An optimized instrument variable selection approach to improve causality estimation in association studies.

Mendelian randomization (MR) is an emerging tool for inferring causality in genetic epidemiology. MR studies suffer bias from weak genetic instrument variables (IVs) and horizontal pleiotropy. We introduce a robust integrative framework strictly adhering with STROBE-MR guidelines to improve causality inference through MR studies. We implemented novel t-statistics-based criteria to improve the reliability of selected IVs followed by various MR methods. Further, we include sensitivity analyses to remove horizontal-pleiotropy bias. For functional validation, we perform enrichment analysis of identified causal SNPs. We demonstrate effectiveness of our proposed approach on 5 different MR datasets selected from diverse populations. Our pipeline outperforms its counterpart MR analyses using default parameters on these datasets. Notably, we found a significant association between total cholesterol and coronary artery disease (P = 1.16 × 10-71) in a single-sample dataset using our pipeline. Contrarily, this same association was deemed ambiguous while using default parameters. Moreover, in a two-sample dataset, we uncover 13 new causal SNPs with enhanced statistical significance (P = 1.06 × 10-11) for liver-iron-content and liver-cell-carcinoma. Likewise, these SNPs remained undetected using the default parameters (P = 7.58 × 10-4). Furthermore, our analysis confirmed previously known pathways, such as hyperlipidemia in heart diseases and gene ME1 in liver cancer. In conclusion, we propose a robust and powerful framework to infer causality across diverse populations and easily adaptable to different diseases.

Causal effects of serum calcium, phosphate, and 25-hydroxyvitamin D on kidney function: a genetic correlation, pleiotropic analysis, and Mendelian randomization study.

Background: Existing studies investigating the impact of serum calcium (Ca), phosphate (P), 25 hydroxyvitamin D (25[OH]D), and parathyroid hormone (PTH) levels on kidney function have produced inconsistent results. Further research is needed to establish the direct causal relationship between these factors and kidney function. Methods: The study used genome-wide association study datasets for exposure and outcome, mainly derived from the UK Biobank and CKDGen Consortium, with sample sizes ranging from 3,310 to 480,699 individuals of European ancestry. Heritability and genetic correlations among these phenotypes were assessed using linkage disequilibrium score regression (LDSC) and phenotypes with a heritability z-score <4 were excluded from further analyses. Pleiotropic analyses were performed to identify potential horizontal pleiotropic variants at gene and LD-independent locus levels. Mendelian randomization (MR) analysis, using instrumental variables (IVs) based on two distinct selection criteria, was conducted to investigate the potential causal relationships between serum Ca, P, 25(OH)D, PTH, and kidney function. Results: PTH was excluded from further analysis due to a heritability z-score < 4. Genetic correlations were observed between serum Ca and urine albumin-to-creatinine ratio (UACR) (rg = 0.202, P-value = 5.0E-04), between serum 25(OH)D and estimated glomerular filtration rate using serum creatinine (eGFRcrea) (rg = -0.094; P-value = 1.4E-05), and between serum 25(OH)D and blood urea nitrogen (BUN) (rg = 0.127; P-value = 1.7E-06). In univariable MR analysis using IVs based on two different selection criteria, it consistently demonstrated that genetically predicted serum Ca consistently showed an increase in UACR (beta 0.11, P-value 2.0E-03; beta 0.13, P-value 2.0E-04). Similarly, serum P was associated with a decrease in eGFRcrea (beta -0.01, P-value 2.0E-04; beta -0.005, P-value 2.0E-03) and an increase in BUN (beta 0.02, P-value 3.0E-03; beta 0.02, P-value 7.5E-07). The influence of serum P on kidney function was further supported in multivariable MR analysis. However, genetically predicted 25(OH)D did not have a significant impact on kidney function. Conclusions: Elevated serum Ca or P levels could both impair kidney function, whereas 25(OH)D has no impact on renal function.

Genomic Correlation, Shared Loci, and Causal Relationship Between Bullous Pemphigoid and Atopic Dermatitis: A Large-Scale Genome-Wide Cross-Trait Analysis.

BACKGROUND: Bullous pemphigoid (BP) and atopic dermatitis (AD) are currently thought to be tightly related, yet studies of the mechanisms of co-morbidities are lacking. METHODS: We obtained GWAS data for BP (N = 376,274) and AD (N = 796,661) from the Finnish Genetic Research Program dataset and the UK Biobank, separately. Then, the following four analyses were performed: (1) cross-trait linkage disequilibrium score regression (LDSC) to assess the genetic correlation between BP and AD, (2) cross-phenotype association analysis (CPASSOC) to identify multiple effector loci shared by BP and AD, (3) transcriptome-wide association study (TWAS) to determine whether their cross-organizational expression patterns share genes with a common biological mechanism of relevance, and (4) bidirectional Mendelian randomization (MR) analysis to assess bidirectional causal effects of BP and AD. RESULTS: We found a positive genetic association between BP and AD (rg = 0.5476, p = 0.0495) as well as identified four pleiotropic loci and 59 common genes affecting BP and AD. Bidirectional MR analysis suggested that BP promotes the risk of AD. CONCLUSIONS: We revealed a genetic link between BP and AD, which is associated with biological pleiotropy and causality. Awareness of the association between BP and AD helps dermatologists manage patients with these illnesses.

Genomic structural equation modeling reveals latent phenotypes in the human cortex with distinct genetic architecture.

Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to map the genetic architecture of cortical surface area (SA) and cortical thickness (CT) for 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance, which we are denoting genetically informed brain networks (GIBNs). Genomic SEM can fit a multivariate GWAS from summary statistics for each of the GIBNs, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA identified 6 GIBNs and CT identified 4 GIBNs, although sensitivity analyses indicated that other structures were plausible. The multivariate GWASs of the GIBNs identified 74 genome-wide significant (GWS) loci (p < 5 × 10-8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of GIBN GWASs found that SA-derived GIBNs had a positive genetic correlation with bipolar disorder (BPD), and cannabis use disorder, indicating genetic predisposition to a larger SA in the specific GIBN is associated with greater genetic risk of these disorders. A negative genetic correlation was observed between attention deficit hyperactivity disorder (ADHD) and major depressive disorder (MDD). CT GIBNs displayed a negative genetic correlation with alcohol dependence. Even though we observed model instability in our application of genomic SEM to high-dimensional data, jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across neuroimaging phenotypes offers new insights into the genetics of cortical structure and relationships to psychopathology.

Multi-Omics Integration Analysis Pinpoint Proteins Influencing Brain Structure and Function: Toward Drug Targets and Neuroimaging Biomarkers for Neuropsychiatric Disorders.

Integrating protein quantitative trait loci (pQTL) data and summary statistics from genome-wide association studies (GWAS) of brain image-derived phenotypes (IDPs) can benefit in identifying IDP-related proteins. Here, we developed a systematic omics-integration analytic framework by sequentially using proteome-wide association study (PWAS), Mendelian randomization (MR), and colocalization (COLOC) analyses to identify the potentially causal brain and plasma proteins for IDPs, followed by pleiotropy analysis, mediation analysis, and drug exploration analysis to investigate potential mediation pathways of pleiotropic proteins to neuropsychiatric disorders (NDs) as well as candidate drug targets. A total of 201 plasma proteins and 398 brain proteins were significantly associated with IDPs from PWAS analysis. Subsequent MR and COLOC analyses further identified 313 potentially causal IDP-related proteins, which were significantly enriched in neural-related phenotypes, among which 91 were further identified as pleiotropic proteins associated with both IDPs and NDs, including EGFR, TMEM106B, GPT, and HLA-B. Drug prioritization analysis showed that 6.33% of unique pleiotropic proteins had drug targets or interactions with medications for NDs. Nine potential mediation pathways were identified to illustrate the mediating roles of the IDPs in the causal effect of the pleiotropic proteins on NDs, including the indirect effect of TMEM106B on Alzheimer's disease (AD) risk via radial diffusivity (RD) of the posterior limb of the internal capsule (PLIC), with the mediation proportion being 11.18%, and the indirect effect of EGFR on AD through RD of PLIC, RD of splenium of corpus callosum (SCC), and fractional anisotropy (FA) of SCC, with the mediation proportion being 18.99%, 22.79%, and 19.91%, respectively. These findings provide novel insights into pathogenesis, drug targets, and neuroimaging biomarkers of NDs.

Pleiotropic effects of PAB1 deletion: Extensive changes in the yeast proteome, transcriptome, and translatome.

Cytoplasmic poly(A)-binding protein (PABPC; Pab1 in yeast) is thought to be involved in multiple steps of post-transcriptional control, including translation initiation, translation termination, and mRNA decay. To understand both the direct and indirect roles of PABPC in more detail, we have employed mass spectrometry to assess the abundance of the components of the yeast proteome, as well as RNA-Seq and Ribo-Seq to analyze changes in the abundance and translation of the yeast transcriptome, in cells lacking the PAB1 gene. We find that pab1Δ cells manifest drastic changes in the proteome and transcriptome, as well as defects in translation initiation and termination. Defects in translation initiation and the stabilization of specific classes of mRNAs in pab1Δ cells appear to be partly indirect consequences of reduced levels of specific initiation factors, decapping activators, and components of the deadenylation complex in addition to the general loss of Pab1's direct role in these processes. Cells devoid of Pab1 also manifested a nonsense codon readthrough phenotype indicative of a defect in translation termination. Collectively, our results indicate that, unlike the loss of simpler regulatory proteins, elimination of cellular Pab1 is profoundly pleiotropic and disruptive to numerous aspects of post-transcriptional regulation.

Alterations of pleiotropic neuropeptide-receptor gene couples in Cetacea.

BACKGROUND: Habitat transitions have considerable consequences in organism homeostasis, as they require the adjustment of several concurrent physiological compartments to maintain stability and adapt to a changing environment. Within the range of molecules with a crucial role in the regulation of different physiological processes, neuropeptides are key agents. Here, we examined the coding status of several neuropeptides and their receptors with pleiotropic activity in Cetacea. RESULTS: Analysis of 202 mammalian genomes, including 41 species of Cetacea, exposed an intricate mutational landscape compatible with gene sequence modification and loss. Specifically for Cetacea, in the 12 genes analysed we have determined patterns of loss ranging from species-specific disruptive mutations (e.g. neuropeptide FF-amide peptide precursor; NPFF) to complete erosion of the gene across the cetacean stem lineage (e.g. somatostatin receptor 4; SSTR4). CONCLUSIONS: Impairment of some of these neuromodulators may have contributed to the unique energetic metabolism, circadian rhythmicity and diving response displayed by this group of iconic mammals.

Most Pleiotropic Effects of Gene Knockouts Are Evolutionarily Transient in Yeasts.

Pleiotropy, the phenomenon in which a single gene influences multiple traits, is a fundamental concept in genetics. However, the evolutionary mechanisms underlying pleiotropy require further investigation. In this study, we conducted parallel gene knockouts targeting 100 transcription factors in 2 strains of Saccharomyces cerevisiae. We systematically examined and quantified the pleiotropic effects of these knockouts on gene expression levels for each transcription factor. Our results showed that the knockout of a single gene generally affected the expression levels of multiple genes in both strains, indicating various degrees of pleiotropic effects. Strikingly, the pleiotropic effects of the knockouts change rapidly between strains in different genetic backgrounds, and ∼85% of them were nonconserved. Further analysis revealed that the conserved effects tended to be functionally associated with the deleted transcription factors, while the nonconserved effects appeared to be more ad hoc responses. In addition, we measured 184 yeast cell morphological traits in these knockouts and found consistent patterns. In order to investigate the evolutionary processes underlying pleiotropy, we examined the pleiotropic effects of standing genetic variations in a population consisting of ∼1,000 hybrid progenies of the 2 strains. We observed that newly evolved expression quantitative trait loci impacted the expression of a greater number of genes than did old expression quantitative trait loci, suggesting that natural selection is gradually eliminating maladaptive or slightly deleterious pleiotropic responses. Overall, our results show that, although being prevalent for new mutations, the majority of pleiotropic effects observed are evolutionarily transient, which explains how evolution proceeds despite complicated pleiotropic effects.

Genetic interactions and pleiotropy in metabolic diseases: Insights from a comprehensive GWAS analysis.

This study offers insights into the genetic and biological connections between nine common metabolic diseases using data from genome-wide association studies. Our goal is to unravel the genetic interactions and biological pathways of these complex diseases, enhancing our understanding of their genetic architecture. We employed a range of advanced analytical techniques to explore the genetic correlations and shared genetic variants of these diseases. These methods include Linked Disequilibrium Score Regression, High-Definition Likelihood (HDL), genetic analysis combining multiplicity and annotation (GPA), two-sample Mendelian randomization analyses, analysis under the multiplicity-complex null hypothesis (PLACO), and Functional mapping and annotation of genetic associations (FUMA). Additionally, Bayesian co-localization analyses were used to examine associations of specific loci across traits. Our study discovered significant genomic correlations and shared loci, indicating complex genetic interactions among these metabolic diseases. We found several shared single nucleotide variants and risk loci, notably highlighting the role of the immune system and endocrine pathways in these diseases. Particularly, rs2476601 and its associated gene PTPN22 appear to play a crucial role in the connection between type 2 diabetes mellitus, hypothyroidism/mucous oedema and hypoglycaemia. These findings enhance our understanding of the genetic underpinnings of these diseases and open new potential avenues for targeted therapeutic and preventive strategies. The results underscore the importance of considering pleiotropic effects in deciphering the genetic architecture of complex diseases, especially metabolic ones.

Novel insights into the pleiotropic health effects of growth differentiation factor 11 gained from genome-wide association studies in population biobanks.

BACKGROUND: Growth differentiation factor 11 (GDF11) is a member of the transforming growth factor-ß (TGF-ß) superfamily that has gained considerable attention over the last decade for its observed ability to reverse age-related deterioration of multiple tissues, including the heart. Yet as many researchers have struggled to confirm the cardioprotective and anti-aging effects of GDF11, the topic has grown increasingly controversial, and the field has reached an impasse. We postulated that a clearer understanding of GDF11 could be gained by investigating its health effects at the population level. METHODS AND RESULTS: We employed a comprehensive strategy to interrogate results from genome-wide association studies in population Biobanks. Interestingly, phenome-wide association studies (PheWAS) of GDF11 tissue-specific cis-eQTLs revealed associations with asthma, immune function, lung function, and thyroid phenotypes. Furthermore, PheWAS of GDF11 genetic variants confirmed these results, revealing similar associations with asthma, immune function, lung function, and thyroid health. To complement these findings, we mined results from transcriptome-wide association studies, which uncovered associations between predicted tissue-specific GDF11 expression and the same health effects identified from PheWAS analyses. CONCLUSIONS: In this study, we report novel relationships between GDF11 and disease, namely asthma and hypothyroidism, in contrast to its formerly assumed role as a rejuvenating factor in basic aging and cardiovascular health. We propose that these associations are mediated through the involvement of GDF11 in inflammatory signaling pathways. Taken together, these findings provide new insights into the health effects of GDF11 at the population level and warrant future studies investigating the role of GDF11 in these specific health conditions.

Shared genetic correlations between kidney diseases and sepsis.

Background: Clinical studies have indicated a comorbidity between sepsis and kidney diseases. Individuals with specific mutations that predispose them to kidney conditions are also at an elevated risk for developing sepsis, and vice versa. This suggests a potential shared genetic etiology that has not been fully elucidated. Methods: Summary statistics data on exposure and outcomes were obtained from genome-wide association meta-analysis studies. We utilized these data to assess genetic correlations, employing a pleiotropy analysis method under the composite null hypothesis to identify pleiotropic loci. After mapping the loci to their corresponding genes, we conducted pathway analysis using Generalized Gene-Set Analysis of GWAS Data (MAGMA). Additionally, we utilized MAGMA gene-test and eQTL information (whole blood tissue) for further determination of gene involvement. Further investigation involved stratified LD score regression, using diverse immune cell data, to study the enrichment of SNP heritability in kidney-related diseases and sepsis. Furthermore, we employed Mendelian Randomization (MR) analysis to investigate the causality between kidney diseases and sepsis. Results: In our genetic correlation analysis, we identified significant correlations among BUN, creatinine, UACR, serum urate, kidney stones, and sepsis. The PLACO analysis method identified 24 pleiotropic loci, pinpointing a total of 28 nearby genes. MAGMA gene-set enrichment analysis revealed a total of 50 pathways, and tissue-specific analysis indicated significant enrichment of five pairs of pleiotropic results in kidney tissue. MAGMA gene test and eQTL information (whole blood tissue) identified 33 and 76 pleiotropic genes, respectively. Notably, genes PPP2R3A for BUN, VAMP8 for UACR, DOCK7 for creatinine, and HIBADH for kidney stones were identified as shared risk genes by all three methods. In a series of immune cell-type-specific enrichment analyses of pleiotropy, we identified a total of 37 immune cells. However, MR analysis did not reveal any causal relationships among them. Conclusions: This study lays the groundwork for shared etiological factors between kidney and sepsis. The confirmed pleiotropic loci, shared pathogenic genes, and enriched pathways and immune cells have enhanced our understanding of the multifaceted relationships among these diseases. This provides insights for early disease intervention and effective treatment, paving the way for further research in this field.

A novel framework with automated horizontal pleiotropy adjustment in mendelian randomization.

The presence of horizontal pleiotropy in Mendelian randomization (MR) analysis has long been a concern due to its potential to induce substantial bias. In recent years, many robust MR methods have been proposed to address this by relaxing the "no horizontal pleiotropy" assumption. Here, we propose a novel two-stage framework called CMR, which integrates a conditional analysis of multiple genetic variants to remove pleiotropy induced by linkage disequilibrium, followed by the application of robust MR methods to model the conditional genetic effect estimates. We demonstrate how the conditional analysis can reduce horizontal pleiotropy and improve the performance of existing MR methods. Extensive simulation studies covering a wide range of scenarios of horizontal pleiotropy showcased the superior performance of the proposed CMR framework over the standard MR framework in which marginal genetic effects are modeled. Moreover, the application of CMR in a negative control outcome analysis and investigation into the causal role of body mass index across various diseases highlighted its potential to deliver more reliable results in real-world applications.

Genome-wide large-scale multi-trait analysis characterizes global patterns of pleiotropy and unique trait-specific variants.

Genome-wide association studies (GWAS) have found widespread evidence of pleiotropy, but characterization of global patterns of pleiotropy remain highly incomplete due to insufficient power of current approaches. We develop fastASSET, a method that allows efficient detection of variant-level pleiotropic association across many traits. We analyze GWAS summary statistics of 116 complex traits of diverse types collected from the GRASP repository and large GWAS Consortia. We identify 2293 independent loci and find that the lead variants in nearly all these loci (~99%) to be associated with ≥ 2 traits (median = 6). We observe that degree of pleiotropy estimated from our study predicts that observed in the UK Biobank for a much larger number of traits (K = 4114) (correlation = 0.43, p-value < 2.2 × 10 - 16 ). Follow-up analyzes of 21 trait-specific variants indicate their link to the expression in trait-related tissues for a small number of genes involved in relevant biological processes. Our findings provide deeper insight into the nature of pleiotropy and leads to identification of highly trait-specific susceptibility variants.

Polygenic prediction of human longevity on the supposition of pervasive pleiotropy.

The highly polygenic nature of human longevity renders pleiotropy an indispensable feature of its genetic architecture. Leveraging the genetic correlation between aging-related traits (ARTs), we aimed to model the additive variance in lifespan as a function of the cumulative liability from pleiotropic segregating variants. We tracked allele frequency changes as a function of viability across different age bins and prioritized 34 variants with an immediate implication on lipid metabolism, body mass index (BMI), and cognitive performance, among other traits, revealed by PheWAS analysis in the UK Biobank. Given the highly complex and non-linear interactions between the genetic determinants of longevity, we reasoned that a composite polygenic score would approximate a substantial portion of the variance in lifespan and developed the integrated longevity genetic scores (iLGSs) for distinguishing exceptional survival. We showed that coefficients derived from our ensemble model could potentially reveal an interesting pattern of genomic pleiotropy specific to lifespan. We assessed the predictive performance of our model for distinguishing the enrichment of exceptional longevity among long-lived individuals in two replication cohorts (the Scripps Wellderly cohort and the Medical Genome Reference Bank (MRGB)) and showed that the median lifespan in the highest decile of our composite prognostic index is up to 4.8 years longer. Finally, using the proteomic correlates of iLGS, we identified protein markers associated with exceptional longevity irrespective of chronological age and prioritized drugs with repurposing potentials for gerotherapeutics. Together, our approach demonstrates a promising framework for polygenic modeling of additive liability conferred by ARTs in defining exceptional longevity and assisting the identification of individuals at a higher risk of mortality for targeted lifestyle modifications earlier in life. Furthermore, the proteomic signature associated with iLGS highlights the functional pathway upstream of the PI3K-Akt that can be effectively targeted to slow down aging and extend lifespan.

The shared genetic etiology of antisocial behavior and psychiatric disorders: Insights from pleiotropy and causality analysis.

BACKGROUND: Antisocial behavior (ASB) infringes on the rights of others and significantly disrupts social order. Studies have shown that ASB is phenotypically associated with various psychiatric disorders. However, these studies often neglected the importance of genetic foundations. METHODS: This study utilized genome-wide association studies and pleiotropy analysis to explore the genetic correlation between ASB and psychiatric disorders. Linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL) methods were employed to assess genetic correlations, and the PLACO method was used for pleiotropy analysis. Functional annotation and biological pathway analysis of identified pleiotropic genes were performed using enrichment analysis. Furthermore, Mendelian randomization (MR) analysis was conducted to validate these causal relationships. RESULTS: LDSC and HDL analysis showed that significant positive genetic correlations were between ASB and attention deficit hyperactivity disorder (ADHD), schizophrenia (SCZ), major depressive disorder (MDD), and post-traumatic stress disorder (PTSD). Multiple potential pleiotropic genetic loci were identified, particularly the FOXP2 and MDFIC genes located at the 7q31.1 locus. Enrichment analysis showed that these pleiotropic genes are highly expressed in several brain regions (such as the hypothalamus, cerebellar hemisphere, cortex, and amygdala) and immune-related cells. MR analysis further confirmed the causal effects ADHD, SCZ, and MDD on ASB risk. CONCLUSION: This study reveals significant genetic correlations and potential causal mechanisms between ASB and various psychiatric disorders. The MR analysis confirmed the causal effects of psychiatric disorders on ASB. These findings deepen our understanding of the genetic architecture of psychiatric disorders and ASB.

Two dwarfing genes Rht-B1b and Rht-D1b show pleiotropic effects on grain protein content in bread wheat (Triticum aestivum L.).

KEY MESSAGE: Five QTL for wheat grain protein content were identified, and the effects of two dwarfing genes Rht-B1b and Rht-D1b on grain protein content were validated in multiple populations. Grain protein content (GPC) plays an important role in wheat quality. Here, a recombinant inbred line (RIL) population derived from a cross between Yangmai 12 (YM12) and Yanzhan 1 (YZ1) was used to identify quantitative trait loci (QTL) for GPC. Two hundred and five RILs and their parents were grown in three years in randomized complete blocks each with two replications, and genotyped using the wheat 55 K SNP array. Five QTL were identified for GPC on chromosomes 1A, 1B, 2D, 4B, and 4D. Notably, QGpc.yaas-4B (co-located with Rht-B1) and QGpc.yaas-4D (co-located with Rht-D1) were consistently detected across all experiments and best linear unbiased estimating, accounting for 6.61-8.39% and 6.05-10.21% of the phenotypic variances, respectively. The effects of these two dwarfing alleles Rht-B1b and Rht-D1b on reducing GPC and plant height were validated in two additional RIL populations and one natural population. This study lays a foundation for further investigating the effects of dwarfing genes Rht-B1b and Rht-D1b on wheat GPC.

Exploring the shared genetic basis of major depressive disorder and frailty.

BACKGROUND: Major depressive disorder (MDD) and frailty impose substantial health and economic burdens. MDD is recognized as a significant risk factor for frailty, but the genetic associations between these conditions remain unclear. This study investigates the genetic correlation, shared pleiotropic loci, causal relationships, and comorbid genes between MDD and frailty. METHODS: The genetic correlation between MDD and frailty was assessed using linkage disequilibrium score regression (LDSC) based on data from genome-wide association studies (GWAS). A detailed analysis was performed to identify shared pleiotropic loci and causal relationships through cross-phenotype association tests and Mendelian randomization. Additionally, tissue enrichment analysis was conducted using stratified LDSC, gene-based associations with both conditions were assessed using Multimarker Analysis of Genomic Annotation (MAGMA), and pathway analysis of comorbid genes was performed using the g: GOSt tool. RESULTS: Our findings revealed a significant positive genetic correlation between MDD and frailty (rg = 0.65, P = 1.49E-219). We identified 57 shared risk SNPs between the two conditions, including 6 novel SNPs. Mendelian randomization analyses indicated robust causal effects of MDD on frailty and vice versa. Furthermore, we observed tissue-specific heritability enrichment in 9 brain tissues. By combining MAGMA and CPASSOC analyses, we identified 10 comorbid genes associated with both MDD and frailty, primarily involved in synapse formation, modulation, plasticity, and desaturase activity. CONCLUSION: This study provides strong evidence for a shared genetic basis between MDD and frailty. The identification of comorbid genes offers new insights into the mechanisms underlying the relationship between these conditions.

Mutant Huntingtin Drives Development of an Advantageous Brain Early in Life: Evidence in Support of Antagonistic Pleiotropy.

OBJECTIVE: Huntington's disease (HD) is a neurodegenerative disease caused by a triplet repeat expansion within the gene huntingtin (HTT). Antagonistic pleiotropy is a theory of aging that posits that some genes, facilitating individual fitness early in life through adaptive evolutionary changes, also augment detrimental aging-related processes. Antagonistic pleiotropy theory may explain a positive evolutionary pressure toward functionally advantageous brain development that is vulnerable to rapid degeneration. The current study investigated antagonistic pleiotropy in HD using a years-to-onset paradigm in a unique sample of children and young adults at risk for HD. METHODS: Cognitive, behavioral, motor, and brain structural measures from premanifest gene-expanded (n = 79) and gene nonexpanded (n = 112) participants (6-21 years) in the Kids-HD study were examined. All measures in the gene-expanded group were modeled using a mixed-effects regression approach to assess years-to-onset-based changes while controlling for normal growth. Simultaneously, structure-function associations were also examined. RESULTS: Decades from motor onset, gene-expanded participants showed significantly better cognitive, behavioral, and motor scores versus gene nonexpanded controls, along with larger cerebral volumes and cortical features. After this initial peak, a prolonged deterioration was observed in both functional and structural measures. Far from onset, brain measures were positively correlated with functional measures, supporting the view that functional advantages were mediated by structural differences. INTERPRETATION: Mutant HTT may drive the development of a larger than normal brain that subserves superior early-life function. These findings support the antagonistic pleiotropy theory of HTT in HD, where this gene drives early advantage followed by accelerated aging processes. ANN NEUROL 2024;96:1006-1019.

Identifying pleiotropic genes via the composite test amidst the complexity of polygenic traits.

Identifying the causal relationship between genotype and phenotype is essential to expanding our understanding of the gene regulatory network spanning the molecular level to perceptible traits. A pleiotropic gene can act as a central hub in the network, influencing multiple outcomes. Identifying such a gene involves testing under a composite null hypothesis where the gene is associated with, at most, one trait. Traditional methods such as meta-analyses of top-hit $P$-values and sequential testing of multiple traits have been proposed, but these methods fail to consider the background of genome-wide signals. Since Huang's composite test produces uniformly distributed $P$-values for genome-wide variants under the composite null, we propose a gene-level pleiotropy test that entails combining the aforementioned method with the aggregated Cauchy association test. A polygenic trait involves multiple genes with different functions to co-regulate mechanisms. We show that polygenicity should be considered when identifying pleiotropic genes; otherwise, the associations polygenic traits initiate will give rise to false positives. In this study, we constructed gene-trait functional modules using the results of the proposed pleiotropy tests. Our analysis suite was implemented as an R package PGCtest. We demonstrated the proposed method with an application study of the Taiwan Biobank database and identified functional modules comprising specific genes and their co-regulated traits.

A robust cis-Mendelian randomization method with application to drug target discovery.

Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs) to investigate causal relationships between traits. Unlike conventional MR, cis-MR focuses on a single genomic region using only cis-SNPs. For example, using cis-pQTLs for a protein as exposure for a disease opens a cost-effective path for drug target discovery. However, few methods effectively handle pleiotropy and linkage disequilibrium (LD) of cis-SNPs. Here, we propose cisMR-cML, a method based on constrained maximum likelihood, robust to IV assumption violations with strong theoretical support. We further clarify the severe but largely neglected consequences of the current practice of modeling marginal, instead of conditional genetic effects, and only using exposure-associated SNPs in cis-MR analysis. Numerical studies demonstrated our method's superiority over other existing methods. In a drug-target analysis for coronary artery disease (CAD), including a proteome-wide application, we identified three potential drug targets, PCSK9, COLEC11 and FGFR1 for CAD.