Mapping Complex Agronomic Traits in Autotetraploid Potato Completed Project uri icon

description

  • The world is facing an unprecedented challenge to provide a sustainable food supply, caused by a rapid population increase and shrinkage of land for growing traditional food crops such as wheat, rice etc., largely due to urbanization and climate change. The FAO has ranked potato as the world's third most important food crop based on its high yield, nutritional value and less stringent requirements for irrigation and arable land to grow compared with many crops. However, there is an urgent need for the development of new varieties with genetically improved agronomic performance, particularly adaptability to harsh cultivation environments such as low rainfall and temperature, resistance to disease, and high tuber yield and quality. A major challenge is posed by the polyploid nature of the potato genome. Polyploid organisms have multiple sets of chromosomes per cell. When polyploid cells divide, they show much more complicated chromosome pairing behaviour compared to diploid cells with two sets of chromosomes, creating a wider range of outcomes for recombination (gene shuffling) and gene segregation (partitioning). Polyploidy has played a key role in the evolution of plants and animals, particularly flowering plants, many of which are currently polyploid, while the rest have experienced polyploidy in their evolutionary history. Potato is an autotetraploid with four copies of the same genome and shows tetrasomic inheritance, a characteristic shared by many other important crops including leek, sugarcane, alfalfa and some economically important aquaculture species, including Atlantic salmon and trout. To develop new potato varieties with genetically improved performance requires knowledge of the number and location of genes that affect the target traits. Most observable traits in nature are quantitative or complex, including key agronomic traits, such as yield and resistance to disease, as well as most traits relevant to health and disease, in humans and other animals. Therefore understanding how phenotypic variation in quantitative traits is genetically controlled provides an essential and rational basis for plant breeding. Discovery of abundant DNA sequence variants in the genome of most species provides a source of information for locating genes that underlie quantitative trait phenotypes, the so called mapping of Quantitative Trait Loci (QTL). QTL mapping provides estimates of genome locations, the number and effects of the genes controlling a quantitative trait. Theory and methods for QTL mapping have been well established and QTL mapping is routinely practiced in diploid species. However, the same type of study lags far behind in autotetraploid species, primarily due to the lack of appropriate methods for these analyses. Since inheritance in autotetraploids differs markedly from that in diploids, it is inappropriate to use the methods developed for diploids to conduct the same analysis in autotetraploids. This project will deliver the scientific basis and novel analytical tools for DNA-marker assisted mapping of QTL and other quantitative genetic analyses in autotetraploid species. The methods to be developed will take proper account of the essential yet complex features of autotetraploid inheritance. We will carry out experiments to sequence an outbred segregating population of cultivated potato for evenly distributed DNA sequence variants in the potato genome. The sequence data will be integrated with phenotype data of several agronomically important quantitative traits from the same population to enable mapping of QTL for these traits using the analytical methods to be developed. This will provide the first example of QTL mapping practice on a rigorous tetrasomic basis. Accomplishment of this project will open unprecedented opportunities for basic genetics and genomics research in autotetraploid species, and facilitate genetic breeding for elite autotetraploid crop cultivars and aquaculture animal varieties.

date/time interval

  • June 30, 2016 - November 30, 2021