Haplotype associated RNA expression (HARE) improves prediction of complex traits in maize

Abstract

Genomic prediction typically relies on associations between single-site polymorphisms and traits of interest. This representation of genomic variability has been successful for predicting many complex traits. However, it usually cannot capture the combination of alleles in haplotypes and it has generated little insight about the biological function of polymorphisms. Here we present a novel and cost-effective method for imputing cis haplotype associated RNA expression (HARE), studied their transferability across tissues, and evaluated genomic prediction models within and across populations. HARE focuses on tightly linked cis acting causal variants in the immediate vicinity of the gene, while excluding trans effects from diffusion and metabolism. Therefore, HARE estimates were more transferrable across different tissues and populations compared to measured transcript expression. We also showed that HARE estimates captured one-third of the variation in gene expression. HARE estimates were used in genomic prediction models evaluated within and across two diverse maize panels–a diverse association panel (Goodman Association panel) and a large half-sib panel (Nested Association Mapping panel)–for predicting 26 complex traits. HARE resulted in up to 15% higher prediction accuracy than control approaches that preserved haplotype structure, suggesting that HARE carried functional information in addition to information about haplotype structure. The largest increase was observed when the model was trained in the Nested Association Mapping panel and tested in the Goodman Association panel. Additionally, HARE yielded higher within-population prediction accuracy as compared to measured expression values. The accuracy achieved by measured expression was variable across tissues, whereas accuracy by HARE was more stable across tissues. Therefore, imputing RNA expression of genes by haplotype is stable, cost-effective, and transferable across populations.

Combining GWAS and TWAS to identify candidate causal genes for tocochromanol levels in maize grain

“…Although cis -eQTL were almost always the largest effect QTL detected for expression variation of candidate casual genes, our study was limited by statistical power and the multiple testing burden, thus not allowing a more complete genetic dissection of trans -eQTL ( Albert et al 2018 ). However, cis effects tend to be more stable than trans effects across environments, thus cis -acting causal variants have the potential to be more transferable across populations when incorporated at the haplotype level in genomic prediction models ( Giri et al 2021 ).…”

Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain

“…As it relates to an applied maize hybrid breeding program, the available ‘omics’ data of the inbred lines could also be used, in theory, to improve prediction of hybrid performance for grain tocochromanol levels (Schrag et al., 2018). Considering the decreasing cost of RNA sequencing (Lohman et al., 2016) and availability of a maize practical haplotype graph (Valdes Franco et al., 2020), the robustness and cost‐effectiveness of our transcriptome‐based prediction approaches can be improved upon for breeding programs by modeling the expression profiles of key candidate causal genes at multiple kernel developmental time points with Bayesian networks (dos Santos et al., 2020) and operationalizing the between‐population transfer of imputed expression effects associated with cis haplotypes (Giri et al., 2021).…”

Combining GWAS and TWAS to identify candidate causal genes for tocochromanol levels in maize grain

“…2018). However, cis effects tend to be more stable than trans effects across environments, thus cis -acting causal variants have the potential to be more transferable across populations when incorporated at the haplotype level in genomic prediction models (Giri et al . 2021).…”