Documentation for CRI-MAP, version 2.4 (3/26/90)

Phil Green, Kathy Falls, and Steve Crooks


The main purpose of CRI-MAP is to allow rapid, largely automated 
construction of multilocus linkage maps (and facilitate the attendant
tasks of assessing support relative to alternative locus orders, 
generating LOD tables, and detecting data errors). Although originally 
designed to handle codominant loci (e.g. RFLPs) scored on pedigrees 
"without missing individuals", such as CEPH or nuclear families, it 
can now (with some caveats described below) be used on general pedigrees, 
and some disease loci. 

This version of CRI-MAP is distributed as source code in the language C, 
on a 360K DOS formatted diskette. To use it, you will need to obtain 
access to a C compiler for your computer, and compile the program 
yourself (see the section GETTING STARTED, below).  The present version 
of the program adheres fairly rigidly to the conventions of "standard C" 
as described in Ker- nighan and Ritchie (1978) (the main exception 
being that some variable and function names have more than 8 significant 
characters), and should be compatible with most C compilers.  The program 
was developed and tested using vers. 2.0 and 2.3 of the VAX C compiler, 
on a Microvax II with 5 Mb memory under the MicroVMS operating system. 
It has been successfully ported to a number of other computers, 
including other VAXes, SUN and Apollo workstations, and the Mac II. 

CRI-MAP requires a lot of memory; it is desirable to run it on a com- 
puter with at least 1 Mb (RAM or virtual memory) if you will be 
analyzing more than 10 loci simultaneously. It may be possible to run 
it on an IBM AT under DOS if your data set is small and you reduce the 
default memory allocations, although we have not tried this yet.  

A small data set with several chromosome 7 markers is provided with the 
program for the purpose of testing it (only). I would appreciate being 
informed of any difficulties in implementing the program, bugs, errors 
or gaps in the documentation, or suggestions for improve- ment.

Historical note: Version 1 of CRI-MAP was originally written by me in
the summer of 1986 in the language APL; the portion of that version
which does maximum likelihood estimation for a fixed locus order was
based on algorithms developed in collaboration with Eric Lander (Lander
and Green, 1987). Collaborative's chromo- some 7 map (Barker et al.,
1987) was constructed using that version of CRI-MAP, running on an IBM
XT. In the summer of 1987 parts of the original version were translated
into C, with the help of Steve Crooks, and used in constructing the
genome map published in Cell (Eric and his group at MIT independently
constructed maps using the program MAPMAKER). At this time I also
discovered the "layered EM" maximum likelihood search method, described
in (Green, 1988). In January, 1988, I worked out a much faster algorithm
for likelihood cal- culation (the method of switch algebras -- actually
a family of related algorithms), also described in (Green, 1988). I have
since written and tested the code for these new algorithms and
incorporated them into version 2.0 of the program; Kathy Falls has
worked on improving various other parts of the program. Versions
subsequent to 2.3 were developed by me at Washington University. I soon
hope to incorporate a full likelihood analysis for pedigrees with
missing individuals, and allow for disease loci with incomplete
penetrance. Any user feedback will be gratefully received, and I will do
my best to incorporate suggestions into future versions of the program.

 Phil Green

 Dept. of Genetics  Box 8232 
 Washington University School of Medicine
 St. Louis, MO 63110
 (314) 362-5192  FAX: (314) 362-4137 

    1. Using CRI-MAP with general pedigrees and disease loci
    2. Getting started
    3. File structures  .gen file  .par file  .dat file  .ord file
    4. Program options
        all       build      chrompic     fixed     flipsn     instant
        merge     prepare    quick        twopoint
    5. Technical notes
    6. Changes incorporated in versions 2.2 - 2.4
    7. Bibliography


CRI-MAP can only handle disease loci to the extent that genotypes are
known: this means that with autosomal dominant loci, affected individuals
are to be scored as heterozygotes at the disease locus, while unaffecteds
are scored either as homozygotes for the normal allele (if you are willing
to assume complete penetrance for that individual), or as genotype
missing (alleles both 0) otherwise. For recessive loci, only affecteds and
obligate car- riers should be scored for the disease locus. 

With general pedigrees, CRI-MAP deduces missing genotypes where possible,
and computes a likelihood based only on analysis of the known or deduced
genotypes. (Partial geno- types, i.e. those in which only one allele is
known or deducible, are also utilized in the likelihood calculation to the
extent possible). This likelihood is the usual likelihood (as defined for
example by Ott (1985)) when the pedigree data are "complete" (i.e.
genotypes are known or can be deduced at every locus, for every individual
having descendants in the pedigree). 

If data are missing for an individual at a particular locus, and the
possible genotypes at that locus (i.e. those genotypes compatible with the
genotypes of ancestors and descendants) in- clude a homozygous genotype,
then all meioses in that individual are treated by CRI-MAP as 
uninformative for that locus. A full likelihood analysis (as provided for
example by LIPED or LINKAGE) would instead consider in turn each possible
genotype at the locus, assign relative probabilities to each based on the
genotypes of ances- tors and descendants, and compute a likelihood which
is the weighted sum of the likelihood expressions for each particular
choice of genotype. CRI-MAP thus ignores some of the available
information. In cases that we have examined, however, the information loss
appears to be small. 

If the missing locus genotype is in an "original parent" (i.e. an
individual with no ancestors in the pedigree), then in a full likelihood
analysis the population allele frequencies are used to assign
probabilities to various possible genotypes. These enter into the
likelihood by influencing the probability that the allele in any child of
the original parent is derived from that parent. CRI- MAP does not make
use of allele frequencies; for any allele in a child of an original
parent, CRI-MAP deter- mines the parental origin when this can be deduced
from the other genotype information, but otherwise assigns equal
probability to the two possible parental origins for the allele. In fact,
little information is lost by this pro- cedure, except when the allele is
rare. For a rare dominant disease, if one parent is known to be affected,
the other should therefore be scored as unaffected (rather than as
missing, for example). 

It should be noted in any case that the frequencies of RFLP alleles have
usually been estimated in a different population (e.g. the CEPH family
parents) from the population from which the disease family is drawn. It is
our experience that allele fre- quencies may vary dramatically between

Therefore it may be inappropriate to perform a full likeli- hood analysis
of disease linkage, if the results of that study depend in an essential
way on parameters estimated from another population. A somewhat more
limited analysis which makes no assumptions concerning allele fre-
quencies, such as that given by CRIMAP, may be preferable. If (for
reasons of power) a full likelihood analysis is necessary, that analysis
should be performed using a range of allele frequencies at each locus, in
ord- er to ensure that the results do not depend in an essential way on
these frequencies.

Finally, it should be noted that whenever some available information is
arbitrarily excluded, as is the case with CRI-MAP's analysis of general
pedigrees, there is a potential that artifactual biases in parameter
estimates can be created. In a preliminary examination of this
possibility (cf. Goldgar et al, 1989), we have compared the results of
CRI-MAP with those of a full likelihood analysis program (LINKAGE) for
all pairs of a set of 20 linked loci, in a data set having 136
pedigrees, most with missing individuals. The information loss (as
estimat- ed by comparing the effective number of informative meioses for
the two programs) averaged about 15%, and the estimated recombination
fractions were almost exactly the same. This suggests that bias is not
significant and information loss is small, at least in this particular
data set. However, the possibility of bias clearly needs to be
considered more thoroughly by means of simulation studies involving
multiple loci, and we are in the process of doing this. I would
recommend that you check the extent of information loss and/or possible
bias in your data set by comparing the results of CRI-MAP to those of a
full likelihood program for small sets of loci.


  1. Put all files on the diskette provided into a single directory on
     your computer.

  2. Compile the source code files (i.e. those whose names
     end in .c), and link them together with your C stdio and math 
     libraries, producing an executable file called crimap.

There are slight differences in the conventions used by some C compilers
which may make it necessary for you to modify some of the source files
before they will compile correctly; usu- ally these will be evident from
the error messages generated by the compiler. For example, if your
operating system is case-sensitive (e.g. UNIX), you will need to rename
all files in lower case (DOS converts all file names to upper case). We
have declared some stdio and math library functions in the crimap
functions where they are used; if your compiler objects to this, you will
need to delete the offending declarations. 

Note to VAX C users: there is a bug in the optimizer of the VAX C
compiler, ver. 2.3 (and possibly in other versions), which causes it to
compile the source file "e_conv.c" incorrectly. When the program is run,
this will result in an incorrectly created phase-known data structure in
the .dat file (see below). To avoid this, compile e_conv.c with the
/NOOPTIMIZE option.  All other source files should be compiled with the
default /OPTIMIZE option.

Also see comments on "underflow" under TECHNICAL NOTES. 

3. The command format for running the program is

 crimap {chromosome number} {option}

Example:  crimap 7a twopoint 

The option name must be entered in lower case letters. Chromosome "number"
(which may consist of any string of digits, possibly followed by letters
-- for example 7p, or 17nf, or 0a) may be replaced by the name of the
parameter file (described below); for example,

  crimap chr7a.par twopoint

You must provide a .gen file (named in accordance with the conventions 
described below, and residing in the same directory as CRI-MAP) which 
contains the raw genotype data, and run the prepare option first in order 
to create the other files required by the program. 

All program output (apart from specific information written to one of the
four files  described in the next section) is displayed using the "printf"
function in C. It will thus be displayed on the terminal (unless
redirected to a  file by means of commands to the operating system) if the
pro- gram is run interactively, or written to a log file if the program
is run in batch mode. (The latter procedure is the most convenient way to
make a copy of the output. In UNIX and some other operating systems, one
can simply redirect the output to a file.) The prepare and merge options
are the only ones requiring interactive input. 

4. As a test run with the chromosome 7 data set, chr7a.gen, provided with 
   the program, use the command

   crimap 7a prepare

to create a .dat file and a .par file for subsequent use by the option
all, with the loci 2 8 9 10. (Specify any two of these as the "ordered
loci", and the other two as the "inserted loci".) Use default values for
the other parame- ters. 

NOTE: If the program stops prematurely, displaying the message "Your
compiler uses a different size for integers; see documentation for changes
that will have to be made in the source code", then you will need to make
certain minor modifications in the source files and recompile. See "16 bit
integers" under TECHNICAL NOTES, below. 

If the program stops prematurely, displaying the message "ERROR:
ALLOCATION FAILED IN MORECORE", then the program's default memory
allocations exceed what your operating system can provide. Reduce the
value of nb_our_alloc in the .par file (using prepare, or a text editor)
and see "Memory management" under TECHNICAL NOTES, below.

Then run CRI-MAP with the command line

   crimap 7a all

The program prints out parameter values, tests all 12 possible orders of
these 4 loci, and finally prints out a sort- ed list of all orders whose
log 10 likelihood is at least that of the best order, less 3.0.  You
should get the answer:

  2 10 9 8 -97.937
  2 10 8 9 -100.839

The run to evaluate these 12 orders should take less than a minute on
most workstations or moderately powerful personal computers (for
example, about 50 secs on a Mac II, about 25 secs on a MICROVAX II
without a nu- merical coprocessor, about 8 seconds on a SUN 3/60 or 2
seconds on a 4/60; exact times will depend on the efficiency of the C
compiler used as well as the computer).


CRI-MAP uses four different types of files. These all have names of the
form chrx.y, where x is the chromo- some "number" and the suffix .y is
one of

 .gen the raw genotype data file

 .par the parameter file

 .dat the processed data file

 .ord the orders file, or "orders database"

The user must supply the .gen file. The .dat and .par files are created
using the option prepare, and must be constructed before using any other
CRI-MAP options (except merge). The .ord file is initialized by prepare,
but required only for the map building options (build, instant, and
quick). You will need to learn about the structures of the .gen and .par
files, but can ignore the descriptions of the other file types if you

Each file is in ASCII format, and can be edited with a text editor. 
For readability, the user can insert additional blank or end of line
characters into these files if desired, since the C statements which read
the files treat any string of such characters as a single "white space"
delimiter between data items. All data items are either numbers or
character strings containing no blank spaces; they must be separated by
white space delimiters. In the following descriptions, brackets {} are
used to enclose data items appearing in the file but do not themselves
appear in the file.

 .gen file 

The data items in this file are as follows: 

{# of families}
{# of loci} 
{locus name1} {locus name2} . . . (each name consisting of at most 15 

For each family:  

{family ID} (consisting of an arbitrary character string)  
{# of members}  

For each member in the family:  

{ID #} {mother's ID #} {father's ID #} {sex: 0 if female, 1 if male, 
                                             3 if unknown} 
{locus1 allele1}{locus1 allele2}{locus2 allele1}{locus2 allele2} . . .

The pedigree structure must be completely specified; this means that for
any individual with an ancestor in the pedigree, both parents must be
assigned ID #'s and must appear in the file. The family ID may be any
string of characters (without embedded blanks) but individual IDs must
be numbers. For "original parents" (individuals without ancestors in the
pedigree), the mother and father IDs are coded as 0. Alleles must be
represented by in- tegers, with missing alleles scored as 0. To handle
new mutations in a dominant disease gene correctly, ances- tors of the
mutant individual should have their disease locus genotypes coded as
missing. To handle non- pseudoautosomal X linked loci, a dummy allele
number (e.g. 9) must be created and assigned as the second al- lele for
all males in the pedigree.

Example: For a data set with two loci, LOCA and LOCB, scored on the
single pedigree depicted on the next page, the corresponding .gen file
would be

1001 0 0 0 
1 2 2 2 
1002 1001 1009 0 
1 2 1 2 
1003 0 0 1 
1 1 1 2 
1004 1002 1003 0 
1 2 1 1 
1005 1002 1003 0 
1 1 2 2 
1009 0 0 1
0 0 0 0

Note that the missing maternal grandparent has been assigned an ID of
1009 and included in the file.

 .par file 

This file may either be created using the prepare option, or directly
using a text editor. The format has been changed effective with version
2.4, and differs somewhat from that of the other files. Each line con-
tains a parameter or variable name and its value(s) (separated by spaces),
and concludes with an asterisk *. The parameters may appear in any order
within the file. If a parameter is omitted (either by omitting the
corresponding line altogether, or by omitting the value between the
parameter name and the asterisk), then CRI- MAP will automatically assign
a default value to the parameter.  In the following descriptions, each
line ending in an asterisk is displayed as it would appear in the .par
file, and gives a parameter name together with its default value(s) (if
any). Any number of hap_sys, hap_sys0, and fixed_dist lines may appear in
the file. The other parameters should appear only once. (If they appear
more than once, only the last specified value will be used).

  dat_file chrx.dat *

  gen_file chrx.gen * 

  ord_file chrx.ord *  

These give the names of the .dat, .gen, and .ord files to be used in 
the analysis.  When the name of the .par file is not of the form chrx.par, 
the default names for the above files are obtained by replacing the ".par" 
(which must always appear) in the name of the .par file with ".dat", 
".gen", or ".ord", respectively. (This is sometimes useful if one wants 
to keep all files in a separate directory from the program itself, in 
which case the name of the .par file passed to crimap may include the 
full path name; the default names for the other files will then include 
the same path).

   nb_our_alloc 3000000 *  

The initial memory allocation 
(in bytes).  The default value will suffice for most runs, except with 
a very large number of loci, and in fact is much more than is usually 
needed for analyzing a small number of loci. If additional memory is needed
during the run, it will be allocated automatically (up to the limits of
your system); however it is advantageous to choose values for the
initial allocation which will suffice for the entire run. See "Memory
management" under TECHNICAL NOTES.

  SEX_EQ 1 * 

SEX_EQ is 1 if recombination rates are assumed equal in the two sexes, 
0 if sex- specific rates are to be al- lowed in each interval.

  TOL .01 *  

The tolerance for determining convergence of the layered EM algorithm; when
log 10 likelihoods from succes- sive "phase unknown" iterations increase
by less than this amount, iteration terminates. The tolerance used to
detect convergence in the "non-informative locus" part of layered EM,
and in the option twopoint is TOL/10. 

In extensive tests using our RFLP CEPH family data sets (several hundred
maximum likelihood estimations) the default value .01 was always found to
be adequate (it was occasionally not adequate when ordinary EM was used as
the search method, instead of layered EM). If you are concerned about the
possibility that this may not be stringent enough for for your data set
(for example, if the likelihood surface is relatively flat), try using
.001 instead; the linear nature of EM convergence guarantees that, if
the estimates had not converged with TOL = .01, then a substantial
improvement in likelihood should be apparent with the more stringent
tolerance. If such an improvement is seen, use successively smaller
values for TOL until no further improvement in the likelihood results.


Applies only to the option build; gives the maximum number of orders
allowed in the current map, in the phase unknown part of the analysis.

   PK_NUM_ORDS_TOL 8 *  

Similar to PUK_NUM_ORDS_TOL, but applies instead to the phase known
analysis in build. If 0, the phase known analysis will be skipped entirely
during mapbuilding.

   PUK_LIKE_TOL 3.0 *  

The tolerance for discarding locus orders. If the log 10 likelihood of an
order is less than the log 10 likelihood of some other order for the same
loci by an amount exceeding PUK_LIKE_TOL, that order is discarded (or not
printed). Used by mapbuilding options, all, and flipsn. With twopoint, LOD
tables are displayed only for locus pairs whose LOD exceeds PUK_LIKE_TOL.

   PK_LIKE_TOL 3.0 *  

As above, but applies only to analysis of the phase known data in the
option build (and is not used by the oth- er options).

   use_ord_file 0 *  

This parameter applies only to the options all and flipsn. When it is 1,
the orders generated by those options are prescreened against the .ord
file to eliminate orders incompatible with the orders database, prior to
comput- ing likelihoods. When use_ord_file is 0, the information in the
orders database is not used.

   write_ord_file 1 * 

Applies only to the option build. When it is 1, the results of the current
build run are used to update the ord- ers database. When it is 0, the
orders database will not be updated (but will still be used to prescreen
orders during the course of the run).

   ordered_loci {index # of 1st ordered locus} {index of 2d ordered locus}
                  . . . *
   inserted_loci {index # of 1st inserted locus} {index of 2d inserted locus}
                  . . . * 

Note: locus indices start at 0.  

For all options except twopoint, the "ordered_loci" are assumed to be in 
their known, unique order; the remaining loci, called the "inserted_loci", 
are to be placed in the framework defined by the ordered loci. For the options
chrompic, fixed, and flipsn, there are no inserted loci. 

For twopoint, if both ordered_loci and inserted_loci are specified, then
LOD tables are only computed for pairs of loci for which one is in the
ordered list and the other is in the inserted list. Otherwise the analysis
uses all pairs of loci in the specified list (ordered or inserted).

   hap_sys {index # of 1st locus in system} {index # of 2d locus} . . . *
   hap_sys0 {index # of 1st locus in system} {index # of 2d locus} . . . *  

Each haplotyped system is a list of loci (for example, different RFLPs
detected by the same probe) which are to be grouped together in an
analysis. The first locus in any system is called "primary", and the
remaining loci are called "secondary". When the parameter use_haps (see
below) is 1, the secondary loci in a system "tag along" with the primary
locus whenever ordered sets of loci are constructed. The operations
which construct new orders (for example, by inserting a new locus into
the map, or by permuting a collection of loci) utilize only the primary
loci; once the order is constructed, however, it is "filled out" by
inserting secondary loci im- mediately following the corresponding
primary locus, prior to calculating likelihoods and map distances.
Secon- dary loci are automatically deleted from the input lists of
ordered loci and inserted loci. Thus any system which is to be included
in the analysis must be represented by (at least) its primary locus.

For systems specified using hap_sys, the loci within a system are
treated as independent in all calculations; i.e. they are not forced to
have 0 recombination fraction. In particular, intralocus recombinants
between loci in the same system are permitted. For systems specified
using hap_sys0, distances within the system are forced to 0 (the program
will stop, displaying the message ERROR: 0 likelihood, if there are in
fact intralocus recom- binants).

Example: two haplotyped systems, the first having loci 3, 4, and 5 with
distances not forced to 0.0, and the second having loci 9 and 11 with
distances forced to 0.0, would be entered in the .par file as

   hap_sys 3 4 5 *
   hap_sys0 9 11 *
   use_haps 1 *

When use_haps is set to 1, haplotyping is performed; when it is 0, any
haplotyped systems specified in the .par file are ignored (i.e. the input
lists of loci are taken as is, and no secondary loci are deleted or 

  fixed_dist {rec.  frac.} {index # of 1st locus} {index # of 2d locus} 
  {sex (optional)} *

For the options fixed and chrompic (only), the recombination fraction
between a pair of adjacent loci may be held fixed using fixed_dist. If
the recombination fraction to be fixed is sex-specific, specify the sex
as 0 for fe- male, or 1 for male. If either locus is part of a
haplotyped system, it must be the primary locus from its system (it is
not possible to force a distance within a haplotyped system, except by
using hap_sys0 as described above). Note: any recombination fractions
held fixed, either using fixed_dist or hap_sys0, are flagged by an
asterisk in the map displayed following the analysis.

The last line of the .par file must contain the single word END.
Example: if you wished to construct a .par file chr7a.par to perform the
all analysis described in GETTING STARTED, above, the following three
lines would suffice (since default values are to be used for all other

  ordered_loci 2 8 * 
  inserted_loci 9 10 * 

 .dat file 

This file, which is created by the program (using the prepare option), not
the user, has two data struc- tures, each having the following data items:

    {# of loci}
    {# of families} {family id1} {family id2} . . . (may be any character
                    strings not containing blanks)
    {locus name1} {locus name2} . . .  (each locus name consists of at most 
                    15 characters, with no embedded blanks)

For each family:

    {# of chromosomes}
    {phase chromosomes: each is a character string of length numloci with
           values 0, 1, or X, denoting the phase}
    {# of switches}

For each switch:

    {index of locus affected by the switch (in this file, locus indices
     start at 1, not 0)} {string of  length numchroms, with entry 1 if
     the corresponding chromosome is affected by the switch, 0 otherwise}

The first such data structure has the "phase known" data: there is 1
"family" with all the chromosomes in the data set, and 0 switches. All
loci of unknown phase are given phase X, as are loci on the chromosomes of
children of identical heterozygotes (the latter restriction is necessary
to avoid bias in the estimation of recombination fractions, cf. Ott
(1985)). The second data structure contains the full phase information for
the data set, arranged by families. (For definitions of switches and phase
chromosomes, see (Green, 1988)).

The "phase known" data structure is used only by the option build.

   .ord file

(For definition of terminology, see description of the build option, below.)

    {# of orders objects}

For each orders object:

    {# of orders} {# of loci}

For each order in the orders object:

    {index of 1st locus in the order} {index of 2d locus in the order} ...



Finds log 10 likelihoods for all locus orders which result from placing
the inserted loci in all possible positions with respect to the ordered
loci (see description of the .par file, above, for definition of these
terms). The pro- gram finds the order with the highest log 10 likelihood L
and prints out, in decreasing order of log 10 likelihood, those orders
whose associated log 10 likelihoods are greater than or equal to L -
PUK_LIKE_TOL. In particular, when the number of ordered loci is two, all
possible orders of the specified loci are analyzed. When using this
option, keep in mind that the number of orders analyzed is n!/f!, where n
is the total number of loci and f is the number of ordered loci (and !
denotes factorial), and that analysis of all 360 orders of a typical set
of 6 loci on chromosome 7 takes about 30 mins on a MICROVAX II; thus it is
probably impractical to use this option with more than 6 inserted loci,
except on a much faster computer. When use_ord_file is set to 1 in the
.par file, any orders incompatible with the orders database are eliminated
prior to calculating likelihoods, which may make it feasible to use all
with larger sets of loci. 

One convenient use of this option is to position a new locus (which would be
specified as the single inserted locus) with respect to a set of loci of
known order.


Builds a map by sequential incorporation of loci. This is the main
option used for RFLP map construc- tion. 

To describe the method used in greater detail, define an "orders object"
to be a collection of loci, together with a set of permissible orders for
those loci. The "orders database" contains a collection of orders objects.
At any given stage during the map construction "the map" (designated
"current_orders" in the program source code, and output) consists of one
such orders object. At the beginning of the run the orders database is
empty, and the map consists of the "ordered loci" specified in the .par
file. (In the absence of any prior information con- cerning locus order,
such as physical localization data, one usually chooses the "ordered loci"
to be a pair of linked and highly informative loci, in order to accelerate
the map construction process). The first locus in the list of inserted
loci (specified in the .par file) is placed in each possible interval in
the map. The resulting locus orders are then tested for compatibility with
the database; each order not excluded is subjected to a full max- imum
likelihood estimation. The order having the highest log 10 likelihood is
found, and any order whose log 10 likelihood is less than this one by
more than a specified tolerance (PUK_LIKE_TOL or PK_LIKE_TOL) is
eliminated. The resulting collection of non-excluded orders form an
orders object (called orders_temp in the program output). The database
is then updated by (1) appending orders_temp to it; and (2) for each
orders ob- ject already in the database, eliminating any order which is
incompatible with orders_temp. If orders_temp has fewer, or the same
number, of orders as does the map, then it becomes the new map and the
added locus is deleted from the list of inserted loci. Otherwise the
next locus in the list is tried in the same manner. If no locus in the
list meets this criterion, the one with the smallest orders_temp is
added to the map. Each time a locus is added to the map, the program
returns to the beginning of the (revised) list of inserted loci. The
orders database is kept in memory during the program run; a copy of it
is written to the .ord file every time a new locus is added to the map,
so that the program can be stopped and restarted later without loss of
the information in the database. 

Build first analyzes the "phase known" data (see description of .dat file
structure), for which maximum likelihood computation is much more rapid
than for the full data set. When the map gets too large (i.e. the number
of orders in the map exceeds PK_NUM_ORDS_TOL), build proceeds to find a
set of uniquely ordered loci, starting with the original ordered loci and
adding additional loci which now have a unique place- ment (during this
latter step, the program only makes use of information in the data base;
it performs no likelihood calculations). Using this uniquely ordered set
of loci as the new "map", the program then proceeds to analyze the full
data set, again adding loci sequentially according to the procedure
described above. This portion of the program stops when the number of
orders in the map exceeds PUK_NUM_ORDS_TOL. Build then again extracts a
set of uniquely ordered loci on the basis of information in the orders
database, and prints out sex-specific and sex-averaged recombination
fractions and the corresponding Kosambi centiMorgans for these loci. For
each remaining locus, it prints out the possible placements with respect
to the uniquely ordered loci, along with the log 10 likelihoods for each

If one starts the map with a pair of unlinked loci, then construction of
the map is slower, because in the initial stages the map will consist of
two unlinked linkage groups which can be in either orientation with
respect to each other so that there are more orders to keep track of (and
to place remaining loci in). Conversely, if the initial loci are at 0
recombination fraction (for example, two RFLPs detected by the same
probe), then in all subsequent maps the loci will appear in both possible
orientations, thereby doubling the number of orders. What is worse, the
parts of the program which extract the maximum set of uniquely ordered
loci will never get past these two (since no other locus is uniquely
placed with respect to them). To avoid this, it is advisable to put these
loci later in the list, or (better) to haplotype them us- ing hap_sys or
hap_sys0 in the .par file. It is a good idea to exclude relatively
uninformative loci from the initial map construction process: they tend to
have multiple possible placements, resulting in a large number of orders
to test. Their positions can be determined later using all, or instant. 

With large numbers of loci, it may be necessary to perform several map 
runs using build until one arrives at "the best" set of uniquely ordered 
loci.  Information in the orders database is cumulative between runs, 
provided it is not reinitialized using prepare; it is not necessary to
reinitialize the orders file when using different sets of loci from the
same file.

NOTE: The goal of multilocus linkage analysis is to find the locus order
having the highest likelihood, and identify alternative orders with
comparable likelihoods. Because it is impossible to consider all
possible orders for a large set of loci, it is necessary to adopt a
strategy which makes decisions on the basis of subsets of the loci.
Multiple statistical tests are performed, each with a nonzero
probability for Type I error. Because in- correct orders will sometimes
have significantly higher likelihoods than the correct one, and the
chance of this happening increases with the number of sets of loci which
are examined during the map construction process, it is possible that
the maximum likelihood order would in fact be rejected by build (or any
other published method for map construction, for that matter) because
for some subset of the loci, an alternative order has a significantly
higher likelihood. In our experience this happens only very rarely
(probably because the tests are not indepen- dent), and when it does is
usually due to errors in the data itself. Nevertheless, three
precautions should be tak- en to guard against the possibility of an
erroneous order being adopted by build. First, we usually construct ini-
tial maps using a fairly stringent tolerance for rejecting orders --
PUK_LIKE_TOL at least 3.0 -- and then lower it to 2.0 (and omit the
phase known part of the analysis) for construction of the final map.
Second, it is advis- able to check the final order by running flipsn to
look at permutations of successive triples or quadruples of loci.
Finally, it is a good idea to construct the map several different times,
using different starting pairs of loci and a different sequence for
adding the loci each time.


Displays the grandparental origins of alleles in each child's
chromosomes, for the phase choice having the highest likelihood; gives
the relative probability for this phase choice and for the next most
likely alterna- tive; gives the number and location of recombinations on
each chromosome, and provides, for each chromosome interval defined by
the loci, a list of the chromosomes having a recombination in that
interval. Flags candidate data errors. 

The program begins by finding
maximum likelihood estimates of the recombination fractions (for the
specified locus order); these are then used to find, for each pedigree
in the data set, the particular phase choice having the highest
likelihood for that pedigree. (The algorithm used for this purpose is
described in (Green, 1988)). For each individual with parents in the
pedigree, the chromosomes are displayed (maternal first, then paternal).
(The individual's ID # appears to the left of the maternal chromosome.)
A '0' (resp. '1') means the allele is of known phase and is of
grandmaternal (resp. grandpaternal) origin. For alleles of unknown
phase, 'o' and 'i' are used instead, denoting the grandparental origin
in the most likely phase choice. '-' means that the locus is

To the right of the chromosome appears the number of recombinations. 
Beneath the chromosome appear the names of any informative loci which 
are out of phase with the nearest informative loci on either side; when 
the loci are closely spaced, these are candidate data errors. 

Following the chromosome pictures for all the families is a list of 
recombinant chromosomes. For each pair of loci, the chromosomes are 
listed in which there is a recombination between those two loci (and no 
intervening locus is informative). The recombinant chromosomes are 
designated by family number, individual ID #, and M or P (for maternal 
or paternal). 

Following the list of recombinant chromosomes is a list of groups of loci 
not separated by any crossovers in the data set; and finally, the maximum 
likelihood recombination fractions which were used in computing pro- 
babilities of the phase choices.


For a set of loci in a specified order, finds the associated maximum 
likelihood recombination fractions and map distances and the corresponding 
log 10 likelihood. The recombination fraction between any pair of adjacent 
loci may be held fixed by using fixed_dist in the .par file.


For each locus order obtained by permuting an n-tuple (n >= 2) of
adjacent loci within an initial (refer- ence) locus order, displays the
relative log 10 likelihood (i.e. the log 10 likelihood of the reference
order, minus that of the permuted order). For example, flips3 will find
relative log 10 likelihoods for all permutations involving tri- ples of
adjacent loci. (If n is omitted it is assumed to be 2). When n is 2,
relative log 10 likelihoods for all per- mutations are displayed; for
higher values of n, only the permutations whose log 10 likelihoods are
at least that of the original order, less PUK_LIKE_TOL, are displayed.

Note: the total # of orders which must be evaluated is

  (m - n + 1)(n! - (n-1)!) + (n - 1)!

where m is the total # of loci; thus it is impractical to use this
option with large values of n. However, when use_ord_file is set to 1 in
the .par file, permutations incompatible with the orders database are
eliminated before calculating likelihoods, which will sometimes make it
feasible to use larger n's.


Finds a uniquely ordered set of loci, and the possible placements of the 
remaining loci with respect to them, using only the information already 
existing in the .ord file (the .ord file must have been created in a 
previous build run). Log 10 likelihoods for the different placements 
of the non-uniquely ordered loci are computed.


Merges two .gen files, having overlapping sets of families and/or loci.
The option is run interactively: in response to prompts, the user enters
the names of the two files to be merged, and the name for the merged out-
put file. File names must be complete (i.e. the .gen suffix needs to be
included). The program compares family IDs and locus names in the two
input files, and for families having the same ID compares individual ID
#s; it then merges the data from the two files into a single .gen file,
taking overlaps into account. A family appearing in both files need not
have exactly the same individuals (in which case the output file will have
a merged family with all individuals appearing in either input file); the
program checks individuals which do occur in both input files to make sure
their mother and father ID #s and sex agree, and if they don't displays an
error message. If the two files have some loci in common, the genotypes at
those loci in the output file for an individual appearing in both input
files will be those from the second file.  

Note: although the command format for using this option is the same as for
the other options, the chromosome number is ignored (although it must
appear), and no .par file is needed.  N.B. Be careful when merging files
containing, say, CEPH and disease families that there are no spurious
matches between family IDs. For this purpose it is useful to use the fact
that family IDs may be arbitrary character strings; so for example CEPH
family IDs may be prefixed with "CEPH".


This option (which is run interactively) must be run before any of the
other options, to create the .dat and .ord files used by them. It will
also create a .par file (or modify an existing one), however once you are
familiar with the format of the .par file you will probably find it
quicker to create and modify that file with a text editor, rather than use
prepare repeatedly. 

Example of usage:

 crimap 7a prepare

Prepare first searches for the .gen and .dat files of the correct name
(in the above example, chr7a.gen and chr7a.dat). If there is no .dat
file, one is created from the .gen file; if both exist, the program asks
whether a new .dat file should be created from the .gen file (select
this option if the .gen file is more recent). If neither exists, a
message to that effect is given and the program terminates. As the .dat
file is being created, family IDs are displayed and any non-inheritances
are noted. (These should be checked carefully: a missing data item in
the .gen file will generally result in multiple non-inheritances and
incorrect family IDs for the subsequent families. If non-inheritances
are found, these should be corrected in the .gen file, and prepare run

Prepare then displays values for several parameter values, including 
whether to compute sex-averaged or sex-specific likeli- hoods, and 
tolerances which control convergence of the maximum likelihood search 
and the map building pro- cess (see description of .par file, above). 
The displayed values are the defaults (given in the description of the 
.par file) or the values specified in any preexisting .par file of the 
same name. The user is then prompted to change any of these values, 
if desired. 

Any haplotyped systems, or fixed distances (see description of .par file) 
in a preexisting .par file are then displayed, and you are prompted to 
enter any new ones if desired. (prepare cannot be used to modify or delete 
systems in the preexisting file; you must use a text editor for this). 

Prepare then displays the names of the other CRI-MAP options, and asks 
which one it should set up the .par file for. Depending on the option 
chosen, the user is now prompted to specify the indices of the "ordered 
loci", and of the "inserted loci". The ordered loci are generally fixed 
in that order during subsequent analysis, while the in- serted loci are 
tried in all possible positions.  When nothing is to be assumed about 
locus order only two ordered loci should be specified. (Twopoint is a 
special case; see its description below). 

For the map-building options (build, instant, and quick), the ordered 
loci form the initial map. As the default (in this case only), if request- 
ed, prepare will sort the loci by informativeness of the phase known 
data and designate the two most informa- tive loci as the ordered loci, 
and the remaining loci as inserted. For the options fixed, flipsn, and 
chrompic, all loci are specified as ordered, and none are inserted. 
See the description of the .par file, or the relevant option, for further 

If the option build has been specified, prepare initializes an orders
file, unless one already exists, in which case it asks whether the
existing one should be used.  Finally, prepare asks whether to create a
new .par file containing the information you have input. You have the
option at this point of aborting the creation of a new file, in which case
any preexisting .par file will remain intact. 


Similar to instant, except that log 10 likelihoods are not computed.


Performs twopoint linkage analysis for each pair of loci. In the case of
sex specific analyses, the out- put for each pair appears on three lines:
the first has the locus names, followed by the maximum likelihood esti-
mates, f and m, of the female and male recombination fractions, and the
sex-specific LODS ( = log 10 like(f,m) - log 10 like(.5,.5)). The second
line has the values of log 10 like(r,m) - log 10 like(.5,.5) for r = .001,
.01, .05, and all multiples of .05 up through .5. The last line similarly
has the values of log 10 like(f,r) - log 10 like(.5,.5). Only pairs for
which the LODS exceed PUK_LIKE_TOL are printed out. (You can get tables
for all pairs by setting PUK_LIKE_TOL = 0.0). 

When SEX_EQ is 1, the sex-averaged recombination fractions, LODS, and 
log 10 likelihoods are given instead. 

Haplotyped systems may be used for either locus in a pair. If the loci 
within a system are not forced to have 0 distance, then maximum likelihood
intralocus distances are found simultaneously with the maximum
likelihood interlocus distances, and are subsequently held fixed at
these values when likelihoods are calculated at the above specified
interlocus distances. 

If both ordered_loci and inserted_loci are specifed in the .par file,
tables are only computed for pairs consisting of one locus from each list.
This is con- venient, for example, when one has just added data for a new
set of loci to a file and wishes to look for linkages with the old loci.
If only one list (either ordered_loci or inserted_loci) is non-empty, LOD
tables are computed for all pairs of loci within that list.


Memory management 

Memory is allocated dynamically by the program, as needed; however, since
repeated re- quests of memory from the operating system (via the C stdio
library function malloc) are quite timeconsuming, we have adopted a
strategy in which two large initial blocks of memory are first allocated.
One of these is reserved for the objects comprising the orders database
and is apportioned out as needed by the function our_orders_alloc, while
the other is used for all other memory requirements and is apportioned by
our_alloc. If the amounts initially allocated are insufficient, additional
memory is requested from the operating system (via the library function
malloc). (When this happens, a statement to that effect is printed). The
default initial request of 3Mb (for our_alloc) will suffice for nearly all
runs. If the program requests more memory than the operating system can
provide, the program terminates with the message "ERROR: ALLOCATION FAILED
IN MORECORE". If this happens, try setting the amount of memory specified
in the .par file (using the parameter nb_our_alloc) to the maximum amount
the operating system will allow you (and see if your system manager can
increase that amount by adjusting operating system parameters). If that
fails, you may need to reduce the number of loci being analyzed (often,
deletion of a single locus having a large number of switches may suffice),
or to split a large pedigree into subpedigrees. I would be interested to
learn of cases where that is necessary, since it may be possible to
improve the switch algebra algorithm to avoid it.


When mapping large numbers of loci, the individual family likelihoods
involve products of many factors numerically less than 1, and so can
become quite small. If possible, at compilation time you should select an
option for representation of floating point numbers which allows them to
be as small as possible. With VAX C, this is done by using the /g_float
option with the compile command cc, and then linking with the vaxcrtlg

16 bit integers 

C compilers usually represent integer variables with either 16 or 32 bits.
The source code pro- vided to you assumes that you have a "32 bit"
compiler. If, when you first try to run the program, it terminates
prematurely, displaying the message "Your compiler uses a different size
for integers; see documentation for changes that will have to be made in
the source code", then you have a 16 bit compiler. To get the program to
run correctly you must then make the following two changes in the source
code and recompile:

 (1) in the file "defs.h", replace the line

     typedef int INT;
     typedef long INT;

 (2) Consult the manual for your compiler and determine the name of a
     library memory allocation function which takes long integers, 
     instead of integers, as arguments (the function malloc takes 
     integer arguments).  The name of this function will be something
     like "mlalloc" (Lightspeed C) or "_halloc" (Microsoft C, ver 4.0
     or later). Whatever it is, substitute it for "malloc" throughout
     the source files our_allo.c and our_orde.c. If your compiler does
     not have such a function, you will be unable to run CRI-MAP.


The main change in version 2.2 is the incorporation of a more efficient
"switch algebra" algorithm which in many cases (particularly for large
pedigrees) substantially reduces memory requirements and running time.
Thus for many runs, even with a relatively large number of loci, the
default memory request of 3 MB (for nb_our_alloc in the .par file) is
overly generous. 

Version 2.3 incorporates a better switch algebra algorithm which further
reduces memory and running time. Additional changes have been made which
should make this version compatible with most C compilers. 

Version 2.4 incorporates the following changes: 

(1) Haplotyping: 
it is now possible to constrain the loci in a haplotyped
system to be at 0 distance from each other for all likelihood
calculations. In addition, haplotyping can be used with all of the
options; in particular, twopoint LOD tables can be generated for pairs
of haplotyped systems. Haplotyped systems are now specified in the .par
file (and there is no longer a .hap file). 

(2) Fixed distances:
it is now possible to fix the distance between a pair of adja- cent loci, 
for the purpose of likelihood calculations, with the options fixed and

(3) The .ord file:
it can optionally be used to prescreen locus orders generated by all and
flipsn, to reduce the number of orders for which likelihoods need be
calculated. (Previously the .ord file was only used by the mapbuilding
options). The parameter use_ord_file in the .par file controls the use of
the .ord file with these options. 

(4) twopoint:
it is now possible to divide the loci into two different groups, and
generate LOD tables only for pairs consisting of one locus from each
group (this makes it possible, for example, to conveniently generate LOD
tables between "new" loci and "old" loci). The parameter PUK_LIKE_TOL is
now used as a LOD cutoff: tables are displayed only for locus pairs
whose LOD exceeds PUK_LIKE_TOL. 

(5) chrompic: 
chromosomes are now labelled by individual ID # (not position within
.gen file); phase designations are now always consistent across pedigrees
(not merely across chromosomes) even in the phase unknown case; the
relative probabilities for the best phase choice and the next most likely
alternative are given; the phase designations now distinguish phase
known from phase unknown. 

(6) merge:
a new option, merge, makes it easy to merge two .gen files having
overlapping sets of families and/or loci.  Other changes in v. 2.4,
include: family IDs are now allowed to be arbitrary character strings (not
merely integers as in previous versions); the format of the .par file has
been changed to make it easier to use; the orders displayed by all are
sorted by decreasing log 10 likelihood; output of flipsn is easier to read
(the "flipped" loci in each order are highlighted).


Barker D, Green P, Knowlton R, Schumm J, Lander E, Oliphant A, Willard
   H, Akots G, Brown V, Gravius T, Helms C, Nelson C, Parker C, Rediker K,
   Watt D, Weiffenbach B, Donis-Keller H (1987): Genetic linkage map of
   human chromosome 7 with 63 DNA markers. Proc. Natl. Acad. Sci. USA 84:
Donis-Keller H, Green P, Helms C, Cartinhour S, Weiffenbach
   B, Stephens K, Keith T, Bowden D, Smith D, Lander E, Botstein D, Akots
   G, Rediker K, Gravius T, Brown V, Rising M, Parker C, Powers J, Watt D,
   Kauffman E, Bricker A, Phipps P, Muller-Kahle H, Fulton T, Ng S, Schumm
   J, Braman J, Knowlton R, Barker D, Crooks S, Lincoln S, Daly M,
   Abrahamson J (1987): A genetic linkage map of the human genome. Cell 51,
Goldgar D, Green P, Parry D, Mulvihill J (1989): Multipoint
   linkage analysis in Neurofibromatosis Type I: An international
   collaboration. Am J Human Genetics 44: 6-12. Green P (1988): Rapid
   construction of multilocus genetic linkage maps. I. Maximum likelihood
   estimation. (draft manuscript). 
Kernighan B, Ritchie D (1978): The C program- ming language. Prentice-Hall. 
Lander E, Green P (1987): Construction of multilocus genetic linkage maps 
   in humans. PNAS 84, 2363-2367. 
Ott J (1985): Analysis of human genetic linkage. Johns Hopkins University