Breeding With Major and Minor Genes: Genomic Selection for Quantitative Disease Resistance

Disease resistance in plants is mostly quantitative, with both major and minor genes controlling resistance. This research aimed to optimize genomic selection (GS) models for use in breeding programs that are needed to select both major and minor genes for resistance. In this study, stripe rust (Puccinia striiformis Westend. f. sp. tritici Erikss.) of wheat (Triticum aestivum L.) was used as a model for quantitative disease resistance. The quantitative nature of stripe rust is usually phenotyped with two disease traits, infection type (IT) and disease severity (SEV). We compared two types of training populations composed of 2,630 breeding lines (BLs) phenotyped in single-plot trials from 4 years (2016–2020) and 475 diversity panel (DP) lines from 4 years (2013–2016), both across two locations. We also compared the accuracy of models using four different major gene markers and genome-wide association study (GWAS) markers as fixed effects. The prediction models used 31,975 markers that are replicated 50 times using a 5-fold cross-validation. We then compared GS models using a marker-assisted selection (MAS) to compare the prediction accuracy of the markers alone and in combination. GS models had higher accuracies than MAS and reached an accuracy of 0.72 for disease SEV. The major gene and GWAS markers had only a small to nil increase in the prediction accuracy more than the base GS model, with the highest accuracy increase of 0.03 for the major markers and 0.06 for the GWAS markers. There was a statistical increase in the accuracy using the disease SEV trait, BLs, population type, and combining years. There was also a statistical increase in the accuracy using the major markers in the validation sets as the mean accuracy decreased. The inclusion of fixed effects in low prediction scenarios increased the accuracy up to 0.06 for GS models using significant GWAS markers. Our results indicate that GS can accurately predict quantitative disease resistance in the presence of major and minor genes.

Breeding with Major and Minor Genes: Genomic Selection for Quantitative Disease Resistance

Most disease resistance in plants is quantitative, with both major and minor genes controlling resistance. This research aimed to optimize genomic selection (GS) models for use in breeding programs needing to select both major and minor genes for resistance. In this experiment, stripe rust (Puccinia striiformis Westend. f. sp. tritici Erikss.) of wheat (Triticum aestivum L.) was used as a model for quantitative disease resistance. The quantitative nature of stripe rust is usually phenotyped with two disease traits, infection type and disease severity. We compared two types of training populations composed of 2,630 breeding lines phenotyped in single plot trials from four years (2016-2020) and 475 diversity panel lines from four years (2013-2016), both across two locations. We also compared the accuracy of models with four different major gene markers and genome-wide association (GWAS) markers as fixed effects. The prediction models used 31,975 markers replicated 50 times using 5-fold cross-validation. We then compared the GS models with marker-assisted selection to compare the prediction accuracy of the markers alone and in combination. The GS models had higher accuracies than marker-assisted selection and reached an accuracy of 0.72 for disease severity. The major gene and GWAS markers had only a small to zero increase in prediction accuracy over the base GS model, with the highest accuracy increase of 0.03 for major markers and 0.06 for GWAS markers. There was a statistical increase in accuracy by using the disease severity trait, the breeding lines, population type, and by combing years. There was also a statistical increase in accuracy using major markers within the validation sets as the mean accuracy decreased. The inclusion of fixed effects in low prediction scenarios increased accuracy up to 0.06 for GS models using significant GWAS markers. Our results indicate that GS can accurately predict quantitative disease resistance in the presence of major and minor genes.

Abstract 12059: Incidental Findings in Cardiomyopathy and Channelopathy Genes Among 5891 Individuals Undergoing Whole-exome Sequencing. What Should be Reported?

Background: Whole-exome-sequencing (WES) is becoming a common molecular diagnostic test for patients with genetic disorders. However, this technique allows the identification not only of mutations responsible for the disease under investigation, but also of variants potentially causing other diseases, the so called “incidental findings” (IFs). The American College of Medical Genetics and Genomics (ACMG) stated that IFs should be reported based on clinical validity and utility and indicated a list of 56 actionable genes. Among these, nearly half (20/56) are major genes associated with channelopathies and cardiomyopathies. Despite these recommendations, most of the studies so far published, reported also mutations in minor genes among the actionable findings. Methods: WES was performed in 5891 individuals without known channelopathies or cardiomyopathies. Exome data were first filtered based on genotype quality. Subsequently, a frequency filter was applied, considering 1000 Genomes, ExAC and our internal exome database. Variants reported as pathogenic in ClinVar or novel but expected to be pathogenic (nonsense, frameshift and splice) were further investigated, following the ACMG guidelines. Major (20) and minor (73) genes associated with channelopathies and cardiomyopathies were evaluated. Results: We identified 3514 variants in the 93 genes under investigation, after applying the quality and frequency filters. Eight variants were classified as pathogenic and 52 as likely pathogenic and they were detected in around 1% of the individuals. The vast majority (85%) of pathogenic or likely pathogenic variants were located in the 20 actionable genes indicated by ACMG. The inclusion of minor genes increased the number of variants of unknown significance (VUS), from 865 to 3454. Conclusion: Our data support the ACMG recommendations in reporting only IFs identified in the 20 major cardiac actionable genes. Indeed, the inclusion of minor genes is mainly increasing the number of VUS, without significantly impacting the number of pathogenic and likely pathogenic variants. The percentage of individuals with potentially clinical relevant variants in these genes is too high in relation to the disease-prevalence: a cardiologic evaluation is warranted.