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Mutating replication-dependent (RD) histone genes is an important tool for understanding chromatin-based epigenetic regulation. Deploying this tool in metazoans is particularly challenging because RD histones in these organisms are typically encoded by many genes, often located at multiple loci. Such gene arrangements make the ability to generate homogenous histone mutant genotypes by site-specific gene editing quite difficult. Drosophila melanogaster provides a solution to this problem because the RD histone genes are organized into a single large tandem array that can be deleted and replaced with transgenes containing mutant histone genes. In the last ∼15 years several different RD histone gene replacement platforms were developed using this simple strategy. However, each platform contains weaknesses that preclude full use of the powerful developmental genetic capabilities available to Drosophila researchers. Here we describe the development of a newly engineered platform that rectifies many of these weaknesses. We used CRISPR to precisely delete the RD histone gene array (HisC), replacing it with a multifunctional cassette that permits site-specific insertion of either one or two synthetic gene arrays using selectable markers. We designed this cassette with the ability to selectively delete each of the integrated gene arrays in specific tissues using site-specific recombinases. We also present a method for rapidly synthesizing histone gene arrays of any genotype using Golden Gate cloning technologies. These improvements facilitate the generation of histone mutant cells in various tissues at different stages of Drosophila development and provide an opportunity to apply forward genetic strategies to interrogate chromatin structure and gene regulation. [ABSTRACT FROM AUTHOR]

Cryptococcus neoformans is a fungal pathogen of the top critical priority recognized by the World Health Organization. This clinically important fungus also serves as a eukaryotic model organism. A variety of resources have been generated to facilitate investigation of the C. neoformans species complex, including congenic pairs, well-annotated genomes, genetic editing tools, and gene deletion sets. Here, we generated a set of strains with all major organelles fluorescently marked. We tested short organelle-specific targeting sequences and successfully labeled the following organelles by fusing the targeting sequences with a fluorescence protein: the plasma membrane, the nucleus, the peroxisome, and the mitochondrion. We used native cryptococcal Golgi and late endosomal proteins fused with a fluorescent protein to label these two organelles. These fluorescence markers were verified via colocalization using organelle-specific dyes. All the constructs for the fluorescent protein tags were integrated in an intergenic safe haven region. These organelle-marked strains were examined for growth and various phenotypes. We demonstrated that these tagged strains could be employed to track cryptococcal interaction with the host in phagocytosis assays. These strains also allowed us to discover remarkable differences in the dynamics of proteins targeted to different organelles during sexual reproduction. Additionally, we revealed that "dormant" spores transcribed and synthesized their own proteins and trafficked the proteins to the appropriate subcellular compartments, demonstrating that spores are metabolically active. We anticipate that these newly generated fluorescent markers will greatly facilitate further investigation of cryptococcal biology and pathogenesis. [ABSTRACT FROM AUTHOR]

In humans, MAPK8IP3 (also known as JIP3) is a neurodevelopmental disorder-associated gene. In Caenorhabditis elegans , the UNC-16 ortholog of the MAPK8IP3 protein can regulate the termination of axon growth. However, its role in this process is not well understood. Here, we report that UNC-16 promotes axon termination through a process that includes the LRK-1 (LRRK-1/LRRK-2) kinase and the WDFY-3 (WDFY3/Alfy) selective autophagy protein. Genetic analysis suggests that UNC-16 promotes axon termination through an interaction between its RH1 domain and the dynein complex. Loss of unc-16 function causes accumulation of late endosomes specifically in the distal axon. Moreover, we observe synergistic interactions between loss of unc-16 function and disruptors of endolysosomal function, indicating that the endolysosomal system promotes axon termination. We also find that the axon termination defects caused by loss of UNC-16 function require the function of a genetic pathway that includes lrk-1 and wdfy-3 , 2 genes that have been implicated in autophagy. These observations suggest a model where UNC-16 promotes axon termination by interacting with the endolysosomal system to regulate a pathway that includes LRK-1 and WDFY-3. [ABSTRACT FROM AUTHOR]

Since 1999, The Arabidopsis Information Resource (www.arabidopsis.org) has been curating data about the Arabidopsis thaliana genome. Its primary focus is integrating experimental gene function information from the peer-reviewed literature and codifying it as controlled vocabulary annotations. Our goal is to produce a "gold standard" functional annotation set that reflects the current state of knowledge about the Arabidopsis genome. At the same time, the resource serves as a nexus for community-based collaborations aimed at improving data quality, access, and reuse. For the past decade, our work has been made possible by subscriptions from our global user base. This update covers our ongoing biocuration work, some of our modernization efforts that contribute to the first major infrastructure overhaul since 2011, the introduction of JBrowse2, and the resource's role in community activities such as organizing the structural reannotation of the genome. For gene function assessment, we used gene ontology annotations as a metric to evaluate: (1) what is currently known about Arabidopsis gene function and (2) the set of "unknown" genes. Currently, 74% of the proteome has been annotated to at least one gene ontology term. Of those loci, half have experimental support for at least one of the following aspects: molecular function, biological process, or cellular component. Our work sheds light on the genes for which we have not yet identified any published experimental data and have no functional annotation. Drawing attention to these unknown genes highlights knowledge gaps and potential sources of novel discoveries. [ABSTRACT FROM AUTHOR]

PRDM9-mediated reproductive isolation was first described in the progeny of Mus musculus musculus (MUS) PWD/Ph and Mus musculus domesticus (DOM) C57BL/6J inbred strains. These male F1 hybrids fail to complete chromosome synapsis and arrest meiosis at prophase I, due to incompatibilities between the Prdm9 gene and hybrid sterility locus Hstx2. We identified 14 alleles of Prdm9 in exon 12, encoding the DNA-binding domain of the PRDM9 protein in outcrossed wild mouse populations from Europe, Asia, and the Middle East, 8 of which are novel. The same allele was found in all mice bearing introgressed t -haplotypes encompassing Prdm9. We asked whether 7 novel Prdm9 alleles in MUS populations and the t -haplotype allele in 1 MUS and 3 DOM populations induce Prdm9 -mediated reproductive isolation. The results show that only combinations of the dom2 allele of DOM origin and the MUS msc1 allele ensure complete infertility of intersubspecific hybrids in outcrossed wild populations and inbred mouse strains examined so far. The results further indicate that MUS mice may share the erasure of PRDM9msc1 binding motifs in populations with different Prdm9 alleles, which implies that erased PRDM9 binding motifs may be uncoupled from their corresponding Prdm9 alleles at the population level. Our data corroborate the model of Prdm9-mediated hybrid sterility beyond inbred strains of mice and suggest that sterility alleles of Prdm9 may be rare. [ABSTRACT FROM AUTHOR]

Numerous genetic loci and several functionally characterized genes have been linked to determination of lint percentage (lint%), one of the most important cotton yield components, but we still know little about the major genetic components underlying lint%. Here, we first linked the genetic loci containing MYB25-like_At and HD1_At to the fiberless seed trait of 'SL1-7-1' and found that MYB25-like_At and HD1_At were very lowly expressed in 'SL1-7-1' ovules during fiber initiation. We then dissected the genetic components involved in determination of lint% using segregating populations derived from crosses of fuzzless mutants and intermediate segregants with different lint%, which not only confirmed the HD1_At locus but identified the HD1_Dt locus as being the major genetic components contributing to fiber initiation and lint%. The segregating populations also allowed us to evaluate the relative contributions of MYB25-like_At , MYB25-like_Dt , HD1_At , and HD1_Dt to lint%. Haplotype analysis of an Upland cotton (Gossypium hirsutum) population with 723 accessions (including 81 fuzzless seed accessions) showed that lint% of the accessions with the LP allele (higher lint%) at MYB25-like_At , MYB25-like_Dt , or HD1_At was significantly higher than that with the lp allele (lower lint%). The lint% of the Upland cotton accessions with 3 or 4 LP alleles at MYB25-like and HD1 was significantly higher than that with 2 LP alleles. The results prompted us to propose a strategy for breeding high-yielding cotton varieties, i.e. pyramiding the LP alleles of MYB25-like and HD1 with new lint% LP alleles without negative impact on seed size and fiber quality. [ABSTRACT FROM AUTHOR]

Genome sequencing and genetic mapping of molecular markers have demonstrated nearly complete Y-linkage across much of the guppy (Poecilia reticulata) XY chromosome pair. Predominant Y-linkage of factors controlling visible male-specific coloration traits also suggested that these polymorphisms are sexually antagonistic (SA). However, occasional exchanges with the X are detected, and recombination patterns also appear to differ between natural guppy populations, suggesting ongoing evolution of recombination suppression under selection created by partially sex-linked SA polymorphisms. We used molecular markers to directly estimate genetic maps in sires from 4 guppy populations. The maps are very similar, suggesting that their crossover patterns have not recently changed. Our maps are consistent with population genomic results showing that variants within the terminal 5 Mb of the 26.5 Mb sex chromosome, chromosome 12, are most clearly associated with the maleness factor, albeit incompletely. We also confirmed occasional crossovers proximal to the male-determining region, defining a second, rarely recombining, pseudo-autosomal region, PAR2. This fish species may therefore have no completely male-specific region (MSY) more extensive than the male-determining factor. The positions of the few crossover events suggest a location for the male-determining factor within a physically small repetitive region. A sex-reversed XX male had few crossovers in PAR2, suggesting that this region's low crossover rate depends on the phenotypic, not the genetic, sex. Thus, rare individuals whose phenotypic and genetic sexes differ, and/or occasional PAR2 crossovers in males can explain the failure to detect fully Y-linked variants. [ABSTRACT FROM AUTHOR]

Genetic selection has been applied for many generations in animal, plant, and experimental populations. Selection changes the allelic architecture of traits to create genetic gain. It remains unknown whether the changes in allelic architecture are different for the recently introduced technique of genomic selection compared to traditional selection methods and whether they depend on the genetic architectures of traits. Here, we investigate the allele frequency changes of old and new causal loci under 50 generations of phenotypic, pedigree, and genomic selection, for a trait controlled by either additive, additive and dominance, or additive, dominance, and epistatic effects. Genomic selection resulted in slightly larger and faster changes in allele frequencies of causal loci than pedigree selection. For each locus, allele frequency change per generation was not only influenced by its statistical additive effect but also to a large extent by the linkage phase with other loci and its allele frequency. Selection fixed a large number of loci, and 5 times more unfavorable alleles became fixed with genomic and pedigree selection than with phenotypic selection. For pedigree selection, this was mainly a result of increased genetic drift, while genetic hitchhiking had a larger effect on genomic selection. When epistasis was present, the average allele frequency change was smaller (~15% lower), and a lower number of loci became fixed for all selection methods. We conclude that for long-term genetic improvement using genomic selection, it is important to consider hitchhiking and to limit the loss of favorable alleles. [ABSTRACT FROM AUTHOR]

Understanding how plants adapt to specific environmental changes and identifying genetic markers associated with phenotypic plasticity can help breeders develop plant varieties adapted to a rapidly changing climate. Here, we propose the use of marker effect networks as a novel method to identify markers associated with environmental adaptability. These marker effect networks are built by adapting commonly used software for building gene coexpression networks with marker effects across growth environments as the input data into the networks. To demonstrate the utility of these networks, we built networks from the marker effects of ~2,000 nonredundant markers from 400 maize hybrids across 9 environments. We demonstrate that networks can be generated using this approach, and that the markers that are covarying are rarely in linkage disequilibrium, thus representing higher biological relevance. Multiple covarying marker modules associated with different weather factors throughout the growing season were identified within the marker effect networks. Finally, a factorial test of analysis parameters demonstrated that marker effect networks are relatively robust to these options, with high overlap in modules associated with the same weather factors across analysis parameters. This novel application of network analysis provides unique insights into phenotypic plasticity and specific environmental factors that modulate the genome. [ABSTRACT FROM AUTHOR]

Telomere healing occurs when telomerase, normally restricted to chromosome ends, acts upon a double-strand break to create a new, functional telomere. De novo telomere addition (dnTA) on the centromere-proximal side of a break truncates the chromosome but, by blocking resection, may allow the cell to survive an otherwise lethal event. We previously identified several sequences in the baker's yeast, Saccharomyces cerevisiae, that act as hotspots of dnTA [termed Sites of Repair-associated Telomere Addition (SiRTAs)], but the distribution and functional relevance of SiRTAs is unclear. Here, we describe a high-throughput sequencing method to measure the frequency and location of telomere addition within sequences of interest. Combining this methodology with a computational algorithm that identifies SiRTA sequence motifs, we generate the first comprehensive map of telomere-addition hotspots in yeast. Putative SiRTAs are strongly enriched in subtelomeric regions where they may facilitate formation of a new telomere following catastrophic telomere loss. In contrast, outside of subtelomeres, the distribution and orientation of SiRTAs appears random. Since truncating the chromosome at most SiRTAs would be lethal, this observation argues against selection for these sequences as sites of telomere addition per se. We find, however, that sequences predicted to function as SiRTAs are significantly more prevalent across the genome than expected by chance. Sequences identified by the algorithm bind the telomeric protein Cdc13, raising the possibility that association of Cdc13 with single-stranded regions generated during the response to DNA damage may facilitate DNA repair more generally. [ABSTRACT FROM AUTHOR]

Reduced-representation sequencing (RRS) provides cost-effective and time-saving genotyping platforms. Despite the outstanding advantage of RRS in throughput, the obtained genotype data usually contain a large number of errors. Several error correction methods employing the hidden Markov model (HMM) have been developed to overcome these issues. These methods assume that markers have a uniform error rate with no bias in the allele read ratio. However, bias does occur because of uneven amplification of genomic fragments and read mismapping. In this paper, we introduce an error correction tool, GBScleanR, which enables robust and precise error correction for noisy RRS-based genotype data by incorporating marker-specific error rates into the HMM. The results indicate that GBScleanR improves the accuracy by more than 25 percentage points at maximum compared to the existing tools in simulation data sets and achieves the most reliable genotype estimation in real data even with error-prone markers. [ABSTRACT FROM AUTHOR]

The identification of individuals that have a recent hybrid ancestry (between populations or species) has been a goal of naturalists for centuries. Since the 1960s, codominant genetic markers have been used with statistical and computational methods to identify F1 hybrids and backcrosses. Existing hybrid inference methods assume that alleles at different loci undergo independent assortment (are unlinked or in population linkage equilibrium). Genomic datasets include thousands of markers that are located on the same chromosome and are in population linkage disequilibrium which violate this assumption. Existing methods may therefore be viewed as composite likelihoods when applied to genomic datasets and their performance in identifying hybrid ancestry (which is a model-choice problem) is unknown. Here, we develop a new program Mongrail that implements a full-likelihood Bayesian hybrid inference method that explicitly models linkage and recombination, generating the posterior probability of different F1 or F2 hybrid, or backcross, genealogical classes. We use simulations to compare the statistical performance of Mongrail with that of an existing composite likelihood method (NewHybrids) and apply the method to analyze genome sequence data for hybridizing species of barred and spotted owls. [ABSTRACT FROM AUTHOR]

The challenges of breeding autotetraploid potato (Solanum tuberosum) have motivated the development of alternative breeding strategies. A common approach is to obtain uniparental dihaploids from a tetraploid of interest through pollination with S. tuberosum Andigenum Group (formerly S. phureja) cultivars. The mechanism underlying haploid formation of these crosses is unclear, and questions regarding the frequency of paternal DNA transmission remain. Previous reports have described aneuploid and euploid progeny that, in some cases, displayed genetic markers from the haploid inducer (HI). Here, we surveyed a population of 167 presumed dihaploids for large-scale structural variation that would underlie chromosomal addition from the HI, and for small-scale introgression of genetic markers. In 19 progeny, we detected 10 of the 12 possible trisomies and, in all cases, demonstrated the noninducer parent origin of the additional chromosome. Deep sequencing indicated that occasional, short-tract signals appearing to be of HI origin were better explained as technical artifacts. Leveraging recurring copy number variation patterns, we documented subchromosomal dosage variation indicating segregation of polymorphic maternal haplotypes. Collectively, 52% of the assayed chromosomal loci were classified as dosage variable. Our findings help elucidate the genomic consequences of potato haploid induction and suggest that most potato dihaploids will be free of residual pollinator DNA.

Identification of adaptive targets in experimental evolution typically relies on extensive replication and genetic reconstruction. An alternative approach is to directly assay all mutations in an evolved clone by generating pools of segregants that contain random combinations of evolved mutations. Here, we apply this method to 6 Saccharomyces cerevisiae clones isolated from 4 diploid populations that were clonally evolved for 2,000 generations in rich glucose medium. Each clone contains 17-26 mutations relative to the ancestor.We derived intermediate genotypes between the founder and the evolved clones by bulk mating sporulated cultures of the evolved clones to a barcoded haploid version of the ancestor. We competed the resulting barcoded diploids en masse and quantified fitness in the experimental and alternative environments by barcode sequencing. We estimated average fitness effects of evolved mutations using barcode-based fitness assays and whole-genome sequencing for a subset of segregants. In contrast to our previous work with haploid evolved clones, we find that diploids carry fewer beneficial mutations, with modest fitness effects (up to 5.4%) in the environment in which they arose. In agreement with theoretical expectations, reconstruction experiments show that all mutations with a detectable fitness effect manifest some degree of dominance over the ancestral allele, and most are overdominant. Genotypes with lower fitness effects in alternative environments allowed us to identify conditions that drive adaptation in our system. [ABSTRACT FROM AUTHOR]

Meiotic recombination is a critical process for sexually reproducing organisms. This exchange of genetic information between homologous chromosomes during meiosis is important not only because it generates genetic diversity, but also because it is often required for proper chromosome segregation. Consequently, the frequency and distribution of crossovers are tightly controlled to ensure fertility and offspring viability. However, in many systems, it has been shown that environmental factors can alter the frequency of crossover events. Two studies in flies and yeast point to nutritional status affecting the frequency of crossing over. However, this question remains unexplored in mammals. Here, we test how crossover frequency varies in response to diet in Mus musculus males. We use immunohistochemistry to estimate crossover frequency in multiple genotypes under two diet treatments. Our results indicate that while crossover frequency was unaffected by diet in some strains, other strains were sensitive even to small composition changes between two common laboratory chows. Therefore, recombination is both resistant and sensitive to certain dietary changes in a strain-dependent manner and, hence, this response is genetically determined. Our study is the first to report a nutrition effect on genome-wide levels of recombination. Moreover, our work highlights the importance of controlling diet in recombination studies and may point to diet as a potential source of variability among studies, which is relevant for reproducibility. [ABSTRACT FROM AUTHOR]

Entomopathogenic nematodes (EPNs), including Heterorhabditis and Steinernema, are parasitic to insects and contain mutualistically symbiotic bacteria in their intestines (Photorhabdus and Xenorhabdus, respectively) and therefore offer opportunities to study both mutualistic and parasitic symbiosis. The establishment of genetic tools in EPNs has been impeded by limited genetic tractability, inconsistent growth in vitro, variable cryopreservation, and low mating efficiency. We obtained the recently described Steinernema hermaphroditum strain CS34 and optimized its in vitro growth, with a rapid generation time on a lawn of its native symbiotic bacteria Xenorhabdus griffiniae. We developed a simple and efficient cryopreservation method. Previously, S. hermaphroditum isolated from insect hosts was described as producing hermaphrodites in the first generation. We discovered that CS34, when grown in vitro, produced consecutive generations of autonomously reproducing hermaphrodites accompanied by rare males. We performed mutagenesis screens in S. hermaphroditum that produced mutant lines with visible and heritable phenotypes. Genetic analysis of the mutants demonstrated that this species reproduces by self-fertilization rather than parthenogenesis and that its sex is determined chromosomally. Genetic mapping has thus far identified markers on the X chromosome and three of four autosomes. We report that S. hermaphroditum CS34 is the first consistently hermaphroditic EPN and is suitable for genetic model development to study naturally occurring mutualistic symbiosis and insect parasitism. [ABSTRACT FROM AUTHOR]

The Beavis effect in quantitative trait locus (QTL) mapping describes a phenomenon that the estimated effect size of a statistically significant QTL (measured by the QTL variance) is greater than the true effect size of the QTL if the sample size is not sufficiently large. This is a typical example of the Winners' curse applied to molecular quantitative genetics. Theoretical evaluation and correction for the Winners' curse have been studied for interval mapping. However, similar technologies have not been available for current models of QTL mapping and genome-wide association studies where a polygene is often included in the linear mixed models to control the genetic background effect. In this study, we developed the theory of the Beavis effect in a linear mixed model using a truncated noncentral Chi-square distribution. We equated the observed Wald test statistic of a significant QTL to the expectation of a truncated noncentral Chi-square distribution to obtain a bias-corrected estimate of the QTL variance. The results are validated from replicated Monte Carlo simulation experiments. We applied the new method to the grain width (GW) trait of a rice population consisting of 524 homozygous varieties with over 300 k single nucleotide polymorphism markers. Two loci were identified and the estimated QTL heritability were corrected for the Beavis effect. Bias correction for the larger QTL on chromosome 5 (GW5) with an estimated heritability of 12% did not change the QTL heritability due to the extremely large test score and estimated QTL effect. The smaller QTL on chromosome 9 (GW9) had an estimated QTL heritability of 9% reduced to 6% after the bias-correction. [ABSTRACT FROM AUTHOR]

In diploid species, many multiparental populations have been developed to increase genetic diversity and quantitative trait loci (QTL) mapping resolution. In these populations, haplotype reconstruction has been used as a standard practice to increase the power of QTL detection in comparison with the marker-based association analysis. However, such software tools for polyploid species are few and limited to a single biparental F1 population. In this study, a statistical framework for haplotype reconstruction has been developed and implemented in the software PolyOrigin for connected tetraploid F1 populations with shared parents, regardless of the number of parents or mating design. Given a genetic or physical map of markers, PolyOrigin first phases parental genotypes, then refines the input marker map, and finally reconstructs offspring haplotypes. PolyOrigin can utilize single nucleotide polymorphism (SNP) data coming from arrays or from sequencebased genotyping; in the latter case, bi-allelic read counts can be used (and are preferred) as input data to minimize the influence of genotype calling errors at low depth. With extensive simulation we show that PolyOrigin is robust to the errors in the input genotypic data and marker map. It works well for various population designs with ≥ 30 offspring per parent and for sequences with read depth as low as 10x. PolyOrigin was further evaluated using an autotetraploid potato dataset with a 3×3 half-diallel mating design. In conclusion, PolyOrigin opens up exciting new possibilities for haplotype analysis in tetraploid breeding populations. [ABSTRACT FROM AUTHOR]

Community association populations are composed of phenotypically and genetically diverse accessions. Once these populations are genotyped, the resulting marker data can be reused by different groups investigating the genetic basis of different traits. Because the same genotypes are observed and scored for a wide range of traits in different environments, these populations represent a unique resource to investigate pleiotropy. Here, we assembled a set of 234 separate trait datasets for the Sorghum Association Panel, a group of 406 sorghum genotypes widely employed by the sorghum genetics community. Comparison of genome-wide association studies (GWAS) conducted with two independently generated marker sets for this population demonstrate that existing genetic marker sets do not saturate the genome and likely capture only 35-43% of potentially detectable loci controlling variation for traits scored in this population. While limited evidence for pleiotropy was apparent in cross-GWAS comparisons, a multivariate adaptive shrinkage approach recovered both known pleiotropic effects of existing loci and new pleiotropic effects, particularly significant impacts of known dwarfing genes on root architecture. In addition, we identified new loci with pleiotropic effects consistent with known trade-offs in sorghum development. These results demonstrate the potential for mining existing trait datasets from widely used community association populations to enable new discoveries from existing trait datasets as new, denser genetic marker datasets are generated for existing community association populations. [ABSTRACT FROM AUTHOR]

We present a novel semiparametric method for quantitative trait loci (QTL) mapping in experimental crosses. Conventional genetic mapping methods typically assume parametric models with Gaussian errors and obtain parameter estimates through maximum-likelihood estimation. In contrast with univariate regression and interval-mapping methods, our model requires fewer assumptions and also accommodates various machine-learning algorithms. Estimation is performed with targeted maximum-likelihood learning methods. We demonstrate our semiparametric targeted learning approach in a simulation study and a well-studied barley data set.

Facilitated by recent advances using CRISPR/Cas9, genome editing technologies now permit custom genetic modifications in a wide variety of organisms. Ideally, modified animals could be both efficiently made and easily identified with minimal initial screening and without introducing exogenous sequence at the locus of interest or marker mutations elsewhere. To this end, we describe a coconversion strategy, using CRISPR/Cas9 in which screening for a dominant phenotypic oligonucleotide-templated conversion event at one locus can be used to enrich for custom modifications at another unlinked locus. After the desired mutation is identified among the F1 progeny heterozygous for the dominant marker mutation, F2 animals that have lost the marker mutation are picked to obtain the desired mutation in an unmarked genetic background. We have developed such a coconversion strategy for Caenorhabditis elegans, using a number of dominant phenotypic markers. Examining the coconversion at a second (unselected) locus of interest in the marked F1 animals, we observed that 14-84% of screened animals showed homologous recombination. By reconstituting the unmarked background through segregation of the dominant marker mutation at each step, we show that custom modification events can be carried out recursively, enabling multiple mutant animals to be made. While our initial choice of a coconversion marker [rol-6(su1006)] was readily applicable in a single round of coconversion, the genetic properties of this locus were not optimal in that CRISPR-mediated deletion mutations at the unselected rol-6 locus can render a fraction of coconverted strains recalcitrant to further rounds of similar mutagenesis. An optimal marker in this sense would provide phenotypic distinctions between the desired mutant/+ class and alternative +/+, mutant/null, null/null, and null/+ genotypes. Reviewing dominant alleles from classical C. elegans genetics, we identified one mutation in dpy-10 and one mutation in sqt-1 that meet these criteria and demonstrate that these too can be used as effective conversion markers. Coconversion was observed using a variety of donor molecules at the second (unselected) locus, including oligonucleotides, PCR products, and plasmids. We note that the coconversion approach described here could be applied in any of the variety of systems where suitable coconversion markers can be identified from previous intensive genetic analyses of gain-of-function alleles.

We revisited, in a genomic context, the theory of hybrid genetic evaluation models of hybrid crosses of pure lines, as the current practice is largely based on infinitesimal model assumptions. Expressions for covariances between hybrids due to additive substitution effects and dominance and epistatic deviations were analytically derived. Using dense markers in a GBLUP analysis, it is possible to split specific combining ability into dominance and across-groups epistatic deviations, and to split general combining ability (GCA) into within-line additive effects and within-line additive by additive (and higher order) epistatic deviations. We analyzed a publicly available maize data set of Dent x Flint hybrids using our new model (called GCA-model) up to additive by additive epistasis. To model higher order interactions within GCAs, we also fitted "residual genetic" line effects. Our new GCA-model was compared with another genomic model which assumes a uniquely defined effect of genes across origins. Most variation in hybrids is accounted by GCA. Variances due to dominance and epistasis have similar magnitudes. Models based on defining effects either differently or identically across heterotic groups resulted in similar predictive abilities for hybrids. The currently used model inflates the estimated additive genetic variance. This is not important for hybrid predictions but has consequences for the breeding scheme--e.g. overestimation of the genetic gain within heterotic group. Therefore, we recommend using GCA-model, which is appropriate for genomic prediction and variance component estimation in hybrid crops using genomic data, and whose results can be practically interpreted and used for breeding purposes. [ABSTRACT FROM AUTHOR]

qpAdm is a statistical tool for studying the ancestry of populations with histories that involve admixture between two or more source populations. Using qpAdm, it is possible to identify plausible models of admixture that fit the population history of a group of interest and to calculate the relative proportion of ancestry that can be ascribed to each source population in the model. Although qpAdm is widely used in studies of population history of human (and nonhuman) groups, relatively little has been done to assess its performance. We performed a simulation study to assess the behavior of qpAdm under various scenarios in order to identify areas of potential weakness and establish recommended best practices for use. We find that qpAdm is a robust tool that yields accurate results in many cases, including when data coverage is low, there are high rates of missing data or ancient DNA damage, or when diploid calls cannot be made. However, we caution against co-analyzing ancient and present-day data, the inclusion of an extremely large number of reference populations in a single model, and analyzing population histories involving extended periods of gene flow. We provide a user guide suggesting best practices for the use of qpAdm. [ABSTRACT FROM AUTHOR]

Ghost quantitative trait loci (QTL) are the false discoveries in QTL mapping, that arise due to the "accumulation" of the polygenic effects, uniformly distributed over the genome. The locations on the chromosome that are strongly correlated with the total of the polygenic effects depend on a specific sample correlation structure determined by the genotypes at all loci. The problem is particularly severe when the same genotypes are used to study multiple QTL, e.g. using recombinant inbred lines or studying the expression QTL. In this case, the ghost QTL phenomenon can lead to false hotspots, where multiple QTL show apparent linkage to the same locus. We illustrate the problem using the classic backcross design and suggest that it can be solved by the application of the extended mixed effect model, where the random effects are allowed to have a nonzero mean. We provide formulas for estimating the thresholds for the corresponding t-test statistics and use them in the stepwise selection strategy, which allows for a simultaneous detection of several QTL. Extensive simulation studies illustrate that our approach eliminates ghost QTL/false hotspots, while preserving a high power of true QTL detection. [ABSTRACT FROM AUTHOR]

Allele frequencies vary across populations and loci, even in the presence of migration. While most differences may be due to genetic drift, divergent selection will further increase differentiation at some loci. Identifying those is key in studying local adaptation, but remains statistically challenging. A particularly elegant way to describe allele frequency differences among populations connected by migration is the F-model, which measures differences in allele frequencies by population specific FST coefficients. This model readily accounts for multiple evolutionary forces by partitioning FST coefficients into locus- and population-specific components reflecting selection and drift, respectively. Here we present an extension of this model to linked loci by means of a hidden Markov model (HMM), which characterizes the effect of selection on linked markers through correlations in the locus specific component along the genome. Using extensive simulations, we show that the statistical power of our method is up to twofold higher than that of previous implementations that assume sites to be independent. We finally evidence selection in the human genome by applying our method to data from the Human Genome Diversity Project (HGDP). [ABSTRACT FROM AUTHOR]

Populations structured into genetic groups may display group-specific linkage disequilibrium, mutations, and/or interactions between quantitative trait loci and the genetic background. These factors lead to heterogeneous marker effects affecting the efficiency of genomic prediction, especially for admixed individuals. Such individuals have a genome that is a mosaic of chromosome blocks from different origins, and may be of interest to combine favorable group-specific characteristics. We developed two genomic prediction models adapted to the prediction of admixed individuals in presence of heterogeneous marker effects: multigroup admixed genomic best linear unbiased prediction random individual (MAGBLUP-RI), modeling the ancestry of alleles; and multigroup admixed genomic best linear unbiased prediction random allele effect (MAGBLUP-RAE), modeling group-specific distributions of allele effects. MAGBLUP-RI can estimate the segregation variance generated by admixture while MAGBLUP-RAE can disentangle the variability that is due to main allele effects from the variability that is due to group-specific deviation allele effects. Both models were evaluated for their genomic prediction accuracy using a maize panel including lines from the Dent and Flint groups, along with admixed individuals. Based on simulated traits, both models proved their efficiency to improve genomic prediction accuracy compared to standard GBLUP models. For real traits, a clear gain was observed at low marker densities whereas it became limited at high marker densities. The interest of including admixed individuals in multigroup training sets was confirmed using simulated traits, but was variable using real traits. Both MAGBLUP models and admixed individuals are of interest whenever group-specific SNP allele effects exist. [ABSTRACT FROM AUTHOR]

Plant breeders make selection decisions based on multiple traits, such as yield, plant height, flowering time, and disease resistance. A commonly used approach in multi-trait genomic selection is index selection, which assigns weights to different traits relative to their economic importance. However, classical index selection only optimizes genetic gain in the next generation, requires some experimentation to find weights that lead to desired outcomes, and has difficulty optimizing nonlinear breeding objectives. Multi-objective optimization has also been used to identify the Pareto frontier of selection decisions, which represents different tradeoffs across multiple traits. We propose a new approach, which maximizes certain traits while keeping others within desirable ranges. Optimal selection decisions are made using a new version of the look-ahead selection (LAS) algorithm, which was recently proposed for single-trait genomic selection, and achieved superior performance with respect to other state-of-the-art selection methods. To demonstrate the effectiveness of the new method, a case study is developed using a realistic data set where our method is compared with conventional index selection. Results suggest that the multi-trait LAS is more effective at balancing multiple traits compared with index selection. [ABSTRACT FROM AUTHOR]

Sex determination is remarkably variable among animals with examples of environmental sex determination, male heterogametic (XX/XY) and female heterogametic (ZZ/ZW) chromosomal sex determination, and other genetic mechanisms. The cephalochordate amphioxus occupies a key phylogenetic position as a basal chordate and outgroup to vertebrates, but its sex determination mechanism is unknown. During the course of generating Nodal mutants with transcription activator-like effector nucleases (TALENs) in amphioxus Branchiostoma floridae, serendipitously, we generated three mutant strains that reveal the sex determination mechanism of this animal. In one mutant strain, all heterozygous mutant offspring over three generations were female and all wild-type descendants were male. This pattern suggests the Nodal allele targeted is on a female-specific W chromosome. A second mutant showed the same W-linked inheritance pattern, with a female heterozygote passing the mutation only to daughters. In a third mutant strain, both male and female offspring could be heterozygous, but a female heterozygote passed the mutation only to sons. This pattern is consistent with the targeted allele being on a Z chromosome. We found an indel polymorphism linked to a Nodal allele present in most females, but no males in our cultured population. Together, these results indicate that Nodal is sex chromosomelinked in B. floridae, and that B. floridae has a ZZ/ZW sex chromosome system. [ABSTRACT FROM AUTHOR]

A multiple-trait Bayesian LASSO (MBL) for genome-based analysis and prediction of quantitative traits is presented and applied to two real data sets. The data-generating model is a multivariate linear Bayesian regression on possibly a huge number of molecular markers, and with a Gaussian residual distribution posed. Each (one per marker) of the T 31 vectors of regression coefficients (T: number of traits) is assigned the same T2variate Laplace prior distribution, with a null mean vector and unknown scale matrix Σ. The multivariate prior reduces to that of the standard univariate Bayesian LASSO when T ¼ 1: The covariance matrix of the residual distribution is assigned a multivariate Jeffreys prior, and Σ is given an inverse-Wishart prior. The unknown quantities in the model are learned using a Markov chain Monte Carlo sampling scheme constructed using a scale-mixture of normal distributions representation. MBL is demonstrated in a bivariate context employing two publicly available data sets using a bivariate genomic best linear unbiased prediction model (GBLUP) for benchmarking results. The first data set is one where wheat grain yields in two different environments are treated as distinct traits. The second data set comes from genotyped Pinus trees, with each individual measured for two traits: rust bin and gall volume. In MBL, the bivariate marker effects are shrunk differentially, i.e., "short" vectors are more strongly shrunk toward the origin than in GBLUP; conversely, "long" vectors are shrunk less. A predictive comparison was carried out as well in wheat, where the comparators of MBL were bivariate GBLUP and bivariate Bayes Cp--a variable selection procedure. A trainingtesting layout was used, with 100 random reconstructions of training and testing sets. For the wheat data, all methods produced similar predictions. In Pinus, MBL gave better predictions that either a Bayesian bivariate GBLUP or the single trait Bayesian LASSO. MBL has been implemented in the Julia language package JWAS, and is now available for the scientific community to explore with different traits, species, and environments. It is well known that there is no universally best prediction machine, and MBL represents a new resource in the armamentarium for genome-enabled analysis and prediction of complex traits. [ABSTRACT FROM AUTHOR]

Testing for Hardy--Weinberg equilibrium (HWE) is an important component in almost all analyses of population genetic data. Genetic markers that violate HWE are often treated as special cases; for example, they may be flagged as possible genotyping errors, or they may be investigated more closely for evolutionary signatures of interest. The presence of population structure is one reason why genetic markers may fail a test of HWE. This is problematic because almost all natural populations studied in the modern setting show some degree of structure. Therefore, it is important to be able to detect deviations from HWE for reasons other than structure. To this end, we extend statistical tests of HWE to allow for population structure, which we call a test of "structural HWE." Additionally, our new test allows one to automatically choose tuning parameters and identify accurate models of structure. We demonstrate our approach on several important studies, provide theoretical justification for the test, and present empirical evidence for its utility. We anticipate the proposed test will be useful in a broad range of analyses of genome-wide population genetic data. [ABSTRACT FROM AUTHOR]

Knowledge of the genetic basis underlying variation in response to environmental exposures or treatments is important in many research areas. For example, knowing the set of causal genetic variants for drug responses could revolutionize personalized medicine. We used Drosophila melanogaster to investigate the genetic signature underlying behavioral variability in response to methylphenidate (MPH), a drug used in the treatment of attention-deficit/hyperactivity disorder. We exposed a wild-type D. melanogaster population to MPH and a control treatment, and observed an increase in locomotor activity in MPH-exposed individuals. Whole-genome transcriptomic analyses revealed that the behavioral response to MPH was associated with abundant gene expression alterations. To confirm these patterns in a different genetic background and to further advance knowledge on the genetic signature of drug response variability, we used a system of inbred lines, the Drosophila Genetic Reference Panel (DGRP). Based on the DGRP, we showed that the behavioral response to MPH was strongly genotype-dependent. Using an integrative genomic approach, we incorporated known gene interactions into the genomic analyses of the DGRP, and identified putative candidate genes for variability in drug response. We successfully validated 71% of the investigated candidate genes by gene expression knockdown. Furthermore, we showed that MPH has cross-generational behavioral and transcriptomic effects. Our findings establish a foundation for understanding the genetic mechanisms driving genotype-specific responses to medical treatment, and highlight the opportunities that integrative genomic approaches have in optimizing medical treatment of complex diseases. [ABSTRACT FROM AUTHOR]

Crossovers (COs) between homologous chromosomes are critical for meiotic chromosome segregation and form in the context of the synaptonemal complex (SC), a meiosis-specific structure that assembles between aligned homologs. During Caenorhabditis elegans meiosis, central region components of the SC (SYP proteins) are essential to repair double-strand DNA breaks (DSBs) as COs. Here, we investigate the relationships between the SYP proteins and conserved pro-CO factors by examining the immunolocalization of these proteins in meiotic mutants where SYP proteins are absent, reduced, or mislocalized. Although COs do not form in syp null mutants, pro-CO factors COSA-1, MSH-5, and ZHP-3 nevertheless colocalize at DSB-dependent sites during late prophase, reflecting an inherent affinity of these factors for DSB repair sites. In contrast, in mutants where SYP proteins are present but form aggregates or display abnormal synapsis, pro-CO factors consistently track with SYP-1 localization. Further, pro-CO factors usually localize to a single site per SYP-1 structure, even in SYP aggregates or in mutants where the SC forms between sister chromatids, suggesting that CO regulation occurs within these aberrant SC structures. Moreover, we find that the meiotic cohesin REC-8 is required to ensure that SC formation occurs between homologs and not sister chromatids. Taken together, our findings support a model in which SYP proteins promote CO formation by promoting the localization of pro-CO factors to recombination events within an SC compartment, thereby ensuring that pro-CO factors identify a recombination event within an SC structure and that CO maturation occurs only between properly aligned homologous chromosomes. [ABSTRACT FROM AUTHOR]

The additive genomic variance in linear models with random marker effects can be defined as a random variable that is in accordance with classical quantitative genetics theory. Common approaches to estimate the genomic variance in random-effects linear models based on genomic marker data can be regarded as estimating the unconditional (or prior) expectation of this random additive genomic variance, and result in a negligence of the contribution of linkage disequilibrium (LD). We introduce a novel best prediction (BP) approach for the additive genomic variance in both the current and the base population in the framework of genomic prediction using the genomic best linear unbiased prediction (gBLUP) method. The resulting best predictor is the conditional (or posterior) expectation of the additive genomic variance when using the additional information given by the phenotypic data, and is structurally in accordance with the genomic equivalent of the classical additive genetic variance in random-effects models. In particular, the best predictor includes the contribution of (marker) LD to the additive genomic variance and possibly fully eliminates the missing contribution of LD that is caused by the assumptions of statistical frameworks such as the random-effects model. We derive an empirical best predictor (eBP) and compare its performance with common approaches to estimate the additive genomic variance in random-effects models on commonly used genomic datasets. [ABSTRACT FROM AUTHOR]

For more than 80 years, the euchromatic right arm of the Drosophila fourth chromosome (101F-102F) has been one of the least genetically accessible regions of the fly genome despite the fact that many important genes reside there. To improve the mapping of genes on the fourth chromosome, we describe a strategy to generate targeted deficiencies and we describe 13 deficiencies that subdivide the 300 kb between the cytological coordinates 102A6 and 102C1 into five discrete regions plus a 200-kb region from 102C1 to 102D6. Together these deficiencies substantially improve the mapping capabilities for mutant loci on the fourth chromosome.

Because of the huge size of the common wheat (Triticum aestivum L., 2n = 6x = 42, AABBDD) genome of 17,300 Mb, sequencing and mapping of the expressed portion is a logical first step for gene discovery. Here we report mapping of 7104 expressed sequence tag (EST) unigenes by Southern hybridization into a chromosome bin map using a set of wheat aneuploids and deletion stocks. Each EST detected a mean of 4.8 restriction fragments and 2.8 loci. More loci were mapped in the B genome (5774) than in the A (5173) or D (5146) genomes. The EST density was significantly higher for the D genome than for the A or B. In general, EST density increased relative to the physical distance from the centromere. The majority of EST-dense regions are in the distal parts of chromosomes. Most of the agronomically important genes are located in EST-dense regions. The chromosome bin map of ESTs is a unique resource for SNP analysis, comparative mapping, structural and functional analysis, and polyploid evolution, as well as providing a framework for constructing a sequence-ready, BAC-contig map of the wheat genome.

The objectives of this study were to develop a high-density chromosome bin map of homoeologous group 7 in hexaploid wheat (Triticum aestivum L.), to identify gene distribution in these chromosomes, and to perform comparative studies of wheat with rice and barley. We mapped 2148 loci from 919 EST clones onto group 7 chromosomes of wheat. In the majority of cases the numbers of loci were significantly lower in the centromeric regions and tended to increase in the distal regions. The level of duplicated loci in this group was 24% with most of these loci being localized toward the distal regions. One hundred nineteen EST probes that hybridized to three fragments and mapped to the three group 7 chromosomes were designated landmark probes and were used to construct a consensus homoeologous group 7 map. An additional 49 probes that mapped to 7AS, 7DS, and the ancestral translocated segment involving 7BS also were designated landmarks. Landmark probe orders and comparative maps of wheat, rice, and barley were produced on the basis of corresponding rice BAC/PAC and genetic markers that mapped on chromosomes 6 and 8 of rice. Identification of landmark ESTs and development of consensus maps may provide a framework of conserved coding regions predating the evolution of wheat genomes.

Understanding the relatedness of individuals within or between populations is a common goal in biology. Increasingly, relatedness features in genetic epidemiology studies of pathogens. These studies are relatively new compared to those in humans and other organisms, but are important for designing interventions and understanding pathogen transmission. Only recently have researchers begun to routinely apply relatedness to apicomplexan eukaryotic malaria parasites, and to date have used a range of different approaches on an ad hoc basis. Therefore, it remains unclear how to compare different studies and which measures to use. Here, we systematically compare measures based on identity-by-state (IBS) and identity-by-descent (IBD) using a globally diverse data set of malaria parasites, Plasmodium falciparum and P. vivax, and provide marker requirements for estimates based on IBD. We formally show that the informativeness of polyallelic markers for relatedness inference is maximized when alleles are equifrequent. Estimates based on IBS are sensitive to allele frequencies, which vary across populations and by experimental design. For portability across studies, we thus recommend estimates based on IBD. To generate estimates with errors below an arbitrary threshold of 0.1, we recommend ~100 polyallelic or 200 biallelic markers. Marker requirements are immediately applicable to haploid malaria parasites and other haploid eukaryotes. C.I.s facilitate comparison when different marker sets are used. This is the first attempt to provide rigorous analysis of the reliability of, and requirements for, relatedness inference in malaria genetic epidemiology. We hope it will provide a basis for statistically informed prospective study design and surveillance strategies. [ABSTRACT FROM AUTHOR]

The concept of haplotype blocks has been shown to be useful in genetics. Fields of application range from the detection of regions under positive selection to statistical methods that make use of dimension reduction. We propose a novel approach ("HaploBlocker") for defining and inferring haplotype blocks that focuses on linkage instead of the commonly used population-wide measures of linkage disequilibrium. We define a haplotype block as a sequence of genetic markers that has a predefined minimum frequency in the population, and only haplotypes with a similar sequence of markers are considered to carry that block, effectively screening a dataset for group-wise identity-by-descent. From these haplotype blocks, we construct a haplotype library that represents a large proportion of genetic variability with a limited number of blocks. Our method is implemented in the associated R-package HaploBlocker, and provides flexibility not only to optimize the structure of the obtained haplotype library for subsequent analyses, but also to handle datasets of different marker density and genetic diversity. By using haplotype blocks instead of single nucleotide polymorphisms (SNPs), local epistatic interactions can be naturally modeled, and the reduced number of parameters enables a wide variety of new methods for further genomic analyses such as genomic prediction and the detection of selection signatures. We illustrate our methodology with a dataset comprising 501 doubled haploid lines in a European maize landrace genotyped at 501,124 SNPs. With the suggested approach, we identified 2991 haplotype blocks with an average length of 2685 SNPs that together represent 94% of the dataset. [ABSTRACT FROM AUTHOR]

Construction of genetic linkage maps has become a routine step for mapping quantitative trait loci (QTL), particularly in animal and plant breeding populations. Many multiparental populations have recently been produced to increase genetic diversity and QTL mapping resolution. However, few software packages are available for map construction in these populations. In this paper, we build a general framework for the construction of genetic linkage maps from genotypic data in diploid populations, including bi- and multiparental populations, cross-pollinated (CP) populations, and breeding pedigrees. The framework is implemented as an automatic pipeline called magicMap, where the maximum multilocus likelihood approach utilizes genotypic information efficiently. We evaluate magicMap by extensive simulations and eight real datasets: one biparental, one CP, four multiparent advanced generation intercross (MAGIC), and two nested association mapping (NAM) populations, the number of markers ranging from a few hundred to tens of thousands. Not only is magicMap the only software capable of accommodating all of these designs, it is more accurate and robust to missing genotypes and genotyping errors than commonly used packages. [ABSTRACT FROM AUTHOR]

Pedigrees provide the genealogical relationships among individuals at a fine resolution and serve an important function in many areas of genetic studies. One such use of pedigree information is in the estimation of the short-term effective population size (Ne), which is of great relevance in fields such as conservation genetics. Despite the usefulness of pedigrees, however, they are often an unknown parameter and must be inferred from genetic data. In this study, we present a Bayesian method to jointly estimate pedigrees and Ne from genetic markers using Markov Chain Monte Carlo. Our method supports analysis of a large number of markers and individuals within a single generation with the use of a composite likelihood, which significantly increases computational efficiency. We show, on simulated data, that our method is able to jointly estimate relationships up to first cousins and Ne with high accuracy. We also apply the method on a real dataset of house sparrows to reconstruct their previously unreported pedigree. [ABSTRACT FROM AUTHOR]

Hybridization between related species results in the formation of an allopolyploid with multiple subgenomes. These subgenomes will each contain complete, yet evolutionarily divergent, sets of genes. Like a diploid hybrid, allopolyploids will have two versions, or homeoalleles, for every gene. Partial functional redundancy between homeologous genes should result in a deviation from additivity. These epistatic interactions between homeoalleles are analogous to dominance effects, but are fixed across subgenomes through self pollination. An allopolyploid can be viewed as an immortalized hybrid, with the opportunity to select and fix favorable homeoallelic interactions within inbred varieties. We present a subfunctionalization epistasis model to estimate the degree of functional redundancy between homeoallelic loci and a statistical framework to determine their importance within a population. We provide an example using the homeologous dwarfing genes of allohexaploid wheat, Rht-1, and search for genome-wide patterns indicative of homeoallelic subfunctionalization in a breeding population. Using the IWGSC RefSeq v1.0 sequence, 23,796 homeoallelic gene sets were identified and anchored to the nearest DNA marker to form 10,172 homeologous marker sets. Interaction predictors constructed from products of marker scores were used to fit the homeologous main and interaction effects, as well as estimate whole genome genetic values. Some traits displayed a pattern indicative of homeoallelic subfunctionalization, while other traits showed a less clear pattern or were not affected. Using genomic prediction accuracy to evaluate importance of marker interactions, we show that homeologous interactions explain a portion of the nonadditive genetic signal, but are less important than other epistatic interactions. [ABSTRACT FROM AUTHOR]

Heterosis (hybrid vigor) and inbreeding depression, commonly considered as corollary phenomena, could nevertheless be decoupled under certain assumptions according to theoretical population genetics works. To explore this issue on real data, we analyzed the components of genetic variation in a population derived from a half-diallel cross between strains from Saccharomyces cerevisiae and S. uvarum, two related yeast species involved in alcoholic fermentation. A large number of phenotypic traits, either molecular (coming from quantitative proteomics) or related to fermentation and life history, were measured during alcoholic fermentation. Because the parental strains were included in the design, we were able to distinguish between inbreeding effects, which measure phenotypic differences between inbred and hybrids, and heterosis, which measures phenotypic differences between a specific hybrid and the other hybrids sharing a common parent. The sources of phenotypic variation differed depending on the temperature, indicating the predominance of genotype-by-environment interactions. Decomposing the total genetic variance into variances of additive (intra- and interspecific) effects, of inbreeding effects, and of heterosis (intra- and interspecific) effects, we showed that the distribution of variance components defined clear-cut groups of proteins and traits. Moreover, it was possible to cluster fermentation and life-history traits into most proteomic groups. Within groups, we observed positive, negative, or null correlations between the variances of heterosis and inbreeding effects. To our knowledge, such a decoupling had never been experimentally demonstrated. This result suggests that, despite a common evolutionary history of individuals within a species, the different types of traits have been subject to different selective pressures. [ABSTRACT FROM AUTHOR]

Ty3 is a retrotransposon of Saccharomyces cerevisiae that integrates just upstream of the transcription initiation site of genes transcribed by RNA polymerase III. Ty3 transcription is pheromone-inducible in haploid cells and is mating-type regulated in diploid cells. The specificity of Ty3 integration was exploited in the design of a novel target into which transposition of Ty3 elements could be selected. The target plasmid contains divergently oriented tRNA genes with 19 base pairs separating the two tRNA gene coding sequences. An inactive ochre suppressor tRNA(Tyr) gene with a modified transcription initiation region was used as the selectable marker and a tRNA(Val) (AAC) gene was used to direct Ty3 integration into the transcription initiation region of the suppressor tRNA(Tyr) gene. Integration of Ty3 activated expression of the suppressor tRNA gene, which resulted in suppression of ochre nonsense alleles ade2-101(0) and lys2-1(0) and allowed cell growth on selective medium. Based on the activity of this target, Ty3, under control of a galactose-inducible promoter and present on a high copy-number plasmid, was estimated to transpose into the genome at a rate of 5.6 x 10(-3) per cell division. We show here that induction of Ty3 transcription from its natural promoter results in transposition. Ty3 elements in strains of the a or alpha mating-type transposed efficiently to target plasmids in cells of the opposite mating-type. Thus, natural transposition of Ty3 is regulated temporally to occur in mating populations.

Overdominance is often invoked to account for the extensive polymorphisms found in natural populations of organisms; overcompensation, however, may be equally or more important. Overcompensation occurs when limiting resources are better exploited by a genetically mixed than by a uniform population, and is often causally related to frequency-dependent selection. We have designed experiments to test whether overcompensation occurs in Drosophila melanogaster, using the Sod locus as a marker. Tests are made at each of two densities and two temperatures for cultures with desired genetic compositions. Both temperature and density have statistically significant effects on the per-female productivity of the cultures. More important, there are strong effects due to overcompensation. Cultures that are more polymorphic are also more productive than less polymorphic ones even when the level of individual heterozygosity is the same in all. There is also overdominance for the Sod locus: the heterozygotes are more productive than either homozygote at every temperature and density, and the differences are statistically significant in several cases. These results corroborate previous studies showing that overdominance may contribute to the maintenance of the Sod polymorphisms. Moreover, our results indicate that the significance of overcompensation as a mechanism to account for polymorphism in natural populations deserves further investigation.

Understanding how plants adapt to specific environmental changes and identifying genetic markers associated with phenotypic plasticity can help breeders develop plant varieties adapted to a rapidly changing climate. Here, we propose the use of marker effect networks as a novel method to identify markers associated with environmental adaptability. These marker effect networks are built by adapting commonly used software for building gene coexpression networks with marker effects across growth environments as the input data into the networks. To demonstrate the utility of these networks, we built networks from the marker effects of ∼2,000 nonredundant markers from 400 maize hybrids across 9 environments. We demonstrate that networks can be generated using this approach, and that the markers that are covarying are rarely in linkage disequilibrium, thus representing higher biological relevance. Multiple covarying marker modules associated with different weather factors throughout the growing season were identified within the marker effect networks. Finally, a factorial test of analysis parameters demonstrated that marker effect networks are relatively robust to these options, with high overlap in modules associated with the same weather factors across analysis parameters. This novel application of network analysis provides unique insights into phenotypic plasticity and specific environmental factors that modulate the genome., Competing Interests: Conflicts of interest The author(s) declare no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The advent of high throughput sequencing and genotyping technologies enables the comparison of patterns of polymorphisms at a very large number of markers. While the characterization of genetic structure from individual sequencing data remains expensive for many nonmodel species, it has been shown that sequencing pools of individual DNAs (Pool-seq) represents an attractive and cost-effective alternative. However, analyzing sequence read counts from a DNA pool instead of individual genotypes raises statistical challenges in deriving correct estimates of genetic differentiation. In this article, we provide a method-of-moments estimator of FST for Pool-seq data, based on an analysis-of-variance framework. We show, by means of simulations, that this new estimator is unbiased and outperforms previously proposed estimators. We evaluate the robustness of our estimator to model misspecification, such as sequencing errors and uneven contributions of individual DNAs to the pools. Finally, by reanalyzing published Pool-seq data of different ecotypes of the prickly sculpin Cottus asper, we show how the use of an unbiased FST estimator may question the interpretation of population structure inferred from previous analyses. [ABSTRACT FROM AUTHOR]

Many different types of multiparental populations have recently been produced to increase genetic diversity and resolution in QTL mapping. Low-coverage, genotyping-by-sequencing (GBS) technology has become a cost-effective tool in these populations, despite large amounts of missing data in offspring and founders. In this work, we present a general statistical framework for genotype imputation in such experimental crosses from low-coverage GBS data. Generalizing a previously developed hidden Markov model for calculating ancestral origins of offspring DNA, we present an imputation algorithm that does not require parental data and that is applicable to bi- and multiparental populations. Our imputation algorithm allows heterozygosity of parents and offspring as well as error correction in observed genotypes. Further, our approach can combine imputation and genotype calling from sequencing reads, and it also applies to called genotypes from SNP array data. We evaluate our imputation algorithm by simulated and real data sets in four different types of populations: the F2, the advanced intercross recombinant inbred lines, the multiparent advanced generation intercross, and the cross-pollinated population. Because our approach uses marker data and population design information efficiently, the comparisons with previous approaches show that our imputation is accurate at even very low (,13) sequencing depth, in addition to having accurate genotype phasing and error detection. [ABSTRACT FROM AUTHOR]

This study presents a method for genomic prediction that uses individual-level data and summary statistics from multiple populations. Genome-wide markers are nowadays widely used to predict complex traits, and genomic prediction using multipopulation data are an appealing approach to achieve higher prediction accuracies. However, sharing of individual-level data across populations is not always possible. We present a method that enables integration of summary statistics from separate analyses with the available individual-level data. The data can either consist of individuals with single or multiple (weighted) phenotype records per individual. We developed a method based on a hypothetical joint analysis model and absorption of population-specific information. We show that population-specific information is fully captured by estimated allele substitution effects and the accuracy of those estimates, i.e., the summary statistics. The method gives identical result as the joint analysis of all individual-level data when complete summary statistics are available. We provide a series of easy-to-use approximations that can be used when complete summary statistics are not available or impractical to share. Simulations show that approximations enable integration of different sources of information across a wide range of settings, yielding accurate predictions. The method can be readily extended to multiple-traits. In summary, the developed method enables integration of genome-wide data in the individual-level or summary statistics from multiple populations to obtain more accurate estimates of allele substitution effects and genomic predictions. [ABSTRACT FROM AUTHOR]

In vitro studies suggest that stress may generate random standing variation and that different cellular and ploidy states may evolve more rapidly under stress. Yet this idea has not been tested with pathogenic fungi growing within their host niche in vivo. Here, we analyzed the generation of both genotypic and phenotypic diversity during exposure of Candida albicans to the mouse oral cavity. Ploidy, aneuploidy, loss of heterozygosity (LOH), and recombination were determined using flow cytometry and double digest restriction site-associated DNA sequencing. Colony phenotypic changes in size and filamentous growth were evident without selection and were enriched among colonies selected for LOH of the GAL1 marker. Aneuploidy and LOH occurred on all chromosomes (Chrs), with aneuploidy more frequent for smaller Chrs and whole Chr LOH more frequent for larger Chrs. Large genome shifts in ploidy to haploidy often maintained one or more heterozygous disomic Chrs, consistent with random Chr missegregation events. Most isolates displayed several different types of genomic changes, suggesting that the oral environment rapidly generates diversity de novo. In sharp contrast, following in vitro propagation, isolates were not enriched for multiple LOH events, except in those that underwent haploidization and/or had high levels of Chr loss. The frequency of events was overall 100 times higher for C. albicans populations following in vivo passage compared with in vitro. These hyper-diverse in vivo isolates likely provide C. albicans with the ability to adapt rapidly to the diversity of stress environments it encounters inside the host. [ABSTRACT FROM AUTHOR]

Dominance genetic effects are rarely included in pedigree-based genetic evaluation. With the availability of single nucleotide polymorphism markers and the development of genomic evaluation, estimates of dominance genetic effects have become feasible using genomic best linear unbiased prediction (GBLUP). Usually, studies involving additive and dominance genetic effects ignore possible relationships between them. It has been often suggested that the magnitude of functional additive and dominance effects at the quantitative trait loci are related, but there is no existing GBLUP-like approach accounting for such correlation. Wellmann and Bennewitz (2012) showed two ways of considering directional relationships between additive and dominance effects, which they estimated in a Bayesian framework. However, these relationships cannot be fitted at the level of individuals instead of loci in a mixed model, and are not compatible with standard animal or plant breeding software. This comes from a fundamental ambiguity in assigning the reference allele at a given locus. We show that, if there has been selection, assigning the most frequent as the reference allele orients the correlation between functional additive and dominance effects. As a consequence, the most frequent reference allele is expected to have a positive value. We also demonstrate that selection creates negative covariance between genotypic additive and dominance genetic values. For parameter estimation, it is possible to use a combined additive and dominance relationship matrix computed from marker genotypes, and to use standard restricted maximum likelihood algorithms based on an equivalent model. Through a simulation study, we show that such correlations can easily be estimated by mixed model software and that the accuracy of prediction for genetic values is slightly improved if such correlations are used in GBLUP. However, a model assuming uncorrelated effects and fitting orthogonal breeding values and dominant deviations performed similarly for prediction. [ABSTRACT FROM AUTHOR]