QTL Mapping in Outbred Tetraploid (and Diploid) Diallel Populations

Over the last decade, multiparental populations have become a mainstay of genetics research in diploid species. Our goal was to extend this paradigm to autotetraploids by developing software for quantitative trait locus (QTL) mapping in connected F1 populations derived from a set of shared parents. For QTL discovery, phenotypes are regressed on the dosage of parental haplotypes to estimate additive effects. Statistical properties of the model were explored by simulating half-diallel diploid and tetraploid populations with different population sizes and numbers of parents. Across scenarios, the number of progeny per parental haplotype (pph) largely determined the statistical power for QTL detection and accuracy of the estimated haplotype effects. Multi-allelic QTL with heritability 0.2 were detected with 90% probability at 25 pph and genome-wide significance level 0.05, and the additive haplotype effects were estimated with over 90% accuracy. Following QTL discovery, the software enables a comparison of models with multiple QTL and non-additive effects. To illustrate, we analyzed potato tuber shape in a half-diallel population with 3 tetraploid parents. A well-known QTL on chromosome 10 was detected, for which the inclusion of digenic dominance lowered the Deviance Information Criterion (DIC) by 17 points compared to the additive model. The final model also contained a minor QTL on chromosome 1, but higher order dominance and epistatic effects were excluded based on the DIC. In terms of practical impacts, the software is already being used to select offspring based on the effect and dosage of particular haplotypes in breeding programs.

Functionally prioritised whole-genome sequence variants improve the accuracy ofgenomic prediction for heat tolerance

Background Heat tolerance is a trait of economic importance in the context of warm climates and the effects of global warming on livestock, production, reproduction, health, and well-being. It is desirable to improve the prediction accuracy for heat tolerance to help accelerate the genetic gain for this trait. This study investigated the improvement in prediction accuracy for heat tolerance when selected sets of sequence variants from a large genome-wide association study (GWAS) were incorporated into a standard 50k SNP panel used by the industry. Methods Over 40,000 dairy cattle (Holsteins, Jersey, and crossbreds) with genotype and phenotype data were analysed. The phenotypes used to measure an individual’s heat tolerance were defined as the rate of milk production decline (slope traits for the yield of milk, fat, and protein) with a rising temperature-humidity index. We used Holstein and Jersey cows to select sequence variants linked to heat tolerance based on GWAS. We then investigated the accuracy of prediction when sets of these pre-selected sequence variants were added to the 50k industry SNP array used routinely for genomic evaluations in Australia. We used a bull reference set to develop the genomic prediction equations and then validated them in an independent set of Holsteins, Jersey, and crossbred cows. The genomic prediction analyses were performed using BayesR and BayesRC methods. Results The accuracy of genomic prediction for heat tolerance improved by up to 7%, 5%, and 10% in Holsteins, Jersey, and crossbred cows, respectively, when sets of selected sequence markers from Holsteins (i.e., single-breed QTL discovery set) were added to the 50k industry SNP panel. Using pre-selected sequence variants identified based on a combined set of Holstein and Jersey cows in a multi-breed QTL discovery, a set of 6,132 to 6,422 SNPs generally improved accuracy, especially in the Jersey validation set. Combining Holstein and Jersey bulls (multi-breed) in the reference set improved prediction accuracy compared to using only Holstein bulls in the reference set. Conclusion Informative sequence markers can be prioritised to improve the genetic prediction of heat tolerance in different breeds, and these variants, in addition to providing biological insight, have direct application in the development of customized SNP arrays or can be utilised via imputation into current SNP sets.

Quantitative Trait Loci (QTLs) for Intumescence Severity in Eucalyptus globulus and Validation of QTL Detection Based on Phenotyping Using Open-Pollinated Families of a Mapping Population

Intumescence is a nonpathogenic physiological disorder characterized by leaf blistering. This disorder can affect growth and development in glasshouses and growth chambers and may be confused with pathogenic diseases. We used quantitative trait loci (QTL) analysis to examine the genetic basis of variation in intumescence severity in Eucalyptus globulus, and test for colocation with previously detected QTLs for pathogen susceptibility. QTL analysis used the phenotype means of open-pollinated (OP) families of an outcrossed F2 mapping family (OP F3; n = 300) of E. globulus and the linkage map constructed in the F2. We validate this phenotyping approach for QTL analysis by assessing a trait previously used for QTL discovery in the F2 and showing the same major QTL was detected with the OP F3. For intumescence severity, five putative QTLs were detected across four linkage groups. Four of these did not colocate with previously reported QTLs for fungal pathogen susceptibility in Eucalyptus, suggesting the mechanisms underlying susceptibility to intumescence and to the two fungal pathogens are largely independent. This study demonstrates there is a genetic basis for variation in intumescence severity, reports the first QTL for intumescence severity in plants, and provides a robust framework for investigating the potential mechanisms involved.