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Social interactions have large effects on individual physiology and fitness. In the immediate sense, social stimuli are often highly salient and engaging. Over longer time scales, competitive interactions often lead to distinct social ranks and differences in physiology and behavior. Understanding how initial responses lead to longer-term effects of social interactions requires examining the changes in responses over time. Here we examined the effects of social interactions on transcriptomic signatures at two times, at the end of a 45-minute interaction and 4 hours later, in female Polistes fuscatus paper wasp foundresses. Female P. fuscatus have variable facial patterns that are used for visual individual recognition, so we separately examined the transcriptional dynamics in the optic lobe and the non-visual brain. Results demonstrate much stronger transcriptional responses to social interactions in the non-visual brain compared to the optic lobe. Differentially regulated genes in response to social interactions are enriched for memory-related transcripts. Comparisons between winners and losers of the encounters revealed similar overall transcriptional profiles at the end of an interaction, which significantly diverged over the course of 4 hours, with losers showing changes in expression levels of genes associated with aggression and reproduction in paper wasps. On nests, subordinate foundresses are less aggressive, do more foraging and lay fewer eggs compared to dominant foundresses and we find losers shift expression of many genes in the non-visual brain, including vitellogenin, related to aggression, worker behavior, and reproduction within hours of losing an encounter. These results highlight the early neurogenomic changes that likely contribute to behavioral and physiological effects of social status changes in a social insect.

Social interactions have large effects on individual physiology and fitness. In the immediate sense, social stimuli are often highly salient and engaging. Over longer time scales, competitive interactions often lead to distinct social ranks and differences in physiology and behavior. Understanding how initial responses lead to longer-term effects of social interactions requires examining the changes in responses over time. Here we examined the effects of social interactions on transcriptomic signatures at two times, at the end of a 45-minute interaction and 4 hours later, in female Polistes fuscatus paper wasp foundresses. Female P. fuscatus have variable facial patterns that are used for visual individual recognition, so we separately examined the transcriptional dynamics in the optic lobe and the non-visual brain. Results demonstrate much stronger transcriptional responses to social interactions in the non-visual brain compared to the optic lobe. Differentially regulated genes in response to social interactions are enriched for memory-related transcripts. Comparisons between winners and losers of the encounters revealed similar overall transcriptional profiles at the end of an interaction, which significantly diverged over the course of 4 hours, with losers showing changes in expression levels of genes associated with aggression and reproduction in paper wasps. On nests, subordinate foundresses are less aggressive, do more foraging and lay fewer eggs compared to dominant foundresses and we find losers shift expression of many genes in the non-visual brain, including vitellogenin, related to aggression, worker behavior, and reproduction within hours of losing an encounter. These results highlight the early neurogenomic changes that likely contribute to behavioral and physiological effects of social status changes in a social insect. Author summary: Aggressive interactions often create inequalities–some individuals win while others lose. Winning versus losing can lead to large physiological differences between individuals, including different neurogenomic profiles between winners and losers. How this information about contest outcome leads to distinct neurogenomic profiles is poorly understood. Here we examine gene expression in response to aggressive social encounters in paper wasps, which naturally form dominance hierarchies on their nests in the wild. Shortly following encounters winners and losers have similar expression profiles, likely because similar mechanisms are engaged by social experiences. Four hours later, we find divergent neurogenomic profiles between winners and losers, with losers showing larger shifts in expression compared to winners. Many of the most dynamically expressed genes have been previously associated with dominance and caste differences in paper wasps showing how a single interaction can engage many of the same genomic networks that are involved in mediating more dramatic differences in queen-worker behavioral differences are also involved in responses shortly following social interactions. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) have identified hundreds of SNPs responsible for variation in human quantitative traits. However, genome-wide-significant associations often fail to replicate across independent cohorts, in apparent inconsistency with their apparent strong effects in discovery cohorts. This limited success of replication raises pervasive questions about the utility of the GWAS field. We identify all 332 studies of quantitative traits from the NHGRI-EBI GWAS Database with attempted replication. We find that the majority of studies provide insufficient data to evaluate replication rates. The remaining papers replicate significantly worse than expected (p < 10−14), even when adjusting for regression-to-the-mean of effect size between discovery- and replication-cohorts termed the Winner’s Curse (p < 10−16). We show this is due in part to misreporting replication cohort-size as a maximum number, rather than per-locus one. In 39 studies accurately reporting per-locus cohort-size for attempted replication of 707 loci in samples with similar ancestry, replication rate matched expectation (predicted 458, observed 457, p = 0.94). In contrast, ancestry differences between replication and discovery (13 studies, 385 loci) cause the most highly-powered decile of loci to replicate worse than expected, due to difference in linkage disequilibrium. [ABSTRACT FROM AUTHOR]

Mendelian Randomization (MR) is a widely embraced approach to assess causality in epidemiological studies. Two-stage least squares (2SLS) method is a predominant technique in MR analysis. However, it can lead to biased estimates when instrumental variables (IVs) are weak. Moreover, the issue of the winner's curse could emerge when utilizing the same dataset for both IV selection and causal effect estimation, leading to biased estimates of causal effects and high false positives. Focusing on one-sample MR analysis, this paper introduces a novel method termed Mendelian Randomization with adaptive Sample-sPLitting with cross-fitting InstrumenTs (MR-SPLIT), designed to address bias issues due to IV selection and weak IVs, under the 2SLS IV regression framework. We show that the MR-SPLIT estimator is more efficient than its counterpart cross-fitting MR (CFMR) estimator. Additionally, we introduce a multiple sample-splitting technique to enhance the robustness of the method. We conduct extensive simulation studies to compare the performance of our method with its counterparts. The results underscored its superiority in bias reduction, effective type I error control, and increased power. We further demonstrate its utility through the application of a real-world dataset. Our study underscores the importance of addressing bias issues due to IV selection and weak IVs in one-sample MR analyses and provides a robust solution to the challenge. Author summary: Mendelian randomization (MR) is a method used in genetic epidemiology to determine whether a specific exposure has a causal effect on a health outcome. Ensuring the accuracy of this method is crucial for its reliability and for making informed decisions that can enhance public health and medical practices. Typically, researchers employ the two-stage least squares (2SLS) method which involves selecting a set of valid instrumental variables (IVs) to estimate and infer the causal effect. However, 2SLS can produce biased results when the effects of the IVs are weak, known as weak instrument bias. Additionally, the "winner's curse" problem may occur when using the same dataset for both IV selection and causal effect estimation, introducing additional bias. Here we introduce a novel approach called MR-SPLIT, which addresses these two bias issues by randomly splitting the data into two parts: one for IV selection and the other for IV construction and causal effect estimation. Through effective integration, this strategy enhances the power, reduces bias, and provides more precise estimates. Our approach is validated through extensive simulation studies, and its effectiveness is demonstrated by an application to a real-world dataset. [ABSTRACT FROM AUTHOR]

Many traits are polygenic, affected by multiple genetic variants throughout the genome. Selection acting on these traits involves co–ordinated allele–frequency changes at these underlying variants, and this process has been extensively studied in random–mating populations. Yet many species self–fertilise to some degree, which incurs changes to genetic diversity, recombination and genome segregation. These factors cumulatively influence how polygenic selection is realised in nature. Here, we use analytical modelling and stochastic simulations to investigate to what extent self–fertilisation affects polygenic adaptation to a new environment. Our analytical solutions show that while selfing can increase adaptation to an optimum, it incurs linkage disequilibrium that can slow down the initial spread of favoured mutations due to selection interference, and favours the fixation of alleles with opposing trait effects. Simulations show that while selection interference is present, high levels of selfing (at least 90%) aids adaptation to a new optimum, showing a higher long–term fitness. If mutations are pleiotropic then only a few major–effect variants fix along with many neutral hitchhikers, with a transient increase in linkage disequilibrium. These results show potential advantages to self–fertilisation when adapting to a new environment, and how the mating system affects the genetic composition of polygenic selection. Author summary: Many biological traits of scientific interest are polygenic, which are influenced by multiple genetic variants present throughout the genome. Emerging whole-genome data from several species is shedding light on how such traits respond to selection, traditionally through co-ordinated changes in variant frequencies. However, many species in nature reproduce via self-fertilisation, where hermaphrodite individuals produce both male and female gametes that can be used to propagate without mates. This reproductive mode can reduce population-level diversity and the reassorting effects of recombination, which affects how polygenic traits respond to selection. In this paper, we theoretically explore how polygenic selection is realised under self-fertilisation, following a shift in the environment. We first show analytically how the mating–system affects the dynamics of polygenic selection, showing that there are two competing effects. First, it can expose mutations to selection more quickly, strengthening adaptation to a changing environment. Conversely, it can reduce the efficacy of selection through weakening the efficacy of recombination. We then use multi–locus stochastic simulations to investigate outcomes under more realistic scenarios, and find that high selfing can lead to higher fitness in the long–term, in contrast to classic expectations. We also investigate how many traits each variant influences, a property known as pleiotropy. If pleiotropy is absent we see that under very high levels of self-fertilisation, populations fix mutations with opposite effects on a trait. If pleiotropy is present then we instead see only a few major-effect genetic variants fixing in the population, alongside many neutral mutations. These findings provide insights into how natural populations adapt to changing environments. [ABSTRACT FROM AUTHOR]

Polygenic risk score (PRS) is a quantity that aggregates the effects of variants across the genome and estimates an individual's genetic predisposition for a given trait. PRS analysis typically contains two input data sets: base data for effect size estimation and target data for individual-level prediction. Given the availability of large-scale base data, it becomes more common that the ancestral background of base and target data do not perfectly match. In this paper, we treat the GWAS summary information obtained in the base data as knowledge learned from a pre-trained model, and adopt a transfer learning framework to effectively leverage the knowledge learned from the base data that may or may not have similar ancestral background as the target samples to build prediction models for target individuals. Our proposed transfer learning framework consists of two main steps: (1) conducting false negative control (FNC) marginal screening to extract useful knowledge from the base data; and (2) performing joint model training to integrate the knowledge extracted from base data with the target training data for accurate trans-data prediction. This new approach can significantly enhance the computational and statistical efficiency of joint-model training, alleviate over-fitting, and facilitate more accurate trans-data prediction when heterogeneity level between target and base data sets is small or high. Author summary: Polygenic risk score (PRS) can quantify the genetic predisposition for a trait. PRS construction typically contains two input datasets: base data for variant-effect estimation and target data for individual-level prediction. Given the availability of large-scale base data, it becomes common that the ancestral background of base and target data do not perfectly match. In this paper, we introduce a PRS method under a transfer learning framework to effectively leverage the knowledge learned from the base data that may or may not have similar background as the target samples to build prediction models for target individuals. Our method first utilizes a unique false-negative control strategy to extract useful information from base data while ensuring to retain a high proportion of true signals; it then applies the extracted information to re-train PRS models in a statistically and computationally efficient fashion. We use numerical studies based on simulated and real data to show that the proposed method can increase the accuracy and robustness of polygenic prediction across different ranges of heterogeneities between base and target data and sample sizes, reduce computational cost in model re-training, and result in more parsimonious models that can facilitate PRS interpretation and/or exploration of complex, non-additive PRS models. [ABSTRACT FROM AUTHOR]

Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits. Meanwhile, constructing a network based on associations between phenotypes and genotypes provides a new insight to analyze multiple phenotypes, which can explore whether phenotypes and genotypes might be related to each other at a higher level of cellular and organismal organization. In this paper, we first develop a bipartite signed network by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). The GPN can be constructed by a mixture of quantitative and qualitative phenotypes and is applicable to binary phenotypes with extremely unbalanced case-control ratios in large-scale biobank datasets. We then apply a powerful community detection method to partition phenotypes into disjoint network modules based on GPN. Finally, we jointly test the association between multiple phenotypes in a network module and a single nucleotide polymorphism (SNP). Simulations and analyses of 72 complex traits in the UK Biobank show that multiple phenotype association tests based on network modules detected by GPN are much more powerful than those without considering network modules. The newly proposed GPN provides a new insight to investigate the genetic architecture among different types of phenotypes. Multiple phenotypes association studies based on GPN are improved by incorporating the genetic information into the phenotype clustering. Notably, it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy. Author summary: Biological pleiotropy refers to a SNP or gene that has a direct biological influence on more than one phenotypic trait, which can offer significant insights in understanding the complex genotype-phenotype relationships. Network analyses provide an integrative approach to characterize complex genomic associations by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). Jointly analyzing multiple phenotypes and incorporating the genetic information into the phenotype clustering may increase the statistical power to discover the cross-phenotype association and pleiotropy. We evaluate our proposed multiple phenotype association tests based on network modules detected by GPN for 72 EHR-derived phenotypes in the diseases of the musculoskeletal system and connective tissue in the UK Biobank. From the post-GWAS analyses, we observe that the test based on GPN can identify more significantly enriched biological pathways than that without considering the network modules. Meanwhile, some of the uniquely identified SNPs by the test based on GPN are also colocalized in the eQTL study of the gene expression in the Muscle Skeletal tissue. [ABSTRACT FROM AUTHOR]

Transcriptional cis-regulatory modules, e.g., enhancers, control the time and location of metazoan gene expression. While changes in enhancers can provide a powerful force for evolution, there is also significant deep conservation of enhancers for developmentally important genes, with function and sequence characteristics maintained over hundreds of millions of years of divergence. Not well understood, however, is how the overall regulatory composition of a locus evolves, with important outstanding questions such as how many enhancers are conserved vs. novel, and to what extent are the locations of conserved enhancers within a locus maintained? We begin here to address these questions with a comparison of the respective single-minded (sim) loci in the two dipteran species Drosophila melanogaster (fruit fly) and Aedes aegypti (mosquito). sim encodes a highly conserved transcription factor that mediates development of the arthropod embryonic ventral midline. We identify two enhancers in the A. aegypti sim locus and demonstrate that they function equivalently in both transgenic flies and transgenic mosquitoes. One A. aegypti enhancer is highly similar to known Drosophila counterparts in its activity, location, and autoregulatory capability. The other differs from any known Drosophila sim enhancers with a novel location, failure to autoregulate, and regulation of expression in a unique subset of midline cells. Our results suggest that the conserved pattern of sim expression in the two species is the result of both conserved and novel regulatory sequences. Further examination of this locus will help to illuminate how the overall regulatory landscape of a conserved developmental gene evolves. Author summary: The expression patterns and roles of genes, especially those involved in core developmental processes, are often conserved over vast evolutionary distances. Paradoxically, the DNA sequences surrounding these genes, which contain the cis-regulatory sequences (enhancers) that regulate gene expression, tend to be highly diverged. The manner and extent to which enhancers are functionally conserved, and how the overall organization of regulatory sequences within a locus is preserved or restructured, is not well understood. In this paper, we investigate these questions by identifying enhancers controlling expression of a master nervous system regulatory gene named sim in the mosquito Aedes aegypti, and comparing their functions and locations to those in the well-characterized sim locus of the fruit fly Drosophila melanogaster. Our results suggest that the two species generate identical patterns of sim expression through a mix of conserved and novel regulatory sequences. Continued exploration of the sim locus in these two species will help to build a comprehensive picture of how a regulatory locus for a master developmental regulator has evolved. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) have identified many genetic loci associated with complex traits and diseases in the past 20 years. Multiple heritable covariates may be added into GWAS regression models to estimate direct effects of genetic variants on a focal trait, or to improve the power by accounting for environmental effects and other sources of trait variations. When one or more covariates are causally affected by both genetic variants and hidden confounders, adjusting for them in GWAS will produce biased estimation of SNP effects, known as collider bias. Several approaches have been developed to correct collider bias through estimating the bias by Mendelian randomization (MR). However, these methods work for only one covariate, some of which utilize MR methods with relatively strong assumptions, both of which may not hold in practice. In this paper, we extend the bias-correction approaches in two aspects: first we derive an analytical expression for the collider bias in the presence of multiple covariates, then we propose estimating the bias using a robust multivariable MR (MVMR) method based on constrained maximum likelihood (called MVMR-cML), allowing the presence of invalid instrumental variables (IVs) and correlated pleiotropy. We also established the estimation consistency and asymptotic normality of the new bias-corrected estimator. We conducted simulations to show that all methods mitigated collider bias under various scenarios. In real data analyses, we applied the methods to two GWAS examples, the first a GWAS of waist-hip ratio with adjustment for only one covariate, body-mass index (BMI), and the second a GWAS of BMI adjusting metabolomic principle components as multiple covariates, illustrating the effectiveness of bias correction. Author summary: Genome-wide association studies (GWAS) are powerful in identifying genetic variants influencing complex traits and diseases. However, adjusting for heritable covariates in GWAS may introduce collider bias when both genetic variants and confounders may causally influence these covariates. In this study, for the first time we derived the analytical form of the bias term in GWAS with multiple covariates, enabling bias estimation and correction using any MVMR method. On the other hand, many existing MVMR methods may not be robust to invalid IVs and are designed for independent samples. Since GWAS data of multiple traits are needed, overlapping samples become inevitable. Hence, while investigating the performance of many MVMR methods, we mainly adopt MVMR-cML, a novel MVMR approach robust to invalid IVs and sample overlap. Our simulations underscore that most MVMR methods effectively reduce collider bias across various scenarios. Furthermore, by accounting for correlations among GWAS statistics, as well as the linkage disequilibrium (LD) between the target SNP and IVs, we establish the consistency and asymptotic normality of the bias-corrected estimator based on MVMR-cML. The application of our bias-correction approach to two published GWAS data examples illustrates its utility and efficacy. [ABSTRACT FROM AUTHOR]

engrailed (en) encodes a homeodomain transcription factor crucial for the proper development of Drosophila embryos and adults. Like many developmental transcription factors, en expression is regulated by many enhancers, some of overlapping function, that drive expression in spatially and temporally restricted patterns. The en embryonic enhancers are located in discrete DNA fragments that can function correctly in small reporter transgenes. In contrast, the en imaginal disc enhancers (IDEs) do not function correctly in small reporter transgenes. En is expressed in the posterior compartment of wing imaginal discs; in contrast, small IDE-reporter transgenes are expressed mainly in the anterior compartment. We found that En binds to the IDEs and suggest that it may directly repress IDE function and modulate En expression levels. We identified two en IDEs, O and S. Deletion of either of these IDEs from a 79kb HA-en rescue transgene (HAen79) caused a loss-of-function en phenotype when the HAen79 transgene was the sole source of En. In contrast, flies with a deletion of the same IDEs from an endogenous en gene had no phenotype, suggesting a resiliency not seen in the HAen79 rescue transgene. Inserting a gypsy insulator in HAen79 between en regulatory DNA and flanking sequences strengthened the activity of HAen79, giving better function in both the ON and OFF transcriptional states. Altogether our data suggest that the en IDEs stimulate expression in the entire imaginal disc, and that the ON/OFF state is set by epigenetic memory set by the embryonic enhancers. This epigenetic regulation is similar to that of the Ultrabithorax IDEs and we suggest that the activity of late-acting enhancers in other genes may be similarly regulated. Author summary: Genes that control development are often used at different times and places in a developing embryo. Transcription of these important genes must be tightly regulated; therefore, these genes often have large arrays of regulatory DNA. In Drosophila, discrete fragments of DNA (enhancers) can be identified that turn genes on in patterns in the early embryo. In cells where the genes are transcriptionally ON, there are active modifications on chromatin, setting later enhancers in a transcription-permissive environment. In cells where the genes are OFF, repressive chromatin marks keep later enhancers inactive. In this paper we studied two late enhancers of the Drosophila en gene. We show that the correct activity of these enhancers is dependent on being next to other, earlier acting en enhancers. Our data also show that En can repress its own expression, likely directly by acting on these late enhancers. The chromatin-regulated activity of these en late enhancers is similar to what was described for a late enhancer of another Drosophila developmental gene, Ubx. We suggest that this mode of regulation is likely to be important for many late-acting developmental enhancers in many different organisms. [ABSTRACT FROM AUTHOR]

Activating Transcription Factor 4 (ATF4) is an important regulator of gene expression in stress responses and developmental processes in many cell types. Here, we catalogued ATF4 binding sites in the human genome and identified overlaps with trait-associated genetic variants. We probed these genetic variants for allelic regulatory activity using a massively parallel reporter assay (MPRA) in HepG2 hepatoma cells exposed to tunicamycin to induce endoplasmic reticulum stress and ATF4 upregulation. The results revealed that in the majority of cases, the MPRA allelic activity of these SNPs was in agreement with the nucleotide preference seen in the ATF4 binding motif from ChIP-Seq. Luciferase and electrophoretic mobility shift assays in additional cellular models further confirmed ATF4-dependent regulatory effects for the SNPs rs532446 (GADD45A intronic; linked to hematological parameters), rs7011846 (LPL upstream; myocardial infarction), rs2718215 (diastolic blood pressure), rs281758 (psychiatric disorders) and rs6491544 (educational attainment). CRISPR-Cas9 disruption and/or deletion of the regulatory elements harboring rs532446 and rs7011846 led to the downregulation of GADD45A and LPL, respectively. Thus, these SNPs could represent examples of GWAS genetic variants that affect gene expression by altering ATF4-mediated transcriptional activation. Author summary: Genome-wide association studies (GWAS) have gathered a large amount of data connecting genetic variations (mostly single-nucleotide polymorphisms (SNPs)) to phenotypic traits, including diseases, but the molecular mechanisms through which the SNPs lead to phenotypic effects are often obscure. In this paper, we compiled a whole-genome catalogue of chromatin loci interacting with activating transcription factor 4 (ATF4), an important regulator of gene expression in cellular stress responses and other biological processes, and identified trait-associated SNPs overlapping ATF4 binding motifs. We then evaluated the consequences of more than 500 ATF4 binding motif-altering SNPs on allelic transcriptional activity using a massively parallel reporter assay in hepatoma cells in unstressed and stressful conditions. Further characterization of five selected SNPs in additional cellular models confirmed ATF4-dependent regulatory effects for the SNPs rs532446 (GADD45A intronic; linked to blood cell parameters), rs7011846 (LPL upstream; myocardial infarction), rs2718215 (diastolic blood pressure), rs281758 (psychiatric disorders) and rs6491544 (educational attainment). CRISPR/Cas9 technology was used to confirm the effect of rs532446 region on GADD45A and rs7011846 region on LPL gene expression. In conclusion, the data indicate that allelic variants of rs532446, rs7011846 and several other GWAS trait-linked SNPs located in ATF4 binding motifs differ in the ability to activate ATF4-mediated gene expression. [ABSTRACT FROM AUTHOR]

In recent years, genome and exome sequencing studies have implicated a plethora of new disease genes with rare causal variants. Here, I review 150 exome sequencing studies that claim to have discovered that a disease can be caused by different rare variants in the same gene, and I determine whether their methods followed the current best-practice guidelines in the interpretation of their data. Specifically, I assess whether studies appropriately assess controls for rare variants throughout the entire gene or implicated region as opposed to only investigating the specific rare variants identified in the cases, and I assess whether studies present sufficient co-segregation data for statistically significant linkage. I find that the proportion of studies performing gene-based analyses has increased with time, but that even in 2015 fewer than 40% of the reviewed studies used this method, and only 10% presented statistically significant co-segregation data. Furthermore, I find that the genes reported in these papers are explaining a decreasing proportion of cases as the field moves past most of the low-hanging fruit, with 50% of the genes from studies in 2014 and 2015 having variants in fewer than 5% of cases. As more studies focus on genes explaining relatively few cases, the importance of performing appropriate gene-based analyses is increasing. It is becoming increasingly important for journal editors and reviewers to require stringent gene-based evidence to avoid an avalanche of misleading disease gene discovery papers. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) are commonly used to identify genomic variants that are associated with complex traits, and estimate the magnitude of this association for each variant. However, it has been widely observed that the association estimates of variants tend to be lower in a replication study than in the study that discovered those associations. A phenomenon known as Winner's Curse is responsible for this upward bias present in association estimates of significant variants in the discovery study. We review existing Winner's Curse correction methods which require only GWAS summary statistics in order to make adjustments. In addition, we propose modifications to improve existing methods and propose a novel approach which uses the parametric bootstrap. We evaluate and compare methods, first using a wide variety of simulated data sets and then, using real data sets for three different traits. The metric, estimated mean squared error (MSE) over significant SNPs, was primarily used for method assessment. Our results indicate that widely used conditional likelihood based methods tend to perform poorly. The other considered methods behave much more similarly, with our proposed bootstrap method demonstrating very competitive performance. To complement this review, we have developed an R package, 'winnerscurse' which can be used to implement these various Winner's Curse adjustment methods to GWAS summary statistics. Author summary: A genome-wide association study is designed to analyse many common genetic variants in thousands of samples and identify which variants are associated with a trait of interest. It provides estimates of association strength for each variant and variants are classified as associated if their test statistics obtained in the study pass a chosen significance threshold. However, due to a phenomenon known as Winner's Curse, the association estimates of these significant variants tend to be upward biased and greater in magnitude than their true values. Naturally, this bias has adverse consequences for downstream statistical techniques which use these estimates. In this paper, we look at current methods which have been designed to combat Winner's Curse and propose modifications to these methods in order to improve performance. Using a wide variety of simulated data sets as well as real data, we perform a thorough evaluation of these methods. We use a metric which allows us to identify which methods, on average, produce adjusted estimates for significant variants that are closest to the true values. To accompany our work, we have created an R package, 'winnerscurse', which allows users to easily apply Winner's Curse correction methods to their data sets. [ABSTRACT FROM AUTHOR]

Evidence of interbreeding between archaic hominins and humans comes from methods that infer the locations of segments of archaic haplotypes, or 'archaic coverage' using the genomes of people living today. As more estimates of archaic coverage have emerged, it has become clear that most of this coverage is found on the autosomes— very little is retained on chromosome X. Here, we summarize published estimates of archaic coverage on autosomes and chromosome X from extant human samples. We find on average 7 times more archaic coverage on autosomes than chromosome X, and identify broad continental patterns in this ratio: greatest in European samples, and least in South Asian samples. We also perform extensive simulation studies to investigate how the amount of archaic coverage, lengths of coverage, and rates of purging of archaic coverage are affected by sex-bias caused by an unequal sex ratio within the archaic introgressors. Our results generally confirm that, with increasing male sex-bias, less archaic coverage is retained on chromosome X. Ours is the first study to explicitly model such sex-bias and its potential role in creating the dearth of archaic coverage on chromosome X. Author summary: Tens of thousands of years ago, humans interbred with our close hominin relatives (e.g. Neanderthals), which we know from finding segments of archaic hominin DNA in our genomes. Up to 4% of a human genome may be archaic DNA, but most of that archaic part is on the autosomes (the non-sex chromosomes). Chromosome X usually contains 3 to 10 times less archaic DNA than the autosomes. Also unlike the autosomes, it is always passed down by mothers, but only sometimes by fathers. There are several hypotheses for why chromosome X has less archaic DNA than the autosomes; one that has not been fully explored is whether the archaic hominins that interbred with our ancestors were mostly male or mostly female, known as 'sex-bias'. In this paper, we use simulations to investigate whether sex-bias could produce less archaic DNA on chromosome X. Using simulation studies, we find that when the archaics are mostly male, modern humans end up with less archaic DNA on chromosome X than their autosomes, compared to when there is a female-bias or no sex-bias. Therefore, male sex-bias could be contributing to the difference in the amount of archaic DNA on chromosome X versus the autosomes. Of course, there are still plenty of other factors to be explored about how demography and selection have shaped our DNA. Studying patterns like this helps us learn more about early hominin natural history, and contextualizes archaic interbreeding events among other sex-biased events in human history. [ABSTRACT FROM AUTHOR]

Predicting phenotypes from genotypes is a fundamental task in quantitative genetics. With technological advances, it is now possible to measure multiple phenotypes in large samples. Multiple phenotypes can share their genetic component; therefore, modeling these phenotypes jointly may improve prediction accuracy by leveraging effects that are shared across phenotypes. However, effects can be shared across phenotypes in a variety of ways, so computationally efficient statistical methods are needed that can accurately and flexibly capture patterns of effect sharing. Here, we describe new Bayesian multivariate, multiple regression methods that, by using flexible priors, are able to model and adapt to different patterns of effect sharing and specificity across phenotypes. Simulation results show that these new methods are fast and improve prediction accuracy compared with existing methods in a wide range of settings where effects are shared. Further, in settings where effects are not shared, our methods still perform competitively with state-of-the-art methods. In real data analyses of expression data in the Genotype Tissue Expression (GTEx) project, our methods improve prediction performance on average for all tissues, with the greatest gains in tissues where effects are strongly shared, and in the tissues with smaller sample sizes. While we use gene expression prediction to illustrate our methods, the methods are generally applicable to any multi-phenotype applications, including prediction of polygenic scores and breeding values. Thus, our methods have the potential to provide improvements across fields and organisms. Author summary: Predicting phenotypes from genotypes is a fundamental problem in quantitative genetics. Thanks to recent advances, it is increasingly feasible to collect data on many phenotypes and genome-wide genotypes in large samples. Here, we tackle the problem of predicting multiple phenotypes from genotypes using a new method based on a multivariate, multiple linear regression model. Although the use of a multivariate, multiple linear regression model is not new, in this paper we introduce a flexible and computationally efficient empirical Bayes approach based on this model. This approach uses a prior that captures how the effects of genotypes on phenotypes are shared across the different phenotypes, and then the prior is adapted to the data in order to capture the most prominent sharing patterns present in the data. We assess the benefits of this flexible Bayesian approach in simulated genetic data sets, and we illustrate its application in predicting gene expression measured in multiple human tissues. We show that our methods can outperform competing methods in terms of prediction accuracy, and the computations involved in fitting the model and making the predictions scale well to large data sets. [ABSTRACT FROM AUTHOR]

Population differences in risk of disease are common, but the potential genetic basis for these differences is not well understood. A standard approach is to compare genetic risk across populations by testing for mean differences in polygenic scores, but existing studies that use this approach do not account for statistical noise in effect estimates (i.e., the GWAS betas) that arise due to the finite sample size of GWAS training data. Here, we show using Bayesian polygenic score methods that the level of uncertainty in estimates of genetic risk differences across populations is highly dependent on the GWAS training sample size, the polygenicity (number of causal variants), and genetic distance (FST) between the populations considered. We derive a Wald test for formally assessing the difference in genetic risk across populations, which we show to have calibrated type 1 error rates under a simplified assumption that all SNPs are independent, which we achieve in practise using linkage disequilibrium (LD) pruning. We further provide closed-form expressions for assessing the uncertainty in estimates of relative genetic risk across populations under the special case of an infinitesimal genetic architecture. We suggest that for many complex traits and diseases, particularly those with more polygenic architectures, current GWAS sample sizes are insufficient to detect moderate differences in genetic risk across populations, though more substantial differences in relative genetic risk (relative risk > 1.5) can be detected. We show that conventional approaches that do not account for sampling error from the training sample, such as using a simple t-test, have very high type 1 error rates. When applying our approach to prostate cancer, we demonstrate a higher genetic risk in African Ancestry men, with lower risk in men of European followed by East Asian ancestry. Author summary: Many diseases and complex traits, such as prostate cancer, exhibit differences in incidence across populations. Yet the potential contribution of genetic factors towards such disparities is unclear. Polygenic scores summarise genetic effects across the genome and can in principle provide a valuable tool for assessing and comparing disease risk across populations. In practise, current approaches based on polygenic scores assume that such scores perfectly measure genetic risk of disease without measurement error, and thus do not account for uncertainty that arises in the construction of the score from a finite genome-wide association study (GWAS) training sample, which can be substantial. We introduce a Bayesian approach based on the LDpred2 polygenic score model that accounts fully for training sample uncertainty, and we propose a Wald test for formally testing such genetic risk differences across populations. Simulations show that the method properly controls for type 1 errors assuming independent SNPs (achieved by pruning), and that statistical power is sensitive to both the genetic architecture (heritability and polygenicity) and training sample size. In application to prostate cancer, this framework enables us to identify a higher genetic risk in African Ancestry men, with lower risk in men of European followed by East Asian ancestry. [ABSTRACT FROM AUTHOR]

In this paper we review the methodological underpinnings of the general pharmacogenetic approach for uncovering genetically-driven treatment effect heterogeneity. This typically utilises only individuals who are treated and relies on fairly strong baseline assumptions to estimate what we term the 'genetically moderated treatment effect' (GMTE). When these assumptions are seriously violated, we show that a robust but less efficient estimate of the GMTE that incorporates information on the population of untreated individuals can instead be used. In cases of partial violation, we clarify when Mendelian randomization and a modified confounder adjustment method can also yield consistent estimates for the GMTE. A decision framework is then described to decide when a particular estimation strategy is most appropriate and how specific estimators can be combined to further improve efficiency. Triangulation of evidence from different data sources, each with their inherent biases and limitations, is becoming a well established principle for strengthening causal analysis. We call our framework 'Triangulation WIthin a STudy' (TWIST)' in order to emphasise that an analysis in this spirit is also possible within a single data set, using causal estimates that are approximately uncorrelated, but reliant on different sets of assumptions. We illustrate these approaches by re-analysing primary-care-linked UK Biobank data relating to CYP2C19 genetic variants, Clopidogrel use and stroke risk, and data relating to APOE genetic variants, statin use and Coronary Artery Disease. Author summary: Understanding how much a specific treatment's effect is moderated by common genetic variation is an important public health question. If a person's genetics means they will experience a much reduced treatment effect, as measured with respect to a particular health outcome, then they could be switched to an alternative therapy. When assessing the impact of such a switch at the population level, it is typical to only use data on those who are treated with the said drug. However, this analysis is compromised if genetic variants exist which moderate the treatment's effect and affect the outcome through alternative pathways. In this paper we describe an extended analysis framework to estimating the 'genetically moderated treatment effect' (GMTE) that incorporates information on both treated and untreated individuals. With this larger set of information we show that four analysis approaches for estimating the GMTE are possible. Each one relies on a different set of assumptions to work correctly and provides estimates that are largely uncorrelated with one another. Our paper describes a decision framework for triangulating the findings from these four approaches in order to provide a more robust basis for decision making in public health. [ABSTRACT FROM AUTHOR]

Over the last decade the availability of SNP-trait associations from genome-wide association studies has led to an array of methods for performing Mendelian randomization studies using only summary statistics. A common feature of these methods, besides their intuitive simplicity, is the ability to combine data from several sources, incorporate multiple variants and account for biases due to weak instruments and pleiotropy. With the advent of large and accessible fully-genotyped cohorts such as UK Biobank, there is now increasing interest in understanding how best to apply these well developed summary data methods to individual level data, and to explore the use of more sophisticated causal methods allowing for non-linearity and effect modification. In this paper we describe a general procedure for optimally applying any two sample summary data method using one sample data. Our procedure first performs a meta-analysis of summary data estimates that are intentionally contaminated by collider bias between the genetic instruments and unmeasured confounders, due to conditioning on the observed exposure. These estimates are then used to correct the standard observational association between an exposure and outcome. Simulations are conducted to demonstrate the method's performance against naive applications of two sample summary data MR. We apply the approach to the UK Biobank cohort to investigate the causal role of sleep disturbance on HbA1c levels, an important determinant of diabetes. Our approach can be viewed as a generalization of Dudbridge et al. (Nat. Comm. 10: 1561), who developed a technique to adjust for index event bias when uncovering genetic predictors of disease progression based on case-only data. Our work serves to clarify that in any one sample MR analysis, it can be advantageous to estimate causal relationships by artificially inducing and then correcting for collider bias. Author summary: Uncovering causal mechanisms between risk factors and disease is challenging with observational data because of unobserved confounding. Mendelian randomization offers a potential solution by replacing an individual's observed risk factor data with an unconfounded genetic proxy measure. Over the last decade an array of methods for performing Mendelian randomization studies (MR) using publicly available summary statistics gleaned from two separate genome-wide association studies. With the advent of large and accessible fully-genotyped cohorts such as UK Biobank, there is now increasing interest in understanding how best to apply these well-developed summary data methods to individual level data. In this paper we describe a general procedure for optimally applying any summary data MR method using individual level data from one cohort study. Our approach may at first seem nonsensical: we create summary statistics that are intentionally biased by confounding. This bias can, however, be very accurately estimated, and the estimate then used to correct the results of a standard observational analysis. We apply our new way of performing an MR analysis to data from UK Biobank to investigate the causal role of sleep disturbance on HbA1c levels, an important determinant of diabetes. [ABSTRACT FROM AUTHOR]

Genetic studies of disease progression can be used to identify factors that may influence survival or prognosis, which may differ from factors that influence on disease susceptibility. Studies of disease progression feed directly into therapeutics for disease, whereas studies of incidence inform prevention strategies. However, studies of disease progression are known to be affected by collider (also known as "index event") bias since the disease progression phenotype can only be observed for individuals who have the disease. This applies equally to observational and genetic studies, including genome-wide association studies and Mendelian randomisation (MR) analyses. In this paper, our aim is to review several statistical methods that can be used to detect and adjust for index event bias in studies of disease progression, and how they apply to genetic and MR studies using both individual- and summary-level data. Methods to detect the presence of index event bias include the use of negative controls, a comparison of associations between risk factors for incidence in individuals with and without the disease, and an inspection of Miami plots. Methods to adjust for the bias include inverse probability weighting (with individual-level data), or Slope-Hunter and Dudbridge et al.'s index event bias adjustment (when only summary-level data are available). We also outline two approaches for sensitivity analysis. We then illustrate how three methods to minimise bias can be used in practice with two applied examples. Our first example investigates the effects of blood lipid traits on mortality from coronary heart disease, while our second example investigates genetic associations with breast cancer mortality. [ABSTRACT FROM AUTHOR]

Neonicotinoid insecticides target insect nicotinic acetylcholine receptors (nAChRs) and their adverse effects on non-target insects are of serious concern. We recently found that cofactor TMX3 enables robust functional expression of insect nAChRs in Xenopus laevis oocytes and showed that neonicotinoids (imidacloprid, thiacloprid, and clothianidin) exhibited agonist actions on some nAChRs of the fruit fly (Drosophila melanogaster), honeybee (Apis mellifera) and bumblebee (Bombus terrestris) with more potent actions on the pollinator nAChRs. However, other subunits from the nAChR family remain to be explored. We show that the Dα3 subunit co-exists with Dα1, Dα2, Dβ1, and Dβ2 subunits in the same neurons of adult D. melanogaster, thereby expanding the possible nAChR subtypes in these cells alone from 4 to 12. The presence of Dα1 and Dα2 subunits reduced the affinity of imidacloprid, thiacloprid, and clothianidin for nAChRs expressed in Xenopus laevis oocytes, whereas the Dα3 subunit enhanced it. RNAi targeting Dα1, Dα2 or Dα3 in adults reduced expression of targeted subunits but commonly enhanced Dβ3 expression. Also, Dα1 RNAi enhanced Dα7 expression, Dα2 RNAi reduced Dα1, Dα6, and Dα7 expression and Dα3 RNAi reduced Dα1 expression while enhancing Dα2 expression, respectively. In most cases, RNAi treatment of either Dα1 or Dα2 reduced neonicotinoid toxicity in larvae, but Dα2 RNAi enhanced neonicotinoid sensitivity in adults reflecting the affinity-reducing effect of Dα2. Substituting each of Dα1, Dα2, and Dα3 subunits by Dα4 or Dβ3 subunit mostly increased neonicotinoid affinity and reduced efficacy. These results are important because they indicate that neonicotinoid actions involve the integrated activity of multiple nAChR subunit combinations and counsel caution in interpreting neonicotinoid actions simply in terms of toxicity. Author summary: In this paper, we show that the Drosophila melanogaster nicotinic acetylcholine receptor (nAChR) Dα3 subunit is co-expressed in ejaculatory duct neurons with Dα1, Dα2, Dβ1, and Dβ2 subunits. All 5 subunits combine to form 12 functional nAChRs in Xenopus laevis oocytes. The functional expression of 18 nAChRs generated from combinations of subunits Dα1−4 and Dβ1−3 are also reported. Dα1 and Dα2 reduced the affinity of D. melanogaster heteromeric nAChRs for imidacloprid, thiacloprid, and clothianidin, whereas Dα3 enhanced it. RNAi of Dα1, Dα2 or Dα3 in adult flies reduced expression of the targeted subunits but commonly enhanced Dβ3 expression; other subunits were also affected in some cases. RNAi targeting either Dα1 or Dα2 reduced neonicotinoid toxicity in larvae but targeting Dα2 led to hyper-neonicotinoid sensitivity in adults consistent with the affinity-reducing effect on neonicotinoids of Dα2. Since RNAi induced subunit compensation was detected, each of Dα1, Dα2, and Dα3 subunits was substituted by Dα4 or Dβ3 subunit. Such subunit compensation mostly increased neonicotinoid affinity and reduced efficacy, impairing the climbing ability of the flies. These results are important because they indicate that neonicotinoid action and toxicity involve the integrated actions of multiple nAChR subunit combinations and counsel caution in interpreting neonicotinoid actions in terms of reduced toxicity. [ABSTRACT FROM AUTHOR]

Admixture graphs are mathematical structures that describe the ancestry of populations in terms of divergence and merging (admixing) of ancestral populations as a graph. An admixture graph consists of a graph topology, branch lengths, and admixture proportions. The branch lengths and admixture proportions can be estimated using numerous numerical optimization methods, but inferring the topology involves a combinatorial search for which no polynomial algorithm is known. In this paper, we present a reversible jump MCMC algorithm for sampling high-probability admixture graphs and show that this approach works well both as a heuristic search for a single best-fitting graph and for summarizing shared features extracted from posterior samples of graphs. We apply the method to 11 Native American and Siberian populations and exploit the shared structure of high-probability graphs to characterize the relationship between Saqqaq, Inuit, Koryaks, and Athabascans. Our analyses show that the Saqqaq is not a good proxy for the previously identified gene flow from Arctic people into the Na-Dene speaking Athabascans. Author summary: One way of summarizing historical relationships between genetic samples is by constructing an admixture graph. An admixture graph describes the demographic history of a set of populations as a directed acyclic graph representing population splits and mergers. The greedy search algorithms that are typically used to infer admixture graphs may fail to find the globally optimal graph. We here improve on these approaches by developing a novel MCMC sampling method, AdmixtureBayes, that can sample from the posterior distribution of admixture graphs. This enables an effective search of the entire state space as well as the ability to report a level of confidence in the sampled graphs. We apply AdmixtureBayes to a set of Native American and Arctic genomes to reconstruct the demographic history of these populations and report posterior probabilities of specific admixture events. While some previous studies have identified the ancient Saqqaq culture as a source of introgression into Athabascans, we instead find that it is the Siberian Koryak population, not the Saqqaq, that serves as the best proxy for gene flow into Athabascans. [ABSTRACT FROM AUTHOR]

The eye is the window through which light is transmitted and visual sensory signalling originates. It is also a window through which elements of the cardiovascular and nervous systems can be directly inspected, using ophthalmoscopy or retinal imaging. Measurements of ocular parameters may therefore offer important information on the physiology and homeostasis of these two important systems. Here we report the results of a genetic characterisation of retinal vasculature. Four genome-wide association studies performed on different aspects of retinal vasculometry phenotypes, such as arteriolar and venular tortuosity and width, found significant similarities between retinal vascular characteristics and cardiometabolic health. Our analyses identified 119 different regions of association with traits of retinal vasculature, including 89 loci associated arteriolar tortuosity, the strongest of which was rs35131825 (p = 2.00×10−108), 2 loci with arteriolar width (rs12969347, p = 3.30×10−09 and rs5442, p = 1.9E-15), 17 other loci associated with venular tortuosity and 11 novel associations with venular width. Our causal inference analyses also found that factors linked to arteriolar tortuosity cause elevated diastolic blood pressure and not vice versa. Author summary: Vessels at the back of the eye (the "retina") can be imaged easily. This paper reports on the largest genetic study of retinal vessel shape and size characteristics so far undertaken, to the best of our knowledge. Our study is novel in using an automated artificial intelligence imaging approach to distinguish between arteries and veins, and in demonstrating more genetic associations with vessel characteristics than any previous study (119 genetic loci in all). We also show that the tortuosity of retinal arteries is the most strongly genetically determined vessel characteristic (replicated remarkable well in a separate second large dataset). In addition, using a particular type of genetic analysis (so called "Mendelian Randomization") we show for the first time that the tortuosity of arteries in the retina is causally related to elevated diastolic blood pressure and not the other way around. This is important as it provides unique insights into the mechanism of elevated blood pressure and hypertension, providing pointers to novel therapeutic targets for future treatment. [ABSTRACT FROM AUTHOR]

The Epidermal Growth Factor Receptor (EGFR) signaling pathway plays a critical role in regulating tissue patterning. Drosophila EGFR signaling achieves specificity through multiple ligands and feedback loops to finetune signaling outcomes spatiotemporally. The principal Drosophila EGF ligand, cleaved Spitz, and the negative feedback regulator, Argos are diffusible and can act both in a cell autonomous and non-autonomous manner. The expression dose of Spitz and Argos early in photoreceptor cell fate determination has been shown to be critical in patterning the Drosophila eye, but the exact identity of the cells expressing these genes in the larval eye disc has been elusive. Using single molecule RNA Fluorescence in situ Hybridization (smFISH), we reveal an intriguing differential expression of spitz and argos mRNA in the Drosophila third instar eye imaginal disc indicative of directional non-autonomous EGFR signaling. By genetically tuning EGFR signaling, we show that rather than absolute levels of expression, the ratio of expression of spitz-to-argos to be a critical determinant of the final adult eye phenotype. Proximate effects on EGFR signaling in terms of cell cycle and differentiation markers are affected differently in the different perturbations. Proper ommatidial patterning is robust to thresholds around a tightly maintained wildtype spitz-to-argos ratio, and breaks down beyond. This provides a powerful instance of developmental buffering against gene expression fluctuations. Author summary: Sexual multicellular organisms start life as a single cell–the fertilized egg. One of the fundamental questions of Developmental Biology is to understand how cells proliferate and assume specific identities to faithfully reproduce the organismal tissue patterning. Cells communicate via signaling pathways to achieve complex patterning outcomes. Epidermal Growth Factor Receptor (EGFR) signaling is known to coordinate both cell division and fate choices in animals ranging from humans to the fruit-fly (Drosophila melanogaster). The fruit-fly eye with its strikingly patterned, hexagonally arranged units is a remarkable example of tissue patterning by EGFR signaling. In this paper, we investigate how expression levels of an activator (Spitz) and inhibitor (Argos) that tune the activation level of EGFR pathway regulate the patterning of the Drosophila eye. We find the activator-to-inhibitor ratio to be a critical factor in this process. The pattern is robust in a range around a tightly-controlled wildtype ratio. Beyond this biological range, both cell fate and cell division are affected producing different 'rough-eye' phenotypes. This is a striking example of how developmental patterning may be buffered against reasonable fluctuations in gene expression. [ABSTRACT FROM AUTHOR]

Selfish genetic elements (historically also referred to as selfish genes, ultra-selfish genes, selfish DNA, parasitic DNA, genomic outlaws) are genetic segments that can enhance their own transmission at the expense of other genes in the genome, even if this has no or a negative effect on organismal fitness. [–] Genomes have traditionally been viewed as cohesive units, with genes acting together to improve the fitness of the organism. However, when genes have some control over their own transmission, the rules can change, and so just like all social groups, genomes are . Early observations of selfish genetic elements were made almost a century ago, but the topic did not get widespread attention until several decades later. Inspired by the popularized by [] and ,[] two papers were published back-to-back in Nature in 1980—by and [] and and Carmen Sapienza[] respectively—introducing the concept of selfish genetic elements (at the time called “selfish DNA”) to the wider scientific community. Both papers emphasized that genes can spread in a population regardless of their effect on organismal fitness as long as they have a transmission advantage. Selfish genetic elements have now been described in most groups of organisms, and they demonstrate a remarkable diversity in the ways by which they promote their own transmission.[] Though long dismissed as genetic curiosities, with little relevance for evolution, they are now recognized to affect a wide swath of biological processes, ranging from genome size and architecture to speciation.[] [ABSTRACT FROM AUTHOR]

Dobzhansky and Muller proposed a general mechanism through which microevolution, the substitution of alleles within populations, can cause the evolution of reproductive isolation between populations and, therefore, macroevolution. As allopatric populations diverge, many combinations of alleles differing between them have not been tested by natural selection and may thus be incompatible. Such genetic incompatibilities often cause low fitness in hybrids between species. Furthermore, the number of incompatibilities grows with the genetic distance between diverging populations. However, what determines the rate and pattern of accumulation of incompatibilities remains unclear. We investigate this question by simulating evolution on holey fitness landscapes on which genetic incompatibilities can be identified unambiguously. We find that genetic incompatibilities accumulate more slowly among genetically robust populations and identify two determinants of the accumulation rate: recombination rate and population size. In large populations with abundant genetic variation, recombination selects for increased genetic robustness and, consequently, incompatibilities accumulate more slowly. In small populations, genetic drift interferes with this process and promotes the accumulation of genetic incompatibilities. Our results suggest a novel mechanism by which genetic drift promotes and recombination hinders speciation. Author summary: As geographically isolated populations evolve, genetic incompatibilities accumulate between them. Eventually, these incompatibilities may cause the populations to become different species. What determines how quickly species are formed in this way remains unclear. We investigate this question using computer simulations and find that genetic incompatibilities accumulate more slowly among populations with individuals that are robust to genetic perturbations. We identify two factors that influence the accumulation rate via their effects on genetic robustness: the size of the populations and how much recombination takes place in them. Small populations with rare recombination accumulate incompatibilities more quickly. [ABSTRACT FROM AUTHOR]

In a letter to his sister, Bateson confessed that "Sedgwick tells me he would not wish me to have Weldon's lectureship if W. goes to University College. By 1902, the two schools of thought were cemented and colloquially named, the Mendelians (Bateson, Saunders, and Punnett) in Cambridge, and the Biometricians (Weldon and Pearson) in London. The event was so explosive that 2 years later, when Weldon died suddenly of pneumonia, the New York Times described the scene in his obituary saying that "The debate, which was conducted before a large and somewhat agitated audience, resolved itself into a dialectical dual between the president of the section [Bateson] and Professor Weldon, and developed quite a considerable amount of heat" [[32]]. After Weldon's death, the raw bitterness of the Mendelian-Biometrician debate gave way to a quieter resentment that seethed between Pearson and Bateson for another decade, and Weldon's manuscript book remained in archives. [Extracted from the article]

Synthetic gene drive constructs could, in principle, provide the basis for highly efficient interventions to control disease vectors and other pest species. This efficiency derives in part from leveraging natural processes of dispersal and gene flow to spread the construct and its impacts from one population to another. However, sometimes (for example, with invasive species) only specific populations are in need of control, and impacts on non-target populations would be undesirable. Many gene drive designs use nucleases that recognise and cleave specific genomic sequences, and one way to restrict their spread would be to exploit sequence differences between target and non-target populations. In this paper we propose and model a series of low threshold double drive designs for population suppression, each consisting of two constructs, one imposing a reproductive load on the population and the other inserted into a differentiated locus and controlling the drive of the first. Simple deterministic, discrete-generation computer simulations are used to assess the alternative designs. We find that the simplest double drive designs are significantly more robust to pre-existing cleavage resistance at the differentiated locus than single drive designs, and that more complex designs incorporating sex ratio distortion can be more efficient still, even allowing for successful control when the differentiated locus is neutral and there is up to 50% pre-existing resistance in the target population. Similar designs can also be used for population replacement, with similar benefits. A population genomic analysis of CRISPR PAM sites in island and mainland populations of the malaria mosquito Anopheles gambiae indicates that the differentiation needed for our methods to work can exist in nature. Double drives should be considered when efficient but localised population genetic control is needed and there is some genetic differentiation between target and non-target populations. Author summary: Some disease vectors, invasive species, and other pests cannot be satisfactorily controlled with existing interventions, and new methods are required. Synthetic gene drive systems that are able to spread though populations because they are inherited at a greater-than-Mendelian rate have the potential to form the basis for new, highly efficient pest control measures. The most efficient such strategies use natural gene flow to spread a construct throughout a species' range, but if control is only desired in a particular location then these approaches may not be appropriate. As some of the most promising gene drive designs use nucleases to target specific DNA sequences, it ought to be possible to exploit sequence differences between target and non-target populations to restrict the spread and impact of a gene drive. In this paper we propose using two-construct "double drive" designs that exploit pre-existing sequence differences between target and non-target populations. Our approaches maintain the efficiencies associated with only small release rates being needed and can work if the differentiated locus is selectively neutral and if the differentiation is far from complete, and therefore expand the range of options to be considered in developing genetic approaches to control pest species. [ABSTRACT FROM AUTHOR]

HIV can evolve remarkably quickly in response to antiretroviral therapies and the immune system. This evolution stymies treatment effectiveness and prevents the development of an HIV vaccine. Consequently, there has been a great interest in using population genetics to disentangle the forces that govern the HIV adaptive landscape (selection, drift, mutation, and recombination). Traditional population genetics approaches look at the current state of genetic variation and infer the processes that can generate it. However, because HIV evolves rapidly, we can also sample populations repeatedly over time and watch evolution in action. In this paper, we demonstrate how time series data can bound evolutionary parameters in a way that complements and informs traditional population genetic approaches. Specifically, we focus on our recent paper (Feder et al., 2016, eLife), in which we show that, as improved HIV drugs have led to fewer patients failing therapy due to resistance evolution, less genetic diversity has been maintained following the fixation of drug resistance mutations. Because soft sweeps of multiple drug resistance mutations spreading simultaneously have been previously documented in response to the less effective HIV therapies used early in the epidemic, we interpret the maintenance of post-sweep diversity in response to poor therapies as further evidence of soft sweeps and therefore a high population mutation rate (θ) in these intra-patient HIV populations. Because improved drugs resulted in rarer resistance evolution accompanied by lower post-sweep diversity, we suggest that both observations can be explained by decreased population mutation rates and a resultant transition to hard selective sweeps. A recent paper (Harris et al., 2018, PLOS Genetics) proposed an alternative interpretation: Diversity maintenance following drug resistance evolution in response to poor therapies may have been driven by recombination during slow, hard selective sweeps of single mutations. Then, if better drugs have led to faster hard selective sweeps of resistance, recombination will have less time to rescue diversity during the sweep, recapitulating the decrease in post-sweep diversity as drugs have improved. In this paper, we use time series data to show that drug resistance evolution during ineffective treatment is very fast, providing new evidence that soft sweeps drove early HIV treatment failure. [ABSTRACT FROM AUTHOR]

The UK Biobank is a very large, prospective population-based cohort study across the United Kingdom. It provides unprecedented opportunities for researchers to investigate the relationship between genotypic information and phenotypes of interest. Multiple regression methods, compared with genome-wide association studies (GWAS), have already been showed to greatly improve the prediction performance for a variety of phenotypes. In the high-dimensional settings, the lasso, since its first proposal in statistics, has been proved to be an effective method for simultaneous variable selection and estimation. However, the large-scale and ultrahigh dimension seen in the UK Biobank pose new challenges for applying the lasso method, as many existing algorithms and their implementations are not scalable to large applications. In this paper, we propose a computational framework called batch screening iterative lasso (BASIL) that can take advantage of any existing lasso solver and easily build a scalable solution for very large data, including those that are larger than the memory size. We introduce snpnet, an R package that implements the proposed algorithm on top of glmnet and optimizes for single nucleotide polymorphism (SNP) datasets. It currently supports ℓ1-penalized linear model, logistic regression, Cox model, and also extends to the elastic net with ℓ1/ℓ2 penalty. We demonstrate results on the UK Biobank dataset, where we achieve competitive predictive performance for all four phenotypes considered (height, body mass index, asthma, high cholesterol) using only a small fraction of the variants compared with other established polygenic risk score methods. Author summary: With the advent and evolution of large-scale and comprehensive biobanks, there come up unprecedented opportunities for researchers to further uncover the complex landscape of human genetics. One major direction that attracts long-standing interest is the investigation of the relationships between genotypes and phenotypes. This includes but doesn't limit to the identification of genotypes that are significantly associated with the phenotypes, and the prediction of phenotypic values based on the genotypic information. Genome-wide association studies (GWAS) is a very powerful and widely used framework for the former task, having produced a number of very impactful discoveries. However, when it comes to the latter, its performance is fairly limited by the univariate nature. To address this, multiple regression methods have been suggested to fill in the gap. That said, challenges emerge as the dimension and the size of datasets both become large nowadays. In this paper, we present a novel computational framework that enables us to solve efficiently the entire lasso or elastic-net solution path on large-scale and ultrahigh-dimensional data, and therefore make simultaneous variable selection and prediction. Our approach can build on any existing lasso solver for small or moderate-sized problems, scale it up to a big-data solution, and incorporate other extensions easily. We provide a package snpnet that extends the glmnet package in R and optimizes for large phenotype-genotype data. On the UK Biobank, we observe competitive prediction performance of the lasso and the elastic-net for all four phenotypes considered from the UK Biobank. That said, the scope of our approach goes beyond genetic studies. It can be applied to general sparse regression problems and build scalable solution for a variety of distribution families based on existing solvers. [ABSTRACT FROM AUTHOR]

This paper considers how polygenic local adaptation and reproductive isolation between hybridizing populations is influenced by linkage disequilibria (LD) between loci, in scenarios where both gene flow and genetic drift counteract selection. It shows that the combined effects of multi-locus LD and genetic drift on allele frequencies at selected loci and on heterozygosity at neutral loci are predicted accurately by incorporating (deterministic) effective migration rates into the diffusion approximation (for selected loci) and into the structured coalescent (for neutral loci). Theoretical approximations are tested against individual-based simulations and used to investigate conditions for the maintenance of local adaptation on an island subject to one-way migration from a differently adapted mainland, and in an infinite-island population with two habitats under divergent selection. The analysis clarifies the conditions under which LD between sets of locally deleterious alleles allows these to be collectively eliminated despite drift, causing sharper and (under certain conditions) shifted migration thresholds for loss of adaptation. Local adaptation also has counter-intuitive effects on neutral (relative) divergence: FST is highest for a pair of subpopulations belonging to the same (rare) habitat, despite the lack of reproductive isolation between them. Author summary: Environmental adaptation often involves spatially heterogeneous selection at many genetic loci. Thus, the evolutionary consequences of hybridisation between populations adapted to different environments depend on the coupled dynamics of multiple loci under selection, migration and genetic drift, making them challenging to predict. Here, I introduce theoretical approximations that accurately capture the effect of such coupling on allele frequencies at individual loci, while also accounting for the stochastic effects of genetic drift. I then use these approximations to study hybridisation in a metapopulation consisting of many interconnected subpopulations, where each subpopulation belongs to one of two habitats under divergent selection. The analysis clarifies how subpopulations belonging to a rare habitat can maintain local adaptation despite high levels of migration if net selection against multi-locus genotypes is stronger than a threshold which depends on the relative abundances of the two habitats. Further, local adaptation in a metapopulation can significantly elevate FST between subpopulations belonging to the same habitat, even though these are not reproductively isolated. These findings highlight the importance of carefully considering the genetic architecture and spatial context of divergence when interpreting patterns of genomic differentiation between speciating populations. [ABSTRACT FROM AUTHOR]

Puf5, a Puf-family RNA-binding protein, binds to 3´ untranslated region of target mRNAs and negatively regulates their expression in Saccharomyces cerevisiae. The puf5Δ mutant shows pleiotropic phenotypes including a weakened cell wall, a temperature-sensitive growth, and a shorter lifespan. To further analyze a role of Puf5 in cell growth, we searched for a multicopy suppressor of the temperature-sensitive growth of the puf5Δ mutant in this study. We found that overexpression of CLB2 encoding B-type cyclin suppressed the temperature-sensitive growth of the puf5Δ mutant. The puf5Δ clb2Δ double mutant displayed a severe growth defect, suggesting that Puf5 positively regulates the expression of a redundant factor with Clb2 in cell cycle progression. We found that expression of CLB1 encoding a redundant B-type cyclin was decreased in the puf5Δ mutant, and that this decrease of the CLB1 expression contributed to the growth defect of the puf5Δ clb2Δ double mutant. Since Puf5 is a negative regulator of the gene expression, we hypothesized that Puf5 negatively regulates the expression of a factor that represses CLB1 expression. We found such a repressor, Ixr1, which is an HMGB (High Mobility Group box B) protein. Deletion of IXR1 restored the decreased expression of CLB1 caused by the puf5Δ mutation and suppressed the growth defect of the puf5Δ clb2Δ double mutant. The expression of IXR1 was negatively regulated by Puf5 in an IXR1 3´ UTR-dependent manner. Our results suggest that IXR1 mRNA is a physiologically important target of Puf5, and that Puf5 and Ixr1 contribute to the cell cycle progression through the regulation of the cell cycle-specific expression of CLB1. Author summary: Cell cycle progression is strictly regulated by cyclin-dependent kinases (CDKs). The activity of CDK is determined by the expression of cell cycle-specific cyclins. In this paper, we demonstrate a novel regulatory mechanism for expression of a cyclin, CLB1, in budding yeast provoked by the RNA-binding protein Puf5 and the HMGB protein Ixr1. The RNA-binding protein Puf5 binds to the 3' untranslated region of IXR1 mRNA, inducing sequential negative regulation of the Ixr1 protein levels. The Ixr1 protein negatively regulates CLB1 expression via the CLB1 promoter. This discovery of new regulators of cyclins indicates that the control of cyclin gene expression may be elaborately regulated in response to environmental conditions. [ABSTRACT FROM AUTHOR]

In recent work, Wang et al introduced the "Sum of Single Effects" (SuSiE) model, and showed that it provides a simple and efficient approach to fine-mapping genetic variants from individual-level data. Here we present new methods for fitting the SuSiE model to summary data, for example to single-SNP z-scores from an association study and linkage disequilibrium (LD) values estimated from a suitable reference panel. To develop these new methods, we first describe a simple, generic strategy for extending any individual-level data method to deal with summary data. The key idea is to replace the usual regression likelihood with an analogous likelihood based on summary data. We show that existing fine-mapping methods such as FINEMAP and CAVIAR also (implicitly) use this strategy, but in different ways, and so this provides a common framework for understanding different methods for fine-mapping. We investigate other common practical issues in fine-mapping with summary data, including problems caused by inconsistencies between the z-scores and LD estimates, and we develop diagnostics to identify these inconsistencies. We also present a new refinement procedure that improves model fits in some data sets, and hence improves overall reliability of the SuSiE fine-mapping results. Detailed evaluations of fine-mapping methods in a range of simulated data sets show that SuSiE applied to summary data is competitive, in both speed and accuracy, with the best available fine-mapping methods for summary data. Author summary: The goal of fine-mapping is to identify the genetic variants that causally affect some trait of interest. Fine-mapping is challenging because the genetic variants can be highly correlated due to a phenomenon called linkage disequilibrium (LD). The most successful current approaches to fine-mapping frame the problem as a variable selection problem, and here we focus on one such approach based on the "Sum of Single Effects" (SuSiE) model. The main contribution of this paper is to extend SuSiE to work with summary data, which is often accessible when the full genotype and phenotype data are not. In the process of extending SuSiE, we developed a new mathematical framework that helps to explain existing fine-mapping methods for summary data, why they work well (or not), and under what circumstances. In simulations, we show that SuSiE applied to summary data is competitive with the best available fine-mapping methods for summary data. We also show how different factors such as accuracy of the LD estimates can affect the quality of the fine-mapping. [ABSTRACT FROM AUTHOR]

Mendelian Randomisation (MR) is a powerful tool in epidemiology that can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilising genetic variants as instrumental variables (IVs) for the exposure. The effect estimates obtained from MR studies are often interpreted as the lifetime effect of the exposure in question. However, the causal effects of some exposures are thought to vary throughout an individual's lifetime with periods during which an exposure has a greater effect on a particular outcome. Multivariable MR (MVMR) is an extension of MR that allows for multiple, potentially highly related, exposures to be included in an MR estimation. MVMR estimates the direct effect of each exposure on the outcome conditional on all the other exposures included in the estimation. We explore the use of MVMR to estimate the direct effect of a single exposure at different time points in an individual's lifetime on an outcome. We use simulations to illustrate the interpretation of the results from such analyses and the key assumptions required. We show that causal effects at different time periods can be estimated through MVMR when the association between the genetic variants used as instruments and the exposure measured at those time periods varies. However, this estimation will not necessarily identify exact time periods over which an exposure has the most effect on the outcome. Prior knowledge regarding the biological basis of exposure trajectories can help interpretation. We illustrate the method through estimation of the causal effects of childhood and adult BMI on C-Reactive protein and smoking behaviour. Author summary: Mendelian Randomisation (MR) can be used to estimate whether a potential exposure has a causal effect on an outcome in the presence of a third (unobserved) variable that affects both of them and so biases the observed association between them. The effect estimates obtained from MR studies can be interpreted as the effect of the exposure on the outcome over the lifetime. However, for some exposures there may be periods during which the causal effect on an outcome is greater or lesser than other periods. Multivariable MR (MVMR) is an extension of MR that allows for estimation of the causal effect of multiple, potentially highly related, exposures. In this paper we investigate how MVMR can be used to estimate the causal effect of the same exposure at different points across the lifecourse. We show that these effects can be estimated through MVMR when the association between the genetic variants used as instruments and the exposure varies between measurements of the exposures. However, we find that this estimation will not necessarily identify exact time periods over which an exposure has the most effect on the outcome as different periods in the lifecourse can only be separated when they are differently associated with genetic variants. [ABSTRACT FROM AUTHOR]

Hyaluronan (HA) is a major extracellular matrix component whose tissue levels are dynamically regulated during embryonic development. Although the synthesis of HA has been shown to exert a substantial influence on embryonic morphogenesis, the functional importance of the catabolic aspect of HA turnover is poorly understood. Here, we demonstrate that the transmembrane hyaluronidase TMEM2 plays an essential role in neural crest development and the morphogenesis of neural crest derivatives, as evidenced by the presence of severe craniofacial abnormalities in Wnt1-Cre–mediated Tmem2 knockout (Tmem2CKO) mice. Neural crest cells (NCCs) are a migratory population of cells that gives rise to diverse cell lineages, including the craniofacial complex, the peripheral nervous system, and part of the heart. Analysis of Tmem2 expression during NCC formation and migration reveals that Tmem2 is expressed at the site of NCC delamination and in emigrating Sox9-positive NCCs. In Tmem2CKO embryos, the number of NCCs emigrating from the neural tube is greatly reduced. Furthermore, linage tracing reveals that the number of NCCs traversing the ventral migration pathway and the number of post-migratory neural crest derivatives are both significantly reduced in a Tmem2CKO background. In vitro studies using Tmem2-depleted mouse O9-1 neural crest cells demonstrate that Tmem2 expression is essential for the ability of these cells to form focal adhesions on and to migrate into HA-containing substrates. Additionally, we show that Tmem2-deficient NCCs exhibit increased apoptotic cell death in NCC-derived tissues, an observation that is corroborated by in vitro experiments using O9-1 cells. Collectively, our data demonstrate that TMEM2-mediated HA degradation plays an essential role in normal neural crest development. This study reveals the hitherto unrecognized functional importance of HA degradation in embryonic development and highlights the pivotal role of Tmem2 in the developmental process. Author summary: As a major component of the extracellular matrix, hyaluronan is particularly abundant in the extracellular matrix of embryonic tissues, where its expression is dynamically regulated during tissue morphogenetic processes. Tissue levels of hyaluronan are regulated not only by its synthesis but also by its degradation. Curiously, mice lacking known hyaluronidase molecules, including HYAL1 and HYAL2, exhibit minimal embryonic phenotypes. As a result, our understanding of the role of the catabolic aspect of hyaluronan metabolism in embryonic development is quite limited. Here, we show that TMEM2, a recently identified hyaluronidase that degrades hyaluronan on the cell surface, plays a critical role in the development of neural crest cells and their derivatives. Our analyses of Tmem2 conditional knockout mice, Tmem2 knock-in reporter mice, and in vitro cell cultures demonstrate that TMEM2 is essential for generating a tissue environment needed for efficient migration of neural crest cells from the neural tube. Our paper reveals for the first time that the degradation of hyaluronan plays a specific regulatory role in embryonic morphogenesis, and that dysregulation of hyaluronan degradation leads to severe developmental defects. [ABSTRACT FROM AUTHOR]

Thereafter it was only by 1) realizing that the phenotypes of I Esp-/- i and I Osteocalcin-/- i mice were mirror images of each other 2) performing genetic complementation experiments and 3) injecting osteocalcin in wild type mice or treating cells with osteocalcin that the notion that osteocalcin was a hormone was advanced [[6], [50], [53]-[55]]. These studies, through different I in vivo i and I in vitro i assays, all show that osteocalcin, and by that I mean bioactive (undercarboxylated) and not total osteocalcin, has the properties of a hormone. If a hormone is defined by what it does when in excess of baseline in normal animals, then unlike what the Perspective claims from its first to its last paragraph, neither of these papers addresses the question of whether osteocalcin is a hormone. [Extracted from the article]

Investigators have recently introduced powerful methods for population genetic inference that rely on supervised machine learning from simulated data. Despite their performance advantages, these methods can fail when the simulated training data does not adequately resemble data from the real world. Here, we show that this "simulation mis-specification" problem can be framed as a "domain adaptation" problem, where a model learned from one data distribution is applied to a dataset drawn from a different distribution. By applying an established domain-adaptation technique based on a gradient reversal layer (GRL), originally introduced for image classification, we show that the effects of simulation mis-specification can be substantially mitigated. We focus our analysis on two state-of-the-art deep-learning population genetic methods—SIA, which infers positive selection from features of the ancestral recombination graph (ARG), and ReLERNN, which infers recombination rates from genotype matrices. In the case of SIA, the domain adaptive framework also compensates for ARG inference error. Using the domain-adaptive SIA (dadaSIA) model, we estimate improved selection coefficients at selected loci in the 1000 Genomes CEU population. We anticipate that domain adaptation will prove to be widely applicable in the growing use of supervised machine learning in population genetics. Author summary: Population genetic simulation is a powerful tool in the study of evolution. A number of supervised machine learning methods have been developed that take advantage of inexpensive simulations as training data. Despite their outstanding performance in benchmarks, these models can fail when the simulated training data deviate from the real data. In this work, we employed domain adaptation techniques to address this "simulation mis-specification" problem by training the machine learning model jointly with simulated and real data. We performed extensive benchmark experiments to demonstrate the improvement of the domain-adaptive models over standard machine learning models in the presence of different types of mis-specification. In addition, we applied dadaSIA, a domain-adaptive selection inference model, to improve the estimates of selection coefficients at selected loci in a European population. The domain adaptation framework proposed in our work is widely applicable to models relying on synthetic training data and therefore opens the door to many more applications in population genetics. [ABSTRACT FROM AUTHOR]

Insertion of new material into the Escherichia coli peptidoglycan (PG) sacculus between the cytoplasmic membrane and the outer membrane requires a well-organized balance between synthetic and hydrolytic activities to maintain cell shape and avoid lysis. Since most bacteria carry multiple enzymes carrying the same type of PG hydrolytic activity, we know little about the specific function of given enzymes. Here we show that the DD-carboxy/endopeptidase PBP4 localizes in a PBP1A/LpoA and FtsEX dependent fashion at midcell during septal PG synthesis. Midcell localization of PBP4 requires its non-catalytic domain 3 of unknown function, but not the activity of PBP4 or FtsE. Microscale thermophoresis with isolated proteins shows that PBP4 interacts with NlpI and the FtsEX-interacting protein EnvC, an activator of amidases AmiA and AmiB, which are needed to generate denuded glycan strands to recruit the initiator of septal PG synthesis, FtsN. The domain 3 of PBP4 is needed for the interaction with NlpI and EnvC, but not PBP1A or LpoA. In vivo crosslinking experiments confirm the interaction of PBP4 with PBP1A and LpoA. We propose that the interaction of PBP4 with EnvC, whilst not absolutely necessary for mid-cell recruitment of either protein, coordinates the activities of PBP4 and the amidases, which affects the formation of denuded glycan strands that attract FtsN. Consistent with this model, we found that the divisome assembly at midcell was premature in cells lacking PBP4, illustrating how the complexity of interactions affect the timing of cell division initiation. Author summary: Peptidoglycan biosynthesis is a major target for antibacterials. The covalently closed peptidoglycan mesh, called sacculus, protects the bacterium from lysis due to its turgor. Sacculus growth is facilitated by the balanced activities of synthases and hydrolases, and disturbing this balance leads to cell lysis and bacterial death. Because of the large number and possible redundant functions of peptidoglycan hydrolases, it has been difficult to decipher their individual functions. In this paper we show that the DD-endopeptidase PBP4 localizes at midcell during septal peptidoglycan synthesis in Escherichia coli and is involved in the timing of the assembly and activation of the division machinery. This shows that inhibition of certain hydrolases could weaken the cells and might enhance antibiotic action. [ABSTRACT FROM AUTHOR]

In the ciliate Tetrahymena thermophila, lysosome-related organelles called mucocysts accumulate at the cell periphery where they secrete their contents in response to extracellular events, a phenomenon called regulated exocytosis. The molecular bases underlying regulated exocytosis have been extensively described in animals but it is not clear whether similar mechanisms exist in ciliates or their sister lineage, the Apicomplexan parasites, which together belong to the ecologically and medically important superphylum Alveolata. Beginning with a T. thermophila mutant in mucocyst exocytosis, we used a forward genetic approach to uncover MDL1 (Mucocyst Discharge with a LamG domain), a novel gene that is essential for regulated exocytosis of mucocysts. Mdl1p is a 40 kDa membrane glycoprotein that localizes to mucocysts, and specifically to a tip domain that contacts the plasma membrane when the mucocyst is docked. This sub-localization of Mdl1p, which occurs prior to docking, underscores a functional asymmetry in mucocysts that is strikingly similar to that of highly polarized secretory organelles in other Alveolates. A mis-sense mutation in the LamG domain results in mucocysts that dock but only undergo inefficient exocytosis. In contrast, complete knockout of MDL1 largely prevents mucocyst docking itself. Mdl1p is physically associated with 9 other proteins, all of them novel and largely restricted to Alveolates, and sedimentation analysis supports the idea that they form a large complex. Analysis of three other members of this putative complex, called MDD (for Mucocyst Docking and Discharge), shows that they also localize to mucocysts. Negative staining of purified MDD complexes revealed distinct particles with a central channel. Our results uncover a novel macromolecular complex whose subunits are conserved within alveolates but not in other lineages, that is essential for regulated exocytosis in T. thermophila. Author summary: All cells, whether single-celled protists or multicellular organisms, interact dynamically with their environments. One important mode of interaction is the release of molecules, a phenomenon called secretion, which can then modify the environment to promote the organism's well-being. Moreover, many cells have the capacity to rapidly adjust the pathways that underlie secretion, allowing them to tailor their secretory behavior in response to changes in their surroundings. A dramatic example of this is the capacity to synthesize and then store reservoirs of secretory molecules, whose eventual release is triggered when the cell senses specific environmental conditions. This phenomenon is called 'regulated exocytosis' and has been long studied in animals, because it serves as the basis for communication between different cells and tissues. Many single-celled organisms can also secrete via regulated exocytosis, and understanding the mechanisms involved could have practical consequences for developing therapies against several devastating human parasites. In this paper, we took a genetic approach to identifying factors involved in exocytosis in a single-celled protist, the ciliate Tetrahymena thermophila. We find that a novel gene, which appears to be present only in Tetrahymena and its relatively close evolutionary relatives, plays an important role in the pathway. Our results add another layer to recent findings that cells like Tetrahymena evolved unique mechanisms for regulated exocytosis, expanding our appreciation of cellular biodiversity. [ABSTRACT FROM AUTHOR]

To infer a causal relationship between two traits, several correlation-based causal direction (CD) methods have been proposed with the use of SNPs as instrumental variables (IVs) based on GWAS summary data for the two traits; however, none of the existing CD methods can deal with SNPs with correlated pleiotropy. Alternatively, reciprocal Mendelian randomization (MR) can be applied, which however may perform poorly in the presence of (unknown) invalid IVs, especially for bi-directional causal relationships. In this paper, first, we propose a CD method that performs better than existing CD methods regardless of the presence of correlated pleiotropy. Second, along with a simple but yet effective IV screening rule, we propose applying a closely related and state-of-the-art MR method in reciprocal MR, showing its almost identical performance to that of the new CD method when their model assumptions hold; however, if the modeling assumptions are violated, the new CD method is expected to better control type I errors. Notably bi-directional causal relationships impose some unique challenges beyond those for uni-directional ones, and thus requiring special treatments. For example, we point out for the first time several scenarios where a bi-directional relationship, but not a uni-directional one, can unexpectedly cause the violation of some weak modeling assumptions commonly required by many robust MR methods. We also offer some numerical support and a modeling justification for the application of our new methods (and more generally MR) to binary traits. Finally we applied the proposed methods to 12 risk factors and 4 common diseases, confirming mostly well-known uni-directional causal relationships, while identifying some novel and plausible bi-directional ones such as between body mass index and type 2 diabetes (T2D), and between diastolic blood pressure and stroke. Author summary: Inferring causal relationships between two traits based on observational data is one of the most important as well as challenging problems in scientific research. The potential impact of such an application on and beyond genetics/genomics is significant, such as in prioritizing molecular, clinical and behavioral targets for therapeutic and behavioral interventions. However, this problem, especially on a possibly bi-directional causal relationship between a pair of traits (in which the causal direction may be from either trait to the other, and both causal directions may be present at the same time), had been largely neglected until very recently. The increasing availability of large-scale GWAS summary data of various traits has popularized the development and application of Mendelian randomization (MR) methods for such a purpose. We point out some severe limitations with the current methods, mainly due to some new and unique challenges facing inference of bi-directional relationships as compared to that of uni-directional relationships that has been more commonly and exclusively considered in MR. By combining two basic ideas of bidirectional (or reciprocal) MR and Steiger's correlation-based screening methods, we develop two new approaches based on constrained maximum likelihood (cML) and GWAS summary data to infer causal effects (as in typical MR) and SNP-trait correlations (as in Steiger's method), called MR-cML and CD-cML respectively, demonstrating their similar effectiveness and more importantly their advantages over existing methods through extensive simulations, real data examples and statistical theory. In particular, our proposed two methods are robust to violations of all three valid IV assumptions, including presence of correlated pleiotropy. [ABSTRACT FROM AUTHOR]

With the advent of high throughput genetic data, there have been attempts to estimate heritability from genome-wide SNP data on a cohort of distantly related individuals using linear mixed model (LMM). Fitting such an LMM in a large scale cohort study, however, is tremendously challenging due to its high dimensional linear algebraic operations. In this paper, we propose a new method named PredLMM approximating the aforementioned LMM motivated by the concepts of genetic coalescence and Gaussian predictive process. PredLMM has substantially better computational complexity than most of the existing LMM based methods and thus, provides a fast alternative for estimating heritability in large scale cohort studies. Theoretically, we show that under a model of genetic coalescence, the limiting form of our approximation is the celebrated predictive process approximation of large Gaussian process likelihoods that has well-established accuracy standards. We illustrate our approach with extensive simulation studies and use it to estimate the heritability of multiple quantitative traits from the UK Biobank cohort. Author summary: In recent years, there is an increased interest of estimating heritability from genome-wide SNP data in large scale cohort studies. Here, we propose the PredLMM, a computationally rapid and memory-efficient linear mixed model for heritability estimation. The proposed approach can estimate SNP heritability on Biobank-scale datasets in a fraction of time compared to the existing mixed model based approaches. Along with the extensive simulations illustrating the precision and robustness of the PredLMM, we have also estimated heritability of several anthropometric traits from the UK Biobank cohort. [ABSTRACT FROM AUTHOR]

Shotgun metagenomics is a powerful tool to identify antimicrobial resistance (AMR) genes in microbiomes but has the limitation that extrachromosomal DNA, such as plasmids, cannot be linked with the host bacterial chromosome. Here we present a comprehensive laboratory and bioinformatics pipeline HAM-ART (Hi-C Assisted Metagenomics for Antimicrobial Resistance Tracking) optimised for the generation of metagenome-assembled genomes including both chromosomal and extrachromosomal AMR genes. We demonstrate the performance of the pipeline in a study comparing 100 pig faecal microbiomes from low- and high-antimicrobial use pig farms (organic and conventional farms). We found significant differences in the distribution of AMR genes between low- and high-antimicrobial use farms including a plasmid-borne lincosamide resistance gene exclusive to high-antimicrobial use farms in three species of Lactobacilli. The bioinformatics pipeline code is available at https://github.com/lkalmar/HAM-ART. Author summary: Antimicrobial resistance (AMR) is one of the biggest global health threats humanity is facing. Understanding the emergence and spread of AMR between different bacterial species is crucial for the development of effective countermeasures. In this paper we describe a user-friendly, affordable and comprehensive (laboratory and bioinformatics) workflow that can identify, associate and track AMR genes in bacteria. We demonstrate the efficiency and reliability of the method by comparing 50 faecal microbiomes from pig farms with high-antibiotic use (conventional farms), and 50 faecal microbiomes from pig farms with low-antibiotic use (organic farms). Our method provides a novel approach to resistance gene tracking, that also leads to the generation of high quality metagenomic assembled genomes that includes genes on mobile genetic elements, such as plasmids, that would not otherwise be included in these assembled genomes. [ABSTRACT FROM AUTHOR]

The formation of a diploid zygote is a highly complex cellular process that is entirely controlled by maternal gene products stored in the egg cytoplasm. This highly specialized transcriptional program is tightly controlled at the chromatin level in the female germline. As an extreme case in point, the massive and specific ovarian expression of the essential thioredoxin Deadhead (DHD) is critically regulated in Drosophila by the histone demethylase Lid and its partner, the histone deacetylase complex Sin3A/Rpd3, via yet unknown mechanisms. Here, we identified Snr1 and Mod(mdg4) as essential for dhd expression and investigated how these epigenomic effectors act with Lid and Sin3A to hyperactivate dhd. Using Cut&Run chromatin profiling with a dedicated data analysis procedure, we found that dhd is intriguingly embedded in an H3K27me3/H3K9me3-enriched mini-domain flanked by DNA regulatory elements, including a dhd promoter-proximal element essential for its expression. Surprisingly, Lid, Sin3a, Snr1 and Mod(mdg4) impact H3K27me3 and this regulatory element in distinct manners. However, we show that these effectors activate dhd independently of H3K27me3/H3K9me3, and that dhd remains silent in the absence of these marks. Together, our study demonstrates an atypical and critical role for chromatin regulators Lid, Sin3A, Snr1 and Mod(mdg4) to trigger tissue-specific hyperactivation within a unique heterochromatin mini-domain. Author summary: Multicellular development depends on a tight control of gene expression in each cell type. This relies on the coordinated activities of nuclear proteins that interact with DNA or its histone scaffold to promote or restrict gene transcription. For example, we previously showed that the histone modifying enzymes Lid and Sin3A/Rpd3 are required in Drosophila ovaries for the massive expression of deadhead (dhd), a gene encoding for a thioredoxin that is essential for fertility. In this paper, we have further identified two additional dhd regulators, Snr1 and Mod(mdg4) and dissected the mechanism behind hyperactivation of this gene. Using the epigenomic profiling method Cut&Run with a dedicated data analysis approach, we unexpectedly found that dhd is embedded in an unusual chromatin mini-domain featuring repressive histone modifications H3K27me3 and H3K9me3 and flanked by two regulatory elements. However, we further showed that Lid, Sin3A, Snr1 and Mod(mdg4) behave like obligatory activators of dhd independently of this mini-domain. Our study unveils how multiple broad-acting epigenomic effectors operate in non-canonical manners to ensure a critical and specialized gene activation event. These findings challenge our knowledge on these regulatory mechanisms and their roles in development and pathology. [ABSTRACT FROM AUTHOR]

The conserved adapter protein Scribble (Scrib) plays essential roles in a variety of cellular processes, including polarity establishment, proliferation, and directed cell migration. While the mechanisms through which Scrib promotes epithelial polarity are beginning to be unraveled, its roles in other cellular processes including cell migration remain enigmatic. In C. elegans, the Scrib ortholog LET-413 is essential for apical–basal polarization and junction formation in embryonic epithelia. However, whether LET-413 is required for postembryonic development or plays a role in migratory events is not known. Here, we use inducible protein degradation to investigate the functioning of LET-413 in larval epithelia. We find that LET-413 is essential in the epidermal epithelium for growth, viability, and junction maintenance. In addition, we identify a novel role for LET-413 in the polarized outgrowth of the epidermal seam cells. These stem cell-like epithelial cells extend anterior and posterior directed apical protrusions in each larval stage to reconnect to their neighbors. We show that the role of LET-413 in seam cell outgrowth is likely mediated largely by the junctional component DLG-1 discs large, which we demonstrate is also essential for directed outgrowth of the seam cells. Our data uncover multiple essential functions for LET-413 in larval development and show that the polarized outgrowth of the epithelial seam cells is controlled by LET-413 Scribble and DLG-1 Discs large. Author summary: Most cells in multicellular organisms are organized along a directional axis of cell polarity. One protein that is important for this polarized organization is the conserved polarity regulator Scribble. This protein has several functions, including forming the basolateral domains of cells, promoting the formation of cell junctions, and promoting cell migration. How Scribble performs these functions is not fully understood. In this paper we study the role of Scribble during larval development of the small nematode Caenorhabditis elegans using an inducible protein degradation system. We show that Scribble, called LET-413 in C. elegans, is essential in the epidermal epithelium for animal development, as depletion of LET-413 in only this tissue blocks growth. We also demonstrate that LET-413 is required for the polarized outgrowth of an epithelial cell type called the seam cells, a process resembling cell migration. Finally, we show that one major function of LET-413 in seam cell outgrowth is the localization of the junctional component Discs large (DLG-1), which we demonstrate is also essential for this process. Our data thus uncover multiple essential functions for LET-413 in larval development and provide new insights into how the directional outgrowth of epithelial seam cells is controlled. [ABSTRACT FROM AUTHOR]

Will be part of a Tenth Anniversary Collection [ABSTRACT FROM AUTHOR]

The early pathogenesis and underlying molecular causes of motor neuron degeneration in Parkinson's Disease (PD) remains unresolved. In the model organism Drosophila melanogaster, loss of the early-onset PD gene parkin (the ortholog of human PRKN) results in impaired climbing ability, damage to the indirect flight muscles, and mitochondrial fragmentation with swelling. These stressed mitochondria have been proposed to activate innate immune pathways through release of damage associated molecular patterns (DAMPs). Parkin-mediated mitophagy is hypothesized to suppress mitochondrial damage and subsequent activation of the cGAS/STING innate immunity pathway, but the relevance of this interaction in the fly remains unresolved. Using a combination of genetics, immunoassays, and RNA sequencing, we investigated a potential role for STING in the onset of parkin-null phenotypes. Our findings demonstrate that loss of Drosophila STING in flies rescues the thorax muscle defects and the climbing ability of parkin-/- mutants. Loss of STING also suppresses the disrupted mitochondrial morphology in parkin-/- flight muscles, suggesting unexpected feedback of STING on mitochondria integrity or activation of a compensatory mitochondrial pathway. In the animals lacking both parkin and sting, PINK1 is activated and cell death pathways are suppressed. These findings support a unique, non-canonical role for Drosophila STING in the cellular and organismal response to mitochondria stress. Author summary: Neurodegenerative diseases such as Parkinson's Disease have been associated with excessive inflammation. The anti-viral immune regulators STING (STimulator of INterferon Genes) and NF-kB have been indicated in mammalian studies to respond to unmitigated mitochondria damage linked to mutations in quality control pathways such as the Parkinson's Disease-associated Pink1/Parkin pathway. In the well-characterized model system Drosophila melanogaster (fruit fly), mutants lacking either of these genes show physical deformities and movement defects caused by mitochondria damage and progressive neurodegeneration, yielding a powerful and efficient model to study the effects of blocking the conserved STING pathway. We find that loss of STING prevents multiple phenotypes of the parkin mutant fly, by multiple complementary approaches. Further, this suppression extends to decreasing the severity of the mitochondria dysfunction normally seen in parkin mutant animals. Finally, assays of gene expression in animals lacking both STING and parkin reveal a significant increase in anti-stress enzymes, which have been previously linked to suppressing degeneration in parkin fly models. [ABSTRACT FROM AUTHOR]

Much has been written about gene modifying technologies (GMTs), with a particularly strong focus on human germline genome editing (HGGE) sparked by its unprecedented clinical research application in 2018, shocking the scientific community. This paper applies political, ethical, and social lenses to aspects of HGGE to uncover previously underexplored considerations that are important to reflect on in global discussions. By exploring 4 areas—(1) just distribution of HGGE benefits through a realist lens; (2) HGGE through a national interest lens; (3) "broad societal consensus" through a structural injustice lens; and (4) HGGE through a scientific trustworthiness lens—a broader perspective is offered, which ultimately aims to enrich further debates and inform well-considered solutions for developments in this field. The application of these lenses also brings to light the fact that all discussions about scientific developments involve a conscious or unconscious application of a lens that shapes the direction of our thinking. [ABSTRACT FROM AUTHOR]

Inherited genetic variation contributes to individual risk for many complex diseases and is increasingly being used for predictive patient stratification. Previous work has shown that genetic factors are not equally relevant to human traits across age and other contexts, though the reasons for such variation are not clear. Here, we introduce methods to infer the form of the longitudinal relationship between genetic relative risk for disease and age and to test whether all genetic risk factors behave similarly. We use a proportional hazards model within an interval-based censoring methodology to estimate age-varying individual variant contributions to genetic relative risk for 24 common diseases within the British ancestry subset of UK Biobank, applying a Bayesian clustering approach to group variants by their relative risk profile over age and permutation tests for age dependency and multiplicity of profiles. We find evidence for age-varying relative risk profiles in nine diseases, including hypertension, skin cancer, atherosclerotic heart disease, hypothyroidism and calculus of gallbladder, several of which show evidence, albeit weak, for multiple distinct profiles of genetic relative risk. The predominant pattern shows genetic risk factors having the greatest relative impact on risk of early disease, with a monotonic decrease over time, at least for the majority of variants, although the magnitude and form of the decrease varies among diseases. As a consequence, for diseases where genetic relative risk decreases over age, genetic risk factors have stronger explanatory power among younger populations, compared to older ones. We show that these patterns cannot be explained by a simple model involving the presence of unobserved covariates such as environmental factors. We discuss possible models that can explain our observations and the implications for genetic risk prediction. Author summary: The genes we inherit from our parents influence our risk for almost all diseases, from cancer to severe infections. With the explosion of genomic technologies, we are now able to use an individual's genome to make useful predictions about future disease risk. However, recent work has shown that the predictive value of genetic information varies by context, including age, sex and ethnicity. In this paper we introduce, validate and apply new statistical methods for investigating the relationship between age and the contributions of genetic risk. These methods allow us to ask questions such as whether relative risk is constant over time, precisely how relative risk changes over time and whether all genetic risk factors have similar age profiles. By applying the methods to data from the UK Biobank, a prospective study of 500,000 people, we show that there is a tendency for genetic relative risk to decline with increasing age. We consider a series of possible explanations for the observation and conclude that there must be processes acting that we are currently unaware of, such as distinct phases of life in which genetic risk manifests itself, or interactions between genes and the environment. [ABSTRACT FROM AUTHOR]

All of these animals have a functioning visual system, so how does the operation of the visual circuit change, given twice as many horizontal cells or retinal ganglion cells? Geneticists, or perhaps more accurately genomicists, are used to big science, enormous projects that take years to complete and consume vast sums of money: sequencing mammalian genomes, genotyping a million people, obtaining ever more extensive catalogs of epigenetic marks, and so on. Yet some mouse strains have nearly twice as many horizontal cells or retinal ganglion cells than others; strains with high numbers of cells of one type do not necessarily also have high numbers of the other [[31]]. Entitled "The mind of a mouse", a position paper in Cell described a visionary project to construct the map of the mouse brain down to the level of the synapse, requiring electron microscopy to obtain the necessary resolution [[1]]. [Extracted from the article]