multi-GPA-Tree: Statistical approach for pleiotropy informed and functional annotation tree guided prioritization of GWAS results.

Genome-wide association studies (GWAS) have successfully identified over two hundred thousand genotype-trait associations. Yet some challenges remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), most with small or moderate effect sizes, making them difficult to detect. Second, many complex traits share a common genetic basis due to 'pleiotropy' and and though few methods consider it, leveraging pleiotropy can improve statistical power to detect genotype-trait associations with weaker effect sizes. Third, currently available statistical methods are limited in explaining the functional mechanisms through which genetic variants are associated with specific or multiple traits. We propose multi-GPA-Tree to address these challenges. The multi-GPA-Tree approach can identify risk SNPs associated with single as well as multiple traits while also identifying the combinations of functional annotations that can explain the mechanisms through which risk-associated SNPs are linked with the traits. First, we implemented simulation studies to evaluate the proposed multi-GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that multi-GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs for multiple traits. Second, we applied multi-GPA-Tree to a systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), and to a Crohn's disease (CD) and ulcertive colitis (UC) GWAS, and functional annotation data including GenoSkyline and GenoSkylinePlus. Our results demonstrate that multi-GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits.

Pleiotropy promotes the evolution of inducible immune responses in a model of host-pathogen coevolution.

Components of immune systems face significant selective pressure to efficiently use organismal resources, mitigate infection, and resist parasitic manipulation. A theoretically optimal immune defense balances investment in constitutive and inducible immune components depending on the kinds of parasites encountered, but genetic and dynamic constraints can force deviation away from theoretical optima. One such potential constraint is pleiotropy, the phenomenon where a single gene affects multiple phenotypes. Although pleiotropy can prevent or dramatically slow adaptive evolution, it is prevalent in the signaling networks that compose metazoan immune systems. We hypothesized that pleiotropy is maintained in immune signaling networks despite slowed adaptive evolution because it provides some other advantage, such as forcing network evolution to compensate in ways that increase host fitness during infection. To study the effects of pleiotropy on the evolution of immune signaling networks, we used an agent-based modeling approach to evolve a population of host immune systems infected by simultaneously co-evolving parasites. Four kinds of pleiotropic restrictions on evolvability were incorporated into the networks, and their evolutionary outcomes were compared to, and competed against, non-pleiotropic networks. As the networks evolved, we tracked several metrics of immune network complexity, relative investment in inducible and constitutive defenses, and features associated with the winners and losers of competitive simulations. Our results suggest non-pleiotropic networks evolve to deploy highly constitutive immune responses regardless of parasite prevalence, but some implementations of pleiotropy favor the evolution of highly inducible immunity. These inducible pleiotropic networks are no less fit than non-pleiotropic networks and can out-compete non-pleiotropic networks in competitive simulations. These provide a theoretical explanation for the prevalence of pleiotropic genes in immune systems and highlight a mechanism that could facilitate the evolution of inducible immune responses.

Genetic correlation and gene-based pleiotropy analysis for four major neurodegenerative diseases with summary statistics.

Recent genome-wide association studies suggested shared genetic components between neurodegenerative diseases. However, pleiotropic association patterns among them remain poorly understood. We here analyzed 4 major neurodegenerative diseases including Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS), and found suggestively positive genetic correlation. We next implemented a gene-centric pleiotropy analysis with a powerful method called PLACO and detected 280 pleiotropic associations (226 unique genes) with these diseases. Functional analyses demonstrated that these genes were enriched in the pancreas, liver, heart, blood, brain, and muscle tissues; and that 42 pleiotropic genes exhibited drug-gene interactions with 341 drugs. Using Mendelian randomization, we discovered that AD and PD can increase the risk of developing ALS, and that AD and ALS can also increase the risk of developing FTD, respectively. Overall, this study provides in-depth insights into shared genetic components and causal relationship among the 4 major neurodegenerative diseases, indicating genetic overlap and causality commonly drive their co-occurrence. It also has important implications on the etiology understanding, drug development and therapeutic targets for neurodegenerative diseases.

Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model.

With the increasing availability of large-scale GWAS summary data on various traits, Mendelian randomization (MR) has become commonly used to infer causality between a pair of traits, an exposure and an outcome. It depends on using genetic variants, typically SNPs, as instrumental variables (IVs). The inverse-variance weighted (IVW) method (with a fixed-effect meta-analysis model) is most powerful when all IVs are valid; however, when horizontal pleiotropy is present, it may lead to biased inference. On the other hand, Egger regression is one of the most widely used methods robust to (uncorrelated) pleiotropy, but it suffers from loss of power. We propose a two-component mixture of regressions to combine and thus take advantage of both IVW and Egger regression; it is often both more efficient (i.e. higher powered) and more robust to pleiotropy (i.e. controlling type I error) than either IVW or Egger regression alone by accounting for both valid and invalid IVs respectively. We propose a model averaging approach and a novel data perturbation scheme to account for uncertainties in model/IV selection, leading to more robust statistical inference for finite samples. Through extensive simulations and applications to the GWAS summary data of 48 risk factor-disease pairs and 63 genetically uncorrelated trait pairs, we showcase that our proposed methods could often control type I error better while achieving much higher power than IVW and Egger regression (and sometimes than several other new/popular MR methods). We expect that our proposed methods will be a useful addition to the toolbox of Mendelian randomization for causal inference.

Transcriptome-Wide Profile of 25-Hydroxyvitamin D3 in Primary Immune Cells from Human Peripheral Blood.

Vitamin D3 is an essential micronutrient mediating pleiotropic effects in multiple tissues and cell types via its metabolite 1α,25-dihydroxyvitamin D3 (1,25(OH)2D3), which activates the transcription factor vitamin D receptor. In this study, we used peripheral blood mononuclear cells (PBMCs) obtained from five healthy adults and investigated transcriptome-wide, whether the precursor of 1,25(OH)2D3, 25-hydroxyvitamin D3 (25(OH)D3), has gene regulatory potential on its own. Applying thresholds of >2 in fold change of gene expression and <0.05 as a false discovery rate, in this ex vivo approach the maximal physiological concentration of 25(OH)D3 (250 nM (nmol/L)) none of the study participants had a significant effect on their PBMC transcriptome. In contrast, 1000 and 10,000 nM 25(OH)D3 regulated 398 and 477 genes, respectively, which is comparable to the 625 genes responding to 10 nM 1,25(OH)2D3. The majority of these genes displayed specificity to the tested individuals, but not to the vitamin D metabolite. Interestingly, the genes MYLIP (myosin regulatory light chain interacting protein) and ABCG1 (ATP binding cassette subfamily G member 1) showed to be specific targets of 10,000 nM 25(OH)D3. In conclusion, 100- and 1000-fold higher 25(OH)D3 concentrations than the reference 10 nM 1,25(OH)2D3 are able to affect the transcriptome of PBMCs with a profile comparable to that of 1,25(OH)2D3.

Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR.

Genome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with complex traits. However, a stringent significance threshold is required to identify robust genetic associations. Leveraging relevant auxiliary covariates has the potential to boost statistical power to exceed the significance threshold. Particularly, abundant pleiotropy and the non-random distribution of SNPs across various functional categories suggests that leveraging GWAS test statistics from related traits and/or functional genomic data may boost GWAS discovery. While type 1 error rate control has become standard in GWAS, control of the false discovery rate can be a more powerful approach. The conditional false discovery rate (cFDR) extends the standard FDR framework by conditioning on auxiliary data to call significant associations, but current implementations are restricted to auxiliary data satisfying specific parametric distributions, typically GWAS p-values for related traits. We relax these distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary covariates from arbitrary continuous distributions ("Flexible cFDR"). Our method can be applied iteratively, thereby supporting multi-dimensional covariate data. Through simulations we show that Flexible cFDR increases sensitivity whilst controlling FDR after one or several iterations. We further demonstrate its practical potential through application to an asthma GWAS, leveraging various functional genomic data to find additional genetic associations for asthma, which we validate in the larger, independent, UK Biobank data resource.

Genotype Phenotype Correlation in Dent Disease 2 and Review of the Literature: OCRL Gene Pleiotropism or Extreme Phenotypic Variability of Lowe Syndrome?

Dent disease is a rare X-linked renal tubulopathy due to CLCN5 and OCRL (DD2) mutations. OCRL mutations also cause Lowe syndrome (LS) involving the eyes, brain and kidney. DD2 is frequently described as a mild form of LS because some patients may present with extra-renal symptoms (ESs). Since DD2 is a rare disease and there are a low number of reported cases, it is still unclear whether it has a clinical picture distinct from LS. We retrospectively analyzed the phenotype and genotype of our cohort of 35 DD2 males and reviewed all published DD2 cases. We analyzed the distribution of mutations along the OCRL gene and evaluated the type and frequency of ES according to the type of mutation and localization in OCRL protein domains. The frequency of patients with at least one ES was 39%. Muscle findings are the most common ES (52%), while ocular findings are less common (11%). Analysis of the distribution of mutations revealed (1) truncating mutations map in the PH and linker domain, while missense mutations map in the 5-phosphatase domain, and only occasionally in the ASH-RhoGAP module; (2) five OCRL mutations cause both DD2 and LS phenotypes; (3) codon 318 is a DD2 mutational hot spot; (4) a correlation was found between the presence of ES and the position of the mutations along OCRL domains. DD2 is distinct from LS. The mutation site and the mutation type largely determine the DD2 phenotype.

Exploring the Pleiotropic Genes and Therapeutic Targets Associated with Heart Failure and Chronic Kidney Disease by Integrating metaCCA and SGLT2 Inhibitors' Target Prediction.

BACKGROUND: Previous studies have shown that heart failure (HF) and chronic kidney disease (CKD) have common genetic mechanisms, overlapping pathophysiological pathways, and therapeutic drug-sodium-glucose cotransporter 2 (SGLT2) inhibitors. METHODS: The genetic pleiotropy metaCCA method was applied on summary statistics data from two independent meta-analyses of GWAS comprising more than 1 million people to identify shared variants and pleiotropic effects between HF and CKD. Targets of SGLT2 inhibitors were predicted by SwissTargetPrediction and DrugBank databases. To refine all genes, we performed using versatile gene-based association study 2 (VEGAS2) and transcriptome-wide association studies (TWAS) for HF and CKD, respectively. Gene enrichment and KEGG pathway analyses were used to explore the potential functional significance of the identified genes and targets. RESULTS: After metaCCA analysis, 4,624 SNPs and 1,745 genes were identified to be potentially pleiotropic in the univariate and multivariate SNP-multivariate phenotype analyses, respectively. 21 common genes were detected in both metaCCA and SGLT2 inhibitors' target prediction. In addition, 169 putative pleiotropic genes were identified, which met the significance threshold both in metaCCA analysis and in the VEGAS2 or TWAS analysis for at least one disease. CONCLUSION: We identified novel variants associated with HF and CKD using effectively incorporating information from different GWAS datasets. Our analysis may provide new insights into HF and CKD therapeutic approaches based on the pleiotropic genes, common targets, and mechanisms by integrating the metaCCA method, TWAS and VEGAS2 analyses, and target prediction of SGLT2 inhibitors.

The antagonistic pleiotropy of insulin-like growth factor 1.

While insulin-like growth factor-1 (IGF-1) is a well-established modulator of aging and longevity in model organisms, its role in humans has been controversial. In this study, we used the UK Biobank (n = 440,185) to resolve previous ambiguities in the relationship between serum IGF-1 levels and clinical disease. We examined prospective associations of serum IGF-1 with mortality, dementia, vascular disease, diabetes, osteoporosis, and cancer, finding two generalized patterns: First, IGF-1 interacts with age to modify risk in a manner consistent with antagonistic pleiotropy; younger individuals with high IGF-1 are protected from disease, while older individuals with high IGF-1 are at increased risk for incident disease or death. Second, the association between IGF-1 and risk is generally U-shaped, indicating that both high and low levels of IGF-1 may be detrimental. With the exception of a more uniformly positive relationship between IGF-1 and cancer, these effects were remarkably consistent across a wide range of conditions, providing evidence for a unifying pathway that determines risk for most age-associated diseases. These data suggest that IGF-1 signaling could be harmful in older adults, who may actually benefit from the attenuation of biological growth pathways.

The molecular basis, genetic control and pleiotropic effects of local gene co-expression.

Nearby genes are often expressed as a group. Yet, the prevalence, molecular mechanisms and genetic control of local gene co-expression are far from being understood. Here, by leveraging gene expression measurements across 49 human tissues and hundreds of individuals, we find that local gene co-expression occurs in 13% to 53% of genes per tissue. By integrating various molecular assays (e.g. ChIP-seq and Hi-C), we estimate the ability of several mechanisms, such as enhancer-gene interactions, in distinguishing gene pairs that are co-expressed from those that are not. Notably, we identify 32,636 expression quantitative trait loci (eQTLs) which associate with co-expressed gene pairs and often overlap enhancer regions. Due to affecting several genes, these eQTLs are more often associated with multiple human traits than other eQTLs. Our study paves the way to comprehend trait pleiotropy and functional interpretation of QTL and GWAS findings. All local gene co-expression identified here is available through a public database ( https://glcoex.unil.ch/ ).

Type 2 Diabetes Mellitus and Amyotrophic Lateral Sclerosis: Genetic Overlap, Causality, and Mediation.

CONTEXT: Understanding phenotypic connection between type II diabetes (T2D) mellitus and amyotrophic lateral sclerosis (ALS) can offer valuable sight into shared disease etiology and have important implication in drug repositioning and therapeutic intervention. OBJECTIVE: This work aims to disentangle the nature of the inverse relationship between T2D mellitus and ALS. METHODS: Depending on summary statistics of T2D (n = 898 130) and ALS (n = 80 610), we estimated the genetic correlation between them and prioritized pleiotropic genes through a multiple-tissue expression quantitative trait loci-weighted integrative analysis and the conjunction conditional false discovery rate (ccFDR) method. We implemented mendelian randomization (MR) analyses to evaluate the causal relationship between the 2 diseases. A mediation analysis was performed to assess the mediating role of T2D in the pathway from T2D-related glycemic/anthropometric traits to ALS. RESULTS: We found supportive evidence of a common genetic foundation between T2D and ALS (rg = -0.223, P = .004) and identified 8 pleiotropic genes (ccFDR < 0.10). The MR analyses confirmed that T2D exhibited a neuroprotective effect on ALS, leading to an approximately 5% (95% CI, 0% ~ 9.6%, P = .038) reduction in disease risk. In contrast, no substantial evidence was observed that supported the causal influence of ALS on T2D. The mediation analysis revealed T2D can also serve as an active mediator for several glycemic/anthropometric traits, including high-density lipoprotein cholesterol, overweight, body mass index, obesity class 1, and obesity class 2, with the mediation effect estimated to be 0.024, -0.022, -0.041, -0.016, and -0.012, respectively. CONCLUSION: We provide new evidence supporting the observed inverse link between T2D and ALS, and revealed that a shared genetic component and causal association commonly drove such a relationship. We also demonstrate the mediating role of T2D standing in the pathway from T2D-related glycemic/anthropometric traits to ALS.

Transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits.

Structural variations of the human brain are heritable and highly polygenic traits, with hundreds of associated genes identified in recent genome-wide association studies (GWAS). Transcriptome-wide association studies (TWAS) can both prioritize these GWAS findings and also identify additional gene-trait associations. Here we perform cross-tissue TWAS analysis of 211 structural neuroimaging and discover 278 associated genes exceeding Bonferroni significance threshold of 1.04 × 10-8. The TWAS-significant genes for brain structures have been linked to a wide range of complex traits in different domains. Through TWAS gene-based polygenic risk scores (PRS) prediction, we find that TWAS PRS gains substantial power in association analysis compared to conventional variant-based GWAS PRS, and up to 6.97% of phenotypic variance (p-value = 7.56 × 10-31) can be explained in independent testing data sets. In conclusion, our study illustrates that TWAS can be a powerful supplement to traditional GWAS in imaging genetics studies for gene discovery-validation, genetic co-architecture analysis, and polygenic risk prediction.

Multivariate genome-wide analysis of immunoglobulin G N-glycosylation identifies new loci pleiotropic with immune function.

The N-glycosylation of immunoglobulin G (IgG) affects its structure and function. It has been demonstrated that IgG N-glycosylation patterns are inherited as complex quantitative traits. Genome-wide association studies identified loci harboring genes encoding enzymes directly involved in protein glycosylation as well as loci likely to be involved in regulation of glycosylation biochemical pathways. Many of these loci could be linked to immune functions and risk of inflammatory and autoimmune diseases. The aim of the present study was to discover and replicate new loci associated with IgG N-glycosylation and to investigate possible pleiotropic effects of these loci onto immune function and the risk of inflammatory and autoimmune diseases. We conducted a multivariate genome-wide association analysis of 23 IgG N-glycosylation traits measured in 8090 individuals of European ancestry. The discovery stage was followed up by replication in 3147 people and in silico functional analysis. Our study increased the total number of replicated loci from 22 to 29. For the discovered loci, we suggest a number of genes potentially involved in the control of IgG N-glycosylation. Among the new loci, two (near RNF168 and TNFRSF13B) were previously implicated in rare immune deficiencies and were associated with levels of circulating immunoglobulins. For one new locus (near AP5B1/OVOL1), we demonstrated a potential pleiotropic effect on the risk of asthma. Our findings underline an important link between IgG N-glycosylation and immune function and provide new clues to understanding their interplay.

Conserved pleiotropy of an ancient plant homeobox gene uncovered by cis-regulatory dissection.

Divergence of gene function is a hallmark of evolution, but assessing functional divergence over deep time is not trivial. The few alleles available for cross-species studies often fail to expose the entire functional spectrum of genes, potentially obscuring deeply conserved pleiotropic roles. Here, we explore the functional divergence of WUSCHEL HOMEOBOX9 (WOX9), suggested to have species-specific roles in embryo and inflorescence development. Using a cis-regulatory editing drive system, we generate a comprehensive allelic series in tomato, which revealed hidden pleiotropic roles for WOX9. Analysis of accessible chromatin and conserved cis-regulatory sequences identifies the regions responsible for this pleiotropic activity, the functions of which are conserved in groundcherry, a tomato relative. Mimicking these alleles in Arabidopsis, distantly related to tomato and groundcherry, reveals new inflorescence phenotypes, exposing a deeply conserved pleiotropy. We suggest that targeted cis-regulatory mutations can uncover conserved gene functions and reduce undesirable effects in crop improvement.

Power analysis of transcriptome-wide association study: Implications for practical protocol choice.

The transcriptome-wide association study (TWAS) has emerged as one of several promising techniques for integrating multi-scale 'omics' data into traditional genome-wide association studies (GWAS). Unlike GWAS, which associates phenotypic variance directly with genetic variants, TWAS uses a reference dataset to train a predictive model for gene expressions, which allows it to associate phenotype with variants through the mediating effect of expressions. Although effective, this core innovation of TWAS is poorly understood, since the predictive accuracy of the genotype-expression model is generally low and further bounded by expression heritability. This raises the question: to what degree does the accuracy of the expression model affect the power of TWAS? Furthermore, would replacing predictions with actual, experimentally determined expressions improve power? To answer these questions, we compared the power of GWAS, TWAS, and a hypothetical protocol utilizing real expression data. We derived non-centrality parameters (NCPs) for linear mixed models (LMMs) to enable closed-form calculations of statistical power that do not rely on specific protocol implementations. We examined two representative scenarios: causality (genotype contributes to phenotype through expression) and pleiotropy (genotype contributes directly to both phenotype and expression), and also tested the effects of various properties including expression heritability. Our analysis reveals two main outcomes: (1) Under pleiotropy, the use of predicted expressions in TWAS is superior to actual expressions. This explains why TWAS can function with weak expression models, and shows that TWAS remains relevant even when real expressions are available. (2) GWAS outperforms TWAS when expression heritability is below a threshold of 0.04 under causality, or 0.06 under pleiotropy. Analysis of existing publications suggests that TWAS has been misapplied in place of GWAS, in situations where expression heritability is low.

Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks.

Causal inference via Mendelian randomization requires making strong assumptions about horizontal pleiotropy, where genetic instruments are connected to the outcome not only through the exposure. Here, we present causal Graphical Analysis Using Genetics (cGAUGE), a pipeline that overcomes these limitations using instrument filters with provable properties. This is achievable by identifying conditional independencies while examining multiple traits. cGAUGE also uses ExSep (Exposure-based Separation), a novel test for the existence of causal pathways that does not require selecting instruments. In simulated data we illustrate how cGAUGE can reduce the empirical false discovery rate by up to 30%, while retaining the majority of true discoveries. On 96 complex traits from 337,198 subjects from the UK Biobank, our results cover expected causal links and many new ones that were previously suggested by correlation-based observational studies. Notably, we identify multiple risk factors for cardiovascular disease, including red blood cell distribution width.

Pleiotropy and epistasis within and between signaling pathways defines the genetic architecture of fungal virulence.

Cryptococcal disease is estimated to affect nearly a quarter of a million people annually. Environmental isolates of Cryptococcus deneoformans, which make up 15 to 30% of clinical infections in temperate climates such as Europe, vary in their pathogenicity, ranging from benign to hyper-virulent. Key traits that contribute to virulence, such as the production of the pigment melanin, an extracellular polysaccharide capsule, and the ability to grow at human body temperature have been identified, yet little is known about the genetic basis of variation in such traits. Here we investigate the genetic basis of melanization, capsule size, thermal tolerance, oxidative stress resistance, and antifungal drug sensitivity using quantitative trait locus (QTL) mapping in progeny derived from a cross between two divergent C. deneoformans strains. Using a "function-valued" QTL analysis framework that exploits both time-series information and growth differences across multiple environments, we identified QTL for each of these virulence traits and drug susceptibility. For three QTL we identified the underlying genes and nucleotide differences that govern variation in virulence traits. One of these genes, RIC8, which encodes a regulator of cAMP-PKA signaling, contributes to variation in four virulence traits: melanization, capsule size, thermal tolerance, and resistance to oxidative stress. Two major effect QTL for amphotericin B resistance map to the genes SSK1 and SSK2, which encode key components of the HOG pathway, a fungal-specific signal transduction network that orchestrates cellular responses to osmotic and other stresses. We also discovered complex epistatic interactions within and between genes in the HOG and cAMP-PKA pathways that regulate antifungal drug resistance and resistance to oxidative stress. Our findings advance the understanding of virulence traits among diverse lineages of Cryptococcus, and highlight the role of genetic variation in key stress-responsive signaling pathways as a major contributor to phenotypic variation.

Mendelian randomization integrating GWAS and mQTL data identified novel pleiotropic DNA methylation loci for neuropathology of Alzheimer's disease.

The pathogenesis of Alzheimer's disease (AD) remains largely unclear. Exploring the genetic/epigenetic loci showing pleiotropic association with the neuropathologies of AD may greatly enhance understanding of the mechanisms underlying the development of AD. In this study, using data from the Religious Orders Study and the Rush Memory and Aging Project, we undertook a Mendelian randomization approach integrating genome-wide association studies (GWASs) and DNA methylation quantitative trait locus data to explore pleiotropic epigenetic loci for AD neuropathologies, including amyloid-ß (Aß) load and tau-containing neurofibrillary tangle density. We performed GWASs of DNA methylation in brain tissues from 592 participants and mapped 60,595 cis-SNP-CpG pairs after correction for multiple testing. By linking cis-DNA methylation quantitative trait locus with GWAS results for Aß load and tau tangles, we identified 47 CpGs showing pleiotropic association with Aß load by the Mendelian randomization analysis. We then used gene expression data from 537 individuals and performed quantitative trait methylation analysis. We found that 18 of the 47 CpGs were in cis associated with 25 mRNAs/genes, comprising 41 unique CpG-mRNA/gene pairs. Our findings shed light on the role of DNA methylation in the pathogenesis of Aß.

PLEIO: a method to map and interpret pleiotropic loci with GWAS summary statistics.

Identifying and interpreting pleiotropic loci is essential to understanding the shared etiology among diseases and complex traits. A common approach to mapping pleiotropic loci is to meta-analyze GWAS summary statistics across multiple traits. However, this strategy does not account for the complex genetic architectures of traits, such as genetic correlations and heritabilities. Furthermore, the interpretation is challenging because phenotypes often have different characteristics and units. We propose PLEIO (Pleiotropic Locus Exploration and Interpretation using Optimal test), a summary-statistic-based framework to map and interpret pleiotropic loci in a joint analysis of multiple diseases and complex traits. Our method maximizes power by systematically accounting for genetic correlations and heritabilities of the traits in the association test. Any set of related phenotypes, binary or quantitative traits with different units, can be combined seamlessly. In addition, our framework offers interpretation and visualization tools to help downstream analyses. Using our method, we combined 18 traits related to cardiovascular disease and identified 13 pleiotropic loci, which showed four different patterns of associations.

Batesian mimicry has evolved with deleterious effects of the pleiotropic gene doublesex.

Dimorphic female-limited Batesian mimicry in the swallowtail butterfly Papilio polytes is regulated by the supergene locus H, harbouring the mimetic (H) and non-mimetic (h) doublesex (dsx) gene. In the present study, we demonstrated that dsx-H negatively affects the number of eggs laid, hatching rate, larval survival rate, and adult lifespan. When crossed with hh males, the number of eggs laid of mimetic females (genotype HH) was lower than that of non-mimetic females (hh). Moreover, hh and Hh females laid fewer eggs when crossed with HH males. The hatching and larval survival rates were lower when both female and male parents harboured dsx-H. The adult lifespan of HH females was shorter than that of hh females, while it was similar in males regardless of the genotype. These findings suggest the presence of a cost-benefit balance of Batesian mimicry, which is evolved to avoid predation but is accompanied by physiological deficits, in this species.