Abundant pleiotropy in human complex diseases and traits

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.

Novel genetic variants in long non-coding RNA MEG3 are associated with the risk of asthma

Background

Asthma is the most common chronic inflammatory airway disease worldwide. Asthma is a complex disease whose exact etiologic mechanisms remain elusive; however, it is increasingly evident that genetic factors play essential roles in the development of asthma. The purpose of this study is to identify novel genetic susceptibility loci for asthma in Taiwanese. We selected a well-studied long non-coding RNA (lncRNA), MEG3, which is involved in multiple cellular functions and whose expression has been associated with asthma. We hypothesize that genetic variants in MEG3 may influence the risk of asthma.

Methods

We genotyped four single nucleotide polymorphisms (SNPs) in MEG3, rs7158663, rs3087918, rs11160608, and rs4081134, in 198 patients with asthma and 453 healthy controls and measured serum MEG3 expression level in a subset of controls.

Results

The variant AG and AA genotypes of MEG3 rs7158663 were significantly over-represented in the patients compared to the controls (P = 0.0024). In logistic regression analyses, compared with the wild-type GG genotype, the heterozygous variant genotype (AG) was associated with a 1.62-fold [95% confidence interval (CI) [1.18-2.32], P = 0.0093] increased risk and the homozygous variant genotype (AA) conferred a 2.68-fold (95% CI [1.52-4.83], P = 0.003) increased risk of asthma. The allelic test showed the A allele was associated with a 1.63-fold increased risk of asthma (95% CI [1.25-2.07], P = 0.0004). The AG plus AA genotypes were also associated with severe symptoms (P = 0.0148). Furthermore, the AG and AA genotype carriers had lower serum MEG3 expression level than the GG genotype carriers, consistent with the reported downregulation of MEG3 in asthma patients.

Conclusion

MEG3 SNP rs7158663 is a genetic susceptibility locus for asthma in Taiwanese. Individuals carrying the variant genotypes have lower serum MEG3 level and are at increased risks of asthma and severe symptoms.

Genetic analyses implicate complex links between adult testosterone levels and health and disease

BACKGROUND

Testosterone levels are linked with diverse characteristics of human health, yet, whether these associations reflect correlation or causation remains debated. Here, we provide a broad perspective on the role of genetically determined testosterone on complex diseases in both sexes.

METHODS

Leveraging genetic and health registry data from the UK Biobank and FinnGen (total N = 625,650), we constructed polygenic scores (PGS) for total testosterone, sex-hormone binding globulin (SHBG) and free testosterone, associating these with 36 endpoints across different disease categories in the FinnGen. These analyses were combined with Mendelian Randomization (MR) and cross-sex PGS analyses to address causality.

RESULTS

We show testosterone and SHBG levels are intricately tied to metabolic health, but report lack of causality behind most associations, including type 2 diabetes (T2D). Across other disease domains, including 13 behavioral and neurological diseases, we similarly find little evidence for a substantial contribution from normal variation in testosterone levels. We nonetheless find genetically predicted testosterone affects many sex-specific traits, with a pronounced impact on female reproductive health, including causal contribution to PCOS-related traits like hirsutism and post-menopausal bleeding (PMB). We also illustrate how testosterone levels associate with antagonistic effects on stroke risk and reproductive endpoints between the sexes.

CONCLUSIONS

Overall, these findings provide insight into how genetically determined testosterone correlates with several health parameters in both sexes. Yet the lack of evidence for a causal contribution to most traits beyond sex-specific health underscores the complexity of the mechanisms linking testosterone levels to disease risk and sex differences.

Investigating the morphology and genetics of scalp and facial hair characteristics for phenotype prediction

Microscopic traits and ultrastructure of hair such as cross-sectional shape, pigmentation, curvature, and internal structure help determine the level of variations between and across human populations. Apart from cosmetics and anthropological applications, such as determining species, somatic origin (body area), and biogeographic ancestry, the evidential value of hair has increased with rapid progression in the area of forensic DNA phenotyping (FDP). Individuals differ in the features of their scalp hair (greying, shape, colour, balding, thickness, and density) and facial hair (eyebrow thickness, monobrow, and beard thickness) features. Scalp and facial hair characteristics are genetically controlled and lead to visible inter-individual variations within and among populations of various ethnic origins. Hence, these characteristics can be exploited and made more inclusive in FDP, thereby leading to more comprehensive, accurate, and robust prediction models for forensic purposes. The present article focuses on understanding the genetics of scalp and facial hair characteristics with the goal to develop a more inclusive approach to better understand hair biology by integrating hair microscopy with genetics for genotype-phenotype correlation research.

The genetics of bipolar disorder with obesity and type 2 diabetes

BACKGROUND

Bipolar disorder (BD) presents with high obesity and type 2 diabetes (T2D) and pathophysiological and phenomenological abnormalities shared with cardiometabolic disorders. Genomic studies may help define if they share genetic liability. This selective review of BD with obesity and T2D will focus on genomic studies, stress their current limitations and guide future steps in developing the field.

METHODS

We searched electronic databases (PubMed, Scopus) until December 2021 to identify genome-wide association studies, polygenic risk score analyses, and functional genomics of BD accounting for body mass index (BMI), obesity, or T2D.

RESULTS

The first genome-wide association studies (GWAS) of BD accounting for obesity found a promising genome-wide association in an intronic gene variant of TCF7L2 that was further replicated. Polygenic risk scores of obesity and T2D have also been associated with BD, yet, no genetic correlations have been demonstrated. Finally, human-induced stem cell studies of the intronic variant in TCF7L2 show a potential biological impact of the products of this genetic variant in BD risk.

LIMITATIONS

The narrative nature of this review.

CONCLUSIONS

Findings from BD GWAS accounting for obesity and their functional testing, have prompted potential biological insights. Yet, BD, obesity, and T2D display high phenotypic, genetic, and population-related heterogeneity, limiting our ability to detect genetic associations. Further studies should refine cardiometabolic phenotypes, test gene-environmental interactions and add population diversity.

Lung- and liver-dominant phenotypes of Korean eight constitution medicine have different profiles of genotype associated with each organ function

Eight Constitution Medicine (ECM), a ramification of traditional Korean medicine, has categorized people into eight constitutions. The main criteria of classification are inherited differences or predominance in the functions of organs, such as the liver or lung, diagnosed through ECM-specific pulse patterns. This study investigated the association between single nucleotide polymorphism (SNP) genotypes and ECM phenotypes and explored candidate genetic makeups responsible for each constitution using a genome-wide association study (GWAS). Sixty-three healthy volunteers, who were either categorized as the Hepatonia (HEP, n = 32) or Pulmotonia (PUL, n = 31) constitution, were enrolled. HEP and PUL are two contrasting ECM types representing the dominant liver and lung phenotypes, respectively. SNPs were analyzed from the oral mucosa DNA using a commercially available microarray chip that can identify 820,000 SNPs. We conducted GWAS using logistic regression analysis and additive mode genotypes and constructed phylogenetic trees using the SNPhylo program with 8 SNPs specific for the liver phenotype and 15 SNPs for the lung phenotype. Although genome-wide significant SNPs were not found, the phylogenetic tree showed a clear difference between the two constitutions. This is the first observation suggesting genetic involvement in the ECM and can be extended to all ECM constitutions.

In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model

Pleiotropy, which refers to the ability of different mutations on the same gene to cause different pathological effects in human genetic diseases, is important in understanding system-level biological diseases. Although some biological experiments have been proposed, still little is known about pleiotropy on gene–gene dynamics, since most previous studies have been based on correlation analysis. Therefore, a new perspective is needed to investigate pleiotropy in terms of gene–gene dynamical characteristics. To quantify pleiotropy in terms of network dynamics, we propose a measure called in silico Pleiotropic Scores (sPS), which represents how much a gene is affected against a pair of different types of mutations on a Boolean network model. We found that our model can identify more candidate pleiotropic genes that are not known to be pleiotropic than the experimental database. In addition, we found that many types of functionally important genes tend to have higher sPS values than other genes; in other words, they are more pleiotropic. We investigated the relations of sPS with the structural properties in the signaling network and found that there are highly positive relations to degree, feedback loops, and centrality measures. This implies that the structural characteristics are principles to identify new pleiotropic genes. Finally, we found some biological evidence showing that sPS analysis is relevant to the real pleiotropic data and can be considered a novel candidate for pleiotropic gene research. Taken together, our results can be used to understand the dynamics pleiotropic characteristics in complex biological systems in terms of gene–phenotype relations.

Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores

Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants identified from genome-wide association studies. PRS can predict a broad spectrum of diseases and have therefore been widely used in research settings. Some work has investigated their potential applications as biomarkers in preventative medicine, but significant work is still needed to definitively establish and communicate absolute risk to patients for genetic and modifiable risk factors across demographic groups. However, the biggest limitation of PRS currently is that they show poor generalizability across diverse ancestries and cohorts. Major efforts are underway through methodological development and data generation initiatives to improve their generalizability. This review aims to comprehensively discuss current progress on the development of PRS, the factors that affect their generalizability, and promising areas for improving their accuracy, portability, and implementation.

PolarMorphism enables discovery of shared genetic variants across multiple traits from GWAS summary statistics

MOTIVATION

Pleiotropic SNPs are associated with multiple traits. Such SNPs can help pinpoint biological processes with an effect on multiple traits or point to a shared etiology between traits. We present PolarMorphism, a new method for the identification of pleiotropic SNPs from genome-wide association studies (GWAS) summary statistics. PolarMorphism can be readily applied to more than two traits or whole trait domains. PolarMorphism makes use of the fact that trait-specific SNP effect sizes can be seen as Cartesian coordinates and can thus be converted to polar coordinates r (distance from the origin) and theta (angle with the Cartesian x-axis, in the case of two traits). r describes the overall effect of a SNP, while theta describes the extent to which a SNP is shared. r and theta are used to determine the significance of SNP sharedness, resulting in a P-value per SNP that can be used for further analysis.

RESULTS

We apply PolarMorphism to a large collection of publicly available GWAS summary statistics enabling the construction of a pleiotropy network that shows the extent to which traits share SNPs. We show how PolarMorphism can be used to gain insight into relationships between traits and trait domains and contrast it with genetic correlation. Furthermore, pathway analysis of the newly discovered pleiotropic SNPs demonstrates that analysis of more than two traits simultaneously yields more biologically relevant results than the combined results of pairwise analysis of the same traits. Finally, we show that PolarMorphism is more efficient and more powerful than previously published methods.

AVAILABILITY AND IMPLEMENTATION

code: https://github.com/UMCUGenetics/PolarMorphism, results: 10.5281/zenodo.5844193.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Large-scale genomic analyses reveal insights into pleiotropy across circulatory system diseases and nervous system disorders

Clinical and epidemiological studies have shown that circulatory system diseases and nervous system disorders often co-occur in patients. However, genetic susceptibility factors shared between these disease categories remain largely unknown. Here, we characterized pleiotropy across 107 circulatory system and 40 nervous system traits using an ensemble of methods in the eMERGE Network and UK Biobank. Using a formal test of pleiotropy, five genomic loci demonstrated statistically significant evidence of pleiotropy. We observed region-specific patterns of direction of genetic effects for the two disease categories, suggesting potential antagonistic and synergistic pleiotropy. Our findings provide insights into the relationship between circulatory system diseases and nervous system disorders which can provide context for future prevention and treatment strategies.

Shared components of heritability across genetically correlated traits

Most disease-associated genetic variants are pleiotropic, affecting multiple genetically correlated traits. Their pleiotropic associations can be mechanistically informative: if many variants have similar patterns of association, they may act via similar pleiotropic mechanisms, forming a shared component of heritability. We developed pleiotropic decomposition regression (PDR) to identify shared components and their underlying genetic variants. We validated PDR on simulated data and identified limitations of existing methods in recovering the true components. We applied PDR to three clusters of five to six traits genetically correlated with coronary artery disease (CAD), asthma, and type II diabetes (T2D), producing biologically interpretable components. For CAD, PDR identified components related to BMI, hypertension, and cholesterol, and it clarified the relationship among these highly correlated risk factors. We assigned variants to components, calculated their posterior-mean effect sizes, and performed out-of-sample validation. Our posterior-mean effect sizes pool statistical power across traits and substantially boost the correlation (r2) between true and estimated effect sizes (compared with the original summary statistics) by 94% and 70% for asthma and T2D out of sample, respectively, and by a predicted 300% for CAD.

Multi-phenotype analyses of hemostatic traits with cardiovascular events reveal novel genetic associations

BACKGROUND

Multi-phenotype analysis of genetically correlated phenotypes can increase the statistical power to detect loci associated with multiple traits, leading to the discovery of novel loci. This is the first study to date to comprehensively analyze the shared genetic effects within different hemostatic traits, and between these and their associated disease outcomes.

OBJECTIVES

To discover novel genetic associations by combining summary data of correlated hemostatic traits and disease events.

METHODS

Summary statistics from genome wide-association studies (GWAS) from seven hemostatic traits (factor VII [FVII], factor VIII [FVIII], von Willebrand factor [VWF] factor XI [FXI], fibrinogen, tissue plasminogen activator [tPA], plasminogen activator inhibitor 1 [PAI-1]) and three major cardiovascular (CV) events (venous thromboembolism [VTE], coronary artery disease [CAD], ischemic stroke [IS]), were combined in 27 multi-trait combinations using metaUSAT. Genetic correlations between phenotypes were calculated using Linkage Disequilibrium Score Regression (LDSC). Newly associated loci were investigated for colocalization. We considered a significance threshold of 1.85 × 10-9 obtained after applying Bonferroni correction for the number of multi-trait combinations performed (n = 27).

RESULTS

Across the 27 multi-trait analyses, we found 4 novel pleiotropic loci (XXYLT1, KNG1, SUGP1/MAU2, TBL2/MLXIPL) that were not significant in the original individual datasets, were not described in previous GWAS for the individual traits, and that presented a common associated variant between the studied phenotypes.

CONCLUSIONS

The discovery of four novel loci contributes to the understanding of the relationship between hemostasis and CV events and elucidate common genetic factors between these traits.

Skewed X-Chromosome Inactivation as a Possible Marker of X-Linked CNV in Women with Pregnancy Loss

Skewed X-chromosome inactivation (sXCI) can be a marker of lethal genetic variants on the X chromosome in a woman since sXCI modifies the pathological phenotype. The aim of this study was to search for CNVs in women with miscarriages and sXCI. XCI was assayed using the classical method based on the amplification of highly polymorphic exon 1 of the androgen receptor (AR) gene. The XCI status was analysed in 313 women with pregnancy loss and in 87 spontaneously aborted embryos with 46,XX karyotype, as well as in control groups of 135 women without pregnancy loss and 64 embryos with 46,XX karyotype from induced abortions in women who terminated a normal pregnancy. The frequency of sXCI differed significantly between women with miscarriages and women without pregnancy losses (6.3% and 2.2%, respectively; p = 0.019). To exclude primary causes of sXCI, sequencing of the XIST and XACT genes was performed. The XIST and XACT gene sequencing revealed no known pathogenic variants that could lead to sXCI. Molecular karyotyping was performed using aCGH, followed by verification of X-linked CNVs by RT-PCR and MLPA. Microdeletions at Xp11.23 and Xq24 as well as gains of Xq28 were detected in women with sXCI and pregnancy loss.

Exploring the association between birthweight and breast cancer using summary statistics from a perspective of genetic correlation, mediation, and causality

BACKGROUND

Previous studies demonstrated a positive relationship between birthweight and breast cancer; however, inconsistent, sometimes even controversial, observations also emerged, and the nature of such relationship remains unknown.

METHODS

Using summary statistics of birthweight and breast cancer, we assessed the fetal/maternal-specific genetic correlation between them via LDSC and prioritized fetal/maternal-specific pleiotropic genes through MAIUP. Relying on summary statistics we conducted Mendelian randomization (MR) to evaluate the fetal/maternal-specific origin of causal relationship between birthweight, age of menarche, age at menopause and breast cancer.

RESULTS

With summary statistics we identified a positive genetic correlation between fetal-specific birthweight and breast cancer (r_g = 0.123 and P = 0.013) as well as a negative but insignificant correlation between maternal-specific birthweight and breast cancer (r_g = - 0.068, P = 0.206); and detected 84 pleiotropic genes shared by fetal-specific birthweight and breast cancer, 49 shared by maternal-specific birthweight and breast cancer. We also revealed fetal-specific birthweight indirectly influenced breast cancer risk in adulthood via the path of age of menarche or age at menopause in terms of MR-based mediation analysis.

CONCLUSION

This study reveals that shared genetic foundation and causal mediation commonly drive the connection between the two traits, and that fetal/maternal-specific birthweight plays substantially distinct roles in such relationship. However, our work offers little supportive evidence for the fetal origins hypothesis of breast cancer originating in utero.

A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS

There has been an increasing interest in joint analysis of multiple phenotypes in genome-wide association studies (GWAS) because jointly analyzing multiple phenotypes may increase statistical power to detect genetic variants associated with complex diseases or traits. Recently, many statistical methods have been developed for joint analysis of multiple phenotypes in genetic association studies, including the Clustering Linear Combination (CLC) method. The CLC method works particularly well with phenotypes that have natural groupings, but due to the unknown number of clusters for a given data, the final test statistic of CLC method is the minimum p-value among all p-values of the CLC test statistics obtained from each possible number of clusters. Therefore, a simulation procedure needs to be used to evaluate the p-value of the final test statistic. This makes the CLC method computationally demanding. We develop a new method called computationally efficient CLC (ceCLC) to test the association between multiple phenotypes and a genetic variant. Instead of using the minimum p-value as the test statistic in the CLC method, ceCLC uses the Cauchy combination test to combine all p-values of the CLC test statistics obtained from each possible number of clusters. The test statistic of ceCLC approximately follows a standard Cauchy distribution, so the p-value can be obtained from the cumulative density function without the need for the simulation procedure. Through extensive simulation studies and application on the COPDGene data, the results demonstrate that the type I error rates of ceCLC are effectively controlled in different simulation settings and ceCLC either outperforms all other methods or has statistical power that is very close to the most powerful method with which it has been compared.

Predicting Archaic Hominin Phenotypes from Genomic Data

Ancient DNA provides a powerful window into the biology of extant and extinct species, including humans' closest relatives: Denisovans and Neanderthals. Here, we review what is known about archaic hominin phenotypes from genomic data and how those inferences have been made. We contend that understanding the influence of variants on lower-level molecular phenotypes-such as gene expression and protein function-is a promising approach to using ancient DNA to learn about archaic hominin traits. Molecular phenotypes have simpler genetic architectures than organism-level complex phenotypes, and this approach enables moving beyond association studies by proposing hypotheses about the effects of archaic variants that are testable in model systems. The major challenge to understanding archaic hominin phenotypes is broadening our ability to accurately map genotypes to phenotypes, but ongoing advances ensure that there will be much more to learn about archaic hominin phenotypes from their genomes. Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 23 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Systems genetics in the rat HXB/BXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose

Neurogenesis in the adult hippocampus contributes to learning and memory in the healthy brain but is dysregulated in metabolic and neurodegenerative diseases. The molecular relationships between neural stem cell activity, adult neurogenesis, and global metabolism are largely unknown. Here we applied unbiased systems genetics methods to quantify genetic covariation among adult neurogenesis and metabolic phenotypes in peripheral tissues of a genetically diverse family of rat strains, derived from a cross between the spontaneously hypertensive (SHR/OlaIpcv) strain and Brown Norway (BN-Lx/Cub). The HXB/BXH family is a very well established model to dissect genetic variants that modulate metabolic and cardiovascular diseases and we have accumulated deep phenome and transcriptome data in a FAIR-compliant resource for systematic and integrative analyses. Here we measured rates of precursor cell proliferation, survival of new neurons, and gene expression in the hippocampus of the entire HXB/BXH family, including both parents. These data were combined with published metabolic phenotypes to detect a neurometabolic quantitative trait locus (QTL) for serum glucose and neuronal survival on Chromosome 16: 62.1-66.3 Mb. We subsequently fine-mapped the key phenotype to a locus that includes the Telo2-interacting protein 2 gene (Tti2)-a chaperone that modulates the activity and stability of PIKK kinases. To verify the hypothesis that differences in neurogenesis and glucose levels are caused by a polymorphism in Tti2, we generated a targeted frameshift mutation on the SHR/OlaIpcv background. Heterozygous SHR-Tti2+/- mutants had lower rates of hippocampal neurogenesis and hallmarks of dysglycemia compared to wild-type littermates. Our findings highlight Tti2 as a causal genetic link between glucose metabolism and structural brain plasticity. In humans, more than 800 genomic variants are linked to TTI2 expression, seven of which have associations to protein and blood stem cell factor concentrations, blood pressure and frontotemporal dementia.

Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy

Mendelian Randomization (MR) studies are threatened by population stratification, batch effects, and horizontal pleiotropy. Although a variety of methods have been proposed to mitigate those problems, residual biases may still remain, leading to highly statistically significant false positives in large databases. Here we describe a suite of sensitivity analysis tools that enables investigators to quantify the robustness of their findings against such validity threats. Specifically, we propose the routine reporting of sensitivity statistics that reveal the minimal strength of violations necessary to explain away the MR results. We further provide intuitive displays of the robustness of the MR estimate to any degree of violation, and formal bounds on the worst-case bias caused by violations multiple times stronger than observed variables. We demonstrate how these tools can aid researchers in distinguishing robust from fragile findings by examining the effect of body mass index on diastolic blood pressure and Townsend deprivation index.

Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy

Mendelian Randomization (MR) studies are threatened by population stratification, batch effects, and horizontal pleiotropy. Although a variety of methods have been proposed to mitigate those problems, residual biases may still remain, leading to highly statistically significant false positives in large databases. Here we describe a suite of sensitivity analysis tools that enables investigators to quantify the robustness of their findings against such validity threats. Specifically, we propose the routine reporting of sensitivity statistics that reveal the minimal strength of violations necessary to explain away the MR results. We further provide intuitive displays of the robustness of the MR estimate to any degree of violation, and formal bounds on the worst-case bias caused by violations multiple times stronger than observed variables. We demonstrate how these tools can aid researchers in distinguishing robust from fragile findings by examining the effect of body mass index on diastolic blood pressure and Townsend deprivation index.

Human Genomics and Drug Development

Insights into the genetic basis of human disease are helping to address some of the key challenges in new drug development including the very high rates of failure. Here we review the recent history of an emerging, genomics-assisted approach to pharmaceutical research and development, and its relationship to Mendelian randomization (MR), a well-established analytical approach to causal inference. We demonstrate how human genomic data linked to pharmaceutically relevant phenotypes can be used for (1) drug target identification (mapping relevant drug targets to diseases), (2) drug target validation (inferring the likely effects of drug target perturbation), (3) evaluation of the effectiveness and specificity of compound-target engagement (inferring the extent to which the effects of a compound are exclusive to the target and distinguishing between on-target and off-target compound effects), and (4) the selection of end points in clinical trials (the diseases or conditions to be evaluated as trial outcomes). We show how genomics can help identify indication expansion opportunities for licensed drugs and repurposing of compounds developed to clinical phase that proved safe but ineffective for the original intended indication. We outline statistical and biological considerations in using MR for drug target validation (drug target MR) and discuss the obstacles and challenges for scaled applications of these genomics-based approaches.

From Genome-Wide to Environment-Wide: Capturing the Environome

Genome-wide association (GWA) studies have shown that genetic influences on individual differences in affect, behavior, and cognition are driven by thousands of DNA variants, each with very small effect sizes. Here, we propose taking inspiration from GWA studies for understanding and modeling the influence of the environment on complex phenotypes. We argue that the availability of DNA microarrays in genetic research is comparable with the advent of digital technologies in psychological science that enable collecting rich, naturalistic observations in real time of the environome, akin to the genome. These data can capture many thousand environmental elements, which we speculate each influence individual differences in affect, behavior, and cognition with very small effect sizes, akin to findings from GWA studies about DNA variants. We outline how the principles and mechanisms of genetic influences on psychological traits can be applied to improve the understanding and models of the environome.

Virtual Gene Concept and a Corresponding Pragmatic Research Program in Genetical Data Science

Mendel proposed an experimentally verifiable paradigm of particle-based heredity that has been influential for over 150 years. The historical arguments have been reflected in the near past as Mendel's concept has been diversified by new types of omics data. As an effect of the accumulation of omics data, a virtual gene concept forms, giving rise to genetical data science. The concept integrates genetical, functional, and molecular features of the Mendelian paradigm. I argue that the virtual gene concept should be deployed pragmatically. Indeed, the concept has already inspired a practical research program related to systems genetics. The program includes questions about functionality of structural and categorical gene variants, about regulation of gene expression, and about roles of epigenetic modifications. The methodology of the program includes bioinformatics, machine learning, and deep learning. Education, funding, careers, standards, benchmarks, and tools to monitor research progress should be provided to support the research program.

A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics

BACKGROUND

Recent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear.

METHODS

We analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders.

RESULTS

We confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders.

CONCLUSIONS

Our study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.

Glycoprotein 2 in health and disease: lifting the veil

In 2020, we discovered glycoprotein 2 (GP2) variants associated with pancreatic cancer susceptibility in a genome-wide association study involving the Japanese population. Individuals carrying a missense coding variant (rs78193826) in the GP2 gene resulting in a p.V432M substitution had an approximately 1.5-fold higher risk of developing pancreatic cancer than those without this variant. GP2 is expressed on the inner surface of zymogen granules in pancreatic acinar cells, which are responsible for the sorting, storage and secretion of digestive enzymes. Upon neuronal, hormonal, or other stimulation, GP2 is cleaved from the membrane of zymogen granules and then secreted into the pancreatic duct and intestinal lumen. While the functions of GP2 remain poorly understood, emerging evidence suggests that it plays an antibacterial role in the gastrointestinal tract after being secreted from pancreatic acinar cells. Impaired GP2 functions may facilitate the adhesion of bacteria to the intestinal mucosa. In this review article, we summarize the role of GP2 in health and disease, emphasizing its functions in the gastrointestinal tract, as well as genetic variations in the GP2 gene and their associations with disease susceptibility. We hope that its robust genetic associations with pancreatic cancer, coupled with its emerging role in gastrointestinal mucosal immunity, will spur renewed research interest in GP2, which has been understudied over the past 30 years compared with its paralog uromodulin (UMOD).

Systematic review on the association between genetic polymorphisms and dental implant-related biological complications

OBJECTIVES

The aim of this systematic review was to evaluate the association between specific genetic polymorphisms and dental implant-related biological complications in patients having a follow-up period of at least 12-months post-loading.

MATERIAL AND METHODS

A sensitive search strategy was developed to identify implant-related genetic-association studies. This was performed by searching five databases. A three-stage screening (titles, abstract, full text) was carried out in duplicate and independently by two reviewers. Assessment was carried out according to the suggested scale for quality assessment of periodontal genetic-association studies and adapted to genetic analyses of implant-related studies leading to an overall final score 0-20 based on the summation of positive answers.

RESULTS

The initial search resulted in 1838 articles. Sixty-seven full-text articles were assessed for eligibility and four studies met the defined inclusion criteria. IL-6 G174C, TNF-α -308, IL-1A-889 and IL-1B+3954 and CD14-159 C/T polymorphisms were evaluated. The quality assessment scores ranged from 6 to 11 positive answers from out of a maximum score of 20. The great heterogeneity among the studies did not allow a meta-analysis.

CONCLUSIONS

The published evidence on genetic predisposition and implant biologic complications is limited. The small number of identified studies evaluating the association between genetic polymorphisms and peri-implant disease presented methodological and reporting inadequacies. Thus, the potential link between genetic polymorphisms and biological complications should be further investigated and clarified through well-designed clinical studies on adequately powered and appropriately included study populations.

A Multilayer Interactome Network Constructed in a Forest Poplar Population Mediates the Pleiotropic Control of Complex Traits

The effects of genes on physiological and biochemical processes are interrelated and interdependent; it is common for genes to express pleiotropic control of complex traits. However, the study of gene expression and participating pathways in vivo at the whole-genome level is challenging. Here, we develop a coupled regulatory interaction differential equation to assess overall and independent genetic effects on trait growth. Based on evolutionary game theory and developmental modularity theory, we constructed multilayer, omnigenic networks of bidirectional, weighted, and positive or negative epistatic interactions using a forest poplar tree mapping population, which were organized into metagalactic, intergalactic, and local interstellar networks that describe layers of structure between modules, submodules, and individual single nucleotide polymorphisms, respectively. These multilayer interactomes enable the exploration of complex interactions between genes, and the analysis of not only differential expression of quantitative trait loci but also previously uncharacterized determinant SNPs, which are negatively regulated by other SNPs, based on the deconstruction of genetic effects to their component parts. Our research framework provides a tool to comprehend the pleiotropic control of complex traits and explores the inherent directional connections between genes in the structure of omnigenic networks.

The flashfm approach for fine-mapping multiple quantitative traits

Joint fine-mapping that leverages information between quantitative traits could improve accuracy and resolution over single-trait fine-mapping. Using summary statistics, flashfm (flexible and shared information fine-mapping) fine-maps signals for multiple traits, allowing for missing trait measurements and use of related individuals. In a Bayesian framework, prior model probabilities are formulated to favour model combinations that share causal variants to capitalise on information between traits. Simulation studies demonstrate that both approaches produce broadly equivalent results when traits have no shared causal variants. When traits share at least one causal variant, flashfm reduces the number of potential causal variants by 30% compared with single-trait fine-mapping. In a Ugandan cohort with 33 cardiometabolic traits, flashfm gave a 20% reduction in the total number of potential causal variants from single-trait fine-mapping. Here we show flashfm is computationally efficient and can easily be deployed across publicly available summary statistics for signals in up to six traits.

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.

A large-scale genome-wide association analysis of lung function in the Chinese population identifies novel loci and highlights shared genetic aetiology with obesity

BACKGROUND

Lung function is a heritable complex phenotype with obesity being one of its important risk factors. However, knowledge of their shared genetic basis is limited. Most genome-wide association studies (GWASs) for lung function have been based on European populations, limiting the generalisability across populations. Large-scale lung function GWASs in other populations are lacking.

METHODS

We included 100 285 subjects from the China Kadoorie Biobank (CKB). To identify novel loci for lung function, single-trait GWAS analyses were performed on forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and FEV1/FVC in the CKB. We then performed genome-wide cross-trait analysis between lung function and obesity traits (body mass index (BMI), BMI-adjusted waist-to-hip ratio and BMI-adjusted waist circumference) to investigate the shared genetic effects in the CKB. Finally, polygenic risk scores (PRSs) of lung function were developed in the CKB and their interaction with BMI's association on lung function were examined. We also conducted cross-trait analysis in parallel with the CKB using up to 457 756 subjects from the UK Biobank (UKB) for replication and investigation of ancestry-specific effects.

RESULTS

We identified nine genome-wide significant novel loci for FEV1, six for FVC and three for FEV1/FVC in the CKB. FEV1 and FVC showed significant negative genetic correlation with obesity traits in both the CKB and UKB. Genetic loci shared between lung function and obesity traits highlighted important biological pathways, including cell proliferation, embryo, skeletal and tissue development, and regulation of gene expression. Mendelian randomisation analysis suggested significant negative causal effects of BMI on FEV1 and on FVC in both the CKB and UKB. Lung function PRSs significantly modified the effect of change in BMI on change in lung function during an average follow-up of 8 years.

CONCLUSION

This large-scale GWAS of lung function identified novel loci and shared genetic aetiology between lung function and obesity. Change in BMI might affect change in lung function differently according to a subject's polygenic background. These findings may open new avenues for the development of molecular-targeted therapies for obesity and lung function improvement.

Results of the Seventh Scientific Workshop of ECCO: Precision Medicine in IBD-What, Why, and How

Many diseases that affect modern humans fall in the category of complex diseases, thus called because they result from a combination of multiple aetiological and pathogenic factors. Regardless of the organ or system affected, complex diseases present major challenges in diagnosis, classification, and management. Current forms of therapy are usually applied in an indiscriminate fashion based on clinical information, but even the most advanced drugs only benefit a limited number of patients and to a variable and unpredictable degree. This 'one measure does not fit all' situation has spurred the notion that therapy for complex disease should be tailored to individual patients or groups of patients, giving rise to the notion of 'precision medicine' [PM]. Inflammatory bowel disease [IBD] is a prototypical complex disease where the need for PM has become increasingly clear. This prompted the European Crohn's and Colitis Organisation to focus the Seventh Scientific Workshop on this emerging theme. The articles in this special issue of the Journal address the various complementary aspects of PM in IBD, including what PM is; why it is needed and how it can be used; how PM can contribute to prediction and prevention of IBD; how IBD PM can aid in prognosis and improve response to therapy; and the challenges and future directions of PM in IBD. This first article of this series is structured on three simple concepts [what, why, and how] and addresses the definition of PM, discusses the rationale for the need of PM in IBD, and outlines the methodology required to implement PM in IBD in a correct and clinically meaningful way.

Identifying pleiotropic genes for complex phenotypes with summary statistics from a perspective of composite null hypothesis testing

Pleiotropy has important implication on genetic connection among complex phenotypes and facilitates our understanding of disease etiology. Genome-wide association studies provide an unprecedented opportunity to detect pleiotropic associations; however, efficient pleiotropy test methods are still lacking. We here consider pleiotropy identification from a methodological perspective of high-dimensional composite null hypothesis and propose a powerful gene-based method called MAIUP. MAIUP is constructed based on the traditional intersection-union test with two sets of independent P-values as input and follows a novel idea that was originally proposed under the high-dimensional mediation analysis framework. The key improvement of MAIUP is that it takes the composite null nature of pleiotropy test into account by fitting a three-component mixture null distribution, which can ultimately generate well-calibrated P-values for effective control of family-wise error rate and false discover rate. Another attractive advantage of MAIUP is its ability to effectively address the issue of overlapping subjects commonly encountered in association studies. Simulation studies demonstrate that compared with other methods, only MAIUP can maintain correct type I error control and has higher power across a wide range of scenarios. We apply MAIUP to detect shared associated genes among 14 psychiatric disorders with summary statistics and discover many new pleiotropic genes that are otherwise not identified if failing to account for the issue of composite null hypothesis testing. Functional and enrichment analyses offer additional evidence supporting the validity of these identified pleiotropic genes associated with psychiatric disorders. Overall, MAIUP represents an efficient method for pleiotropy identification.

A comprehensive evaluation of methods for Mendelian randomization using realistic simulations and an analysis of 38 biomarkers for risk of type 2 diabetes

BACKGROUND

Previous studies have often evaluated methods for Mendelian randomization (MR) analysis based on simulations that do not adequately reflect the data-generating mechanisms in genome-wide association studies (GWAS) and there are often discrepancies in the performance of MR methods in simulations and real data sets.

METHODS

We use a simulation framework that generates data on full GWAS for two traits under a realistic model for effect-size distribution coherent with the heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank and performed down sampling to investigate trends in estimates of causal effects of these biomarkers on the risk of type 2 diabetes (T2D).

RESULTS

Simulation studies show that weighted mode and MRMix are the only two methods that maintain the correct type I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS whereas the opposite is true for smaller sample sizes. Among the other methods, random-effect IVW (inverse-variance weighted method), MR-Robust and MR-RAPS (robust adjust profile score) tend to perform best in maintaining a low mean-squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real-data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on the risk of T2D across the different methods and estimates from many methods trended in one direction with increasing sample size with patterns similar to those observed in simulation studies.

CONCLUSION

The relative performance of different MR methods depends heavily on the sample sizes of the underlying GWAS, the proportion of valid instruments and the validity of the InSIDE assumption. Down-sampling analysis can be used in large GWAS for the possible detection of bias in the MR methods.

Psychiatry in the Digital Age: A Blessing or a Curse?

Social distancing and the shortage of healthcare professionals during the COVID-19 pandemic, the impact of population aging on the healthcare system, as well as the rapid pace of digital innovation are catalyzing the development and implementation of new technologies and digital services in psychiatry. Is this transformation a blessing or a curse for psychiatry? To answer this question, we conducted a literature review covering a broad range of new technologies and eHealth services, including telepsychiatry; computer-, internet-, and app-based cognitive behavioral therapy; virtual reality; digital applied games; a digital medicine system; omics; neuroimaging; machine learning; precision psychiatry; clinical decision support; electronic health records; physician charting; digital language translators; and online mental health resources for patients. We found that eHealth services provide effective, scalable, and cost-efficient options for the treatment of people with limited or no access to mental health care. This review highlights innovative technologies spearheading the way to more effective and safer treatments. We identified artificially intelligent tools that relieve physicians from routine tasks, allowing them to focus on collaborative doctor-patient relationships. The transformation of traditional clinics into digital ones is outlined, and the challenges associated with the successful deployment of digitalization in psychiatry are highlighted.

Highly pleiotropic variants of human traits are enriched in genomic regions with strong background selection

Recent studies have shown the ubiquity of pleiotropy for variants affecting human complex traits. These studies also show that rare variants tend to be less pleiotropic than common ones, suggesting that purifying natural selection acts against highly pleiotropic variants of large effect. Here, we investigate the mean frequency, effect size and recombination rate associated with pleiotropic variants, and focus particularly on whether highly pleiotropic variants are enriched in regions with putative strong background selection. We evaluate variants for 41 human traits using data from the NHGRI-EBI GWAS Catalog, as well as data from other three studies. Our results show that variants involving a higher degree of pleiotropy tend to be more common, have larger mean effect sizes, and contribute more to heritability than variants with a lower degree of pleiotropy. This is consistent with the fact that variants of large effect and frequency are more likely detected by GWAS. Using data from four different studies, we also show that more pleiotropic variants are enriched in genome regions with stronger background selection than less pleiotropic variants, suggesting that highly pleiotropic variants are subjected to strong purifying selection. From the above results, we hypothesized that a number of highly pleiotropic variants of low effect/frequency may pass undetected by GWAS.

A unified framework identifies new links between plasma lipids and diseases from electronic medical records across large-scale cohorts

Plasma lipids are known heritable risk factors for cardiovascular disease, but increasing evidence also supports shared genetics with diseases of other organ systems. We devised a comprehensive three-phase framework to identify new lipid-associated genes and study the relationships among lipids, genotypes, gene expression and hundreds of complex human diseases from the Electronic Medical Records and Genomics (347 traits) and the UK Biobank (549 traits). Aside from 67 new lipid-associated genes with strong replication, we found evidence for pleiotropic SNPs/genes between lipids and diseases across the phenome. These include discordant pleiotropy in the HLA region between lipids and multiple sclerosis and putative causal paths between triglycerides and gout, among several others. Our findings give insights into the genetic basis of the relationship between plasma lipids and diseases on a phenome-wide scale and can provide context for future prevention and treatment strategies.

Molecular Evolution of Ecological Specialisation: Genomic Insights from the Diversification of Murine Rodents

Adaptive radiations are characterized by the diversification and ecological differentiation of species, and replicated cases of this process provide natural experiments for understanding the repeatability and pace of molecular evolution. During adaptive radiation, genes related to ecological specialization may be subject to recurrent positive directional selection. However, it is not clear to what extent patterns of lineage-specific ecological specialization (including phenotypic convergence) are correlated with shared signatures of molecular evolution. To test this, we sequenced whole exomes from a phylogenetically dispersed sample of 38 murine rodent species, a group characterized by multiple, nested adaptive radiations comprising extensive ecological and phenotypic diversity. We found that genes associated with immunity, reproduction, diet, digestion, and taste have been subject to pervasive positive selection during the diversification of murine rodents. We also found a significant correlation between genome-wide positive selection and dietary specialization, with a higher proportion of positively selected codon sites in derived dietary forms (i.e., carnivores and herbivores) than in ancestral forms (i.e., omnivores). Despite striking convergent evolution of skull morphology and dentition in two distantly related worm-eating specialists, we did not detect more genes with shared signatures of positive or relaxed selection than in a nonconvergent species comparison. Although a small number of the genes we detected can be incidentally linked to craniofacial morphology or diet, protein-coding regions are unlikely to be the primary genetic basis of this complex convergent phenotype. Our results suggest a link between positive selection and derived ecological phenotypes, and highlight specific genes and general functional categories that may have played an integral role in the extensive and rapid diversification of murine rodents.

Shared genetic etiology and causality between body fat percentage and cardiovascular diseases: a large-scale genome-wide cross-trait analysis

BACKGROUND

Accumulating evidences have suggested that high body fat percentage (BF%) often occurs in parallel with cardiovascular diseases (CVDs), implying a common etiology between them. However, the shared genetic etiology underlying BF% and CVDs remains unclear.

METHODS

Using large-scale genome-wide association study (GWAS) data, we investigated shared genetics between BF% (N = 100,716) and 10 CVD-related traits (n = 6968-977,323) with linkage disequilibrium score regression, multi-trait analysis of GWAS, and transcriptome-wide association analysis, and evaluated causal associations using Mendelian randomization.

RESULTS

We found strong positive genetic correlations between BF% and heart failure (HF) (Rg = 0.47, P = 1.27 × 10^- 22) and coronary artery disease (CAD) (Rg = 0.22, P = 3.26 × 10^- 07). We identified 5 loci and 32 gene-tissue pairs shared between BF% and HF, as well as 16 loci and 28 gene-tissue pairs shared between BF% and CAD. The loci were enriched in blood vessels and brain tissues, while the gene-tissue pairs were enriched in the nervous, cardiovascular, and exo-/endocrine system. In addition, we observed that BF% was causally related with a higher risk of HF (odds ratio 1.63 per 1-SD increase in BF%, P = 4.16 × 10-04) using a MR approach.

CONCLUSIONS

Our findings suggest that BF% and CVDs have shared genetic etiology and targeted reduction of BF% may improve cardiovascular outcomes. This work advances our understanding of the genetic basis underlying co-morbid obesity and CVDs and opens up a new way for early prevention of CVDs.

Detecting local genetic correlations with scan statistics

Genetic correlation analysis has quickly gained popularity in the past few years and provided insights into the genetic etiology of numerous complex diseases. However, existing approaches oversimplify the shared genetic architecture between different phenotypes and cannot effectively identify precise genetic regions contributing to the genetic correlation. In this work, we introduce LOGODetect, a powerful and efficient statistical method to identify small genome segments harboring local genetic correlation signals. LOGODetect automatically identifies genetic regions showing consistent associations with multiple phenotypes through a scan statistic approach. It uses summary association statistics from genome-wide association studies (GWAS) as input and is robust to sample overlap between studies. Applied to seven phenotypically distinct but genetically correlated neuropsychiatric traits, we identify 227 non-overlapping genome regions associated with multiple traits, including multiple hub regions showing concordant effects on five or more traits. Our method addresses critical limitations in existing analytic strategies and may have wide applications in post-GWAS analysis.

Achieved educational attainment, inherited genetic endowment for education, and obesity

This study investigates two sources of education effects on obesity - achieved educational attainment and inherited genetic endowment for education. In doing so, we accomplish two goals. First, we assess the role of genetic confounding in the association between education and health. Second, we consider the heterogeneity in the extent to which genetic potential for education is realized, and we examine its impact on obesity. Data come from the National Longitudinal Study of Adolescent to Adult Health. Using a polygenic score approach, we find that, net of genetic confounding, holding a college degree is associated with a lower likelihood of obesity. Moreover, among individuals who hold a college degree, those with a high education polygenic score (a greater genetic propensity to succeed in education) are less likely to be obese than those with a relatively low education polygenic score. However, when individuals with a high education polygenic score do not have a college degree, their risk of obesity is similar to that of non-college-educated individuals with a low education polygenic score, suggesting that the effect of genetic endowment for education on obesity is conditional on college education.

A general approach to sensitivity analysis for Mendelian randomization

Mendelian Randomization (MR) represents a class of instrumental variable methods using genetic variants. It has become popular in epidemiological studies to account for the unmeasured confounders when estimating the effect of exposure on outcome. The success of Mendelian Randomization depends on three critical assumptions, which are difficult to verify. Therefore, sensitivity analysis methods are needed for evaluating results and making plausible conclusions. We propose a general and easy to apply approach to conduct sensitivity analysis for Mendelian Randomization studies. Bound et al. (1995) derived a formula for the asymptotic bias of the instrumental variable estimator. Based on their work, we derive a new sensitivity analysis formula. The parameters in the formula include sensitivity parameters such as the correlation between instruments and unmeasured confounder, the direct effect of instruments on outcome and the strength of instruments. In our simulation studies, we examined our approach in various scenarios using either individual SNPs or unweighted allele score as instruments. By using a previously published dataset from researchers involving a bone mineral density study, we demonstrate that our proposed method is a useful tool for MR studies, and that investigators can combine their domain knowledge with our method to obtain bias-corrected results and make informed conclusions on the scientific plausibility of their findings.

Genetic variations in the PSMA6 and PSMC6 proteasome genes are associated with multiple sclerosis and response to interferon-β therapy in Latvians

Several polymorphisms in genes related to the ubiquitin-proteasome system exhibit an association with pathogenesis and prognosis of various human autoimmune diseases. Our previous study reported the association between multiple sclerosis (MS) and the PSMA3-rs2348071 polymorphism in the Latvian population. The current study aimed to evaluate the PSMA6 and PSMC6 genetic variations, their interaction between each other and with the rs2348071, on the susceptibility to MS risk and response to therapy in the Latvian population. PSMA6-rs2277460, -rs1048990 and PSMC6-rs2295826, -rs2295827 were genotyped in the MS case/control study and analysed in terms of genotype-protein correlation network. The possible association with the disease and alleles, single- and multi-locus genotypes and haplotypes of the studied loci was assessed. Response to therapy was evaluated in terms of 'no evidence of disease activity'. To the best of our knowledge, the present study was the first to report that single- and multi-loci variations in the PSMA6, PSMC6 and PSMA3 proteasome genes may have contributed to the risk of MS in the Latvian population. The results of the current study suggested a potential for the PSMA6-rs1048990 to be an independent marker for the prognosis of interferon-β therapy response. The genotype-phenotype network presented in the current study provided a new insight into the pathogenesis of MS and perspectives for future pharmaceutical interventions.

A regulatory variant at 3q21.1 confers an increased pleiotropic risk for hyperglycemia and altered bone mineral density

Skeletal and glycemic traits have shared etiology, but the underlying genetic factors remain largely unknown. To identify genetic loci that may have pleiotropic effects, we studied Genome-wide association studies (GWASs) for bone mineral density and glycemic traits and identified a bivariate risk locus at 3q21. Using sequence and epigenetic modeling, we prioritized an adenylate cyclase 5 (ADCY5) intronic causal variant, rs56371916. This SNP changes the binding affinity of SREBP1 and leads to differential ADCY5 gene expression, altering the chromatin landscape from poised to repressed. These alterations result in bone- and type 2 diabetes-relevant cell-autonomous changes in lipid metabolism in osteoblasts and adipocytes. We validated our findings by directly manipulating the regulator SREBP1, the target gene ADCY5, and the variant rs56371916, which together imply a novel link between fatty acid oxidation and osteoblast differentiation. Our work, by systematic functional dissection of pleiotropic GWAS loci, represents a framework to uncover biological mechanisms affecting pleiotropic traits.

Genetic Association Studies of Age-Related Traits: New Perspectives

Understanding the role of genetic factors in non-Mendelian traits characteristic for post-reproductive life, herein referred to as age-related traits, is lagged behind the understanding of the genetic architecture of Mendelian traits. This lag calls for new, more comprehensive approaches in the analyses of age-related traits leveraging their characteristic features. This paper discusses the role of the inherent heterogeneity in genetic predisposition to age-related traits and pleiotropy. It shows that the comprehensive analyses leveraging such heterogeneity can substantially increase the efficiency and accelerate the progress in uncovering genetic predisposition to such traits.

Circulating chemerin levels are determined through circulating platelet counts in nondiabetic Taiwanese people: A bidirectional Mendelian randomization study

BACKGROUND AND AIMS

Platelet count (PLT) is a predictor of metabolic and inflammation-related disorders. Platelets can release prochemerin, which acts as a link between coagulation and inflammation and between innate and adaptive immunity. The causal effect between PLT and circulating chemerin level has not been elucidated.

METHODS

Nondiabetic participants with samples in the Taiwan Biobank were recruited for a genome-wide association study (GWAS) based on PLT (17,037 participants) and chemerin levels (3887 participants). A bidirectional Mendelian randomization (MR) study was conducted to determine the association between circulating PLT and chemerin levels.

RESULTS

For a GWAS of PLT, 11 gene loci were found to have genome-wide significance. For a GWAS of chemerin levels, two gene loci, RARRES2 and HLADQA2-HLADQB1, were found to have genome-wide significance. Age, sex, body mass index, leukocyte count, hemoglobin, mean blood pressure, hemoglobin A1C, serum total bilirubin, aspartate aminotransferase, triglyceride, and low-density-lipoprotein cholesterol levels, estimated glomerular filtration rate, and circulating chemerin level were found to be independently associated with PLT through a stepwise regression analysis. A bidirectional MR study revealed weighted genetic risk scores (WGRSs) for PLT were significantly associated with chemerin levels by using a two-stage least-square method in a multivariate analysis (p = 0.0031), and no significant association between chemerin level WGRSs and PLT was noted. Sensitivity analysis further revealed no violation of the exclusion-restriction assumption with PLT-determining genotypes on chemerin levels.

CONCLUSIONS

Through a bidirectional MR analysis, our data revealed that chemerin levels were determined based on circulating PLT. Circulating chemerin levels can be intermediates between PLT and future metabolic and inflammation-related disorders.

Meta-analysis of sample-level dbGaP data reveals novel shared genetic link between body height and Crohn's disease

To further explore genetic links between complex traits, we developed a comprehensive framework to harmonize and integrate extensive genotype and phenotype data from the four well-characterized cohorts with the focus on cardiometabolic diseases deposited to the database of Genotypes and Phenotypes (dbGaP). We generated a series of polygenic risk scores (PRS) to investigate pleiotropic effects of loci that confer genetic risk for 19 common diseases and traits on body height, type 2 diabetes (T2D), and myocardial infarction (MI). In a meta-analysis of 20,021 subjects, we identified shared genetic determinants of Crohn's Disease (CD), a type of inflammatory bowel disease, and body height (p = 5.5 × 10^-5). The association of PRS-CD with height was replicated in UK Biobank (p = 1.1 × 10^-5) and an independent cohort of 510 CD cases and controls (1.57 cm shorter height per PRS-CD interquartile increase, p = 5.0 × 10^-3 and a 28% reduction in CD risk per interquartile increase in PRS-height, p = 1.1 × 10^-3, with the effect independent of CD diagnosis). A pathway analysis of the variants overlapping between PRS-height and PRS-CD detected significant enrichment of genes from the inflammatory, immune-mediated and growth factor regulation pathways. This finding supports the clinical observation of growth failure in patients with childhood-onset CD and demonstrates the value of using individual-level data from dbGaP in searching for shared genetic determinants. This information can help provide a refined insight into disease pathogenesis and may have major implications for novel therapies and drug repurposing.

A fast and powerful aggregated Cauchy association test for joint analysis of multiple phenotypes

BACKGROUND

Pleiotropy is a widespread phenomenon in complex human diseases. Jointly analyzing multiple phenotypes can improve power performance of detecting genetic variants and uncover the underlying genetic mechanism.

OBJECTIVE

This study aims to detect the association between genetic variants in a genomic region and multiple phenotypes.

METHODS

We develop the aggregated Cauchy association test to detect the association between rare variants in a genomic region and multiple phenotypes (abbreviated as "Multi-ACAT"). Multi-ACAT first detects the association between each rare variant and multiple phenotypes based on reverse regression and obtains variant-level p-values, then takes linear combination of transformed p-values as the test statistic which approximately follows Cauchy distribution under the null hypothesis.

RESULTS

Extensive simulation studies show that when the proportion of causal variants in a genomic region is extremely small, Multi-ACAT is more powerful than the other several methods and is robust to bi-directional effects of causal variants. Finally, we illustrate our proposed method by analyzing two phenotypes [systolic blood pressure (SBP) and diastolic blood pressure (DBP)] from Genetic Analysis Workshop 19 (GAW19).

CONCLUSION

The Multi-ACAT computes extremely fast, does not consider complex distributions of multiple correlated phenotypes, and can be applied to the case with noise phenotypes.

Pleiotropic genetic influence on birth weight and childhood obesity

Childhood obesity is a global public health problem. Understanding the molecular mechanisms that underlie early origins of childhood obesity can facilitate interventions. Consistent phenotypic and genetic correlations have been found between childhood obesity traits and birth weight (a proxy for in-utero growth), suggesting shared genetic influences (pleiotropy). We aimed to (1) investigate whether there is significant shared genetic influence between birth weight and childhood obesity traits, and (2) to identify genetic loci with shared effects. Using a statistical approach that integrates summary statistics and functional annotations for paired traits, we found strong evidence of pleiotropy (P < 3.53 × 10^–127) and enrichment of functional annotations (P < 1.62 × 10^–39) between birth weight and childhood body mass index (BMI)/obesity. The pleiotropic loci were enriched for regulatory features in skeletal muscle, adipose and brain tissues and in cell lines derived from blood lymphocytes. At 5% false discovery rate, 6 loci were associated with birth weight and childhood BMI and 13 loci were associated with birth weight and childhood obesity. Out of these 19 loci, one locus (EBF1) was novel to childhood obesity and one locus (LMBR1L) was novel to both birth weight and childhood BMI/obesity. These findings give evidence of substantial shared genetic effects in the regulation of both fetal growth and childhood obesity.