Pleiotropy in complex traits: challenges and strategies

The brain-heart axis: Integrative analysis of the shared genetic etiology between neuropsychiatric disorders and cardiovascular disease

BACKGROUND

Multiple observational studies have reported substantial comorbidity between neuropsychiatric disorders and cardiovascular disease (CVD), but the underlying mechanisms remain largely unknown.

METHODS

Using GWAS summary datasets of 8 neuropsychiatric disorders and 6 cardiovascular diseases, an integrative analysis incorporating linkage-disequilibrium-score-regression (LDSC), Mendelian randomization (MR), functional mapping and annotation (FUMA), and functional enrichment analysis, was conducted to investigate shared genetic etiology of the brain-heart axis from the whole genome level, single-nucleotide polymorphism (SNP) level, gene level, and biological pathway level.

RESULTS

In LDSC analysis, 18 pairwise traits between neuropsychiatric disorders and CVD were identified with significant genetic overlaps, revealing extensive genome-wide genetic correlations. In bidirectional MR analysis, 19 pairwise traits were identified with significant causal relationships. Genetic liabilities to neuropsychiatric disorders, particularly attention-deficit hyperactivity disorder and major depressive disorder, conferred extensive significant causal effects on the risk of CVD, while hypertension seemed to be a risk factor for multiple neuropsychiatric disorders, with no significant heterogeneity or pleiotropy. In FUMA analysis, 13 shared independent significant SNPs and 887 overlapping protein-coding genes were detected between neuropsychiatric disorders and CVD. With GO and KEEG functional enrichment analysis, biological pathways of the brain-heart axis were highly concentrated in neurotransmitter synaptic transmission, lipid metabolism, aldosterone synthesis and secretion, glutathione metabolism, and MAPK signaling pathway.

CONCLUSION

Extensive genetic correlations and genetic overlaps between neuropsychiatric disorders and CVD were identified in this study, which might provide some new insights into the brain-heart axis and the therapeutic targets in clinical practice.

Whole-exome sequencing identifies high-confidence genes for tic disorders in a Chinese Han population

BACKGROUND

Tic disorder (TD) is a polygenic neurodevelopmental disorder with high susceptibility. However, identifying high-confidence risk genes has been challenging due to poor replication across multiple studies.

METHODS

Whole-exome sequencing was performed on 390 TD patients and 372 unaffected individuals in a Chinese Han population. Analysis of variance, burden analysis and in silico prediction were used to identify candidate genes for TD. To facilitate data analysis and to focus on high-confidence genes, we defined a panel of 160 genes as known causal or candidate TD genes from previous studies. Gene enrichment and protein-protein interaction analysis were utilized to detect potential novel TD risk genes.

RESULTS

Totally, 14 variants across 12 known TD candidate genes were considered potential susceptibility variants. Ten variants across 10 known TD candidate genes were identified as potential disease-causing variants. Burden analysis identified variants of 28 known genes were significantly excess in TD patients. In addition, 354 previously unproven TD genes are over-represented in patients. Genes enriched in the PI3K-Akt signaling, sphingolipid metabolism and serotonergic synaptic pathways, as well as those interacting with FN1, were considered potential new candidate genes for TD.

CONCLUSIONS

This is the largest WES study focusing on TD patients in a Chinese Han population. Several variants recurring in our cohort were identified as high-confidence risk loci for TD. Moreover, we provided potential new risk genes that may be prioritized for further investigation.

Mendelian randomization: causal inference leveraging genetic data

Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples - the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.

Exploring the Fetal Origins Hypothesis Using Genetic Data

Birth weight is a robust predictor of valued life course outcomes, emphasizing the importance of prenatal development. But does birth weight act as a proxy for environmental conditions in utero, or do biological processes surrounding birth weight themselves play a role in healthy development? To answer this question, we leverage variation in birth weight that is, within families, orthogonal to prenatal environmental conditions: one's genes. We construct polygenic scores in two longitudinal studies (Born in Bradford, N = 2008; Wisconsin Longitudinal Study, N = 8488) to empirically explore the molecular genetic correlates of birth weight. A 1 standard deviation increase in the polygenic score is associated with an ~100-grams increase in birth weight and a 1.4 pp (22 percent) decrease in low birth weight probability. Sibling comparisons illustrate that this association largely represents a causal effect. The polygenic score-birth weight association is increased for children who spend longer in the womb and whose mothers have higher body mass index, though we find no differences across maternal socioeconomic status. Finally, the polygenic score affects social and cognitive outcomes, suggesting that birth weight is itself related to healthy prenatal development.

Selecting Invalid Instruments to Improve Mendelian Randomization with Two-Sample Summary Data

Mendelian randomization (MR) is a widely-used method to estimate the causal relationship between a risk factor and disease. A fundamental part of any MR analysis is to choose appropriate genetic variants as instrumental variables. Genome-wide association studies often reveal that hundreds of genetic variants may be robustly associated with a risk factor, but in some situations investigators may have greater confidence in the instrument validity of only a smaller subset of variants. Nevertheless, the use of additional instruments may be optimal from the perspective of mean squared error even if they are slightly invalid; a small bias in estimation may be a price worth paying for a larger reduction in variance. For this purpose, we consider a method for "focused" instrument selection whereby genetic variants are selected to minimise the estimated asymptotic mean squared error of causal effect estimates. In a setting of many weak and locally invalid instruments, we propose a novel strategy to construct confidence intervals for post-selection focused estimators that guards against the worst case loss in asymptotic coverage. In empirical applications to: (i) validate lipid drug targets; and (ii) investigate vitamin D effects on a wide range of outcomes, our findings suggest that the optimal selection of instruments does not involve only a small number of biologically-justified instruments, but also many potentially invalid instruments.

An adaptive and robust method for multi-trait analysis of genome-wide association studies using summary statistics

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human traits or diseases in the past decade. Nevertheless, much of the heritability of many traits is still unaccounted for. Commonly used single-trait analysis methods are conservative, while multi-trait methods improve statistical power by integrating association evidence across multiple traits. In contrast to individual-level data, GWAS summary statistics are usually publicly available, and thus methods using only summary statistics have greater usage. Although many methods have been developed for joint analysis of multiple traits using summary statistics, there are many issues, including inconsistent performance, computational inefficiency, and numerical problems when considering lots of traits. To address these challenges, we propose a multi-trait adaptive Fisher method for summary statistics (MTAFS), a computationally efficient method with robust power performance. We applied MTAFS to two sets of brain imaging derived phenotypes (IDPs) from the UK Biobank, including a set of 58 Volumetric IDPs and a set of 212 Area IDPs. Through annotation analysis, the underlying genes of the SNPs identified by MTAFS were found to exhibit higher expression and are significantly enriched in brain-related tissues. Together with results from a simulation study, MTAFS shows its advantage over existing multi-trait methods, with robust performance across a range of underlying settings. It controls type 1 error well and can efficiently handle a large number of traits.

Use of polygenic risk scores to assess weight loss after bariatric surgery: a five-year follow-up study

BACKGROUND

Bariatric surgery is currently the most effective long-term treatment for severe obesity. However, the interindividual variability observed in surgical outcomes suggests a moderating effect of several factors, including individual genetic background. Our goal was to investigate the contribution of the genetic architecture of BMI to the variability in weight loss outcomes after bariatric surgery.

METHODS

A total of 106 patients with severe obesity who underwent Roux-en-Y gastric bypass or sleeve gastrectomy were followed up for five years. Changes in body mass index (BMIchange) and percentage of total weight loss (%TWL) were evaluated during the postoperative period. Polygenic risk scores including 50 genetic variants were calculated for each participant to determine their individual genetic risk for high BMI based on a previous GWAS. Generalized estimating equation models were used to study the role of the individual's polygenic score, as well as other factors, on BMIchange and %TWL in the long term after surgery.

RESULTS

We found an effect of the polygenic score on %TWL and BMIchange, where subjects with lower scores had better outcomes after surgery than those with higher scores. Furthermore, when analyzing only patients who underwent Roux-en-Y gastric bypass, these results were replicated showing greater weight loss after surgery for those with lower polygenic scores.

DISCUSSION

These results indicate that genetic background assessed with polygenic risk scores, along with other individual factors such as sex, age, and preoperative BMI, has an impact on bariatric surgery outcomes and could represent a useful tool for estimating surgical outcomes in advance.

The Impact of Non-alcohol Fatty Liver Disease on Bone Mineral Density is Mediated by Sclerostin by Mendelian Randomization Study

Non-alcoholic fatty liver disease (NAFLD) has been found to be associated with osteoporosis (OP) in observational studies. However, the precise causal relationship between NAFLD and OP remains unclear. Here, we used Mendelian randomization (MR) to explore the causal relationship. We selected NAFLD-related single-nucleotide polymorphisms from a genome-wide meta-analysis (8434 cases and 434,770 controls) as instrumental variants. We used inverse variance weighted analysis for the primary MR analysis. Furthermore, we used similar methodologies in parallel investigations of other chronic liver diseases (CLDs). We performed sensitivity analyses to ensure the reliability of the results. We observed a causality between NAFLD and forearm bone mineral density (FABMD) (beta-estimate [β]: - 0.212; p-value: 0.034). We also found that sclerostin can act as a mediator to influence the NAFLD and FABMD pathways to form a mediated MR network (mediated proportion = 8.8%). We also identified indications of causal relationships between other CLDs and OP. However, we were unable to establish any associated mediators. Notably, our analyses did not yield any evidence of pleiotropy. Our findings have implications in the development of preventive and interventional measures aimed at managing low bone mineral density in patients with NAFLD.

A novel method for multiple phenotype association studies based on genotype and phenotype network

Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits. Meanwhile, constructing a network based on associations between phenotypes and genotypes provides a new insight to analyze multiple phenotypes, which can explore whether phenotypes and genotypes might be related to each other at a higher level of cellular and organismal organization. In this paper, we first develop a bipartite signed network by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). The GPN can be constructed by a mixture of quantitative and qualitative phenotypes and is applicable to binary phenotypes with extremely unbalanced case-control ratios in large-scale biobank datasets. We then apply a powerful community detection method to partition phenotypes into disjoint network modules based on GPN. Finally, we jointly test the association between multiple phenotypes in a network module and a single nucleotide polymorphism (SNP). Simulations and analyses of 72 complex traits in the UK Biobank show that multiple phenotype association tests based on network modules detected by GPN are much more powerful than those without considering network modules. The newly proposed GPN provides a new insight to investigate the genetic architecture among different types of phenotypes. Multiple phenotypes association studies based on GPN are improved by incorporating the genetic information into the phenotype clustering. Notably, it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy.

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.

Multi-trait analysis of gene-by-environment interactions in large-scale genetic studies

Identifying genotype-by-environment interaction (GEI) is challenging because the GEI analysis generally has low power. Large-scale consortium-based studies are ultimately needed to achieve adequate power for identifying GEI. We introduce Multi-Trait Analysis of Gene-Environment Interactions (MTAGEI), a powerful, robust, and computationally efficient framework to test gene-environment interactions on multiple traits in large data sets, such as the UK Biobank (UKB). To facilitate the meta-analysis of GEI studies in a consortium, MTAGEI efficiently generates summary statistics of genetic associations for multiple traits under different environmental conditions and integrates the summary statistics for GEI analysis. MTAGEI enhances the power of GEI analysis by aggregating GEI signals across multiple traits and variants that would otherwise be difficult to detect individually. MTAGEI achieves robustness by combining complementary tests under a wide spectrum of genetic architectures. We demonstrate the advantages of MTAGEI over existing single-trait-based GEI tests through extensive simulation studies and the analysis of the whole exome sequencing data from the UKB.

Childhood Reading Ability and Pain in Childhood Through to Midlife

Dyslexia and pain have recently been shown to correlate on a genetic level, but there has been little exploration of this association on the phenotypic level despite reports of increased pain in Attention Deficit Hyperactivity Disorder, which commonly co-occurs with dyslexia. In this study we test for an association between reading ability, which is the primary feature of dyslexia, and pain both in childhood and adulthood. Logistic regression modeling was used to test associations between reading ability in childhood and pain from childhood to midlife in a large UK birth cohort; the 1958 National Child Development Study. Associations were found between poor childhood reading ability and increased headache and abdominal pain in childhood, and between poor childhood reading ability and headache, eye pain, back pain, and rheumatism in adulthood. Mediation analyses indicated that socioeconomic status (defined by employment) fully mediated the association between poor reading ability in childhood and back pain at age 42. By contrast, the association between reading ability and eye pain acted independently of socioeconomic status. Different mechanisms were thus indicated for the association of reading with different pain types, including manual labor and a potential shared biological pathway. PERSPECTIVE: This study found a relationship between poor reading ability in childhood and pain in childhood and adulthood. Those with reading difficulties should be monitored for pain symptoms. Future research may uncover shared biological mechanisms, increasing our understanding of pain and potential treatments.

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Shared genetic architecture between hypothyroidism and rheumatoid arthritis: A large-scale cross-trait analysis

BACKGROUND

In recent years, mounting evidence has indicated a co-morbid relationship between hypothyroidism and rheumatoid arthritis (RA), however, the shared genetic factors underlying this association remain unclear. This study aims to investigate the common genetic architecture between hypothyroidism and RA.

METHODS

Genome-wide association study (GWAS) summary statistics from recently published studies were utilized to examine the genetic correlation, shared genetic loci, and potential causal relationship between hypothyroidism and RA. Statistical methods included linkage disequilibrium score regression (LDSC), high-definition likelihood (HDL), cross-trait meta-analyses, colocalization analysis, multi-marker analysis of genomic annotation (MAGMA), tissue-specific enrichment analysis (TSEA), functional enrichment analysis, and latent causal variable method (LCV).

RESULTS

Our study demonstrated a significant genetic correlation between hypothyroidism and RA(LDSC:rg=0.3803,p=7.23e-11;HDL:rg=0.3849,p=1.02e-21). Through cross-trait meta-analysis, we identified 1035 loci, including 43 novel genetic loci. By integrating colocalization analysis and the MAGMA algorithm, we found a substantial number of genes, such as PTPN22, TYK2, and CTLA-4, shared between the two diseases, which showed significant enrichment across 14 tissues. These genes were primarily associated with the regulation of alpha-beta T cell proliferation, positive regulation of T cell activation, positive regulation of leukocyte cell-cell adhesion, T cell receptor signaling pathway, and JAK-STAT signaling pathway. However, our study did not reveal a significant causal association between the two diseases using the LCV approach.

CONCLUSION

Based on these findings, there is a significant genetic correlation between hypothyroidism and RA, suggesting a shared genetic basis for these conditions.

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.

Benchmarking machine learning and parametric methods for genomic prediction of feed efficiency-related traits in Nellore cattle

Genomic selection (GS) offers a promising opportunity for selecting more efficient animals to use consumed energy for maintenance and growth functions, impacting profitability and environmental sustainability. Here, we compared the prediction accuracy of multi-layer neural network (MLNN) and support vector regression (SVR) against single-trait (STGBLUP), multi-trait genomic best linear unbiased prediction (MTGBLUP), and Bayesian regression (BayesA, BayesB, BayesC, BRR, and BLasso) for feed efficiency (FE) traits. FE-related traits were measured in 1156 Nellore cattle from an experimental breeding program genotyped for ~ 300 K markers after quality control. Prediction accuracy (Acc) was evaluated using a forward validation splitting the dataset based on birth year, considering the phenotypes adjusted for the fixed effects and covariates as pseudo-phenotypes. The MLNN and SVR approaches were trained by randomly splitting the training population into fivefold to select the best hyperparameters. The results show that the machine learning methods (MLNN and SVR) and MTGBLUP outperformed STGBLUP and the Bayesian regression approaches, increasing the Acc by approximately 8.9%, 14.6%, and 13.7% using MLNN, SVR, and MTGBLUP, respectively. Acc for SVR and MTGBLUP were slightly different, ranging from 0.62 to 0.69 and 0.62 to 0.68, respectively, with empirically unbiased for both models (0.97 and 1.09). Our results indicated that SVR and MTGBLUBP approaches were more accurate in predicting FE-related traits than Bayesian regression and STGBLUP and seemed competitive for GS of complex phenotypes with various degrees of inheritance.

Evidence for the gut-skin axis: Common genetic structures in inflammatory bowel disease and psoriasis

BACKGROUND

Inflammatory bowel disease (IBD) and psoriasis (Ps) are common immune-mediated diseases that exhibit clinical comorbidity, possibly due to a common genetic structure. However, the exact mechanism remains unknown.

METHODS

The study population consisted of IBD and Ps genome-wide association study (GWAS) data. Genetic correlations were first evaluated. Then, the overall evaluation employed LD score regression (LDSC), while the local assessment utilized heritability estimation from summary statistics (HESS). Causality assessment was conducted through two-sample Mendelian randomization (2SMR), and genetic overlap analysis utilized the conditional false discovery rate/conjunctional FDR (cond/conjFDR) method. Finally, LDSC applied to specifically expressed genes (LDSC-SEG) was performed at the tissue level. For IBD and Ps-specific expressed genes, genetic correlation, causality, shared genetics, and trait-specific associated tissues were methodically examined.

RESULTS

At the genomic level, both overall and local genetic correlations were found between IBD and Ps. MR analysis indicated a positive causal relationship between Ps and IBD. The conjFDR analysis with a threshold of < 0.01 identified 43 loci shared between IBD and Ps. Subsequent investigations into disease-associated tissues indicated a close association of IBD and Ps with whole blood, lung, spleen, and EBV-transformed lymphocytes.

CONCLUSION

The current research offers a novel perspective on the association between IBD and Ps. It contributes to an enhanced comprehension of the genetic structure and mechanisms of comorbidities in both diseases.

Investigation of causal relationships between cortical structure and osteoporosis using two-sample Mendelian randomization

The mutual interaction between bone characteristics and brain had been reported previously, yet whether the cortical structure has any relevance to osteoporosis is questionable. Therefore, we applied a two-sample bidirectional Mendelian randomization analysis to investigate this relationship. We utilized the bone mineral density measurements of femoral neck (n = 32,735) and lumbar spine (n = 28,498) and data on osteoporosis (7300 cases and 358,014 controls). The global surficial area and thickness and 34 specific functional regions of 51,665 patients were screened by magnetic resonance imaging. For the primary estimate, we utilized the inverse-variance weighted method. The Mendelian randomization-Egger intercept test, MR-PRESSO, Cochran's Q test, and "leave-one-out" sensitivity analysis were conducted to assess heterogeneity and pleiotropy. We observed suggestive associations between decreased thickness in the precentral region (OR = 0.034, P = 0.003) and increased chance of having osteoporosis. The results also revealed suggestive causality of decreased bone mineral density in femoral neck to declined total cortical surface area (β = 1400.230 mm2, P = 0.003), as well as the vulnerability to osteoporosis and reduced thickness in the Parstriangularis region (β = -0.006 mm, P = 0.002). Our study supports that the brain and skeleton exhibit bidirectional crosstalk, indicating the presence of a mutual brain-bone interaction.

Computational dissection of genetic variation modulating the response of multiple photosynthetic phenotypes to the light environment

BACKGROUND

The expression of biological traits is modulated by genetics as well as the environment, and the level of influence exerted by the latter may vary across characteristics. Photosynthetic traits in plants are complex quantitative traits that are regulated by both endogenous genetic factors and external environmental factors such as light intensity and CO2 concentration. The specific processes impacted occur dynamically and continuously as the growth of plants changes. Although studies have been conducted to explore the genetic regulatory mechanisms of individual photosynthetic traits or to evaluate the effects of certain environmental variables on photosynthetic traits, the systematic impact of environmental variables on the dynamic process of integrated plant growth and development has not been fully elucidated.

RESULTS

In this paper, we proposed a research framework to investigate the genetic mechanism of high-dimensional complex photosynthetic traits in response to the light environment at the genome level. We established a set of high-dimensional equations incorporating environmental regulators to integrate functional mapping and dynamic screening of gene‒environment complex systems to elucidate the process and pattern of intrinsic genetic regulatory mechanisms of three types of photosynthetic phenotypes of Populus simonii that varied with light intensity. Furthermore, a network structure was established to elucidate the crosstalk among significant QTLs that regulate photosynthetic phenotypic systems. Additionally, the detection of key QTLs governing the response of multiple phenotypes to the light environment, coupled with the intrinsic differences in genotype expression, provides valuable insights into the regulatory mechanisms that drive the transition of photosynthetic activity and photoprotection in the face of varying light intensity gradients.

CONCLUSIONS

This paper offers a comprehensive approach to unraveling the genetic architecture of multidimensional variations in photosynthetic phenotypes, considering the combined impact of integrated environmental factors from multiple perspectives.

mixWAS: An efficient distributed algorithm for mixed-outcomes genome-wide association studies

Genome-wide association studies (GWAS) have been instrumental in identifying genetic associations for various diseases and traits. However, uncovering genetic underpinnings among traits beyond univariate phenotype associations remains a challenge. Multi-phenotype associations (MPA), or genetic pleiotropy, offer important insights into shared genes and pathways among traits, enhancing our understanding of genetic architectures of complex diseases. GWAS of biobank-linked electronic health record (EHR) data are increasingly being utilized to identify MPA among various traits and diseases. However, methodologies that can efficiently take advantage of distributed EHR to detect MPA are still lacking. Here, we introduce mixWAS, a novel algorithm that efficiently and losslessly integrates multiple EHRs via summary statistics, allowing the detection of MPA among mixed phenotypes while accounting for heterogeneities across EHRs. Simulations demonstrate that mixWAS outperforms the widely used MPA detection method, Phenome-wide association study (PheWAS), across diverse scenarios. Applying mixWAS to data from seven EHRs in the US, we identified 4,534 MPA among blood lipids, BMI, and circulatory diseases. Validation in an independent EHR data from UK confirmed 97.7% of the associations. mixWAS fundamentally improves the detection of MPA and is available as a free, open-source software.

Asymmetric and parallel subgenome selection co-shape common carp domestication

BACKGROUND

The common carp (Cyprinus carpio) might best represent the domesticated allopolyploid animals. Although subgenome divergence which is well-known to be a key to allopolyploid domestication has been comprehensively characterized in common carps, the link between genetic architecture underlying agronomic traits and subgenome divergence is unknown in the selective breeding of common carps globally.

RESULTS

We utilized a comprehensive SNP dataset in 13 representative common carp strains worldwide to detect genome-wide genetic variations associated with scale reduction, vibrant skin color, and high growth rate in common carp domestication. We identified numerous novel candidate genes underlie the three agronomically most desirable traits in domesticated common carps, providing potential molecular targets for future genetic improvement in the selective breeding of common carps. We found that independently selective breeding of the same agronomic trait (e.g., fast growing) in common carp domestication could result from completely different genetic variations, indicating the potential advantage of allopolyploid in domestication. We observed that candidate genes associated with scale reduction, vibrant skin color, and/or high growth rate are repeatedly enriched in the immune system, suggesting that domestication of common carps was often accompanied by the disease resistance improvement.

CONCLUSIONS

In common carp domestication, asymmetric subgenome selection is prevalent, while parallel subgenome selection occurs in selective breeding of common carps. This observation is not due to asymmetric gene retention/loss between subgenomes but might be better explained by reduced pleiotropy through transposable element-mediated expression divergence between ohnologs. Our results demonstrate that domestication benefits from polyploidy not only in plants but also in animals.

Gender-dependent multiple cross-phenotype association of interferon lambda genetic variants with peripheral blood profiles in healthy individuals

BACKGROUND

Type III interferons (IFN), also called as lambda IFNs (IFN-λs), are antiviral and immunomodulatory cytokines that are evolutionarily important in humans. Given their central roles in innate immunity, they could be influencing other aspects of human biology. This study aimed to examine the association of genetic variants that control the expression and/or activity of IFN-λ3 and IFN-λ4 with multiple phenotypes in blood profiles of healthy individuals.

METHODS

In a cohort of about 550 self-declared healthy individuals, after applying several exclusion criteria to determine their health status, we measured 30 blood parameters, including cellular, biochemical, and metabolic profiles. We genotyped them at rs12979860 and rs28416813 using competitive allele-specific PCR assays and tested their association with the blood profiles under dominant and recessive models for the minor allele. IFN-λ4 variants rs368234815 and rs117648444 were also genotyped or inferred.

RESULTS

We saw no association in the combined cohort under either of the models for any of the phenotypes. When we stratified the cohort based on gender, we saw a significant association only in males with monocyte (p = 1 × 10-3 ) and SGOT (p = 7 × 10-3 ) levels under the dominant model and with uric acid levels (p = 0.01) under the recessive model. When we tested the IFN-λ4 activity modifying variant within groupings based on absence or presence of one or two copies of IFN-λ4 and on different activity levels of IFN-λ4, we found significant (p < 0.05) association with several phenotypes like monocyte, triglyceride, VLDL, ALP, and uric acid levels, only in males. All the above significant associations did not show any confounding when we tested for the same with up to ten different demographic and lifestyle variables.

CONCLUSIONS

These results show that lambda interferons can have pleiotropic effects. However, gender seems to be an effect modifier, with males being more sensitive than females to the effect.

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.

Multitrait meta-analyses identify potential candidate genes for growth-related traits in Holstein heifers

Understanding the underlying pleiotropic relationships among growth and body size traits is important for refining breeding strategies in dairy cattle for optimal body size and growth rate. Therefore, we performed single-trait GWAS for monthly-recorded body weight (BW), hip height, body length, and chest girth from birth to 12 mo of age in Holstein animals, followed by stepwise multiple regression of independent or lowly-linked markers from GWAS loci using conditional and joint association analyses (COJO). Subsequently, we conducted a multitrait meta-analysis to detect pleiotropic markers. Based on the single-trait GWAS, we identified 170 significant SNPs, in which 59 of them remained significant after the COJO analyses. The most significant SNP, located at BTA7:3,676,741, explained 2.93% of the total phenotypic variance for BW6 (BW at 6 mo of age). We identified 17 SNPs with potential pleiotropic effects based on the multitrait meta-analyses, which resulted in 3 additional SNPs in comparison to those detected based on the single-trait GWAS. The identified quantitative trait loci regions overlap with genes known to influence human growth-related traits. According to positional and functional analyses, we proposed HMGA2, HNF4G, MED13L, BHLHE40, FRZB, DMP1, TRIB3, and GATAD2A as important candidate genes influencing the studied traits. The combination of single-trait GWAS and meta-analyses of GWAS results improved the efficiency of detecting associated SNPs, and provided new insights into the genetic mechanisms of growth and development in Holstein cattle.

Genome-wide Association Analysis of Schizophrenia and Vitamin D Levels Shows Shared Genetic Architecture and Identifies Novel Risk Loci

Low vitamin D (vitD) levels have been consistently reported in schizophrenia (SCZ) suggesting a role in the etiopathology. However, little is known about the role of underlying shared genetic mechanisms. We applied a conditional/conjunctional false discovery rate approach (FDR) on large, nonoverlapping genome-wide association studies for SCZ (N cases = 53 386, N controls = 77 258) and vitD serum concentration (N = 417 580) to evaluate shared common genetic variants. The identified genomic loci were characterized using functional analyses and biological repositories. We observed cross-trait SNP enrichment in SCZ conditioned on vitD and vice versa, demonstrating shared genetic architecture. Applying the conjunctional FDR approach, we identified 72 loci jointly associated with SCZ and vitD at conjunctional FDR < 0.05. Among the 72 shared loci, 40 loci have not previously been reported for vitD, and 9 were novel for SCZ. Further, 64% had discordant effects on SCZ-risk and vitD levels. A mixture of shared variants with concordant and discordant effects with a predominance of discordant effects was in line with weak negative genetic correlation (rg = -0.085). Our results displayed shared genetic architecture between SCZ and vitD with mixed effect directions, suggesting overlapping biological pathways. Shared genetic variants with complex overlapping mechanisms may contribute to the coexistence of SCZ and vitD deficiency and influence the clinical picture.

Identification of genetic loci jointly influencing COVID-19 and coronary heart diseases

BACKGROUND

Comorbidities of coronavirus disease 2019 (COVID-19)/coronary heart disease (CHD) pose great threats to disease outcomes, yet little is known about their shared pathology. The study aimed to examine whether comorbidities of COVID-19/CHD involved shared genetic pathology, as well as to clarify the shared genetic variants predisposing risks common to COVID-19 severity and CHD risks.

METHODS

By leveraging publicly available summary statistics, we assessed the genetically determined causality between COVID-19 and CHD with bidirectional Mendelian randomization. To further quantify the causality contributed by shared genetic variants, we interrogated their genetic correlation with the linkage disequilibrium score regression method. Bayesian colocalization analysis coupled with conditional/conjunctional false discovery rate analysis was applied to decipher the shared causal single nucleotide polymorphisms (SNPs).

FINDINGS

Briefly, we observed that the incident CHD risks post COVID-19 infection were partially determined by shared genetic variants. The shared genetic variants contributed to the causality at a proportion of 0.18 (95% CI 0.18-0.19) to 0.23 (95% CI 0.23-0.24). The SNP (rs10490770) located near LZTFL1 suggested direct causality (SNPs → COVID-19 → CHD), and SNPs in ABO (rs579459, rs495828), ILRUN(rs2744961), and CACFD1(rs4962153, rs3094379) may simultaneously influence COVID-19 severity and CHD risks.

INTERPRETATION

Five SNPs located near LZTFL1 (rs10490770), ABO (rs579459, rs495828), ILRUN (rs2744961), and CACFD1 (rs4962153, rs3094379) may simultaneously influence their risks. The current study suggested that there may be shared mechanisms predisposing to both COVID-19 severity and CHD risks. Genetic predisposition to COVID-19 is a causal risk factor for CHD, supporting that reducing the COVID-19 infection risk or alleviating COVID-19 severity among those with specific genotypes might reduce their subsequent CHD adverse outcomes. Meanwhile, the shared genetic variants identified may be of clinical implications for identifying the target population who are more vulnerable to adverse CHD outcomes post COVID-19 and may also advance treatments of 'Long COVID-19.'

The impact of common variants on gene expression in the human brain: from RNA to protein to schizophrenia risk

Background

The impact of genetic variants on gene expression has been intensely studied at the transcription level, yielding in valuable insights into the association between genes and the risk of complex disorders, such as schizophrenia (SCZ). However, the downstream impact of these variants and the molecular mechanisms connecting transcription variation to disease risk are not well understood.

Results

We quantitated ribosome occupancy in prefrontal cortex samples of the BrainGVEX cohort. Together with transcriptomics and proteomics data from the same cohort, we performed cis-Quantitative Trait Locus (QTL) mapping and identified 3,253 expression QTLs (eQTLs), 1,344 ribosome occupancy QTLs (rQTLs), and 657 protein QTLs (pQTLs) out of 7,458 genes quantitated in all three omics types from 185 samples. Of the eQTLs identified, only 34% have their effects propagated to the protein level. Further analysis on the effect size of prefrontal cortex eQTLs identified from an independent dataset showed clear post-transcriptional attenuation of eQTL effects. To investigate the biological relevance of the attenuated eQTLs, we identified 70 expression-specific QTLs (esQTLs), 51 ribosome-occupancy-specific QTLs (rsQTLs), and 107 protein-specific QTLs (psQTLs). Five of these omics-specific QTLs showed strong colocalization with SCZ GWAS signals, three of them are esQTLs. The limited number of GWAS colocalization discoveries from omics-specific QTLs and the apparent prevalence of eQTL attenuation prompted us to take a complementary approach to investigate the functional relevance of attenuated eQTLs. Using S-PrediXcan we identified 74 SCZ risk genes, 34% of which were novel, and 67% of these risk genes were replicated in a MR-Egger test. Notably, 52 out of 74 risk genes were identified using eQTL data and 70% of these SCZ-risk-gene-driving eQTLs show little to no evidence of driving corresponding variations at the protein level.

Conclusion

The effect of eQTLs on gene expression in the prefrontal cortex is commonly attenuated post-transcriptionally. Many of the attenuated eQTLs still correlate with SCZ GWAS signal. Further investigation is needed to elucidate a mechanistic link between attenuated eQTLs and SCZ disease risk.

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.

A scalable approach to characterize pleiotropy across thousands of human diseases and complex traits using GWAS summary statistics

Genome-wide association studies (GWASs) across thousands of traits have revealed the pervasive pleiotropy of trait-associated genetic variants. While methods have been proposed to characterize pleiotropic components across groups of phenotypes, scaling these approaches to ultra-large-scale biobanks has been challenging. Here, we propose FactorGo, a scalable variational factor analysis model to identify and characterize pleiotropic components using biobank GWAS summary data. In extensive simulations, we observe that FactorGo outperforms the state-of-the-art (model-free) approach tSVD in capturing latent pleiotropic factors across phenotypes while maintaining a similar computational cost. We apply FactorGo to estimate 100 latent pleiotropic factors from GWAS summary data of 2,483 phenotypes measured in European-ancestry Pan-UK BioBank individuals (N = 420,531). Next, we find that factors from FactorGo are more enriched with relevant tissue-specific annotations than those identified by tSVD (p = 2.58E-10) and validate our approach by recapitulating brain-specific enrichment for BMI and the height-related connection between reproductive system and muscular-skeletal growth. Finally, our analyses suggest shared etiologies between rheumatoid arthritis and periodontal condition in addition to alkaline phosphatase as a candidate prognostic biomarker for prostate cancer. Overall, FactorGo improves our biological understanding of shared etiologies across thousands of GWASs.

Disentangling the interplay between genes, cognitive skills, and educational level in adolescent and young adult smoking - The TRAILS study

Recent studies suggest that smoking and lower educational attainment may have genetic influences in common. However, little is known about the mechanisms through which genetics contributes to educational inequalities in adolescent and young adult smoking. Common genetic liabilities may underlie cognitive skills associated with both smoking and education, such as IQ and effortful control, in line with indirect health-related selection explanations. Additionally, by affecting cognitive skills, genes may predict educational trajectories and hereby adolescents' social context, which may be associated with smoking, consistent with social causation explanations. Using data from the Dutch TRAILS Study (N = 1581), we estimated the extent to which polygenic scores (PGSs) for ever smoking regularly (PGSSMOK) and years of education (PGSEDU) predict IQ and effortful control, measured around age 11, and whether these cognitive skills then act as shared predictors of smoking and educational level around age 16, 19, 22, and 26. Second, we assessed if educational level mediated associations between PGSs and smoking. Both PGSs were associated with lower effortful control, and PGSEDU also with lower IQ. Lower IQ and effortful control, in turn, predicted having a lower educational level. However, neither of these cognitive skills were directly associated with smoking behaviour after controlling for covariates and PGSs. This suggests that IQ and effortful control are not shared predictors of smoking and education (i.e., no indirect health-related selection related to cognitive skills). Instead, PGSSMOK and PGSEDU, partly through their associations with lower cognitive skills, predicted selection into a lower educational track, which in turn was associated with more smoking, in line with social causation explanations. Our findings suggest that educational differences in the social context contribute to associations between genetic liabilities and educational inequalities in smoking.

Genetic Correlation, Shared Loci, and Causal Association Between Sex Hormone-Binding Globulin and Bone Mineral Density: Insights From a Large-Scale Genomewide Cross-Trait Analysis

Although the impact of sex hormones on bone metabolism is well-documented, effect of their primary modulator, sex hormone-binding globulin (SHBG), remains inconclusive. This study aims to elucidate the genetic overlap between SHBG and heel estimated bone mineral density (eBMD), a widely-accepted tool for osteoporosis management and fracture risk assessment. Using summary statistics from large-scale genomewide association studies conducted for SHBG (N = 370,125), SHBG adjusted for body mass index (SHBGa, N = 368,929), and eBMD (N = 426,824), a comprehensive genomewide cross-trait approach was performed to quantify global and local genetic correlations, identify pleiotropic loci, and infer causal associations. A significant overall inverse genetic correlation was found for SHBG and eBMD (rg  = -0.11, p = 3.34 × 10-10 ), which was further supported by the significant local genetic correlations observed in 11 genomic regions. Cross-trait meta-analysis revealed 219 shared loci, of which seven were novel. Notably, four novel loci (rs6542680, rs8178616, rs147110934, and rs815625) were further demonstrated to colocalize. Mendelian randomization identified a robust causal effect of SHBG on eBMD (beta = -0.22, p = 3.04 × 10-13 ), with comparable effect sizes observed in both men (beta = -0.16, p = 1.99 × 10-6 ) and women (beta = -0.19, p = 2.73 × 10-9 ). Replacing SHBG with SHBGa, the observed genetic correlations, pleiotropic loci and causal associations did not change substantially. Our work reveals a shared genetic basis between SHBG and eBMD, substantiated by multiple pleiotropic loci and a robust causal relationship. Although SHBG has been implicated in preventing and screening aging-related diseases, our findings support its etiological role in osteoporosis. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

A copula-based set-variant association test for bivariate continuous, binary or mixed phenotypes

In genome wide association studies (GWAS), researchers are often dealing with dichotomous and non-normally distributed traits, or a mixture of discrete-continuous traits. However, most of the current region-based methods rely on multivariate linear mixed models (mvLMMs) and assume a multivariate normal distribution for the phenotypes of interest. Hence, these methods are not applicable to disease or non-normally distributed traits. Therefore, there is a need to develop unified and flexible methods to study association between a set of (possibly rare) genetic variants and non-normal multivariate phenotypes. Copulas are multivariate distribution functions with uniform margins on the [0, 1] interval and they provide suitable models to deal with non-normality of errors in multivariate association studies. We propose a novel unified and flexible copula-based multivariate association test (CBMAT) for discovering association between a genetic region and a bivariate continuous, binary or mixed phenotype. We also derive a data-driven analytic p-value procedure of the proposed region-based score-type test. Through simulation studies, we demonstrate that CBMAT has well controlled type I error rates and higher power to detect associations compared with other existing methods, for discrete and non-normally distributed traits. At last, we apply CBMAT to detect the association between two genes located on chromosome 11 and several lipid levels measured on 1477 subjects from the ASLPAC study.

Ankylosing spondylitis and psychiatric disorders in European population: a Mendelian randomization study

Background

Epidemiologic evidence has demonstrated a correlation between ankylosing spondylitis and psychiatric disorders. However, little is known about the common genetics and causality of this association. This study aimed to investigate the common genetics and causality between ankylosing spondylitis (AS) and psychiatric disorders.

Methods

A two-sample Mendelian Randomization (MR) analysis was carried out to confirm causal relationships between ankylosing spondylitis and five mental health conditions including major depressive disorder (MDD), anxiety disorder (AXD), schizophrenia (SCZ), bipolar disorder (BIP), and anorexia nervosa (AN). Genetic instrumental variables associated with exposures and outcomes were derived from the largest available summary statistics of genome-wide association studies (GWAS). Bidirectional causal estimation of MR was primarily obtained using the inverse variance weighting (IVW) method. Other MR methods include MR-Egger regression, Weighted Median Estimator (WME), Weighted Mode, Simple Mode, and Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO). Sensitivity analyses are conducted to estimate the robustness of MR results.

Results

The findings suggest that AS may be causally responsible for the risk of developing SCZ (OR = 1.18, 95% confidence interval = (1.06, 1.31), P = 2.58 × 10-3) and AN (OR = 1.32, 95% confidence interval = (1.07, 1.64), P = 9.43 × 10-3). In addition, MDD, AXD, SCZ, AN, and BIP were not inversely causally related to AS (all p > 0.05).

Conclusion

Our study provides fresh insights into the relationship between AS and psychiatric disorders (SCZ and AN). Furthermore, it may provide new clues for risk management and preventive interventions for mental disorders in patients with AS.

Cauchy combination methods for the detection of gene-environment interactions for rare variants related to quantitative phenotypes

The characterization of gene-environment interactions (GEIs) can provide detailed insights into the biological mechanisms underlying complex diseases. Despite recent interest in GEIs for rare variants, published GEI tests are underpowered for an extremely small proportion of causal rare variants in a gene or a region. By extending the aggregated Cauchy association test (ACAT), we propose three GEI tests to address this issue: a Cauchy combination GEI test with fixed main effects (CCGEI-F), a Cauchy combination GEI test with random main effects (CCGEI-R), and an omnibus Cauchy combination GEI test (CCGEI-O). ACAT was applied to combine p values of single-variant GEI analyses to obtain CCGEI-F and CCGEI-R and p values of multiple GEI tests were combined in CCGEI-O. Through numerical simulations, for small numbers of causal variants, CCGEI-F, CCGEI-R and CCGEI-O provided approximately 5% higher power than the existing GEI tests INT-FIX and INT-RAN; however, they had slightly higher power than the existing GEI test TOW-GE. For large numbers of causal variants, although CCGEI-F and CCGEI-R exhibited comparable or slightly lower power values than the competing tests, the results were still satisfactory. Among all simulation conditions evaluated, CCGEI-O provided significantly higher power than that of competing GEI tests. We further applied our GEI tests in genome-wide analyses of systolic blood pressure or diastolic blood pressure to detect gene-body mass index (BMI) interactions, using whole-exome sequencing data from UK Biobank. At a suggestive significance level of 1.0 × 10-4, KCNC4, GAR1, FAM120AOS and NT5C3B showed interactions with BMI by our GEI tests.

Mendelian randomization mixed-scale treatment effect robust identification and estimation for causal inference

Standard Mendelian randomization (MR) analysis can produce biased results if the genetic variant defining an instrumental variable (IV) is confounded and/or has a horizontal pleiotropic effect on the outcome of interest not mediated by the treatment variable. We provide novel identification conditions for the causal effect of a treatment in the presence of unmeasured confounding by leveraging a possibly invalid IV for which both the IV independence and exclusion restriction assumptions may be violated. The proposed Mendelian randomization mixed-scale treatment effect robust identification (MR MiSTERI) approach relies on (i) an assumption that the treatment effect does not vary with the possibly invalid IV on the additive scale; (ii) that the confounding bias does not vary with the possibly invalid IV on the odds ratio scale; and (iii) that the residual variance for the outcome is heteroskedastic with respect to the possibly invalid IV. Although assumptions (i) and (ii) have, respectively, appeared in the IV literature, assumption (iii) has not; we formally establish that their conjunction can identify a causal effect even with an invalid IV. MR MiSTERI is shown to be particularly advantageous in the presence of pervasive heterogeneity of pleiotropic effects on the additive scale. We propose a simple and consistent three-stage estimator that can be used as a preliminary estimator to a carefully constructed efficient one-step-update estimator. In order to incorporate multiple, possibly correlated, and weak invalid IVs, a common challenge in MR studies, we develop a MAny Weak Invalid Instruments (MR MaWII MiSTERI) approach for strengthened identification and improved estimation accuracy. Both simulation studies and UK Biobank data analysis results demonstrate the robustness of the proposed methods.

GCPBayes pipeline: a tool for exploring pleiotropy at the gene level

Cross-phenotype association using gene-set analysis can help to detect pleiotropic genes and inform about common mechanisms between diseases. Although there are an increasing number of statistical methods for exploring pleiotropy, there is a lack of proper pipelines to apply gene-set analysis in this context and using genome-scale data in a reasonable running time. We designed a user-friendly pipeline to perform cross-phenotype gene-set analysis between two traits using GCPBayes, a method developed by our team. All analyses could be performed automatically by calling for different scripts in a simple way (using a Shiny app, Bash or R script). A Shiny application was also developed to create different plots to visualize outputs from GCPBayes. Finally, a comprehensive and step-by-step tutorial on how to use the pipeline is provided in our group's GitHub page. We illustrated the application on publicly available GWAS (genome-wide association studies) summary statistics data to identify breast cancer and ovarian cancer susceptibility genes. We have shown that the GCPBayes pipeline could extract pleiotropic genes previously mentioned in the literature, while it also provided new pleiotropic genes and regions that are worthwhile for further investigation. We have also provided some recommendations about parameter selection for decreasing computational time of GCPBayes on genome-scale data.

A causal inference and Bayesian optimisation framework for modelling multi-trait relationships-Proof-of-concept using Brassica napus seed yield under controlled conditions

The improvement of crop yield is a major breeding target and there is a long history of research that has focussed on unravelling the mechanisms and processes that contribute to yield. Quantitative prediction of the interplay between morphological traits, and the effects of these trait-trait relationships on seed production remains, however, a challenge. Consequently, the extent to which crop varieties optimise their morphology for a given environment is largely unknown. This work presents a new combination of existing methodologies by framing crop breeding as an optimisation problem and evaluates the extent to which existing varieties exhibit optimal morphologies under the test conditions. In this proof-of-concept study using spring and winter oilseed rape plants grown under greenhouse conditions, we employ causal inference to model the hierarchically structured effects of 27 morphological yield traits on each other. We perform Bayesian optimisation of seed yield, to identify and quantify the morphologies of ideotype plants, which are expected to be higher yielding than the varieties in the studied panels. Under the tested growth conditions, we find that existing spring varieties occupy the optimal regions of trait-space, but that potentially high yielding strategies are unexplored in extant winter varieties. The same approach can be used to evaluate trait (morphology) space for any environment.

The Co-Twin Control Design: Implementation and Methodological Considerations

Establishing causal relationships in observational studies is an important step in research and policy decision making. The association between an exposure and an outcome can be confounded by multiple factors, often making it hard to draw causal conclusions. The co-twin control design (CTCD) is a powerful approach that allows for the investigation of causal effects while controlling for genetic and shared environmental confounding factors. This article introduces the CTCD and offers an overview of analysis methods for binary and continuous outcome and exposure variables. Tools for data simulation are provided, along with practical guidance and accompanying scripts for implementing the CTCD in R, SPSS, and Stata. While the CTCD offers valuable insights into causal inference, it depends on several assumptions that are important when interpreting CTCD results. By presenting a broad overview of the CTCD, this article aims to equip researchers with actionable recommendations and a comprehensive understanding of the design's strengths and limitations.

Inferring sparse structure in genotype-phenotype maps

Correlation among multiple phenotypes across related individuals may reflect some pattern of shared genetic architecture: individual genetic loci affect multiple phenotypes (an effect known as pleiotropy), creating observable relationships between phenotypes. A natural hypothesis is that pleiotropic effects reflect a relatively small set of common "core" cellular processes: each genetic locus affects one or a few core processes, and these core processes in turn determine the observed phenotypes. Here, we propose a method to infer such structure in genotype-phenotype data. Our approach, sparse structure discovery (SSD) is based on a penalized matrix decomposition designed to identify latent structure that is low-dimensional (many fewer core processes than phenotypes and genetic loci), locus-sparse (each locus affects few core processes), and/or phenotype-sparse (each phenotype is influenced by few core processes). Our use of sparsity as a guide in the matrix decomposition is motivated by the results of a novel empirical test indicating evidence of sparse structure in several recent genotype-phenotype datasets. First, we use synthetic data to show that our SSD approach can accurately recover core processes if each genetic locus affects few core processes or if each phenotype is affected by few core processes. Next, we apply the method to three datasets spanning adaptive mutations in yeast, genotoxin robustness assay in human cell lines, and genetic loci identified from a yeast cross, and evaluate the biological plausibility of the core process identified. More generally, we propose sparsity as a guiding prior for resolving latent structure in empirical genotype-phenotype maps.

Multivariate genome-wide associations for immune traits in two maternal pig lines

BACKGROUND

Immune traits are considered to serve as potential biomarkers for pig's health. Medium to high heritabilities have been observed for some of the immune traits suggesting genetic variability of these phenotypes. Consideration of previously established genetic correlations between immune traits can be used to identify pleiotropic genetic markers. Therefore, genome-wide association study (GWAS) approaches are required to explore the joint genetic foundation for health biomarkers. Usually, GWAS explores phenotypes in a univariate (uv), trait-by-trait manner. Besides two uv GWAS methods, four multivariate (mv) GWAS approaches were applied on combinations out of 22 immune traits for Landrace (LR) and Large White (LW) pig lines.

RESULTS

In total 433 (LR: 351, LW: 82) associations were identified with the uv approach implemented in PLINK and a Bayesian linear regression uv approach (BIMBAM) software. Single Nucleotide Polymorphisms (SNPs) that were identified with both uv approaches (n = 32) were mostly associated with immune traits such as haptoglobin, red blood cell characteristics and cytokines, and were located in protein-coding genes. Mv GWAS approaches detected 647 associations for different mv immune trait combinations which were summarized to 133 Quantitative Trait Loci (QTL). SNPs for different trait combinations (n = 66) were detected with more than one mv method. Most of these SNPs are associated with red blood cell related immune trait combinations. Functional annotation of these QTL revealed 453 immune-relevant protein-coding genes. With uv methods shared markers were not observed between the breeds, whereas mv approaches were able to detect two conjoint SNPs for LR and LW. Due to unmapped positions for these markers, their functional annotation was not clarified.

CONCLUSIONS

This study evaluated the joint genetic background of immune traits in LR and LW piglets through the application of various uv and mv GWAS approaches. In comparison to uv methods, mv methodologies identified more significant associations, which might reflect the pleiotropic background of the immune system more accurately. In genetic research of complex traits, the SNP effects are generally small. Furthermore, one genetic variant can affect several correlated immune traits at the same time, termed pleiotropy. As mv GWAS methods consider strong dependencies among traits, the power to detect SNPs can be boosted. Both methods revealed immune-relevant potential candidate genes. Our results indicate that one single test is not able to detect all the different types of genetic effects in the most powerful manner and therefore, the methods should be applied complementary.

Guidelines for performing Mendelian randomization investigations: update for summer 2023

This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into ten sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), extensions and additional analyses, data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 24 months.

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.

A systematic review of Mendelian randomization studies on multiple sclerosis

Mendelian randomization (MR) is a powerful approach for assessing the causal effect of putative risk factors on an outcome, using genetic variants as instrumental variables. The methodology and application developed in the framework of MR have been dramatically improved, taking advantage of the many public genome-wide association study (GWAS) data. The availability of summary-level data allowed to perform numerous MR studies especially for complex diseases, pinpointing modifiable exposures causally related to increased or decreased disease risk. Multiple sclerosis (MS) is a complex multifactorial disease whose aetiology involves both genetic and non-genetic risk factors and their interplay. Previous observational studies have revealed associations between candidate modifiable exposures and MS risk; although being prone to confounding, and reverse causation, these studies were unable to draw causal conclusions. MR analysis addresses the limitations of observational studies and allows to establish reliable and accurate causal conclusions. Here, we systematically reviewed the studies evaluating the causal effect, through MR, of genetic and non-genetic exposures on MS risk. Among 107 papers found, only 42 were eligible for final evaluation and qualitative synthesis. We found that, above all, low vitamin D levels and high adult body mass index (BMI) appear to be uncontested risk factors for increased MS risk.

Intermediate Molecular Phenotypes to Identify Genetic Markers of Anthracycline-Induced Cardiotoxicity Risk

Cardiotoxicity due to anthracyclines (CDA) affects cancer patients, but we cannot predict who may suffer from this complication. CDA is a complex trait with a polygenic component that is mainly unidentified. We propose that levels of intermediate molecular phenotypes (IMPs) in the myocardium associated with histopathological damage could explain CDA susceptibility, so variants of genes encoding these IMPs could identify patients susceptible to this complication. Thus, a genetically heterogeneous cohort of mice (n = 165) generated by backcrossing were treated with doxorubicin and docetaxel. We quantified heart fibrosis using an Ariol slide scanner and intramyocardial levels of IMPs using multiplex bead arrays and QPCR. We identified quantitative trait loci linked to IMPs (ipQTLs) and cdaQTLs via linkage analysis. In three cancer patient cohorts, CDA was quantified using echocardiography or Cardiac Magnetic Resonance. CDA behaves as a complex trait in the mouse cohort. IMP levels in the myocardium were associated with CDA. ipQTLs integrated into genetic models with cdaQTLs account for more CDA phenotypic variation than that explained by cda-QTLs alone. Allelic forms of genes encoding IMPs associated with CDA in mice, including AKT1, MAPK14, MAPK8, STAT3, CAS3, and TP53, are genetic determinants of CDA in patients. Two genetic risk scores for pediatric patients (n = 71) and women with breast cancer (n = 420) were generated using machine-learning Least Absolute Shrinkage and Selection Operator (LASSO) regression. Thus, IMPs associated with heart damage identify genetic markers of CDA risk, thereby allowing more personalized patient management.

Polygenic overlap with body-mass index improves prediction of treatment-resistant schizophrenia

Treatment resistant schizophrenia (TRS) is characterized by repeated treatment failure with antipsychotics. A recent genome-wide association study (GWAS) of TRS showed a polygenic architecture, but no significant loci were identified. Clozapine is shown to be the superior drug in terms of clinical effect in TRS; at the same time it has a serious side effect profile, including weight gain. Here, we sought to increase power for genetic discovery and improve polygenic prediction of TRS, by leveraging genetic overlap with Body Mass Index (BMI). We analysed GWAS summary statistics for TRS and BMI applying the conditional false discovery rate (cFDR) framework. We observed cross-trait polygenic enrichment for TRS conditioned on associations with BMI. Leveraging this cross-trait enrichment, we identified 2 novel loci for TRS at cFDR <0.01, suggesting a role of MAP2K1 and ZDBF2. Further, polygenic prediction based on the cFDR analysis explained more variance in TRS when compared to the standard TRS GWAS. These findings highlight putative molecular pathways which may distinguish TRS patients from treatment responsive patients. Moreover, these findings confirm that shared genetic mechanisms influence both TRS and BMI and provide new insights into the biological underpinnings of metabolic dysfunction and antipsychotic treatment.

Cross-phenotype association analysis of gastric cancer: in-silico functional annotation based on the disease-gene network

BACKGROUND

A gene or variant has pleiotropic effects, and genetic variant identification across multiple phenotypes can provide a comprehensive understanding of biological pathways shared among different diseases or phenotypes. Discovery of genetic loci associated with multiple diseases can simultaneously support general interventions. Several meta-analyses have shown genetic associations with gastric cancer (GC); however, no study has identified associations with other phenotypes using this approach.

METHODS

Here, we applied disease network analysis and gene-based analysis (GBA) to examine genetic variants linked to GC and simultaneously associated with other phenotypes. We conducted a single-nucleotide polymorphism (SNP) level meta-analysis and GBA through a systematic genome-wide association study (GWAS) linked to GC, to integrate published results for the SNP variants and group them into major GC-associated genes. We then performed disease network and expression quantitative trait loci (eQTL) analyses to evaluate cross-phenotype associations and expression levels of GC-related genes.

RESULTS

Seven genes (MTX1, GBAP1, MUC1, TRIM46, THBS3, PSCA, and ABO) were associated with GC as well as blood urea nitrogen (BUN), glomerular filtration rate (GFR), and uric acid (UA). In addition, 17 SNPs regulated the expression of genes located on 1q22, 24 SNPs regulated the expression of PSCA on 8q24.3, and rs7849820 regulated the expression of ABO on 9q34.2. Furthermore, rs1057941 and rs2294008 had the highest posterior causal probabilities of being a causal candidate SNP in 1q22, and 8q24.3, respectively.

CONCLUSIONS

These findings identified seven GC-associated genes exhibiting a cross-association with GFR, BUN, and UA.

A genome-wide search for pleiotropy in more than 100,000 harmonized longitudinal cognitive domain scores

BACKGROUND

More than 75 common variant loci account for only a portion of the heritability for Alzheimer's disease (AD). A more complete understanding of the genetic basis of AD can be deduced by exploring associations with AD-related endophenotypes.

METHODS

We conducted genome-wide scans for cognitive domain performance using harmonized and co-calibrated scores derived by confirmatory factor analyses for executive function, language, and memory. We analyzed 103,796 longitudinal observations from 23,066 members of community-based (FHS, ACT, and ROSMAP) and clinic-based (ADRCs and ADNI) cohorts using generalized linear mixed models including terms for SNP, age, SNP × age interaction, sex, education, and five ancestry principal components. Significance was determined based on a joint test of the SNP's main effect and interaction with age. Results across datasets were combined using inverse-variance meta-analysis. Genome-wide tests of pleiotropy for each domain pair as the outcome were performed using PLACO software.

RESULTS

Individual domain and pleiotropy analyses revealed genome-wide significant (GWS) associations with five established loci for AD and AD-related disorders (BIN1, CR1, GRN, MS4A6A, and APOE) and eight novel loci. ULK2 was associated with executive function in the community-based cohorts (rs157405, P = 2.19 × 10-9). GWS associations for language were identified with CDK14 in the clinic-based cohorts (rs705353, P = 1.73 × 10-8) and LINC02712 in the total sample (rs145012974, P = 3.66 × 10-8). GRN (rs5848, P = 4.21 × 10-8) and PURG (rs117523305, P = 1.73 × 10-8) were associated with memory in the total and community-based cohorts, respectively. GWS pleiotropy was observed for language and memory with LOC107984373 (rs73005629, P = 3.12 × 10-8) in the clinic-based cohorts, and with NCALD (rs56162098, P = 1.23 × 10-9) and PTPRD (rs145989094, P = 8.34 × 10-9) in the community-based cohorts. GWS pleiotropy was also found for executive function and memory with OSGIN1 (rs12447050, P = 4.09 × 10-8) and PTPRD (rs145989094, P = 3.85 × 10-8) in the community-based cohorts. Functional studies have previously linked AD to ULK2, NCALD, and PTPRD.

CONCLUSION

Our results provide some insight into biological pathways underlying processes leading to domain-specific cognitive impairment and AD, as well as a conduit toward a syndrome-specific precision medicine approach to AD. Increasing the number of participants with harmonized cognitive domain scores will enhance the discovery of additional genetic factors of cognitive decline leading to AD and related dementias.

Genome-wide analyses characterize shared heritability among cancers and identify novel cancer susceptibility regions

BACKGROUND

The shared inherited genetic contribution to risk of different cancers is not fully known. In this study, we leverage results from 12 cancer genome-wide association studies (GWAS) to quantify pairwise genome-wide genetic correlations across cancers and identify novel cancer susceptibility loci.

METHODS

We collected GWAS summary statistics for 12 solid cancers based on 376 759 participants with cancer and 532 864 participants without cancer of European ancestry. The included cancer types were breast, colorectal, endometrial, esophageal, glioma, head and neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancers. We conducted cross-cancer GWAS and transcriptome-wide association studies to discover novel cancer susceptibility loci. Finally, we assessed the extent of variant-specific pleiotropy among cancers at known and newly identified cancer susceptibility loci.

RESULTS

We observed widespread but modest genome-wide genetic correlations across cancers. In cross-cancer GWAS and transcriptome-wide association studies, we identified 15 novel cancer susceptibility loci. Additionally, we identified multiple variants at 77 distinct loci with strong evidence of being associated with at least 2 cancer types by testing for pleiotropy at known cancer susceptibility loci.

CONCLUSIONS

Overall, these results suggest that some genetic risk variants are shared among cancers, though much of cancer heritability is cancer-specific and thus tissue-specific. The increase in statistical power associated with larger sample sizes in cross-disease analysis allows for the identification of novel susceptibility regions. Future studies incorporating data on multiple cancer types are likely to identify additional regions associated with the risk of multiple cancer types.

Pleiotropic loci for cannabis use disorder severity in multi-ancestry high-risk populations

Cannabis use disorder (CUD) is common and has in part a genetic basis. The risk factors underlying its development likely involve multiple genes that are polygenetic and interact with each other and the environment to ultimately lead to the disorder. Co-morbidity and genetic correlations have been identified between CUD and other disorders and traits in select populations primarily of European descent. If two or more traits, such as CUD and another disorder, are affected by the same genetic locus, they are said to be pleiotropic. The present study aimed to identify specific pleiotropic loci for the severity level of CUD in three high-risk population cohorts: American Indians (AI), Mexican Americans (MA), and European Americans (EA). Using a previously developed computational method based on a machine learning technique, we leveraged the entire GWAS catalog and identified 114, 119, and 165 potentially pleiotropic variants for CUD severity in AI, MA, and EA respectively. Ten pleiotropic loci were shared between the cohorts although the exact variants from each cohort differed. While majority of the pleiotropic genes were distinct in each cohort, they converged on numerous enriched biological pathways. The gene ontology terms associated with the pleiotropic genes were predominately related to synaptic functions and neurodevelopment. Notable pathways included Wnt/β-catenin signaling, lipoprotein assembly, response to UV radiation, and components of the complement system. The pleiotropic genes were the most significantly differentially expressed in frontal cortex and coronary artery, up-regulated in adipose tissue, and down-regulated in testis, prostate, and ovary. They were significantly up-regulated in most brain tissues but were down-regulated in the cerebellum and hypothalamus. Our study is the first to attempt a large-scale pleiotropy detection scan for CUD severity. Our findings suggest that the different population cohorts may have distinct genetic factors for CUD, however they share pleiotropic genes from underlying pathways related to Alzheimer's disease, neuroplasticity, immune response, and reproductive endocrine systems.