Dna markers and mapping of quantitative trait loci for yield and bunch quality in Deli dura X Yangambi pisifera oil palm (Elaeis guineensis) population

Increased modern farming of the oil biosynthesis efficient oil palm, Elaeis guineensis Jacq., has propelled it to be the world's largest source of edible oil today. However, further oil yield improvement by conventional breeding is increasingly limited by lengthy time and costs due to long reproductive cycles, large plant size and an evaluation period of 10-15 years. Molecular tools which allow rapid, large scale evaluation over a short time, independent of plant age, will be particularly valuable in the face of such constraints. Towards such a goal, the aim of this particular study was to construct a genetic linkage map of a high yield oil palm population using DNA markers and to identify Quantitative Trait Loci (QTLs) related to oil yield components. This was followed by configuration of Quantitative Trait Alleles (QTA) with favourable and unfavourable effects on their respective oil yield components. The mapping population was a high-yielding Felda breeding cross, coded DA41, represented by 118 progeny palms. Besides the genotypic data generated in this study, phenotypic data of 21 yield components were available from ongoing field trials. The DNA markers employed for genotype data were microsatellites (SSR), Amplified Fragment Length Polymorphism (AFLP) and Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP) markers. A total 804 segregating marker loci (489 SSRs, 307 AFLPs and 8 PCR-RFLP) were used for final linkage analysis and map construction. The map of DA41 (ARK86D X ML161P) was 2398.8 cM long with 512 marker loci (368 SSRs, 135 AFLPs and 9 PCR-RFLPs), at an average 32 markers and a range of 15-59 markers per linkage group, and an average map density of 5cM. The linkage group length was 77.5 cM to 223 cM, with an average of 150 cM. Taking the yield components phenotype data on board resulted in the detection of 164 QTLs associated with oil yield components. The QTLs had an average confidence region of 15.4 cM and no marker interval exceeded 50 cM. In the DA41 population, cumulative QTL effects increased in tandem with the number of QTL markers, matching the QT+ allele for each of the traits tested. The many QTLs detected per trait suggested that the traits studied are polygenic with many genes of individual small effects on independent loci. However, the scope of the study did not rule out or rule in epistasis between different QTLs affecting a particular trait. Furthermore, several QTLs probably also show pleiotropic effects as seen by QTL clustering of inter-related traits on almost all the linkage groups, confirming the complexity of the genetic architecture of not only oil yield but also its components in the oil palm. The overall picture suggests that certain regions of the chromosomes are richer in the genes that affect the expression of a particular yield component trait and encompass pleiotropic, epistatic and heterotic effects. Hence, it will not be surprising if a large proportion of the identified additive effects from QTLs actually arise from digenic interactions between loci. For practical applications from this work, it will be necessary to test these yield component QTLs in a broader array of genetic backgrounds and in different environments. Also, more closely linked markers or flanking markers to the QTLs should be sought because recombinations between the markers and QTLs can occur when transferring the results from one population to another. Clearly, while this study has generated results that can be used in initial marker-assisted selection (MAS) for oil palm breeding, such as in population selection and enrichment, more detailed knowledge of marker-trait association will further contribute to more precise applications.

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