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A new advanced backcross tomato population enables high resolution leaf QTL mapping and gene identification

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Title A new advanced backcross tomato population enables high resolution leaf QTL mapping and gene identification
 
Creator Fulop, Daniel
Ranjan, Aashish
Ofner, Itai
Covington, Michael F.
Chitwood, Daniel H.
West, Donelly
Ichihashi, Yasunori
Headland, Lauren
Zamir, Daniel
Maloof, Julin N.
Sinha, Neelima R.
 
Description Accepted date: August 1, 2016
Quantitative Trait Loci (QTL) mapping is a powerful technique for dissecting the genetic basis of traits and species differences. Established tomato mapping populations between domesticated tomato (Solanum lycopersicum) and its more distant interfertile relatives typically follow a near isogenic line (NIL) design, such as the S. pennellii Introgression Line (IL) population, with a single wild introgression per line in an otherwise domesticated genetic background. Here, we report on a new advanced backcross QTL mapping resource for tomato, derived from a cross between the M82 tomato cultivar and S. pennellii This so-called Backcrossed Inbred Line (BIL) population is comprised of a mix of BC2 and BC3 lines, with domesticated tomato as the recurrent parent. The BIL population is complementary to the existing S. pennellii IL population, with which it shares parents. Using the BILs, we mapped traits for leaf complexity, leaflet shape, and flowering time. We demonstrate the utility of the BILs for fine-mapping QTL, particularly QTL initially mapped in the ILs, by fine-mapping several QTL to single or few candidate genes. Moreover, we confirm the value of a backcrossed population with multiple introgressions per line, such as the BILs, for epistatic QTL mapping. Our work was further enabled by the development of our own statistical inference and visualization tools, namely a heterogeneous hidden Markov model for genotyping the lines, and by using state-of-the-art sparse regression techniques for QTL mapping.
We thank Yaniv Brandvain and Paul Lott for discussions and assistance with early versions of the HMM. We thank the Vincent J. Coates Genomics Sequencing Laboratory at University of California Berkeley (supported by National Institutes of Health S10 Instrumentation grants S10RR029668 and S10RR027303), and computational resources/cyber infrastructure provided by the iPlant Collaborative (www.iplantcollaborative.org), funded by the National Science Foundation (grant DBI-0735191). This work was supported through a National Science Foundation grant (IOS-0820854) awarded to N.R.S. and J.N.M. D.H.C. was a fellow of the Life Sciences Research Foundation funded through the Gordon and Betty Moore Foundation.
 
Date 2016-11-25T07:23:04Z
2016-11-25T07:23:04Z
2016
 
Type Article
 
Identifier G3: Genes, Genomes, Genetics, 6(10): 3169-3184
2160-1836
http://59.163.192.83:8080/jspui/handle/123456789/695
http://www.g3journal.org/content/6/10/3169.long
10.1534/g3.116.030536
 
Language en_US
 
Publisher Genetics Society of America