IMPUTE 2 is freely available for academic use only. Marker data (such as 1000 Genomes Project haplotypes) IMPUTE 2Ī program for genotype imputation and phasing in genome-wideĪssociation studies and fine-mapping studies based on a dense set of To see rules for non-academic use see the (also included with each software download). IMPUTE 4 is freely available for academic use only. It was written to impute genotypes for the UK Biobank dataset that consists of genetic data on ~500,000 individuals IMPUTE 4 implements the haploid imputation options included in IMPUTE 2, but is much faster and more memory efficient. IMPUTE 4Ī program for efficient genotype imputation. IMPUTE 5 is freely available for academic use only. Marchini (2019) Genotype imputation using the Positional Burrows Wheeler Transform PLoS Genetics IMPUTE5 is up to 30x faster than MINIMAC4 and up to 3x faster than The method then uses the selected haplotypes as conditioning states within the IMPUTE model. By using the PBWT data structure at genotyped markers, IMPUTE 5 identifies locally best matching haplotypes and long identical by state segments. It achieves fast, accurate, and memory-efficient imputation by selecting haplotypes using the Positional Burrows Wheeler Transform (PBWT). This method continues to refine the observation made in the IMPUTE2 method, that accuracy is optimized via use of a custom subset of haplotypes when imputing each individual. IMPUTE 5 is a genotype imputation method that can scale to reference panels with millions of samples. (freely available under an MIT licence) Imputation IMPUTE 5 The GPLEMMA method is implemented as an option in the LEMMA code Haseman-Elston regression for estimation of gene-environment Matthew Kerin and Jonathan Marchini (2020) Non-linear randomized Estimation of the ES provides a readily interpretable way to examine the combined effect of many environmental variables. The method simultaneously estimates a linear combination of environmental variables, called an environmental score (ES), that interacts with genetic markers throughout the genome, and it’s associated heritability. GPLEMMA (Gaussian Prior Linear Environment Mixed Model Analysis) is a non-linear randomized Haseman-Elston regression method for flexible modeling of gene-environment interactions in large datasets such as the UK Biobank. (freely available under an MIT licence) GPLEMMA Gene-by-Environment Interactions with a Bayesian Whole-Genome Matthew Kerin and Jonathan Marchini (2020) Inferring G圎 effects at genetic variants across the genome. Phenotypic variance attributable to G圎 effects, and also to test for The ES can be used both to estimate the proportion of Way to examine the combined effect of many environmental Markers throughout the genome, and provides a readily interpretable The method estimates a linear combination of environmental variables,Ĭalled an environmental score (ES), that interacts with genetic LEMMA (Linear Environment Mixed Model Analysis) is a whole genome wide regression method for flexible modeling of gene-environment interactions in large datasets such as the UK Biobank. This is now hosted on CRAN and maintained by others. R package that implements the FastICA algorithm. Tensor decomposition for multi-tissue gene expression experiments. Victoria Hore, Ana Viñuela, Alfonso Buil, Julian Knight, Mark I McCarthy, Kerrin Small, Jonathan Marchini. Please read the Tensor and Matrix Decomposition SDA4DĪ program for sparse Bayesian 4D tensor decompositionĬhristopher Gill and Jonathan Marchini (2020) Four-Dimensional Sparse Bayesian Tensor Decomposition for Gene Expression Data Ī program for sparse Bayesian matrix and tensor decomposition. Some of these programs are licenced for academic use only. If you have questions use the, and post a question there. This page contains links to software packages that were developed by our group.
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