A Multiparent Advanced Generation Inter-Cross to Fine-Map Quantitative Traits in Arabidopsis thaliana

Identifying natural allelic variation that underlies quantitative trait variation remains a fundamental problem in genetics. Most studies have employed either simple synthetic populations with restricted allelic variation or performed association mapping on a sample of naturally occurring haplotypes. Both of these approaches have some limitations, therefore alternative resources for the genetic dissection of complex traits continue to be sought. Here we describe one such alternative, the Multiparent Advanced Generation Inter-Cross (MAGIC). This approach is expected to improve the precision with which QTL can be mapped, improving the outlook for QTL cloning. Here, we present the first panel of MAGIC lines developed: a set of 527 recombinant inbred lines (RILs) descended from a heterogeneous stock of 19 intermated accessions of the plant Arabidopsis thaliana. These lines and the 19 founders were genotyped with 1,260 single nucleotide polymorphisms and phenotyped for development-related traits. Analytical methods were developed to fine-map quantitative trait loci (QTL) in the MAGIC lines by reconstructing the genome of each line as a mosaic of the founders. We show by simulation that QTL explaining 10% of the phenotypic variance will be detected in most situations with an average mapping error of about 300 kb, and that if the number of lines were doubled the mapping error would be under 200 kb. We also show how the power to detect a QTL and the mapping accuracy vary, depending on QTL location. We demonstrate the utility of this new mapping population by mapping several known QTL with high precision and by finding novel QTL for germination data and bolting time. Our results provide strong support for similar ongoing efforts to produce MAGIC lines in other organisms

Genome-wide association of multiple complex traits in outbred mice by ultra low-coverage sequencing

The authors wish to acknowledge excellent technical assistance from A. Kurioka, L. Swadling, C. de Lara, J. Ussher, R. Townsend, S. Lionikaite, A.S. Lionikiene, R. Wolswinkel and I. van der Made. We would like to thank T.M. Keane and A.G. Doran for their help in annotating variants and adding the FVB/NJ strain to the MGP. We thank the High-Throughput Genomics Group at the Wellcome Trust Centre for Human Genetics and the Wellcome Trust Sanger Institute for the generation of the sequencing data. This work was funded by Wellcome Trust grant 090532/Z/09/Z (J.F.). Primary phenotyping of the mice was supported by the Mary Lyon Centre and Mammalian Genetics Unit (Medical Research Council, UK Hub grant G0900747 91070 and Medical Research Council, UK grant MC U142684172). D.A.B. acknowledges support from NIH R01AR056280. The sleep work was supported by the state of Vaud (Switzerland) and the Swiss National Science Foundation (SNF 14694 and 136201 to P.F.). The ECG work was supported by the Netherlands CardioVascular Research Initiative (Dutch Heart Foundation, Dutch Federation of University Medical Centres, Netherlands Organization for Health Research and Development and the Royal Netherlands Academy of Sciences) PREDICT project, InterUniversity Cardiology Institute of the Netherlands (ICIN; 061.02; C.A.R. and C.R.B.). N.C. is supported by the Agency of Science, Technology and Research (A*STAR) Graduate Academy. R.W.D. is supported by a grant from the Wellcome Trust (097308/Z/11/Z).Peer reviewedPostprin

Management, presentation and interpretation of genome scans using GSCANDB.

MOTIVATION: Advances in high-throughput genotyping have made it possible to carry out genome-wide association studies using very high densities of genetic markers. This has led to the problem of the storage, management, quality control, presentation and interpretation of results. In order to achieve a successful outcome, it may be necessary to analyse the data in different ways and compare the results with genome annotations and other genome scans. RESULTS: We created GSCANDB, a database for genome scan data, using a MySQL backend and Perl-CGI web interface. It displays genome scans of multiple phenotypes analysed in different ways and projected onto genome annotations derived from EnsMart. The current version is optimized for analysis of mouse data, but is customizable to other species. AVAILABILITY: Source code and example data are available under the GPL, in versions tailored to either human or mouse association studies, from http://gscan.well.ox.ac.uk/software