Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension

Dissecting shared genetic architecture between obesity and multiple sclerosis

BACKGROUND

Observational studies have associated obesity with an increased risk of multiple sclerosis (MS). However, the role of genetic factors in their comorbidity remains largely unknown. Our study aimed to investigate the shared genetic architecture underlying obesity and MS.

METHODS

By leveraging data from genome-wide association studies, we investigated the genetic correlation of body mass index (BMI) and MS by linkage disequilibrium score regression and genetic covariance analyser. The casualty was identified by bidirectional Mendelian randomisation. Linkage disequilibrium score regression in specifically expressed genes and multimarker analysis of GenoMic annotation was utilised to explore single-nucleotide polymorphism (SNP) enrichment at the tissue and cell-type levels. Shared risk SNPs were derived using cross-trait meta-analyses and Heritability Estimation from Summary Statistics. We explored the potential functional genes using summary-data-based Mendelian randomization (SMR). The expression profiles of the risk gene in tissues were further examined.

FINDINGS

We found a significantly positive genetic correlation between BMI and MS, and the causal association of BMI with MS was supported (β = 0.22, P = 8.03E-05). Cross-trait analysis yielded 39 shared risk SNPs, and the risk gene GGNBP2 was consistently identified in SMR. We observed tissue-specific level SNP heritability enrichment for BMI mainly in brain tissues for MS in immune-related tissues, and cell-type-specific level SNP heritability enrichment in 12 different immune cell types in brain, spleen, lung, and whole blood. The expressions of GGNBP2 were significantly altered in the tissues of patients with obesity or MS compared to those of control subjects.

INTERPRETATION

Our study indicates the genetic correlation and shared risk genes between obesity and MS. These findings provide insights into the potential mechanisms behind their comorbidity and the future development of therapeutics.

FUNDING

This work was funded by the National Natural Science Foundation of China (82171698, 82170561, 81300279, and 81741067), the Program for High-level Foreign Expert Introduction of China (G2022030047L), the Natural Science Foundation for Distinguished Young Scholars of Guangdong Province (2021B1515020003), Natural Science Foundation of Guangdong Province (2022A1515012081), the Foreign Distinguished Teacher Program of Guangdong Science and Technology Department (KD0120220129), the Climbing Programme of Introduced Talents and High-level Hospital Construction Project of Guangdong Provincial People's Hospital (DFJH201803, KJ012019099, KJ012021143, and KY012021183), and in part by VA Clinical Merit and ASGE clinical research funds (FWL).

Investigating the relationship between depression and breast cancer: observational and genetic analyses

BACKGROUND

Both depression and breast cancer (BC) contribute to a substantial global burden of morbidity and mortality among women, and previous studies have observed a potential depression-BC link. We aimed to comprehensively characterize the phenotypic and genetic relationships between depression and BC.

METHODS

We first evaluated phenotypic association using longitudinal follow-up data from the UK Biobank (N = 250,294). We then investigated genetic relationships leveraging summary statistics from the hitherto largest genome-wide association study of European individuals conducted for depression (N = 500,199), BC (N = 247,173), and its subtypes based on the status of estrogen receptor (ER + : N = 175,475; ER - : N = 127,442).

RESULTS

Observational analysis suggested an increased hazard of BC in depression patients (HR = 1.10, 95%CIs = 0.95-1.26). A positive genetic correlation between depression and overall BC was observed ([Formula: see text] = 0.08, P = 3.00 × 10-4), consistent across ER + ([Formula: see text] = 0.06, P = 6.30 × 10-3) and ER - subtypes ([Formula: see text] = 0.08, P = 7.20 × 10-3). Several specific genomic regions showed evidence of local genetic correlation, including one locus at 9q31.2, and four loci at, or close, to 6p22.1. Cross-trait meta-analysis identified 17 pleiotropic loci shared between depression and BC. TWAS analysis revealed five shared genes. Bi-directional Mendelian randomization suggested risk of depression was causally associated with risk of overall BC (OR = 1.12, 95%Cis = 1.04-1.19), but risk of BC was not causally associated with risk of depression.

CONCLUSIONS

Our work demonstrates a shared genetic basis, pleiotropic loci, and a putative causal relationship between depression and BC, highlighting a biological link underlying the observed phenotypic relationship; these findings may provide important implications for future studies aimed reducing BC risk.

Genetic correlation, causal relationship, and shared loci between vitamin D and COVID-19: A genome-wide cross-trait analysis

Observational studies have shown that vitamin D supplementation reduces the risk of COVID-19 infection, yet little is known about the shared genomic architectures between them. Leveraging large-scale genome-wide association study (GWAS) summary statistics, we investigated the genetic correlation and causal relationship between genetically determined vitamin D and COVID-19 using linkage disequilibrium score regression and Mendelian randomization (MR) analyses, and conducted a cross-trait GWAS meta-analysis to identify the overlapping susceptibility loci of them. We observed a significant genetic correlation between genetically predicted vitamin D and COVID-19 (r_g  = -0.143, p = 0.011), and the risk of COVID-19 infection would decrease by 6% for every 0.76 nmol L^-1 increase of serum 25 hydroxyvitamin D (25OHD) concentrations in generalized MR (OR = 0.94, 95% CI: 0.89-0.99, p = 0.019). We identified rs4971066 (EFNA1) as a risk locus for the joint phenotype of vitamin D and COVID-19. In conclusion, genetically determined vitamin D is associated with COVID-19. Increased levels of serum 25OHD concentration may benefit the prevention and treatment of COVID-19.

Comparison of two multi-trait association testing methods and sequence-based fine mapping of six additive QTL in Swiss Large White pigs

BACKGROUND

Genetic correlations between complex traits suggest that pleiotropic variants contribute to trait variation. Genome-wide association studies (GWAS) aim to uncover the genetic underpinnings of traits. Multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS enable detecting variants associated with multiple phenotypes. In this study, we used array-derived genotypes and phenotypes for 24 reproduction, production, and conformation traits to explore differences between the two methods and used imputed sequence variant genotypes to fine-map six quantitative trait loci (QTL).

RESULTS

We considered genotypes at 44,733 SNPs for 5,753 pigs from the Swiss Large White breed that had deregressed breeding values for 24 traits. Single-trait association analyses revealed eleven QTL that affected 15 traits. Multi-trait association testing and the meta-analysis of the single-trait GWAS revealed between 3 and 6 QTL, respectively, in three groups of traits. The multi-trait methods revealed three loci that were not detected in the single-trait GWAS. Four QTL that were identified in the single-trait GWAS, remained undetected in the multi-trait analyses. To pinpoint candidate causal variants for the QTL, we imputed the array-derived genotypes to the sequence level using a sequenced reference panel consisting of 421 pigs. This approach provided genotypes at 16 million imputed sequence variants with a mean accuracy of imputation of 0.94. The fine-mapping of six QTL with imputed sequence variant genotypes revealed four previously proposed causal mutations among the top variants.

CONCLUSIONS

Our findings in a medium-size cohort of pigs suggest that multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS provide very similar results. Although multi-trait association methods provide a useful overview of pleiotropic loci segregating in mapping populations, the investigation of single-trait association studies is still advised, as multi-trait methods may miss QTL that are uncovered in single-trait GWAS.

StocSum: stochastic summary statistics for whole genome sequencing studies

Genomic summary statistics, usually defined as single-variant test results from genome-wide association studies, have been widely used to advance the genetics field in a wide range of applications. Applications that involve multiple genetic variants also require their correlations or linkage disequilibrium (LD) information, often obtained from an external reference panel. In practice, it is usually difficult to find suitable external reference panels that represent the LD structure for underrepresented and admixed populations, or rare genetic variants from whole genome sequencing (WGS) studies, limiting the scope of applications for genomic summary statistics. Here we introduce StocSum, a novel reference-panel-free statistical framework for generating, managing, and analyzing stochastic summary statistics using random vectors. We develop various downstream applications using StocSum including single-variant tests, conditional association tests, gene-environment interaction tests, variant set tests, as well as meta-analysis and LD score regression tools. We demonstrate the accuracy and computational efficiency of StocSum using two cohorts from the Trans-Omics for Precision Medicine Program. StocSum will facilitate sharing and utilization of genomic summary statistics from WGS studies, especially for underrepresented and admixed populations.

An adaptive test based on principal components for detecting multiple phenotype associations using GWAS summary data

Extensive evidence from genome-wide association studies (GWAS) has shown that jointly analyzing multiple phenotypes can improve the power of the association test compared to the traditional single variant versus single trait approach. Here we propose an adaptive test based on principal components (ATPC) that is powerful and efficient for discovering the association between a single variant and multiple traits. Our method only needs GWAS summary statistics that are often available. We first estimate the trait correlation matrix by LD score regression. Then, based on the correlation matrix, we construct a series of test statistics that contain different numbers of principal components. The ultimate test statistic combines the P values of these principal component-based statistics by using the aggregated Cauchy association test. The analytical P-value of the test statistic can be computed quickly without the permutation process, which is the notable feature of our proposed method. The extensive simulation studies demonstrate that ATPC can control the type I error rates and have powerful and robust performance compared to several existing tests in a wide range of simulation settings. The analysis of the lipids GWAS summary data from the Global Lipids Genetics Consortium shows that ATPC identifies 230 new SNPs that are missed by the original single trait association analysis. By searching the GWAS Catalog, some SNPs and mapped genes identified by ATPC are reported to be associated with lipid traits. Through further analysis for GWAS results, we also find some Gene Ontology terms and biological pathways related to lipids.

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.

A clustering linear combination method for multiple phenotype association studies based on GWAS summary statistics

There is strong evidence showing that joint analysis of multiple phenotypes in genome-wide association studies (GWAS) can increase statistical power when detecting the association between genetic variants and human complex diseases. We previously developed the Clustering Linear Combination (CLC) method and a computationally efficient CLC (ceCLC) method to test the association between multiple phenotypes and a genetic variant, which perform very well. However, both of these methods require individual-level genotypes and phenotypes that are often not easily accessible. In this research, we develop a novel method called sCLC for association studies of multiple phenotypes and a genetic variant based on GWAS summary statistics. We use the LD score regression to estimate the correlation matrix among phenotypes. The test statistic of sCLC is constructed by GWAS summary statistics and has an approximate Cauchy distribution. We perform a variety of simulation studies and compare sCLC with other commonly used methods for multiple phenotype association studies using GWAS summary statistics. Simulation results show that sCLC can control Type I error rates well and has the highest power in most scenarios. Moreover, we apply the newly developed method to the UK Biobank GWAS summary statistics from the XIII category with 70 related musculoskeletal system and connective tissue phenotypes. The results demonstrate that sCLC detects the most number of significant SNPs, and most of these identified SNPs can be matched to genes that have been reported in the GWAS catalog to be associated with those phenotypes. Furthermore, sCLC also identifies some novel signals that were missed by standard GWAS, which provide new insight into the potential genetic factors of the musculoskeletal system and connective tissue phenotypes.

Genome-wide Association Study Meta-analysis of Blood Pressure Traits and Hypertension in Sub-Saharan African Populations: An AWI-Gen Study

Most hypertension-related genome-wide association studies (GWAS) focus on non-African populations, despite hypertension (a major risk factor for cardiovascular disease) being highly prevalent in Africa. The AWI-Gen study GWAS meta-analysis for blood pressure-related traits (systolic and diastolic blood pressure, pulse pressure, mean-arterial pressure and hypertension) from three sub-Saharan African geographic regions (N=10,775), identified two genome-wide significant signals (p<5E-08): systolic blood pressure near P2RY1 (rs77846204; intergenic variant, p=4.25E-08) and pulse pressure near Linc01256 (rs80141533; intergenic variant, p=4.25E-08). No genome-wide signals were detected for the AWI-Gen GWAS meta-analysis with previous African-ancestry GWASs (UK Biobank (African), Uganda Genome Resource). Suggestive signals (p<5E-06) were observed for all traits, with 29 displaying pleiotropic effects and several replicating known associations. Polygenic risk scores developed from studies on different ancestries had limited transferability, with multi-ancestry models providing better prediction. This study provides insights into the genetics and physiology of blood pressure variation in African populations.

A Meta-Analysis of the Genome-Wide Association Studies on Two Genetically Correlated Phenotypes Suggests Four New Risk Loci for Headaches

Headache is one of the commonest complaints that doctors need to address in clinical settings. The genetic mechanisms of different types of headache are not well understood while it has been suggested that self-reported headache and self-reported migraine were genetically correlated. In this study, we performed a meta-analysis of genome-wide association studies (GWAS) on the self-reported headache phenotype from the UK Biobank and the self-reported migraine phenotype from the 23andMe using the Unified Score-based Association Test (metaUSAT) software for genetically correlated phenotypes (N = 397,385). We identified 38 loci for headaches, of which 34 loci have been reported before and four loci were newly suggested. The LDL receptor related protein 1 (LRP1)-Signal Transducer and Activator of Transcription 6 (STAT6)-S hort chain D ehydrogenase/R eductase family 9C member 7 (SDR9C7) region in chromosome 12 was the most significantly associated locus with a leading p value of 1.24 × 10-62 of rs11172113. The One Cut homeobox 2 (ONECUT2) gene locus in chromosome 18 was the strongest signal among the four new loci with a p value of 1.29 × 10-9 of rs673939. Our study demonstrated that the genetically correlated phenotypes of self-reported headache and self-reported migraine can be meta-analysed together in theory and in practice to boost study power to identify more variants for headaches. This study has paved way for a large GWAS meta-analysis involving cohorts of different while genetically correlated headache phenotypes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00078-7.

MRBEE: A novel bias-corrected multivariable Mendelian Randomization method

Mendelian Randomization (MR) has been widely applied to infer causality of exposures on outcomes in the genome wide association (GWAS) era. Existing approaches are often subject to biases from multiple sources including weak instruments, sample overlap, and measurement error. We introduce MRBEE, a computationally efficient multivariable MR method that can correct for all known biases simultaneously, which is demonstrated in theory, simulations, and real data analysis. In comparison, all existing MR methods are biased. In two independent real data analyses, we observed that the causal effect of BMI on coronary artery disease risk is completely mediated by blood pressure, and that existing MR methods drastically underestimate the causal effect of cannabis use disorder on schizophrenia risk compared to MRBEE. We demonstrate that MRBEE can be a useful tool in studying causality between multiple risk factors and a disease outcome, especially as more GWAS summary statistics are being made publicly available.

Multimarker omnibus tests by leveraging individual marker summary statistics from large biobanks

As biobanks become increasingly popular, access to genotypic and phenotypic data continues to increase in the form of precomputed summary statistics (PCSS). Widespread accessibility of PCSS alleviates many issues related to biobank data, including that of data privacy and confidentiality, as well as high computational costs. However, questions remain about how to maximally leverage PCSS for downstream statistical analyses. Here we present a novel method for testing the association of an arbitrary number of single nucleotide variants (SNVs) on a linear combination of phenotypes after adjusting for covariates for common multimarker tests (e.g., SKAT, SKAT-O) without access to individual patient-level data (IPD). We validate exact formulas for each method, and demonstrate their accuracy through simulation studies and an application to fatty acid phenotypic data from the Framingham Heart Study.

CACNA1D-Related Channelopathies: From Hypertension to Autism

Tightly controlled Ca²⁺ influx through voltage-gated Ca²⁺ channels (Cavs) is indispensable for proper physiological function. Thus, it is not surprising that Cav loss and/or gain of function have been implicated in human pathology. Deficiency of Cav1.3 L-type Ca²⁺ channels (LTCCs) causes deafness and bradycardia, whereas several genetic variants of CACNA1D, the gene encoding the pore-forming α1 subunit of Cav1.3, have been linked to various disease phenotypes, such as hypertension, congenital hypoglycemia, or autism. These variants include not only common polymorphisms associated with an increased disease risk, but also rare de novo missense variants conferring high risk. This review provides a concise summary of disease-associated CACNA1D variants, whereas the main focus lies on de novo germline variants found in individuals with a neurodevelopmental disorder of variable severity. Electrophysiological recordings revealed activity-enhancing gating changes induced by these de novo variants, and tools to predict their pathogenicity and to study the resulting pathophysiological consequences will be discussed. Despite the low number of affected patients, potential phenotype-genotype correlations and factors that could impact the severity of symptoms will be covered. Since increased channel activity is assumed as the disease-underlying mechanism, pharmacological inhibition could be a treatment option. In the absence of Cav1.3-selective blockers, dihydropyridine LTCC inhibitors clinically approved for the treatment of hypertension may be used for personalized off-label trials. Findings from in vitro studies and treatment attempts in some of the patients seem promising as outlined. Taken together, due to advances in diagnostic sequencing techniques the number of reported CACNA1D variants in human diseases is constantly rising. Evidence from in silico, in vitro, and in vivo disease models can help to predict the pathogenic potential of such variants and to guide diagnosis and treatment in the clinical practice when confronted with patients harboring CACNA1D variants.

Identification of shared genetic architecture between non-alcoholic fatty liver disease and type 2 diabetes: A genome-wide analysis

BACKGROUND

The incidence of complications of non-alcoholic fatty liver disease (NAFLD) and type 2 diabetes (T2D) has been increasing.

METHOD

In order to identify the shared genetic architecture of the two disease phenotypes of NAFLD and T2D, a European population-based GWAS summary and a cross-trait meta-analysis was used to identify significant shared genes for NAFLD and T2D. The enrichment of shared genes was then determined through the use of functional enrichment analysis to investigate the relationship between genes and phenotypes. Additionally, differential gene expression analysis was performed, significant differentially expressed genes in NAFLD and T2D were identified, genes that overlapped between those that were differentially expressed and cross-trait results were reported, and enrichment analysis was performed on the core genes that had been obtained in this way. Finally, the application of a bidirectional Mendelian randomization (MR) approach determined the causal link between NAFLD and T2D.

RESULT

A total of 115 genes were discovered to be shared between NAFLD and T2D in the GWAS analysis. The enrichment analysis of these genes showed that some were involved in the processes such as the decomposition and metabolism of lipids, phospholipids, and glycerophospholipids. Additionally, through the use of differential gene expression analysis, 15 core genes were confirmed to be linked to both T2D and NAFLD. They were correlated with carcinoma cells and inflammation. Furthermore, the bidirectional MR identified a positive causal relationship between NAFLD and T2D.

CONCLUSION

Our study determined the genetic structure shared between NAFLD and T2D, offering a new reference for the genetic pathogenesis and mechanism of NAFLD and T2D comorbidities.

Prevailing Antagonistic Risks in Pleiotropic Associations with Alzheimer's Disease and Diabetes

BACKGROUND

The lack of efficient preventive interventions against Alzheimer's disease (AD) calls for identifying efficient modifiable risk factors for AD. As diabetes shares many pathological processes with AD, including accumulation of amyloid plaques and neurofibrillary tangles, insulin resistance, and impaired glucose metabolism, diabetes is thought to be a potentially modifiable risk factor for AD. Mounting evidence suggests that links between AD and diabetes may be more complex than previously believed.

OBJECTIVE

To examine the pleiotropic architecture of AD and diabetes mellitus (DM).

METHODS

Univariate and pleiotropic analyses were performed following the discovery-replication strategy using individual-level data from 10 large-scale studies.

RESULTS

We report a potentially novel pleiotropic NOTCH2 gene, with a minor allele of rs5025718 associated with increased risks of both AD and DM. We confirm previously identified antagonistic associations of the same variants with the risks of AD and DM in the HLA and APOE gene clusters. We show multiple antagonistic associations of the same variants with AD and DM in the HLA cluster, which were not explained by the lead SNP in this cluster. Although the ɛ2 and ɛ4 alleles played a major role in the antagonistic associations with AD and DM in the APOE cluster, we identified non-overlapping SNPs in this cluster, which were adversely and beneficially associated with AD and DM independently of the ɛ2 and ɛ4 alleles.

CONCLUSION

This study emphasizes differences and similarities in the heterogeneous genetic architectures of AD and DM, which may differentiate the pathogenic mechanisms of these diseases.

Combining genome-wide association studies highlight novel loci involved in human facial variation

Standard genome-wide association studies (GWASs) rely on analyzing a single trait at a time. However, many human phenotypes are complex and composed by multiple correlated traits. Here we introduce C-GWAS, a method for combining GWAS summary statistics of multiple potentially correlated traits. Extensive computer simulations demonstrated increased statistical power of C-GWAS compared to the minimal p-values of multiple single-trait GWASs (MinGWAS) and the current state-of-the-art method for combining single-trait GWASs (MTAG). Applying C-GWAS to a meta-analysis dataset of 78 single trait facial GWASs from 10,115 Europeans identified 56 study-wide suggestively significant loci with multi-trait effects on facial morphology of which 17 are novel loci. Using data from additional 13,622 European and Asian samples, 46 (82%) loci, including 9 (53%) novel loci, were replicated at nominal significance with consistent allele effects. Functional analyses further strengthen the reliability of our C-GWAS findings. Our study introduces the C-GWAS method and makes it available as computationally efficient open-source R package for widespread future use. Our work also provides insights into the genetic architecture of human facial appearance.

Exploring the Genetic Association between Obesity and Serum Lipid Levels Using Bivariate Methods

It is crucial to understand the genetic mechanisms and biological pathways underlying the relationship between obesity and serum lipid levels. Structural equation models (SEMs) were constructed to calculate heritability for body mass index (BMI), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and the genetic connections between BMI and the four classes of lipids using 1197 pairs of twins from the Chinese National Twin Registry (CNTR). Bivariate genomewide association studies (GWAS) were performed to identify genetic variants associated with BMI and lipids using the records of 457 individuals, and the results were further validated in 289 individuals. The genetic background affecting BMI may differ by gender, and the heritability of males and females was 71% (95% CI [.66, .75]) and 39% (95% CI [.15, .71]) respectively. BMI was positively correlated with TC, TG and LDL-C in phenotypic and genetic correlation, while negatively correlated with HDL-C. There were gender differences in the correlation between BMI and lipids. Bivariate GWAS analysis and validation stage found 7 genes (LOC105378740, LINC02506, CSMD1, MELK, FAM81A, ERAL1 and MIR144) that were possibly related to BMI and lipid levels. The significant biological pathways were the regulation of cholesterol reverse transport and the regulation of high-density lipoprotein particle clearance (p < .001). BMI and blood lipid levels were affected by genetic factors, and they were genetically correlated. There might be gender differences in their genetic correlation. Bivariate GWAS analysis found MIR144 gene and its related biological pathways may influence obesity and lipid levels.

Evidence that the pituitary gland connects type 2 diabetes mellitus and schizophrenia based on large-scale trans-ethnic genetic analyses

BACKGROUND

Previous studies on European (EUR) samples have obtained inconsistent results regarding the genetic correlation between type 2 diabetes mellitus (T2DM) and Schizophrenia (SCZ). A large-scale trans-ethnic genetic analysis may provide additional evidence with enhanced power.

OBJECTIVE

We aimed to explore the genetic basis for both T2DM and SCZ based on large-scale genetic analyses of genome-wide association study (GWAS) data from both East Asian (EAS) and EUR subjects.

METHODS

A range of complementary approaches were employed to cross-validate the genetic correlation between T2DM and SCZ at the whole genome, autosomes (linkage disequilibrium score regression, LDSC), loci (Heritability Estimation from Summary Statistics, HESS), and causal variants (MiXeR and Mendelian randomization, MR) levels. Then, genome-wide and transcriptome-wide cross-trait/ethnic meta-analyses were performed separately to explore the effective shared organs, cells and molecular pathways.

RESULTS

A weak genome-wide negative genetic correlation between SCZ and T2DM was found for the EUR (r_g = - 0.098, P = 0.009) and EAS (r_g =- 0.053 and P = 0.032) populations, which showed no significant difference between the EUR and EAS populations (P = 0.22). After Bonferroni correction, the r_g remained significant only in the EUR population. Similar results were obtained from analyses at the levels of autosomes, loci and causal variants. 25 independent variants were firstly identified as being responsible for both SCZ and T2DM. The variants associated with the two disorders were significantly correlated to the gene expression profiles in the brain (P = 1.1E-9) and pituitary gland (P = 1.9E-6). Then, 61 protein-coding and non-coding genes were identified as effective genes in the pituitary gland (P < 9.23E-6) and were enriched in metabolic pathways related to glutathione mediated arsenate detoxification and to D-myo-inositol-trisphosphate.

CONCLUSION

Here, we show that a negative genetic correlation exists between SCZ and T2DM at the whole genome, autosome, locus and causal variant levels. We identify pituitary gland as a common effective organ for both diseases, in which non-protein-coding effective genes, such as lncRNAs, may be responsible for the negative genetic correlation. This highlights the importance of molecular metabolism and neuroendocrine modulation in the pituitary gland, which may be responsible for the initiation of T2DM in SCZ patients.

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.

Recent advances and challenges of rare variant association analysis in the biobank sequencing era

Causal variants for rare genetic diseases are often rare in the general population. Rare variants may also contribute to common complex traits and can have much larger per-allele effect sizes than common variants, although power to detect these associations can be limited. Sequencing costs have steadily declined with technological advancements, making it feasible to adopt whole-exome and whole-genome profiling for large biobank-scale sample sizes. These large amounts of sequencing data provide both opportunities and challenges for rare-variant association analysis. Herein, we review the basic concepts of rare-variant analysis methods, the current state-of-the-art methods in utilizing variant annotations or external controls to improve the statistical power, and particular challenges facing rare variant analysis such as accounting for population structure, extremely unbalanced case-control design. We also review recent advances and challenges in rare variant analysis for familial sequencing data and for more complex phenotypes such as survival data. Finally, we discuss other potential directions for further methodology investigation.

Inflammation and immunity connect hypertension with adverse COVID-19 outcomes

Objectives: To explore the connection of hypertension and severe COVID-19 outcomes. Methods: A total of 68 observational studies recording mortality and/or general severity of COVID-19 were pooled for meta-analyses of the relationship of severe COVID-19 outcomes with hypertension as well as systolic and diastolic blood pressure. Genome-wide cross-trait meta-analysis (GWCTM) was performed to explore the genes linking between hypertension and COVID-19 severity. Results: The results of meta-analysis with the random effect model indicated that pooled risk ratios of hypertension on mortality and severity of COVID-19 were 1.80 [95% confidence interval (CI) 1.54-2.1] and 1.78 (95% confidence interval 1.56-2.04), respectively, although the apparent heterogeneity of the included studies was detected. In subgroup analysis, cohorts of severe and mild patients of COVID-19 assessed in Europe had a significant pooled weighted mean difference of 6.61 mmHg (95% CI 3.66-9.55) with no heterogeneity found (p = 0.26). The genes in the shared signature of hypertension and the COVID-19 severity were mostly expressed in lungs. Analysis of molecular networks commonly affected both by hypertension and by severe COVID-19 highlighted CCR1/CCR5 and IL10RB signaling, as well as Th1 and Th2 activation pathways, and also a potential for a shared regulation with multiple sclerosis. Conclusion: Hypertension is significantly associated with the severe course of COVID-19. Genetic variants within inflammation- and immunity-related genes may affect their expression in lungs and confer liability to both elevated blood pressure and to severe COVID-19.

A genome-wide cross-trait analysis identifies shared loci and causal relationships of type 2 diabetes and glycaemic traits with polycystic ovary syndrome

AIMS/HYPOTHESIS

The link underlying abnormal glucose metabolism, type 2 diabetes and polycystic ovary syndrome (PCOS) that is independent of BMI remains unclear in observational studies. We aimed to clarify this association using a genome-wide cross-trait approach.

METHODS

Summary statistics from the hitherto largest genome-wide association studies conducted for type 2 diabetes, type 2 diabetes mellitus adjusted for BMI (T2DM_adjBMI), fasting glucose, fasting insulin, 2h glucose after an oral glucose challenge (all adjusted for BMI), HbA_1c and PCOS, all in populations of European ancestry, were used. We quantified overall and local genetic correlations, identified pleiotropic loci and expression-trait associations, and made causal inferences across traits.

RESULTS

A positive overall genetic correlation between type 2 diabetes and PCOS was observed, largely influenced by BMI (r_g=0.31, p=1.63×10^-8) but also independent of BMI (T2DM_adjBMI-PCOS: r_g=0.12, p=0.03). Sixteen pleiotropic loci affecting type 2 diabetes, glycaemic traits and PCOS were identified, suggesting mechanisms of association that are independent of BMI. Two shared expression-trait associations were found for type 2 diabetes/T2DM_adjBMI and PCOS targeting tissues of the cardiovascular, exocrine/endocrine and digestive systems. A putative causal effect of fasting insulin adjusted for BMI and type 2 diabetes on PCOS was demonstrated.

CONCLUSIONS/INTERPRETATION

We found a genetic link underlying type 2 diabetes, glycaemic traits and PCOS, driven by both biological pleiotropy and causal mediation, some of which is independent of BMI. Our findings highlight the importance of controlling fasting insulin levels to mitigate the risk of PCOS, as well as screening for and long-term monitoring of type 2 diabetes in all women with PCOS, irrespective of BMI.

Insights From a Large-Scale Whole-Genome Sequencing Study of Systolic Blood Pressure, Diastolic Blood Pressure, and Hypertension

BACKGROUND

The availability of whole-genome sequencing data in large studies has enabled the assessment of coding and noncoding variants across the allele frequency spectrum for their associations with blood pressure.

METHODS

We conducted a multiancestry whole-genome sequencing analysis of blood pressure among 51 456 Trans-Omics for Precision Medicine and Centers for Common Disease Genomics program participants (stage-1). Stage-2 analyses leveraged array data from UK Biobank (N=383 145), Million Veteran Program (N=318 891), and Reasons for Geographic and Racial Differences in Stroke (N=10 643) participants, along with whole-exome sequencing data from UK Biobank (N=199 631) participants.

RESULTS

Two blood pressure signals achieved genome-wide significance in meta-analyses of stage-1 and stage-2 single variant findings (P<5×10-8). Among them, a rare intergenic variant at novel locus, LOC100506274, was associated with lower systolic blood pressure in stage-1 (beta [SE]=-32.6 [6.0]; P=4.99×10-8) but not stage-2 analysis (P=0.11). Furthermore, a novel common variant at the known INSR locus was suggestively associated with diastolic blood pressure in stage-1 (beta [SE]=-0.36 [0.07]; P=4.18×10-7) and attained genome-wide significance in stage-2 (beta [SE]=-0.29 [0.03]; P=7.28×10-23). Nineteen additional signals suggestively associated with blood pressure in meta-analysis of single and aggregate rare variant findings (P<1×10-6 and P<1×10-4, respectively).

DISCUSSION

We report one promising but unconfirmed rare variant for blood pressure and, more importantly, contribute insights for future blood pressure sequencing studies. Our findings suggest promise of aggregate analyses to complement single variant analysis strategies and the need for larger, diverse samples, and family studies to enable robust rare variant identification.

Investigating the shared genetic architecture of uterine leiomyoma and breast cancer: A genome-wide cross-trait analysis

Little is known regarding the shared genetic architecture or causality underlying the phenotypic association observed for uterine leiomyoma (UL) and breast cancer (BC). Leveraging summary statistics from the hitherto largest genome-wide association study (GWAS) conducted in each trait, we investigated the genetic overlap and causal associations of UL with BC overall, as well as with its subtypes defined by the status of estrogen receptor (ER). We observed a positive genetic correlation between UL and BC overall (rg = 0.09, p = 6.00 × 10-3), which was consistent in ER+ subtype (rg = 0.06, p = 0.01) but not in ER- subtype (rg = 0.06, p = 0.08). Partitioning the whole genome into 1,703 independent regions, local genetic correlation was identified at 22q13.1 for UL with BC overall and with ER+ subtype. Significant genetic correlation was further discovered in 9 out of 14 functional categories, with the highest estimates observed in coding, H3K9ac, and repressed regions. Cross-trait meta-analysis identified 9 novel loci shared between UL and BC. Mendelian randomization demonstrated a significantly increased risk of BC overall (OR = 1.09, 95% CI = 1.01-1.18) and ER+ subtype (OR = 1.09, 95% CI = 1.01-1.17) for genetic liability to UL. No reverse causality was found. Our comprehensive genome-wide cross-trait analysis demonstrates a shared genetic basis, pleiotropic loci, as well as a putative causal relationship between UL and BC, highlighting an intrinsic link underlying these two complex female diseases.

TwinEQTL: Ultra Fast and Powerful Association Analysis for eQTL and GWAS in Twin Studies

We develop a computationally efficient alternative, TwinEQTL, to a linear mixed-effects model (LMM) for twin genome-wide association study (GWAS) data. Instead of analyzing all twin samples together with LMM, TwinEQTL first splits twin samples into two independent groups on which multiple linear regression analysis can be validly performed separately, followed by an appropriate meta-analysis-like approach to combine the two non-independent test results. Through mathematical derivations, we prove the validity of TwinEQTL algorithm and show that the correlation between two dependent test statistics at each single-nucleotide polymorphism (SNP) are independent of its minor allele frequency (MAF). Thus the correlation is constant across all SNPs. Through simulations, we show empirically that TwinEQTL has well controlled type I error with negligible power loss compared to the gold-standard linear mixed effects models. To accommodate eQTL analysis with twin subjects, we further implement TwinEQTL into a R package with much improved computational efficiency. Our approaches provide a significant leap in terms of computing speed for GWAS and eQTL analysis with twin samples.

Sleep and Late-Onset Alzheimer's Disease: Shared Genetic Risk Factors, Drug Targets, Molecular Mechanisms, and Causal Effects

Late-onset Alzheimer's disease (AD) is associated with sleep-related phenotypes (SRPs). The fact that whether they share a common genetic etiology remains largely unknown. We explored the shared genetics and causality between AD and SRPs by using high-definition likelihood (HDL), cross-phenotype association study (CPASSOC), transcriptome-wide association study (TWAS), and bidirectional Mendelian randomization (MR) in summary-level data for AD (N = 455,258) and summary-level data for seven SRPs (sample size ranges from 359,916 to 1,331,010). AD shared a strong genetic basis with insomnia (r _g = 0.20; p = 9.70 × 10^-5), snoring (r _g = 0.13; p = 2.45 × 10^-3), and sleep duration (r _g = -0.11; p = 1.18 × 10^-3). The CPASSOC identifies 31 independent loci shared between AD and SRPs, including four novel shared loci. Functional analysis and the TWAS showed shared genes were enriched in liver, brain, breast, and heart tissues and highlighted the regulatory roles of immunological disorders, very-low-density lipoprotein particle clearance, triglyceride-rich lipoprotein particle clearance, chylomicron remnant clearance, and positive regulation of T-cell-mediated cytotoxicity pathways. Protein-protein interaction analysis identified three potential drug target genes (APOE, MARK4, and HLA-DRA) that interacted with known FDA-approved drug target genes. The CPASSOC and TWAS demonstrated three regions 11p11.2, 6p22.3, and 16p11.2 may account for the shared basis between AD and sleep duration or snoring. MR showed insomnia had a causal effect on AD (OR_IVW = 1.02, P _IVW = 6.7 × 10^-6), and multivariate MR suggested a potential role of sleep duration and major depression in this association. Our findings provide strong evidence of shared genetics and causation between AD and sleep abnormalities and advance our understanding of the genetic overlap between them. Identifying shared drug targets and molecular pathways can be beneficial for treating AD and sleep disorders more efficiently.

Genome-Wide Associations with Resistance to Bipolaris Leaf Spot (Bipolaris oryzae (Breda de Haan) Shoemaker) in a Northern Switchgrass Population (Panicum virgatum L.)

Switchgrass (Panicum virgatum L.), a northern native perennial grass, suffers from yield reduction from Bipolaris leaf spot caused by Bipolaris oryzae (Breda de Haan) Shoemaker. This study aimed to determine the resistant populations via multiple phenotyping approaches and identify potential resistance genes from genome-wide association studies (GWAS) in the switchgrass northern association panel. The disease resistance was evaluated from both natural (field evaluations in Ithaca, New York and Phillipsburg, Philadelphia) and artificial inoculations (detached leaf and leaf disk assays). The most resistant populations based on a combination of three phenotyping approaches—detached leaf, leaf disk, and mean from two locations—were ‘SW788’, ‘SW806’, ‘SW802’, ‘SW793’, ‘SW781’, ‘SW797’, ‘SW798’, ‘SW803’, ‘SW795’, ‘SW805’. The GWAS from the association panel showed 27 significant SNPs on 12 chromosomes: 1K, 2K, 2N, 3K, 3N, 4N, 5K, 5N, 6N, 7K, 7N, and 9N. These markers accumulatively explained the phenotypic variance of the resistance ranging from 3.28 to 26.52%. Within linkage disequilibrium of 20 kb, these SNP markers linked with the potential resistance genes included the genes encoding for NBS-LRR, PPR, cell-wall related proteins, homeostatic proteins, anti-apoptotic proteins, and ABC transporter.

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

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

A Novel Hierarchical Clustering Approach for Joint Analysis of Multiple Phenotypes Uncovers Obesity Variants Based on ARIC

Genome-wide association studies (GWASs) have successfully discovered numerous variants underlying various diseases. Generally, one-phenotype one-variant association study in GWASs is not efficient in identifying variants with weak effects, indicating that more signals have not been identified yet. Nowadays, jointly analyzing multiple phenotypes has been recognized as an important approach to elevate the statistical power for identifying weak genetic variants on complex diseases, shedding new light on potential biological mechanisms. Therefore, hierarchical clustering based on different methods for calculating correlation coefficients (HCDC) is developed to synchronously analyze multiple phenotypes in association studies. There are two steps involved in HCDC. First, a clustering approach based on the similarity matrix between two groups of phenotypes is applied to choose a representative phenotype in each cluster. Then, we use existing methods to estimate the genetic associations with the representative phenotypes rather than the individual phenotypes in every cluster. A variety of simulations are conducted to demonstrate the capacity of HCDC for boosting power. As a consequence, existing methods embedding HCDC are either more powerful or comparable with those of without embedding HCDC in most scenarios. Additionally, the application of obesity-related phenotypes from Atherosclerosis Risk in Communities via existing methods with HCDC uncovered several associated variants. Among these, UQCC1-rs1570004 is reported as a significant obesity signal for the first time, whose differential expression in subcutaneous fat, visceral fat, and muscle tissue is worthy of further functional studies.

Charcot-Marie-Tooth neuropathy score and ambulation index are both predictors of orthotic need for patients with CMT

Charcot-Marie-Tooth (CMT) disease is the most common hereditary neuropathy with an estimated prevalence of 1 person affected on 2500. Frequent symptoms include distal weakness and muscle wasting, sensory loss, reduced deep tendon reflexes, and skeletal deformities, such as hammer toes and pes cavus. CMT is a progressive disease and patients' needs change over their lifetime. In particular, ambulation aids are increasingly needed to maintain ambulation and reduce the risk of falls. We performed a retrospective analysis of medical records from 149 patients with confirmed CMT to evaluate patients ambulation needs related to the severity of their CMT as measured by the CMT Neuropathy Score (CMTNS) and Ambulation Index (AI). Most patients required some form of orthotics (86.6%). The CMTNS and AI scores both differed significantly between patients with no orthotics compared to those who wore insoles/inserts. The CMTNS and AI also differed significantly between patients wearing insoles and those with ankle foot orthotics (AFOs). CMTNS and the AI were valid predictors of the type and choice of the orthotics. Both the CMTNS and AI can be effective tools to aid in the correct choice of orthotics in patients affected by CMT.

Shared Genetic Basis and Causal Relationship Between Television Watching, Breakfast Skipping and Type 2 Diabetes: Evidence From a Comprehensive Genetic Analysis

Background

Epidemiological investigations have established unhealthy lifestyles, such as excessive leisurely sedentary behavior (especially TV/television watching) and breakfast skipping, increase the risk of type 2 diabetes (T2D), but the causal relationship is unclear. We aimed to understand how single nucleotide variants contribute to the co-occurrence of unhealthy lifestyles and T2D, thereby providing meaningful insights into disease mechanisms.

Methods

Combining summary statistics from genome-wide association studies (GWAS) on TV watching (N = 422218), breakfast skipping (N = 193860) and T2D (N = 159208) in European pedigrees, we conducted comprehensive pairwise genetic analysis, including high-definition likelihood (HDL-method), cross-phenotype association studies (CPASSOC), GWAS-eQTL colocalization analysis and transcriptome-wide association studies (TWAS), to understand the genetic overlap between them. We also performed bidirectional two-sample Mendelian randomization (MR) analysis for causal inference using genetic instrumental variables, and two-step MR mediation analysis was used to assess any effects explained by body mass index, lipid traits and glycemic traits.

Results

HDL-method showed that T2D shared a strong genetic correlation with TV watching (rg = 0.26; P = 1.63×10-29) and skipping breakfast (rg = 0.15; P =2.02×10-6). CPASSOC identifies eight independent SNPs shared between T2D and TV watching, including one novel shared locus. TWAS and CPASSOC showed that shared genes were enriched in lung, esophageal, adipose, and thyroid tissues and highlighted potential shared regulatory pathways for lipoprotein metabolism, pancreatic β-cell function, cellular senescence and multi-mediator factors. MR showed TV watching had a causal effect on T2D (βIVW = 0.629, PIVW = 1.80×10-10), but no significant results were observed between breakfast skipping and T2D. Mediation analysis provided evidence that body mass index, fasting glucose, hemoglobin A1c and high-density lipoprotein are potential factors that mediate the causal relationship between TV and T2D.

Conclusions

Our findings provide strong evidence of shared genetics and causation between TV watching and T2D and facilitate our identification of common genetic architectures shared between them.

Overlapping common genetic architecture between major depressive disorders and anxiety and stress-related disorders

Major depressive disorders (MDDs) and anxiety and stress-related disorders (ASRDs) have overlapping symptoms and high rates of comorbidity. However, the underlying mechanisms remain largely unknown. Here, we aimed to examine whether MDD and ASRD share genetic risk factors utilizing recent large-scale genome-wide association studies (GWASs). To examine the genetic overlap between MDD and ASRD, we applied genetic correlation analysis to analyze GWAS summary statistics for MDD (16,823 cases and 25,632 controls) and ASRD (12,665 cases and 19,225 controls). We found positive and significant genetic correlations between MDD and ASRD (GNOVA: rho = 0.59, se = 0.01, P = 5.32 × 10^-45). Our latent causal variable (LCV) analysis indicated the genetic correlation result from pleiotropic effects (gcp = -0.56, se = 0.31, P_gcp = 0.1). Based on pleiotropic enrichment, we performed a cross-trait meta-analysis of MDD and ASRD GWAS and fine-mapped the identified loci. In total, we identified 5 pleiotropic loci simultaneously associated with MDD and ASRD at P < 5 × 10^-8. At the gene level, we further demonstrated that MDD- and ASRD-inferred gene expression overlapped across 48 tissues and highlighted the NUP210L gene as a potential mediator of the genetic correlation. Our study highlights a shared underlying genetic risk for MDD and ASRD, which may help to improve the understanding of high comorbidity and overlapping genetic mechanisms between the two traits.

Genome-wide pleiotropy analysis identifies novel blood pressure variants and improves its polygenic risk scores

Systolic and diastolic blood pressure (S/DBP) are highly correlated modifiable risk factors for cardiovascular disease (CVD). We report here a bidirectional Mendelian Randomization (MR) and horizontal pleiotropy analysis of S/DBP summary statistics from the UK Biobank (UKB)-International Consortium for Blood Pressure (ICBP) (UKB-ICBP) BP genome-wide association study and construct a composite genetic risk score (GRS) by including pleiotropic variants. The composite GRS captures greater (1.11-3.26 fold) heritability for BP traits and increases (1.09- and 2.01-fold) Nagelkerke's R² for hypertension and CVD. We replicated 118 novel BP horizontal pleiotropic variants including 18 novel BP loci using summary statistics from the Million Veteran Program (MVP) study. An additional 219 novel BP signals and 40 novel loci were identified after a meta-analysis of the UKB-ICBP and MVP summary statistics but without further independent replication. Our study provides further insight into BP regulation and provides a novel way to construct a GRS by including pleiotropic variants for other complex diseases.

High blood pressure in children and adolescents: current perspectives and strategies to improve future kidney and cardiovascular health

Hypertension is one of the most common causes of preventable death worldwide. The prevalence of pediatric hypertension has increased significantly in recent decades. The cause of this is likely multifactorial, related to increasing childhood obesity, high dietary sodium intake, sedentary lifestyles, perinatal factors, familial aggregation, socioeconomic factors, and ethnic blood pressure (BP) differences. Pediatric hypertension represents a major public health threat. Uncontrolled pediatric hypertension is associated with subclinical cardiovascular disease and adult-onset hypertension. In children with chronic kidney disease (CKD), hypertension is also a strong risk factor for progression to kidney failure. Despite these risks, current rates of pediatric BP screening, hypertension detection, treatment, and control remain suboptimal. Contributing to these shortcomings are the challenges of accurately measuring pediatric BP, limited access to validated pediatric equipment and hypertension specialists, complex interpretation of pediatric BP measurements, problematic normative BP data, and conflicting society guidelines for pediatric hypertension. To date, limited pediatric hypertension research has been conducted to help address these challenges. However, there are several promising signs in the field of pediatric hypertension. There is greater attention being drawn on the cardiovascular risks of pediatric hypertension, more emphasis on the need for childhood BP screening and management, new public health initiatives being implemented, and increasing research interest and funding. This article summarizes what is currently known about pediatric hypertension, the existing knowledge-practice gaps, and ongoing research aimed at improving future kidney and cardiovascular health.

Rare coding variants in RCN3 are associated with blood pressure

BACKGROUND

While large genome-wide association studies have identified nearly one thousand loci associated with variation in blood pressure, rare variant identification is still a challenge. In family-based cohorts, genome-wide linkage scans have been successful in identifying rare genetic variants for blood pressure. This study aims to identify low frequency and rare genetic variants within previously reported linkage regions on chromosomes 1 and 19 in African American families from the Trans-Omics for Precision Medicine (TOPMed) program. Genetic association analyses weighted by linkage evidence were completed with whole genome sequencing data within and across TOPMed ancestral groups consisting of 60,388 individuals of European, African, East Asian, Hispanic, and Samoan ancestries.

RESULTS

Associations of low frequency and rare variants in RCN3 and multiple other genes were observed for blood pressure traits in TOPMed samples. The association of low frequency and rare coding variants in RCN3 was further replicated in UK Biobank samples (N = 403,522), and reached genome-wide significance for diastolic blood pressure (p = 2.01 × 10- 7).

CONCLUSIONS

Low frequency and rare variants in RCN3 contributes blood pressure variation. This study demonstrates that focusing association analyses in linkage regions greatly reduces multiple-testing burden and improves power to identify novel rare variants associated with blood pressure traits.

Genomic correlation, shared loci, and causal relationship between obesity and polycystic ovary syndrome: a large-scale genome-wide cross-trait analysis

BACKGROUND

The comorbidity between polycystic ovary syndrome (PCOS) and obesity has long been observed in clinical settings, but their shared genetic basis remains unclear.

METHODS

Leveraging summary statistics of large-scale GWAS(s) conducted in European-ancestry populations on body mass index (adult BMI, Nfemale=434,794; childhood BMI, N=39,620), waist-to-hip ratio (WHR, Nfemale=381,152), WHR adjusted for BMI (WHRadjBMI, Nfemale=379,501), and PCOS (Ncase=10,074, Ncontrol=103,164), we performed a large-scale genome-wide cross-trait analysis to quantify overall and local genetic correlation, to identify shared loci, and to infer causal relationship.

RESULTS

We found positive genetic correlations between PCOS and adult BMI (rg=0.47, P=2.19×10-16), childhood BMI (rg=0.31, P=6.72×10-5), and WHR (rg=0.32, P=1.34×10-10), all withstanding Bonferroni correction. A suggestive significant genetic correlation was found between PCOS and WHRadjBMI (rg=0.09, P=0.04). Partitioning the whole genome into 1703 nearly independent regions, we observed a significant local genetic correlation for adult BMI and PCOS at chromosome 18: 57630483-59020751. We identified 16 shared loci underlying PCOS and obesity-related traits via cross-trait meta-analysis including 9 loci shared between BMI and PCOS (adult BMI and PCOS: 5 loci; childhood BMI and PCOS: 4 loci), 6 loci shared between WHR and PCOS, and 5 loci shared between WHRadjBMI and PCOS. Mendelian randomization (MR) supported the causal roles of both adult BMI (OR=2.92, 95% CI=2.33-3.67) and childhood BMI (OR=2.76, 95% CI=2.09-3.66) in PCOS, but not WHR (OR=1.19, 95% CI=0.93-1.52) or WHRadjBMI (OR=1.03, 95% CI=0.87-1.22). Genetic predisposition to PCOS did not seem to influence the risk of obesity-related traits.

CONCLUSIONS

Our cross-trait analysis suggests a shared genetic basis underlying obesity and PCOS and provides novel insights into the biological mechanisms underlying these complex traits. Our work informs public health intervention by confirming the important role of weight management in PCOS prevention.

A flexible summary statistics-based colocalization method with application to the mucin cystic fibrosis lung disease modifier locus

Mucus obstruction is a central feature in the cystic fibrosis (CF) airways. A genome-wide association study (GWAS) of lung disease by the CF Gene Modifier Consortium (CFGMC) identified a significant locus containing two mucin genes, MUC20 and MUC4. Expression quantitative trait locus (eQTL) analysis using human nasal epithelia (HNE) from 94 CF-affected Canadians in the CFGMC demonstrated MUC4 eQTLs that mirrored the lung association pattern in the region, suggesting that MUC4 expression may mediate CF lung disease. Complications arose, however, with colocalization testing using existing methods: the locus is complex and the associated SNPs span a 0.2 Mb region with high linkage disequilibrium (LD) and evidence of allelic heterogeneity. We previously developed the Simple Sum (SS), a powerful colocalization test in regions with allelic heterogeneity, but SS assumed eQTLs to be present to achieve type I error control. Here we propose a two-stage SS (SS2) colocalization test that avoids a priori eQTL assumptions, accounts for multiple hypothesis testing and the composite null hypothesis, and enables meta-analysis. We compare SS2 to published approaches through simulation and demonstrate type I error control for all settings with the greatest power in the presence of high LD and allelic heterogeneity. Applying SS2 to the MUC20/MUC4 CF lung disease locus with eQTLs from CF HNE revealed significant colocalization with MUC4 (p = 1.31 × 10-5) rather than with MUC20. The SS2 is a powerful method to inform the responsible gene(s) at a locus and guide future functional studies. SS2 has been implemented in the application LocusFocus.

Pleiotropic predisposition to Alzheimer's disease and educational attainment: insights from the summary statistics analysis

Epidemiological studies report beneficial associations of higher educational attainment (EDU) with Alzheimer's disease (AD). Prior genome-wide association studies (GWAS) also reported variants associated with AD and EDU separately. The analysis of pleiotropic associations with these phenotypes may shed light on EDU-related protection against AD. We performed pleiotropic meta-analyses using Fisher's method and omnibus test applied to summary statistics for single nucleotide polymorphisms (SNPs) associated with AD and EDU in large-scale univariate GWAS at suggestive-effect (5 × 10-8 < p < 0.1) and genome-wide (p ≤ 5 × 10-8) significance levels. We report 53 SNPs that attained p ≤ 5 × 10-8 at least in one of the pleiotropic meta-analyses and were reported in the univariate GWAS at 5 × 10-8 < p < 0.1. Of them, there were 46 pleiotropic SNPs according to Fisher's method. Additionally, Fisher's method identified 25 of 206 SNPs with pleiotropic effects, which attained p ≤ 5 × 10-8 in the univariate GWAS. We showed that a large fraction of the pleiotropic associations was affected by a counterintuitive phenomenon of antagonistic genetic heterogeneity, which explains the increase, rather than decrease, of the significance of the pleiotropic associations in the omnibus test. Functional enrichment analysis showed that apart from cancers, gene set harboring the non-pleiotropic SNPs was characterized by late-onset AD and neurodevelopmental disorders. The pleiotropic gene set was characterized by a broad spectrum of progressive neurological and neuromuscular diseases and immune-mediated conditions, including progressive motor neuropathy, multiple sclerosis, Parkinson's disease, and severe AD. Our results suggest that disentangling genes harboring variants with and without pleiotropic associations with AD and EDU is promising for dissecting heterogeneity in biological mechanisms of AD.

Shared genetic influences on resting‐state functional networks of the brain

The amplitude of activation in brain resting state networks (RSNs), measured with resting‐state functional magnetic resonance imaging, is heritable and genetically correlated across RSNs, indicating pleiotropy. Recent univariate genome‐wide association studies (GWASs) explored the genetic underpinnings of individual variation in RSN activity. Yet univariate genomic analyses do not describe the pleiotropic nature of RSNs. In this study, we used a novel multivariate method called genomic structural equation modeling to model latent factors that capture the shared genomic influence on RSNs and to identify single nucleotide polymorphisms (SNPs) and genes driving this pleiotropy. Using summary statistics from GWAS of 21 RSNs reported in UK Biobank (N = 31,688), the genomic latent factor analysis was first conducted in a discovery sample (N = 21,081), and then tested in an independent sample from the same cohort (N = 10,607). In the discovery sample, we show that the genetic organization of RSNs can be best explained by two distinct but correlated genetic factors that divide multimodal association networks and sensory networks. Eleven of the 17 factor loadings were replicated in the independent sample. With the multivariate GWAS, we found and replicated nine independent SNPs associated with the joint architecture of RSNs. Further, by combining the discovery and replication samples, we discovered additional SNP and gene associations with the two factors of RSN amplitude. We conclude that modeling the genetic effects on brain function in a multivariate way is a powerful approach to learn more about the biological mechanisms involved in brain function.

Shared Genetic Architecture and Causal Relationship Between Asthma and Cardiovascular Diseases: A Large-Scale Cross-Trait Analysis

Background: Accumulating evidence has suggested that there is a positive association between asthma and cardiovascular diseases (CVDs), implying a common architecture between them. However, the shared genetic architecture and causality of asthma and CVDs remain unclear.

Methods: Based on the genome-wide association study (GWAS) summary statistics of recently published studies, our study examined the genetic correlation, shared genetic variants, and causal relationship between asthma (N = 127,669) and CVDs (N = 86,995–521,612). Statistical methods included high-definition likelihood (HDL), cross-trait meta-analyses of large-scale GWAS, transcriptome-wide association studies (TWAS), and Mendelian randomization (MR).

Results: First, we observed a significant genetic correlation between asthma and heart failure (HF) (Rg = 0.278, P = 5 × 10⁻⁴). Through cross-trait analyses, we identified a total of 145 shared loci between asthma and HF. Fifteen novel loci were not previously reported for association with either asthma or HF. Second, we mapped these 145 loci to a total of 99 genes whose expressions are enriched in a broad spectrum of tissues, including the seminal vesicle, tonsil, appendix, spleen, skin, lymph nodes, breast, cervix and uterus, skeletal muscle, small intestine, lung, prostate, cardiac muscle, and liver. TWAS analysis identified five significant genes shared between asthma and HF in tissues from the hemic and immune system, digestive system, integumentary system, and nervous system. GSDMA, GSDMB, and ORMDL3 are statistically independent genetic effects from all shared TWAS genes between asthma and HF. Third, through MR analysis, genetic liability to asthma was significantly associated with heart failure at the Bonferroni-corrected significance level. The odds ratio (OR) is 1.07 [95% confidence interval (CI): 1.03–1.12; p = 1.31 × 10⁻³] per one-unit increase in log_e odds of asthma.

Conclusion: These findings provide strong evidence of genetic correlations and causal relationship between asthma and HF, suggesting a shared genetic architecture for these two diseases.

Shared Genetics and Causality Between Decaffeinated Coffee Consumption and Neuropsychiatric Diseases: A Large-Scale Genome-Wide Cross-Trait Analysis and Mendelian Randomization Analysis

Coffee or caffeine consumption has been associated with neuropsychiatric disorders, implying a shared etiology. However, whether these associations reflect causality remains largely unknown. To understand the genetic structure of the association between decaffeinated coffee consumption (DCC) and neuropsychiatric traits, we examined the genetic correlation, causality, and shared genetic structure between DCC and neuropsychiatric traits using linkage disequilibrium score regression, bidirectional Mendelian randomization (MR), and genome-wide cross-trait meta-analysis in large GWAS Consortia for coffee consumption (N = 329,671) and 13 neuropsychiatric traits (sample size ranges from 36,052 to 500,199). We found strong positive genetic correlations between DCC and lifetime cannabis use (LCU; Rg = 0.48, P = 8.40 × 10^-19), alcohol use disorder identification test (AUDIT) total score (AUDIT_T; Rg = 0.40, P = 4.63 × 10^-13), AUDIT_C score (alcohol consumption component of the AUDIT; Rg = 0.40, P = 5.26 × 10^-11), AUDIT_P score (dependence and hazardous-use component of the AUDIT; Rg = 0.28, P = 1.36 × 10^-05), and strong negative genetic correlations between DCC and neuroticism (Rg = -0.15, P = 7.27 × 10^-05), major depressed diseases (MDD; Rg = -0.15, P = 0.0010), and insomnia (Rg= -0.15, P = 0.0007). In the cross-trait meta-analysis, we identified 6, 5, 1, 1, 2, 31, and 27 shared loci between DCC and Insomnia, LCU, AUDIT_T, AUDIT_C, AUDIT_P, neuroticism, and MDD, respectively, which were mainly enriched in bone marrow, lymph node, cervix, uterine, lung, and thyroid gland tissues, T cell receptor signaling pathway, antigen receptor-mediated signaling pathway, and epigenetic pathways. A large of TWAS-significant associations were identified in tissues that are part of the nervous system, digestive system, and exo-/endocrine system. Our findings further indicated a causal influence of liability to DCC on LCU and low risk of MDD (odds ratio: 0.90, P = 9.06 × 10^-5 and 1.27, P = 7.63 × 10^-4 respectively). We also observed that AUDIT_T and AUDIT_C were causally related to DCC (odds ratio: 1.83 per 1-SD increase in AUDIT_T, P = 1.67 × 10^-05, 1.80 per 1-SD increase in AUDIT_C, P = 5.09 × 10^-04). Meanwhile, insomnia and MDD had a causal negative influence on DCC (OR: 0.91, 95% CI: 0.86-0.95, P = 1.51 × 10^-04 for Insomnia; OR: 0.93, 95% CI: 0.89-0.99, P = 6.02 × 10^-04 for MDD). These findings provided evidence for the shared genetic basis and causality between DCC and neuropsychiatric diseases, and advance our understanding of the shared genetic mechanisms underlying their associations, as well as assisting with making recommendations for clinical works or health education.

Smoking Status and Type 2 Diabetes, and Cardiovascular Disease: A Comprehensive Analysis of Shared Genetic Etiology and Causal Relationship

OBJECTIVE

This study aimed to explore shared genetic etiology and the causality between smoking status and type 2 diabetes (T2D), cardiovascular diseases (CVDs), and related metabolic traits.

METHODS

Using summary statistics from publicly available genome-wide association studies (GWASs), we estimated genetic correlations between smoking status and T2D, 6 major CVDs, and 8 related metabolic traits with linkage disequilibrium score regression (LDSC) analysis; identified shared genetic loci with large-scale genome-wide cross-trait meta-analysis; explored potential shared biological mechanisms with a series of post-GWAS analyses; and determined causality with Mendelian randomization (MR).

RESULTS

We found significant positive genetic associations with smoking status for T2D (Rg = 0.170, p = 9.39 × 10^-22), coronary artery disease (CAD) (Rg = 0.234, p = 1.96 × 10^-27), myocardial infarction (MI) (Rg = 0.226, p = 1.08 × 10^-17), and heart failure (HF) (Rg = 0.276, p = 8.43 × 10^-20). Cross-trait meta-analysis and transcriptome-wide association analysis of smoking status identified 210 loci (32 novel loci) and 354 gene-tissue pairs jointly associated with T2D, 63 loci (12 novel loci) and 37 gene-tissue pairs with CAD, 38 loci (6 novel loci) and 17 gene-tissue pairs with MI, and 28 loci (3 novel loci) and one gene-tissue pair with HF. The shared loci were enriched in the exo-/endocrine, cardiovascular, nervous, digestive, and genital systems. Furthermore, we observed that smoking status was causally related to a higher risk of T2D (β = 0.385, p = 3.31 × 10^-3), CAD (β = 0.670, p = 7.86 × 10^-11), MI (β = 0.725, p = 2.32 × 10^-9), and HF (β = 0.520, p = 1.53 × 10^-6).

CONCLUSIONS

Our findings provide strong evidence on shared genetic etiology and causal associations between smoking status and T2D, CAD, MI, and HF, underscoring the potential shared biological mechanisms underlying the link between smoking and T2D and CVDs. This work opens up a new way of more effective and timely prevention of smoking-related T2D and CVDs.

Genetic analysis of dietary intake identifies new loci and functional links with metabolic traits

Dietary intake is a major contributor to the global obesity epidemic and represents a complex behavioural phenotype that is partially affected by innate biological differences. Here, we present a multivariate genome-wide association analysis of overall variation in dietary intake to account for the correlation between dietary carbohydrate, fat and protein in 282,271 participants of European ancestry from the UK Biobank (n = 191,157) and Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (n = 91,114), and identify 26 distinct genome-wide significant loci. Dietary intake signals map exclusively to specific brain regions and are enriched for genes expressed in specialized subtypes of GABAergic, dopaminergic and glutamatergic neurons. We identified two main clusters of genetic variants for overall variation in dietary intake that were differently associated with obesity and coronary artery disease. These results enhance the biological understanding of interindividual differences in dietary intake by highlighting neural mechanisms, supporting functional follow-up experiments and possibly providing new avenues for the prevention and treatment of prevalent complex metabolic diseases.

Coffee Types and Type 2 Diabetes Mellitus: Large-Scale Cross-Phenotype Association Study and Mendelian Randomization Analysis

PURPOSE

To explore whether coffee intake is associated with the risk of type 2 diabetes mellitus (T2DM) from a genetic perspective, and whether this association remains the same among different types of coffee consumers.

METHODS

We utilized the summary-level results of 12 genome-wide association studies. First, we used linkage disequilibrium score regression and cross-phenotype association analysis to estimate the genetic correlation and identify shared genes between coffee intake and T2DM in addition to some other T2DM-related phenotypes. Second, we used Mendelian randomization (MR) analysis to test whether there is a significant genetically predicted causal association between coffee intake and the risk of T2DM or other T2DM-related phenotypes. For all the analyses above, we also conducted a separate analysis for different types of coffee consumers, in addition to total coffee intake.

RESULTS

Genetically, choice for ground coffee was significantly negatively associated with the risk of T2DM and some other related risks. While coffee intake and choice for decaffeinated/instant coffee had significant positive correlation with these risks. Between these genetically related phenotypes, there were 1571 genomic shared regions, of which 134 loci were novel. Enrichment analysis showed that these shared genes were significantly enriched in antigen processing related biological processes. MR analysis indicated that higher genetically proxied choice for ground coffee can reduce the risk of T2DM (T2DM: b: -0.2, p-value: 4.70×10^-10; T2DM adjusted for body mass index (BMI): b: -0.11, p-value: 4.60×10^-5), and BMI (b: -0.08, p-value: 6.50×10^-5).

CONCLUSIONS

Compared with other types of coffee, ground coffee has a significant negative genetic and genetically predicated causal relationship with the risk of T2DM. And this association is likely to be mediated by immunity. The effect of different coffee types on T2DM is not equal, researchers on coffee should pay more attention to distinguishing between coffee types.

Phenotypic and Genotypic Associations Between Migraine and Lipoprotein Subfractions

BACKGROUND AND OBJECTIVE

To evaluate phenotypic and genetic relationships between migraine and lipoprotein subfractions.

METHODS

We evaluated phenotypic associations between migraine and 19 lipoprotein subfraction measures in the Women's Genome Health Study (n = 22,788). We then investigated genetic relationships between these traits using summary statistics from the International Headache Genetics Consortium for migraine (n_case = 54,552, n_control = 297,970) and combined summary data for lipoprotein subfractions (n up to 47,713).

RESULTS

There was a significant phenotypic association (odds ratio 1.27 [95% confidence interval 1.12-1.44]) and a significant genetic correlation at 0.18 (p = 0.001) between migraine and triglyceride-rich lipoproteins (TRLPs) concentration but not for low-density lipoprotein or high-density lipoprotein subfractions. Mendelian randomization (MR) estimates were largely null, implying that pleiotropy rather than causality underlies the genetic correlation between migraine and lipoprotein subfractions. Pleiotropy was further supported in cross-trait meta-analysis, revealing significant shared signals at 4 loci (chr2p21 harboring THADA, chr5q13.3 harboring HMGCR, chr6q22.31 harboring HEY2, and chr7q11.23 harboring MLXIPL) between migraine and lipoprotein subfractions. Three of these loci were replicated for migraine (p < 0.05) in a smaller sample from the UK Biobank. The shared signal at chr5q13.3 colocalized with expression of HMGCR, ANKDD1B, and COL4A3BP in multiple tissues.

CONCLUSIONS

The study supports the association between certain lipoprotein subfractions, especially for TRLP, and migraine in populations of European ancestry. The corresponding shared genetic components may help identify potential targets for future migraine therapeutics.

CLASSIFICATION OF EVIDENCE

This study provides Class I evidence that migraine is significantly associated with some lipoprotein subfractions.

Systematic Review of Genomic Associations with Blood Pressure and Hypertension in Populations with African-Ancestry

Background: Despite hypertension being highly prevalent in individuals with African-ancestry, they are under-represented in large genome-wide association studies. Inclusion of African participants is essential to better understand genetic associations with blood pressure-related traits in Africans. This systematic review critically evaluates existing studies with African-ancestry participants and identifies knowledge gaps.

Methods: We followed the PRISMA protocol, HuGE Review handbook to identify literature on original research, in English, on genetic association studies for blood pressure-related traits (systolic and diastolic blood pressure, pulse and mean-arterial pressure, and hypertension) in populations with African-ancestry (January 2007 to April 2020). A narrative synthesis of the evidence was conducted.

Results: Twelve studies with African-ancestry participants met the eligibility criteria, within which 10 studies met the additional genetic association data criteria (i.e., reporting only on African-ancestry participants). Across the five blood pressure-related traits, 26 genome-wide significantly associated SNPs were identified, with six SNPs linked to more than one trait, illustrating pleiotropic effects. Among the SNP associations, 12 had not previously been described in non-African studies.

Discussion: The limited number of relevant studies highlights the dearth of genomic association studies on participants with African-ancestry, especially those located within Africa. Variations in study methodology, participant inclusion, adjustment for covariates (e.g., antihypertensive medication) and relatively small sample sizes make comparisons challenging, and have resulted in fewer significant associations, compared to large European studies. Regional variation in the prevalence and associated risk factors of hypertension across Africa makes a compelling argument to develop African cohorts to facilitate large genomic studies, using African-centric arrays. Data harmonisation and comparable study designs, such as described in the H3Africa CHAIR initiative, provide a good example toward achieving this goal.

Other relevant information: SS and J-TB were funded by the South African National Research Foundation. MR is a South African Research Chair in Genomics and Bioinformatics of African populations hosted by the University of the Witwatersrand, funded by the Department of Science and Innovation, and administered by the NRF. This review was registered at PROSPERO (registration number: CRD42020179221) and OSF (registration DOI: 10.17605/OSF.IO/QT2HA).

Characterisation of insomnia as an environmental risk factor for asthma via Mendelian randomization and gene environment interaction

Asthma is a complex disease that is reportedly associated with insomnia. However, the causal directionality of this association is still unclear. We used asthma and insomnia-associated single nucleotide polymorphisms (SNPs) and genome-wide association study (GWAS) summary statistics to test the causal directionality between insomnia and asthma via Mendelian randomization (MR) analysis. We also performed a cross-trait meta-analysis using UK Biobank GWAS summary statistics and a gene–environment interaction study using data from UK Biobank. The interaction of genetic risk score for asthma (GRS_asthma) with insomnia on asthma was tested by logistic regression. Insomnia was a risk factor for the incidence of asthma, as revealed by three different methods of MR analysis. However, asthma did not act as a risk factor for insomnia. The cross-trait meta-analysis identified 28 genetic loci shared between asthma and insomnia. In the gene–environment interaction study, GRS_asthma interacted with insomnia to significantly affect the risk of asthma. The results of this study highlight the importance of insomnia as a risk factor of asthma, and warrant further analysis of the mechanism through which insomnia affects the risk of asthma.