986], which organized
families under 226 categories in 848
pages and included 30,853 individuals.
The AAMG book focused on individuals born before 1870. As the title
implies, AAMG contains information
about many Anabaptist individuals who
were not Old Order Amish; also, many
of the individuals never lived in eastern
Pennsylvania. Because of the date limitation, AAMG has limited use for studying present-day individuals, but the
combination of FFH and AAMG works
well. For example, we were able to find
potential founders for a pedigree of 33
sibships with Amish nemaline myopathy
[Johnston et al., 2000], even though
Christian Fisher (or anybody else in
FFH) appears not to be an ancestor of all
66 obligate carrier parents. The engineering challenges of digitizing the
AAMG data and merging it with the
FFH data were described previously in
Agarwala et al. [1999].
Through Dr. Judith Westman (Ohio
State University), we became aware of a
much larger computerized Anabaptist
genealogy maintained by Mr. James
34
AMERICAN JOURNAL OF MEDICAL GENETICS (SEMIN. MED. GENET.)
Hostetler (Richmond, VA), who kindly
provided us with his file. Mr. Hostetler’s
file is in GEDCOM format, which is
widely used by genealogists [GEDCOM, 1997], making it easier to parse
than the first two sources. The merger of
Mr. Hostetler’s data with the earlier
sources to make AGDB 3.0 was summarized previously [Agarwala et al., 2001].
Mr. Hostetler continues to find and add
more individuals to his Anabaptist genealogy, and we hope to update AGDB in
the future with his information.
Access to AGDB
Creation and maintenance of AGDB is
considered human subjects research and
was done under a research protocol
approved by an Institutional Review
Board (IRB) at NIH. In response to
concerns expressed by the board and
Amish bishops, AGDB information is
not accessible from any web page on the
World Wide Web. Queries (see description of query software below) are sent to
R.A. or A.A.S. by e-mail or in person,
and output files representing the answers
are returned by e-mail. All queries are
kept confidential. In most cases, we do
not know what phenotype was being
studied, nor do we know who among
the submitted individuals is affected by
that phenotype. To the IRB, we report
only the number of queries we received,
not the diseases that were studied. Any
researcher who has an IRB-approved
protocol or an IRB exemption for a
study of Amish or Mennonites may apply
to obtain access to AGDB, and all such
requests have been approved.
AGDB Contents
AGDB version 3.0 currently contains
information on 295,122 Amish and
Mennonite individuals and 68,216 marriages. The number of individuals is
slightly higher than reported in Agarwala et al. [2001] because we subsequently added those individuals to the
database for whom we had only one
known parent. A few duplicate entries
were also discovered. The data are organized in four tables: person table,
relationship table, ID table, and genera-
tion table. These tables together contain
the following information:
1. A consistent set of family relationships among individuals
2. The name, gender, address, and birth
and death dates for most individuals
(names may be missing and may
never have been given if a child was
stillborn or died as a neonate)
3. A marriage date for most couples
Information about adoptions, occupation, and religious designation is present
but is incomplete.
Unlike some project-specific medical genealogy databases (e.g., PhenoDB
[Cheung et al., 1996]), AGDB contains
no information on disease phenotypes or
genotypes. Information on a few traits of
interest to geneticists such as twinning
[Agarwala et al., 2001], lifespan [Mitchell et al., 2001], and family size [Pollin
et al., 2001] can be extracted by analyzing birth dates, death dates, and family
relationships. The lack of disease information is essential to protect patient
confidentiality and is desirable to allow
multiple, competing researchers to use
AGDB to study the same disease.
PEDHUNTER QUERY
SOFTWARE AND SAMPLE
APPLICATIONS
We developed query software called
PedHunter to efficiently extract information from AGDB.
We developed query software
called PedHunter to efficiently
extract information from
AGDB.
PedHunter 1.0 was described in Agarwala et al. [1998]; the current version
(v 1.2) includes additional queries, a
few of which are mentioned below.
PedHunter is freely available and can
be downloaded by following links
from http://www.ncbi.nlm.nih.gov/
CBBresearch/Schaffer/pedhunter. html.
PedHunter can be, and has been, used to
ARTICLE
query genealogy databases other than
AGDB [Greenwood et al., 2001]. It
provides several useful features not
available in earlier systems such as
PEDSYS [Dyke, 1992]. We made two
variants of PedHunter depending on
whether the user wished to store the data
in a traditional relational database using
the query language SQL [Date, 1990] or
instead with the tables in a structured
ASCII format, but no use of SQL.
Within the main PedHunter programs
and AGDB, individuals are numbered 1,
2, 3, . . . For AGDB usage, we have
developed utility programs that convert
from the identifier formats used in the
three sources into the AGDB identifiers
and back.
PedHunter 1.2 supports four categories of queries as basic operations:
1. Queries testing a relationship. For
example, Is X an ancestor of Y? Is X a
first cousin of Y?
2. Queries to find all individuals satisfying a relationship. For example, find
all aunts and uncles of X; find all
descendants of Y; find all founders.
3. Requests to print information. For
example, print name, birth date, and
death date for every identifier in a file.
4. Complex queries. One example is to
find the inbreeding coefficient (with
respect to the genealogy) of every
individual in a file. Another example
is, given a set of individuals, find a
maximal subset that has a common
ancestor. A third example is to find all
the connected sets in the genealogy
when allowing parent-child and
marriage links.
Queries of the first two types are
essential building blocks to answer the
complex queries, but can also be used
alone to check previous information or
construct lists of individuals who might
be of interest in an ongoing study. For
example, we used PedHunter to check
and correct information in a previously
published pedigree for McKusick-Kaufman syndrome [McKusick, 1978b], and
McBride et al. [2002] used information
on ancestors and descendants to identify
likely carriers of a known mutation.
Queries regarding demographic infor-
ARTICLE
mation have been essential in large-scale
studies of family relationships [Agarwala
et al., 2001] and aging [Mitchell et al.,
2001]. The query to construct all connected sets in the genealogy was useful
for discovering duplicates in the sources
that were not obvious due to major
discrepancies in the two versions of the
individual’s data; one version of the
duplicate individual was stranded in a
small connected set. AGDB 3.0 contains
one large connected set of 294,895
individuals and 52 small sets ranging in
size from 1 to 13 individuals.
Two complex queries in PedHunter, ASP (all shortest paths) and minimal,
were especially pertinent to the problem
of selecting a pedigree for linkage analysis, given a set, C, of affected individuals. The ASP query finds the set L of
lowest common ancestors of C, and then
for each ancestor A in L, it finds all
minimum-length parent-child paths
(i.e., fewest generations) from A to each
individual in C. The output is presented
as a set of LINKAGE-format pedigree
files, one for each common ancestor.
The common ancestors and shortest
paths can be computed quickly using
algorithms well known in computer
science [Dijkstra, 1959; Even, 1979].
The minimal query takes as input an
ASP pedigree and a required set of
individuals, R, and produces as output a
pedigree with a minimal-size set of
parent-child links, such that there is a
path of inheritance from the founder to
each required individual. In the canonical application, one is studying a disease
inherited in an autosomal recessive
pattern and R is the set of obligate
carrier parents of any affected children.
In this application, a minimal pedigree is
one that has the fewest possible number
of meioses, while still providing an
explanation of how each affected child
might be homozygous for the causative
mutation. The problem of constructing
minimal pedigrees is one type of problem in a well-studied class called Steiner
tree problems [Hwang and Richards,
1982] or, more generally, Steiner arborescence problems [Zelikovsky, 1997;
Cong et al., 1998]. Most Steiner tree
problems are intractable in a formal sense
for large instances, and the special case of
AMERICAN JOURNAL OF MEDICAL GENETICS (SEMIN. MED. GENET.)
minimal pedigree construction turns out
to be intractable also [Provan, 1983].
The heuristic method we implemented
to solve smaller instances of minimal
pedigree construction was described
previously in Agarwala et al. [1998,
appendix]. For the larger instances that
arose in the nemaline myopathy and
microcephaly projects, we used Steiner
tree software that is much more sophisticated but not freely available [Koch and
Martin, 1998].
The pedigree outputs from the two
pedigree construction queries can be
easily combined with phenotype and
genotype data for genetic linkage analysis, as we have done in several studies.
The pedigrees can also be drawn with
programs such as CYRILLIC [Chapman, 1990], PEDDRAW [Curtis,
1990], or PedigreeDraw [Mamelka
et al., 1993]. The pedigrees in Stone
et al. [1998], Johnston et al. [2000], and
Rosenberg et al. [2002] were all drawn
initially with PedigreeDraw, but manual
editing was required to make the layout
publication ready.
We illustrate the functionality of
PedHunter and information in AGDB
3.0 by reconsidering the genealogical
search done manually in Lee et al. [2001]
to link two couples, each of whom had a
child with the disease sitosterolemia,
which is inherited in an autosomal
recessive pattern. They used the AAMG
hard copy book, and they reported
finding five possible pedigrees headed
by founders from different surnames:
Hertzler, Schmucker, Blank, Mast, and
Yoder. The names, partial birth dates,
and partial AAMG identifiers for the
four obligate carriers analyzed by Lee
et al. [2001] were kindly given to us by
Dr. Alan Shuldiner (University of Maryland). Using the utility program that
converts name and identifier information, we found that AGDB 3.0 has three
of the four individuals. We explored
information on a website (http://
www.omii.org/omii.htm) that has more
recent Hostetler data than what we used
while making AGDB 3.0 (but cannot be
easily used for pedigree construction)
and found that the parents of the fourth
individual are present in AGDB 3.0 as
well. Using ASP, we found six couples
35
with both spouses as possible minimal
ancestors connecting all four individuals. We found four of the five founders
found by Lee et al. [2001], but did not
find a Yoder as a founder because one of
his descendants, a spouse of Hertzler, was
found as a more recent ancestor common to all four carriers. Two additional
founder couples we found were Siever
(AAMG id SV) and Beiler (AAMG id
BY3). Minimal pedigrees for six ASP
pedigrees have 40 (Hertzler), 37
(Schmucker), 42 (Blank), 40 (Mast), 44
(Siever), and 40 (Beiler) individuals. Our
analysis, including tracking parents for
the fourth individual, took us less than
1 day. We show the Beiler pedigree in
Figure 1 because 1) it was not found at all
by Lee et al. [2001]; 2) it has the unusual
property, which we have not seen
before, where ASP and minimal pedigrees are identical; and 3) it illustrates
that in order to exhaustively find all
minimal common ancestors, one should
continue to trace back even those paths
that do not find a common ancestor for
several generations, which is hard to do
without software. If we were to choose
one of the six pedigrees for linkage analysis, we would follow our criterion of
picking the one with the fewest individuals and choose the Schmucker pedigree.
DISCUSSION
We created AGDB and PedHunter to aid
medical genetics researchers in mapping
disease-causing genes. Specifically, we
sought to enable clinicians studying
Anabaptist populations to have more
time in the clinic and the lab, by
eliminating the need to peruse genealogy books and to connect hypothetical
pedigrees. AGDB has been used in early
steps of mapping three disease genes and
in studying some complex traits. One of
us (L.G.B.) estimated that the work to
construct the McKusick-Kaufman syndrome pedigrees in Stone et al. [1998]
would have taken 50 hr of manual checking of one source only, with limited
confidence that all relevant parent-child
links had been explored. We can extrapolate to 142 AGDB queries to date and
estimate that 3.5 person-years of book
searching have been saved.
36
AMERICAN JOURNAL OF MEDICAL GENETICS (SEMIN. MED. GENET.)
ARTICLE
Figure 1. This pedigree was derived from a semiautomated search of the AGDB using PedHunter. It connected two nuclear families
affected with sitosterolemia (originally published in Lee et al. [2001]) to a shared ancestral couple six generations back in the genealogy. This
pedigree was not found by Lee et al. [2001]; see text for discussion.
Ideally, finding a disease gene and
the causative mutation(s) in isolated
populations is intended to help the study
population, in addition to the obvious
benefit to scientific and medical knowledge. Side projects from AGDB gave us
three opportunities to aid members of
the Lancaster community in their avid
pursuit of Anabaptist genealogy information. First, the data sources contained
numerous errors and inconsistencies,
even ignoring intersource discrepancies.
If one knows what types of problems to
look for, they can be systematically
detected by using computer programs.
Thus, we were able to provide the
keepers of FFH and AAMG with lists
of items to check and correct. Second,
one of us (R.A.) implemented a set of
computer programs that a member of
the Anabaptist community has used to
add new entries to the computerized
version of FFH, and these programs can
also produce a new edition using a
format similar to that of Beiler [1988]
in Microsoft Word. Third, the Lancaster
Mennonite Historical Society (current
holders of the copyright on AAMG
[Gingerich and Kreider, 1986]) sought
and received massive data files from us,
toward the goal of producing a new
edition. This was desirable because the
source data for the original was on disks
for which no reading device could be
found, and because we have corrected
numerous typographical errors and
inconsistencies.
The large size of the AGDB affords
some opportunities for geneticists to
study common traits, for social scientists
to study demographic trends, and for
computer scientists to study practical instances of some algorithmic problems.
We give some examples that we have
touched on, but hardly put to rest, in our
research.
Geneticists might be interested in
AGDB as a tool to study factors controlling twinning [Agarwala et al., 2001],
aging [Mitchell et al., 2001], family size
[Agarwala et al., 2001; Pollin et al.,
2001], etc. One could extract a twin
registry from AGDB, with the limitation
that one would not know, without
fieldwork, if same-gender twins were
monozygotic. Social scientists might be
interested in AGDB to test hypotheses
about migration within North America,
immigration from Europe, changes in
surname spelling, isonymy, etc. Computer scientists might wish to use AGDB
as a source of Steiner tree problems,
pedigree drawing problems, or subgraph
isomorphism problems (e.g., testing
whether the individuals and parentchild relationships in two pedigrees correspond one to one). The problems we
tackled in merging the three discrepant
and partially overlapping data sources are
quite analogous to, and special cases of,
problems in synthesizing data from
different pages on the World Wide
Web. Another type of problem we have
seen, at the interface of genetics
and computer science, is how to use
ARTICLE
the genealogy information to optimize
sample collection and fieldwork. A basic
example arose in recent work on locating and testing Amish individuals who
might carry a mutation of the COL1A2
gene that sometimes has a subclinical
phenotype [McBride et al., 2002]. In
summary, the construction and usage of
AGDB has assisted medical geneticists,
aided the Lancaster community, and
provided us some challenging, practical
scientific research problems. We are
committed to maintaining AGDB and
PedHunter for the foreseeable future.
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