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. 2013 Apr;23(4):679-86.
doi: 10.1101/gr.147322.112. Epub 2013 Jan 8.

RNA-seq-based Mapping and Candidate Identification of Mutations From Forward Genetic Screens

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Free PMC article

RNA-seq-based Mapping and Candidate Identification of Mutations From Forward Genetic Screens

Adam C Miller et al. Genome Res. .
Free PMC article

Abstract

Forward genetic screens have elucidated molecular pathways required for innumerable aspects of life; however, identifying the causal mutations from such screens has long been the bottleneck in the process, particularly in vertebrates. We have developed an RNA-seq-based approach that identifies both the region of the genome linked to a mutation and candidate lesions that may be causal for the phenotype of interest. We show that our method successfully identifies zebrafish mutations that cause nonsense or missense changes to codons, alter transcript splicing, or alter gene expression levels. Furthermore, we develop an easily accessible bioinformatics pipeline allowing for implementation of all steps of the method. Overall, we show that RNA-seq is a fast, reliable, and cost-effective method to map and identify mutations that will greatly facilitate the power of forward genetics in vertebrate models.

Figures

Figure 1.
Figure 1.
RNA-seq–based mapping identifies single peaks of linkage centered on the known mutations in all experiments. (A,B) Rows represent individual experiments and are labeled by genotype and the number of mutant embryos used for mapping. (A) Genome-wide mapping data. The average frequency of mutant markers (black marks) is plotted against genomic position. In each case, a single region emerges with an allele frequency near one (red arrow). Each chromosome is separated by vertical lines and labeled at the bottom. (B) Detail of the chromosome containing the linked interval for a given experiment (row). The average frequency of mutant markers (green discs) is plotted against chromosomal position. A red box marks each region of linkage, and a red line marks the position of the known mutation. Each tick mark on the x-axis represents 10 Mb. Each y-axis is the same as in A, first row.
Figure 2.
Figure 2.
Increasing the number of embryos in an RNA-seq–based mapping experiment decreases the linkage size of the mapped region. (A) Detail of chromosome 12 containing the linked interval for each hoxb1b1219 mapping experiment. Each row is labeled with the number of embryos used in the experiment. The average frequency of mutant markers (green discs) is plotted against chromosomal position. A red box marks each region of linkage, and a red line marks the position of the hoxb1b gene; linkage was defined as the region between the “leftmost” and “rightmost” positions within 1% of homozygosity. Each y-axis is the same as in the first row. (B) Comparison of linked regions to the number of embryos used in each RNA-seq–based mapping experiment. The hoxb1b1219 experiments are labeled in red; nhsl1bfh131, in green; vanglm209, in magenta; and egr2bfh227, in blue; and unknown mutations mapped using this method, in cyan. Increasing the number of embryos decreases the linked region with diminishing returns.
Figure 3.
Figure 3.
RNA-seq–based mapping identifies candidate mutations creating nonsense and missense changes, affecting splicing, and affecting gene expression. Reads are shown aligned to each known lesion site. Aligned reads are shown as gray boxes; differences from reference (ref) are highlighted by colored letters. (aa) Amino acid; (cov) coverage; (aln) aligned. (AC) RNA-seq data from the mutant pool identified the known A-to-T transversion in hoxb1bb1219 (A), the G-to-T transversion in nhsl1bfh131 (B), both creating stop codons, and the creation of a splice acceptor introducing 15 bp of intronic sequence in the vangl2m209 mutation (C). (D) The down-regulation of egr2b (via NMD of the egr2bfh227 nonsense mutation) is evident in a comparison of the wild-type and mutant aligned reads (identified as significantly down-regulated by 25-fold via Cufflinks, q = 0.00011423).

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