Genetics最新文献

Aneuploidy, arising from the gain or loss of chromosomes due to nondisjunction, is a special class of mutation. It can create significant phenotypic changes by altering the 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 the 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 3 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.

Standard measures of linkage disequilibrium (LD) are affected by admixture and population structure, such that loci that are not in LD within each ancestral population appear linked when considered jointly across the populations. The influence of population structure on LD can cause problems for downstream analysis methods, in particular those that rely on LD pruning or clumping. To address this issue, we propose a measure of LD that accommodates population structure using the top inferred principal components. We estimate LD from the correlation of genotype residuals and prove that this LD measure remains unaffected by population structure when analyzing multiple populations jointly, even with admixed individuals. Based on this adjusted measure of LD, we can perform LD pruning to remove the correlation between markers for downstream analysis. Traditional LD pruning is more likely to remove markers with high differences in allele frequencies between populations, which biases measures for genetic differentiation and removes markers that are not in LD in the ancestral populations. Using data from moderately differentiated human populations and highly differentiated giraffe populations we show that traditional LD pruning biases FST and principal component analysis (PCA), which can be alleviated with the adjusted LD measure. In addition, we show that the adjusted LD leads to better PCA when pruning and that LD clumping retains more sites with the retained sites having stronger associations.

Expansion of nucleotide repeat sequences is associated with more than 40 human neuromuscular disorders. The different pathogenic mechanisms associated with the expression of nucleotide repeats are not well understood. We use a Caenorhabditis elegans model that expresses expanded CUG repeats only in cells of the body wall muscle and recapitulate muscle dysfunction and impaired organismal motility to identify the basis by which expression of RNA repeats is toxic to muscle function. Here, we performed 2 consecutive RNA interference screens and uncovered coenzyme Q metabolism and mitochondrial dysfunction as critical genetic modifiers of the motility phenotype. Furthermore, coenzyme Q supplementation reduced the toxic phenotypes, ameliorating the motility impairment and mitochondrial phenotypes. Together our data show how the expression of expanded RNA repeats can be toxic to mitochondrial homeostasis.

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 midparent 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 (identity-by-descent) 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 with 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.

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 initiative Genotype by Environment prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements, and field management notes gathered by the project over 9 years. The competition attracted registrants from around the world with representation from academic, government, industry, and nonprofit institutions as well as unaffiliated. These participants came from diverse disciplines, including 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 2 models combining machine learning and traditional breeding tools: 1 model emphasized environment using features extracted by random forest, ridge regression, and least squares, and 1 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.

Nuclear RNAi in Caenorhabditis elegans induces a set of transgenerationally heritable marks of H3K9me3, H3K23me3, and H3K27me3 at the target genes. The function of H3K23me3 in the nuclear RNAi pathway is largely unknown due to the limited knowledge of H3K23 histone methyltransferase (HMT). In this study we identified SET-21 as a novel H3K23 HMT. By taking combined genetic, biochemical, imaging, and genomic approaches, we found that SET-21 functions synergistically with a previously reported H3K23 HMT SET-32 to deposit H3K23me3 at the native targets of germline nuclear RNAi. We identified a subset of native nuclear RNAi targets that are transcriptionally activated in the set-21;set-32 double mutant. SET-21 and SET-32 are also required for robust transgenerational gene silencing induced by exogenous dsRNA. The set-21;set-32 double mutant strain exhibits an enhanced temperature-sensitive mortal germline phenotype compared to the set-32 single mutant, while the set-21 single mutant animals are fertile. We also found that HRDE-1 and SET-32 are required for cosuppression, a transgene-induced gene silencing phenomenon, in C. elegans germline. Together, these results support a model in which H3K23 HMTs SET-21 and SET-32 function cooperatively as germline nuclear RNAi factors and promote the germline immortality under the heat stress.

Pathogen genomics is a powerful tool for tracking infectious disease transmission. In malaria, identity-by-descent is used to assess the genetic relatedness between parasites and has been used to study transmission and importation. In theory, identity-by-descent can be used to distinguish genealogical relationships to reconstruct transmission history or identify parasites for QTL experiments. MalKinID (Malaria Kinship Identifier) is a new classification model designed to identify genealogical relationships among malaria parasites based on genome-wide identity-by-descent proportions and identity-by-descent segment distributions. MalKinID was calibrated to the genomic data from 3 laboratory-based genetic crosses (yielding 440 parent-child and 9060 full-sibling 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 parent-child and full-sibling and <0.05 for second-degree and third-degree relatives. Disentangling inbred relationships required adapting MalKinID to perform multisample comparisons. Genealogical inference is most powered when (1) outcrossing is the norm or (2) multisample comparisons based on a predefined pedigree are used. MalKinID lays the foundations for using identity-by-descent to track parasite transmission history and for separating progeny for quantitative-trait-locus experiments.

The Zebrafish Information Network (ZFIN, zfin.org) is the database resource for genetic, genomic, and phenotypic data from research using zebrafish, Danio rerio. ZFIN curates information about genetic perturbations, gene expression, phenotype, gene function, and human disease models from zebrafish research publications and makes these data available to researchers worldwide. Over the past 20 years, zebrafish have increasingly been used to investigate the effects of environmental exposures, becoming an ideal model to study toxicity, phenotypic outcomes, and gene-chemical interactions. Despite this, database resources supporting zebrafish toxicology and environmental exposure research are limited. To fill this gap, ZFIN has expanded functionality to incorporate and convey toxicology data better. ZFIN annotations for gene expression, phenotype, and human disease models include information about genotypes and experimental conditions used. One type of experimental condition the database captures is the application of chemicals to zebrafish. ZFIN annotates chemicals using the Chemical Entities of Biological Interest Ontology (ChEBI) along with the Zebrafish Experimental Conditions Ontology (ZECO) to denote route of exposure and other experimental conditions. These features allow researchers to search phenotypes and human disease models linked to chemicals more efficiently. Here we discuss how experimental conditions are displayed on ZFIN web pages, the data displayed on chemical term pages, and how to search and download data associated with chemical exposure experiments.