Biology subjects

Della Coletta, R.

Publications and source records attributed to Della Coletta, R..

7 recordsLinked to original sources

bioRxiv2025

Optimizing population simulations to accurately parallel empirical data for digital breeding

The use of computational and data-driven approaches to accelerate and optimize breeding programs is becoming common practice among plant breeders. Simulations allow breeders to evaluate potential changes in breeding schemes in a time- and cost-efficient manner. However, accurately simulating traits that match empirical trait data remains a challenge. Here we tested if incorporating information about the genetic architecture from genome-wide association studies (GWAS) of maize agronomic traits with varying heritabilities into simulations can improve the concordance between simulated and empirical data in a population of hybrids developed from crosses of 333 maize recombinant inbred lines grown in four to eleven environments. Using at least 200 non-redundant top GWAS hits as causative variants, regardless of statistical significance, resulted in mean correlations between simulated and empirical trait data of 0.397 to 0.616 within environments and 0.610 to 0.915 across environments. Reducing the GWAS estimated marker effect sizes in the simulations further improved concordance with empirical data. This study provides valuable insights into methods for simulating more realistic phenotypes for digital breeding to parallel empirical trait distributions, and that these simulated traits are highly concordant with observed variance partitioning (i.e. genotype, environment, etc.), and genomic prediction performance. PLAIN LANGUAGE SUMMARYPlant breeders need to evaluate many possible changes to breeding schemes and resource allocation. One method of optimizing these changes is through computer-based simulations, which can be time- and cost-efficient. The genetic architecture of randomly simulated traits rarely parallels the genetic architecture of real-world traits, which can mislead the interpretation of such simulations. In this study, the genetic architecture of real-world traits was used to inform the simulation of digital traits to develop an optimized simulation pipeline for creating and assessing simulated breeding programs. The utility of these informed simulations is demonstrated through genomic prediction assessment, in which we show that the rank order of individuals and the distribution of traits is similar between simulated and real-world data. CORE IDEAS[bullet] Simulations allow breeders to evaluate potential changes in breeding schemes in a time- and cost-efficient manner. [bullet]Incorporating the genetic architecture of traits in simulations can improve the concordance with empirical data. [bullet]Reducing GWAS marker effect size improves correlation and distribution concordance of simulated and empirical data. [bullet]Increasing the number of causative variants beyond GWAS significance thresholds improves simulation performance. [bullet]Simulated data used in genomic prediction produces results similar to empirical genomic predicted data.

plant biology↗

bioRxiv2023

Importance of genetic architecture in marker selection decisions for genomic prediction

Breeders commonly use genetic markers to predict the performance of untested individuals as a way to improve the efficiency of breeding programs. These genomic prediction models have almost exclusively used single nucleotide polymorphisms (SNPs) as their source of genetic information, even though other types of markers exist, such as structural variants (SVs). Given that SVs are associated with environmental adaptation and not all of them are in linkage disequilibrium to SNPs, SVs have the potential to bring additional information to multi-environment prediction models that are not captured by SNPs alone. Here, we evaluated different marker types (SNPs and/or SVs) on prediction accuracy across a range of genetic architectures for simulated traits across multiple environments. Our results show that SVs can improve prediction accuracy by up to 19%, but it is highly dependent on the genetic architecture of the trait. Differences in prediction accuracy across marker types were more pronounced for traits with high heritability, high number of QTLs, and SVs as causative variants. In these scenarios, using SV markers resulted in better prediction accuracies than SNP markers, especially when predicting untested genotypes across environments, likely due to more predictors being in linkage disequilibrium with causative variants. The simulations revealed little impact of different effect sizes between SNPs and SVs as causative variants on prediction accuracy. This study demonstrates the importance of knowing the genetic architecture of a trait in deciding what markers and marker types to use in large scale genomic prediction modeling in a breeding program. Key messageWe demonstrate potential for improved multi-environment genomic prediction accuracy using structural variant markers. However, the degree of observed improvement is highly dependent on the genetic architecture of the trait.

plant biology↗

bioRxiv2023

Linking genetic and environmental factors through marker effect networks to understand trait plasticity

Understanding how plants adapt to specific environmental changes and identifying genetic markers associated with phenotypic plasticity can help breeders develop plant varieties adapted to a rapidly changing climate. Here, we propose the use of marker effect networks as a novel method to identify markers associated with environmental adaptability. These marker effect networks are built by adapting commonly used software for building gene co-expression networks with marker effects across growth environments as the input data into the networks. To demonstrate the utility of these networks, we built networks from the marker effects of [~]10,000 non-redundant markers from 400 maize hybrids across nine environments. We demonstrate that networks can be generated using this approach, and that the markers that are co-varying are rarely in linkage disequilibrium, thus representing higher biological relevance. Multiple covarying marker modules associated with different weather factors throughout the growing season were identified within the marker effect networks. Finally, a factorial test of analysis parameters demonstrated marker effect networks are relatively robust to these options, with high overlap in modules associated with the same weather factors across analysis parameters. This novel application of network analysis provides unique insights into phenotypic plasticity, and specific environmental factors that modulate the genome.

plant biology↗

bioRxiv2021

Identification of candidate susceptibility genes to Puccinia graminis f. sp. tritici in wheat

Wheat stem rust disease caused by Puccinia graminis f. sp. tritici (Pgt) is a global threat to wheat production. Fast evolving populations of Pgt limit the efficacy of plant genetic resistance and constrain disease management strategies. Understanding molecular mechanisms that lead to rust infection and disease susceptibility could deliver novel strategies to deploy crop resistance through genetic loss of disease susceptibility. We used comparative transcriptome-based and orthology-guided approaches to characterize gene expression changes associated with Pgt infection in susceptible and resistant Triticum aestivum genotypes as well as the non-host Brachypodium distachyon. We targeted our analysis to genes with differential expression in T. aestivum and genes suppressed or not affected in B. distachyon and report several processes potentially linked to susceptibility to Pgt, such as cell death suppression and impairment of photosynthesis. We complemented our approach with a gene co-expression network analysis to identify wheat targets to deliver resistance to Pgt through removal or modification of putative susceptibility genes.

plant biology↗

bioRxiv2021

De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes

We report de novo genome assemblies, transcriptomes, annotations, and methylomes for the 26 inbreds that serve as the founders for the maize nested association mapping population. The data indicate that the number of pan-genes exceeds 103,000 and that the ancient tetraploid character of maize continues to degrade by fractionation to the present day. Excellent contiguity over repeat arrays and complete annotation of centromeres further reveal the locations and internal structures of major cytological landmarks. We show that combining structural variation with SNPs can improve the power of quantitative mapping studies. Finally, we document variation at the level of DNA methylation, and demonstrate that unmethylated regions are enriched for cis-regulatory elements that overlap QTL and contribute to changes in gene expression. One sentence summaryA multi-genome analysis of maize reveals previously unknown variation in gene content, genome structure, and methylation.

genomics↗

bioRxiv2020

Population Level Variation of Transposable Elements in a Maize Diversity Panel

Intact transposable elements (TEs) account for 65% of the maize genome and can impact gene function and regulation. Although TEs comprise the majority of the maize genome and affect important phenotypes, genome wide patterns of TE polymorphisms in maize have only been studied in a handful of maize genotypes, due to the challenging nature of assessing highly repetitive sequences. We implemented a method to use short read sequencing data from 509 diverse inbred lines to classify the presence/absence of 445,418 non-redundant TEs that were previously annotated in four genome assemblies including B73, Mo17, PH207, and W22. Different orders of TEs (i.e. LTRs, Helitrons, TIRs) had different frequency distributions within the population. LTRs with lower LTR similarity were generally more frequent in the population than LTRs with higher LTR similarity, though high frequency insertions with very high LTR similarity were observed. LTR similarity and frequency estimates of nested elements and the outer elements in which they insert revealed that most nesting events occurred very near the timing of the outer element insertion. TEs within genes were at higher frequency than those that were outside of genes and this is particularly true for those not inserted into introns. Many TE insertional polymorphisms observed in this population were tagged by SNP markers. However, there were also 19.9% of the TE polymorphisms that were not well tagged by SNPs (R2 < 0.5) that potentially represent information that has not been well captured in previous SNP based marker-trait association studies. This study provides a population scale genome-wide assessment of TE variation in maize, and provides valuable insight on variation in TEs in maize and factors that contribute to this variation.

plant biology↗

bioRxiv2019

Highly Genotype- and Tissue-specific Single-Parent Expression Drives Dynamic Gene Expression Complementation in Maize Hybrids

Maize exhibits tremendous gene expression variation between different lines. Complementation of diverse gene expression patterns in hybrids could play an important role in the manifestation of heterosis. In this study, we used transcriptome data of five different tissues from 33 maize inbreds and 89 hybrids (430 samples in total) to survey the global gene expression landscape of F1-hybrids relative to their inbred parents. Analysis of this data set revealed that single parent expression (SPE), which is defined as gene expression in only one of the two parents, while commonly observed, is highly genotype- and tissue-specific. Genes that have SPE in at least one pair of inbreds also tend to be tissue-specific. Genes with SPE caused by genomic presence/absence variation (PAV SPE) are much more frequently expressed in hybrids than genes that are present in the genome of both inbreds, but expressed in only a single-parent (non-PAV SPE) (74.7% vs. 59.7%). For non-PAV SPE genes, allele specific expression was used to investigate whether parental alleles not expressed in the inbred line (\"silent allele\") can be actively transcribed in the hybrid. We found that expression of the silent allele in the hybrid is relatively rare ([~]6.3% of non-PAV SPE genes), but is observed in almost all hybrids and tissues. Non-PAV SPE genes with expression of the silent allele in the hybrid are more likely to exhibit above high-parent expression level in the hybrid than those that do not express the silent allele. Finally, both PAV SPE and non-PAV SPE genes are highly enriched for being classified as non-syntenic, but depleted for curated genes with experimentally determined functions. This study provides a more comprehensive understanding of the potential role of non-PAV SPE and PAV SPE genes in heterosis.

genomics↗