RESUMEN

A RARSeq based Association mapping study was performed in a population of 104 Elaeis oleifera x E. guineensis hybrids of five origins with the aim of finding functional markers associated to six productive and 19 oil quality traits. For this purpose mRNA of each genotype was isolated and double stranded cDNA was synthesized. Following digestion with two restriction enzymes and adapter ligation, a size selected pool of barcoded amplicons was produced and sequenced using Illumina MiSeq. The obtained sequences were processed with a "snakemake" pipeline, filtered and missing values were imputed. For all traits except two significant effects of the origin was observed. Genetic diversity analyses revealed high variability within origins and an excess of heterozygosity in the population. Two GLM models with Q matrix or PCA matrix as covariates and two MLM models incorporating in addition a Kinship matrix were tested for genotype-phenotype associations using GAPIT software. Using unadjusted p values (< 0.01) 78 potential associations were detected involving 25 SNP and 20 traits. When applying FDR multiple testing with p < 0.05, 25 significant associations remained involving eight SNP and six quality traits. Four SNP were located in genes with a potential relevant biological meaning.

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RESUMEN

Oil palm production is gaining importance in Central and South America. However, the main species Elaeis guineensis (Eg) is suffering severely from bud rod disease, restricting the potential cultivation areas. Therefore, breeding companies have started to work with interspecific Elaeis oleifera × Eg (Eo × Eg) hybrids which are tolerant to this disease. We performed association studies between candidate gene (CG) single nucleotide polymorphisms (SNP) and six production and 19 oil quality traits in 198 accessions of interspecific oil palm hybrids from five different origins. For this purpose, barcoded amplicons of initially 167 CG were produced from each genotype and sequenced with Ion Torrent. After sequence cleaning 115 SNP remained targeting 62 CG. The influence of the origins on the different traits was analyzed and a genetic diversity study was performed. Two generalized linear models (GLM) with principle component analysis (PCA) or structure (Q) matrixes as covariates and two mixed linear models (MLM) which included in addition a Kinship (K) matrix were applied for association mapping using GAPIT. False discovery rate (FDR) multiple testing corrections were applied in order to avoid Type I errors. However, with FDR adjusted p values no significant associations between SNP and traits were detected. If using unadjusted p values below 0.05, seven of the studied CG showed potential associations with production traits, while 23 CG may influence different quality traits. Under these conditions the current approach and the detected candidate genes could be exploited for selecting genotypes with superior CG alleles in Marker Assisted Selection systems.