Abundant pleiotropy in human complex diseases and traits

Improved genetic discovery and fine-mapping resolution through multivariate latent factor analysis of high-dimensional traits

Genome-wide association studies (GWASs) of high-dimensional traits, such as blood cell or metabolic traits, often use univariate approaches, ignoring trait relationships. Biological mechanisms generating variation in high-dimensional traits can be captured parsimoniously through a GWAS of latent factors. Here, we introduce flashfmZero, a zero-correlation latent-factor-based multi-trait fine-mapping approach. In an application to 25 latent factors derived from 99 blood cell traits in the INTERVAL cohort, we show that latent factor GWASs enable the detection of signals generating sub-threshold associations with several blood cell traits. The 99% credible sets (CS99) from flashfmZero were equal to or smaller in size than those from univariate fine-mapping of blood cell traits in 87% of our comparisons. In all cases univariate latent factor CS99 contained those from flashfmZero. Our latent factor approaches can be applied to GWAS summary statistics and will enhance power for the discovery and fine-mapping of associations for many traits.

A latent outcome variable approach for Mendelian randomization using the stochastic expectation maximization algorithm

Mendelian randomization (MR) is a widely used tool to uncover causal relationships between exposures and outcomes. However, existing MR methods can suffer from inflated type I error rates and biased causal effects in the presence of invalid instruments. Our proposed method enhances MR analysis by augmenting latent phenotypes of the outcome, explicitly disentangling horizontal and vertical pleiotropy effects. This allows for explicit assessment of the exclusion restriction assumption and iteratively refines causal estimates through the expectation-maximization algorithm. This approach offers a unique and potentially more precise framework compared to existing MR methods. We rigorously evaluate our method against established MR approaches across diverse simulation scenarios, including balanced and directional pleiotropy, as well as violations of the Instrument Strength Independent of Direct Effect (InSIDE) assumption. Our findings consistently demonstrate superior performance of our method in terms of controlling type I error rates, bias, and robustness to genetic confounding, regardless of whether individual-level or summary data is used. Additionally, our method facilitates testing for directional horizontal pleiotropy and outperforms MR-Egger in this regard, while also effectively testing for violations of the InSIDE assumption. We apply our method to real data, demonstrating its effectiveness compared to traditional MR methods. This analysis reveals the causal effects of body mass index (BMI) on metabolic syndrome (MetS) and a composite MetS score calculated by the weighted sum of its component factors. While the causal relationship is consistent across most methods, our proposed method shows fewer violations of the exclusion restriction assumption, especially for MetS scores where horizontal pleiotropy persists and other methods suffer from inflation.

Low-dimensional genotype-fitness mapping across divergent environments suggests a limiting functions model of fitness

A central goal in evolutionary biology is to be able to predict the effect of a genetic mutation on fitness. This is a major challenge because fitness depends both on phenotypic changes due to the mutation, and how these phenotypes map onto fitness in a particular environment. Genotype, phenotype, and environment spaces are all extremely complex, rendering bottom-up prediction unlikely. Here we show, using a large collection of adaptive yeast mutants, that fitness across a set of lab environments can be well-captured by top-down, low-dimensional linear models that generate abstract genotype-phenotype-fitness maps. We find that these maps are low-dimensional not only in the environment where the adaptive mutants evolved, but also in more divergent environments. We further find that the genotype-phenotype-fitness spaces implied by these maps overlap only partially across environments. We argue that these patterns are consistent with a "limiting functions" model of fitness, whereby only a small number of limiting functions can be modified to affect fitness in any given environment. The pleiotropic side-effects on non-limiting functions are effectively hidden from natural selection locally, but can be revealed globally. These results combine to emphasize the importance of environmental context in genotype-phenotype-fitness mapping, and have implications for the predictability and trajectory of evolution in complex environments.

Quantifying pleiotropy through directed signaling networks: A synchronous Boolean network approach and in-silico pleiotropic scoring

Pleiotropy refers to a gene's ability to influence multiple phenotypes or traits. In the context of human genetic diseases, pleiotropy manifests as different pathological effects resulting from mutations in the same gene. This phenomenon plays a crucial role in understanding gene-gene interactions in system-level biological diseases. Previous studies have largely focused on pleiotropy within undirected molecular correlation networks, leaving a gap in examining pleiotropy induced by directed signaling networks, which can better explain dynamic gene-gene interactions. In this study, we utilized a synchronous Boolean network model to explore pleiotropic dynamics induced by various mutations in large-scale networks. We introduced an in-silico Pleiotropic Score (sPS) to quantify the impact of gene mutations and validated the model against observational pleiotropy data from the Human Phenotype Ontology (HPO). Our results indicate a significant correlation between sPS and network structural characteristics, including degree centrality and feedback loop involvement. The highest correlation was observed between closeness centrality and sPS (0.6), suggesting that genes more central in the network exhibit higher pleiotropic potential. Furthermore, genes involved in feedback loops demonstrated higher sPS values (p < 0.0001), supporting the role of feedback loops in amplifying pleiotropic behavior. Our model provides a novel approach for quantifying pleiotropy through directed network dynamics, complementing traditional observational methods.

Bayesian estimation of shared polygenicity identifies drug targets and repurposable medicines for human complex diseases

Background

Complex diseases may share portions of their polygenic architectures which can be leveraged to identify drug targets with low off-target potential or repurposable candidates. However, the literature lacks methods which can make these inferences at scale using publicly available data.

Methods

We introduce a Bayesian model to estimate the polygenic structure of a trait using only gene-based association test statistics from GWAS summary data and returns gene-level posterior risk probabilities (PRPs). PRPs were used to infer shared polygenicity between 496 trait pairs and we introduce measures that can prioritize drug targets with low off-target effects or drug repurposing potential.

Results

Across 32 traits, we estimated that 69.5 to 97.5% of disease-associated genes are shared between multiple traits, and the estimated number of druggable genes that were only associated with a single disease ranged from 1 (multiple sclerosis) to 59 (schizophrenia). Estimating the shared genetic architecture of ALS with all other traits identified the KIT gene as a potentially harmful drug target because of its deleterious association with triglycerides, but also identified TBK1 and SCN11B as putatively safer because of their non-association with any of the other 31 traits. We additionally found 21 genes which are candidate repourposable targets for Alzheimer's disease (AD) (e.g., PLEKHA1, PPIB) and 5 for ALS (e.g., GAK, DGKQ).

Conclusions

The sets of candidate drug targets which have limited off-target potential are generally smaller compared to the sets of pleiotropic and putatively repurposable drug targets, but both represent promising directions for future experimental studies.

A pleiotropy scan to discover new susceptibility loci for pancreatic ductal adenocarcinoma

Pleiotropic variants (i.e. genetic polymorphisms influencing more than one phenotype) are often associated with cancer risk. A scan of pleiotropic variants was successfully conducted 10 years ago in relation to pancreatic ductal adenocarcinoma susceptibility. However, in the last decade, genetic association studies performed on several human traits have greatly increased the number of known pleiotropic variants. Based on the hypothesis that variants already associated with a least one trait have a higher probability of association with other traits, 61 052 variants reported to be associated by at least one genome-wide association study with at least one human trait were tested in the present study consisting of two phases (discovery and validation), comprising a total of 16 055 pancreatic ductal adenocarcinoma (PDAC) cases and 212 149 controls. The meta-analysis of the two phases showed two loci (10q21.1-rs4948550 (P = 6.52 × 10-5) and 7q36.3-rs288762 (P = 3.03 × 10-5) potentially associated with PDAC risk. 10q21.1-rs4948550 shows a high degree of pleiotropy and it is also associated with colorectal cancer risk while 7q36.3-rs288762 is situated 28,558 base pairs upstream of the Sonic Hedgehog (SHH) gene, which is involved in the cell-differentiation process and PDAC etiopathogenesis. In conclusion, none of the single nucleotide polymorphisms (SNPs) showed a formally statistically significant association after correction for multiple testing. However, given their pleiotropic nature and association with various human traits including colorectal cancer, the two SNPs showing the best associations with PDAC risk merit further investigation through fine mapping and ad hoc functional studies.

A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally scalable analytical pipeline for functionally informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits in 61,838 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered and replicated new associations with lipid traits missed by single-trait analysis.

Inferring the energy cost of resistance to parasitic infection and its link to a trade-off

BACKGROUND

In infected hosts, immune responses trigger a systemic energy reallocation away from energy storage and growth, to fuel a costly defense program. The exact energy costs of immune defense are however unknown in general. Life history theory predicts that such costs underpin trade-offs between host disease resistance and other fitness related traits, yet this has been seldom assessed. Here we investigate immune energy cost induced by infection, and their potential link to a trade-off between host resistance and fat storage that we previously exposed in sheep divergently selected for resistance to a pathogenic helminth.

RESULTS

To this purpose, we developed a mathematical model of host-parasite interaction featuring individual changes in energy allocation over the course of infection. The model was fitted to data from an experimental infectious challenge in sheep from genetically resistant and susceptible lines to infer the magnitude of immune energy costs. A relatively small and transient immune energy cost in early infection best explained within-individual changes in growth, energy storage and parasite burden. Among individuals, predicted responses assuming this positive energy cost conformed to the observed trade-off between resistance and storage, whereas a cost-free scenario incorrectly predicted no trade-off.

CONCLUSIONS

Our mechanistic model fitting to experimental data provides novel insights into the link between energy costs and reallocation due to induced resistance within-individual, and trade-offs among individuals of selected lines. These will be useful to better understand the exact role of energy allocation in the evolution of host defenses, and for predicting the emergence of trade-offs in genetic selection.

ASSOCIATIONS BETWEEN EPILEPSY-RELATED POLYGENIC RISK AND BRAIN MORPHOLOGY IN CHILDHOOD

Temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) is associated with a complex genetic architecture, but the translation from genetic risk factors to brain vulnerability remains unclear. Here, we examined associations between epilepsy-related polygenic risk scores for HS (PRS-HS) and brain structure in a large sample of neurotypical children, and correlated these signatures with case-control findings in in multicentric cohorts of patients with TLE-HS. Imaging-genetic analyses revealed PRS-related cortical thinning in temporo-parietal and fronto-central regions, strongly anchored to distinct functional and structural network epicentres. Compared to disease-related effects derived from epilepsy case-control cohorts, structural correlates of PRS-HS mirrored atrophy and epicentre patterns in patients with TLE-HS. By identifying a potential pathway between genetic vulnerability and disease mechanisms, our findings provide new insights into the genetic underpinnings of structural alterations in TLE-HS and highlight potential imaging-genetic biomarkers for early risk stratification and personalized interventions.

Accelerometer-derived sleep metrics in adolescents reveal shared genetic influences with obesity and stress in a Brazilian birth cohort study

We aimed to test the association between sleep-related polygenic scores (PGSs) and accelerometer-based sleep metrics among Brazilian adolescents and to evaluate potential mechanisms underlying the association through the enrichment of obesity, and cortisol pathway-specific polygenic scores (PRSet). Utilizing data from The 2004 Pelotas (Brazil) Birth Cohort, sleep time window and sleep efficiency were measured at the 11-year-old follow-up using ActiGraph accelerometers. Three sleep PGSs were developed based on the most recent genome-wide association study of accelerometer-based sleep measures. PRSet, calculated using variants linked to body mass index (BMI) and plasmatic cortisol concentration, aimed to assess pleiotropic effects. Linear regression models, adjusted for sex and the first 10 principal components of ancestry, were employed to explore the impact of sleep PGS and specific-PRSet on sleep phenotypes. The number of nocturnal sleep episodes-PGS was positively associated with sleep time window (β = 2.306, SE: 0.92, p = .011). Nocturnal sleep episodes were also associated with sleep time window when restricted to BMI-PRSet (β = 2.682, SE: 0.912, competitive p = .003). Both the number of sleep episodes and sleep time window cortisol-PRSets were associated (β = .002, SE: 0.001, p = .013; β = .003, SE: 0.001, p = .003, respectively) and exhibited enrichment in molecular pathways (competitive p = .011; competitive p = .003, respectively) with sleep efficiency. Sleep polygenetic components observed in European adults may partially explain the accelerometer-based sleep time window in Brazilian adolescents. Specific BMI molecular pathways strengthened the association between sleep PGS and sleep time window, while the cortisol concentration pathway had a significant impact on the genetic liability for sleep efficiency. Our results suggest genetic overlap as a potential etiological pathway for sleep-related comorbidities, emphasizing common genetic mechanisms.

AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships

Despite the wealth of single-cell multi-omics data, it remains challenging to predict the consequences of novel genetic and chemical perturbations in the human body. It requires knowledge of molecular interactions at all biological levels, encompassing disease models and humans. Current machine learning methods primarily establish statistical correlations between genotypes and phenotypes but struggle to identify physiologically significant causal factors, limiting their predictive power. Key challenges in predictive modeling include scarcity of labeled data, generalization across different domains, and disentangling causation from correlation. In light of recent advances in multi-omics data integration, we propose a new artificial intelligence (AI)-powered biology-inspired multi-scale modeling framework to tackle these issues. This framework will integrate multi-omics data across biological levels, organism hierarchies, and species to predict genotype-environment-phenotype relationships under various conditions. AI models inspired by biology may identify novel molecular targets, biomarkers, pharmaceutical agents, and personalized medicines for presently unmet medical needs.

A multi-trait GWAS identifies novel genes influencing albuminuria

BACKGROUND

Albuminuria is common and is associated with increased risks of end-stage kidney disease and cardiovascular diseases, yet its underlying mechanism remains obscure. Previous genome-wide association studies (GWAS) for albuminuria did not consider gene pleiotropy and primarily focused on European ancestry populations. This study adopted a multi-trait analysis of GWAS (MTAG) approach to jointly analyze two vital kidney traits, estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR) to identify and prioritize the genes associated with UACR.

METHODS

Data from the Taiwan Biobank from 2012 to 2023 were analyzed. GWAS of UACR and eGFR were performed separately and the summary statistics from these GWAS were jointly analyzed using MTAG. The polygenic risk scores (PRS) of UACR were constructed for validation. The UACR-associated loci were further fine-mapped and prioritized based on their deleteriousness, eQTL associations and relatedness to Mendelian kidney diseases.

RESULTS

MTAG analysis of the UACR revealed 15 genetic loci, including 12 novel loci. The PRS for UACR was significantly associated with urinary albumin level (P < .001) and microalbuminuria (P = .001-.045). A list of priority genes was generated. Twelve genes with high priority included the albumin endocytic receptor gene LRP2 and ciliary gene IFT172.

CONCLUSIONS

The findings of this multi-trait GWAS suggest that primary cilia play a role in sensing mechanical stimuli, leading to albumin endocytosis. The priority list of genes warrants further translational investigation to reduce albuminuria.

Deciphering proteins in Alzheimer's disease: A new Mendelian randomization method integrated with AlphaFold3 for 3D structure prediction

Hidden confounding biases hinder identifying causal protein biomarkers for Alzheimer's disease in non-randomized studies. While Mendelian randomization (MR) can mitigate these biases using protein quantitative trait loci (pQTLs) as instrumental variables, some pQTLs violate core assumptions, leading to biased conclusions. To address this, we propose MR-SPI, a novel MR method that selects valid pQTL instruments using Leo Tolstoy's Anna Karenina principle and performs robust post-selection inference. Integrating MR-SPI with AlphaFold3, we developed a computational pipeline to identify causal protein biomarkers and predict 3D structural changes. Applied to genome-wide proteomics data from 54,306 UK Biobank participants and 455,258 subjects (71,880 cases and 383,378 controls) for a genome-wide association study of Alzheimer's disease, we identified seven proteins (TREM2, PILRB, PILRA, EPHA1, CD33, RET, and CD55) with structural alterations due to missense mutations. These findings offer insights into the etiology and potential drug targets for Alzheimer's disease.

Pleiotropy increases with gene age in six model multicellular eukaryotes

Fundamental traits of genes, including function, length and GC content, all vary with gene age. Pleiotropy, where a single gene affects multiple traits, arises through selection for novel traits and is expected to be removed from the genome through subfunctionalization following duplication events. It is unclear, however, how these opposing forces shape the prevalence of pleiotropy through time. We hypothesized that the prevalence of pleiotropy would be lowest in young genes, peak in middle aged genes, and then either decrease to a middling level in ancient genes or stay near the middle-aged peak, depending on the balance between exaptation and subfunctionalization. To address this question, we have calculated gene age and pleiotropic status for several model multicellular eukaryotes, including Homo sapiens, Mus musculus, Danio rerio, Drosophila melanogaster, Caenorhabditis elegans, and Arabidopsis thaliana. Gene age was determined by finding the most distantly related species that shared an ortholog using the Open Tree of Life and the Orthologous Matrix Database (OMAdb). Pleiotropic status was determined using both protein-protein interactions (STRINGdb) and associated biological processes (Gene Ontology). We found that middle-aged and ancient genes tend to be more pleiotropic than young genes, and that this relationship holds across all species evaluated and across both modalities of measuring pleiotropy. We also found absolute differences in the degree of pleiotropy based on gene functional class, but only when looking at biological process count. From these results we propose that there is a fundamental relationship between pleiotropy and gene age and further study of this relationship may shed light on the mechanism behind the functional changes genes undergo as they age.

Shared genetic architecture between COVID-19 and irritable bowel syndrome: a large-scale genome-wide cross-trait analysis

BACKGROUND

It has been reported that COVID-19 patients have an increased risk of developing IBS; however, the underlying genetic mechanisms of these associations remain largely unknown. The aim of this study was to investigate potential shared SNPs, genes, proteins, and biological pathways between COVID-19 and IBS by assessing pairwise genetic correlations and cross-trait genetic analysis.

MATERIALS AND METHODS

We assessed the genetic correlation between three COVID-19 phenotypes and IBS using linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL) methods. Two different sources of IBS data were combined using METAL, and the Multi-trait analysis of GWAS (MTAG) method was applied for multi-trait analysis to enhance statistical robustness and discover new genetic associations. Independent risk loci were examined using genome-wide complex trait analysis (GCTA)-conditional and joint analysis (COJO), multi-marker analysis of genomic annotation (MAGMA), and functional mapping and annotation (FUMA), integrating various QTL information and methods to further identify risk genes and proteins. Gene set variation analysis (GSVA) was employed to compute pleiotropic gene scores, and combined with immune infiltration algorithms, IBS patients were categorized into high and low immune infiltration groups.

RESULTS

We found a positive genetic correlation between COVID-19 infection, COVID-19 hospitalization, and IBS. Subsequent multi-trait analysis identified nine significantly associated genomic loci. Among these, eight genetic variants were closely related to the comorbidity of IBS and COVID-19. The study also highlighted four genes and 231 proteins associated with the susceptibility to IBS identified through various analytical strategies and a stratification approach for IBS risk populations.

CONCLUSIONS

Our study reveals a shared genetic architecture between these two diseases, providing new insights into potential biological mechanisms and laying the groundwork for more effective interventions.

A genome-wide cross-trait analysis identifying shared genetic basis and causal relationships between Hunner-type interstitial cystitis and autoimmune diseases in East Asian populations

PURPOSE

Epidemiological studies have demonstrated the clinical link between Hunner interstitial cystitis (HIC) and autoimmune diseases (ADs), suggesting potential shared genetic bases for their comorbidity. We aimed to investigate the shared genetic architecture and causal relationships between HIC and ADs.

METHODS

We conducted a genome-wide cross-trait study with ~170000 individuals of East Asian ancestry to investigate the shared architecture between HIC and ADs. Bidirectional Mendelian randomization (MR) was used to assess potential causal relationships and a multi-trait analysis of GWAS (MTAG) was conducted to identify their associated pleiotropic loci. Fine-mapping analysis narrowed candidate gene susceptibility loci and colocalization analysis was performed to identify shared variants at specific locus. Lastly, transcriptome-wide association (TWAS) and functional analysis were utilized to explore potential shared gene-tissue associations.

RESULTS

Through bidirectional MR analysis, we observed a positive causal effect of AIH(ORIVW=1.09, PIVW=1.00×10-3) and RA (ORIVW=1.47, PIVW<1.00×10-4) on HIC and a negative causal effect of UC on HIC (ORIVW=0.89, PIVW< 1.00×10-4). Furthermore, we unveiled a robust positive causal effect of HIC on T1D(ORConMix=1.05, PConMix=1.77×10-3). Cross-trait meta-analysis identified a total of 64 independent SNPs associated with HIC and ADs. Functional analysis revealed that the identified variants regulated gene expression in major tissues belonging to the autoimmune system.

CONCLUSIONS

Our findings might offer insights into the shared underlying etiology of HIC and ADs.

Identifying Common Genetic Etiologies Between Inflammatory Bowel Disease and Related Immune-Mediated Diseases

BACKGROUND

Patients with inflammatory bowel disease (IBD) have an increased risk of developing immune-mediated diseases. However, the genetic basis of IBD is complex, and an integrated approach should be used to elucidate the complex genetic relationship between IBD and immune-mediated diseases.

METHODS

The genetic relationship between IBD and 16 immune-mediated diseases was examined using linkage disequilibrium score regression. GWAS data were synthesized from two IBD databases using the METAL, and multi-trait analysis of genome-wide association studies was performed to enhance statistical robustness and identify novel genetic associations. Independent risk loci were meticulously examined using conditional and joint genome-wide multi-trait analysis, multi-marker analysis of genomic annotation, and functional mapping and annotation of significant genetic loci, integrating the information of quantitative trait loci and different methodologies to identify risk-related genes and proteins.

RESULTS

The results revealed four immune-mediated diseases (AS, psoriasis, iridocyclitis, and PsA) with a significant relationship with IBD. The multi-trait analysis revealed 909 gene loci of statistical significance. Of these loci, 28 genetic variants were closely related to IBD, and 7 single-nucleotide polymorphisms represented novel independent risk loci. In addition, 14 genes and 514 proteins were found to be associated with susceptibility to immune-mediated diseases. Notably, IL1RL1 emerged as a key player, present within pleiotropic genes across multiple protein databases, highlighting its potential as a therapeutic target.

CONCLUSIONS

This study suggests that the common polygenic determinants between IBD and immune-mediated diseases are widely distributed across the genome. The findings not only support a shared genetic relationship between IBD and immune-mediated diseases but also provide novel therapeutic targets for these diseases.

Genome-Wide and Exome-Wide Association Study Identifies Genetic Underpinning of Comorbidity between Myocardial Infarction and Severe Mental Disorders

BACKGROUND

Myocardial Infarction (MI) and severe mental disorders (SMDs) are two types of highly prevalent and complex disorders and seem to have a relatively high possibility of mortality. However, the contributions of common and rare genetic variants to their comorbidity arestill unclear.

METHODS

We conducted a combined genome-wide association study (GWAS) and exome-wide association study (EWAS) approach.

RESULTS

Using gene-based and gene-set association analyses based on the results of GWAS, we found the common genetic underpinnings of nine genes (GIGYF2, KCNJ13, PCCB, STAG1, HLA-C, HLA-B, FURIN, FES, and SMG6) and nine pathways significantly shared between MI and SMDs. Through Mendelian randomization analysis, we found that twenty-seven genes were potential causal genes for SMDs and MI. Based on the exome sequencing data of MI and SMDs patients from the UK Biobank, we found that MUC2 was exome-wide significant in the two diseases. The gene-set analyses of the exome-wide association study indicated that pathways related to insulin processing androgen catabolic process and angiotensin receptor binding may be involved in the comorbidity between SMDs and MI. We also found that six candidate genes were reported to interact with known therapeutic drugs based on the drug-gene interaction information in DGIdb.

CONCLUSIONS

Altogether, this study revealed the overlap of common and rare genetic underpinning between SMDs and MI and may provide useful insights for their mechanism study and therapeutic investigations.

GENIUS-MAWII: for robust Mendelian randomization with many weak invalid instruments

Mendelian randomization (MR) addresses causal questions using genetic variants as instrumental variables. We propose a new MR method, G-Estimation under No Interaction with Unmeasured Selection (GENIUS)-MAny Weak Invalid IV, which simultaneously addresses the 2 salient challenges in MR: many weak instruments and widespread horizontal pleiotropy. Similar to MR-GENIUS, we use heteroscedasticity of the exposure to identify the treatment effect. We derive influence functions of the treatment effect, and then we construct a continuous updating estimator and establish its asymptotic properties under a many weak invalid instruments asymptotic regime by developing novel semiparametric theory. We also provide a measure of weak identification, an overidentification test, and a graphical diagnostic tool.

Parallel evolution of integrated craniofacial traits in trophic specialist pupfishes

Populations may adapt to similar environments via parallel or non-parallel genetic changes, but the frequency of these alternative mechanisms and underlying contributing factors are still poorly understood outside model systems. We used QTL mapping to investigate the genetic basis of highly divergent craniofacial traits between the scale-eater (Cyprinodon desquamator) and molluscivore (C. brontotheroides) pupfish adapting to two different hypersaline lake environments on San Salvador Island, Bahamas. We lab-reared F2 scale-eater x molluscivore intercrosses from two different lake populations, estimated linkage maps, scanned for significant QTL for 29 skeletal and craniofacial traits, female mate preference, and sex. We compared the location of QTL between lakes to quantify parallel and non-parallel genetic changes. We detected significant QTL for six craniofacial traits in at least one lake. However, nearly all shared QTL loci were associated with a different craniofacial trait within each lake. Therefore, our estimate of parallel evolution of craniofacial genetic architecture could range from one out of six identical trait QTL (low parallelism) to five out of six integrated trait QTL (high parallelism). We suggest that pleiotropy and trait integration can affect estimates of parallel evolution, particularly within rapid radiations. We also observed increased adaptive introgression in shared QTL regions, suggesting that gene flow contributed to parallel evolution. Overall, our results suggest that the same genomic regions may contribute to parallel adaptation across integrated suites of craniofacial traits, rather than specific traits, and highlight the need for a more expansive definition of parallel evolution.

Pleiotropy alleviates the fitness costs associated with resource allocation trade-offs in immune signalling networks

Many genes and signalling pathways within plant and animal taxa drive the expression of multiple organismal traits. This form of genetic pleiotropy instigates trade-offs among life-history traits if a mutation in the pleiotropic gene improves the fitness contribution of one trait at the expense of another. Whether or not pleiotropy gives rise to conflict among traits, however, likely depends on the resource costs and timing of trait deployment during organismal development. To investigate factors that could influence the evolutionary maintenance of pleiotropy in gene networks, we developed an agent-based model of co-evolution between parasites and hosts. Hosts comprise signalling networks that must faithfully complete a developmental programme while also defending against parasites, and trait signalling networks could be independent or share a pleiotropic component as they evolved to improve host fitness. We found that hosts with independent developmental and immune networks were significantly more fit than hosts with pleiotropic networks when traits were deployed asynchronously during development. When host genotypes directly competed against each other, however, pleiotropic hosts were victorious regardless of trait synchrony because the pleiotropic networks were more robust to parasite manipulation, potentially explaining the abundance of pleiotropy in immune systems despite its contribution to life history trade-offs.

Regulatory elements in SEM1-DLX5-DLX6 (7q21.3) locus contribute to genetic control of coronal nonsyndromic craniosynostosis and bone density-related traits

Purpose

The etiopathogenesis of coronal nonsyndromic craniosynostosis (cNCS), a congenital condition defined by premature fusion of 1 or both coronal sutures, remains largely unknown.

Methods

We conducted the largest genome-wide association study of cNCS followed by replication, fine mapping, and functional validation of the most significant region using zebrafish animal model.

Results

Genome-wide association study identified 6 independent genome-wide-significant risk alleles, 4 on chromosome 7q21.3 SEM1-DLX5-DLX6 locus, and their combination conferred over 7-fold increased risk of cNCS. The top variants were replicated in an independent cohort and showed pleiotropic effects on brain and facial morphology and bone mineral density. Fine mapping of 7q21.3 identified a craniofacial transcriptional enhancer (eDlx36) within the linkage region of the top variant (rs4727341; odds ratio [95% confidence interval], 0.48[0.39-0.59]; P = 1.2E-12) that was located in SEM1 intron and enriched in 4 rare risk variants. In zebrafish, the activity of the transfected human eDlx36 enhancer was observed in the frontonasal prominence and calvaria during skull development and was reduced when the 4 rare risk variants were introduced into the sequence.

Conclusion

Our findings support a polygenic nature of cNCS risk and functional role of craniofacial enhancers in cNCS susceptibility with potential broader implications for bone health.

The full spectrum of SLC22 OCT1 mutations illuminates the bridge between drug transporter biophysics and pharmacogenomics

Mutations in transporters can impact an individual's response to drugs and cause many diseases. Few variants in transporters have been evaluated for their functional impact. Here, we combine saturation mutagenesis and multi-phenotypic screening to dissect the impact of 11,213 missense single-amino-acid deletions, and synonymous variants across the 554 residues of OCT1, a key liver xenobiotic transporter. By quantifying in parallel expression and substrate uptake, we find that most variants exert their primary effect on protein abundance, a phenotype not commonly measured alongside function. Using our mutagenesis results combined with structure prediction and molecular dynamic simulations, we develop accurate structure-function models of the entire transport cycle, providing biophysical characterization of all known and possible human OCT1 polymorphisms. This work provides a complete functional map of OCT1 variants along with a framework for integrating functional genomics, biophysical modeling, and human genetics to predict variant effects on disease and drug efficacy.

Identifying latent genetic interactions in genome-wide association studies using multiple traits

The "missing" heritability of complex traits may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. We propose a new kernel-based method called Latent Interaction Testing (LIT) to screen for genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Using simulated data, we demonstrate that LIT increases power to detect latent genetic interactions compared to univariate methods. We then apply LIT to obesity-related traits in the UK Biobank and detect variants with interactive effects near known obesity-related genes (URL: https://CRAN.R-project.org/package=lit ).

Shared genetic architecture and causal relationship between liver and heart disease

This study investigates the relationship and genetic mechanisms of liver and heart diseases, focusing on the liver-heart axis (LHA) as a fundamental biological basis. Through genome-wide association study analysis, we explore shared genes and pathways related to LHA. Shared genetic factors are found in 8 out of 20 pairs, indicating genetic correlations. The analysis reveals 53 loci with pleiotropic effects, including 8 loci exhibiting shared causality across multiple traits. Based on SNP-p level tissue-specific multi-marker analysis of genomic annotation (MAGMA) analysis demonstrates significant enrichment of pleiotropy in liver and heart diseases within different cardiovascular tissues and female reproductive appendages. Gene-specific MAGMA analysis identifies 343 pleiotropic genes associated with various traits; these genes show tissue-specific enrichment primarily in the liver, cardiovascular system, and other tissues. Shared risk loci between immune cells and both liver and cardiovascular diseases are also discovered. Mendelian randomization analyses provide support for causal relationships among the investigated trait pairs.

Convergent genetic adaptation of Escherichia coli in minimal media leads to pleiotropic divergence

Adaptation in an environment can either be beneficial, neutral or disadvantageous in another. To test the genetic basis of pleiotropic behaviour, we evolved six lines of E. coli independently in environments where glucose and galactose were the sole carbon sources, for 300 generations. All six lines in each environment exhibit convergent adaptation in the environment in which they were evolved. However, pleiotropic behaviour was observed in several environmental contexts, including other carbon environments. Genome sequencing reveals that mutations in global regulators rpoB and rpoC cause this pleiotropy. We report three new alleles of the rpoB gene, and one new allele of the rpoC gene. The novel rpoB alleles confer resistance to Rifampicin, and alter motility. Our results show how single nucleotide changes in the process of adaptation in minimal media can lead to wide-scale pleiotropy, resulting in changes in traits that are not under direct selection.

Abundant pleiotropy across neuroimaging modalities identified through a multivariate genome-wide association study

Genetic pleiotropy is abundant across spatially distributed brain characteristics derived from one neuroimaging modality (e.g. structural, functional or diffusion magnetic resonance imaging [MRI]). A better understanding of pleiotropy across modalities could inform us on the integration of brain function, micro- and macrostructure. Here we show extensive genetic overlap across neuroimaging modalities at a locus and gene level in the UK Biobank (N = 34,029) and ABCD Study (N = 8607). When jointly analysing phenotypes derived from structural, functional and diffusion MRI in a genome-wide association study (GWAS) with the Multivariate Omnibus Statistical Test (MOSTest), we boost the discovery of loci and genes beyond previously identified effects for each modality individually. Cross-modality genes are involved in fundamental biological processes and predominantly expressed during prenatal brain development. We additionally boost prediction of psychiatric disorders by conditioning independent GWAS on our multimodal multivariate GWAS. These findings shed light on the shared genetic mechanisms underlying variation in brain morphology, functional connectivity, and tissue composition.

Reducing Epistasis and Pleiotropy Can Avoid the Survival of the Flattest Tragedy

This study investigates whether reducing epistasis and pleiotropy enhances mutational robustness in evolutionary adaptation, utilizing an indirect encoded model within the "survival of the flattest" (SoF) fitness landscape. By simulating genetic variations and their phenotypic consequences, we explore organisms' adaptive mechanisms to maintain positions on higher, narrower evolutionary peaks amidst environmental and genetic pressures. Our results reveal that organisms can indeed sustain their advantageous positions by minimizing the complexity of genetic interactions-specifically, by reducing the levels of epistasis and pleiotropy. This finding suggests a counterintuitive strategy for evolutionary stability: simpler genetic architectures, characterized by fewer gene interactions and multifunctional genes, confer a survival advantage by enhancing mutational robustness. This study contributes to our understanding of the genetic underpinnings of adaptability and robustness, challenging traditional views that equate complexity with fitness in dynamic environments.

Chromosomal single-strand break repair and neurological disease: Implications on transcription and emerging genomic tools

Cells are constantly exposed to various sources of DNA damage that pose a threat to their genomic integrity. One of the most common types of DNA breaks are single-strand breaks (SSBs). Mutations in the repair proteins that are important for repairing SSBs have been reported in several neurological disorders. While several tools have been utilised to investigate SSBs in cells, it was only through recent advances in genomics that we are now beginning to understand the architecture of the non-random distribution of SSBs and their impact on key cellular processes such as transcription and epigenetic remodelling. Here, we discuss our current understanding of the genome-wide distribution of SSBs, their link to neurological disorders and summarise recent technologies to investigate SSBs at the genomic level.

A common allele increases endometrial Wnt4 expression, with antagonistic implications for pregnancy, reproductive cancers, and endometriosis

The common human SNP rs3820282 is associated with multiple phenotypes including gestational length and likelihood of endometriosis and cancer, presenting a paradigmatic pleiotropic variant. Deleterious pleiotropic mutations cause the co-occurrence of disorders either within individuals, or across population. When adverse and advantageous effects are combined, pleiotropy can maintain high population frequencies of deleterious alleles. To reveal the causal molecular mechanisms of this pleiotropic SNP, we introduced this substitution into the mouse genome by CRISPR/Cas 9. Previous work showed that rs3820282 introduces a high-affinity estrogen receptor alpha-binding site at the Wnt4 locus. Here, we show that this mutation upregulates Wnt4 transcription in endometrial stroma, following the preovulatory estrogen peak. Effects on uterine transcription include downregulation of epithelial proliferation and induction of progesterone-regulated pro-implantation genes. We propose that these changes increase uterine permissiveness to embryo invasion, whereas they decrease resistance to invasion by cancer and endometriotic foci in other estrogen-responsive tissues.

Pleiotropic genetic association analysis with multiple phenotypes using multivariate response best-subset selection

Genetic pleiotropy refers to the simultaneous association of a gene with multiple phenotypes. It is widely distributed in the whole genome and can help to understand the common genetic mechanism of diseases or traits. In this study, a multivariate response best-subset selection (MRBSS) model based pleiotropic association analysis method is proposed. Different from the traditional genetic association model, the high-dimensional genotypic data are viewed as response variables while the multiple phenotypic data as predictor variables. Moreover, the response best-subset selection procedure is converted into an 0-1 integer optimization problem by introducing a separation parameter and a tuning parameter. Furthermore, the model parameters are estimated by using the curve search under the modified Bayesian information criterion. Simulation experiments show that the proposed method MRBSS remarkably reduces the computational time, obtains higher statistical power under most of the considered scenarios, and controls the type I error rate at a low level. The application studies in the datasets of maize yield traits and pig lipid traits further verifies the effectiveness.

Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies using multi-layer network

MOTIVATION

Genome-wide association studies is an essential tool for analyzing associations between phenotypes and single nucleotide polymorphisms (SNPs). Most of binary phenotypes in large biobanks are extremely unbalanced, which leads to inflated type I error rates for many widely used association tests for joint analysis of multiple phenotypes. In this article, we first propose a novel method to construct a Multi-Layer Network (MLN) using individuals with at least one case status among all phenotypes. Then, we introduce a computationally efficient community detection method to group phenotypes into disjoint clusters based on the MLN. Finally, we propose a novel approach, MLN with Omnibus (MLN-O), to jointly analyse the association between phenotypes and a SNP. MLN-O uses the score test to test the association of each merged phenotype in a cluster and a SNP, then uses the Omnibus test to obtain an overall test statistic to test the association between all phenotypes and a SNP.

RESULTS

We conduct extensive simulation studies to reveal that the proposed approach can control type I error rates and is more powerful than some existing methods. Meanwhile, we apply the proposed method to a real data set in the UK Biobank. Using phenotypes in Chapter XIII (Diseases of the musculoskeletal system and connective tissue) in the UK Biobank, we find that MLN-O identifies more significant SNPs than other methods we compare with.

AVAILABILITY AND IMPLEMENTATION

https://github.com/Hongjing-Xie/Multi-Layer-Network-with-Omnibus-MLN-O.

Leveraging information between multiple population groups and traits improves fine-mapping resolution

Statistical fine-mapping helps to pinpoint likely causal variants underlying genetic association signals. Its resolution can be improved by (i) leveraging information between traits; and (ii) exploiting differences in linkage disequilibrium structure between diverse population groups. Using association summary statistics, MGflashfm jointly fine-maps signals from multiple traits and population groups; MGfm uses an analogous framework to analyse each trait separately. We also provide a practical approach to fine-mapping with out-of-sample reference panels. In simulation studies we show that MGflashfm and MGfm are well-calibrated and that the mean proportion of causal variants with PP > 0.80 is above 0.75 (MGflashfm) and 0.70 (MGfm). In our analysis of four lipids traits across five population groups, MGflashfm gives a median 99% credible set reduction of 10.5% over MGfm. MGflashfm and MGfm only require summary level data, making them very useful fine-mapping tools in consortia efforts where individual-level data cannot be shared.

A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of NIPSNAP3A and an intergenic region on chromosome 1.

The association of immune-related genes and the potential role of IL10 with biliary atresia

BACKGROUND

Biliary atresia (BA) is a severe immune-related disease that is characterized by biliary obstruction and cholestasis. The etiology of BA is unclear, our aim was to explore the relationship between biliary tract inflammation and immune-related genes.

METHODS

We selected 14 SNPs in 13 immune-related genes and investigated their associations with BA by using a large case‒control cohort with a total of 503 cases and 1473 controls from southern China.

RESULTS

SNP rs1518111 in interleukin10 (IL10) was identified as associated with BA (P = 5.79E-03; OR: 0.80; 95% CI: 0.68-0.94). The epistatic effects of the following pairwise interactions among these SNPs were associated with BA: signal transducer and activator of transcription 4 (STAT4) and chemokine (C-X-C motif) ligand 3 (CXCL3); STAT4 and damage-regulated autophagy modulator1 (DRAM1); CXCL3 and RAD51 paralog B (RAD51B); and interferon gamma (IFNG) and interleukin26 (IL26). Furthermore, we explored the potential role of IL-10 in the pathogenesis of the neonatal mouse model of BA. IL-10 effectively prevented biliary epithelial cell injury and biliary obstruction in murine BA as well as inhibit the activation of BA-related immune cells.

CONCLUSIONS

In conclusion, this study provided strong evidence implicating IL10 as a susceptibility gene for BA in the southern Chinese population.

IMPACT

This study provided strong evidence implicating IL10 as a susceptibility gene for BA in the southern Chinese population. This study could infer that IL-10 may play a protective role in BA mouse model. We found that four SNPs (rs7574865, rs352038, rs4622329, and rs4902562) have genetic interactions.

New statistical selection method for pleiotropic variants associated with both quantitative and qualitative traits

BACKGROUND

Identification of pleiotropic variants associated with multiple phenotypic traits has received increasing attention in genetic association studies. Overlapping genetic associations from multiple traits help to detect weak genetic associations missed by single-trait analyses. Many statistical methods were developed to identify pleiotropic variants with most of them being limited to quantitative traits when pleiotropic effects on both quantitative and qualitative traits have been observed. This is a statistically challenging problem because there does not exist an appropriate multivariate distribution to model both quantitative and qualitative data together. Alternatively, meta-analysis methods can be applied, which basically integrate summary statistics of individual variants associated with either a quantitative or a qualitative trait without accounting for correlations among genetic variants.

RESULTS

We propose a new statistical selection method based on a unified selection score quantifying how a genetic variant, i.e., a pleiotropic variant associates with both quantitative and qualitative traits. In our extensive simulation studies where various types of pleiotropic effects on both quantitative and qualitative traits were considered, we demonstrated that the proposed method outperforms the existing meta-analysis methods in terms of true positive selection. We also applied the proposed method to a peanut dataset with 6 quantitative and 2 qualitative traits, and a cowpea dataset with 2 quantitative and 6 qualitative traits. We were able to detect some potentially pleiotropic variants missed by the existing methods in both analyses.

CONCLUSIONS

The proposed method is able to locate pleiotropic variants associated with both quantitative and qualitative traits. It has been implemented into an R package 'UNISS', which can be downloaded from http://github.com/statpng/uniss.

Pleiotropy alleviates the fitness costs associated with resource allocation trade-offs in immune signaling networks

Many genes and signaling pathways within plant and animal taxa drive the expression of multiple organismal traits. This form of genetic pleiotropy instigates trade-offs among life-history traits if a mutation in the pleiotropic gene improves the fitness contribution of one trait at the expense of another. Whether or not pleiotropy gives rise to conflict among traits, however, likely depends on the resource costs and timing of trait deployment during organismal development. To investigate factors that could influence the evolutionary maintenance of pleiotropy in gene networks, we developed an agent-based model of co-evolution between parasites and hosts. Hosts comprise signaling networks that must faithfully complete a developmental program while also defending against parasites, and trait signaling networks could be independent or share a pleiotropic component as they evolved to improve host fitness. We found that hosts with independent developmental and immune networks were significantly more fit than hosts with pleiotropic networks when traits were deployed asynchronously during development. When host genotypes directly competed against each other, however, pleiotropic hosts were victorious regardless of trait synchrony because the pleiotropic networks were more robust to parasite manipulation, potentially explaining the abundance of pleiotropy in immune systems despite its contribution to life history trade-offs.

Identifying latent genetic interactions in genome-wide association studies using multiple traits

Genome-wide association studies of complex traits frequently find that SNP-based estimates of heritability are considerably smaller than estimates from classic family-based studies. This 'missing' heritability may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. To circumvent these challenges, we propose a new method to detect genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Our approach, Latent Interaction Testing (LIT), uses the observation that correlated traits with shared latent genetic interactions have trait variance and covariance patterns that differ by genotype. LIT examines the relationship between trait variance/covariance patterns and genotype using a flexible kernel-based framework that is computationally scalable for biobank-sized datasets with a large number of traits. We first use simulated data to demonstrate that LIT substantially increases power to detect latent genetic interactions compared to a trait-by-trait univariate method. We then apply LIT to four obesity-related traits in the UK Biobank and detect genetic variants with interactive effects near known obesity-related genes. Overall, we show that LIT, implemented in the R package lit, uses shared information across traits to improve detection of latent genetic interactions compared to standard approaches.

Skeletal Class II Malocclusion: From Clinical Treatment Strategies to the Roadmap in Identifying the Genetic Bases of Development in Humans with the Support of the Collaborative Cross Mouse Population

Depending on how severe it is, malocclusion, which may involve misaligned teeth, jaws, or a combination of the two, can hurt a person's overall facial aesthetics. The maxillary molar develops before the mandibular molar in class II malocclusion, which affects 15% of the population in the United States. With a retrusive mandible, patients typically have a convex profile. The goal of this study is to classify the skeletal and dental variability present in class II malocclusion, to reduce heterogeneity, present the current clinical treatment strategies, to summarize the previously published findings of genetic analysis, discuss these findings and their constraints, and finally, propose a comprehensive roadmap to facilitate investigations aimed at determining the genetic bases of malocclusion development using a variety of genomic approaches. To further comprehend the hereditary components involved in the onset and progression of class II malocclusion, a novel animal model for class II malocclusion should be developed while considering the variety of the human population. To overcome the constraints of the previous studies, here, we propose to conduct novel research on humans with the support of mouse models to produce contentious findings. We believe that carrying out a genome-wide association study (GWAS) on a large human cohort to search for significant genes and their modifiers; an epigenetics-wide association study (EWAS); RNA-seq analysis; integrating GWAS and the expression of quantitative trait loci (eQTL); and the testing of microRNAs, small RNAs, and long noncoding RNAs in tissues related to the skeletal class II malocclusion (SCIIMO) phenotype, such as mandibular bone, gum, and jaw in humans and the collaborative cross (CC) mouse model, will identify novel genes and genetic factors affecting this phenotype. We anticipate discovering novel genetic elements to advance our knowledge of how this malocclusion phenotype develops and open the venue for the early identification of patients carrying the susceptible genetic factors so that we can offer early prevention treatment strategies.

Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context

Assessment of the genetic similarity between two phenotypes can provide insight into a common genetic aetiology and inform the use of pleiotropy-informed, cross-phenotype analytical methods to identify novel genetic associations. The genetic correlation is a well-known means of quantifying and testing for genetic similarity between traits, but its estimates are subject to comparatively large sampling error. This makes it unsuitable for use in a small-sample context. We discuss the use of a previously published nonparametric test of genetic similarity for application to GWAS summary statistics. We establish that the null distribution of the test statistic is modelled better by an extreme value distribution than a transformation of the standard exponential distribution. We show with simulation studies and real data from GWAS of 18 phenotypes from the UK Biobank that the test is to be preferred for use with small sample sizes, particularly when genetic effects are few and large, outperforming the genetic correlation and another nonparametric statistical test of independence. We find the test suitable for the detection of genetic similarity in the rare disease context.

graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data

Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand the functional mechanisms underlying many associated variants. This is especially the case when we are interested in variants shared across multiple phenotypes. To address this challenge, we propose graph-GPA 2.0 (GGPA 2.0), a statistical framework to integrate GWAS datasets for multiple phenotypes and incorporate functional annotations within a unified framework. Our simulation studies showed that incorporating functional annotation data using GGPA 2.0 not only improves the detection of disease-associated variants, but also provides a more accurate estimation of relationships among diseases. Next, we analyzed five autoimmune diseases and five psychiatric disorders with the functional annotations derived from GenoSkyline and GenoSkyline-Plus, along with the prior disease graph generated by biomedical literature mining. For autoimmune diseases, GGPA 2.0 identified enrichment for blood-related epigenetic marks, especially B cells and regulatory T cells, across multiple diseases. Psychiatric disorders were enriched for brain-related epigenetic marks, especially the prefrontal cortex and the inferior temporal lobe for bipolar disorder and schizophrenia, respectively. In addition, the pleiotropy between bipolar disorder and schizophrenia was also detected. Finally, we found that GGPA 2.0 is robust to the use of irrelevant and/or incorrect functional annotations. These results demonstrate that GGPA 2.0 can be a powerful tool to identify genetic variants associated with each phenotype or those shared across multiple phenotypes, while also promoting an understanding of functional mechanisms underlying the associated variants.

Cross-cancer pleiotropic analysis identifies three novel genetic risk loci for colorectal cancer

BACKGROUND

To understand the shared genetic basis between colorectal cancer (CRC) and other cancers and identify potential pleiotropic loci for compensating the missing genetic heritability of CRC.

METHODS

We conducted a systematic genome-wide pleiotropy scan to appraise associations between cancer-related genetic variants and CRC risk among European populations. Single nucleotide polymorphism (SNP)-set analysis was performed using data from the UK Biobank and the Study of Colorectal Cancer in Scotland (10 039 CRC cases and 30 277 controls) to evaluate the overlapped genetic regions for susceptibility of CRC and other cancers. The variant-level pleiotropic associations between CRC and other cancers were examined by CRC genome-wide association study meta-analysis and the pleiotropic analysis under composite null hypothesis (PLACO) pleiotropy test. Gene-based, co-expression and pathway enrichment analyses were performed to explore potential shared biological pathways. The interaction between novel genetic variants and common environmental factors was further examined for their effects on CRC.

RESULTS

Genome-wide pleiotropic analysis identified three novel SNPs (rs2230469, rs9277378 and rs143190905) and three mapped genes (PIP4K2A, HLA-DPB1 and RTEL1) to be associated with CRC. These genetic variants were significant expressions quantitative trait loci in colon tissue, influencing the expression of their mapped genes. Significant interactions of PIP4K2A and HLA-DPB1 with environmental factors, including smoking and alcohol drinking, were observed. All mapped genes and their co-expressed genes were significantly enriched in pathways involved in carcinogenesis.

CONCLUSION

Our findings provide an important insight into the shared genetic basis between CRC and other cancers. We revealed several novel CRC susceptibility loci to help understand the genetic architecture of CRC.

Evidence of epistasis in regions of long-range linkage disequilibrium across five complex diseases in the UK Biobank and eMERGE datasets

Leveraging linkage disequilibrium (LD) patterns as representative of population substructure enables the discovery of additive association signals in genome-wide association studies (GWASs). Standard GWASs are well-powered to interrogate additive models; however, new approaches are required for invesigating other modes of inheritance such as dominance and epistasis. Epistasis, or non-additive interaction between genes, exists across the genome but often goes undetected because of a lack of statistical power. Furthermore, the adoption of LD pruning as customary in standard GWASs excludes detection of sites that are in LD but might underlie the genetic architecture of complex traits. We hypothesize that uncovering long-range interactions between loci with strong LD due to epistatic selection can elucidate genetic mechanisms underlying common diseases. To investigate this hypothesis, we tested for associations between 23 common diseases and 5,625,845 epistatic SNP-SNP pairs (determined by Ohta's D statistics) in long-range LD (>0.25 cM). Across five disease phenotypes, we identified one significant and four near-significant associations that replicated in two large genotype-phenotype datasets (UK Biobank and eMERGE). The genes that were most likely involved in the replicated associations were (1) members of highly conserved gene families with complex roles in multiple pathways, (2) essential genes, and/or (3) genes that were associated in the literature with complex traits that display variable expressivity. These results support the highly pleiotropic and conserved nature of variants in long-range LD under epistatic selection. Our work supports the hypothesis that epistatic interactions regulate diverse clinical mechanisms and might especially be driving factors in conditions with a wide range of phenotypic outcomes.

Association of asthma genetic variants with asthma-associated traits reveals molecular pathways of eosinophilic asthma

INTRODUCTION

Asthma is a complex, polygenic, heterogenous inflammatory disease. Recently, we generated a list of 128 independent single nucleotide polymorphisms (SNPs) associated with asthma in genome-wide association studies. However, it is unknown if asthma SNPs are associated with specific asthma-associated traits such as high eosinophil counts, atopy, and airway obstruction, revealing molecular endotypes of this disease. Here, we aim to identify the association between asthma SNPs and asthma-associated traits and assess expression quantitative trait locus (e-QTLs) to reveal their downstream functional effects and find drug targets.

METHODS

Association analyses between 128 asthma SNPs and associated traits (blood eosinophil numbers, atopy, airway obstruction, airway hyperresponsiveness) were conducted using regression modelling in population-based studies (Lifelines N = 32,817/Vlagtwedde-Vlaardingen N = 1554) and an asthma cohort (Dutch Asthma genome-wide association study N = 917). Functional enrichment and pathway analysis were performed with genes linked to the significant SNPs by e-QTL analysis. Genes were investigated to generate novel drug targets.

RESULTS

We identified 69 asthma SNPs that were associated with at least one trait, with 20 SNPs being associated with multiple traits. The SNP annotated to SMAD3 was the most pleiotropic. In total, 42 SNPs were associated with eosinophil counts, 18 SNPs with airway obstruction, and 21 SNPs with atopy. We identified genetically driven pathways regulating eosinophilia. The largest network of eosinophilia contained two genes (IL4R, TSLP) targeted by drugs currently available for eosinophilic asthma. Several novel targets were identified such as IL-18, CCR4, and calcineurin.

CONCLUSION

Many asthma SNPs are associated with blood eosinophil counts and genetically driven molecular pathways of asthma-associated traits were identified.

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.

Divided-and-combined omnibus test for genetic association analysis with high-dimensional data

Advances in biologic technology enable researchers to obtain a huge amount of genetic and genomic data, whose dimensions are often quite high on both phenotypes and variants. Testing their association with multiple phenotypes has been a hot topic in recent years. Traditional single phenotype multiple variant analysis has to be adjusted for multiple testing and thus suffers from substantial power loss due to ignorance of correlation across phenotypes. Similarity-based method, which uses the trace of product of two similarity matrices as a test statistic, has emerged as a useful tool to handle this problem. However, it loses power when the correlation strength within multiple phenotypes is middle or strong, for some signals represented by the eigenvalues of phenotypic similarity matrix are masked by others. We propose a divided-and-combined omnibus test to handle this drawback of the similarity-based method. Based on the divided-and-combined strategy, we first divide signals into two groups in a series of cut points according to eigenvalues of the phenotypic similarity matrix and combine analysis results via the Cauchy-combined method to reach a final statistic. Extensive simulations and application to a pig data demonstrate that the proposed statistic is much more powerful and robust than the original test under most of the considered scenarios, and sometimes the power increase can be more than 0.6. Divided-and-combined omnibus test facilitates genetic association analysis with high-dimensional data and achieves much higher power than the existing similarity based method. In fact, divided-and-combined omnibus test can be used whenever the association analysis between two multivariate variables needs to be conducted.

Elucidating the patterns of pleiotropy and its biological relevance in maize

Pleiotropy-when a single gene controls two or more seemingly unrelated traits-has been shown to impact genes with effects on flowering time, leaf architecture, and inflorescence morphology in maize. However, the genome-wide impact of biological pleiotropy across all maize phenotypes is largely unknown. Here, we investigate the extent to which biological pleiotropy impacts phenotypes within maize using GWAS summary statistics reanalyzed from previously published metabolite, field, and expression phenotypes across the Nested Association Mapping population and Goodman Association Panel. Through phenotypic saturation of 120,597 traits, we obtain over 480 million significant quantitative trait nucleotides. We estimate that only 1.56-32.3% of intervals show some degree of pleiotropy. We then assess the relationship between pleiotropy and various biological features such as gene expression, chromatin accessibility, sequence conservation, and enrichment for gene ontology terms. We find very little relationship between pleiotropy and these variables when compared to permuted pleiotropy. We hypothesize that biological pleiotropy of common alleles is not widespread in maize and is highly impacted by nuisance terms such as population structure and linkage disequilibrium. Natural selection on large standing natural variation in maize populations may target wide and large effect variants, leaving the prevalence of detectable pleiotropy relatively low.

Role of the Gut-Brain Axis in the Shared Genetic Etiology Between Gastrointestinal Tract Diseases and Psychiatric Disorders: A Genome-Wide Pleiotropic Analysis

IMPORTANCE

Comorbidities and genetic correlations between gastrointestinal tract diseases and psychiatric disorders have been widely reported, with the gut-brain axis (GBA) hypothesized as a potential biological basis. However, the degree to which the shared genetic determinants are involved in these associations underlying the GBA is unclear.

OBJECTIVE

To investigate the shared genetic etiology between gastrointestinal tract diseases and psychiatric disorders and to identify shared genomic loci, genes, and pathways.

DESIGN, SETTING, AND PARTICIPANTS

This genome-wide pleiotropic association study using genome-wide association summary statistics from publicly available data sources was performed with various statistical genetic approaches to sequentially investigate the pleiotropic associations from genome-wide single-nucleotide variation (SNV; formerly single-nucleotide polymorphism [SNP]), and gene levels and biological pathways to disentangle the underlying shared genetic etiology between 4 gastrointestinal tract diseases (inflammatory bowel disease, irritable bowel syndrome, peptic ulcer disease, and gastroesophageal reflux disease) and 6 psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, attention-deficit/hyperactivity disorder, posttraumatic stress disorder, and anorexia nervosa). Data were collected from March 10, 2021, to August 25, 2021, and analysis was performed from January 8 through May 30, 2022.

MAIN OUTCOMES AND MEASURES

The primary outcomes consisted of a list of genetic loci, genes, and pathways shared between gastrointestinal tract diseases and psychiatric disorders.

RESULTS

Extensive genetic correlations and genetic overlaps were found among 22 of 24 trait pairs. Pleiotropic analysis under a composite null hypothesis identified 2910 significant potential pleiotropic SNVs in 19 trait pairs, with 83 pleiotropic loci and 24 colocalized loci detected. Gene-based analysis found 158 unique candidate pleiotropic genes, which were highly enriched in certain GBA-related phenotypes and tissues, whereas pathway enrichment analysis further highlighted biological pathways primarily involving cell adhesion, synaptic structure and function, and immune cell differentiation. Several identified pleiotropic loci also shared causal variants with gut microbiomes. Mendelian randomization analysis further illustrated vertical pleiotropy across 8 pairwise traits. Notably, many pleiotropic loci were identified for multiple pairwise traits, such as 1q32.1 (INAVA), 19q13.33 (FUT2), 11q23.2 (NCAM1), and 1p32.3 (LRP8).

CONCLUSIONS AND RELEVANCE

These findings suggest that the pleiotropic genetic determinants between gastrointestinal tract diseases and psychiatric disorders are extensively distributed across the genome. These findings not only support the shared genetic basis underlying the GBA but also have important implications for intervention and treatment targets of these diseases simultaneously.