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The Genetics Society of America is calling for submissions of papers on the Genetics of Bacteria for their journals GENETICS and G3: Genes|Genomes|Genetics. The series aims to highlight recent genetic and genomic work on various aspects of bacterial genetics, including genetic diversity, genome preservation, mutagenesis, gene function and regulation, bacterial defense systems, and the genetic basis of medically important traits. The series will be launched in mid-2025, and authors are invited to submit manuscripts by September 30. The papers will undergo peer review and will be published online shortly after acceptance. [Extracted from the article]

The Genetics Society of America is calling for submissions of papers on biobank-scale genomic data analyses. Biobanks, which are large collections of biological, medical, and genetic data on the same individuals, have revolutionized human genomics research. However, there is substantial heterogeneity between biobanks worldwide, creating both challenges and opportunities for data analysis. The series will focus on various topics, including population structures, selection bias, measurement errors, clinical observations, linkage with external data sources, meta-analysis, computational analysis methods, family-based analyses, ethical and privacy-related issues, and technological considerations. Manuscripts can be submitted until May 31, 2024, and the series will be an ongoing resource in this area. [Extracted from the article]

This paper introduces AlphaSimR, an R package for stochastic simulations of plant and animal breeding programs. AlphaSimR is a highly flexible software package able to simulate a wide range of plant and animal breeding programs for diploid and autopolyploid species. AlphaSimR is ideal for testing the overall strategy and detailed design of breeding programs. AlphaSimR utilizes a scripting approach to building simulations that is particularly well suited for modeling highly complex breeding programs, such as commercial breeding programs. The primary benefit of this scripting approach is that it frees users from preset breeding program designs and allows them to model nearly any breeding program design. This paper lists the main features of AlphaSimR and provides a brief example simulation to show how to use the software [ABSTRACT FROM AUTHOR]

During the last decade, the spotted wing drosophila, Drosophila suzukii , has spread from eastern Asia to the Americas, Europe, and Africa. This fly attacks many species of cultivated and wild fruits with soft, thin skins, where its serrated ovipositor allows it to lay eggs in undamaged fruit. Parasitoids from the native range of D. suzukii may provide sustainable management of this polyphagous pest. Among these parasitoids, host-specificity testing has revealed a lineage of Ganaspis near brasiliensis , referred to in this paper as G1, that appears to be a cryptic species more host-specific to D. suzukii than other parasitoids. Differentiation among cryptic species is critical for introduction and subsequent evaluation of their impact on D. suzukii. Here, we present results on divergence in genomic sequences and architecture and reproductive isolation between lineages of Ganaspis near brasiliensis that appear to be cryptic species. We studied five populations, two from China, two from Japan, and one from Canada, identified as the G1 vs G3 lineages based on differences in cytochrome oxidase l sequences. We assembled and annotated the genomes of these populations and analyzed divergences in sequence and genome architecture between them. We also report results from crosses to test reproductive compatibility between the G3 lineage from China and the G1 lineage from Japan. The combined results on sequence divergence, differences in genome architectures, ortholog divergence, reproductive incompatibility, differences in host ranges and microhabitat preferences, and differences in morphology show that these lineages are different species. Thus, the decision to evaluate the lineages separately and only import and introduce the more host-specific lineage to North America and Europe was appropriate. [ABSTRACT FROM AUTHOR]

Galleria mellonella is a pest of honeybees in many countries because its larvae feed on beeswax. However, G. mellonella larvae can also eat various plastics, including polyethylene, polystyrene, and polypropylene, and therefore, the species is garnering increasing interest as a tool for plastic biodegradation research. This paper presents an improved genome (99.3% completed lepidoptera_odb10 BUSCO; genome mode) for G. mellonella. This 472 Mb genome is in 221 contigs with an N50 of 6.4 Mb and contains 13,604 protein-coding genes. Genes that code for known and putative polyethylene-degrading enzymes and their similarity to proteins found in other Lepidoptera are highlighted. An analysis of secretory proteins more likely to be involved in the plastic catabolic process has also been carried out. [ABSTRACT FROM AUTHOR]

Reception of chemical information from the environment is crucial for insects' survival and reproduction. The chemosensory reception mainly occurs by the antennae and mouth parts of the insect, when the stimulus contacts the chemoreceptors located within the sensilla. Chemosensory receptor genes have been well-studied in some social hymenopterans such as ants, honeybees, and wasps. However, although stingless bees are the most representative group of eusocial bees, little is known about their odorant, gustatory, and ionotropic receptor genes. Here, we analyze the transcriptome of the proboscis and antennae of the stingless bee Tetragonisca fiebrigi. We identified and annotated 9 gustatory and 15 ionotropic receptors. Regarding the odorant receptors, we identified 204, and we were able to annotate 161 of them. In addition, we compared the chemosensory receptor genes of T. fiebrigi with those annotated for other species of Hymenoptera. We found that T. fiebrigi showed the largest number of odorant receptors compared with other bees. Genetic expansions were identified in the subfamilies 9-exon, which was also expanded in ants and paper wasps; in G02A, including receptors potentially mediating social behavior; and in GUnC, which has been related to pollen and nectar scent detection. Our study provides the first report of chemosensory receptor genes in T. fiebrigi and represents a resource for future molecular and physiological research in this and other stingless bee species. [ABSTRACT FROM AUTHOR]

This paper proposes a solution to a long-standing problem concerning the joint distribution of allelic identity by descent between two individuals at two linked loci. Such distributions have important applications across various fields of genetics, and detailed formulas for selected relationships appear scattered throughout the literature. However, these results were obtained essentially by brute force, with no efficient method available for general pedigrees. The recursive algorithm described in this paper, and its implementation in R, allow efficient calculation of two-locus identity coefficients in any pedigree. As a result, many existing procedures and techniques may, for the first time, be applied to complex and inbred relationships. Two such applications are discussed, concerning the expected likelihood ratio in forensic kinship testing, and variances in realized relatedness. [ABSTRACT FROM AUTHOR]

This article emphasizes the importance of recognizing and giving credit to the many contributions that contribute to scientific discoveries. It discusses the challenges of acknowledging the large number of people involved in research and the need to share credit. The article encourages authors to cite others who have conducted similar experiments or research, as well as to acknowledge the collective efforts in creating databases and knowledgebases. It emphasizes the importance of properly citing resources, tools, methods, and web links used in research papers to give credit where it is due and support the continued viability of these resources. [Extracted from the article]

Look-ahead selection is a sophisticated yet effective algorithm for genomic selection, which optimizes not only the selection of breeding parents but also mating strategy and resource allocation by anticipating the implications of crosses in a prespecified future target generation. Simulation results using maize datasets have suggested that look-ahead selection is able to significantly accelerate genetic gain in the target generation while maintaining genetic diversity. In this paper, we propose a new algorithm to address the limitations of look-ahead selection, including the difficulty in specifying a meaningful deadline in a continuous breeding process and slow growth of genetic gain in early generations. This new algorithm uses the present value of genetic gains as the breeding objective, converting genetic gains realized in different generations to the current generation using a discount rate, similar to using the interest rate to measure the time value of cash flows incurred at different time points. By using the look-ahead techniques to anticipate the future gametes and thus present value of future genetic gains, this algorithm yields a better trade-off between short-term and long-term benefits. Results from simulation experiments showed that the new algorithm can achieve higher genetic gains in early generations and a continuously growing trajectory as opposed to the look-ahead selection algorithm, which features a slow progress in early generations and a growth spike right before the deadline. [ABSTRACT FROM AUTHOR]

Many important traits in plants, animals, and microbes are polygenic and challenging to improve through traditional marker-assisted selection. Genomic prediction addresses this by incorporating all genetic data in a mixed model framework. The primary method for predicting breeding values is genomic best linear unbiased prediction, which uses the realized genomic relationship or kinship matrix (K) to connect genotype to phenotype. Genomic relationship matrices share information among entries to estimate the observed entries' genetic values and predict unobserved entries' genetic values. One of the main parameters of such models is genomic variance (r2 g), or the variance of a trait associated with a genome-wide sample of DNA polymorphisms, and genomic heritability (h2 g); however, the seminal papers introducing different forms of K often do not discuss their effects on the model estimated variance components despite their importance in genetic research and breeding. Here, we discuss the effect of several standard methods for calculating the genomic relationship matrix on estimates of r2 g and h2 g. With current approaches, we found that the genomic variance tends to be either overestimated or underestimated depending on the scaling and centering applied to the marker matrix (Z), the value of the average diagonal element of K, and the assortment of alleles and heterozygosity (H) in the observed population. Using the average semivariance, we propose a new matrix, KASV, that directly yields accurate estimates of r2 g and h2 g in the observed population and produces best linear unbiased predictors equivalent to routine methods in plants and animals. [ABSTRACT FROM AUTHOR]

The past few years have seen a surge of novel applications of the Luria-Delbrück fluctuation assay protocol in bacterial research. Appropriate analysis of fluctuation assay data often requires computational methods that are unavailable in the popular web tool FALCOR. This paper introduces an R package named rSalvador to bring improvements to the field. The paper focuses on rSalvador's capabilities to alleviate three kinds of problems found in recent investigations: (i) resorting to partial plating without properly accounting for the effects of partial plating; (ii) conducting attendant fitness assays without incorporating mutants' relative fitness in subsequent data analysis; and (iii) comparing mutation rates using methods that are in general inapplicable to fluctuation assay data. In addition, the paper touches on rSalvador's capabilities to estimate sample size and the difficulties related to parameter nonidentifiability. [ABSTRACT FROM AUTHOR]

Deep sequencing-based bulked segregant analysis (BSA-seq) has become a popular approach for quantitative trait loci (QTL) mapping in recent years. Effective statistical methods for BSA-seq have been developed, but how to design a suitable experiment for BSA-seq remains unclear. In this paper, we show in theory how the major experimental factors (including population size, pool proportion, pool balance, and generation) and the intrinsic factors of a QTL (including heritability and degree of dominance) affect the power of QTL detection and the precision of QTL mapping in BSA-seq. Increasing population size can improve the power and precision, depending on the QTL heritability. The best proportion of each pool in the population is around 0.25. So, 0.25 is generally applicable in BSA-seq. Small pool proportion can greatly reduce the power and precision. Imbalance of pool pair in size also causes decrease of the power and precision. Additive effect is more important than dominance effect for QTL mapping. Increasing the generation of filial population produced by selfing can significantly increase the power and precision, especially from F2 to F3. These findings enable researchers to optimize the experimental design for BSAseq. A web-based program named BSA-seq Design Tool is available at http://124.71.74.135/BSA-seqDesignTool/and https://github.com/huanglikun/BSA-seqDesignTool. [ABSTRACT FROM AUTHOR]

Populations are shaped by their history. It is crucial to interpret population structure in an evolutionary context. Pairwise FST measures population structure, whereas population-specific FST measures deviation from the ancestral population. To understand the current population structure and a population’s history of range expansion, we propose a representation method that overlays population-specific FST estimates on a sampling location map, and on an unrooted neighbor-joining tree and a multi-dimensional scaling plot inferred from a pairwise FST distance matrix. We examined the usefulness of our procedure using simulations that mimicked population colonization from an ancestral population and by analyzing published human, Atlantic cod, and wild poplar data. Our results demonstrated that population-specific FST values identify the source population and trace the evolutionary history of its derived populations. Conversely, pairwise FST values represent the current population structure. By integrating the results of both estimators, we obtained a new picture of the population structure that incorporates evolutionary history. The generalized least squares estimate of genome-wide population-specific FST indicated that the wild poplar population expanded its distribution to the north, where daylight hours are long in summer, to coastal areas with abundant rainfall, and to the south where summers are dry. Genomic data highlight the power of the bias-corrected moment estimators of FST, whether global, pairwise, or population-specific, that provide unbiased estimates of FST. All FST moment estimators described in this paper have reasonable processing times and are useful in population genomics studies. [ABSTRACT FROM AUTHOR]

Multi-parent Advanced Generation Intercross (MAGIC) populations are fast becoming mainstream tools for research and breeding, along with the technology and tools for analysis. This paper demonstrates the analysis of a rice MAGIC population from data filtering to imputation and processing of genetic data to characterizing genomic structure, and finally quantitative trait loci (QTL) mapping. In this study, 1316 S6:8 indica MAGIC (MI) lines and the eight founders were sequenced using Genotyping by Sequencing (GBS). As the GBS approach often includes missing data, the first step was to impute the missing SNPs. The observable number of recombinations in the population was then explored. Based on this case study, a general outline of procedures for a MAGIC analysis workflow is provided, as well as for QTL mapping of agronomic traits and biotic and abiotic stress, using the results from both association and interval mapping approaches. QTL for agronomic traits (yield, flowering time and plant height), physical (grain length and grain width) and cooking properties (amylose content) of the rice grain, abiotic stress (submergence tolerance) and biotic stress (brown spot disease) were mapped. Through presenting this extensive analysis in the MI population in rice, we highlight important considerations when choosing analytical approaches. The methods and results reported in this paper will provide a guide to future genetic analysis methods applied to multi-parent populations. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) is revolutionizing plant breeding since the selection process is done with the help of statistical machine learning methods. A model is trained with a reference population and then it is used for predicting the candidate individuals available in the testing set. However, given that breeding phenotypic values are very noisy, new models must be able to integrate not only genotypic and environmental data but also high-resolution images that have been collected by breeders with advanced image technology. For this reason, this paper explores the use of generalized Poisson regression (GPR) for genome-enabled prediction of count phenotypes using genomic and hyperspectral images. The GPR model allows integrating input information of many sources like environments, genomic data, high resolution data, and interaction terms between these three sources. We found that the best prediction performance was obtained when the three sources of information were taken into account in the predictor, and those measures of high-resolution images close to the harvest day provided the best prediction performance [ABSTRACT FROM AUTHOR]

Ascochyta lentis causes ascochyta blight in lentil (Lens culinaris Medik.) and yield loss can be as high as 50%. With careful agronomic management practices, fungicide use, and advances in breeding resistant lentil varieties, disease severity and impact to farmers have been largely controlled. However, evidence from major lentil producing countries, Canada and Australia, suggests that A. lentis isolates can change their virulence profile and level of aggressiveness over time and under different selection pressures. In this paper, we describe the first genome assembly for A. lentis for the Australian isolate Al4, through the integration of data from Illumina and PacBio SMRT sequencing. The Al4 reference genome assembly is almost 42 Mb in size and encodes 11,638 predicted genes. The Al4 genome comprises 21 full-length and gapless chromosomal contigs and two partial chromosome contigs each with one telomere. We predicted 31 secondary metabolite clusters, and 38 putative protein effectors, many of which were classified as having an unknown function. Comparison of A. lentis genome features with the recently published reference assembly for closely related A. rabiei show that genome synteny between these species is highly conserved. However, there are several translocations and inversions of genome sequence. The location of secondary metabolite clusters near transposable element and repeat-rich genomic regions was common for A. lentis as has been reported for other fungal plant pathogens [ABSTRACT FROM AUTHOR]

In genomic selection choosing the statistical machine learning model is of paramount importance. In this paper, we present an application of a zero altered random forest model with two versions (ZAP_RF and ZAPC_RF) to deal with excess zeros in count response variables. The proposed model was compared with the conventional random forest (RF) model and with the conventional Generalized Poisson Ridge regression (GPR) using two real datasets, and we found that, in terms of prediction performance, the proposed zero inflated random forest model outperformed the conventional RF and GPR models [ABSTRACT FROM AUTHOR]

Following the discovery of western corn rootworm (WCR; Diabrotica virgifera virgifera) populations resistant to the Bacillus thuringiensis (Bt) protein Cry3Bb1, resistance was genetically mapped to a single locus on WCR chromosome 8 and linked SNP markers were shown to correlate with the frequency of resistance among field-collected populations from the US Corn Belt. The purpose of this paper is to further investigate the relationship between one of these resistance-linked markers and the causal resistance locus. Using data from laboratory bioassays and field experiments, we show that one allele of the resistance-linked marker increased in frequency in response to selection, but was not perfectly linked to the causal resistance allele. By coupling the response to selection data with a genetic model of the linkage between the marker and the causal allele, we developed a model that allowed marker allele frequencies to be mapped to causal allele frequencies. We then used this model to estimate the resistance allele frequency distribution in the US Corn Belt based on collections from 40 populations. These estimates suggest that chromosome 8 Cry3Bb1 resistance allele frequency was generally low (<10%) for 65% of the landscape, though an estimated 13% of landscape has relatively high (>25%) resistance allele frequency. [ABSTRACT FROM AUTHOR]

The paradigm called genomic selection (GS) is a revolutionary way of developing new plants and animals. This is a predictive methodology, since it uses learning methods to perform its task. Unfortunately, there is no universal model that can be used for all types of predictions; for this reason, specific methodologies are required for each type of output (response variables). Since there is a lack of efficient methodologies for multivariate count data outcomes, in this paper, a multivariate Poisson deep neural network (MPDN) model is proposed for the genomic prediction of various count outcomes simultaneously. The MPDN model uses the minus log-likelihood of a Poisson distribution as a loss function, in hidden layers for capturing nonlinear patterns using the rectified linear unit (RELU) activation function and, in the output layer, the exponential activation function was used for producing outputs on the same scale of counts. The proposed MPDN model was compared to conventional generalized Poisson regression models and univariate Poisson deep learning models in two experimental data sets of count data. We found that the proposed MPDL outperformed univariate Poisson deep neural network models, but did not outperform, in terms of prediction, the univariate generalized Poisson regression models. All deep learning models were implemented in Tensorflow as back-end and Keras as front-end, which allows implementing these models on moderate and large data sets, which is a significant advantage over previous GS models for multivariate count data. [ABSTRACT FROM AUTHOR]

Admixture is increasingly being recognized as an important factor in evolutionary genetics. The distribution of genomic admixture tracts, and the resulting effects on admixture linkage disequilibrium, can be used to date the timing of admixture between species or populations. However, the theory used for such prediction assumes selective neutrality despite the fact that many famous examples of admixture involve natural selection acting for or against admixture. In this paper, we investigate the effects of positive selection on the distribution of tract lengths. We develop a theoretical framework that relies on approximating the trajectory of the selected allele using a logistic function. By numerically calculating the expected allele trajectory, we also show that the approach can be extended to cases where the logistic approximation is poor due to the effects of genetic drift. Using simulations, we show that the model is highly accurate under most scenarios. We use the model to show that positive selection on average will tend to increase the admixture tract length. However, perhaps counter-intuitively, conditional on the allele frequency at the time of sampling, positive selection will actually produce shorter expected tract lengths. We discuss the consequences of our results in interpreting the timing of the introgression of EPAS1 from Denisovans into the ancestors of Tibetans. [ABSTRACT FROM AUTHOR]

Currently available genome assemblies for Atlantic cod (Gadus morhua) have been constructed from fish belonging to the Northeast Arctic Cod (NEAC) population; a migratory population feeding in the Barents Sea. These assemblies have been crucial for the development of genetic markers which have been used to study population differentiation and adaptive evolution in Atlantic cod, pinpointing four discrete islands of genomic divergence located on linkage groups 1, 2, 7 and 12. In this paper, we present a high-quality reference genome from a male Atlantic cod representing a southern population inhabiting the Celtic sea. The genome assembly (gadMor_Celtic) was produced from long-read nanopore data and has a combined contig length of 686 Mb with an N50 of 10 Mb. Integrating contigs with genetic linkage mapping information enabled us to construct 23 chromosome sequences which mapped with high confidence to the latest NEAC population assembly (gadMor3) and allowed us to characterize, to an extent not previously reported large chromosomal inversions on linkage groups 1, 2, 7 and 12. In most cases, inversion breakpoints could be located within single nanopore contigs. Our results suggest the presence of inversions in Celtic cod on linkage groups 6, 11 and 21, although these remain to be confirmed. Further, we identified a specific repetitive element that is relatively enriched at predicted centromeric regions. Our gadMor_Celtic assembly provides a resource representing a ‘southern’ cod population which is complementary to the existing ‘northern’ population based genome assemblies and represents the first step towards developing pan-genomic resources for Atlantic cod. [ABSTRACT FROM AUTHOR]

Genomic selection has been successfully implemented in plant and animal breeding. The transition of parental selection based on phenotypic characteristics to genomic selection (GS) has reduced breeding time and cost while accelerating the rate of genetic progression. Although breeding methods have been adapted to include genomic selection, parental selection often involves truncation selection, selecting the individuals with the highest genomic estimated breeding values (GEBVs) in the hope that favorable properties will be passed to their offspring. This ensures genetic progression and delivers offspring with high genetic values. However, several favorable quantitative trait loci (QTL) alleles risk being eliminated from the breeding population during breeding. We show that this could reduce the mean genetic value that the breeding population could reach in the long term with up to 40%. In this paper, by means of a simulation study, we propose a new method for parental mating that is able to preserve the genetic variation in the breeding population, preventing premature convergence of the genetic values to a local optimum, thus maximizing the genetic values in the long term. We do not only prevent the fixation of several unfavorable QTL alleles, but also demonstrate that the genetic values can be increased by up to 15 percentage points compared with truncation selection. [ABSTRACT FROM AUTHOR]

The fungal species Candida albicans is both a member of the human microbiome and a fungal pathogen. C. albicans undergoes several different morphological transitions, including one called white-opaque switching. Here, cells reversibly switch between two states, "white" and "opaque," and each state is heritable through many cell generations. Each cell type has a distinct cellular and colony morphology and they differ in many other properties including mating, nutritional specialization, and interactions with the innate immune system. Previous genetic screens to gain insight into white-opaque switching have focused on certain classes of genes (for example transcriptional regulators or chromatin modifying enzymes). In this paper, we examined 172 deletion mutants covering a broad range of cell functions. We identified 28 deletion mutants with at least a fivefold effect on switching frequencies; these cover a wide variety of functions ranging from membrane sensors to kinases to proteins of unknown function. In agreement with previous reports, we found that components of the pheromone signaling cascade affect white-to-opaque switching; however, our results suggest that the major effect of Cek1 on white-opaque switching occurs through the cell wall damage response pathway. Most of the genes we identified have not been previously implicated in white-opaque switching and serve as entry points to understand new aspects of this morphological transition. [ABSTRACT FROM AUTHOR]

The heterogeneous stock (HS) is an outbred rat population derived from eight inbred rat strains. HS rats are ideally suited for genome wide association studies; however, only a few genotyping microarrays have ever been designed for rats and none of them are currently in production. To address the need for an efficient and cost effective method of genotyping HS rats, we have adapted genotype-by-sequencing (GBS) to obtain genotype information at large numbers of single nucleotide polymorphisms (SNPs). In this paper, we have outlined the laboratory and computational steps we took to optimize double digest genotype-bysequencing (ddGBS) for use in rats. We evaluated multiple existing computational tools and explain the workflow we have used to call and impute over 3.7 million SNPs. We have also compared various rat genetic maps, which are necessary for imputation, including a recently developed map specific to the HS. Using our approach, we obtained concordance rates of 99% with data obtained using data from a genotyping array. The principles and computational pipeline that we describe could easily be adapted for use in other species for which reliable reference genome sets are available. [ABSTRACT FROM AUTHOR]

Large groups of species with well-defined phylogenies are excellent systems for testing evolutionary hypotheses. In this paper, we describe the creation of a comparative genomic resource consisting of 23 genomes from the species-rich Drosophila montium species group, 22 of which are presented here for the first time. The montium group is well-positioned for clade genomics. Within the montium clade, evolutionary distances are such that large numbers of sequences can be accurately aligned while also recovering strong signals of divergence; and the distance between the montium group and D. melanogaster is short enough so that orthologous sequence can be readily identified. All genomes were assembled from a single, small-insert library using MaSuRCA, before going through an extensive post-assembly pipeline. Estimated genome sizes within the montium group range from 155 Mb to 223 Mb (mean = 196 Mb). The absence of long-distance information during the assembly process resulted in fragmented assemblies, with the scaffold NG50s varying widely based on repeat content and sample heterozygosity (min = 18 kb, max = 390 kb, mean = 74 kb). The total scaffold length for most assemblies is also shorter than the estimated genome size, typically by 5-15%. However, subsequent analysis showed that our assemblies are highly complete. Despite large differences in contiguity, all assemblies contain at least 96% of known single-copy Dipteran genes (BUSCOs, n = 2,799). Similarly, by aligning our assemblies to the D. melanogaster genome and remapping coordinates for a large set of transcriptional enhancers (n = 3,457), we showed that each montium assembly contains orthologs for at least 91% of D. melanogaster enhancers. Importantly, the genic and enhancer contents of our assemblies are comparable to that of far more contiguous Drosophila assemblies. The alignment of our own D. serrata assembly to a previously published PacBio D. serrata assembly also showed that our longest scaffolds (up to 1 Mb) are free of large-scale misassemblies. Our genome assemblies are a valuable resource that can be used to further resolve the montium group phylogeny; study the evolution of protein-coding genes and cis-regulatory sequences; and determine the genetic basis of ecological and behavioral adaptations. [ABSTRACT FROM AUTHOR]

The evolution of drug resistance in pathogens such as HIV is an important and widely known example in the field of evolutionary medicine. Here, we focus on a unique data set from the late 1990s with multiple viral sequences from multiple time points in 118 patients. We study patterns of evolutionary dynamics in the viral populations in these patients who were treated with Reverse Transcriptase Inhibitors and Protease Inhibitors in the late 1990s. Specifically, we aim to visualize and analyze examples of population genetic processes such as selective sweeps and clonal interference. The figures and descriptions in this paper can be used in evolution and population genetics classes. We show and analyze a wide variety of patterns, specifically: soft sweeps, hard sweeps, softening sweeps and hardening sweeps, simultaneous sweeps, accumulation of mutations and clonal interference. [ABSTRACT FROM AUTHOR]

In this paper we propose a Bayesian multi-output regressor stacking (BMORS) model that is a generalization of the multi-trait regressor stacking method. The proposed BMORS model consists of two stages: in the first stage, a univariate genomic best linear unbiased prediction (GBLUP including genotype · environment interaction GE) model is implemented for each of the L traits under study; then the predictions of all traits are included as covariates in the second stage, by implementing a Ridge regression model. The main objectives of this research were to study alternative models to the existing multi-trait multienvironment (BMTME) model with respect to (1) genomic-enabled prediction accuracy, and (2) potential advantages in terms of computing resources and implementation. We compared the predictions of the BMORS model to those of the univariate GBLUP model using 7 maize and wheat datasets. We found that the proposed BMORS produced similar predictions to the univariate GBLUP model and to the BMTME model in terms of prediction accuracy; however, the best predictions were obtained under the BMTME model. In terms of computing resources, we found that the BMORS is at least 9 times faster than the BMTME method. Based on our empirical findings, the proposed BMORS model is an alternative for predicting multi-trait and multi-environment data, which are very common in genomic-enabled prediction in plant and animal breeding programs. [ABSTRACT FROM AUTHOR]

Modern SNP genotyping technologies allow measurement of the relative abundance of different alleles for a given locus and consequently estimation of their allele dosage, opening a new road for genetic studies in autopolyploids. Despite advances in genetic linkage analysis in autotetraploids, there is a lack of statistical models to perform linkage analysis in organisms with higher ploidy levels. In this paper, we present a statistical method to estimate recombination fractions and infer linkage phases in full-sib populations of autopolyploid species with even ploidy levels for a set of SNP markers using hidden Markov models. Our method uses efficient two-point procedures to reduce the search space for the best linkage phase configuration and reestimate the final parameters by maximizing the likelihood of the Markov chain. To evaluate the method, and demonstrate its properties, we rely on simulations of autotetraploid, autohexaploid and autooctaploid populations and on a real tetraploid potato data set. The results show the reliability of our approach, including situations with complex linkage phase scenarios in hexaploid and octaploid populations. [ABSTRACT FROM AUTHOR]

The DNA is the fundamental basis of genetic information, just as bits are for computers. Whenever computers are used to represent genetic data, the computational encoding must be efficient to allow the representation of processes driving the inheritance and variability. This is especially important across simulations in view of the increasing complexity and dimensions brought by genomics. This paper introduces a new binary representation of genetic information. Algorithms as bitwise operations that mimic the inheritance of a wide range of polymorphisms are also presented. Different kinds and mixtures of polymorphisms are discussed and exemplified. Proposed algorithms and data structures were implemented in C++ programming language and is available to end users in the R package "isqg" which is available at the R repository (CRAN). Supplementary data are available online. [ABSTRACT FROM AUTHOR]

Multiple-trait experiments with mixed phenotypes (binary, ordinal and continuous) are not rare in animal and plant breeding programs. However, there is a lack of statistical models that can exploit the correlation between traits with mixed phenotypes in order to improve prediction accuracy in the context of genomic selection (GS). For this reason, when breeders have mixed phenotypes, they usually analyze them using univariate models, and thus are not able to exploit the correlation between traits, which many times helps improve prediction accuracy. In this paper we propose applying deep learning for analyzing multiple traits with mixed phenotype data in terms of prediction accuracy. The prediction performance of multipletrait deep learning with mixed phenotypes (MTDLMP) models was compared to the performance of univariate deep learning (UDL) models. Both models were evaluated using predictors with and without the genotype · environment (G·E) interaction term (I and WI, respectively). The metric used for evaluating prediction accuracy was Pearson's correlation for continuous traits and the percentage of cases correctly classified (PCCC) for binary and ordinal traits. We found that a modest gain in prediction accuracy was obtained only in the continuous trait under the MTDLMP model compared to the UDL model, whereas for the other traits (1 binary and 2 ordinal) we did not find any difference between the two models. In both models we observed that the prediction performance was better for WI than for I. The MTDLMP model is a good alternative for performing simultaneous predictions of mixed phenotypes (binary, ordinal and continuous) in the context of GS. [ABSTRACT FROM AUTHOR]

RNA-seq has proven to be a powerful tool to unravel various aspects of the transcriptome, especially the quantification of alternative splicing (AS) that leads to isoform diversity. The honey bee (Apis mellifera) is an important model organism for studying the molecular underpinnings of behavioral plasticity and social behavior, and recent RNA-seq studies of honey bees have revealed AS patterns and their regulation by DNA methylation. However, tissue-specific AS patterns have not been fully explored. In this paper, we characterized AS patterns in two different honey bee tissue types, and also explored their conservation and regulation. We used the RNA-seq data from brain and fat body to improve the existing models of honey bee genes and identified tissue-specific AS patterns. We found that AS genes show high conservation between honey bee and Drosophila melanogaster. We also confirmed and extended previous findings of a correlation between gene body DNA methylation and AS patterns, providing further support for the role of DNA methylation in regulating AS. In addition, our analysis suggests distinct functional roles for tissue-specific alternatively spliced genes. Taken together, our work provides new insights into the conservation and dynamics of AS patterns across different tissue types. [ABSTRACT FROM AUTHOR]

Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical models for ordinal phenotypes to improve the accuracy of the selection of candidate genotypes. For this reason, in this paper we explore the genomic based prediction performance of two popular machine learning methods: the Multi Layer Perceptron (MLP) and support vector machine (SVM) methods vs. the Bayesian threshold genomic best linear unbiased prediction (TGBLUP) model. We used the percentage of cases correctly classified (PCCC) as a metric to measure the prediction performance, and seven real data sets to evaluate the prediction accuracy, and found that the best predictions (in four out of the seven data sets) in terms of PCCC occurred under the TGLBUP model, while the worst occurred under the SVM method. Also, in general we found no statistical differences between using 1, 2 and 3 layers under the MLP models, which means that many times the conventional neuronal network model with only one layer is enough. However, although even that the TGBLUP model was better, we found that the predictions of MLP and SVM were very competitive with the advantage that the SVM was the most efficient in terms of the computational time required. [ABSTRACT FROM AUTHOR]

Genomic Selection (GS) is the procedure whereby molecular information is used to predict complex phenotypes and it is standard in many animal and plant breeding schemes. However, only a small number of studies have been reported in horticultural crops, and in polyploid species in particular. In this paper, we have developed a versatile forward simulation tool, called polyploid Sequence Based Virtual Breeding (pSBVB), to evaluate GS strategies in polyploids; pSBVB is an efficient gene dropping software that can simulate any number of complex phenotypes, allowing a very flexible modeling of phenotypes suited to polyploids. As input, it takes genotype data from the founder population, which can vary from single nucleotide polymorphisms (SNP) chips up to sequence, a list of causal variants for every trait and their heritabilities, and the pedigree. Recombination rates between homeologous chromosomes can be specified, so that both allo- and autopolyploid species can be considered. The program outputs phenotype and genotype data for all individuals in the pedigree. Optionally, it can produce several genomic relationship matrices that consider exact or approximate genotype values. pSBVB can therefore be used to evaluate GS strategies in polyploid species (say varying SNP density, genetic architecture or population size, among other factors), or to optimize experimental designs for association studies. We illustrate pSBVB with SNP data from tetraploid potato and partial sequence data from octoploid strawberry, and we show that GS is a promising breeding strategy for polyploid species but that the actual advantage critically depends on the underlying genetic architecture. Source code, examples and a complete manual are freely available in GitHub https://github.com/lauzingaretti/pSBVB. [ABSTRACT FROM AUTHOR]

Medaka is a model organism in medicine, genetics, developmental biology and population genetics. Lab stocks composed of more than 100 local wild populations are available for research in these fields. Thus, medaka represents a potentially excellent bioresource for screening disease-risk- and adaptation-related genes in genome-wide association studies. Although the genetic population structure should be known before performing such an analysis, a comprehensive study on the genome-wide diversity of wild medaka populations has not been performed. Here, we performed genotyping-by-sequencing (GBS) for 81 and 12 medakas captured from a bioresource and the wild, respectively. Based on the GBS data, we evaluated the genetic population structure and estimated the demographic parameters using an approximate Bayesian computation (ABC) framework. The genome-wide data confirmed that there were substantial differences between local populations and supported our previously proposed hypothesis on medaka dispersal based on mitochondrial genome (mtDNA) data. A new finding was that a local group that was thought to be a hybrid between the northern and the southern Japanese groups was actually an origin of the northern Japanese group. Thus, this paper presents the first population-genomic study of medaka and reveals its population structure and history based on chromosomal genetic diversity. [ABSTRACT FROM AUTHOR]

Multi-trait and multi-environment data are common in animal and plant breeding programs. However, what is lacking are more powerful statistical models that can exploit the correlation between traits to improve prediction accuracy in the context of genomic selection (GS). Multi-trait models are more complex than univariate models and usually require more computational resources, but they are preferred because they can exploit the correlation between traits, which many times helps improve prediction accuracy. For this reason, in this paper we explore the power of multi-trait deep learning (MTDL) models in terms of prediction accuracy. The prediction performance ofMTDL models was compared to the performance of the Bayesian multi-trait and multienvironment (BMTME) model proposed by Montesinos-López et al. (2016), which is a multi-trait version of the genomic best linear unbiased prediction (GBLUP) univariate model. Both models were evaluated with predictors with and without the genotype×environment interaction term. The prediction performance of both models was evaluated in terms of Pearson's correlation using cross-validation. We found that the best predictions in two of the three data sets were found under the BMTME model, but in general the predictions of both models, BTMTE and MTDL, were similar. Among models without the genotype×environment interaction, the MTDL model was the best, while among models with genotype×environment interaction, the BMTME model was superior. These results indicate that the MTDL model is very competitive for performing predictions in the context of GS, with the important practical advantage that it requires less computational resources than the BMTME model. [ABSTRACT FROM AUTHOR]

Preeclampsia, a serious multisystem disorder specific to human pregnancy, remains a considerable burden of disease worldwide. Reduced nitric oxide bioavailability is proved to be crucial in the maternal and fetal pathophysiology of preeclampsia. G-protein-coupled Receptor Kinase Interactor-1 (GIT1) is a novel endothelial nitric oxide synthases (eNOS) interactor mediator. The aim of this paper is to investigate the effect of GIT1 on preeclampsia. Blood pressure (BP) was measured using a carotid cathetercalibrated eight-chamber tail-cuff system (CODA) at the same time daily. Urinary albumin excretion (UAE) was determined using Albuwell-M kits (Exocell Inc) and creatinine clearance (CCr) was determined by measuring urinary creatinine concentration with tandem liquid chromatography-mass spectrometry. The release of nitrite was analyzed to detect nitric oxide (NO) production using a Sievers Chemiluminescence NO Analyzer. NOS activity was examined by measuring the conversion of 3H-labeled L-arginine to 3Hlabeled L-citrulline. BP was significantly increased in GIT-/- mice with or without sFIT-1 treatment. In addition, GIT-/- mice possessed higher UAE and lower CCr. Depletion of GIT1 impedes the NO production and placenta eNOS activity. Additional GIT1 attenuates sFlt-1-induced preeclampsia phenotypes. Our findings suggest that GIT1 significantly extenuates the sFlt-1-induced preeclampsia phenotypes by inhibiting eNOS activity, indicating a crucial role of GIT1 in the progression of preeclampsia. [ABSTRACT FROM AUTHOR]

Plant and animal breeders are interested in selecting the best individuals from a candidate set for the next breeding cycle. In this paper, we propose a formal method under the Bayesian decision theory framework to tackle the selection problem based on genomic selection (GS) in single- and multi-trait settings. We proposed and tested three univariate loss functions (Kullback-Leibler, KL; Continuous Ranked Probability Score, CRPS; Linear-Linear loss, LinLin) and their corresponding multivariate generalizations (Kullback-Leibler, KL; Energy Score, EnergyS; and the Multivariate Asymmetric Loss Function, MALF). We derived and expressed all the loss functions in terms of heritability and tested them on a real wheat dataset for one cycle of selection and in a simulated selection program. The performance of each univariate loss function was compared with the standard method of selection (Std) that does not use loss functions. We compared the performance in terms of the selection response and the decrease in the population's genetic variance during recurrent breeding cycles. Results suggest that it is possible to obtain better performance in a long-term breeding program using the single-trait scheme by selecting 30% of the best individuals in each cycle but not by selecting 10% of the best individuals. For the multi-trait approach, results show that the population mean for all traits under consideration had positive gains, even though two of the traits were negatively correlated. The corresponding population variances were not statistically different from the different loss function during the 10th selection cycle. Using the loss function should be a useful criterion when selecting the candidates for selection for the next breeding cycle. [ABSTRACT FROM AUTHOR]

Rhinocladiella mackenziei accounts for the majority of fungal brain infections in the Middle East, and is restricted to the arid climate zone between Saudi Arabia and Pakistan. Neurotropic dissemination caused by this fungus has been reported in immunocompromised, but also immunocompetent individuals. If untreated, the infection is fatal. Outside of humans, the environmental niche of R. mackenziei is unknown, and the fungus has been only cultured from brain biopsies. In this paper, we describe the whole-genome resequencing of two R. mackenziei strains from patients in Saudi Arabia and Qatar. We assessed intraspecies variation and genetic signatures to uncover the genomic basis of the pathogenesis, and potential niche adaptations. We found that the duplicated genes (paralogs) are more susceptible to accumulating significant mutations. Comparative genomics with other filamentous ascomycetes revealed a diverse arsenal of genes likely engaged in pathogenicity, such as the degradation of aromatic compounds and iron acquisition. In addition, intracellular accumulation of trehalose and choline suggests possible adaptations to the conditions of an arid climate region. Specifically, protein family contractions were found, including short-chain dehydrogenase/reductase SDR, the cytochrome P450 (CYP) (E-class), and the G-protein b WD-40 repeat. Gene composition and metabolic potential indicate extremotolerance and hydrocarbon assimilation, suggesting a possible environmental habitat of oil-polluted desert soil. [ABSTRACT FROM AUTHOR]

Multiparent populations (MPP) have become popular resources for complex trait mapping because of their wider allelic diversity and larger population size compared with traditional two-way recombinant inbred (RI) strains. In mice, the collaborative cross (CC) is one of the most popular MPP and is derived from eight genetically diverse inbred founder strains. The strategy of generating RI intercrosses (RIX) from MPP in general and from the CC in particular can produce a large number of completely reproducible heterozygote genomes that better represent the (outbred) human population. Since both maternal and paternal haplotypes of each RIX are readily available, RIX is a powerful resource for studying both standing genetic and epigenetic variations of complex traits, in particular, the parent-of-origin (PoO) effects, which are important contributors to many complex traits. Furthermore, most complex traits are affected by .1 genes, where multiple quantitative trait locus mapping could be more advantageous. In this paper, for MPP-RIX data but taking CC-RIX as a working example, we propose a general Bayesian variable selection procedure to simultaneously search for multiple genes with founder allelic effects and PoO effects. The proposed model respects the complex relationship among RIX samples, and the performance of the proposed method is examined by extensive simulations. [ABSTRACT FROM AUTHOR]

KRAB C2H2 zinc finger proteins (KZNFs) are the largest and most diverse family of human transcription factors, likely due to diversifying selection driven by novel endogenous retroelements (EREs), but the vast majority lack binding motifs or functional data. Two recent studies analyzed a majority of the human KZNFs using either ChIP-seq (60 proteins) or ChIP-exo (221 proteins) in the same cell type (HEK293). The ChIP-exo paper did not describe binding motifs, however. Thirty-nine proteins are represented in both studies, enabling the systematic comparison of the data sets presented here. Typically, only a minority of peaks overlap, but the two studies nonetheless display significant similarity in ERE binding for 32/39, and yield highly similar DNA binding motifs for 23 and related motifs for 34 (MoSBAT similarity score .0.5 and .0.2, respectively). Thus, there is overall (albeit imperfect) agreement between the two studies. For the 242 proteins represented in at least one study, we selected a highest-confidence motif for each protein, utilizing several motif-derivation approaches, and evaluating motifs within and across data sets. Peaks for the majority (158) are enriched (96% with AUC .0.6 predicting peak vs. nonpeak) for a motif that is supported by the C2H2 "recognition code," consistent with intrinsic sequence specificity driving DNA binding in cells. An additional 63 yield motifs enriched in peaks, but not supported by the recognition code, which could reflect indirect binding. Altogether, these analyses validate both data sets, and provide a reference motif set with associated quality metrics. [ABSTRACT FROM AUTHOR]

Autosomal drivers violate Mendel's law of segregation in that they are overrepresented in gametes of heterozygous parents. For drivers to be polymorphic within populations rather than fixing, their transmission advantage must be offset by deleterious effects on other fitness components. In this paper, we develop an analytical model for the evolution of autosomal drivers that is motivated by the neocentromere drive system found in maize. In particular, we model both the transmission advantage and deleterious fitness effects on seed viability, pollen viability, seed to adult survival mediated by maternal genotype, and seed to adult survival mediated by offspring genotype. We derive general, biologically intuitive conditions for the four most likely evolutionary outcomes and discuss the expected evolution of autosomal drivers given these conditions. Finally, we determine the expected equilibrium allele frequencies predicted by the model given recent estimates of fitness components for all relevant genotypes and show that the predicted equilibrium is within the range observed in maize land races for levels of drive at the low end of what has been observed. [ABSTRACT FROM AUTHOR]

Identification of quantitative trait loci (QTL) involved in the variation of hybrid value is of key importance for cross-pollinated species such as maize (Zea mays L.). In a companion paper, we illustrated a new QTL mapping population design involving a factorial mating between two multiparental segregating populations. Six biparental line populations were developed from four founder lines in the Dent and Flint heterotic groups. They were crossed to produce 951 hybrids and evaluated for silage performances. Previously, a linkage analysis (LA) model that assumes each founder line carries a different allele was used to detect QTL involved in General and Specific Combining Abilities (GCA and SCA, respectively) of hybrid value. This previously introduced model requires the estimation of numerous effects per locus, potentially affecting QTL detection power. Using the same design, we compared this "Founder alleles" model to two more parsimonious models, which assume that (i) identity in state at SNP alleles from the same heterotic group implies identity by descent (IBD) at linked QTL ("SNP within-group" model) or (ii) identity in state implies IBD, regardless of population origin of the alleles ("Hybrid genotype" model). This last model assumes biallelic QTL with equal effects in each group. It detected more QTL on average than the two other models but explained lower percentages of variance. The "SNP within-group" model appeared to be a good compromise between the two other models. These results confirm the divergence between the Dent and Flint groups. They also illustrate the need to adapt the QTL detection model to the complexity of the allelic variation, which depends on the trait, the QTL, and the divergence between the heterotic groups. [ABSTRACT FROM AUTHOR]

There are Bayesian and non-Bayesian genomic models that take into account GxE interactions. However, the computational cost of implementing Bayesian models is high and becomes almost impossible when the number of genotypes, environments, and traits is very large, while, in non-Bayesian models, there are often important and unsolved convergence problems. The variational Bayes method is popular in machine learning and by approximating the probability distributions through optimization, it tends to be faster than Markov Chain Monte Carlo methods. For this reason, in this paper, we propose a new genomic variational Bayes version of the Bayesian genomic model with GxE using half-t priors on each standard deviation (SD) term to guarantee highly noninformative and posterior inferences that are not sensitive to the choice of hyper-parameters. We show the complete theoretical derivation of the full conditional and the variational posterior distributions and their implementations. We used eight experimental genomic maize and wheat data sets to illustrate the new proposed variational Bayes approximation and compared its predictions and implementation time with a standard Bayesian genomic model with GxE. Results indicated that prediction accuracies are slightly higher in the standard Bayesian model with GxE than in its variational counterpart, but, in terms of computation time, the variational Bayes genomic model with GxE is, in general, 10 times faster than the conventional Bayesian genomic model with GxE. For this reason, the proposed model may be a useful tool for researchers who need to predict and select genotypes in several environments. [ABSTRACT FROM AUTHOR]

We investigate the utility and scalability of new read cloud technologies to improve the draft genome assemblies of the colossal, and largely repetitive, genomes of conifers. Synthetic long read technologies have existed in various forms as a means of reducing complexity and resolving repeats since the outset of genome assembly. Recently, technologies that combine subhaploid pools of high molecular weight DNA with barcoding on a massive scale have brought new efficiencies to sample preparation and data generation. When combined with inexpensive light shotgun sequencing, the resulting data can be used to scaffold large genomes. The protocol is efficient enough to consider routinely for even the largest genomes. Conifers represent the largest reference genome projects executed to date. The largest of these is that of the conifer Pinus lambertiana (sugar pine), with a genome size of 31 billion bp. In this paper, we report on the molecular and computational protocols for scaffolding the P. lambertiana genome using the library technology from 10· Genomics. At 247,000 bp, the NG50 of the existing reference sequence is the highest scaffold contiguity among the currently published conifer assemblies; this new assembly's NG50 is 1.94 million bp, an eightfold increase. [ABSTRACT FROM AUTHOR]

Meta-analysis has become a popular tool for genetic association studies to combine different genetic studies. A key challenge in meta-analysis is heterogeneity, or the differences in effect sizes between studies. Heterogeneity complicates the interpretation of meta-analyses. In this paper, we describe ForestPMPlot, a flexible visualization tool for analyzing studies included in a meta-analysis. The main feature of the tool is visualizing the differences in the effect sizes of the studies to understand why the studies exhibit heterogeneity for a particular phenotype and locus pair under different conditions. We show the application of this tool to interpret a meta-analysis of 17 mouse studies, and to interpret a multi-tissue eQTL study. [ABSTRACT FROM AUTHOR]

Genetic recombination is a very important evolutionary mechanism that mixes parental haplotypes and produces new raw material for organismal evolution. As a result, information on recombination rates is critical for biological research. In this paper, we introduce a new extremely fast open-source software package (FastEPRR) that uses machine learning to estimate recombination rate r (=4Ner) from intraspecific DNA polymorphism data. When r.10 and the number of sampled diploid individuals is large enough ($50), the variance of rFastEPRR remains slightly smaller than that of rLDhat The new estimate rcomb (calculated by averaging rFastEPRR and rLDhat) has the smallest variance of all cases. When estimating rFastEPRR, the finite-site model was employed to analyze cases with a high rate of recurrent mutations, and an additional method is proposed to consider the effect of variable recombination rates within windows. Simulations encompassing a wide range of parameters demonstrate that different evolutionary factors, such as demography and selection, may not increase the false positive rate of recombination hotspots. Overall, accuracy of FastEPRR is similar to the well-known method, LDhat, but requires far less computation time. Genetic maps for each human population (YRI, CEU, and CHB) extracted from the 1000 Genomes OMNI data set were obtained in less than 3 d using just a single CPU core. The Pearson Pairwise correlation coefficient between the rFastEPRR and rLDhat maps is very high, ranging between 0.929 and 0.987 at a 5-Mb scale. Considering that sample sizes for these kinds of data are increasing dramatically with advances in next-generation sequencing technologies, FastEPRR (freely available at http://www.picb.ac.cn/ evolgen/) is expected to become a widely used tool for establishing genetic maps and studying recombination hotspots in the population genomic era. [ABSTRACT FROM AUTHOR]

Drosophila willistoni is a geographically widespread Neotropical species. The genome of strain Gd-H4-1 from Guadeloupe Island (Caribbean) was sequenced in 2007 as part of the 12 Drosophila Genomes Project. The assembled scaffolds were joined based on conserved linkage and assigned to polytene chromosomes based on a handful of genetic and physical markers. This paucity of markers was particularly striking in the metacentric chromosome II, comprised two similarly sized arms, IIL and IIR, traditionally considered homologous to Muller elements C and B, respectively. In this paper we present the cytological mapping of 22 new gene markers to increase the number of markers mapped by in situ hybridization and to test the assignment of scaffolds to the polytene chromosome II arms. For this purpose, we generated, by polymerase chain reaction amplification, one or two gene probes from each scaffold assigned to the chromosome II arms and mapped these probes to the Gd-H4-1 strain's polytene chromosomes by nonfluorescent in situ hybridization. Our findings show that chromosome arms IIL and IIR correspond to Muller elements B and C, respectively, directly contrasting the current homology assignments in D. willistoni and constituting a major reassignment of the scaffolds to chromosome II arms. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of exact-test based statistical inference for Hardy2Weinberg equilibrium in the presence of missing genotype data. Missing genotypes often are discarded when markers are tested for Hardy2Weinberg equilibrium, which can lead to bias in the statistical inference about equilibrium. Single and multiple imputation can improve inference on equilibrium. We develop tests for equilibrium in the presence of missingness by using both inbreeding coefficients (or, equivalently, X² statistics) and exact p-values. The analysis of a set of markers with a high missing rate from the GENEVA project on prematurity shows that exact inference on equilibrium can be altered considerably when missingness is taken into account. For markers with a high missing rate (>5%), we found that both single and multiple imputation tend to diminish evidence for Hardy2Weinberg disequilibrium. Depending on the imputation method used, 6213% of the test results changed qualitatively at the 5% level. [ABSTRACT FROM AUTHOR]