Genetics最新文献

Genomic prediction models that capture genotype-by-environment interaction are useful for predicting site-specific performance by leveraging information among related individuals and correlated environments, but implementing such models is computationally challenging. This study describes the algorithm of these scalable approaches, including two models with latent representations of genotype-by-environment interactions, namely MegaLMM and MegaSEM, and an efficient multivariate mixed model solver, namely PEGS, fitting different covariance structures (unstructured, XFA, HCS). Accuracy and runtime are benchmarked on simulated scenarios with varying numbers of genotypes and environments. MegaLMM and PEGS-based XFA and HCS models provided the highest accuracy under sparse testing with 100 testing environments. PEGS-based unstructured model was orders of magnitude faster than REML-based multivariate GBLUP while providing the same accuracy. MegaSEM provided the lowest runtime, fitting a model with 200 traits and 20,000 individuals in approximately 5 minutes, and a model with 2,000 traits and 2,000 individuals in less than 3 minutes. With the G2F data, the most accurate predictions were attained with the univariate model fitted across environments and by averaging environment-level GEBVs from models with HCS and XFA covariance structures.

Cryptococcus neoformans is an opportunistic fungal pathogen with a polysaccharide capsule that becomes greatly enlarged in the mammalian host and during in vitro growth under host-like conditions. To understand how individual environmental signals affect capsule size and gene expression, we grew cells in all combinations of five signals implicated in capsule size and systematically measured cell and capsule sizes. We also sampled these cultures over time and performed RNA-Seq in quadruplicate, yielding 881 RNA-Seq samples. Analysis of the resulting data sets showed that capsule induction in tissue culture medium, typically used to represent host-like conditions, requires the presence of either CO2 or exogenous cyclic AMP (cAMP). Surprisingly, adding either of these pushes overall gene expression in the opposite direction from tissue culture media alone, even though both are required for capsule development. Another unexpected finding was that rich medium blocks capsule growth completely. Statistical analysis further revealed many genes whose expression is associated with capsule thickness; deletion of one of these significantly reduced capsule size. Beyond illuminating capsule induction, our massive, uniformly collected dataset will be a significant resource for the research community.

Myosin storage myopathy (MSM) is a rare skeletal muscle disorder caused by mutations in the slow muscle/β-cardiac myosin heavy chain (MHC) gene. MSM missense mutations frequently disrupt the tail's stabilizing heptad repeat motif. Disease hallmarks include subsarcolemmal hyaline-like β-MHC aggregates, muscle weakness and, occasionally, cardiomyopathy. We generated transgenic, heterozygous Drosophila to examine the dominant physiological and structural effects of the L1793P, R1845W, and E1883K MHC MSM mutations on diverse muscles. The MHC variants reduced lifespan and flight and jump abilities. Moreover, confocal and electron microscopy revealed that they provoked indirect flight muscle breaks and myofibrillar disarray/degeneration with filamentous inclusions. Incorporation of GFP-myosin enabled in situ determination of thick filament lengths, which were significantly reduced in all mutants. Semi-automated heartbeat analysis uncovered aberrant cardiac function, which worsened with age. Thus, our fly models phenocopied traits observed among MSM patients. We additionally mapped the mutations onto a recently-determined, 6Å resolution, cryo-EM structure of the human cardiac thick filament. The R1845W mutation replaces a basic arginine with a polar-neutral, bulkier tryptophan, while E1883K reverses charge at critical filament loci. Both would be expected to disrupt the core and the outer shell of the backbone structure. Replacing L1793 with a proline, a potent breaker of alpha-helices, could disturb the coiled-coil of the myosin rod and alter the tail-tail interactome. Hence, all mutations likely destabilize and weaken the filament backbone. This may trigger disease in humans, while potentially analogous perturbations are likely to yield the observed thick filament and muscle disruption in our fly models.

Self-fertile Caenorhabditis nematodes carry a surprising number of Medea elements, alleles that act in heterozygous mothers and cause death or developmental delay in offspring that don't inherit them. At some loci, both alleles in a cross operate as independent Medeas, affecting all the homozygous progeny of a selfing heterozygote. The genomic coincidence of Medea elements and ancient, deeply coalescing haplotypes, which pepper the otherwise homogeneous genomes of these animals, raises questions about how these apparent gene-drive elements persist for long periods of time. Here I investigate how mating system affects the evolution of Medeas, and their paternal-effect counterparts, peels. Despite an intuition that antagonistic alleles should induce balancing selection by killing homozygotes, models show that, under partial selfing, antagonistic elements experience positive frequency dependence: the common allele drives the rare one extinct, even if the rare one is more penetrant. Analytical results for the threshold frequency required for one allele to invade a population show that a very weakly penetrant allele, one whose effects would escape laboratory detection, could nevertheless prevent a much more penetrant allele from invading under high rates of selfing. Ubiquitous weak antagonistic Medeas and peels could then act as localized barriers to gene flow between populations, generating genomic islands of deep coalescence. Analysis of gene expression data, however, suggest that this cannot be the whole story. A complementary explanation is that ordinary ecological balancing selection generates ancient haplotypes on which Medeas can evolve, while high homozygosity in these selfers minimizes the role of gene drive in their evolution.

Multi-environment trials (METs) are crucial for identifying varieties that perform well across a target population of environments (TPE). However, METs are typically too small to sufficiently represent all relevant environment-types, and face challenges from changing environment-types due to climate change. Statistical methods that enable prediction of variety performance for new environments beyond the METs are needed. We recently developed MegaLMM, a statistical model that can leverage hundreds of trials to significantly improve genetic value prediction accuracy within METs. Here, we extend MegaLMM to enable genomic prediction in new environments by learning regressions of latent factor loadings on Environmental Covariates (ECs) across trials. We evaluated the extended MegaLMM using the maize Genome-To-Fields dataset, consisting of 4402 varieties cultivated in 195 trials with 87.1% of phenotypic values missing, and demonstrated its high accuracy in genomic prediction under various breeding scenarios. Furthermore, we showcased MegaLMM's superiority over univariate GBLUP in predicting trait performance of experimental genotypes in new environments. Finally, we explored the use of higher-dimensional quantitative ECs and discussed when and how detailed environmental data can be leveraged for genomic prediction from METs. We propose that MegaLMM can be applied to plant breeding of diverse crops and different fields of genetics where large-scale linear mixed models are utilized.

Ring chromosomes are known in many eukaryotic organisms, including humans. They are typically associated with a variety of maladies, including abnormal development and lethality. Underlying these phenotypes are anaphase chromatin bridges that can lead to chromosome loss, nondisjunction and breakage. By cytological examination of ring chromosomes in Drosophila melanogaster we identified five causes for anaphase bridges produced by ring chromosomes. Catenation of sister chromatids appears to be the most common cause and these bridges frequently resolve during anaphase, presumably by the action of topoisomerase II. Sister chromatid exchange and chromosome breakage followed by sister chromatid union also produce anaphase bridges. Mitotic recombination with the homolog was rare, but was another route to generation of anaphase bridges. Most surprising, was the discovery of homolog capture, where the ring chromosome was connected to its linear homolog in anaphase. We hypothesize that this is a remnant of mitotic pairing and that the linear chromosome is connected to the ring by multiple wraps produced through the action of topoisomerase II during establishment of homolog pairing. In support, we showed that in a ring/ring homozygote the two rings are frequently catenated in mitotic metaphase, a configuration that requires breaking and rejoining of at least one chromosome.

RNA polymerase II (Pol II) has a highly conserved domain, the trigger loop (TL), that controls transcription fidelity and speed. We previously probed pairwise genetic interactions between residues within and surrounding the TL for the purpose of understand functional interactions between residues and to understand how individual mutants might alter TL function. We identified widespread incompatibility between TLs of different species when placed in the Saccharomyces cerevisiae Pol II context, indicating species-specific interactions between otherwise highly conserved TLs and its surroundings. These interactions represent epistasis between TL residues and the rest of Pol II. We sought to understand why certain TL sequences are incompatible with S. cerevisiae Pol II and to dissect the nature of genetic interactions within multiply substituted TLs as a window on higher order epistasis in this system. We identified both positive and negative higher-order residue interactions within example TL haplotypes. Intricate higher-order epistasis formed by TL residues was sometimes only apparent from analysis of intermediate genotypes, emphasizing complexity of epistatic interactions. Furthermore, we distinguished TL substitutions with distinct classes of epistatic patterns, suggesting specific TL residues that potentially influence TL evolution. Our examples of complex residue interactions suggest possible pathways for epistasis to facilitate Pol II evolution.

Plant architecture is a major determinant of planting density, which enhances productivity potential for crops per unit area. Genomic prediction is well positioned to expedite genetic gain of plant architectural traits since they are typically highly heritable. Additionally, the adaptation of genomic prediction models to query predictive abilities of markers tagging certain genomic regions could shed light on the genetic architecture of these traits. Here, we leveraged transcriptional networks from a prior study that contextually described developmental progression during tassel and leaf organogenesis in maize (Zea mays) to inform genomic prediction models for architectural traits. Since these developmental processes underlie tassel branching and leaf angle, 2 important agronomic architectural traits, we tested whether genes prioritized from these networks quantitatively contribute to the genetic architecture of these traits. We used genomic prediction models to evaluate the ability of markers in the vicinity of prioritized network genes to predict breeding values of tassel branching and leaf angle traits for 2 diversity panels in maize and diversity panels from sorghum (Sorghum bicolor) and rice (Oryza sativa). Predictive abilities of markers near these prioritized network genes were similar to those using whole-genome marker sets. Notably, markers near highly connected transcription factors from core network motifs in maize yielded predictive abilities that were significantly greater than expected by chance in not only maize but also closely related sorghum. We expect that these highly connected regulators are key drivers of architectural variation that are conserved across closely related cereal crop species.

Dosage compensation in Drosophila involves upregulating male X-genes two-fold. This process is carried out by the MSL (male-specific lethal) complex, which binds high-affinity sites and spreads to surrounding genes. Current models of MSL spreading focus on interactions betwen MSL3 (male-specific lethal 3) and Set2-dependent histone marks like trimethylated H3 lysine-36 (H3K36me3). However, Set2 could affect DC via another target, or there could be redundancy between canonical H3.2 and variant H3.3 histones. Furthermore, it is important to parse male-specific effects from those that are X-specific. To discriminate among these possibilities, we employed genomic approaches in H3K36 'residue' and Set2 'writer' mutants. The results confirm a role for Set2 in X-gene regulation, but show that expression trends in males are often mirrored in females. Instead of global, male-specific reduction of X-genes in Set2 or H3K36 mutants, we observe heterogeneous effects. Interestingly, we identified groups of differentially expressed genes (DEGs) whose changes were in opposite directions following loss of H3K36 or Set2, suggesting that H3K36me states have reciprocal functions. In contrast to H4K16R controls, differential expression analysis of combined H3.2K36R/H3.3K36R mutants showed neither consistent reduction in X-gene expression, nor correlation with MSL3 binding. Motif analysis of the DEGs implicated BEAF-32 and other insulator proteins in Set2/H3K36-dependent regulation. Overall, the data are inconsistent with the prevailing model wherein H3K36me3 is essential for spreading the MSL complex to genes along the male X. Rather, we propose that Set2 and H3K36 support DC indirectly, via processes that are utilized by MSL but common to both sexes.

Variation in DNA repair genes can increase cancer risk by elevating the rate of oncogenic mutation. Defects in one such gene, MUTYH, are known to elevate the incidence of colorectal cancer in a recessive Mendelian manner. Recent evidence has also linked MUTYH to a mutator phenotype affecting normal somatic cells as well as the female germline. Here, we use whole genome sequencing to measure germline de novo mutation rates in a large extended family containing both mothers and fathers who are affected by pathogenic MUTYH variation. By developing novel methodology that uses siblings as "surrogate parents" to identify de novo mutations, we were able to include mutation data from several children whose parents were unavailable for sequencing. In the children of mothers affected by the pathogenic MUTYH genotype p.Y179C/V234M, we identify an elevation of the C>A mutation rate that is weaker than mutator effects previously reported to be caused by other pathogenic MUTYH genotypes, suggesting that mutation rates in normal tissues may be useful for classifying cancer-associated variation along a continuum of severity. Surprisingly, we detect no significant elevation of the C>A mutation rate in children born to a father with the same MUTYH genotype, and we similarly find that the mutator effect of the mouse homolog Mutyh appears to be localized to embryonic development, not the spermatocytes. Our results suggest that maternal MUTYH variants can cause germline mutations by attenuating the repair of oxidative DNA damage in the early embryo.