Submitted to: Rice Technical Working Group Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 1/1/2006
Publication Date: 2/15/2006
Citation: Pinson, S.R., Jia, Y., Fjellstrom, R.G., Jia, M.H., Hulbert, S., Liu, K., Nelson, J.C. 2006. Bringing quantitative traits under breeder control by combining QTL mapping with candidate gene approaches: A case study of rice sheath blight resistance. Rice Technical Working Group Meeting Proceedings, February 29-March 1, 2006, Houston, Texas. 2006 CDROM.
Technical Abstract: It is inherently difficult to collect reliable phenotypic data on quantitative traits where the individual genetic effect of the multiple genes is generally less than the environmental effect(s), and the genes themselves may interact, producing a genetic-background effect. The difficulty in obtaining reliable phenotypic data for these traits is one reason breeders highly desire molecular tags to facilitate marker-assisted selection for quantitative trait loci (QTLs). Molecular gene-tags are identified through statistical correlation between phenotype and molecular markers. Accurate and precise data on QTL locations are also difficult to achieve without first obtaining reliable phenotypic data. Unfortunately, the same factors that make it desirable to know the precise chromosomal location, or even know the molecular sequence of a QTL also make this information inherently difficult to determine. While several major genes have been cloned and sequenced in rice, we know the DNA sequence of very few QTLs – not just in rice, but in any species. Tens of thousands of QTLs have been mapped in plants, with new QTLs being reported each month. As of January 2006, Gramene (www.gramene.org) cited more than 7500 QTLs for rice alone. Some of Gramene’s QTL listings are redundant in that a locus identified in the same population observed over multiple replications/locations gets a separate listing in the database. This is intentional and allows the information in Gramene to also reflect the replication or confidence levels of each QTL. Even so, of the thousands of QTLs mapped in all plants, only a handful have been fine mapped and cloned. For example, just one of the several QTLs for fruit sugar content in tomato has been cloned. The same for tomato fruit shape and weight. QTLs cloned from Arabidopsis include one for insect resistance controlled by aliphatic glucosinolate structure profiles, one for root morphology, and two QTLs for flowering time. Only heading time QTLs have been cloned from rice, but four have been cloned to date (Se1, Hd3a, 'CK2, and Ehd1), all by researchers in Japan. Nearly all plant QTLs sequenced and cloned to date were identified through positional cloning, which generally proceeds along the following steps. 1) Putative identification and mapping of QTLs to large (10 – 20 cM) chromosomal regions. 2) QTL verification – often from agreement between independent mapping studies. 3) Fine mapping ('5 cM) – often within near-isogenic lines (NILs) where the QTL is now ‘Mendelized’. 4) After location to a sufficiently small genomic region, the region can be sequenced and analyzed for gene features such as start and stop codons or similarity to genes reported from other species. 5) Gene sequence verification - often via transformation. Progressing from phenotype to genotype in this manner is known as forward genetics. It is also possible to conduct ‘reverse genetics’ where one starts with a base sequence, then identifies the gene’s function or affected phenotype. The NRI Rice Coordinated Applied Genomics Program (a.k.a. RiceCAP; http://www.uark.edu/ua/ricecap/) is a multi-disciplinary, multi-State research project aimed at building within the U.S. rice community the “machinery” and experience-base required for developing and using molecular markers to facilitate marker-assisted selection (MAS) of QTLs. RiceCAP focuses on two quantitative rice traits, grain milling quality and sheath blight resistance (SBR). This paper focuses on the SBR-QTL tagging effort to facilitate MAS. RiceCAP includes both forward and reverse genetics studies, with additional markers for the forward genetics efforts being identified through reverse genetics (candidate genes, expression differences), and with the putative genes identified via reverse genetics being verified and prioritized for further analysis with the forward genetics re