Genetics最新文献

Aneuploidy, arising from gain or loss of chromosomes due to nondisjunction, is a special class of mutation. It can create significant phenotypic changes by altering abundance of hundreds of genes in a single event, providing material for adaptive evolution. But it can also incur large fitness costs relative to other types of mutations. Understanding mutational dynamics of aneuploidy is important for modeling its impact in nature, but aneuploidy rates are difficult to measure accurately. One challenge is that aneuploid karyotypes may revert back to euploidy, biasing forward mutation rate estimates - yet the rate of aneuploidy reversion is largely uncharacterized. Furthermore, current rate estimates are confounded because fitness differences between euploids and aneuploids are typically not accounted for in rate calculations. We developed a unique fluctuation assay in a wild-yeast model to measure the rate of extra-chromosome loss across three aneuploid chromosomes, while accounting for fitness differences between aneuploid and euploid cells. We show that incorporating fitness effects is essential to obtain accurate estimates of aneuploidy rates. Furthermore, the rate of extra-chromosome loss, separate from karyotype fitness differences, varies across chromosomes. We also measured rates in a strain lacking RNA-binding protein Ssd1, important for aneuploidy tolerance and implicated in chromosome segregation. We found no role for Ssd1 in the loss of native aneuploid chromosomes, although it did impact an engineered chromosome XV with a perturbed centromeric sequence. We discuss the impacts and challenges of modeling aneuploidy dynamics in real world situations.

Pathogen genomics is a powerful tool for tracking infectious disease transmission. In malaria, identity-by-descent (IBD) is used to assess the genetic relatedness between parasites and has been used to study transmission and importation. In theory, IBD can be used to distinguish genealogical relationships to reconstruct transmission history or identify parasites for quantitative-trait-locus experiments. MalKinID (Malaria Kinship Identifier) is a new classification model designed to identify genealogical relationships among malaria parasites based on genome-wide IBD proportions and IBD segment distributions. MalKinID was calibrated to the genomic data from three laboratory-based genetic crosses (yielding 440 parent-child [PC] and 9060 full-sibling [FS] comparisons). MalKinID identified lab generated F1 progeny with >80% sensitivity and showed that 0.39 (95% CI 0.28, 0.49) of the second-generation progeny of a NF54 and NHP4026 cross were F1s and 0.56 (0.45, 0.67) were backcrosses of an F1 with the parental NF54 strain. In simulated outcrossed importations, MalKinID reconstructs genealogy history with high precision and sensitivity, with F1-scores exceeding 0.84. However, when importation involves inbreeding, such as during serial co-transmission, the precision and sensitivity of MalKinID declined, with F1-scores (the harmonic mean of precision and sensitivity) of 0.76 (0.56, 0.92) and 0.23 (0.0, 0.4) for PC and FS and <0.05 for second-degree and third-degree relatives. Disentangling inbred relationships required adapting MalKinID to perform multi-sample comparisons. Genealogical inference is most powered when 1) outcrossing is the norm or 2) multi-sample comparisons based on a predefined pedigree are used. MalKinID lays the foundations for using IBD to track parasite transmission history and for separating progeny for quantitative-trait-locus experiments.

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 winner's 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, 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.

Variable selection and large-scale hypothesis testing are techniques commonly used to analyze high-dimensional genomic data. Despite recent advances in theory and methodology, variable selection and inference with highly collinear features remain challenging. For instance, collinearity poses a great challenge in genome-wide association studies involving millions of variants, many of which may be in high linkage disequilibrium. In such settings, collinearity can significantly reduce the power of variable selection methods to identify individual variants associated with an outcome. To address such challenges, we developed a Bayesian hierarchical hypothesis testing (BHHT)-a novel multiresolution testing procedure that offers high power with adequate error control and fine-mapping resolution. We demonstrate through simulations that the proposed methodology has a power-FDR performance that is competitive with (and in many scenarios better than) state-of-the-art methods. Finally, we demonstrate the feasibility of using BHHT with large sample size (n∼ 300,000) and ultra dimensional genotypes (∼ 15 million single-nucleotide polymorphisms or SNPs) by applying it to eight complex traits using data from the UK-Biobank. Our results show that the proposed methodology leads to many more discoveries than those obtained using traditional SNP-centered inference procedures. The article is accompanied by open-source software that implements the methods described in this study using algorithms that scale to biobank-size ultra-high-dimensional data.

Near the C-terminus of histone H2A in the yeast S. cerevisiae, there are two serines (S122 and S129) that are targets of phosphorylation. The phosphorylation of Serine 129 in response to DNA damage is dependent on the Tel1 and Mec1 kinases. In S. pombe and S. cerevisiae, the phosphorylation of Serine 122 is dependent on the Bub1 kinase, and S. pombe strains with an alanine mutation of this serine have elevated levels of lagging chromosomes in mitosis. Strains that lack both Tel1 and Mec1 in S. cerevisiae have very elevated rates of non-disjunction. To clarify the functional importance of phosphorylation of serines 122 and 129 in H2A, we measured chromosome loss rates in single mutant strains and double mutant combinations. We also examined the interaction of mutations of BUB1, TEL1, and MEC1 in combination with mutations of serine 122 and 129 in H2A. We conclude that the phosphorylation state of S129 has no effect on chromosome disjunction whereas mutations that inactivate Bub1 or a S122A mutation in the histone H2A greatly elevate the rate of chromosome non-disjunction. Based on this analysis, we suggest that Bub1 exerts its primary effect on chromosome disjunction by phosphorylating S122 of histone H2A. However, Tel1, Mec1 and Bub1 also functionally redundant in a second pathway affecting chromosome disjunction that is at least partially independent of phosphorylation of S122 of H2A.

Chromosome congression and alignment on the metaphase plate involves lateral and microtubule plus-end interactions with the kinetochore. Here we take advantage of our ability to efficiently generate a GFP-marked acentric X chromosome fragment in Drosophila neuroblasts to identify forces acting on chromosome arms that drive congression and alignment. We find acentrics efficiently congress and align on the metaphase plate, often more rapidly than kinetochore-bearing chromosomes. Unlike intact chromosomes, the paired sister acentrics oscillate as they move to and reside on the metaphase plate in a plane distinct and significantly further from the main mass of intact chromosomes. Consequently, at anaphase onset acentrics are oriented either parallel or perpendicular to the spindle. Parallel-oriented sisters separate by sliding while those oriented perpendicularly separate via unzipping. This oscillation, together with the fact that in the presence of spindles with disrupted interpolar microtubules acentrics are rapidly shunted away from the poles, indicates that distributed plus-end directed forces are primarily responsible for acentric migration. This conclusion is supported by the observation that reduction of EB1 preferentially disrupts acentric alignment. Taken together these studies suggest that plus-end forces mediated by the outer interpolar microtubules contribute significantly to acentric congression and alignment. Surprisingly, we observe disrupted telomere pairing and alignment of sister acentrics indicating that the kinetochore is required to ensure proper gene-to-gene alignment of sister chromatids. Finally, we demonstrate that like mammalian cells, the Drosophila congressed chromosomes on occasion exhibit a toroid configuration.

Breeders have long appreciated the need to balance selection for short-term genetic gain with maintaining genetic variance for long-term gain. For outbred populations, the method called Optimum Contribution Selection (OCS) chooses parental contributions to maximize the average breeding value at a prescribed inbreeding rate. With Optimum Mate Allocation (OMA), the contribution of each mating is optimized, which allows for specific combining ability due to dominance. To enable OCS and OMA in polyploid species, new theoretical results were derived to (1) predict mid-parent heterosis due to dominance and (2) control inbreeding in a population of arbitrary ploidy. A new Convex optimization framework for OMA, named COMA, was developed and released as public software. Under stochastic simulation of a genomic selection program, COMA maintained a target inbreeding rate of 0.5% using either pedigree or genomic IBD kinship. Significantly more genetic gain was realized with pedigree kinship, which is consistent with previous studies showing the selective advantage of an individual under OCS is dominated by its Mendelian sampling term. Despite the higher accuracy (+0.2-0.3) when predicting mate performance with OMA compared to OCS, there was little long-term gain advantage. The sparsity of the COMA mating design and flexibility to incorporate mating constraints offer practical incentives over OCS. In a potato breeding case study with 170 candidates, the optimal solution at 0.5% inbreeding involved 43 parents but only 43 of the 903 possible matings.

Trioecy, a rare reproductive system where hermaphrodites, females, and males coexist, is found in certain algae, plants, and animals. Though it has evolved independently multiple times, its rarity suggests it may be an unstable or transitory evolutionary strategy. In the well-studied Caenorhabditis elegans, attempts to engineer a trioecious strain have reverted to the hermaphrodite/male system, reinforcing this view. However, these studies did not consider the sex-determination systems of naturally stable trioecious species. The discovery of free-living nematodes of the Auanema genus, which have naturally stable trioecy, provides an opportunity to study these systems. In Auanema, females produce only oocytes, while hermaphrodites produce both oocytes and sperm for self-fertilization. Crosses between males and females primarily produce daughters (XX hermaphrodites and females), while male-hermaphrodite crosses result in sons only. These skewed sex ratios are due to X-chromosome drive during spermatogenesis, where males produce only X-bearing sperm through asymmetric cell division. The stability of trioecy in Auanema is influenced by maternal control over sex determination and environmental cues. These factors offer insights into the genetic and environmental dynamics that maintain trioecy, potentially explaining its evolutionary stability in certain species.

Homologous recombination is a meiotic process that generates diversity along the genome and interacts with all evolutionary forces. Despite its importance, studies of recombination landscapes are lacking due to methodological limitations and limited data. Frequently used approaches include linkage mapping based on familial data that provides sex-specific broad-scale estimates of realized recombination and inferences based on population LD that reveal a more fine scale resolution of the recombination landscape, albeit dependent on the effective population size and the selective forces acting on the population. In this study, we use a combination of these two methods to elucidate the recombination landscape for the Afro-European barn owl (Tyto alba). We find subtle differences in crossover placement between sexes that leads to differential effective shuffling of alleles. LD based estimates of recombination are concordant with family-based estimates and identify large variation in recombination rates within and among linkage groups. Larger chromosomes show variation in recombination rates, while smaller chromosomes have a universally high rate which shapes the diversity landscape. We find that recombination rates are correlated with gene content, genetic diversity and GC content. We find no conclusive differences in the recombination landscapes between populations. Overall, this comprehensive analysis enhances our understanding of recombination dynamics, genomic architecture, and sex-specific variation in the barn owl, contributing valuable insights to the broader field of avian genomics.