Platform 
File 
Linux
(x86_64)
Static
Executable 
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(x86_64)
Static
Executable
(SuSE
9.3) 
snptest_v2.2.0_SuSE9.3_x86_64_static.tgz 
Linux
(x86_64)
Dynamic
Executable 
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Linux
(i386)
Static
Executable 
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Linux
(i386)
Dynamic
Executable 
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Mac
OS
X
10.410.6
Intel 
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Mac OS X (PowerPC)  snptest_v2.2.0_MacOSX_PowerPC.tgz 
Solaris
5.8
(Sun
SPARC) 
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Solaris
5.10
(AMD
Opterons) 
snptest_v2.2.0_Solaris5.10_Opteron.tgz 
SLES
10
(Intel
Itanium2) 
snptest_v2.2.0_SLES10_Itanium2.tgz 
Windows
MSDOS
(Intel) 
snptest_v2.2.0_Windows_Intel.tgz 
tar zxvf snptest_vX.X.X_i386.tgz 
./snptest
help 
./snptest
\ summary_stats_only \ data ./example/cohort1.gen ./example/cohort1.sample ./example/cohort2.gen ./example/cohort2.sample \ o ./example/ex.out 
id 
SNP
ID
(taken
from
input
files) 
rsid 
RS
ID
of
the
SNP
(taken
from
input
files) 
chromosome 
A 2letter chromosome
identifier (if SNPTEST can
determine it) or the value NA. See the section
on
chromosomes. 
pos 
Base
pair
position
of
the
SNP 
allele_A
allele_B 
The
two
alleles
at
the
SNP.
allele_A
is
coded
0
and
allele_B
is
coded
1. 
average_maximum_posterior_call 
The
average
maximum
posterior
probability
across
all
individuals
in
the
sample
that
are
used
for
the
test
at
each
SNP.This
is a measure of how
much uncertainty there is at each SNP. Samples excluded will be
(a) those excluded using the exclude_samples option, (b) samples with
a missing phenotype or covariate relevant to the test, (c) samples
without genotypes if the method threshold option is used, (d) samples
where the sum of the genotype probabilities is less than 0.1. 
info 
A
measure
of
the
observed
statistical
information
for
the
estimate
of
allele
frequency
of
the
SNP
using
all
individuals
in
the
sample
that
are
used for the
test at each SNP. This measure has a
maximum value of 1 that indicates that perfect information. Samples
excluded will be (a) those excluded using the exclude_samples
option, (b) samples with a missing phenotype or covariate relevant to
the test, (c) samples without genotypes if the method
threshold option
is used, (d) samples where the sum of the genotype probabilities is
less than the value set by the option total_prob_limit
(default 0.1). 
cohort_1_AA
cohort_1_AB
cohort_1_BB
cohort_1_NULL 
Counts
of
AA,
AB,
BB
and
NULL
genotypes
in
the
1st
cohort.
See
Note
below
which
details
exactly
how
genotype
counts
are
calculated
in
SNPETST
v2. 
cohort_2_AA cohort_2_AB cohort_2_BB cohort_2_NULL  Counts of AA, AB, BB and NULL genotypes for the 2nd cohort (see details above). Subsequent cohorts will be included in a similar way. See Note below which details exactly how genotype counts are calculated in SNPETST v2. 
all_AA all_AB all_BB all_NULL  Counts of AA, AB, BB and NULL thresholded genotypes across all cohorts. See Note below which details exactly how genotype counts are calculated in SNPETST v2. 
all_maf 
Minor
allele
frequencies
(MAF)
in
the
combined
controls,
combined
cases
and
combined
across
all
cohorts. 
missing_data_proportion 
The
proportion
of
missing
data
across
all
cohorts. 
controls_AA controls_AB controls_BB controls_NULL  Counts of AA, AB, BB and NULL genotypes across all case cohorts. See Note above which details exactly how genotype counts are calculated in SNPETST v2. 
cases_AA cases_AB cases_BB cases_NULL  Counts of AA, AB, BB and NULL genotypes across all case cohorts. See Note above which details exactly how genotype counts are calculated in SNPETST v2. 
cases_maf
controls_maf 
Minor
allele
frequencies
(MAF)
in
the
controls
and
cases
across
all
cohorts. 
het_OR het_OR_lower het_OR_upper  Estimated
odds
ratios
and
lower
and
upper
95%
confidence
limits
for
the
heterozygote
genotype
AB
versus
the
(baseline)
AA
genotype. 
hom_OR hom_OR_lower hom_OR_upper  Estimated
odds
ratios
and
lower
and
upper
95%
confidence
limits
for
the
homozygote
genotype
BB
versus
the
(baseline)
AA
genotype. 
all_OR, all_OR_lower all_OR_upper  Estimated
allelic
odds
ratios
and
lower
and
upper
95%
confidence
limits
for
the
B
allele
versus
the
(baseline)
A
allele. 
pheno
<name>

This
specifies
which
phenotype
you
wish
to
test.
The
<name>
should
match
one
of
the
phenotypes
in
the
sample
file.
If
the
phenotype
in
the
sample file is binary (B) then a casecontrol test is carried out. If
the
phenotypes in the sample file is continuous (P) then a quantitative
trait test (i.e. Ftest for a linear model) is carried out. See FILE
FORMAT
WEBPAGE for more details about how to specify a phenotype in
the sample file. If no phenotype is specified then the first phenotype
in the sample file is used. 
frequentist
<t1>...<tn>

This option controls the model you wish to test at each SNP versus a model of no association. The five different models are coded as 1=Additive, 2=Dominant, 3=Recessive, 4=General and 5=Heterozygote. When using this option the output file will have a column for each test that contains the pvalue for the test as well as estimates of the model parameters (beta's) and their standard errors. SNPTEST codes allele_A as 0 and allele_B as 1 and this defines the meaning of the beta's and there se's. For example, when using the additive model the beta estimates the increase in logodds that can be attributed to each copy of allele_B. When a model cannot be fitted to the data the pvalue is set to 1. 
method
threshold

Use
thresholded
genotypes.
The
calling
threshold
is
controlled
by
the
flag
call_thresh.
The
default
calling
threshold
is
0.9.
This
is
the
same
as
the
default
option
in
previous
versions. 
method
expected 
Use
expected
genotype
counts
(aka
genotype
dosages). 
method
score 
Use
a
missing
data
likelihood
score
test.
This
is
equivalent
to
the
proper
option in previous versions, except that if the score test experiences
problems at a SNP (usually due to a rare SNP and/or high uncertainty)
then method
em is used for this SNP. 
method
ml 
Use
multiple
NewtonRaphson
iterations
to
estimate
the
parameters
in
the
missing
data
likelihood
for
the
model. 
method
em 
Use
an
EM
algorithm
to
estimate
the
parameters
in
the
missing
data
likelihood
for
the
model. 
renorm

The methods described above to
deal with genotype uncertainty were developed for the use with imputed
SNPs. This implies that the genotype probabilities will sum to 1. If
probabilistic genotype calls from an
algorithm like CHIAMO are used then the probabilities might
sum to less than one and any left over probability is the probability
of a NULL call. The renorm
option renormalizes the genotype
probabilities to sum to 1. The default is not to renormalize the
probablities unless the method
expected option is chosen in which case
it is automatically turned on. 
total_prob_limit
<x>

There is an internal lower limit
set on the sum of genotype probabilities. The default is 0.1. If this
threshold is not met then that genotype is not included in the test.
This protects against SNPs with a high proportion of NULL genotypes. 
<phenotype_name(s)>  The name (or names if mpheno is used) of the phenotypes used in the test. 
<test_type>  frequentist or bayesian 
<genetic_model>  add, dom, rec, gen or het 
<covariate_name(s)>  The name (or names) of the covariates being conditioned upon in the test 
<summary_measure>  One of pvalue, info, beta_X, se_X or log10_bf depending on the column 
./snptest \ data ./example/cohort1.gen ./example/cohort1.sample ./example/cohort2.gen ./example/cohort2.sample \ o ./example/ex.out \ frequentist 1 \ method score \ pheno bin1 
./snptest \ data ./example/cohort1.gen ./example/cohort1.sample ./example/cohort2.gen ./example/cohort2.sample \ o ./example/ex.out \ method score \ frequentist 1 \ pheno pheno1 
bayesian
<t1>...<tn>

This option controls the model you wish to test at each SNP versus a model of no association. The five different models are coded as 1=Additive, 2=Dominant, 3=Recessive, 4=General and 5=Heterozygote. When using this option the output file will have a column for each test that contains the log10 Bayes Factor for the test as well as posterior mean estimates of the model parameters (beta's) and their standard errors. SNPTEST codes allele_A as 0 and allele_B as 1 and this defines the meaning of the beta's and there se's. For example, when using the additive model the beta estimates the increase in logodds that can be attributed to each copy of allele_B. A Bayes factor will always be calculated at a SNP. 
Model 
Linear
Predictor 
Priors 
Default 
Coding 
Command
line
option 
Additive 
log(p_{i}/(1p_{i}))
=
µ
+
ßG_{i} 
µ~N(a_{0},
a_{1}^{2}) ß~N(b_{0}, b_{1}^{2}) 
a_{0}=0,
a_{1}=1 b_{0}=0, b_{1}=0.2 
G_{i}
is the additive coding of the SNP i.e. AA > 0, AB >1, BB > 2. 
prior_add a_{0} a_{1} b_{0} b_{1} 
Dominant 
log(p_{i}/(1p_{i}))
=
µ
+
ßD_{i} 
µ~N(a_{0},
a_{1}^{2}) ß~N(b_{0}, b_{1}^{2}) 
a_{0}=0,
a_{1}=1 b_{0}=0, b_{1}=0.5 
D_{i}
is the dominant coding of the SNP i.e. AA > 0, AB > 1, BB > 1. 
prior_dom a_{0} a_{1} b_{0} b_{1} 
Recessive 
log(p_{i}/(1p_{i}))
=
µ
+
ßR_{i} 
µ~N(a_{0},
a_{1}^{2}) ß~N(b_{0}, b_{1}^{2}) 
a_{0}=0,
a_{1}=1 b_{0}=0, b_{1}=0.5 
R_{i}
is the recessive coding of the SNP i.e. AA > 0, AB > 0, BB > 1. 
prior_rec a_{0} a_{1} b_{0} b_{1} 
General 
log(p_{i}/(1p_{i})) = µ + ßG_{i} + qH_{i}  µ~N(a_{0},
a_{1}^{2}) ß~N(b_{0}, b_{1}^{2}) q~N(c_{0}, c_{1}^{2}) 
a_{0}=0,
a_{1}=1 b_{0}=0, b_{1}=0.2 c_{0}=0, c_{1}=0.5 
G_{i}
is the additive coding of the SNP i.e. AA > 0, AB >1, BB > 2. H_{i} is the heterozygote coding of the SNP i.e. AA > 0, AB >1, BB > 0. 
prior_gen a_{0} a_{1} b_{0} b_{1 }c_{0} c_{1} 
Heterozygote 
log(p_{i}/(1p_{i}))
=
µ
+
ßH_{i} 
µ~N(a_{0},
a_{1}^{2}) ß~N(b_{0}, b_{1}^{2}) 
a_{0}=0,
a_{1}=1 b_{0}=0, b_{1}=0.5 
H_{i}
is the heterozygote coding of the SNP i.e. AA > 0, AB >1, BB > 0. 
prior_het a_{0} a_{1} b_{0} b_{1} 
t_prior

Specfies the use of tdistribution priors on the genetic effects. Effectively, this option modifies the priors described in the table above i.e. the mean and variance of the tdistributions are specified by the options given in the table above, but the normal distributon is replaced by the tdistribution. NOTE : a tdistribution is only used for the genetic effects i.e. the parameters ß and q in the models above. For example, bayesian add t_prior would specify the linear predictor log(p_{i}/(1p_{i})) = µ + ßG_{i} and the priors would be µ~N(a_{0}, a_{1}^{2}) and ß~t(b_{0}, b_{1}^{2}, df = 3). 
t_df
<x>

The degrees of
freedom parameter of the tdistribution.
The
default
value
is
3.
When
this
parameter
is
set
very
large
the
prior
converges
to
the
normal distribution prior. 
./snptest \ data ./example/cohort1.gen ./example/cohort1.sample ./example/cohort2.gen ./example/cohort2.sample \ o ./example/ex.out \ bayesian 1 \ method score \ pheno bin1 
./snptest \ data ./example/cohort1.gen ./example/cohort1.sample ./example/cohort2.gen ./example/cohort2.sample \ o ./example/ex.out \ bayesian 1 \ method expected \ pheno pheno1 \ prior_qt_mean_b 0 \ prior_qt_V_b 0.02 \ prior_qt_a 3 \ prior_qt_b 2 
Model
name 
Model

Priors 
Command
line
options
needed 
Additive 
y_{i} = ßG_{i} + e_{i}, e_{i} ~ N(0, σ^{2})  ß~N(b_{0},
V_{ß}σ^{2}) σ^{2 }~ IG(a,b) 
prior_qt_mean_b b_{0}
prior_qt_V_b V_{ß} prior_qt_a a prior_qt_a b 
Dominant 
y_{i} = ßD_{i} + e_{i}, e_{i} ~ N(0, σ^{2})  ß~N(b_{0},
V_{ß}σ^{2}) σ^{2 }~ IG(a,b) 
prior_qt_mean_b b_{0}
prior_qt_V_b V_{ß} prior_qt_a a prior_qt_a b 
Recessive 
y_{i} = ßR_{i} + e_{i}, e_{i} ~ N(0, σ^{2})  ß~N(b_{0},
V_{ß}σ^{2}) σ^{2 }~ IG(a,b) 
prior_qt_mean_b b_{0}
prior_qt_V_b V_{ß} prior_qt_a a prior_qt_a b 
General 
y_{i} = ßG_{i} + qH_{i }+ e_{i}, e_{i} ~ N(0, σ^{2})  ß~N(b_{0},
V_{ß}σ^{2}) ß~N(b_{1}, V_{q}σ^{2}) σ^{2 }~ IG(a,b) 
prior_qt_mean_b b_{0}
prior_qt_V_b V_{ß} prior_qt_mean_q b_{1} prior_qt_V_q V_{q} prior_qt_a a prior_qt_a b 
Heterozygote 
y_{i }= ßH_{i} + e_{i}, e_{i} ~ N(0, σ^{2})  ß~N(b_{0},
V_{ß}σ^{2}) σ^{2 }~ IG(a,b) 
prior_qt_mean_b b_{0}
prior_qt_V_b V_{ß} prior_qt_a a prior_qt_a b 
mean_bf
<w1>...<wn>

Specify that a log10 Bayes
factor for a weighted average over the models specified by bayesian
with weights given by
<w1>....<wn>. For example, bayesian
1
4
mean_bf
9
1 would calculate a Bayes factor for a weighted
average
of the additive and general models where the additive model is given
weight 9 and the general model is given weight 1. The log10 Bayes
factor will be written in a column with the label mean_bf. 
./snptest \ data ./example/cohort1.gen ./example/cohort1.sample ./example/cohort2.gen ./example/cohort2.sample \ o ./example/ex.out \ bayesian 1 \ method expected \ mpheno pheno1 pheno2 \ prior_qt_mean_b 0 \ prior_qt_V_b 0.02 \ prior_mqt_c 6 \ prior_mqt_Q 4 
cov_names
<name_1>
...
<name_n> 
Condition
upon
the
covariates
in
the
sample
files
with
names
name_1,....,
name_n. 
cov_all 
Condition
upon
all
the
covariates
in
the
sample
files. 
cov_all_discrete 
Condition
upon
all
the
discrete
covariates
(D)
in
the
sample
files. 
cov_all_continuous 
Condition
upon
all
the
continuous
covariates
(C)
in
the
sample
files. 
condition_on
<snp_1>
<model_1>
...
<snp_n>
<model_n> 
Condition
upon
a
list
of
SNPs
with
IDs
given
by
snp_1,...,snp_n. For each SNP a list of models can be supplied; the choices are add, dom, rec, het, or gen. Here "gen" is shorthand for "add het", i.e. condition on additive and heterozygote dosages. If no model is supplied, the default "add" is used. These covariates are internally added to the sample file as continuous (type C) covariates and appear in the covariate summary in the screen output. 
./snptest \ data ./example/cohort1.gen ./example/cohort1.sample ./example/cohort2.gen ./example/cohort2.sample \ o ./example/ex.out \ frequentist 1 \ method score \ pheno bin2 \ cov_names cov1 
./snptest \ data ./example/cohort1.gen ./example/cohort1.sample ./example/cohort2.gen ./example/cohort2.sample \ o ./example/ex.out \ frequentist 1 \ method score \ pheno bin1 \ cov_names cov3 cov4 
./snptest \ data ./example/cohort1.gen ./example/cohort1.sample ./example/cohort2.gen ./example/cohort2.sample \ o ./example/ex.out \ frequentist 1 \ method score \ pheno bin1 \ condition_on RSID_10 add RSID_20 gen 
./snptest \ data ./example/cohort1.gen ./example/cohort1.sample ./example/cohort2.gen ./example/cohort2.sample \ o ./example/ex.out \ frequentist 1 \ method score \ pheno bin1 \ range 2000030000 4000050000 
./snptest \ data ./example/cohort1.gen ./example/cohort1.sample ./example/cohort2.gen ./example/cohort2.sample \ o ./example/ex.out \ frequentist 1 \ method score \ pheno bin1 \ snpid RSID_4 SNPID_7 
./snptest \ data ./example/cohort1.gen ./example/cohort1.sample ./example/cohort2.gen ./example/cohort2.sample \ o ./example/ex.out \ frequentist 1 \ method score \ pheno bin1 \ exclude_snps ./example/snps.list 
./snptest \ data ./example/cohort1.gen ./example/cohort1.sample ./example/cohort2.gen ./example/cohort2.sample \ o ./example/ex.out \ frequentist 1 \ method score \ pheno bin1 \ exclude_samples ./example/samples.list 
./snptest \ data ./example/cohort1.gen ./example/cohort1.sample ./example/cohort2.gen ./example/cohort2.sample \ o ./example/ex.out \ frequentist 1 \ method score \ pheno bin1 \ miss_thresh 0.01 
hwe 
This will produce an output file with columns that contain the pvalues for an exact test of HWE in each cohort. If a test for a binary phenotype is carried out then HWE for all the case individuals and all the control individuals are also reported. 
chunk
<x>

The program works by reading in, analyzing and writing output for chunks of the data at a time. This option is included to control the maximum amount of RAM used by the program at any one time. The default chunk size is 100 SNPs. 
nowarn 
Turns off printing off warnings
to the screen 
log
<filename> 
Copy all screen output to the
specified log file. 
qctool
\ g example/cohort1.gen \ og example/cohort1.bgen \ force 
./snptest
\ data example/cohort1.bgen example/cohort1.sample \ o example/ex.out \ frequentist 1 \ method score \ pheno bin2 
qctool
\ g example/cohort1_#.gen \ og example/cohort1_#.bgen \ force 
2.2.0 
07.12.2010 
This is a substantial
update on the previous version that implements a number of new features

2.1.1 
01.04.2010 
Minor
update.
This
release
can
be
found
here. 
2.1.0 
19.03.2010 
This
is
major
change
to
SNPTEST
from
previous
versions.
Please
read
the
following
carefully

1.1.5 
28.05.2008 
This
release
can
be
found
here
