The power of next-generation sequencing and machine learning for causal gene finding and prediction of phenotypes.

Abstract

Abstract The wide application of next-generation sequencing (NGS) has facilitated and accelerated causal gene finding and breeding in the field of plant sciences. A wide variety of techniques and computational strategies is available that needs to be appropriately tailored to the species, genetic architecture of the trait of interest, breeding system and available resources. Utilizing these NGS methods, the typical computational steps of marker discovery, genetic mapping and identification of causal mutations can be achieved in a single step in a cost- and time-efficient manner. Rather than focusing on a few high-impact genetic variants that explain phenotypes, increased computational power allows modelling of phenotypes based on genome-wide molecular markers, known as genomic selection (GS). Solely based on this genotype information, modern GS approaches can accurately predict breeding values for a given trait (the average effects of alleles over all loci that are anticipated to be transferred from the parent to the progeny) based on a large training population of genotyped and phenotyped individuals (Crossa et al., 2017). Once trained, the model offers great reductions in breeding speed and costs. We advocate for improving conventional GS methods by applying advanced techniques based on machine learning (ML) and outline how this approach can also be used for causal gene finding. Subsequent to genetic causes of agronomically important traits, epigenetic mechanisms such as DNA methylation play a crucial role in shaping phenotypes and can become interesting targets in breeding pipelines. We highlight an ML approach shown to detect functional methylation changes sensitively from NGS data. We give an overview about commonly applied strategies and provide practical considerations in choosing and performing NGS-based gene finding and NGS-assisted breeding.