Biology subjects

Swarts, K.

Publications and source records attributed to Swarts, K..

12 recordsLinked to original sources

bioRxiv2025

A maize near-isogenic line population designed for gene discovery and characterization of allelic effects

In this study we characterized a panel of 1,264 maize near-isogenic lines (NILs), developed from crosses between 18 diverse inbred lines and the recurrent parent B73, referred to as nested NILs (nNILs). 884 of the nNILs were genotyped using genotyping-by-sequencing (GBS). Subsequently, 24 of these nNILs, and all the parental lines, were re-genotyped using a high-density SNP chip. A novel pipeline for calling introgressions, which does not rely on knowing the donor parent of each nNIL, was developed based on a hidden Markov model (HMM) algorithm. By comparing the introgressions detected using GBS data with those identified using chip data, we optimized the HMM parameters for analyzing the entire nNIL population. A total of 2,972 introgressions were identified across the 884 nNILs. Individual introgression blocks ranged from 21 bp to 204 Mbp, with an average size of 17 Mbp. By comparing SNP genotypes within introgressed segments to the known genotypes of the donor lines we determined that in about one third of the lines, the identity of the donors did not match expectation based on their pedigrees. We characterized the entire nNIL population for three foliar diseases. Using these data, we mapped a number of quantitative trait loci (QTL) for disease resistance in the nNIL population and observed extensive variation in effects among the alleles from different donor parents at most QTL identified. This population will be of significant utility for dissecting complex agronomic traits and allelic series in maize. Significance StatementThe study reports the characterization of a publicly available population of 1,264 maize near-isogenic lines largely derived from a single recurrent parent and 18 donor lines. This population is likely to be of significant utility for the characterization of allelic series at loci of interest.

plant biology↗

bioRxiv2024

Population genomics of Marchantia polymorpha subspecies ruderalis reveals evidence of climate adaptation

Sexual reproduction results in the development of haploid and diploid cell states during the life cycle. In bryophytes the dominant multicellular haploid phase produces motile sperm that swim through water to the egg to effect fertilization from which a relatively small diploid phase develops. In angiosperms, the reduced multicellular haploid phase produces non-motile sperm that is delivered to the egg through a pollen tube to effect fertilization from which the dominant diploid phase develops. These different life cycle characteristics are likely to impact the distribution of genetic variation among populations. However, little is known about the distribution of genetic variation among populations of bryophytes. To understand how genetic variation is distributed among populations of a bryophyte and to establish the foundation for population genetics research in bryophytes, we described the genetic diversity of collections of Marchantia polymorpha subspecies ruderalis, a cosmopolitan ruderal liverwort. We explored genetic diversity of this species using 78 genetically unique (non-clonal) accessions from a total of 209 collected from 37 sites in Europe and Japan. There was no detectable population structure among European populations but significant genetic differentiation between Japanese and European populations. By associating genetic variation across the genome with global climate data, we identified summer temperature and precipitation as climate factors influencing the frequency of adaptative alleles. We speculate that the requirement for water through which motile sperm swim imposes a constraint on the life cycle to which the plant genetically adapts.

plant biology↗

bioRxiv2024

In silico analysis of the evolution of root phenotypes during maize domestication in Neolithic soils of Tehuacan

Roots are essential for plant adaptation to changing environments, yet the role of roots in crop domestication remains unclear. This study examines the evolution of root phenotypes from teosinte to maize, a transition resulting in reduced nodal root number (NRN), multiseriate cortical sclerenchyma (MCS), and increased seminal root number (SRN). We reconstructed the root phenotypes of maize and teosinte, as well as the environments of the Tehuacan Valley over the last 18,000 years using a combination of ancient DNA, paleobotany, and functional-structural modeling. Our models reveal that increasing Holocene atmospheric CO2 concentrations favored the appearance of reduced NRN and MCS between 12000 to 8000 years before present (yBP), promoting deeper root systems. The advent of irrigation by 6000 yBP switched nitrogen distribution from topsoil to subsoil domains, a change which increased the utility of reduced NRN and MCS. Comparison of allelic frequencies among ancient samples ranging from 5500 to 500 yBP suggest that increased SRN may have appeared around 3500 yBP, coinciding with a period of increased human population, agricultural intensification, and soil degradation. Our results suggest that root phenotypes that enhance plant performance under nitrogen stress were important for maize adaptation to changing agricultural practices in the Tehuacan Valley. ClassificationPhysiology & Development

plant biology↗

bioRxiv2024

On the contribution of genetic heterogeneity to complex traits

Genetic heterogeneity, where different alleles or loci are responsible for similar phenotypes, reduces the power of genome-wide association studies and can cause misleading results. Although many striking examples have been identified, the general importance of genetic heterogeneity for complex traits is unclear. Here, we use a novel interpretative machine-learning approach to look for evidence of genetic heterogeneity in plants and humans. Our approach helps identify new loci/alleles influencing trait variation in several agriculturally important species, and we show that at least 6% of maize eQTL, half of them newly identified, exhibit evidence of allelic heterogeneity. Finally, we search for evidence of synthetic associations in human GWAS data, and find that as many as 3-5% may be affected. Our results highlight the need to take genetic heterogeneity seriously, and provide a simple approach for doing so.

genetics↗

bioRxiv2023

Unified Multi-caller Ensemble (UME) generates an unbiased maize haplotype map for variable coverage whole genome data

We present a novel diversity-focused haplotype map (HapMap) that characterizes over 64.5 million maize (Zea mays ssp. mays) single nucleotide polymorphisms (SNPs) genotyped across 818 individuals from diverse backgrounds. This HapMap aims to balance the variation obtained from domesticated landraces and inbred lines, outgroup Zea spp. and more distant Tripsacum spp. in order to minimize ascertainment bias for diversity studies. Included individuals derive from public data from various experimental setups and coverages, which is challenging for standard SNP callers to accommodate. We provide evidence of coverage biases associated with standard callers that influence resulting variation and introduce a novel approach called Unified Multi-Caller Ensemble (UME), which enhances variant calling accuracy in low-coverage and mixed-coverage genomic datasets. UME corrects for coverage bias resulting from inter-sample coverage heterogeneity by leveraging evidence from variant callers with orthogonal strategies, re-calibrating the error probabilities across callers to minimize the impact of error biases inherent to a given caller. It outperforms individual strategies and excels in de novo variant calling, taking advantage of instances of higher depth reads, even in low coverage individuals, while preserving biologically informative variant relationships across coverage levels. An important feature of UME is the independence from population allele frequencies in the discovery panel, thus avoiding ascertainment bias resulting from unbalanced input genetic diversity. Discovered variants are unbiased because no population filtering is used, and the full diversity of SNPs is retained in the final variant call set to maximize the utility of the dataset for production calling newly sequenced samples. We present a strategy for filtering the recalibrated error profiles that relies on maximizing demographic signals to retain genetic relationships within the population while reducing sequencing error. After the variant discovery phase, we employ the UME production stage, which enriches genotype calling across all coverage levels, benefiting low-coverage samples. Error introduced in this process is removed through subsequent filtering. Using this approach, we generated a coverage bias-controlled maize HapMap database, providing a comprehensive representation of maize accessions and emphasizing landrace diversity. This diverse panel of domesticated maize and outgroups from across the Americas enables accurate genotyping in low-coverage samples while offering crucial context for interpreting diversity, particularly for natural diversity and paleogenomic analyses.

bioinformatics↗

bioRxiv2022

Domestication and lowland adaptation of coastal preceramic maize from Paredones, Peru

Archaeological cobs from Paredones and Huaca Prieta (Peru) are phenotypically indistinguishable from modern maize. This contrasts with the earliest Mexican macro-specimens from Guila Naquitz and San Marcos, which are phenotypically intermediate even though they date more recently in time. These observations suggest at least two alternative scenarios, one in which maize was domesticated earlier than previously thought in the lowland Mesoamerica, followed by rapid lowland dispersal to Peru, and another in which maize was independently domesticated in South America and subsequently lost, as current evidence supports a single origin for all modern maize. To gain insights into the origins of ancient Peruvian maize, we sequenced DNA from three Paredones specimens dating 6775 to 5000 calibrated years before present (BP) and conducted comparative analyses with two teosinte subspecies (Zea mays ssp. mexicana and parviglumis) and extant maize, including highland and lowland landraces from Mesoamerica and South America. We show that Paredones maize originated from the same domestication event as Mexican maize and was domesticated by 6775 BP, implying rapid dispersal followed by improvement. Paredones maize show minimal levels of gene flow from mexicana, smaller than those observed in teosinte parviglumis. It also harbors significantly fewer alleles previously found to be adaptive to highlands, but not of alleles adaptive to lowlands, supporting a lowland migration route. Our overall results imply that Paredones maize originated in Mesoamerica, arrived in Peru without mexicana introgression through a rapid lowland migration route, and underwent improvements in both Mesoamerica and South America. Significance StatementThe coastal Peruvian preceramic sites of Paredones and Huaca Prieta provide the earliest known maize macro-remains. Found more than 3,800 km away from the maize center of origin and presenting a phenotypically modern cob constitution relative to their antiquity, these specimens represent a paradox for understanding maize evolution and dispersal. We show that Paredones maize originated in Mesoamerica, like all known maize, and arrived in South America without introgression from the teosinte mexicana. Since modern maize has substantial contributions from mexicana, it raises the question of when mexicana introgression spread to South America. Paredones maize preferentially shares adaptive allelic diversity with lowland Mesoamerican samples, suggesting a migration route probably associated with a coastal corridor previously identified with archeological findings.

plant biology↗

bioRxiv2022

Heritable and climatic sources of variation in juvenile tree growth in an Austrian common garden experiment of Central European Norway spruce populations

We leveraged publicly available data on juvenile tree height of 299 Central European Norway spruce populations grown in a common garden experiment across 24 diverse trial locations in Austria and weather data from the trial locations and population provenances to parse the heritable and climatic components of juvenile tree height variation. Principal component analysis of geospatial and weather variables demonstrated high interannual variation among trial environments, largely driven by differences in precipitation, and separation of population provenances based on altitude, temperature, and snowfall. Tree height was highly heritable and modeling the covariance between populations and trial environments based on climatic data led to more stable estimation of heritability and population x environment variance. Climatic similarity among population provenances was highly predictive of population x environment estimates for tree height.

ecology↗

bioRxiv2022

Automation of tree-ring detection and measurements using deep learning

O_LIWe present an implementable neural network-based automated detection and measurement of tree-ring boundaries from coniferous species. C_LIO_LIWe trained our Mask R-CNN extensively on over 8,000 manually annotated rings. We assessed the performance of the trained model from our core processing pipeline on real world data. C_LIO_LIThe CNN performed well, recognizing over 99% of ring boundaries (precision) and a recall value of 95% when tested on real world data. Additionally, we have implemented automatic measurements based on minimum distance between rings. With minimal editing for missed ring detections, these measurements were a 99% match with human measurements of the same samples. C_LIO_LIOur CNN is readily deployable through a Docker container and requires only basic command line skills. Application outputs include editable annotations which facilitate the efficient generation of ring-width measurements from tree-ring samples, an important source of environmental data. C_LI

ecology↗

bioRxiv2022

Multi-GBS: A massively multiplexed GBS-based protocol optimized for large, repetitive conifer genomes

Reduced representation library approaches are still a valuable tool for breeding and population and ecological genomics, even with impressive increases in sequencing capacity in recent years. Unfortunately, current approaches only allow for multiplexing up to 384 samples. To take advantage of increased sequencing capacity, we present Multi-GBS, a massively multiplexable extension to Genotyping-by-Sequencing that is also optimized for large conifer genomes. In Norway Spruce, a highly repetitive 20Gbp diploid genome with high population genetic variation, we call over a million variants in 32 genotypes from three populations, two natural forest in the Alps and Bohemian Alps, and a managed population from southeastern Austria using the existing TASSEL GBSv2 pipeline. Metric MDS analysis of replicated genotypes shows that technical bias in resulting genotype calling is minimal and that populations cluster in biologically meaningful ways.

genomics↗

bioRxiv2017

Parallel Altitudinal Clines Reveal Adaptive Evolution Of Genome Size In Zea mays

While the vast majority of genome size variation in plants is due to differences in repetitive sequence, we know little about how selection acts on repeat content in natural populations. Here we investigate parallel changes in intraspecific genome size and repeat content of domesticated maize (Zea mays) landraces and their wild relative teosinte across altitudinal gradients in Mesoamerica and South America. We combine genotyping, low coverage whole-genome sequence data, and flow cytometry to test for evidence of selection on genome size and individual repeat abundance. We find that population structure alone cannot explain the observed variation, implying that clinal patterns of genome size are maintained by natural selection. Our modeling additionally provides evidence of selection on individual heterochromatic knob repeats, likely due to their large individual contribution to genome size. To better understand the phenotypes driving selection on genome size, we conducted a growth chamber experiment using a population of highland teosinte exhibiting extensive variation in genome size. We find weak support for a positive correlation between genome size and cell size, but stronger support for a negative correlation between genome size and the rate of cell production. Reanalyzing published data of cell counts in maize shoot apical meristems, we then identify a negative correlation between cell production rate and flowering time. Together, our data suggest a model in which variation in genome size is driven by natural selection on flowering time across altitudinal clines, connecting intraspecific variation in repetitive sequence to important differences in adaptive phenotypes.\n\nAuthor summaryGenome size in plants can vary by orders of magnitude, but this variation has long been considered to be of little to no functional consequence. Studying three independent adaptations to high altitude in Zea mays, we find that genome size experiences parallel pressures from natural selection, causing a linear reduction in genome size with increasing altitude. Though reductions in repetitive content are responsible for the genome size change, we find that only those individual loci contributing most to the variation in genome size are individually targeted by selection. To identify the phenotype influenced by genome size, we study how variation in genome size within a single teosinte population impacts leaf growth and cell division. We find that genome size variation correlates negatively with the rate of cell division, suggesting that individuals with larger genomes require longer to complete a mitotic cycle. Finally, we reanalyze data from maize inbreds to show that faster cell division is correlated with earlier flowering, connecting observed variation in genome size to an important adaptive phenotype.

evolutionary biology↗

bioRxiv2016

Identifying the diamond in the rough: a study of allelic diversity underlying flowering time adaptation in maize landraces

Landraces (traditional varieties) of crop species are a reservoir of useful genetic diversity, yet remain untapped due to the genetic linkage between the few useful alleles with hundreds of undesirable alleles1. We integrated two approaches to characterize the genetic diversity of over 3000 maize landraces from across the Americas. First, we mapped the genomic regions controlling latitudinal and altitudinal adaptation, identifying 1498 genes. Second, we developed and used F-One Association Mapping (FOAM) to directly map genes controlling flowering time across 22 environments, identifying 1,005 genes. In total 65% of the SNPs associated with altitude were also associated with flowering time. In particular, we observed many of the significant SNPs were contained in large structural variants (inversions, centromeres, and pericentromeric regions): 29.4% for flowering time, 58.4% for altitude and 13.1% for latitude. The combined mapping results indicate that while floral regulatory network genes contribute substantially to field variation, over 90% of contributing genes likely have indirect effects. Our strategy can be used to harness the diversity of maize and other plant and animal species.

genetics↗

bioRxiv2016

A large scale joint analysis of flowering time reveals independent temperate adaptations in maize

Modulating days to flowering is a key mechanism in plants for adapting to new environments, and variation in days to flowering drives population structure by limiting mating. To elucidate the genetic architecture of flowering across maize, a quantitative trait, we mapped flowering in five global populations, a diversity panel (Ames) and four half-sib mapping designs, Chinese (CNNAM), US (USNAM), and European Dent (EUNAM-Dent) and Flint (EUNAM-Flint). Using whole-genome projected SNPs, we tested for joint association using GWAS, resampling GWAS and two regional approaches; Regional Heritability Mapping (RHM) (1, 2) and a novel method, Boosted Regional Heritability Mapping (BRHM). Direct overlap in significant regions detected between populations and flowering candidate genes was limited, but whole-genome cross-population predictive abilities were [≤]0.78. Poor predictive ability correlated with increased population differentiation (r = 0.41), unless the parents were broadly sampled from across the North American temperate-tropical germplasm gradient; uncorrected GWAS results from populations with broadly sampled parents were well predicted by temperate-tropical FSTs in machine learning. Machine learning between GWAS results also suggested shared architecture between the American panels and, more distantly, the European panels, but not the Chinese panel. Machine learning approaches can reconcile non-linear relationships, but the combined predictive ability of all of the populations did not significantly enhance prediction of physiological candidates. While the North American-European temperate adaption is well studied, this study suggest independent temperate adaptation evolved in the Chinese panel, most likely in China after 1500, a finding supported by differential gene ontology term enrichment between populations.

genetics↗