Jurg Ott / 20 January 2015
ott@rockefeller.edu
(written with KompoZer)
Table of Contents
The
LIPED program (for
LIkelihoods
in PEDigrees)
estimates the recombination fraction by calculating pedigree
likelihoods for various assumed values of the recombination fraction.
The algorithm is based on Elston and Stewart (1971) with some
extensions. Its first application (to the large Alaska pedigree,
Schrott et al. 1972) resulted in mild evidence for linkage of
familial hypercholesterolemia to the C3 polymorphism (Ott et al
1974), which was later confirmed by various authors. This disease
locus (LDLR, previously FH and FHC) is now known to be located on
chromosome 19p13.3.
The program contained one error (in the likelihood calculation for
quantitative traits), which was pointed out to me by Dr. Robert
Elston. Details on the theory underlying LIPED and its likelihood
calculations may be found in Thompson (2011).
This manual
describes the LIPED computer program
for
genetic linkage analysis. Only two loci can be handled at a time, for
example, a disease locus and a marker locus. Originally written in
Fortran IV (Ott 1974), LIPED requires input in fixed format (numbers
must be provided in a fixed number of spaces or columns). The code is
essentially as originally written, with some additions such as proper
treatment of age of onset data. This manual describes a slightly
updated
version of LIPED
suitable for compiling in Fortran 77 (GNU g77 in Windows, GNU gfortran
in SuSE Linux).
Files included:
liped.for Source code of LIPED program
liped .exe, lipedL  Executable codes for Windows, Linux
liped5.dat Input file holding 5 examples. It is highly recommended to carefully look at the examples provided!
EX1.DAT  The first of the sample files in liped5.dat.
LIPED.OUT  Output file resulting from running LIPED5.DAT.
To initiate the program, type LIPED. It will assume that input is furnished in a file called LIPED.DAT, and will write output to LIPED.OUT. To run the 5 examples, copy LIPED5.DAT to LIPED.DAT and then type LIPED. The appropriate literature reference is Ott (1974) or Ott (1976).
Two
kinds of loci are
distinguished in the LIPED program: main locus (internal number zero)
and marker loci (numbered from 1 to NMARK). Lod scores can be
computed for any combination main locus vs. marker locus. With input
item 16 (see section 5, Input File, below), any one of the marker
loci can be declared to represent a new main locus so that lod scores
may also be computed among marker loci. If more than one marker locus
is present, the program creates two temporary disk files that will be
deleted on program termination. Note the following restriction: with
a single marker locus, any number of pedigrees may be analyzed in a
single run. However, with more than one marker locus, only a single
pedigree may be analyzed in a run. One way to overcome this
restriction is as follows. If several independent families are
presented to the program as one single pedigree, LIPED will recognize
this and carry out the proper calculations, the resulting lod score
being the sum over the individual families; however, individual lods
for the families will not be recognizable by the user. Analyzing
several independent families as a single large pedigree requires a
substantial amount of memory. If an error occurs (MNP or MLIST too
small), you may have to analyze the families in the usual manner as
separate pedigrees.
Generally, to analyze several pedigrees
with phenotypes at more than two loci, one might proceed as follows.
First, one decides on the two loci to be analyzed. If one of these is
the main locus, then the comparison main locus vs. marker is
identified on one line of input item 9. Otherwise, all numbers in
input item 9 are set equal to 0, and a comparison among markers is
defined on a new line of input item 9 that appears after a line
containing 5000 which immediately follows the pedigree data. Thus
far, one has decided on the 2 loci to be compared. Now, one must tell
LIPED where on the line (in which columns) to read the phenotypes of
these loci, which is done with FORTRAN Format expressions (see
beginning of section 5) furnished in input item 4.
If you
interrupt LIPED while it is still running and if more than one marker
locus has been defined, scratch files named "ZZ..." or
"for..." will remain on the disk. These files would be
deleted when the program terminates normally. You may simply delete
them.
Penetrance
is defined as
the probability of occurrence of a particular phenotype given the
presence of a certain genotype. Accordingly, with respect to a
disease, penetrance is the probability of being affected given a
certain genotype.
Penetrances are needed to describe the
relation between genotypes and phenotypes. In the following three
simple and common cases, only full penetrance (values 0 or 1 only) is
assumed to occur. Assume a locus with 2 alleles, T and t. When this
is a disease locus, let T be the dominant disease allele and consider
the phenotypes AFF for affected and NA for unaffected.

Phenotypes 


Dominant, T > t 
Recessive, T < t 
Codominant trait 

Genotype 
AFF 
NA 
AFF 
NA 
TT 
Tt 
TT 
T T 
1 
0 
1 
0 
1 
0 
0 
T t 
1 
0 
0 
1 
0 
1 
0 
t t 
0 
1 
0 
1 
0 
0 
1 
To
code for Xlinked
inheritance in LIPED, tables such as the one above are used to
represent the relation between genotypes and phenotypes. They apply
directly for females. For males, for example, the genotype T/T is
interpreted as T/y (hemizygote), and all lines corresponding to
heterozygote genotypes are disregarded. Therefore, it is not
necessary to distinguish male and female phenotypes. For example, TT
can serve as a phenotype for either sex.
To analyze loci on
the Ychromosome, it is easiest to code Ylinkage as a special case
of autosomal linkage, but precautions must be taken. For details, see
Ott (1986); note that the methods described in that reference apply
only to full penetrance.
For most input quantities, their location (column numbers) on an input line is fixed and must strictly be adhered to. The input file must consist of the following lines here numbered 5.1 through 5.16.
Col.12
NMARK,
number of marker loci in addition to the main locus. LIPED stops when
NMARK<1 is encountered.
Col. 4
= 0 LIPED prints results to screen and to file, liped.out
= 1 and 2 LIPED prints internal information not generally interpretable)
= 3 LIPED only writes to output file, no screen output
Col. 5
= 0 usual setting
= 1 to prevent underflows as much as possible.
This should be used only when an underflow has occurred since
underflows are unlikely with double precision calculations.
Col. 6
= 0 for autosomal loci
= 1 for loci on the Xchromosome
Col. 7
= 0 if gene frequencies (rather than
haplotype frequencies) will be read
= 1 if haplotype frequencies
are to be read. Note that in this case, dummy gene
frequencies must still be provided.
Col.
820 Mutation
rate at main locus (see MUTATION below).
Col.2180 Text
In columns 
Provide symbol for 
912 
No parent (e.g., 0) 
1316 
Male sex. The first different sex symbol encountered in item 14 will be considered the symbol for female sex. 
1720 
Unknown phenotype at main locus (e.g., 0) 
2124, etc. 
Unknown phenotype at marker locus 1, etc. 
Col.
12 Number of
alleles at main locus (locus 0)
Col. 34 Number of alleles at
marker locus 1, etc.
Col.
12 Number of
phenotypes at main locus
Col. 34 Number of phenotypes at marker
locus 1, etc.
Note:
For locus types 1, 2, and 3, the program will not read
phenotypes but certain parameters, for example, mean and variance for
a quantitative trait. The number of these parameters is fixed for
each locus type. Here, in line 5.7, you may specify 1 for number of
phenotypes (parameters) as the program will not use any
value given here, but a value has to be provided anyway.
5.5.
Locus type
Col.
12 locus type for main
locus
Col. 34 locus type for marker 1, etc., where the following
values for locus type apply:
1 
Locus with discrete phenotypes and penetrances ranging from 0 through 1. 
0 
Locus with discrete phenotypes and penetrances of 0 and 1 only; runs faster than locus type = 1. 
1 
Quantitative phenotypes following conditional normal distributions (see section 9). The number of parameters to be entered for number of phenotypes is 2: mean and standard deviation. 
2 
Locus with agedependent penetrances following a lognormal distribution (see section 10) 
3 
Locus with straightline agedependent penetrances. 
Col.
2 Output option for main
locus versus marker locus 1
Col. 4 Output option for main locus
versus
marker locus 2, etc., where the option values have the following
effect (below, r_{m} and
r_{f} =
male and female recombination fractions, respectively):
Below
is a graphic
representation of the values of r_{m} and r_{f}
at
which lods will be computed depending on the option value; instead
of 0 as indicated below, recombination fractions are set to
0.0001.
Option = 4 (22 points)
r_{m}
.50  x x x x x
x x x
.40 
x x
.30 
x x
.20 
x x
.10 
x x
.05  x
x
.001 x
x
0  x
x
+
0
.001 .05 .1 .2 .3 .4 .5 r_{f}
Option
= 5 (34 points)
Analogous to above but with 34 points, recombination fractions at rvalues
0 .001 .05 .1 .15
.2 .25 .3 .35 .4 .45 .5
Option
= 6 (9 points) Option = 7 (16 points)
rm
.5  x x x .50 
x x x x
.3
 x x x
.35  x x
x x
.1
 x x x .20  x
x x x
+ .05  x x
x x
0.1 0.3 0.5 rf
+
0.05 0.20 0.35 0.50 rf
Option = 8 (64
points)
rm
.500  x x x x x x
x x
.400  x x x x x x
x x
.300  x x x x x x
x x
.200  x x x x x x
x x
.100  x x x x x x
x x
.050  x x x x x x
x x
.001
 x x x x x x
x x
.0001 x x x x x x
x x
+
.0001 .001 .05 .1 .2 .3 .4 .5
rf
Option 8 is useful for approximate
factorization of joint male and female lods into sexspecific lods.
This text applies
with option = 9 on line 5.6. Each
pedigree will be
analyzed at the r_{m},r_{f}values
provided here.
Col. 1 5 Value for
male recombination fraction
Col. 610
Value for female recombination fraction. These two values are each read
in 5 spaces with an implied decimal point between space 1 and 2. For
example, an input line for r_{m}
= 0.1
and r_{f} = 0.45 may look like this: b1000b4500, or
bbb.1bb.45, where b stands for blank (space). For each likelihood to
be computed, one such line of values must be provided. To terminate
the set of r_{m},r_{f}values,
enter 60000 as the
last line. Maximum number of lines including the terminating line is
equal to MT (see section 6).
The following lines, 5.8 through 5.10, must be
repeated for each locus in the order main locus, marker
locus 1, marker locus 2, etc.
The following items
are expected on this line, each occupying 4 spaces:

name of locus
 symbol for
allele 1
 symbol for allele 2,
etc.
 symbol for phenotype 1
 symbol for phenotype 2, etc.
Col.
1 8 Population
frequency of allele 1
Col. 916 Population frequency of
allele 2, etc.
As many lines specified below are expected as there are genotypes at the given locus. In the case of Xlinkage, this refers to the female genotypes. For males, with Xlinkage, a genotype A/A is interpreted as A/y while heterozygote genotypes such as A/a are disregarded. On each line (for each genotype), the following values are expected, each in 4 spaces. For the penetrance values, an implied decimal point is to the right of the 4 spaces. For example, bbb1 will be read as 1.0, and b.95 will be read as 0.95, where b stands for a blank space.

symbol for first
allele
 symbol for
second allele; these two define a genotype
 probability of
observing phenotype 1 under the given genotype

probability of observing phenotype 2 under the given genotype,
etc.
The above applies to locus types of 0 or 1 (line 5.5). For
quantitative phenotypes (locus type = 1), four items
are expected for each genotype, two alleles (defining the genotype)
plus a mean and a standard deviation, each within 4 spaces. For
agedependent penetrances
(locus types 2 or 3), each line must contain two allele
symbols
plus six parameters, ie. three parameters for females and three
parameters for males (the three sexspecific
parameters are defined
in section 10).
The following
input lines, 5.11 through no. 5.14, are to be repeated for
each
pedigree except that input item 5.14 is needed only once, after the
first pedigree.
Col.
1 4 Number of
individuals in pedigree. Count a "doubled" individual as 2
persons (see section on complex pedigrees).
Col. 5 8 Number
of (pairs of) doubled individuals; = 0 for simple pedigrees
Col. 968 optional comments
For each
individual, the following items
must be provided, one line per individual:
max.length
 symbol identifying the individual (ID) 4
 ID for one
of the parents *)
4
 ID for the other of the
parents *) 4
 symbol for individual's sex
4
 phenotype at main locus
8
 phenotype at marker locus 1, etc. 8
*) Note that each individual must either have two parents in the
pedigree, or both parents' ID may be replaced by the symbol for no
parent. If you have information on only one parent, you must provide
an ID for the other parent who will then have unknown phenotypes.
The above limits seem rather restrictive. However, changing them in the source code is tricky as the code had been written to save as much memory as possible and various shortcuts had been implemented to achieve this. So, rather than changing source code it will be simpler to use the Idnum program to replace IDs by consecutive numbers.
Applies
only to complex
pedigrees (number greater than zero in col. 58, section 5.11).
For simple pedigrees, no input item 5.14a is expected. To be read
with 4 spaces each as any ID. For each pair of doubled
individuals, the following two items are required:
 ID of first
member of pair of doubled individuals
 ID of second member of
pair of doubled individuals
This
information is needed
only once, after the first pedigree, and only with a value of 1 in
col. 7, section 5.1. The haplotype frequencies are read with 8
spaces as the allele frequencies. These values must be
given in the following order. Consider a main locus and a marker
locus where n is the number of alleles at the
marker locus.
Then, the order of the haplotype corresponding to the ith
allele at the main locus and the jth allele at the
marker
locus is given by n(i 1) + j.
As an
example with 2 alleles at the main locus and 3 alleles at the marker
locus, the haplotypes are numbered as follows:
j=1 j=2 j=3

i=1 1 2 3
i=2 4 5 6
Note: there is no check that the haplotype
frequencies sum to 1.
This
information is needed
only with output option = 1 (section 5.6). Then, as many likelihood
calculations will be carried out as sets of recombinations (lines) are
provided here. Each line is read with 5 spaces (cf. section
5.7):
Col. 1 5 value for male recombination fraction
Col. 610
value for female recombination fraction.
As the terminating line,
enter 60000. For output options > 1,
multiple sets of
recombination fractions, separated by
60000, must
be entered.
Col.
14 Value to
determine what action to take next.
= 5000 if new output options (section 5.6) are to be read (allowed only
if no more than one
pedigree is present in this problem) thus allowing for linkage
analyses between marker loci. The new lines are expected immediately
after the 5000 value. On each line, there must be as many values as
there are marker loci. The program will scan these values and the first
marker locus with a value different from zero will be considered the
new main locus. From then
on, on that line, the output optionvalues have the same
meaning as in section 5.6.
Multiple lines of output options may follow a single
5000 value. For example, consider a total of 5
loci, that is, one main locus (locus no. 0) and 4 marker loci
(numbered 1 through 4):
_
_2_2_2_2 ← original output options
_
5000
_1_2_2_2 
_0_1_2_2  extra lines of output options
_0_0_1_3 
9000
Each
of these extra lines of output options has one field for each of the
original marker loci. For instance, the following extra line,
_0_1_2_2, would mean: "now take marker locus 2 as the new
main
locus, and pair it with marker locus 3 (using option 2), then with
marker locus 4 (using option 2)", which could be extended to all
marker loci. Note that whenever option 1 is specified, a
corresponding set of recombination fractions (section 5.15) is expected
immediately
after the line containing option 1. To terminate the set of extra
lines, enter a line with 8000 or 9000 (same meaning
as below) in col. 14.
= 7000 if a new pedigree is to be
read. Then, new lines of pedigree information (section 5.11) etc. are
expected. Note that
this is allowed only when no more than one marker locus is present.
= 8000 if a new problem is to be analyzed. Then, new lines of input
are expected from the beginning (section 5.1).
= 9000 to terminate this run.
Constants
are defined for dimensioning arrays and defining lengths of character
variables. Example values are as follows.
MLIST
= 50 headsibs (nuclear families)
MMARK = 30 marker loci in
addition to the main locus
MNAL = 5 alleles at any locus
MNDI = 5 pairs of doubled individuals
MNFE = 21 phenotypes at any
locus
MNP = 50 genotype vectors stored in memory
MNPT = 500
individuals in a pedigree
MT = 20 pairs of theta values after
item 5.9, including the terminating 6000 line.
IDLEN
= 20 characters in individual IDs
To change
these, simply adjust the values of the constants in the parameter
statements and recompile the program.
The following
information is for programmers only and is not needed for general
program use. With the abbreviations,
KK = MNAL*MNAL
KK1 = MNAL*(MNAL+1)/2,
KK2 = KK*(KK+1)/2,
the
array dimensions are given as follows (the arrays not listed below
have fixed dimensions):
FENO1(MNPT,KK1)
IAD(KK,KK) LIST(MLIST) PHI(KK2)
FENO2(MNPT,KK1) IAU(MMARK) NAL(MMARK) PHIS(KK)
GEN(MNAL) ID(MNPT)
NC(MNPT) PHPROB(MNFE)
GENO(MNP,KK2) IGENO(MNP) NF(MMARK)
THV1(MT)
GF1(MNAL) ISEX(MNPT)
NM(MNPT) THV2(MT)
GF2(MNAL) KONT(MMARK) NS(MNPT)
THVS(MT)
GVX1(MNFE,KK1) LDI(2,MNDI) PHE1(MNFE) UNK(MMARK)
GVX2(MNFE,KK1) LGC(MNDI) PHE2(MNFE)
HOLD(MNDI,KK2) LGENO(MNPT) PHEPED(MMARK)
Note: MNFE must have a value of at
least 8.
In
a socalled simple pedigree, tracing the inheritance of genes by
going backwards through the generations (upwards in the pedigree)
always leads to the same pair of founder parents. Pedigrees for
which this is not the case are called complex pedigrees. In
particular, pedigrees with the following features are examples of
complex pedigrees: (1) both members of a pair of parents have
themselves parents in the pedigree; (2) consanguinity loop, i.e.,
parents are related; (3) marriage loop, e.g., two brothers are
married to two sisters, or an individual has been married twice, the
two spouses being related with each other but not with the individual
who married twice. An example of the last kind is pedigree 1, below,
where [] refers to a female and () refers to a male:
Pedigree
1: marriage loop
[1].(2)

..


(3).[4].(5)


(6)
(7)
Without special measures, LIPED analyzes only
simple pedigrees. The analysis of complex pedigrees is possible by
manipulating the pedigree in a certain way so that it "appears"
to LIPED as a simple pedigree. This manipulation consists of
replacing a particular individual by two individuals as shown in the
example below (pedigree 2), and by identifying the two individuals
actually corresponding to the same individual in the original
pedigree (input item 5.14a). Note that such a "doubling" of
individuals is necessary for breaking up loops, and also whenever
more than one of the two parents has parents in the pedigree.
When
an individual has been "doubled", the number of individuals
in the pedigree must be increased by 1 thus counting a pair of
doubled individuals as two persons. Up to MNDI individuals may be
"doubled" so that, e.g., multiple consanguineous loops can
be accommodated. At the end of a pedigree with doubled individuals,
the two individuals corresponding to the one original person must be
identified (input item 5.14a).
Pedigree 2: Example of a
pedigree, manipulated for processing by LIPED
Original
pedigree
Modified pedigree, acceptable to
LIPED
[1.1].(1.2).[1.3] [1.1].(1.2).[1.3]








(2.1).[2.2]
(2.1a) (2.1b).[2.2]




[3.1]
[3.1]
Here are some important notes
regarding "doubling" of individuals:
Individuals can only be doubled, not tripled. For example, an individual who is an offspring and is also married twice with children from each marriage cannot be manipulated as described.
Computation time generally increases drastically with the number of pairs of doubled individuals. When one has a choice among several candidates to be doubled, it is recommended to take an individual with as much phenotypic information as possible in order to exclude as many genotypes as possible. For example, in pedigree 1, above, any one of individuals 3, 4 or 5 could be chosen for doubling. In the presence of one doubled individual, the QLIK routine for calculating the likelihood is executed for each genotype of that individual, except for those genotypes known to be incompatible with the individual's phenotypes or the phenotypes of his or her offspring. Analogously, for several pairs of doubled individuals, QLIK is called a maximum of m times, where m is calculated as follows. Let n be the number of haplotypes at the two loci jointly, i.e., n is the product of the number of alleles at the two loci under consideration. The number of joint genotypes is then given by g = n(n + 1)/2, so that m = g^{NDI}, where NDI is the number of pairs of doubled individuals. For example, with 2 and 3 alleles at the respective two loci, one has n = 6 haplotypes and g = 21 genotypes. With NDI = 3 pairs of doubled individuals, QLIK may be called up to m = 9261 times. The present version of PCLIPED counts these calls and displays them on the screen.
Whenever the genotype of an individual can unequivocally be inferred with certainty (including phase), such an individual may be represented as multiple individuals in the pedigree if necessary, and this individual must not be counted as a socalled doubled individual (treat it as separate multiple individuals). The likelihood will then not be correct but the lod score will be unaffected by such a manipulation. For example, if an individual is known to be A/A at locus 1 and B/b at locus 2, the joint genotype is known to be AB/Ab. Note that for doubly heterozygous individuals it will not generally be possible to make use of this feature even though phase may be known, as there is no easy way to identify phases in LIPED on the basis of phenotypes.
Mutation
is allowed for at
the current main locus only and is assumed to occur with a constant
rate from any of the alleles no. 2, 3,... towards the first allele,
with the mutation rate being specified in col. 820 of input line
5.1. Backmutation is assumed to be negligible. Also, in the
computation of the likelihood, it is assumed that a mutation occurs
only in one or the other of two parents, but not simultaneously in
both parents.
WARNING: when processing a disease locus with
mutation and subsequently, in the same run, testing marker versus
marker, then the mutation rate keeps applying to the current main
locus unless a new run is carried out with the mutation rate set
equal to zero.
Note that only simple pedigrees can be
processed by LIPED unless special steps are taken to code for a
complex pedigree (see section 7 above).
At
any locus, quantitative rather than qualitative phenotypes can be
read. For a locus with quantitative phenotypes, the following special
rules must be observed.
Input
item Explanation

3 Read one mean and one standard deviation
for each genotype, 4 spaces each as for any phenotype
7 Set the number of phenotypes equal to
2. The program will correct wrong numbers.
8 Set the locus type
equal to 1.
10 Two phenotype symbols will be read by the
program but they are not used in any way.
12 After the
symbol for the second allele, two items are expected, the mean and
the standard deviation of the
phenotype distribution given the
particular genotype specified by the two alleles.
14 The phenotype values must not occupy more than 4 spaces
each.

Agedependent
penetrance
refers to the fact that a carrier of a disease gene may not exhibit
the disease at birth but only later in life, that is, the penetrance
(= probability of showing a certain phenotype given a genotype)
depends on the age of an individual.
The easiest way of
implementing agedependant penetrance is by forming age classes and
having different penetrances in these classes. For affecteds,
irrespective of their age, only one class is required (provided that
no phenocopies are allowed for), but unaffecteds must be grouped into
age classes. For example, in a given disease, if all gene carriers
beyond 10 years of age have expressed the disease, a suitable
assumption is that penetrance rises linearly from 0 at age 0 to 100%
at age 10, as pictured below:
Penetrance

1 
 /
 /
 /
/
0 +age
0 10 20
One might then form 6
classes as follows, where AFF stands for the 'affected' phenotype,
and NA1, NA2, etc. stands for unaffected in age class 1, 2, etc.; NA5
denotes unaffected individuals older than 10 years who are taken to
be known not to carry the disease gene. The disease is assumed
dominant, the disease being T. Note that the probability of being
unaffected is 1 minus the probability of being affected.

Phenotypes

Genotype AFF NA1 NA2 NA3 NA4 NA5

T T 1 .88 .63 .38
.13 0
T t 1 .88 .63 .38
.13 0
t t 0 1 1
1 1 1

Rather
than forming age
classes, the distribution of the age at disease onset may be assumed
to follow a certain distribution. In LIPED, two such distributions
are implemented, the lognormal and a straightline distribution.
Below, F denotes the distribution (cumulative sum) of age at onset
whereas f denotes the corresponding density (histogram).
Whatever
the age of onset distribution used, to represent in a single number
the various pieces of phenotypic information (age at onset, present
age, affection status) at a disease locus, the following conventions
must be observed in LIPED. In principle, the phenotype to be provided
in the input to LIPED is an individual's present age (or age last
seen) or the age at onset, taken with a minus sign for unaffecteds,
and taken to be positive for affecteds. Present age and age at onset
are distinguished as outlined, below.
The phenotype is an individual's present age (or age last seen), taken with a minus sign (the sign distinguishes affecteds from unaffecteds). Example: unaffected, present age is 56, phenotype given in program is 56. If present age is unknown, a guess must be used, for example, based on ages of sibs or parents.
If
actual age at disease
onset is unknown, the phenotype is a person's present age. Example:
56. If present age is unknown, a guess must be used, based on ages of
relatives.
If age at disease onset is known, it is entered
into LIPED by the following coding scheme: The phenotype to be
provided is obtained by adding 500 to the age at onset. Example: age
at disease onset is 23 years; phenotype to be provided is 523.
NOTE: Actual age at disease onset is relevant only when disease can occur
under different genotypes with different penetrances. If this is not
so (it usually is not), then present age may be given for all
affecteds.
The phenotype is given as 0. Alternatively, on input line 5.5, one may define any other code for unknown phenotype, for example, blank (not recommended).
It
is often meaningful to assume that age of onset is lognormally
distributed, that is, that LN(age of onset) follows a normal
distribution where LN denotes natural logarithm (a simpler assumption
for ageofonset distribution is covered in section 10.4, below).
Mean and standard deviation for the lognormal and normal
distributions are defined and connected with each other as follows:
Age
(orig. values) LN(age)
(lognormal distr.) (normal
distr.)

mean
μ
u
std. dev. σ
s

For
ease of presentation, define m = exp(u) and w = exp(s^{2}).
Then one has:
μ = m √w
σ = m √[w(w
1)] = μ √(w 1)
u = 2 LN(μ) 0.5
LN(μ^{2} + σ^{2})
s = √[LN(μ^{2}
+ σ^{2}) 2 LN(μ)] = √[2{LN(μ)
u}],
where LN denotes natural logarithm. Also, with given
mean, μ, of the raw data, and standard deviation, s, of the
transformed data, one obtains the mean of the transformed data as u =
LN(μ) 0.5s^{2}.
Some example values are
given in the following table:

Original scale LN scale (normal distr.)
 
μ σ
u
s

20 5
2.97 0.25
20 10
2.88 0.47
20 15
2.77 0.67
40 5
3.68 0.12
40 10
3.66 0.25
40 15
3.62 0.36

The
LOGNORM program (included) transforms values u and s into the
corresponding values of μ and σ, and vice versa.
If
age at onset for an (affected) individual is known, the corresponding
likelihood is simply f(age at onset), where f is the lognormal
density. If age at onset is unknown, then the likelihood is F(age)
where 'age' denotes current age, or age last seen, and F is the
lognormal distribution function. For unaffecteds, the likelihood is
equal to 1 F(age). If the final penetrance, t, is less than
100% then f and F above are multiplied by t.
Lognormal
agedependent penetrance is modeled in analogy to quantitative
phenotypes (see previous section) except that here, 6 parameters must
be specified (3 for females and 3 for males). For each genotype
(input item 5.3), these are
 the mean, u, of LN(age of onset)
 the standard deviation, s, of LN(age of onset)
 the limiting
penetrance, t, when age is very high, for females, followed by the
analogous three parameters for males.
Depending on the values
of the 6 parameters given (input item 5.3) for each genotype, the
following 2 situations can be distinguished. Assume a disease locus
with two alleles, a dominant disease allele, D, and a normal allele,
d.
1. Age of onset follows a lognormal distribution with
parameters u and s, where the final penetrance attained (at high age)
is equal to t. For example (parameters taken to be the same for males
and females), one may have on input item 5.3:
D
D 3.35 0.17 1.0 3.35 0.17 1.0 → u = 3.35, s = 0.17, final
penetrance 100%
D d 3.35 0.17 0.6 3.35
0.17 0.6 → susceptible individuals express disease with
max. penetrance of 60% when they are very old
d
d 3.0 0.1 0.0 3.0 0.1 3.0 → genotype d/d not
susceptible to disease; values of u and s are irrelevant (likelihood
is zero for affecteds and 1 for unaffecteds)
2. Penetrance
does not depend on age but is a fixed value (t for affecteds, 1t for
unaffecteds). To accommodate this situation, set s = 0.0. The value
of u is then irrelevant. For example, one may have
d
d 0.0 0.0 0.01 0.0 0.0 0.01 → t = 0.01, that is, d/d
genotypes express the disease with probability 1%, irrespective of
age (likelihood is 0.01 for affecteds and 0.99 for unaffecteds); the
value of u is irrelevant. This case should be used with great care
since it does not differentiate between age of onset known or
unknown.
In summary, for a locus with agedependent
(lognormal) penetrance, the following special rules must be
observed.
Input
item
Explanation

3 Provide six values on each line (genotype): One mean,
one standard deviation and one final penetrance for each sex.
7 Set the number
of phenotypes equal to 1 (the program will set the correct number of
phenotypes).
8 Set the locus type equal to 2.
10 Six phenotype
symbols will be read by the program, but they are not used in any
way.
12 After the symbol for the second allele, six items are
expected: mean, standard deviation and final penetrance for females,
and the analogous three parameters for males.
14 The value
for the phenotype (age) must not occupy more than 4 spaces. A positive age value
refers to an affected individual, a negative age figure identifies an
unaffected individual. Phenotypes are coded following the rules given
in section 10.2,
above.

F

t  
 /.
 / .
 / .
 / .
 / .
0 + age
A1 A2
F
is the probability of being affected, that is, the penetrance (or
likelihood) is equal to F for an affected individual (age at onset
unknown) and equal to 1 F for an unaffected individual.
According to the figure, above, the ageofonset curve is defined
as
/
0 if a ≤ A1
F =  t(a  A1)/(A2  A1) if A1
< a < A2
\ t if a ≤ A2
where
"a" is an individual's present age, or age last seen. If
age at onset is known (for an affected individual) then the
likelihood (density) is equal to f = t/(A2 A1) if the age of
onset is between A1 and A2, and equal to zero otherwise. If age at
onset is considered a random variable, according to the present
definition and with t = 1, it follows a uniform distribution with
mean (A2 A1)/2 and standard deviation (A2
A1)/3.464.
For a locus of type 3 (straight line age of onset),
coding is very similar to the conventions used for lognormal age of
onset (specific instructions are given below). The phenotypes are the
ages of each individual, taken to be positive for affected
individuals and taken with a minus sign for unaffected individuals.
Zero will be interpreted as unknown, but any other symbol may also be
designated to represent unknown phenotype. For affected individuals
with known age at onset, enter a number equal to 500 plus age at
onset as the phenotype (see section 10.2, above).
As in
lognormal agedependent penetrance, 6 parameters must be specified
but here, they have the following meaning. For each genotype (input
item 5.3), they are (see graph, above)
 the age, A1, at which
penetrance becomes positive
 the age, A2, at which penetrance
reaches its final values
 the limiting penetrance, t, when age
is very high, for females, followed by the analogous three quantities
for males.
Depending on the values of the 6 parameters given
(input line 5.3) for each genotype, the following 2 situations can be
distinguished. Assume a disease locus with two alleles, a dominant
disease allele, D, and a normal allele, d.
1. Age of onset
follows a straightline distribution with parameters A1 and A2, where
the final penetrance attained (at high age) is equal to t. For
example (parameter values taken to be the same for females and for
males), one may have on input item 5.3:
D
D 10 60 1.0 10 60 1.0 → for
individuals with D/D genotype, susceptibility to disease starts at
age 10 and penetrance reaches its maximum of 100% at age 60.
D
d 10 60 0.6 10 60 0.6 → susceptible
individuals express disease with max. penetrance of 60% when they are
60 years or older.
d
d 10 11 0.0 10 11 0.0 → genotype
d/d not susceptible to disease; values of A1 and A2 are irrelevant
(given genotype d/d, likelihood is zero for affecteds and 1 for
unaffecteds).
2. Penetrance does not depend on age but is a
fixed value (t for affecteds, 1 t for unaffecteds). To
accommodate this situation, set A2 = 0.0. The value of A1 is then
irrelevant. For example (same parameter values for males and
females), one may have
d
d 0.0 0.0 .01 0.0 0.0 .01 → t
= 0.01, that is, d/d genotypes express the disease with probability
1%, irrespective of age (likelihood is 0.01 for affecteds and 0.99
for unaffecteds); the value of A1 is irrelevant. This case should be
used with great care since it does not differentiate between age of
onset known or unknown.
In summary, for a locus with
straightline agedependent penetrance, the following special rules
must be observed. An example input file, agedep.dat, is provided in the program package (run it by copying this file to liped.dat and then typing liped).

Set the number of phenotypes equal to 1 (the program will set the
correct numbers).
Set the locus type equal to 3.
For genotype data, after the symbol for the second allele, six
items are expected: starting age (A1), finishing age (A2) and final
penetrance (t) for females, and the analogous three parameters for
males.
The value for the phenotype (age) must occupy 4 spaces. A
positive age value refers to an affected individual, a negative age
figure identifies an unaffected individual. Phenotypes are coded
following the rules given in section 10.2,
above.

To
calculate conditional
genotype probabilities for a specific individual, given all the
family data, one must carry out several likelihood computations and
combine their results as follows. For example, consider an individual
with phenotype 'unaffected' and penetrances as given in the table
below, where D is the disease allele and d is the normal allele at
the main locus.

Penetrance for
phenotypes

Genotype affected unaffected XDd

D D 0.9
0.1 0
D d 0.6
0.4 0.4
d d 0
1 0

For this
unaffected individual, one wants to compute the risk that he or she
has genotype D/d. To obtain this risk, one runs LIPED twice, each
time with a different phenotype assigned to this individual, that is,
in run 1, the individual has phenotype unaffected, and in run 2, the
individual has phenotype XDd. Denote the resulting likelihoods (not
lod scores) by L(ua) and L(XDd). Then, the risk to this individual of
having genotype D/d is given by L(XDd)/L(ua). Note that other
programs, such as the MLINK program of Dr. Mark Lathrop, can compute
genetic risks directly.
With Xlinked recessive deleterious
traits, for a female founder individual (no parents in pedigree), the
prior probability, q, of being a carrier of the
disease gene
is a multiple of the mutation rate, u. For example,
in
Duchenne muscular dystrophy (DMD), q = 4u
(Murphy and
Chase, "Principles of Genetic Counseling"). In the
likelihood calculation of pedigree data, on the other hand, the prior
probability of a founder's genotype is determined solely by the gene
frequency, p. For example, the prior probability
that a
founder is heterozygous is given by 2p(1 p).
Therefore, to implement the prior probability, q,
that a woman
is heterozygous for an Xlinked recessive deleterious gene, in the
likelihood calculation, one must choose the gene frequency of the
deleterious gene, p, such that q
= 2p(1
p) or, approximately, p = q/2
(in DMD, thus, p
= 2u).
In some applications, the likelihood at a single (disease) locus only is needed. For example, one may want to estimate from family data gene frequencies or ageofonset parameters at a single locus. In LIPED, singlelocus calculations are accommodated easiest by defining a dummy second locus with a single allele of frequency 1.
In
a pedigree to be
processed by LIPED, any individual must have either both parents in
the pedigree, or be a founder individual, that is, have both parents
unknown (not in pedigree). Note that siblings cannot be recognized as
such unless their parents are also in the pedigree. If parents
are not actually known, they still must be present in the
pedigree, possibly with all phenotypes coded as unknown.
When
there is at least one known recombination in a pedigree but the value
of the recombination fraction is set equal to zero, then the
likelihood will be equal to zero, and the log likelihood equal to ∞.
On output, ∞ is represented as 99.99.
When the
likelihood is equal to zero, either because recombinants are present
while the recombination fraction (θ, theta) is set to zero or
because of a genetic inconsistency (incompatible genotypes of some
individuals), LIPED will report this with the message, "L(r_{m},
r_{f} = 0 at r_{m}, r_{f}
=...", and
will print the male and female recombination fractions at which the
likelihood is zero, and sequential number and ID code of the
individual at which this was first detected. An incompatibility then
exists among the indicated individual and his or her spouse(s) and
descendants. Note that additional incompatibilities not yet detected
may exist in the given pedigree. Using θ = 0, this helps to
find recombinations in a pedigree. In families with loops (eg,
inbreeding), this scheme does not work, and LIPED will report a zero
likelihood only at θ = 0.5 but cannot pinpoint where this was
first detected.
In the locus descriptions, when there are
several alleles, the number of possible phenotypes may become quite
large. For the analysis, however, it is not necessary to list all
phenotypes that might possibly occur. One only needs to identify
those phenotypes that are actually present in at least one family
member.
Th
EX1.DAT file contains
input corresponding to a family pedigree with the structure as shown
in section 7, "Complex pedigrees" (pedigree 2), above. Two
dominant loci are used with two alleles each, where A > a and B
>
b. The gene frequencies are p = P(A) = 0.4 and q = P(B) = 0.3.
Calculation of the pedigree likelihood by first principles
yields
L(θ) = (1 p)^{5} p(1 q)^{3}
q^{2} (1 θ)[1 + q(1 θ)^{2}
+ qθ^{2}]/8,
where θ denotes the
recombination fraction. With this, one obtains, for example, log
[L(0.5)] = 4.16106927 and log[L(0.2)] = 3.93702064,
which agrees with the output given by LIPED. The lod score at θ
= 0.2 is thus 0.224.
The EX2.DAT file shows an example with 3 pedigrees and the use of output option 9, ie, summation of lod scores over pedigrees. The second pedigree in this set of 3 pedigrees requires much more computer time per lod score than either pedigree 1 or 3.
Compare linkage relationships among 6 gene markers, here labelled main locus and marker loci 1 through 5. The first comparison made takes more computer time per lod score than the other comparisons. This example shows the use of various output options in combination with locus comparisons.
The EX4.DAT file shows a published pedigree with Norrie's disease (Xlinked recessive) and 2 marker loci. Analysis is disease versus each marker and marker versus marker.
Mode of inheritance of disease locus in example 1 is changed such that penetrance rises linearly from age 0 to 10. As no individual is in that age range, the output is the same as in example 1.
Ban
Y, Davies TF, Greenberg
DA, Concepcion ES, Tomer Y (2002) The influence of human leucocyte
antigen (HLA) genes on autoimmune thyroid disease (AITD): results of
studies in HLADR3 positive AITD families. Clin
Endocrinol (Oxf) 57:8188
[a recent example for the use of the LIPED program]
Cheung KH,
Nadkarni P, Silverstein S, Kidd JR, Pakstis AJ, Miller P, Kidd KK
(1996) PhenoDB: an integrated client/server database for linkage and
population genetics. Comput
Biomed Res 29:327337
[an example of the use of the LIPED program]
Elston RC,
Stewart J (1971) A general model for the analysis of pedigree data.
Hum
Hered
21:523542
Ott J (1974)
Estimation of the recombination fraction in human pedigrees:
efficient computation of the likelihood for human linkage studies. Am
J Hum Genet 26:588597
Ott
J, Schrott HG, Goldstein JL, Hazzard WR, Allen FH Jr, Falk CT,
Motulsky AG (1974) Linkage studies in a large kindred with familial
hypercholesterolemia. Am
J Hum Genet 26,
598603
Ott J (1976) A computer program for linkage analysis
of general human pedigrees. Am
J Hum Genet 28:528529
Ott
J (1986) Ylinkage and pseudoautosomal linkage. Am
J Hum Genet 38:891897
Ott
J (1999) Analysis
of
Human Genetic Linkage,
3rd edition. Johns Hopkins University Press, Baltimore
Schrott
HG, Goldstein JL, Hazzard WR, McGoodwin MM, Motulsky AG (1972)
Familial hypercholesterolemia in a large kindred. Evidence for
monogenic mechanism. Annals
of Internal Medicine 76:711720
[description
of the Alaska
pedigree]
Terwilliger
JD, Ott J (1994) Handbook
of Human Genetic Linkage.
Johns Hopkins University Press, Baltimore (now available online as a pdf file)
Thompson E (2011)
The structure of genetic linkage data: From LIPED to 1 M SNPs. Hum
Hered 71:8696