Statistical Methods for Rice QTL Mapping

Abstract

Rice is the staple food and nutritious resource for about half of the world's population. Therefore, its impact on global food security, poverty alleviation and environmental protection is significant. Much rice-related research is focused on ways to increase grain yield and improve grain quality. The grains on the spikelets of a mature rice plant represent the next generation and consist mainly of diploid (embryo) and triploid (endosperm) tissues. The grain yield is directly related to the traits in the plants, such as large panicles, number of unproductive tillers, sturdy stems, dark green leaf, vigorous root system, intermediate height, growth duration, and pest and disease resistance. These yield-related traits are controlled by the diploid plant genomes. The grain quality depends critically upon the traits in the endosperms, such as amylase content, gel consistency, starch and gum content, etc. These quality-related traits are controlled by the triploid endosperm genomes. Both the yield- and quality-related traits are usually showing quantitative variation, and they are affected by multiple genes (quantitative trait loci, QTL) and environmental factors. To improve the yield potential and grain quality, the understanding of the genetic basis of these traits in plant and endosperm through the mapping for the underlying QTL becomes increasingly important to rice (cereal) breeding. Introduction of QTL mapping and statistical methods for locating the positions and estimating the effects of QTL in rice (cereal) as well as the related statistical issues are presented and discussed.