real-time genome scans for multiple quantitative traits using linear mixed models

We have developed algorithms and a Julia implementation to perform genome scans of a large number of quantitative traits using linear mixed models. It is suitable for genome scans for whole transcriptome data (eQTL scanning) or other high-throughput traits. For the BXD mouse data such computations take a few seconds.

matrix linear models for connecting metabolite composition to individual characteristics

This paper shows how we can enhance metabolomics data analysis using matrix linear models. The framework allows us to connect metabolite characteristics (such as type of lipid or number of double bonds) directly to individual characteristics (such as sex or an intervention).

current collaborations

summary of current collaborations

news items
speeding up eqtl scans in the bxd population using GPUs

To facilitate interactive use of genotype-phenotype relationships in the BXD population, we sought to speed up eQTL scans. We were able to decrease runtimes to approaching real-time computation.

flexible multivariate linear mixed models for structured multiple traits

Flexible modeling of structured traits using multivariate linear mixed models

sparse matrix linear models for structured high-throughput data

We have developed a fast algorithm for fitting L1-penalized multivariate linear models for high throughput data

matrix linear models

Jane Liang has written three interconnected packages for implementing matrix linear models in Julia

matrix linear models for high-throughput genetic screens

A flexible and computationally efficient approach for analyzing high throughput chemical genetic screens

mapping function-valued traits

Supporting information for Xiong et. al. (2011)

qtl mapping from an information perspective

Supporting information for Sen et. al. (2005)