Incorporating Gene Annotation into Genomic Prediction of Complex Phenotypes

Abstract

Today, genomic prediction (GP) is an established technology in plant and animal breeding programs. Current standard methods are purely based on statistical considerations but do not make use of the abundant biological knowledge, which is easily available from public databases. Major questions that have to be answered before biological prior information can be used routinely in GP approaches are which types of information can be used, and at which points they can be incorporated into prediction methods. In this study, we propose a novel strategy to incorporate gene annotation into GP of complex phenotypes by defining haploblocks according to gene positions. Haplotype effects are then modeled as categorical or as numerical allele dosage variables. The underlying concept of this approach is to build the statistical model on variables representing the biologically functional units. We evaluate the new methods with data from a heterogeneous stock mouse population, the (), and a rice breeding population from the Rice Diversity Panel. Our results show that using gene annotation to define haploblocks often leads to a comparable, but for some traits to a higher, predictive ability compared to SNP-based models or to haplotype models that do not use gene annotation information. Modeling gene interaction effects can further improve predictive ability. We also illustrate that the additional use of markers that have not been mapped to any gene in a second separate relatedness matrix does in many cases not lead to a relevant additional increase in predictive ability when the first matrix is based on haploblocks defined with gene annotation data, suggesting that intergenic markers only provide redundant information on the considered data sets. Therefore, gene annotation information seems to be appropriate to perceive the importance of DNA segments. Finally, we discuss the effects of gene annotation quality, marker density, and linkage disequilibrium on the performance of the new methods. To our knowledge, this is the first work that incorporates epistatic interaction or gene annotation into haplotype-based prediction approaches.

Multi-omics-data-assisted genomic feature preselection improves the accuracy of genomic prediction

“…These results are similar to those of a previous study (Morgante et al, 2019), but higher than the results in (Li et al, 2019). This may be due to the quality control of the SNPs, the number of lines, and the line means for phenotypes in the present study, which are the same as those used in (Morgante et al, 2019) and different from those in (Gao et al, 2017). The high heritability of the analyzed traits showed that most loci that affect the traits have additive gene actions or contributions from non-additive gene actions at many loci.…”

“…Therefore, pre-selected potential causal markers or QTLs from WGS are necessary for improving the accuracy of genomic prediction (Raymond et al, 2018). Thus, many preselection variant strategies were used to improve the power of genomic prediction based on the following methods: genomewide association study (GWAS) (Zhang et al, 2014;Veerkamp et al, 2016;Song et al, 2019;Ye et al, 2019), Bayesian procedures (Kemper et al, 2015), genome-wide signatures of selection (Ye et al, 2020), Animal QTLdb (Song et al, 2019), gene annotation (Heidaritabar et al, 2016;Gao et al, 2017), and gene ontology categories (Edwards et al, 2016;Abdollahiarpanahi et al, 2017). These methods mainly depend on the direct link between phenotype and DNA variants or some prior genome annotation information.…”

Homeologous Epistasis in Wheat: The Search for an Immortal Hybrid

“…This has been accomplished by allowing pairwise relationships with all markers across entire subgenomes (Santantonio et al 2019b) or on syntenic chromosome arms (Santantonio et al 2019a), with mixed success. The construction of smaller haplotypes in a manner similar to Gao et al (2017) may also improve functional pairing of homeologous alleles. Higher depth sequencing and advances in marker imputation may also aid in detection of homeologous epistasis.…”

A subfunctionalization epistasis model to evaluate homeologous gene interactions in allopolyploid wheat

“…This has been accomplished by allowing pairwise relationships with all markers across entire subgenomes (Santantonio et al ., 2018b) or on syntenic chromosome arms (Santantonio et al ., 2018a), with mixed success. The construction of smaller haplotypes in a manner similar to Gao et al ., (2017) may also improve functional pairing of homeologous alleles. Higher depth sequencing and advances in marker imputation may also aid in detection of homeologous epistasis.…”