Pleiotropic Associations Research Articles

Multi-ancestry genome-wide analysis identifies shared genetic effects and common genetic variants for self-reported sleep duration.

Both short (≤6h per night) and long sleep duration (≥9h per night) are associated with increased risk of chronic diseases. Despite evidence linking habitual sleep duration and risk of disease, the genetic determinants of sleep duration in the general population are poorly understood, especially outside of European (EUR) populations. Here, we report that a polygenic score of 78 European ancestry sleep duration single-nucleotide polymorphisms (SNPs) is associated with sleep duration in an African (n= 7288; P =0.003), an East Asian (n =13 618; P =6 × 10-4) and a South Asian (n = 7485; P =0.025) genetic ancestry cohort, but not in a Hispanic/Latino cohort (n = 8726; P =0.71). Furthermore, in a pan-ancestry (N = 483 235) meta-analysis of genome-wide association studies (GWAS) for habitual sleep duration, 73 loci are associated with genome-wide statistical significance. Follow-up of five loci (near HACD2, COG5, PRR12, SH3RF1 and KCNQ5) identified expression-quantitative trait loci for PRR12 and COG5 in brain tissues and pleiotropic associations with cardiovascular and neuropsychiatric traits. Overall, our results suggest that the genetic basis of sleep duration is at least partially shared across diverse ancestry groups.

Pan-cancer and cross-population genome-wide association studies dissect shared genetic backgrounds underlying carcinogenesis

Integrating genomic data of multiple cancers allows de novo cancer grouping and elucidating the shared genetic basis across cancers. Here, we conduct the pan-cancer and cross-population genome-wide association study (GWAS) meta-analysis and replication studies on 13 cancers including 250,015 East Asians (Biobank Japan) and 377,441 Europeans (UK Biobank). We identify ten cancer risk variants including five pleiotropic associations (e.g., rs2076295 at DSP on 6p24 associated with lung cancer and rs2525548 at TRIM4 on 7q22 nominally associated with six cancers). Quantifying shared heritability among the cancers detects positive genetic correlations between breast and prostate cancer across populations. Common genetic components increase the statistical power, and the large-scale meta-analysis of 277,896 breast/prostate cancer cases and 901,858 controls identifies 91 newly genome-wide significant loci. Enrichment analysis of pathways and cell types reveals shared genetic backgrounds across said cancers. Focusing on genetically correlated cancers can contribute to enhancing our insights into carcinogenesis.

Identification of multiple novel susceptibility genes associated with autoimmune thyroid disease.

Autoimmune thyroid disease (AITD) is induced by various factors, including inheritability, which regulates gene expression. Multiple loci correlated with AITD have been discovered utilizing genome-wide association studies (GWASs). Nevertheless, demonstrating the biological relevance and function of these genetic loci is difficult. The FUSION software was utilized to define genes that were expressed differentially in AITD using a transcriptome-wide association study (TWAS) method in accordance with GWAS summary statistics from the largest genome-wide association study of 755,406 AITD individuals (30,234 cases and 725,172 controls) and levels of gene expression from two tissue datasets (blood and thyroid). Further analyses were performed such as colocalization, conditional, and fine-mapping analyses to extensively characterize the identified associations, using functional mapping and annotation (FUMA) to conduct functional annotation of the summary statistics of 23329 significant risk SNPs (P < 5 × 10-8) recognized by GWAS, together with summary-data-based mendelian randomization (SMR) for identifying functionally related genes at the loci in GWAS. There were 330 genes with transcriptome-wide significant differences between cases and controls, and the majority of these genes were new. 9 of the 94 unique significant genes had strong, colocalized, and potentially causal correlations with AITD. Such strong associations included CD247, TPO, KIAA1524, PDE8B, BACH2, FYN, FOXK1, NKX2-3, and SPATA13. Subsequently, applying the FUMA approach, novel putative AITD susceptibility genes and involved gene sets were detected. Furthermore, we detected 95 probes that showed strong pleiotropic association with AITD through SMR analysis, such as CYP21A2, TPO, BRD7, and FCRL3. Lastly, we selected 26 genes by integrating the result of TWAS, FUMA, and SMR analysis. A phenome-wide association study (pheWAS) was then carried out to determine the risk of other related or co-morbid phenotypes for AITD-related genes. The current work provides further insight into widespread changes in AITD at the transcriptomic level, as well as characterized the genetic component of gene expression in AITD by validating identified genes, establishing new correlations, and uncovering novel susceptibility genes. Our findings indicate that the genetic component of gene expression plays a significant part in AITD.

Mendelian Randomization and Transcriptome-Wide Association Analysis Identified Genes That Were Pleiotropically Associated with Intraocular Pressure.

Intraocular pressure (IOP) is a major modifiable risk factor for glaucoma. However, the mechanisms underlying the controlling of IOP remain to be elucidated. To prioritize genes that are pleiotropically associated with IOP. We adopted a two-sample Mendelian randomization method, named summary-based Mendelian randomization (SMR), to examine the pleiotropic effect of gene expression on IOP. The SMR analyses were based on summarized data from a genome-wide association study (GWAS) on IOP. We conducted separate SMR analyses using Genotype-Tissue Expression (GTEx) and Consortium for the Architecture of Gene Expression (CAGE) expression quantitative trait loci (eQTL) data. Additionally, we performed a transcriptome-wide association study (TWAS) to identify genes whose cis-regulated expression levels were associated with IOP. We identified 19 and 25 genes showing pleiotropic association with IOP using the GTEx and CAGE eQTL data, respectively. RP11-259G18.3 (PSMR = 2.66 × 10-6), KANSL1-AS1 (PSMR = 2.78 × 10-6), and RP11-259G18.2 (PSMR = 2.91 × 10-6) were the top three genes using the GTEx eQTL data. LRRC37A4 (PSMR = 1.19 × 10-5), MGC57346 (PSMR = 1.19 × 10-5), and RNF167 (PSMR = 1.53 × 10-5) were the top three genes using the CAGE eQTL data. Most of the identified genes were found in or near the 17q21.31 genomic region. Additionally, our TWAS analysis identified 18 significant genes whose expression was associated with IOP. Of these, 12 and 4 were also identified by the SMR analysis using the GTEx and CAGE eQTL data, respectively. Our findings suggest that the 17q21.31 genomic region may play a critical role in the regulation of IOP.

The GCKR-P446L gene variant predisposes to raised blood cholesterol and lower blood glucose in the P446L mouse-a model for GCKR rs1260326

ObjectivesThe Glucokinase Regulatory Protein GKRP, encoded by GCKR, enables acute regulation of liver glucokinase to support metabolic demand. The common human GCKR rs1260326:Pro446 > Leu variant within a large linkage disequilibrium region associates with pleiotropic traits including lower Type 2 diabetes risk and raised blood triglycerides and cholesterol. Whether the GCKR-P446 > L substitution is causal to the raised lipids is unknown. We determined whether mouse GKRP phenocopies the human GKRP:P446 > L substitution and studied a GKRP:P446L knockin mouse to identify physiological consequences to P446 > L. MethodsGKRP-deficient hepatocytes were transfected with adenoviral vectors for human or mouse GKRP:446 P or 446 L for cellular comprehensive analysis including transcriptomics consequent to P446 > L. Physiological traits in the diet-challenged P446L mouse were compared with pleiotropic associations at the human rs1260326 locus. Transcriptomics was compared in P446L mouse liver with hepatocytes overexpressing glucokinase or GKRP:446 P/L. Results1. P446 > L substitution in mouse or human GKRP similarly compromises protein expressivity of GKRP:446 L, nuclear sequestration of glucokinase and counter-regulation of gene expression. 2. The P446L knockin mouse has lower liver glucokinase and GKRP protein similar to human liver homozygous for rs1260326-446 L. 3. The diet-challenged P446L mouse has lower blood glucose, raised blood cholesterol and altered hepatic cholesterol homeostasis consistent with relative glucokinase-to-GKRP excess, but not raised blood triglycerides. ConclusionsMouse GKRP phenocopies the human GKRP:P446 > L substitution despite the higher affinity for glucokinase of human GKRP. The diet-challenged P446L mouse replicates several traits found in association with the rs1260326 locus on chromosome 2 including raised blood cholesterol, lower blood glucose and lower liver glucokinase and GKRP protein but not raised blood triglycerides.

FHL5 Controls Vascular Disease-Associated Gene Programs in Smooth Muscle Cells.

Genome-wide association studies have identified hundreds of loci associated with common vascular diseases, such as coronary artery disease, myocardial infarction, and hypertension. However, the lack of mechanistic insights for many GWAS loci limits their translation into the clinic. Among these loci with unknown functions is UFL1-four-and-a-half LIM (LIN-11, Isl-1, MEC-3) domain 5 (FHL5; chr6q16.1), which reached genome-wide significance in a recent coronary artery disease/ myocardial infarction GWAS meta-analysis. UFL1-FHL5 is also associated with several vascular diseases, consistent with the widespread pleiotropy observed for GWAS loci. We apply a multimodal approach leveraging statistical fine-mapping, epigenomic profiling, and ex vivo analysis of human coronary artery tissues to implicate FHL5 as the top candidate causal gene. We unravel the molecular mechanisms of the cross-phenotype genetic associations through in vitro functional analyses and epigenomic profiling experiments in coronary artery smooth muscle cells. We prioritized FHL5 as the top candidate causal gene at the UFL1-FHL5 locus through expression quantitative trait locus colocalization methods. FHL5 gene expression was enriched in the smooth muscle cells and pericyte population in human artery tissues with coexpression network analyses supporting a functional role in regulating smooth muscle cell contraction. Unexpectedly, under procalcifying conditions, FHL5 overexpression promoted vascular calcification and dysregulated processes related to extracellular matrix organization and calcium handling. Lastly, by mapping FHL5 binding sites and inferring FHL5 target gene function using artery tissue gene regulatory network analyses, we highlight regulatory interactions between FHL5 and downstream coronary artery disease/myocardial infarction loci, such as FOXL1 and FN1 that have roles in vascular remodeling. Taken together, these studies provide mechanistic insights into the pleiotropic genetic associations of UFL1-FHL5. We show that FHL5 mediates vascular disease risk through transcriptional regulation of downstream vascular remodeling gene programs. These transacting mechanisms may explain a portion of the heritable risk for complex vascular diseases.

Integration of Mendelian randomisation and systems biology models to identify novel blood-based biomarkers for stroke

Stroke is the second largest cause of mortality in the world. Genome-wide association studies (GWAS) have identified some genetic variants associated with stroke risk, but their putative functional causal genes are unknown. Hence, we aimed to identify putative functional causal gene biomarkers of stroke risk. We used a summary-based Mendelian randomisation (SMR) approach to identify the pleiotropic associations of genetically regulated traits (i.e., gene expression and DNA methylation) with stroke risk. Using SMR approach, we integrated cis-expression quantitative loci (cis-eQTLs) and cis-methylation quantitative loci (cis-mQTLs) data with GWAS summary statistics of stroke. We also utilised heterogeneity in dependent instruments (HEIDI) test to distinguish pleiotropy from linkage from the observed associations identified through SMR analysis. Our integrative SMR analyses and HEIDI test revealed 45 candidate biomarker genes (FDR < 0.05; PHEIDI > 0.01) that were pleiotropically or potentially causally associated with stroke risk. Of those candidate biomarker genes, 10 genes (HTRA1, PMF1, FBN2, C9orf84, COL4A1, BAG4, NEK6, SH2B3, SH3PXD2A, ACAD10) were differentially expressed in genome-wide blood transcriptomics data from stroke and healthy individuals (FDR < 0.05). Functional enrichment analysis of the identified candidate biomarker genes revealed gene ontologies and pathways involved in stroke, including “cell aging”, “metal ion binding” and “oxidative damage”. Based on the evidence of genetically regulated expression of genes through SMR and directly measured expression of genes in blood, our integrative analysis suggests ten genes as blood biomarkers of stroke risk. Furthermore, our study provides a better understanding of the influence of DNA methylation on the expression of genes linked to stroke risk.

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

To understand the shared genetic basis between colorectal cancer (CRC) and other cancers and identify potential pleiotropic loci for compensating the missing genetic heritability of CRC. We conducted a systematic genome-wide pleiotropy scan to appraise associations between cancer-related genetic variants and CRC risk among European populations. Single nucleotide polymorphism (SNP)-set analysis was performed using data from the UK Biobank and the Study of Colorectal Cancer in Scotland (10 039 CRC cases and 30 277 controls) to evaluate the overlapped genetic regions for susceptibility of CRC and other cancers. The variant-level pleiotropic associations between CRC and other cancers were examined by CRC genome-wide association study meta-analysis and the pleiotropic analysis under composite null hypothesis (PLACO) pleiotropy test. Gene-based, co-expression and pathway enrichment analyses were performed to explore potential shared biological pathways. The interaction between novel genetic variants and common environmental factors was further examined for their effects on CRC. Genome-wide pleiotropic analysis identified three novel SNPs (rs2230469, rs9277378 and rs143190905) and three mapped genes (PIP4K2A, HLA-DPB1 and RTEL1) to be associated with CRC. These genetic variants were significant expressions quantitative trait loci in colon tissue, influencing the expression of their mapped genes. Significant interactions of PIP4K2A and HLA-DPB1 with environmental factors, including smoking and alcohol drinking, were observed. All mapped genes and their co-expressed genes were significantly enriched in pathways involved in carcinogenesis. Our findings provide an important insight into the shared genetic basis between CRC and other cancers. We revealed several novel CRC susceptibility loci to help understand the genetic architecture of CRC.

Gene based markers improve precision of genome-wide association studies and accuracy of genomic predictions in rice breeding.

It is hypothesized that the genome-wide genic markers may increase the prediction accuracy of genomic selection for quantitative traits. To test this hypothesis, a set of candidate gene-based markers for yield and grain traits-related genes cloned across the rice genome were custom-designed. A multi-model, multi-locus genome-wide association study (GWAS) was performed using new genic markers developed to test their effectiveness for gene discovery. Two multi-locus models, FarmCPU and mrMLM, along with a single-locus mixed linear model (MLM), identified 28 significant marker-trait associations. These associations revealed novel causative alleles for grain weight and pleiotropic associations with other traits. For instance, the marker YD91 derived from the gene OsAAP3 on chromosome 1 was consistently associated with grain weight, while the gene has a significant effect on grain yield. Furthermore, nine genomic selection methods, including regression-based and machine learning-based models, were used to predict grain weight using a leave-one-out five-fold cross-validation approach to optimize the genomic selection model with genic markers. Among nine prediction models, Kernel Hilbert Space Regression (RKHS) is the best among regression-based models, and Random Forest Regression (RFR) is the best among machine learning-based models. Genomic prediction accuracies with and without GWAS significant markers were compared to assess the effectiveness of markers. The rapid decreases in prediction accuracy upon dropping GWAS significant markers indicate the effectiveness of new genic markers in genomic selection. Apart from that, the candidate gene-based markers were found to be more effective in genomic selection programs for better accuracy.

Epidemiologic and Genetic Associations of Endometriosis With Depression, Anxiety, and Eating Disorders

Endometriosis is a common chronic gynecologic pathology with a large negative impact on women's health. Beyond severe physical symptoms, endometriosis is also associated with several psychiatric comorbidities, including depression and anxiety. To investigate whether pleiotropy contributes to the association of endometriosis with depression, anxiety, and eating disorders. This genetic association study was performed between September 13, 2021, and June 24, 2022, in 202 276 unrelated female participants. Genotypic and phenotypic information from the UK Biobank was combined with genome-wide association statistics available from the Psychiatric Genomics Consortium (11 countries), the Million Veteran Program (US), the FinnGen study (Finland), and the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium (5 countries). The main outcomes were the phenotypic and genetic associations of endometriosis with anxiety, depression, and eating disorders. A total of 8276 women with endometriosis (mean [SD] age, 53.1 [7.9] years) and 194 000 female controls (mean [SD] age, 56.7 [7.9] years) were included in the study. In a multivariate regression analysis accounting for age, body mass index, socioeconomic status, chronic pain-related phenotypes, irritable bowel syndrome, and psychiatric comorbidities, endometriosis was associated with increased odds of depression (odds ratio [OR], 3.61; 95% CI, 3.32-3.92), eating disorders (OR, 2.94; 95% CI, 1.96-4.41), and anxiety (OR, 2.61; 95% CI, 2.30-2.97). These associations were supported by consistent genetic correlations (rg) (depression rg, 0.36, P = 1.5 × 10-9; anxiety rg, 0.33, P = 1.17 × 10-5; and eating disorders rg, 0.61, P = .02). With the application of a 1-sample mendelian randomization, the genetic liabilities to depression and anxiety were associated with increased odds of endometriosis (depression: OR, 1.09; 95% CI, 1.08-1.11; anxiety: OR, 1.39; 95% CI, 1.13-1.65). A genome-wide analysis of pleiotropic associations shared between endometriosis and psychiatric disorders identified 1 locus, DGKB rs12666606, with evidence of pleiotropy between endometriosis and depression after multiple testing correction (z = -9.46 for endometriosis, z = 8.10 for depression, P = 5.56 × 10-8; false discovery rate q = 4.95 × 10-4). These findings highlight that endometriosis is associated with women's mental health through pleiotropic mechanisms. To our knowledge, this is the first large-scale study to provide genetic and phenotypic evidence of the processes underlying the psychiatric comorbidities of endometriosis.

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

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

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

BackgroundGenetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery.ResultsTo expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism.ConclusionsTaken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits.

Genetic association studies of many heritable traits resulting from physiological testing often have modest sample sizes due to the cost and burden of the required phenotyping. This reduces statistical power and limits discovery of multiple genetic associations. We present a strategy to leverage pleiotropy between traits to both discover new loci and to provide mechanistic hypotheses of the underlying pathophysiology. Specifically, we combine a colocalization test with a locus-level test of pleiotropy. In simulations, we show that this approach is highly selective for identifying true pleiotropy driven by the same causative variant, thereby improves the chance to replicate the associations in underpowered validation cohorts and leads to higher interpretability. Here, as an exemplar, we use Obstructive Sleep Apnea (OSA), a common disorder diagnosed using overnight multi-channel physiological testing. We leverage pleiotropy with relevant cellular and cardio-metabolic phenotypes and gene expression traits to map new risk loci in an underpowered OSA GWAS. We identify several pleiotropic loci harboring suggestive associations to OSA and genome-wide significant associations to other traits, and show that their OSA association replicates in independent cohorts of diverse ancestries. By investigating pleiotropic loci, our strategy allows proposing new hypotheses about OSA pathobiology across many physiological layers. For example, we identify and replicate the pleiotropy across the plateletcrit, OSA and an eQTL of DNA primase subunit 1 (PRIM1) in immune cells. We find suggestive links between OSA, a measure of lung function (FEV1/FVC), and an eQTL of matrix metallopeptidase 15 (MMP15) in lung tissue. We also link a previously known genome-wide significant peak for OSA in the hexokinase 1 (HK1) locus to hematocrit and other red blood cell related traits. Thus, the analysis of pleiotropic associations has the potential to assemble diverse phenotypes into a chain of mechanistic hypotheses that provide insight into the pathogenesis of complex human diseases.

EXOME-WIDE STUDY IDENTIFIED 12 PLEIOTROPIC LOCI ASSOCIATED WITH ALZHEIMER’S AND CARDIOVASCULAR DISEASE RISKS

Abstract Health of brain, heart and blood vessels are closely connected, which means that Alzheimer’s (AD) and cardiovascular (CVD) diseases may have overlapping etiologies including genetic component. Most of previous genetic association studies considered different traits (AD, CVD, and their risk factors) separately. The analysis of pleiotropic predisposition to these traits may shed light on the trait-specific mechanisms in protection against AD. We carried out pair-wise pleiotropic exome-wide association study (~250K common genetic variants) in predisposition to AD and each of 17 traits in a sample of 118K individuals from UK biobank. The analysis included seven qualitative traits (CVD, coronary-heart disease, type 2 diabetes, stroke, myocardial infarction, heart failure and hypertension), as well as their 10 risk factors (blood glucose, body-mass index, height, weight, 4 lipid traits, systolic and diastolic blood pressure). Fisher’s method and omnibus test were used in pleiotropic analyses. In addition to the APOE-TOMM40 locus, the analysis identified 12 genetic loci in which genetic polymorphisms demonstrated significant associations with AD at p≤5×10-4 and pleiotropic associations at genome-wide level, p≤5×10-8. The identified genes are involved in processes of phosphorylation; regulation of cell growth, differentiation, and brain development; signal transduction and neurotransmission; mitochondrial and cytoskeleton organization; regulation of gene expression, apoptotic processes, and adaptive immune response. Our results provide novel evidence supporting the hypothesis of complex pleiotropic mechanisms contributing to the development and progression of AD and provide more insights into understanding of underlying biological functions and regulatory mechanisms behind these effects.

Collective genomic segments with differential pleiotropic patterns between cognitive dimensions and psychopathology

Cognitive deficits are known to be related to most forms of psychopathology. Here, we perform local genetic correlation analysis as a means of identifying independent segments of the genome that show biologically interpretable pleiotropic associations between cognitive dimensions and psychopathology. We identify collective segments of the genome, which we call “meta-loci”, showing differential pleiotropic patterns for psychopathology relative to either cognitive task performance (CTP) or performance on a non-cognitive factor (NCF) derived from educational attainment. We observe that neurodevelopmental gene sets expressed during the prenatal-early childhood period predominate in CTP-relevant meta-loci, while post-natal gene sets are more involved in NCF-relevant meta-loci. Further, we demonstrate that neurodevelopmental gene sets are dissociable across CTP meta-loci with respect to their spatial distribution across the brain. Additionally, we find that GABA-ergic, cholinergic, and glutamatergic genes drive pleiotropic relationships within dissociable meta-loci.

SPOP in Cancer: Phenomena, Mechanisms and Its Role in Therapeutic Implications

Speckle-type POZ (pox virus and zinc finger protein) protein (SPOP) is a cullin 3-based E3 ubiquitin ligase adaptor protein that plays a crucial role in ubiquitin-mediated protein degradation. Recently, SPOP has attracted major research attention as it is frequently mutated in a range of cancers, highlighting pleiotropic tumorigenic effects and associations with treatment resistance. Structurally, SPOP contains a functionally critical N-terminal meprin and TRAF homology (MATH) domain for many SPOP substrates. SPOP has two other domains, including the internal Bric-a-brac-Tramtrack/Broad (BTB) domain, which is linked with SPOP dimerization and binding to cullin3, and a C-terminal nuclear localization sequence (NLS). The dysregulation of SPOP-mediated proteolysis is associated with the development and progression of different cancers since abnormalities in SPOP function dysregulate cellular signaling pathways by targeting oncoproteins or tumor suppressors in a tumor-specific manner. SPOP is also involved in genome stability through its role in the DNA damage response and DNA replication. More recently, studies have shown that the expression of SPOP can be modulated in various ways. In this review, we summarize the current understanding of SPOP's functions in cancer and discuss how to design a rational therapeutic target.

Leveraging pleiotropy for joint analysis of genome-wide association studies with per trait interpretations.

We introduce pleiotropic association test (PAT) for joint analysis of multiple traits using genome-wide association study (GWAS) summary statistics. The method utilizes the decomposition of phenotypic covariation into genetic and environmental components to create a likelihood ratio test statistic for each genetic variant. Though PAT does not directly interpret which trait(s) drive the association, a per trait interpretation of the omnibus p-value is provided through an extension to the meta-analysis framework, m-values. In simulations, we show PAT controls the false positive rate, increases statistical power, and is robust to model misspecifications of genetic effect. Additionally, simulations comparing PAT to three multi-trait methods, HIPO, MTAG, and ASSET, show PAT identified 15.3% more omnibus associations over the next best method. When these associations were interpreted on a per trait level using m-values, PAT had 37.5% more true per trait interpretations with a 0.92% false positive assignment rate. When analyzing four traits from the UK Biobank, PAT discovered 22,095 novel variants. Through the m-values interpretation framework, the number of per trait associations for two traits were almost tripled and were nearly doubled for another trait relative to the original single trait GWAS.

Large-scale genome-wide association study of coronary artery disease in genetically diverse populations.

We report a genome-wide association study (GWAS) of coronary artery disease (CAD) incorporating nearly a quarter of a million cases, in which existing studies are integrated with data from cohorts of white, Black and Hispanic individuals from the Million Veteran Program. We document near equivalent heritability of CAD across multiple ancestral groups, identify 95 novel loci, including nine on the X chromosome, detect eight loci of genome-wide significance in Black and Hispanic individuals, and demonstrate that two common haplotypes at the 9p21 locus are responsible for risk stratification in all populations except those of African origin, in which these haplotypes are virtually absent. Moreover, in the largest GWAS for angiographically derived coronary atherosclerosis performed to date, we find 15 loci of genome-wide significance that robustly overlap with established loci for clinical CAD. Phenome-wide association analyses of novel loci and polygenic risk scores (PRSs) augment signals related to insulin resistance, extend pleiotropic associations of these loci to include smoking and family history, and precisely document the markedly reduced transferability of existing PRSs to Black individuals. Downstream integrative analyses reinforce the critical roles of vascular endothelial, fibroblast, and smooth muscle cells in CAD susceptibility, but also point to a shared biology between atherosclerosis and oncogenesis. This study highlights the value of diverse populations in further characterizing the genetic architecture of CAD.

Shared components of heritability across genetically correlated traits

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

Pleiotropic Genes for Pulmonary Function and Aging-Related Traits: The Long Life Family Study (LLFS)

Pulmonary function progressively declines with aging. Forced expiratory volume 1-second (FEV1) and forced vital capacity (FVC) are predictors of morbidity of cardiovascular diseases and all-cause mortality. Reduced pulmonary function has shown association with elevated chronic low-grade systemic inflammation and low muscle strength, and glycosylated hemoglobin (HbA1c). We evaluated whether FEV1 and FVC share common genetic factors with interleukin-6 (IL-6), high-sensitivity C-reactive protein (hsCRP), body mass index (BMI), handgrip strength, plasma glucose, and HbA1c employing correlated meta-analysis (CMA) in up to 3888 individuals (age range: 26-106). CMA tests whether combining genome-wide association (GWA) P-values from two or more traits enhances the ability to detect variants concomitantly influencing such traits. We considered a variant pleiotropic if the univariate GWA P≤1.0x10-02 and CMA P≤5.0x10-08. We identified pleiotropic loci for FEV1 (or FVC), IL-6 and hsCRP within CYCS, CALN1, TRIM9, AXIN2, MICAL3, and CMIP; FEV1 (or FVC) and BMI within CYP2U1, LINC00871-RPL10L-MDGA2, and LOC105372472; FEV1 (or FVC) and grip strength within XXYLT1 and MAL2; FEV1 and FPG within MAF; and FVC and HbA1c within HDHD3. Some loci were reported as GWAS suggestive (P<10-6) for pulmonary function. Additionally, the identified loci harbor genes with roles in pro-inflammatory cytokine production, immune and inflammatory pathway, T-helper signaling pathway, transforming growth factor-beta, and T-cell receptor-alpha enhancer. Our findings suggest that inflammation is a feature of reduced pulmonary function with IL-6, hsCRP, BMI, low muscle strength, glucose, and HbA1c. Pleiotropic genetic associations across these traits may explain part of the correlated genetic architecture between pulmonary function and aging-related traits.