# Matti Pirinen's Software

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The use of the attached source codes on this page is allowed under the
terms of the GNU
general public license (version 3).

FINEMAP and metaCCA have their own licenses.

### biMM: Efficient estimation of genetic variances and
covariances for cohorts with high-dimensional
phenotype measurements.

Publication
in Bioinformatics (2017) and
the R-package biMM_1.0.0.tar.gz (updated 3 Mar 2017) with a vignette.
### FINEMAP: Efficient variable selection using summary data from genome-wide association studies.

Publication in Bioinformatics (2016)
and the software.
### metaCCA: Multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

Publication in Bioinformatics (2016)
and the software.
## 2015

### Allele specific expression from RNA-seq read count data for multiple tissues

These source codes implement the models described in paper
Assessing allele specific expression across multiple tissues from RNA-seq read count data **Bioinformatics** 2015.

R-codes plus data sets (61 MB); or just the R-codes for functions and examples.

For a user-friendly interface for the same models see MAMBA by Manuel Rivas.

### Linear mixed model software MMM

MMM is a software package for analysing a linear mixed model with one random effect
whose covariance structure can be freely specified by the user.
It is written with large data sets in mind: applied to real data sets where
hundreds of thousands of predictors on over 20,000 individuals
are tested one-by-one. Motivation for MMM came from genome-wide association studies,
but it can be used with other data as well. Written in C (GNU-C) and uses GSL and, preferably, LAPACK libraries.

Current version 1.01 (updated 10-Feb-2014): Software. Manual.

Pirinen M, Donnelly P and Spencer CCA **(2012)**:

*Efficient Computation with a Linear Mixed Model on Large-scale Data Sets with Applications to Genetic Studies.*

**Ann Appl Stat** 7(1): 369-390.

Text and Supplementary text available.

### Non-confounding covariates in logistic regression

R-functions to assess the effect of a single binary or continuous covariate
on power for detecting genetic variants (of small effects) in GWAS.
Includes as examples the codes for plotting Supplementary Figures of the publication.
R-functions. Examples.

Pirinen M, Donnelly P and Spencer CCA **(2012)**:

*Including known covariates can reduce power to detect genetic effects in case-control studies.*

**Nat Genet** 44: 848-851.

## 2010

### Hippo and AEML

Haplotype estimation using incomplete prior information from pooled observations (Hippo), and

Approximate EM-algorithm with list of known haplotypes (AEML) are in a

Tar-package.

These algorithms estimate population haplotype frequencies from pooled SNP data and
can make use of a list of the haplotypes that are known to exist in the population. Written in
ANSI-C using Gnu Scientific Library (GSL v.1.0.0).
For description see:

Pirinen M **(2009)**:

*Estimating population haplotype frequencies from pooled SNP data using incomplete prior information.*

**Bioinformatics** 25(24):3296-3302.

### APE

Allelic Path Explorer (APE)

As a Tar-package.

APE is a program for extending partially known genotype data on a given
pedigree consistently (i.e. in accordance with the Mendelian rules)
to the whole pedigree. Written in ANSI-C. For details, see:

Pirinen M and Gasbarra D **(2006)**:

*Finding Consistent Gene Transmission Patterns on Large and Complex Pedigrees. *

**IEEE/ACM Trans. on Computational Biology and
Bioinformatics **3(3):252-262.