Genomic Selection for Late Blight and Common Scab Resistance in Tetraploid Potato (Solanum tuberosum)

Abstract

Potato (Solanum tuberosum) is a staple food crop and is considered one of the main sources of carbohydrates worldwide. Late blight (Phytophthora infestans) and common scab (Streptomyces scabies) are two of the primary production constraints faced by potato farming. Previous studies have identified a few resistance genes for both late blight and common scab; however, these genes explain only a limited fraction of the heritability of these diseases. Genomic selection has been demonstrated to be an effective methodology for breeding value prediction in many major crops (e.g., maize and wheat). However, the technology has received little attention in potato breeding. We present the first genomic selection study involving late blight and common scab in tetraploid potato. Our data involves 4,110 (Single Nucleotide Polymorphisms, SNPs) and phenotypic field evaluations for late blight (n=1,763) and common scab (n=3,885) collected in seven and nine years, respectively. We report moderately high genomic heritability estimates (0.46 ± 0.04 and 0.45 ± 0.017, for late blight and common scab, respectively). The extent of genotype-by-year interaction was high for late blight and low for common scab. Our assessment of prediction accuracy demonstrates the applicability of genomic prediction for tetraploid potato breeding. For both traits, we found that more than 90% of the genetic variance could be captured with an additive model. For common scab, the highest prediction accuracy was achieved using an additive model. For late blight, small but statistically significant gains in prediction accuracy were achieved using a model that accounted for both additive and dominance effects. Using whole-genome regression models we identified SNPs located in previously reported hotspots regions for late blight, on genes associated with systemic disease resistance responses, and a new locus located in a WRKY transcription factor for common scab.

Exploring Deep Learning for Complex Trait Genomic Prediction in Polyploid Outcrossing Species

“…The simplest one is to incorporate a general matrix into the linear models made up of dummy variables. This model contains as many degrees of freedom as ploidy level per locus and allowing for any interaction structure between alleles (Enciso-Rodriguez et al, 2018;Amadeu et al, 2019). RKHS models (Gianola et al, 2006;Gianola et al, 2008; are also able to capture complex interaction patterns in a relatively straightforward manner.…”

“…The above parameterization assumes additivity of effects, although linear models can address non-linear relationships if properly parameterized. Non-linear interactions can be modeled by expressing g (Equation 2) in a general way, i.e., g = W w where W (centered and scaled) is a matrix of dummy variables that indicates the number of copies of the reference allele ranging from 0 to the ploidy level (Slater et al, 2016;Enciso-Rodriguez et al, 2018). This model is, in principle, a good parameterization to account for nonlinear interactions and we will refer to it as BRR general model (BRR-GM), since Bayes ridge regression was used.…”

“…SNP calling (Slater et al, 2016;Gezan et al, 2017;Bourke et al, 2018). A few studies have demonstrated the potential advantages of GS in allo and autopolyploids (Gezan et al, 2017;Enciso-Rodriguez et al, 2018;Nyine et al, 2018;Amadeu et al, 2019;de Bem Oliveira et al, 2019;Juliana et al, 2019;Zingaretti et al, 2019), although its implementation is still in its infancy.…”

SeqBreed: a python tool to evaluate genomic prediction in complex scenarios

“…We illustrate the software with sequence data from the Drosophila genome reference panel (DGRP, [21]), parsed and filtered as explained in [22], and genotype data from tetraploid potato [23], parsed as described in [7]. Data and scripts are in https ://githu b.com/migue lpere zenci so/ # 10 QTNs are simulated, h2 of the trait is 0.7 qtn = gg.QTNs(h2=[0.7], genome=gfeatures, nqtn=10) # environmental variances are computed qtn.get_var(gfeatures, gbase) Selection is implemented in cycles, the number of generations, the numbers of males and females selected, and family size must be specified.…”

SeqBreed: a python tool to evaluate genomic prediction in complex scenarios

“…Panel (DGRP, [11]), parsed and filtered as detailed in [12], and genotype data from tetraploid potato [13], parsed as described in [2].…”