The effect of marker types and density on genomic prediction and GWAS of key performance traits in tetraploid potato

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Abstract

Genomic prediction and genome-wide association studies are becoming
widely employed in potato key performance trait QTL identifications and to
support potato breeding using genomic selection. Elite cultivars are tetraploid
and highly heterozygous but also share many common ancestors and
generation-spanning inbreeding events, resulting from the clonal
propagation of potatoes through seed potatoes. Consequentially, many
SNP markers are not in a 1:1 relationship with a single allele variant but
shared over several alleles that might exert varying effects on a given trait.
The impact of such redundant “diluted” predictors on the statistical models
underpinning genome-wide association studies (GWAS) and genomic
prediction has scarcely been evaluated despite the potential impact on
model accuracy and performance. We evaluated the impact of marker
location, marker type, and marker density on the genomic prediction and
GWAS of five key performance traits in tetraploid potato (chipping quality, dry
matter content, length/width ratio, senescence, and yield). A 762-offspring
panel of a diallel cross of 18 elite cultivars was genotyped by sequencing, and
markers were annotated according to a reference genome. Genomic
prediction models (GBLUP) were trained on four marker subsets [nonsynonymous
(29,553 SNPs), synonymous (31,229), non-coding (32,388), and
a combination], and robustness to marker reduction was investigated. Singlemarker regression GWAS was performed for each trait and marker subset. The
best cross-validated prediction correlation coefficients of 0.54, 0.75, 0.49,
0.35, and 0.28 were obtained for chipping quality, dry matter content, length/
width ratio, senescence, and yield, respectively. The trait prediction abilities
were similar across all marker types, with only non-synonymous variants
improving yield predictive ability by 16%. Marker reduction response did not
depend on marker type but rather on trait. Traits with high predictive abilities,
e.g., dry matter content, reached a plateau using fewer markers than traits
with intermediate-low correlations, such as yield. The predictions were
unbiased across all traits, marker types, and all marker densities >100 SNPs.
Our results suggest that using non-synonymous variants does not enhance the performance of genomic prediction of most traits. The major known QTLs
were identified by GWAS and were reproducible across exonic and wholegenome
variant sets for dry matter content, length/width ratio, and
senescence. In contrast, minor QTL detection was marker type dependent.
Original languageEnglish
Article number1340189
JournalFrontiers in Plant Science
Volume15
ISSN1664-462X
DOIs
Publication statusPublished - 2024

Bibliographical note

Copyright © 2024 Aalborg, Sverrisdóttir, Kristensen and Nielsen.

Keywords

  • Solanum tuberosum
  • genomic prediction
  • GBLUP
  • tetraploid potato breeding
  • GWAS
  • marker density
  • marker type

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