Bellis, E. S. | Search bioRxiv

bioRxiv2026

Conditional genome-wide associations reveal novel genes

We introduce two novel approaches for gene discovery based on conditional genome-wide associations. Experimental validation of gene targets identified by our top-performing approach uncovers three genes with a previously unknown role in controlling flowering time in Arabidopsis, one of the most well-studied traits in the most well-studied plant genome. This work demonstrates the power of knockoff-based frameworks to uniquely identify novel genes underlying complex traits, a core task across applications in agriculture and human health.

genomics↗

bioRxiv2025

Population genomic structure of sorghum landraces across landscape, environment and culture

The spread of staple crops to diverse environments over time and their current genetic structure may reflect historical dispersal by humans, sustained human preference for particular traits, and adaptation to local environments. Sorghum is a drought-tolerant crop native to Africa cultivated by hundreds of millions of smallholders globally. Here we examined the ecological context of population-genomic structure of 1,806 sorghum landraces across Africa and Eurasia to infer the relative contribution of environmental and cultural factors to sorghum genetic diversity across different relative time periods. Sorghum landraces were spatially and linguistically structured at a large-scale and within subregions, following a pattern of isolation by distance. Within regions, much of genomic structure was best explained by a mechanistic model of human travel time. In our assessment of hierarchical linguistic structure, we found that language families explain 4% of genomic variation while individual languages explain 13% of genomic variation, suggesting the importance of human culture and relationships in gene flow and selection. Variance partitioning showed that travel time, language, and climate explain up to 27% of genomic variation among landraces. We also observed regional differences in the degree of genetic relatedness across space and time in our assessment of shared ancestry. East Africa showed particularly strong geographic turnover in genomic composition and haplotype sharing, while West Africa showed substantial haplotype sharing even over large distances, signifying some rapidly spreading lineages. Thus, space, travel time, and culture likely capture important forces controlling sorghum genomic variation, but these factors operate heterogeneously over space.

plant biology↗

bioRxiv2024

Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winners strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams methods included quantitative genetics, classical machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.

genetics↗

bioRxiv2024

Predicting gene expression responses to environment in Arabidopsis thaliana using natural variation in DNA sequence

The evolution of gene expression responses is a critical component of adaptation to variable environments. Predicting how DNA sequence influences expression is challenging because the genotype to phenotype map is not well resolved for cis regulatory elements, transcription factor binding, regulatory interactions, and epigenetic features, not to mention how these factors respond to environment. We tested if flexible machine learning models could learn some of the underlying cis-regulatory genotype to phenotype map. We tested this approach using cold-responsive transcriptome profiles in 5 diverse Arabidopsis thaliana accessions. We first tested for evidence that cis regulation plays a role in environmental response, finding 14 and 15 motifs that were significantly enriched within the up- and down-stream regions of cold-responsive differentially regulated genes (DEGs). We next applied convolutional neural networks (CNNs), which learn de novo cis-regulatory motifs in DNA sequences to predict expression response to environment. We found that CNNs predicted differential expression with moderate accuracy, with evidence that predictions were hindered by biological complexity of regulation and the large potential regulatory code. Overall, approaches to predict DEGs between specific environments based only on proximate DNA sequences require further development, and additional information may be required.

evolutionary biology↗

bioRxiv2022

Resolving intergenotypic Striga resistance in sorghum

Genetic underpinnings of host-pathogen interactions in the parasitic plant Striga hermonthica,a root parasitic plant that ravages cereals in sub-Saharan Africa, are unclear. We performed a comparative transcriptome study on five genotypes of sorghum exhibiting diverse resistance responses to S. hermonthica using weighted gene co-expression network analysis (WGCNA). We found that S. hermonthica elicits both basal and effector-triggered immunity - like a bona fide pathogen. Resistance response was genotype-specific. Some resistance responses followed the salicylic acid-dependent signaling pathway for systemic acquired resistance characterized by cell wall reinforcements, lignification and callose deposition while in others the WRKY-dependent signaling pathway was activated leading to a hypersensitive response (HR). In some genotypes, both modes of resistance were activated while in others, either mode dominated the resistance response. Cell-wall-based resistance was common to all sorghum genotypes but strongest in IS2814, while HR-based response was specific to N13, IS9830 and IS41724. WGCNA further allowed for pinpointing of S. hermonthica resistance causative genes in sorghum. Some highlights include a Glucan synthase-like 10, a pathogenesis-related thaumatin-like family, and a phosphoinositide phosphatase gene. Such candidate genes will form a good basis for subsequent functional validation and possibly future resistance breeding. HighlightParasitic plants of the Striga genus are major pests to cereals in Africa. We pinpointed genetic causes of Striga resistance in sorghum that can be harnessed for future resistance breeding.

plant biology↗

bioRxiv2022

Genomic signatures of host-specific selection in a parasitic plant

PremiseParasitic plants and their hosts are model systems for studying genetic variation in species interactions across environments. The parasitic plant Striga hermonthica (witchweed) attacks a range of cereal crop hosts in Africa and exhibits substantial variation in performance on different host species. Some of this variation is due to local adaptation, but the genetic basis of specialization on certain hosts is unknown. MethodsTo identify genomic regions that are strongly differentiated between parasites attacking different host species, we present an alignment-free analysis of S. hermonthica population diversity using whole genome sequencing (WGS) data for 68 individuals from western Kenya. We validate our findings with germination experiments and analyses based on a de novo assembled draft genome. ResultsReference-free and reference-based analyses suggest that only a small portion of the S. hermonthica genome is strongly differentiated by host species in populations from western Kenya. Analysis of host-associated k-mers implicated genes involved in development of the parasite haustorium (a specialized structure used to establish vascular connections with host roots) and a potential role of chemocyanins in molecular host-parasitic plant interactions. Conversely, no phenotypic or genomic evidence was observed suggesting host-specific selection on parasite response to strigolactones, hormones exuded by host roots and required for parasite germination. ConclusionsThis study demonstrates the utility of WGS for plant species with large, complex genomes and no available reference. Contrasting with theory emphasizing the role of early recognition loci for host specificity, our findings support host-specific selection on later interaction stages, recurring each generation after homogenizing gene flow.

evolutionary biology↗

bioRxiv2020

Engines of change: Transposable element mutation rates are high and vary widely among genotypes and populations of Daphnia magna

Transposable elements (TEs) represent a large and dynamic portion of most eukaryotic genomes, yet little is known about their mutation rates or the correspondence between rates and long-term patterns of accrual. We compare TE activity over long and short time periods by quantifying TE profiles and mutation rates (with and without minimizing selection) among 9 genotypes from three populations of Daphnia magna sampled along a latitudinal gradient. The patterns of genome-wide variation observed in nature mirror direct estimates of rates and spectra observed in a multi-year laboratory mutation accumulation experiment, where net rates range from -11.98 to 12.79 x 10-5 per copy per generation across genotypes. Overall, gains outnumber losses and both types of events are highly deleterious based on comparing lines with and without selection minimized. The rate and spectrum of TE mutations vary widely among genotypes and across TE families/types, even within the same population. We compare TE mutation rates to previously published rates of base substitution, microsatellite mutation, and gene conversion for the same genotypes, and show a correlation only with the latter. Our study provides strong evidence for the notion that TEs represent a highly mutagenic force in the genome. Furthermore, the variation we observe underscores the need to expand the repertoire of mutations studied to include a wider array of mutation types with different underlying mechanisms in order to better understand the evolution of the mutation rate and the ways in which genetic variation is generated genome wide.

genomics↗

bioRxiv2019

Genomic history and ecology of the geographic spread of rice

Rice (Oryza sativa) is one of the worlds most important food crops. We reconstruct the history of rice dispersal in Asia using whole-genome sequences of >1,400 landraces, coupled with geographic, environmental, archaeobotanical and paleoclimate data. We also identify extrinsic factors that impact genome diversity, with temperature a leading abiotic factor. Originating [~]9,000 years ago in the Yangtze Valley, rice diversified into temperate and tropical japonica during a global cooling event [~]4,200 years ago. Soon after, tropical rice reached Southeast Asia, where it rapidly diversified starting [~]2,500 yBP. The history of indica rice dispersal appears more complicated, moving into China [~]2,000 yBP. Reconstructing the dispersal history of rice and its climatic correlates may help identify genetic adaptation associated with the spread of a key domesticated species.\n\nOne sentence summaryWe reconstructed the ancient dispersal of rice in Asia and identified extrinsic factors that impact its genomic diversity.

evolutionary biology↗

bioRxiv2019

Minor QTLs mining through the combination of GWAS and machine learning feature selection

IntroductionMinor QTLs mining has a very important role in genomic selection, pathway analysis and trait development in agricultural and biological research. Since most individual loci contribute little to complex trait variations, it remains a challenge for traditional statistical methods to identify minor QTLs with subtle phenotypic effects. Here we applied a new framework which combined the GWAS analysis and machine learning feature selection to explore new ways for the study of minor QTLs mining.\n\nResultsWe studied the soybean branching trait with the 2,137 accessions from soybean (Glycine max) diversity panel, which was sequenced by 50k SNP chips with 42,080 valid SNPs. First as a baseline study, we conducted the GWAS GAPIT analysis, and we found that only one SNP marker significantly associated with soybean branching was identified. We then combined the GWAS analysis and feature importance analysis with Random Forest score analysis and permutation analysis. Our analysis results showed that there are 36,077 features (SNPs) identified by Random Forest score analysis, and 2,098 features (SNPs) identified by permutation analysis. In total, there are 1,770 features (SNPs) confirmed by both of the Random Forest score analysis and the permutation analysis. Based on our analysis, 328 branching development related genes were identified. A further analysis on GO (gene ontology) term enrichment were applied on these 328 genes. And the gene location and gene expression of these identified genes were provided.\n\nConclusionsWe find that the combined analysis with GWAS and machine learning feature selection shows significant identification power for minor QTLs mining. The presented research results on minor QTLs mining will help understand the biological activities that lie between genotype and phenotype in terms of causal networks of interacting genes. This study will potentially contribute to effective genomic selection in plant breeding and help broaden the way of molecular breeding in plants.

bioinformatics↗

bioRxiv2019

Minor QTLs mining through the combination of GWAS and machine learning feature selection

IntroductionMinor QTLs mining has a very important role in genomic selection, pathway analysis and trait development in agricultural and biological research. Since most individual loci contribute little to complex trait variations, it remains a challenge for traditional statistical methods to identify minor QTLs with subtle phenotypic effects. Here we applied a new framework which combined the GWAS analysis and machine learning feature selection to explore new ways for the study of minor QTLs mining.\n\nResultsWe studied the soybean branching trait with the 2,137 accessions from soybean (Glycine max) diversity panel, which was sequenced by 50k SNP chips with 42,080 valid SNPs. First as a baseline study, we conducted the GWAS GAPIT analysis, and we found that only one SNP marker significantly associated with soybean branching was identified. We then combined the GWAS analysis and feature importance analysis with Random Forest score analysis and permutation analysis. Our analysis results showed that there are 36,077 features (SNPs) identified by Random Forest score analysis, and 2,098 features (SNPs) identified by permutation analysis. In total, there are 1,770 features (SNPs) confirmed by both of the Random Forest score analysis and the permutation analysis. Based on our analysis, 328 branching development related genes were identified. A further analysis on GO (gene ontology) term enrichment were applied on these 328 genes. And the gene location and gene expression of these identified genes were provided.\n\nConclusionsWe find that the combined analysis with GWAS and machine learning feature selection shows significant identification power for minor QTLs mining. The presented research results on minor QTLs mining will help understand the biological activities that lie between genotype and phenotype in terms of causal networks of interacting genes. This study will potentially contribute to effective genomic selection in plant breeding and help broaden the way of molecular breeding in plants.

bioinformatics↗

bioRxiv2019

Genomics of sorghum local adaptation to a parasitic plant

Host-parasite coevolution can maintain high levels of genetic diversity in traits involved in species interactions. In many systems, host traits exploited by parasites are constrained by use in other functions, leading to complex selective pressures across space and time. Here, we study genome-wide variation in the staple crop Sorghum bicolor (L.) Moench and its association with the parasitic weed Striga hermonthica (Delile) Benth., a major constraint to food security in Africa. We hypothesize that geographic selection mosaics across gradients of parasite occurrence maintain genetic diversity in sorghum landrace resistance. Suggesting a role in local adaptation to parasite pressure, multiple independent loss-of-function alleles at sorghum LOW GERMINATION STIMULANT 1 (LGS1) are broadly distributed among African landraces and geographically associated with S. hermonthica occurrence. However, low frequency of these alleles within S. hermonthica-prone regions and their absence elsewhere implicate potential tradeoffs restricting their fixation. LGS1 is thought to cause resistance by changing stereochemistry of strigolactones, hormones that control plant architecture and belowground signaling to mycorrhizae and are required to stimulate parasite germination. Consistent with tradeoffs, we find signatures of balancing selection surrounding LGS1 and other candidates from analysis of genome-wide associations with parasite distribution. Experiments with CRISPR-Cas9 edited sorghum further indicate the benefit of LGS1-mediated resistance strongly depends on parasite genotype and abiotic environment and comes at the cost of reduced photosystem gene expression. Our study demonstrates long-term maintenance of diversity in host resistance genes across smallholder agroecosystems, providing a valuable comparison to both industrial farming systems and natural communities. SIGNIFICANCE STATEMENTUnderstanding co-evolution in crop-parasite systems is critical to management of myriad pests and pathogens confronting modern agriculture. In contrast to wild plant communities, parasites in agricultural ecosystems are usually expected to gain the upper hand in co-evolutionary arms races due to limited genetic diversity of host crops in cultivation. Here, we develop a framework to characterize associations between genome variants in global landraces (traditional varieties) of the staple crop sorghum with the distribution of the devastating parasitic weed Striga hermonthica. We find long-term maintenance of diversity in genes related to parasite resistance, highlighting an important role of host adaptation for co-evolutionary dynamics in smallholder agroecosystems.

evolutionary biology↗