Estimating colocalization probability from limited summary statistics

Background and ObjectiveGenetic markers associated with disease are often non-functional and generally tag one or more functional “causative” variants in linkage disequilibrium. Markers may not show tight linkage to the causative variants across multiple ethnicities due to evolutionary divergence, and therefore may not be informative across different population groups. Validated markers of disease suggest causative variants exist in the gene and, if the causative variants can be identified, it is reasonable to hypothesize that such variants will be informative across diverse populations. The aim of this study was to test that hypothesis using functional interleukin-1 gene variations across multiple ethnic populations to replace the non-functional markers originally associated with chronic adult periodontitis in Caucasians.Material and MethodsAdult chronic periodontitis cases and controls from 4 ethnic groups (Caucasians, African Americans, Hispanics and Asians) were recruited in the U.S., Chile and China. Genotypes of IL1B gene SNPs, including 3 functional SNPs (rs16944, rs1143623, rs4848306) in the promoter and 1 intronic SNP (rs1143633), were determined using single base extension method or TaqMan 5′ nuclease assay. Logistic regression and other statistical analyses were used to examine the association between moderate to severe periodontitis and IL1B gene variations, including SNPs, haplotypes, and composite genotypes. Genotype patterns associated with disease in the discovery study were then evaluated in independent validation studies.ResultsSignificant associations were identified in the discovery study, consisting of Caucasians and African Americans, between moderate to severe adult chronic periodontitis and functional variations in the IL1B gene, including a pattern of 4 IL1B SNPs (OR=1.87, P-value<0.0001). The association between the disease and this IL1B composite genotype pattern was validated in two additional studies consisting of Hispanics (OR=1.95, P-value=0.04) or Asians (OR=3.27, P-value=0.01). A meta-analysis of the 3 populations supported the association between the IL-1 genotype pattern and moderate to severe periodontitis (OR 1.95; P-value <0.001). Our analysis also demonstrated that IL1B gene variations had added value to conventional risk factors in predicting chronic periodontitis.ConclusionThis study validated the influence of IL-1 genetic factors on the severity of chronic periodontitis in four different ethnicities.

Imputation using external reference panels (e.g. 1000 Genomes) is a widely used approach for increasing power in genome-wide association studies and meta-analysis. Existing hidden Markov models (HMM)-based imputation approaches require individual-level genotypes. Here, we develop a new method for Gaussian imputation from summary association statistics, a type of data that is becoming widely available. In simulations using 1000 Genomes (1000G) data, this method recovers 84% (54%) of the effective sample size for common (>5%) and low-frequency (1-5%) variants [increasing to 87% (60%) when summary linkage disequilibrium information is available from target samples] versus the gold standard of 89% (67%) for HMM-based imputation, which cannot be applied to summary statistics. Our approach accounts for the limited sample size of the reference panel, a crucial step to eliminate false-positive associations, and it is computationally very fast. As an empirical demonstration, we apply our method to seven case-control phenotypes from the Wellcome Trust Case Control Consortium (WTCCC) data and a study of height in the British 1958 birth cohort (1958BC). Gaussian imputation from summary statistics recovers 95% (105%) of the effective sample size (as quantified by the ratio of [Formula: see text] association statistics) compared with HMM-based imputation from individual-level genotypes at the 227 (176) published single nucleotide polymorphisms (SNPs) in the WTCCC (1958BC height) data. In addition, for publicly available summary statistics from large meta-analyses of four lipid traits, we publicly release imputed summary statistics at 1000G SNPs, which could not have been obtained using previously published methods, and demonstrate their accuracy by masking subsets of the data. We show that 1000G imputation using our approach increases the magnitude and statistical evidence of enrichment at genic versus non-genic loci for these traits, as compared with an analysis without 1000G imputation. Thus, imputation of summary statistics will be a valuable tool in future functional enrichment analyses. Publicly available software package available at http://bogdan.bioinformatics.ucla.edu/software/. bpasaniuc@mednet.ucla.edu or aprice@hsph.harvard.edu Supplementary materials are available at Bioinformatics online.

As most of the heritability of complex traits is attributed to common and low frequency genetic variants, imputing them by combining genotyping chips and large sequenced reference panels is the most cost-effective approach to discover the genetic basis of these traits. Association summary statistics from genome-wide meta-analyses are available for hundreds of traits. Updating these to ever-increasing reference panels is very cumbersome as it requires reimputation of the genetic data, rerunning the association scan, and meta-analysing the results. A much more efficient method is to directly impute the summary statistics, termed as summary statistics imputation, which we improved to accommodate variable sample size across SNVs. Its performance relative to genotype imputation and practical utility has not yet been fully investigated. To this end, we compared the two approaches on real (genotyped and imputed) data from 120K samples from the UK Biobank and show that, genotype imputation boasts a 3- to 5-fold lower root-mean-square error, and better distinguishes true associations from null ones: We observed the largest differences in power for variants with low minor allele frequency and low imputation quality. For fixed false positive rates of 0.001, 0.01, 0.05, using summary statistics imputation yielded a decrease in statistical power by 9, 43 and 35%, respectively. To test its capacity to discover novel associations, we applied summary statistics imputation to the GIANT height meta-analysis summary statistics covering HapMap variants, and identified 34 novel loci, 19 of which replicated using data in the UK Biobank. Additionally, we successfully replicated 55 out of the 111 variants published in an exome chip study. Our study demonstrates that summary statistics imputation is a very efficient and cost-effective way to identify and fine-map trait-associated loci. Moreover, the ability to impute summary statistics is important for follow-up analyses, such as Mendelian randomisation or LD-score regression.

<title>Abstract</title> <bold>Background:</bold> Breast cancer incidence has continued to rise over recent decades. Beyond established genetic and hormonal factors, neuropsychiatric conditions including depression, anxiety, and sleep disturbances, have emerged as independent risk factors. Brain structure, as a core neuroendocrine substrate, may share genetic determinants with breast cancer risk; however, this relationship remains insufficiently characterized. <bold>Objective:</bold> This study investigated the potential genetic correlations between brain imaging-derived phenotypes (IDPs) and breast cancer using genome-wide association study (GWAS) summary statistics via Mendelian randomization (MR) analysis. We hypothesized that shared genetic architecture between neuropsychiatric traits and breast cancer may reveal previously unrecognized biological pathways. <bold>Methods:</bold> We conducted MR analysis to systematically evaluate potential causal associations between 4,013 IDPs and 14 breast cancer (BC) traits, encompassing overall BC and molecular subtypes. Genetic instruments for IDPs were obtained from the UK Biobank and ENIGMA consortium. Summary-level BC data were sourced from IEU OpenGWAS, FinnGen, and the GWAS Catalog, covering European and African ancestry populations. A combined multi-ancestry linkage disequilibrium (LD) reference panel was constructed to optimize instrument clumping. The inverse-variance weighted (IVW) method served as the primary analysis, supported by sensitivity tests and colocalization to assess robustness and identify shared causal loci. <bold>Results:</bold> Temporal lobe structure demonstrated the strongest causal associations with estrogen receptor-positive (ER+) breast cancer. Within this region, the fusiform gyrus showed the most consistent negative causal effects across European ancestry datasets: right fusiform volume (IDP: GCST90002807, <italic>P</italic> -value = 2.23×10 ^-7 ), right fusiform surface area (IDP: GCST90003180, <italic>P</italic> -value = 1.11×10 ^-6 ), and left fusiform surface area (IDP: GCST90003079, <italic>P</italic> -value = 5.23×10 ^-6 ). Cross-ancestry analyses highlighted substantial population heterogeneity, as associations identified in European cohorts were not replicated in African ancestry datasets. Colocalization further localized shared genetic signals to chromosomes 17 and 12, implicating genes such as <italic>ARL17B</italic> and <italic>NSFP1</italic> , which are enriched in pathways related to intracellular vesicular transport and membrane dynamics. <bold>Conclusion:</bold> Our findings reveal a causal relationship between brain structure and breast cancer risk, identifying the fusiform gyrus as a key region associated with ER+ BC. The study uncovers a shared genetic basis underlying this brain-breast axis, centered on genes involved in vesicular transport and membrane regulation. These insights suggest that neural processes may contribute to cancer susceptibility and provide new directions for biomarker development and mechanistic research.

MotivationMethods based on summary statistics obtained from genome-wide association studies have gained considerable interest in genetics due to the computational cost and privacy advantages they present. Imputing missing summary statistics has therefore become a key procedure in many bioinformatics pipelines, but available solutions may rely on additional knowledge about the populations used in the original study and, as a result, may not always ensure feasibility or high accuracy of the imputation procedure.ResultsWe present ARDISS, a method to impute missing summary statistics in mixed-ethnicity cohorts through Gaussian Process Regression and automatic relevance determination. ARDISS is trained on an external reference panel and does not require information about allele frequencies of genotypes from the original study. Our method approximates the original GWAS population by a combination of samples from a reference panel relying exclusively on the summary statistics and without any external information. ARDISS successfully reconstructs the original composition of mixed-ethnicity cohorts and outperforms alternative solutions in terms of speed and imputation accuracy both for heterogeneous and homogeneous datasets.Availability and implementationThe proposed method is available at https://github.com/BorgwardtLab/ARDISS.Supplementary information Supplementary data are available at Bioinformatics online.

Understanding the determinants of healthy mental ageing is a priority for society today. So far, we know that intelligence differences show high stability from childhood to old age and there are estimates of the genetic contribution to intelligence at different ages. However, attempts to discover whether genetic causes contribute to differences in cognitive ageing have been relatively uninformative. Here we provide an estimate of the genetic and environmental contributions to stability and change in intelligence across most of the human lifetime. We used genome-wide single nucleotide polymorphism (SNP) data from 1,940 unrelated individuals whose intelligence was measured in childhood (age 11 years) and again in old age (age 65, 70 or 79 years). We use a statistical method that allows genetic (co)variance to be estimated from SNP data on unrelated individuals. We estimate that causal genetic variants in linkage disequilibrium with common SNPs account for 0.24 of the variation in cognitive ability change from childhood to old age. Using bivariate analysis, we estimate a genetic correlation between intelligence at age 11 years and in old age of 0.62. These estimates, derived from rarely available data on lifetime cognitive measures, warrant the search for genetic causes of cognitive stability and change.

SummaryUncovering the functional impact of genetic variation on gene expression is important in understanding tissue biology and the pathogenesis of complex traits. Despite large efforts to map expression quantitative trait loci (eQTLs) across many human tissues, our ability to translate those findings to understanding human disease has been incomplete, and the majority of disease loci are not explained by association with expression of a target gene. Cell-type specificity and the presence of multiple independent causal variants for many eQTLs are potential confounders contributing to the apparent discrepancy with disease loci. In this study, we investigate the tissue specificity of genetic effects on gene expression and the overlap with disease loci while considering the presence of multiple causal variants within and across tissues. We find evidence of pervasive tissue specificity of eQTLs, often masked by linkage disequilibrium that misleads traditional meta-analytic approaches. We propose CAFEH (colocalization and fine-mapping in the presence of allelic heterogeneity), a Bayesian method that integrates genetic association data across multiple traits, incorporating linkage disequilibrium to identify causal variants. CAFEH outperforms previous approaches in colocalization and fine-mapping. Using CAFEH, we show that genes with highly tissue-specific genetic effects are under greater selection, enriched in differentiation and developmental processes, and more likely to be involved in human disease. Last, we demonstrate that CAFEH can efficiently leverage the widespread allelic heterogeneity in genetic regulation of gene expression to prioritize the target tissue in genome-wide association complex trait loci, thereby improving our ability to interpret complex trait genetics.

Genome-wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) that are associated with complex traits. GWAS data allows us to investigate the shared genetic etiologies among different traits. However, linkage disequilibrium (LD) between the SNPs complicates the detection and identification of shared genetic effects. In this paper we model the LD by dividing the genome into LD blocks and linking the genetic variants within a block to a possible latent causal variant. An eigenvector-projected score statistic that leverages the set of variants in LD and a maxtype test statistic (Max-block) are proposed to detect the existence of cross-trait genetic association. The Max-block is easy to calculate and is shown to control the genome-wide error rate. After the detection a stepwise procedure is proposed to identify the significant blocks that explain the genetic sharing between two traits. Simulation experiments show that Max-block is more powerful than standard approaches in the sparse settings and is robust to different signal strengths or levels of sparsity. The method is applied to study shared cross-trait associations in 10 pediatric autoimmune diseases and identified several regions that explain the genetic sharing between juvenile idiopathic arthritis (JIA) and ulcerative colitis (UC) and between UC and Crohn’s disease (CD). In addition, our analysis also indicates the genetic sharing in the MHC region among systemic lupus (SLE), celiac disease (CEL) and common variable immunodeficiency (CVID). Results from real data and simulation studies show that Max-block provides an important alternative to commonly used genetic correlation estimation in understanding genetic correlation among complex diseases.

Motivation: To increase detection power, gene level analysis methods are used to aggregate weak signals. To greatly increase computational efficiency, most methods use as input summary statistics from genome-wide association studies (GWAS). Subsequently, gene statistics are constructed using linkage disequilibrium (LD) patterns from a relevant reference panel. However, all methods, including our own Joint Effect on Phenotype of eQTL/functional single nucleotide polymorphisms (SNPs) associated with a Gene (JEPEG), assume homogeneous panels, e.g. European. However, this renders these tools unsuitable for the analysis of large cosmopolitan cohorts.Results: We propose a JEPEG extension, JEPEGMIX, which similar to one of our software tools, Direct Imputation of summary STatistics of unmeasured SNPs from MIXed ethnicity cohorts, is capable of estimating accurate LD patterns for cosmopolitan cohorts. JEPEGMIX uses this accurate LD estimates to (i) impute the summary statistics at unmeasured functional variants and (ii) test for the joint effect of all measured and imputed functional variants which are associated with a gene. We illustrate the performance of our tool by analyzing the GWAS meta-analysis summary statistics from the multi-ethnic Psychiatric Genomics Consortium Schizophrenia stage 2 cohort. This practical application supports the immune system being one of the main drivers of the process leading to schizophrenia.Availability and implementation: Software, annotation database and examples are available at http://dleelab.github.io/jepegmix/.Contact:donghyung.lee@vcuhealth.orgSupplementary information:Supplementary material is available at Bioinformatics online.

Background/Introduction Genome-wide association studies (GWAS) have identified two novel coronary artery disease (CAD) risk variants, rs17087335 [1] and rs72627509 [2], located near the REST gene, which encodes the RE-1 silencing transcription factor. While REST is recognised as a transcriptional regulator of neuronal genes, its role in atherosclerosis and CAD is poorly understood. Purpose To investigate the molecular mechanisms by which the CAD risk variants rs17087335 and rs72627509 influence REST gene expression and contribute to atherosclerotic phenotypes. Methods In silico analyses were performed to identify variants in linkage disequilibrium with rs17087335 and rs72627509. Reporter gene assays were used to assess regulatory activity at these loci. Mass spectrometry was employed to examine transcription factor binding to variant alleles. Expression quantitative trait loci (eQTL) data were obtained from the GTEx database, and VSMC siRNA knockdown studies were conducted to evaluate cellular effects. Results Linkage disequilibrium (LD) analysis revealed that rs17087335 and rs72627509 are in LD with 14 other variants. To narrow the number of putative causal variants, we performed reporter gene assays that confirmed regulatory activity specifically at rs17087335 and rs72627509, as well as at rs3796529. Mass spectrometry analysis revealed differential transcription factor binding to the alleles of these variants, potentially influencing REST transcription. Indeed, eQTL data demonstrated that CAD patients carrying the risk alleles of rs17087335, rs72627509, and rs3796529, respectively, had significantly decreased REST mRNA levels in the aorta. Functional studies on vascular smooth muscle cells (VSMCs) showed that REST siRNA knockdown increased cell migration and lipid accumulation under inflammatory conditions. RNA sequencing revealed ATF6 and PLA2G4C as potential downstream targets of REST, potentially mediating the observed changes in VSMC migration and lipid accumulation. Conclusion(s) This study provides the first insights into the role of REST in atherosclerosis. The CAD risk variants rs17087335, rs72627509, and rs3796529 influence REST expression, and decreased REST levels promote atherosclerotic phenotypes in VSMCs. These effects are likely mediated through altered lipid metabolism and cell migration, driven by REST targets such as ATF6 and PLA2G4C. Our findings suggest that targeting REST could offer new therapeutic strategies for CAD prevention and treatment.

Genome-wide association studies have identified thousands of genetic variants associated with non-small cell lung cancer (NSCLC), however, it is still challenging to determine the causal variants and to improve disease risk prediction. Here, we applied massively parallel reporter assays to perform NSCLC variant-to-function mapping at scale. A total of 1249 candidate variants were evaluated, and 30 potential causal variants within 12 loci were identified. Accordingly, we proposed three genetic architectures underlying NSCLC susceptibility: multiple causal variants in a single haplotype block (e.g. 4q22.1), multiple causal variants in multiple haplotype blocks (e.g. 5p15.33), and a single causal variant (e.g. 20q11.23). We developed a modified polygenic risk score using the potential causal variants from Chinese populations, improving the performance of risk prediction in 450,821 Europeans from the UK Biobank. Our findings not only augment the understanding of the genetic architecture underlying NSCLC susceptibility but also provide strategy to advance NSCLC risk stratification.

We have applied tests for detecting recombination to genes of foot-and-mouth disease virus (FMDV). Our approach estimated summary statistics of linkage disequilibrium (LD), which are sensitive to recombination. Using the genealogical relationships, rate heterogeneity and mutation parameters estimated from individual sets of aligned gene sequences, we simulated matching RNA sequence datasets without recombination. These simulated datasets allowed for recurrent mutations at any site to mimic homoplasy in virus sequence data and allow construction of null distributions for LD parameters expected in the absence of recombination. We tested for recombination in two ways: by comparing LD in observed data with corresponding null distributions obtained from simulated data; and by testing for a negative relationship between observed LD between pairs of polymorphic nucleotide sites and inter-site distance. We applied these tests to six FMDV datasets from four serotypes and found some evidence for recombination in all of them.

Several gene-based tests, e.g., sequence kernel association test, have been developed for association testing of rare single nucleotide variants (SNVs) in genomic regions with disease traits. A common limitation of these aggregate methods is their inability to discriminate potentially causal variants from null variants within the tested regions. We propose a novel clustering method to classify rare variants into null and signal variant groups using summary statistics from the gene-based tests based on a Gaussian mixture model (GMM). We classify the signal variants into potentially risk and protective subgroups of different effect sizes. We evaluate the performance of the proposed method by a simulation study, considering several statistics such as the adjusted rand index (ARI), mean square error (MSE), and accuracy in specifying the number of clusters. We apply the proposed clustering method to identify possibly risk and protective rare variants in six genes that are significantly associated with blood pressure (BP) traits in the most recent large genomewide association study (GWAS) and meta-analysis. This proposed method may facilitate the identification of potentially causal rare variant clusters in genomic regions and ultimately help understand the genetic architecture underlying human complex traits for the discovery of drug target and the design of gene therapy.

VEGAS (versatile gene-based association study) is a popular methodological framework to perform gene-based tests based on summary statistics from single-variant analyses. The approach incorporates linkage disequilibrium information from reference panels to account for the correlation of test statistics. The gene-based test can utilize three different types of tests. In 2015, the improved framework VEGAS2, using more detailed reference panels, was published. Both versions provide user-friendly web- and offline-based tools for the analysis. However, the implementation of the popular top-percentage test is erroneous in both versions. The p values provided by VEGAS2 are deflated/anti-conservative. Based on real data examples, we demonstrate that this can increase substantially the rate of false-positive findings and can lead to inconsistencies between different test options. We also provide code that allows the user of VEGAS to compute correct p values.

We recently identified a locus on chromosome 18q11.2 for high serum triglycerides in Mexicans. We hypothesize that the lead genome-wide association study single-nucleotide polymorphism rs9949617, or its linkage disequilibrium proxies, regulates 1 of the 5 genes in the triglyceride-associated region. We performed a linkage disequilibrium analysis and found 9 additional variants in linkage disequilibrium (r(2)>0.7) with the lead single-nucleotide polymorphism. To select the variants for functional analyses, we annotated the 10 variants using DNase I hypersensitive sites, transcription factor and chromatin states and identified rs17259126 as the lead candidate variant for functional in vitro validation. Using luciferase transcriptional reporter assay in liver HepG2 cells, we found that the G allele exhibits a significantly lower effect on transcription (P<0.05). The electrophoretic mobility shift and ChIPqPCR (chromatin immunoprecipitation coupled with quantitative polymerase chain reaction) assays confirmed that the minor G allele of rs17259126 disrupts an hepatocyte nuclear factor 4 α-binding site. To find the regional candidate gene, we performed a local expression quantitative trait locus analysis and found that rs17259126 and its linkage disequilibrium proxies alter expression of the regional transmembrane protein 241 (TMEM241) gene in 795 adipose RNAs from the Metabolic Syndrome In Men (METSIM) cohort (P=6.11×10(-07)-5.80×10(-04)). These results were replicated in expression profiles of TMEM241 from the Multiple Tissue Human Expression Resource (MuTHER; n=856). The Mexican genome-wide association study signal for high serum triglycerides on chromosome 18q11.2 harbors a regulatory single-nucleotide polymorphism, rs17259126, which disrupts normal hepatocyte nuclear factor 4 α binding and decreases the expression of the regional TMEM241 gene. Our data suggest that decreased transcript levels of TMEM241 contribute to increased triglyceride levels in Mexicans.