Crop improvement studies based on molecular approaches in interspecific oil palm hybrids

  1. Astorkia Amiama, Maider
Dirigida por:
  1. Mónica Hernández Muñoz Director/a
  2. Begoña Jugo Orrantia Director/a

Universidad de defensa: Universidad del País Vasco - Euskal Herriko Unibertsitatea

Fecha de defensa: 07 de febrero de 2020

Tribunal:
  1. Javier Tamames de la Huerta Presidente/a
  2. Maria de los Angeles Martinez de Pancorbo Gomez Secretario/a
  3. Norbert Billotte Vocal
Departamento:
  1. Genética, Antropología Física y Fisiología Animal

Tipo: Tesis

Teseo: 152034 DIALNET lock_openADDI editor

Resumen

Oil Palm (OP) is the crop with the highest oil yield per hectare and as a result, its use has spread rapidly in tropical regions of Asia, Africa and America. The main OP plantations consist of Elaeis guineensis (Eg) species, known to produce high amounts of oil. However, in American regions this species is being affected by the ¿Pudrición de Cogollo¿ disease leading to dead palms. Therefore, OP companies started crossing this species with E. oleifera (Eo) palms which is resistant to this disease. The obtained interspecific hybrids show interesting characteristic inherited from both parents; resistance to different diseases, interesting oil quality characteristics, competitive oil production and decreased height which prolongs its useful life. However, little work has been done in the improvement of these hybrids. This thesis tries to address this gap applying different molecular approaches. First, an extensive study of an amplicon of the ¿Shell-thickness¿ (Sh) gene has been conducted on 568 Eg, Eo and hybrid genotypes. Then, with the aim to discover promising new Candidate Genes (CG) that could be exploited in further molecular assisted selection systems (MAS) a large phenotypic study of 25 production and quality traits have been performed within 198 hybrid genotypes fllowed by two Association Mapping (AM) assays. These latter have been based on targeted CG and random Restriction site associated RNA sequencing(RARSeq) approaches.