Introduction

Dissecting the genetic basis of complex traits (eg. height, weight, disease gravity,..), namely, identifying which regions of the genome explain part of the heritable phenotypic variation, is a grand question in biology that brings together two complementary approaches carried over by two often distinct communities, GWAS on natural populations on the one hand and QTL mapping in so-called mapping populations on the other hand.

Analysis of GWAS data is indeed purely computational, and performed by scientists immersed into a quantitative environment thus well-aware of good code management practice. Conversely, no small part of QTL mapping approaches remain performed by experimental geneticists, building up on tedious phenotyping experiments that leave little time to catch up with both the statistical and reproducible analysis requirements.

In this broader context, CC-QTL positions itself as a one-stop shop interface dedicated to QTL mapping on Collaborative Cross data, a widely used mouse mapping population that derives from 8 founder strains.

CC-QTL embarks both a user-friendly GUI allowing end-to-end QTL mapping analysis (from data transformation to exploration of the QTL interval, eg, identifying candidate genes) and a database structure guaranteeing the safe and organized storage of phenotypic data along with an advanced permissions system.

CC-QTL’s main goal is to allow non-specialists (mouse experimental geneticists on their own or publicly available data, trainers for demo or teaching purposes) to explore and analyze data by themselves. However, the added bonuses of Galaxy-powered analyses reproducibility and permission system makes CC-QTL also relevant for more experienced users, eg. facilities.

CC-QTL is still under active development and its documentation is being updated regularly. It is thus strongly encouraged that you always use the latest release of CC-QTL.