Deciphering Pleiotropic Signatures of Regulatory SNPs in Zea mays L. Using Multi-Omics Data and Machine Learning Algorithms

Abstract

Maize is one of the most widely grown cereals in the world. However, to address the challenges in maize breeding arising from climatic anomalies, there is a need for developing novel strategies to harness the power of multi-omics technologies. In this regard, pleiotropy is an important genetic phenomenon that can be utilized to simultaneously enhance multiple agronomic phenotypes in maize. In addition to pleiotropy, another aspect is the consideration of the regulatory SNPs (rSNPs) that are likely to have causal effects in phenotypic development. By incorporating both aspects in our study, we performed a systematic analysis based on multi-omics data to reveal the novel pleiotropic signatures of rSNPs in a global maize population. For this purpose, we first applied Random Forests and then Markov clustering algorithms to decipher the pleiotropic signatures of rSNPs, based on which hierarchical network models are constructed to elucidate the complex interplay among transcription factors, rSNPs, and phenotypes. The results obtained in our study could help to understand the genetic programs orchestrating multiple phenotypes and thus could provide novel breeding targets for the simultaneous improvement of several agronomic traits.

Improving genomic prediction of rhizomania resistance in sugar beet (Beta vulgaris L.) by implementing epistatic effects and feature selection

“…Feature selection was used in recent studies to increase the prediction accuracy of genomic prediction models in man 59 and crops. 51,54 However, these studies led to heterogeneous results such that no general recommendation can be given regarding feature selection. Here, we show that in case of rhizomania resistance, prediction accuracy could be increased using feature selection.…”

“…Besides improving prediction accuracy of genomic prediction models, other studies used results from feature selection via genomic prediction to determine the association between certain SNPs and the phenotype. 54,64 Following this approach, it could be interesting to use the method presented here to select the 29 single SNPs or the 16 SNP pairs, respectively, as being associated with rhizomania resistance. However, we found that the estimation of variable importance did not lead to the same results in each training data set.…”

“…The coefficient of determination is defined as the proportion of the explained variability of the total variability 53 and was used in previous studies as measure for the prediction accuracy. 54,55 To perform feature selection, the variable importance of each SNP was estimated using random forest in a first step. Therefore, the R package Boruta, version 7.0.0 was used.…”

“…This function was used in previous studies to assess the variable importance of SNPs. 54,57,58 We chose the mean variable importance as estimate of the variable importance per SNP. We used a confidence level of 10 À10 , 2,000 trees and 200 as the maximum number of runs.…”

Deep learning the cis-regulatory code for gene expression in selected model plants

“…Another important application is the prediction of the effects of genetic variation 70 . While current genomics approaches rarely enable functional annotation of the effect of polymorphisms 71,72 and rely mostly on statistical association, an increasing availability of long-read data enables making mechanistic links between observed variation and the down-stream molecular network 32 . The presented example of gene expression across 14 tomato accessions represents an important step in this direction and opens perspectives for routine prediction pipelines for new genomes, tissues and environmental scenarios.…”