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The tests implemented include. Much of the theory behind the implemented tests is described in this paper [ 3 ]. These files contain data at SNPs on individuals that are split into a control cohort and a case cohort. These datasets can be used to try out the tests using both binary case-control and quantitative phenotypes.

Changes in this release include bug fixes and enhancements as documented here. To get started, download a pre-built binary for your platform from the download page and run an example command. This behaves broadly like -method ml , but supports new features:.

Pre-compiled versions of the program and example files can be downloaded from the links below. For linux, you should use the dynamically linked version unless you run into trouble.

On some systems, library incompatibilities cause problems so we have provided two statically linked versions as well. If you have any problems getting the program to work on your machine please contact us. Please fill out the registration form to receive emails about updates to this software. The data for each cohort is stored in two files. The first file the genotype file stores the genotype data for the cohort. The second file the sample file stores the ID's and associated covariate and phenotype information of the individuals of each cohort.

For the example datasets included with the software the sample and genotype files for each of these cohorts have the suffices. These will be used if the filename extension is. This column is an additional column and must be the first column in the file.

To get this behaviour, specify the input data file as '-' and specify the filetype as bgen using the -filetype option. Support for the BGEN v1. Full support for BGEN v1. A few points to note are:. VCF format version 4.

These are mapped internally to covariate levels. The default missing value for samples is now the two-character string "NA". This feature is currently restricted to BGEN format files.

To use this feature specify "-" as the first input genotype file and specify the file type as bgen using the -filetype bgen option. Metadata reflecting the options used is now written to the top of the file protected by a ' ' comment character.

For example, here is the metadata from the output for an example command:. The desired output format is detected based on the filename extension. It's also possible to write gzipped output files - add the.

Currently the sqlite embedded database is supported. Sqlite databases are entirely contained in a single file, and don't require the use of special server software.

For example, the command. A major motivation for this feature is that large flat files like the ones SNPTEST outputs can be difficult to work with - in particular, rows are not indexed, and the large number of columns can make viewing particular fields awkward.

This is not actually part of the command -- it is just a shorthand notation that means "keep reading the next line as part of a single command. This is a valid way to enter commands in a Unix-style terminal window so, for example, you should be able to directly paste these commands into the terminal and hit 'enter' to make them run , but it would be equivalent to put all of the arguments on a single line, separated by spaces.

Note how the cohorts are specified by placing the relevant genotype and sample files after the -data and option in the command. For each cohort the name of the genotype file should be followed by its associated sample file. There is a limit of 18 cohorts that can be specified.

The -o option specified the output file i. This file contains a line for each SNP and there is a header line which specifies the contents of each column. If a test for a binary phenotype is being carried out then the following additional fields are included:. Odds ratios and their confidence limits are set to NA if they cannot be calculated.

See the section on frequentist tests for association for further columns that are output when performing association tests.

In previous versions, NULL call counts would only reflect samples that had high enough genotype probability to be included in the association test i. Information about which data files were specified, the tests selected, the numbers of SNPs, the total number of cases and the total number of controls, information about the covariates and phenotypes in the sample files and information about individuals and SNPs selected for exclusion is all written to the screen.

Also, information about the progress of the program is written to the screen. Incorrect use of the options or input files with the wrong format may cause the program to terminate.

The screen output can be used to identify any problems that lead to the termination. There are 3 options that control Frequentist testing for association -pheno , -frequentist and -method ,.

The -method option which controls the way genotype uncertainty is taken into account when carrying out association tests. The options are listed in the table below. There are two other options that control how the imputed genotypes are treated. The statistical details of the Frequentist tests implemented are given in this pdf. If score , ml or em are chosen as the method when using a frequentist test then a relative information measure will be calculated at each SNP.

The statistical details of these information measures are given in this pdf. T he following example carries out a case-control test for the binary phenotype named bin1. T he following example carries out a case-control test for the quantitative phenotype named pheno1. The Bayesian tests are specified by the -bayesian option, in a similar way to the use of the -frequentist option.

The statistical details of the Bayesian tests implemented are given in this pdf. The -method option is also used to control the way the Bayesian models are fit, but not all options are valid. The table below gives a description of the linear predictor of the logistic regression used, the form of the priors used on the model parameters, the default priors used in SNPTEST and the command line option that can be used to change the priors.

The fatter tails of the t-distribution allow more flexibility in specifying beliefs about the size of the genetic effects. This option is controlled by the following two options. T he following example calculates a Bayesian additive model Bayes Factor for the binary phenotype bin1 named using the default priors. The Bayesian tests for quantitative traits are carried out using the conjugate prior formulation of the linear model using either thresholded genotypes -method threshold or the expected genotypes -method expected.

The model is most easily explained through an example. For an additive model the formulation is. The residual phenotype is calculated by subtracting off a baseline term and estimates of any specified covariates. This prior has the form. So, if we are happy to put a N 0, 0. By default all quantitative phenotypes are centered and scaled to have zero mean and unit variance before analysis.

This places all the quantitative phenotypes on a comparable scale. The following example uses this model to analyze the phenotype pheno1. This produces a log 10 Bayes Factor in the output file. This can be used for both binary and quantitative phenotype tests. This option does not currently work with the -mpheno option. A Bayesian test for association of a SNP with multiple quantitative phenotypes can be carried out with the -mpheno option. The model we use is the Bayesian Multivariate Linear model which is specified by.

The residual phenotype is calculated by subtracting off an baseline term and estimates of any specified covariates. Further we assume that each of these phenotypes has been centered and scaled to have zero mean and unit variance. Also, G i is the coded version of the SNP genotype for the i th individual. We use the conjugate prior for this model. Dawid Some matrix-variate distribution theory: The Bayes factor calculated then has the form. The following example uses this model to analyze the phenotypes pheno1 and pheno2 jointly.

This specifies an IW c,Q I q. This extends the logistic regression implemented for binary traits to multiple categories. This feature is currently considered experimental and this page provides initial documentation on its use. To allow parameter identification, the output contains columns named in the following way:. To avoid cluttering the output, corresponding standard errors and other columns are simply identified by number, e.

For example, suppose the column 'bin3' contains a phenotype with levels control , case1 and case2. Similarly, number columns for the standard errors, covariances and Wald test p-values will be output.

These options work with both the Frequentist and Bayesian association tests. Conditioning upon one or more covariate means that the test of association being carried out is testing for a genetic effect over and above that explained by the covariate s.

Discrete covariates are added into the model as factors i. If a single Discrete D covariate is conditioned upon then this is equivalent to a Mantel-Hantzel test. This is a test for a common genetic effect where each group is allowed to have it's own baseline effect.