Genomic Selection in Commercial Perennial Crops: Applicability and Improvement in Oil Palm (Elaeis guineensis Jacq.)

Abstract

Genomic selection (GS) uses genome-wide markers to select individuals with the desired overall combination of breeding traits. A total of 1,218 individuals from a commercial population of Ulu Remis x AVROS (UR x AVROS) were genotyped using the OP200K array. The traits of interest included: shell-to-fruit ratio (S/F, %), mesocarp-to-fruit ratio (M/F, %), kernel-to-fruit ratio (K/F, %), fruit per bunch (F/B, %), oil per bunch (O/B, %) and oil per palm (O/P, kg/palm/year). Genomic heritabilities of these traits were estimated to be in the range of 0.40 to 0.80. GS methods assessed were RR-BLUP, Bayes A (BA), Cπ (BC), Lasso (BL) and Ridge Regression (BRR). All methods resulted in almost equal prediction accuracy. The accuracy achieved ranged from 0.40 to 0.70, correlating with the heritability of traits. By selecting the most important markers, RR-BLUP B has the potential to outperform other methods. The marker density for certain traits can be further reduced based on the linkage disequilibrium (LD). Together with in silico breeding, GS is now being used in oil palm breeding programs to hasten parental palm selection.

Genomic preselection with genotyping-by-sequencing increases performance of commercial oil palm hybrid crosses

“…In addition, the difficulty and costs associated with these long term evaluations limit the number of individuals evaluated, resulting in low selection intensity. In this context, the potential of GS for palm oil yield is high, and has been confirmed in the few previous studies dealing with GS in this species [ 15 – 19 ].…”

“…Nevertheless, the empirical GS studies published in oil palm so far [ 17 – 19 ] suffered from some limitations. First, like in many studies in other crops, they used single datasets which can bias accuracies upwards [ 20 – 22 ].…”

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“…Costs for GS ranged from US$75,000 to $194,000 per unit gain, depending on cost per marker data point, population size, QTL number and heritability, compared with US$116,000 to $333,000 per unit gain for 19 years of phenotypic selection per cycle. Also in oil palm, Kwong et al (2017a) found that for 1,218 individuals genotyped using a 200K array, GS model accuracy increased with trait heritability, ranging from 0.40 to 0.70.…”

“…Denis and Bouvet (2013) concluded that perennial crops may have more to gain from GS than annual crops since genetic gain per unit time in perennial crops is critical for improved cultivars. There is a paucity of published studies for tree nut crops, though there has been some work conducted in citrus (Minamikawa et al 2017), apple (Kumar et al 2012b), oil palm (Wong and Bernardo 2008;Kwong et al 2017a), and pear (Iwata et al 2013;Minamikawa et al 2018). In a recent study of fruit quality traits in Japanese pear, model accuracy was highest when data for parents and progeny were combined, at r > 0.7 for most traits (Minamikawa et al 2018).…”

“…The main attraction of GS for perennial crops may be that it can accelerate breeding cycles, thereby increasing the gain per unit time and reducing field trial costs (Jannink et al 2010;Desta and Ortiz 2014;Denis and Bouvet 2013). Sweet cherry (Piaskowski et al 2018), peach (Biscarini et al 2017), oil palm (Kwong et al 2017b;Kwong et al 2017a;Wong and Bernardo 2008), citrus (Minamikawa et al 2017), apple (Muranty et al 2015;Kumar et al 2012b), and pear (Minamikawa et al 2018;Iwata et al 2013) breeders have all investigated GS to increase genetic gain in their breeding programs. A recent study in Japanese chestnut (Nishio et al 2018a) achieved high prediction accuracies for harvest date (r = 0.841) and insect infestation (0.604), though yield was not studied.…”