Comparative evolutionary analysis and prediction of deleterious mutation patterns between sorghum and maize

Abstract

Sorghum and maize share a close evolutionary history that can be explored through comparative genomics. To perform a large-scale comparison of the genomic variation between these two species, we analyzed 13 million variants identified from whole genome resequencing of 468 sorghum lines together with 25 million variants previously identified in 1,218 maize lines.Deleterious mutations in both species were prevalent in pericentromeric regions, enriched in non-syntenic genes, and present at low allele frequencies. A comparison of deleterious burden between sorghum and maize revealed that sorghum, in contrast to maize, departed from the "domestication cost" hypothesis that predicts a higher deleterious burden among domesticates compared to wild lines. Additionally, sorghum and maize population genetic summary statistics were used to predict a gene deleterious index with an accuracy higher than 0.5. This research represents a key step towards understanding the evolutionary dynamics of deleterious variants in sorghum and provides a comparative genomics framework to start prioritizing them for removal through genome editing and breeding. 2 Main text: Sorghum ( Sorghum bicolor L. Moench) and maize ( Zea mays L.) are both members of the Poaceae family and often serve as a model system for comparative plant genomics. Their common Poaceae ancestor underwent a whole-genome duplication (WGD) event ~96 million years ago 1 , and a second WGD in maize corresponds closely with its divergence 12 million years ago from sorghum 2 . The role of polyploidization in maize diversification 1 makes the sorghum-maize system particularly powerful for comparative studies.A rchaeobotanical studies support a single sorghum domestication event around 3000 BC in Eastern Sudan 3 , with genetic studies supporting a potential second independent domestication center in west Africa 4,5 . Maize, in contrast, was domesticated once from teosinte in the Balsas river valley in central Mexico around 9000 years ago 6,7 . While some orthologs between these two species experienced parallel selection during domestication 8 , most domestication related genes appear to be drawn from a non-overlapping set 9 . It is well established that maize experienced a decline in effective population size due to a domestication bottleneck 10,11 that increased the burden of deleterious alleles in the domesticate compared to teosinte 12,13 .Evidence for reduced nucleotide diversity in landraces from a genetic bottleneck 5 or population size decline 14 has also been reported for sorghum.Sorghum has a hermaphroditic inflorescence, contributing to its predominantly self-pollinating nature. Domesticated sorghum has an estimated outcrossing rate of only 7 to 20% 9,15 , while the outcrossing rate tends to be higher (up to 70% 16 ) for weedy and wild sorghum. In contrast, maize and its wild progenitor teosinte are monoecious and generally outcrossing at a rate of over 90% 17 . In this study, we performed a joint analysis of functional variation in sorghum and 3 maize lines that span th...

Purging of deleterious mutations during domestication in the predominant selfing crop soybean

“…In this selfing species, overall deleterious alleles among landraces relative to wild soybean accessions have been drastically reduced by up to almost 35%, similar to the observation in sorghum 20 . Mutation burden was further decreased in improved lines from modern soybean breeding.…”

“…A detailed description of GERP scores is provided below. The patterns of these variables we observed across the genome were significantly correlated with the highest correlation between gene density and GERP score density ( Supplementary Table 3) and thus reflected other reports on plant genomes 20,21 . Interestingly, although correlation coefficients between historical recombination rates and other variables are relatively low with the highest value of 0.344 between recombination and indel density, our visual inspection indicated that the highest peak (hotspot) of historical recombination rates in each of half of the 20 chromosomes corresponded with the densest region of indels in each chromosome.…”

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

“…Significant improvements in cross-population genomic prediction for GY suggest that prioritization of SNPs by predicted PNC could be useful for breeding applications (e.g., genomic pre-breeding [44], or genomic selection in understudied populations [45]). They also suggest that predicted PNC could point to useful causal variants, because accurate cross-population prediction requires very close tagging of causal variants by genomic markers [46,47]. Our improvements in prediction accuracy for GY (+5% and +38%) are on par with those achieved from genome-wide prioritization of causal variants, with many experimental annotations in large human samples (trans-ancestry predictions in cohorts of size > 150,000) [48].…”

A sorghum practical haplotype graph facilitates genome‐wide imputation and cost‐effective genomic prediction

“…Setting the minTaxa parameter to 1 means that all haplotypes are kept, even if taxa are too divergent to group with other individuals in the database. The SNPs were written at all variant sites in the graph, as well as all positions in the sorghum hapmap (Lozano et al, 2019). The The Plant Genome SNP calling accuracy was assessed by comparing PHG SNP calls to a set of 3,468 GBS SNPs (Muleta et al, unpublished data, 2019).…”

“…Many studies have shown that GS can accelerate the breeding process and rate of genetic gain without significantly increasing program costs (e.g., Bernardo & Yu, 2007;Heffner, Lorenz, Jannink, & Sorrells, 2010;Heslot, Jannink, & Sorrells, 2015;Meuwissen et al, 2001;Muleta, Pressoir, & Morris, 2019;Poland et al, 2012). Increasingly dense marker or haplotype maps in major crops like maize (Zea mays; Bukowski et al, 2018) and sorghum (Sorghum bicolor (L.) Moench; Lozano et al, 2019) can now be leveraged to inform breeding decisions.…”