Prediction of evolutionary constraint by genomic annotations improves functional prioritization of genomic variants in maize

Abstract

Background Crop improvement through cross-population genomic prediction and genome editing requires identification of causal variants at high resolution, within fewer than hundreds of base pairs. Most genetic mapping studies have generally lacked such resolution. In contrast, evolutionary approaches can detect genetic effects at high resolution, but they are limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Here we use genomic annotations to accurately predict nucleotide conservation across angiosperms, as a proxy for fitness effect of mutations. Results Using only sequence analysis, we annotate nonsynonymous mutations in 25,824 maize gene models, with information from bioinformatics and deep learning. Our predictions are validated by experimental information: within-species conservation, chromatin accessibility, and gene expression. According to gene ontology and pathway enrichment analyses, predicted nucleotide conservation points to genes in central carbon metabolism. Importantly, it improves genomic prediction for fitness-related traits such as grain yield, in elite maize panels, by stringent prioritization of fewer than 1% of single-site variants. Conclusions Our results suggest that predicting nucleotide conservation across angiosperms may effectively prioritize sites most likely to impact fitness-related traits in crops, without being limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Our approach—Prediction of mutation Impact by Calibrated Nucleotide Conservation (PICNC)—could be useful to select polymorphisms for accurate genomic prediction, and candidate mutations for efficient base editing. The trained PICNC models and predicted nucleotide conservation at protein-coding SNPs in maize are publicly available in CyVerse (https://doi.org/10.25739/hybz-2957).

Cross-species modeling of plant genomes at single nucleotide resolution using a pre-trained DNA language model

“…For each SNP, we calculated the log-likelihood difference between the reference and alternate alleles, where a more negative value indicates higher conservation. Deleterious mutations tend to have lower frequencies within a population due to selective constraints 32 , we therefore used minor allele frequency (MAF) to quantify the deleteriousness of mutations predicted by different methods. Despite the potential for low MAF in neutral/beneficial alleles, we believe this approach provides useful signals for assessing deleterious mutations 32 .…”

“…Deleterious mutations are considered the genetic basis of heterosis, where hybrids yield more due to the suppression of deleterious recessives from one parent by dominant alleles from the other 36 . Traditionally, deleterious mutations have been identified by generating MSAs 32 , 37 , 38 and using evolutionary methods such as phyloP 24 and phastCons 24 . However, the prevalence of transposable elements and polyploidy in plant genomes complicates the MSA generation 39 , 40 .…”

On the contribution of genetic heterogeneity to complex traits

“…First, to cover the noncoding genome, we considered the presence in open chromatin, specifically MNase-hypersensitive regions obtained from shoot tissue were 32 . Second, for coding regions, we employed the PICNC (Prediction of mutation Impact by Calibrated Nucleotide Conservation) 63 . A higher PICNC score indicates a stronger degree of evolutionary constraint, and these metrics of the maize genome are available from Data Commons.…”

Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations

“…For example, in humans, evolutionary conserved sequences are enriched for trait and disease heritability [48, 49], and conservation across related species can be used to prioritize medically relevant variants in fine mapping [50, 51] and rare-variant association studies [52, 53]. Similarly, multi-species alignments are being used by conservation geneticists to estimate the fitness effects of mutations in wild populations [54, 55] and by plant breeders to aid in genomic selection [56, 57]. And there is growing interest in using estimated ancestral recombination graphs (ARGs) to perform explicitly tree-based versions of QTL mapping and complex trait analysis [58, 59].…”