Biology subjects

Francesco Paolo Casale

Publications and source records attributed to Francesco Paolo Casale.

2 recordsLinked to original sources

bioRxiv2016

Common genetic variation drives molecular heterogeneity in human iPSCs

Induced pluripotent stem cell (iPSC) technology has enormous potential to provide improved cellular models of human disease. However, variable genetic and phenotypic characterisation of many existing iPSC lines limits their potential use for research and therapy. Here, we describe the systematic generation, genotyping and phenotyping of 522 open access human iPSCs derived from 189 healthy male and female individuals as part of the Human Induced Pluripotent Stem Cells Initiative (HipSci:http://www.hipsci.org). Our study provides a comprehensive picture of the major sources of genetic and phenotypic variation in iPSCs and establishes their suitability for use in genetic studies of complex human traits and cancer. Using a combination of genomewide analyses we find that 5-25% of the variation in different iPSC phenotypes, including differentiation capacity and cellular morphology, arises from differences betweenindividuals. We also assess the phenotypic effects of rare, genomic copy number mutations that are recurrently seen following iPSC reprogramming and present an initial map of common regulatory variants affecting the transcriptome of pluripotent cells in humans.

Genomics↗

bioRxiv2014

LIMIX: genetic analysis of multiple traits

Multi-trait mixed models have emerged as a promising approach for joint analyses of multiple traits. In principle, the mixed model framework is remarkably general. However, current methods implement only a very specific range of tasks to optimize the necessary computations. Here, we present a multi-trait modeling framework that is versatile and fast: LIMIX enables to flexibly adapt mixed models for a broad range of applications with different observed and hidden covariates, and variable study designs. To highlight the novel modeling aspects of LIMIX we performed three vastly different genetic studies: joint GWAS of correlated blood lipid phenotypes, joint analysis of the expression levels of the multiple transcript-isoforms of a gene, and pathway-based modeling of molecular traits across environments. In these applications we show that LIMIX increases GWAS power and phenotype prediction accuracy, in particular when integrating stepwise multi-locus regression into multi-trait models, and when analyzing large numbers of traits. An open source implementation of LIMIX is freely available at: https://github.com/PMBio/limix.

Genetics↗