Disease resistance in plants can be conferred by single genes of large effect or by multiple genes each conferring incomplete resistance. The latter case, termed quantitative resistance, may be difficult for pathogens to overcome through evolution due to the low selection pressures exerted by the actions of any single gene and, for some diseases, is the only identified source of genetic resistance. We evaluated quantitative resistance to 2 diseases of maize in a biparental mapping population as well as backcrosses to both parents. Quantitative trait locus analysis shows that the genetic architecture of resistance to these diseases is characterized by several modes of gene action including additivity as well as dominance, overdominance, and epistasis. Heterosis or hybrid vigor, the improved performance of a hybrid compared with its parents, can be caused by nonadditive gene action and is fundamental to the breeding of several crops including maize. In the backcross populations and a diverse set of maize hybrids, we find heterosis for resistance in many cases and that the degree of heterosis appears to be dependent on both hybrid genotype and disease.

Topoisomerase I, a nickase that allows swiveling of the DNA substrate, is highly enriched in the ribosomal DNA (rDNA) from yeast to humans, but its function at this locus remains poorly understood. S. cerevisiae mutants lacking topoisomerase I (top1) exhibit pronounced rDNA instability and accumulate bubbles of single-stranded DNA (ssDNA) on 35S ribosomal RNA genes, suggesting a role in relieving transcription-associated topological stress. However, Top1 cleavage complexes are most highly enriched in the rDNA-encoded replication fork barrier, a genetically encoded source of rDNA instability, and only weakly in the 35S promoter, leading to the proposal that top1-associated rDNA instability may be linked to the fork barrier. Here, we show that the rDNA instability phenotypes of top1 mutants, including increased formation of extrachromosomal rDNA circles, elevated genetic marker loss, and instability of critically short rDNA arrays, are independent of the replication fork barrier. In addition, we link Top1 binding at the 35S promoter to the formation of a DNA species with a long ssDNA tail, which originates in the 35S promoter region and is undetectable in top1 mutants. This DNA species is abundant in wild-type cells, occurs independently of S-phase, and may be the resolution product of the ssDNA bubbles seen in top1 mutants. Whether the formation of this DNA species is important for rDNA stability remains unclear, but our findings link Top1 to highly active DNA metabolism in the 35S promoter.

Genetic and phenotypic variation among populations is one of the fundamental subjects of evolutionary genetics. One question that arises often in data on natural populations is whether differentiation among populations on a particular trait might be caused in part by natural selection. For the past several decades, researchers have used QST-FST approaches to compare the amount of trait differentiation among populations on one or more traits (measured by the statistic QST) with differentiation on genome-wide genetic variants (measured by FST). Theory says that under neutrality, FST and QST should be approximately equal in expectation, so QST values much larger than FST are consistent with local adaptation driving subpopulations' trait values apart, and QST values much smaller than FST are consistent with stabilizing selection on similar optima. At the same time, investigators have differed in their definitions of genome-wide FST (such as "ratio of averages" vs. "average of ratios" versions of FST) and in their definitions of the variance components in QST. Here, we show that these details matter. Different versions of FST and QST have different interpretations in terms of coalescence time, and comparing incompatible statistics can lead to elevated type I error rates, with some choices leading to type I error rates near one when the nominal rate is 5%. We conduct simulations under varying genetic architectures and forms of population structure and show how they affect the distribution of QST. When many loci influence the trait, our simulations support procedures grounded in a coalescent-based framework for neutral phenotypic differentiation.

Spinal muscular atrophy and amyotrophic lateral sclerosis are devastating neurodegenerative diseases characterized by motor neuron loss. Although these 2 disorders have distinct genetic origins, recent studies suggest that they share common etiological mechanisms rooted in proteostatic dysfunction. At the heart of this emerging understanding is the survival motor neuron (SMN) complex.

Transcription factors (TFs) play a pivotal role in orchestrating critical intricate patterns of gene regulation. Although gene expression is complex, differential expression of hundreds of genes is often due to regulation by just a handful of TFs. Despite extensive efforts to elucidate TF-target regulatory relationships in Caenorhabditis elegans, existing experimental datasets cover distinct subsets of TFs and leave data integration challenging. Here, I introduce CelEst, a unified gene regulatory network designed to estimate the activity of 487 distinct C. elegans TFs-∼58% of the total-from gene expression data. To integrate data from ChIP-seq, DNA-binding motifs, and eY1H screens, optimal processing of each data type was benchmarked against a set of TF perturbation RNA-seq experiments. Moreover, I showcase how leveraging TF motif conservation in target promoters across genomes of related species can distinguish highly informative interactions, a strategy which can be applied to many model organisms. Integrated analyses of data from commonly studied conditions including heat shock, bacterial infection, and sex differences validates CelEst's performance and highlights overlooked TFs that likely play major roles in coordinating the transcriptional response to these conditions. CelEst can infer TF activity on a standard laptop computer within minutes. Furthermore, an R Shiny app with a step-by-step guide is provided for the community to perform rapid analysis with minimal coding required. I anticipate that widespread adoption of CelEsT will significantly enhance the interpretive power of transcriptomic experiments, both present and retrospective, thereby advancing our understanding of gene regulation in C. elegans and beyond.

Natural selection on complex traits is difficult to study in part due to the ascertainment inherent to genome-wide association studies (GWAS). The power to detect a trait-associated variant in GWAS is a function of its frequency and effect size - but for traits under selection, the effect size of a variant determines the strength of selection against it, constraining its frequency. Recognizing the biases inherent to GWAS ascertainment, we propose studying the joint distribution of allele frequencies across populations, conditional on the frequencies in the GWAS cohort. Before considering these conditional frequency spectra, we first characterized the impact of selection and non-equilibrium demography on allele frequency dynamics forwards and backwards in time. We then used these results to understand conditional frequency spectra under realistic human demography. Finally, we investigated empirical conditional frequency spectra for GWAS variants associated with 106 complex traits, finding compelling evidence for either stabilizing or purifying selection. Our results provide insights into polygenic score portability and other properties of variants ascertained with GWAS, highlighting the utility of conditional frequency spectra.

Reliable methods for detecting and analyzing gene expression are necessary tools for understanding development and investigating biological responses to genetic and environmental perturbation. With its fully sequenced genome, invariant cell lineage, transparent body, wiring diagram, detailed anatomy, and wide array of genetic tools, Caenorhabditis elegans is an exceptionally useful model organism for linking gene expression to cellular phenotypes. The development of new techniques in recent years has greatly expanded our ability to detect gene expression at high resolution. Here, we provide an overview of gene expression methods for C. elegans, including techniques for detecting transcripts and proteins in situ, bulk RNA sequencing of whole worms and specific tissues and cells, single-cell RNA sequencing, and high-throughput proteomics. We discuss important considerations for choosing among these techniques and provide an overview of publicly available online resources for gene expression data.

It was recently reported that a severe ancient bottleneck occurred around 900 thousand years ago in the ancestry of African populations, while this signal is absent in non-African populations. Here, we present evidence to show that this finding is likely a statistical artifact.

Budding yeast (Saccharomyces cerevisiae) is the most extensively characterized eukaryotic model organism and has long been used to gain insight into the fundamentals of genetics, cellular biology, and the functions of specific genes and proteins. The Saccharomyces Genome Database (SGD) is a scientific resource that provides information about the genome and biology of S. cerevisiae. For more than 30 years, SGD has maintained the genetic nomenclature, chromosome maps, and functional annotation for budding yeast along with search and analysis tools to explore these data. Here, we describe recent updates at SGD, including the 2 most recent reference genome annotation updates, expanded biochemical pathway representation, changes to SGD search and data files, and other enhancements to the SGD website and user interface. These activities are part of our continuing effort to promote insights gained from yeast to enable the discovery of functional relationships between sequence and gene products in fungi and higher eukaryotes.