A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits

Genome-wide association meta-analysis of 88,250 individuals highlights pleiotropic mechanisms of five ocular diseases in UK Biobank

BACKGROUND

Ocular diseases may exhibit common clinical symptoms and epidemiological comorbidity. However, the extent of pleiotropic mechanisms across ocular diseases remains unclear. We aim to examine shared genetic etiology in age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma, retinal detachment (RD), and myopia.

METHODS

We analyzed genome-wide association analyses for the five ocular diseases in 43,877 cases and 44,373 controls of European ancestry from UK Biobank, estimated their genetic relationships (LDSC, GNOVA, and Genomic SEM), and identified pleiotropic loci (ASSET and METASOFT).

FINDINGS

The genetic correlation of common SNPs revealed a meaningful genetic structure within these diseases, identifying genetic correlations between AMD, DR, and glaucoma. Cross-trait meta-analysis identified 23 pleiotropic loci associated with at least two ocular diseases and 14 loci unique to individual disorders (non-pleiotropic). We found that the genes associated with these shared genetic loci are involved in neuron differentiation (P = 8.80 × 10^-6) and eye development systems (P = 3.86 × 10^-5), and single cell RNA sequencing data reveals their heightened gene expression from multipotent progenitors to other differentiated retinal cells during retina developmental process.

INTERPRETATION

These results highlighted the potential common genetic architectures among these ocular diseases and can deepen the understanding of the molecular mechanisms underlying the related diseases.

FUNDING

The National Natural Science Foundation of China (61871294), Zhejiang Provincial Natural Science Foundation of China (LR19C060001), and the Scientific Research Foundation for Talents of Wenzhou Medical University (QTJ18023).

Cross-ancestry genome-wide meta-analysis of 61,047 cases and 947,237 controls identifies new susceptibility loci contributing to lung cancer

To identify new susceptibility loci to lung cancer among diverse populations, we performed cross-ancestry genome-wide association studies in European, East Asian and African populations and discovered five loci that have not been previously reported. We replicated 26 signals and identified 10 new lead associations from previously reported loci. Rare-variant associations tended to be specific to populations, but even common-variant associations influencing smoking behavior, such as those with CHRNA5 and CYP2A6, showed population specificity. Fine-mapping and expression quantitative trait locus colocalization nominated several candidate variants and susceptibility genes such as IRF4 and FUBP1. DNA damage assays of prioritized genes in lung fibroblasts indicated that a subset of these genes, including the pleiotropic gene IRF4, potentially exert effects by promoting endogenous DNA damage.

An Atlas of Genetic Correlations and Genetically Informed Associations Linking Psychiatric and Immune-Related Phenotypes

Importance

Certain psychiatric and immune-related disorders are reciprocal risk factors. However, the nature of these associations is unclear.

Objective

To characterize the pleiotropy between psychiatric and immune-related traits, as well as risk factors of hypothesized relevance.

Design, Setting, and Participants

This genetic association study was conducted from July 10, 2020, to January 15, 2022. Analyses used genome-wide association (GWA) statistics related to 14 psychiatric traits; 13 immune-related phenotypes, ie, allergic, autoimmune, and inflammatory disorders; and 15 risk factors related to health-related behaviors, social determinants of health, and stress response. Genetically correlated psychiatric-immune pairs were assessed using 2-sample mendelian randomization (MR) with sensitivity analyses and multivariable adjustment for genetic associations of third variables. False discovery rate correction (Q value < .05) was applied for each analysis.

Exposures

Genetic associations.

Main Outcomes and Measures

Genetic correlations and MR association estimates with SEs and P values. A data-driven approach was used that did not test a priori planned hypotheses.

Results

A total of 44 genetically correlated psychiatric-immune pairs were identified, including 31 positive correlations (most consistently involving asthma, Crohn disease, hypothyroidism, and ulcerative colitis) and 13 negative correlations (most consistently involving allergic rhinitis and type 1 diabetes). Correlations with third variables were especially strong for psychiatric phenotypes. MR identified 7 associations of psychiatric phenotypes on immune-related phenotypes that were robust to multivariable adjustment, including the positive association of (1) the psychiatric cross-disorder phenotype with asthma (odds ratio [OR], 1.04; 95% CI, 1.02-1.06), Crohn disease (OR, 1.09; 95% CI, 1.05-1.14), and ulcerative colitis (OR, 1.09; 95% CI, 1.05-1.14); (2) major depression with asthma (OR, 1.25; 95% CI, 1.13-1.37); (3) schizophrenia with Crohn disease (OR, 1.12; 95% CI, 1.05-1.18) and ulcerative colitis (OR, 1.14; 95% CI, 1.07-1.21); and a negative association of risk tolerance with allergic rhinitis (OR, 0.77; 95% CI, 0.67-0.92).

Conclusions and Relevance

Results of this genetic association study suggest that genetic liability for psychiatric disorders was associated with liability for several immune disorders, suggesting that vertical pleiotropy related to behavioral traits (or correlated third variables) contributes to clinical associations observed in population-scale data.

Pinpointing novel risk loci for Lewy body dementia and the shared genetic etiology with Alzheimer's disease and Parkinson's disease: a large-scale multi-trait association analysis

BACKGROUND

The current genome-wide association study (GWAS) of Lewy body dementia (LBD) suffers from low power due to a limited sample size. In addition, the genetic determinants underlying LBD and the shared genetic etiology with Alzheimer's disease (AD) and Parkinson's disease (PD) remain poorly understood.

METHODS

Using the largest GWAS summary statistics of LBD to date (2591 cases and 4027 controls), late-onset AD (86,531 cases and 676,386 controls), and PD (33,674 cases and 449,056 controls), we comprehensively investigated the genetic basis of LBD and shared genetic etiology among LBD, AD, and PD. We first conducted genetic correlation analysis using linkage disequilibrium score regression (LDSC), followed by multi-trait analysis of GWAS (MTAG) and association analysis based on SubSETs (ASSET) to identify the trait-specific SNPs. We then performed SNP-level functional annotation to identify significant genomic risk loci paired with Bayesian fine-mapping and colocalization analysis to identify potential causal variants. Parallel gene-level analysis including GCTA-fastBAT and transcriptome-wide association analysis (TWAS) was implemented to explore novel LBD-associated genes, followed by pathway enrichment analysis to understand underlying biological mechanisms.

RESULTS

Pairwise LDSC analysis found positive genome-wide genetic correlations between LBD and AD (rg = 0.6603, se = 0.2001; P = 0.0010), between LBD and PD (rg = 0.6352, se = 0.1880; P = 0.0007), and between AD and PD (rg = 0.2136, se = 0.0860; P = 0.0130). We identified 13 significant loci for LBD, including 5 previously reported loci (1q22, 2q14.3, 4p16.3, 4q22.1, and 19q13.32) and 8 novel biologically plausible genetic associations (5q12.1, 5q33.3, 6p21.1, 8p23.1, 8p21.1, 16p11.2, 17p12, and 17q21.31), among which APOC1 (19q13.32), SNCA (4q22.1), TMEM175 (4p16.3), CLU (8p21.1), MAPT (17q21.31), and FBXL19 (16p11.2) were also validated by gene-level analysis. Pathway enrichment analysis of 40 common genes identified by GCTA-fastBAT and TWAS implicated significant role of neurofibrillary tangle assembly (GO:1902988, adjusted P = 1.55 × 10^-2).

CONCLUSIONS

Our findings provide novel insights into the genetic determinants of LBD and the shared genetic etiology and biological mechanisms of LBD, AD, and PD, which could benefit the understanding of the co-pathology as well as the potential treatment of these diseases simultaneously.

Large-scale genomic analyses reveal insights into pleiotropy across circulatory system diseases and nervous system disorders

Clinical and epidemiological studies have shown that circulatory system diseases and nervous system disorders often co-occur in patients. However, genetic susceptibility factors shared between these disease categories remain largely unknown. Here, we characterized pleiotropy across 107 circulatory system and 40 nervous system traits using an ensemble of methods in the eMERGE Network and UK Biobank. Using a formal test of pleiotropy, five genomic loci demonstrated statistically significant evidence of pleiotropy. We observed region-specific patterns of direction of genetic effects for the two disease categories, suggesting potential antagonistic and synergistic pleiotropy. Our findings provide insights into the relationship between circulatory system diseases and nervous system disorders which can provide context for future prevention and treatment strategies.

Shared components of heritability across genetically correlated traits

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

Benchmarking of univariate pleiotropy detection methods applied to epilepsy

Pleiotropy is a widespread phenomenon that may increase insight into the etiology of biological and disease traits. Since genome-wide association studies frequently provide information on a single trait only, only univariate pleiotropy detection methods are applicable, with yet unknown comparative performance. Here, we compared five such methods with respect to their ability to detect pleiotropy, including meta-analysis, ASSET, cFDR, CPBayes, and PLACO, by performing extended computer simulations that varied the underlying etiological model for pleiotropy for a pair of traits, including the number of causal variants, degree of traits' overlap, effect sizes as well as trait prevalence, and varying sample sizes. Our results indicate that ASSET provides the best trade-off between power and protection against false positives. We then applied ASSET to a previously published ILAE consortium dataset on complex epilepsies, comprising genetic generalized epilepsy and focal epilepsy cases and corresponding controls. We identified a novel candidate locus at 17q21.32 and confirmed locus 2q24.3, previously identified to act pleiotropically on both epilepsy subtypes by a mega-analysis. Functional annotation, tissue-specific expression and regulatory function analysis as well as Bayesian co-localization analysis corroborated this result, rendering 17q21.32 a worthwhile candidate for follow-up studies on pleiotropy in epilepsies. This article is protected by copyright. All rights reserved.

Reconstructing SNP allele and genotype frequencies from GWAS summary statistics

The emergence of genome-wide association studies (GWAS) has led to the creation of large repositories of human genetic variation, creating enormous opportunities for genetic research and worldwide collaboration. Methods that are based on GWAS summary statistics seek to leverage such records, overcoming barriers that often exist in individual-level data access while also offering significant computational savings. Such summary-statistics-based applications include GWAS meta-analysis, with and without sample overlap, and case-case GWAS. We compare performance of leading methods for summary-statistics-based genomic analysis and also introduce a novel framework that can unify usual summary-statistics-based implementations via the reconstruction of allelic and genotypic frequencies and counts (ReACt). First, we evaluate ASSET, METAL, and ReACt using both synthetic and real data for GWAS meta-analysis (with and without sample overlap) and find that, while all three methods are comparable in terms of power and error control, ReACt and METAL are faster than ASSET by a factor of at least hundred. We then proceed to evaluate performance of ReACt vs an existing method for case-case GWAS and show comparable performance, with ReACt requiring minimal underlying assumptions and being more user-friendly. Finally, ReACt allows us to evaluate, for the first time, an implementation for calculating polygenic risk score (PRS) for groups of cases and controls based on summary statistics. Our work demonstrates the power of GWAS summary-statistics-based methodologies and the proposed novel method provides a unifying framework and allows further extension of possibilities for researchers seeking to understand the genetics of complex disease.

Causal Association and Shared Genetics Between Asthma and COVID-19

Objectives

Recent studies suggest that asthma may have a protective effect on COVID-19.We aimed to investigate the causality between asthma and two COVID-19 outcomes and explore the mechanisms underlining this connection.

Methods

Summary results of GWAS were used for the analyses, including asthma (88,486 cases and 447,859 controls), COVID-19 hospitalization (6,406 hospitalized COVID-19 cases and 902,088 controls), and COVID-19 infection (14,134 COVID-19 cases and 1,284,876 controls). The Mendelian randomization (MR) analysis was performed to evaluate the causal effects of asthma on the two COVID-19 outcomes. A cross-trait meta-analysis was conducted to analyze genetic variants within two loci shared by COVID-19 hospitalization and asthma.

Results

Asthma is associated with decreased risk both for COVID-19 hospitalization (odds ratio (OR): 0.70, 95% confidence interval (CI): 0.70-0.99) and for COVID-19 infection (OR: 0.83, 95%CI: 0.51-0.95). Asthma and COVID-19 share two genome-wide significant genes, including ABO at the 9q34.2 region and OAS2 at the 12q24.13 region. The meta-analysis revealed that ABO and ATXN2 contain variants with pleiotropic effects on both COVID-19 and asthma.

Conclusion

In conclusion, our results suggest that genetic liability to asthma is associated with decreased susceptibility to SARS-CoV-2 and to severe COVID-19 disease, which may be due to the protective effects of ongoing inflammation and, possibly, related compensatory responses against COVID-19 in its early stage.

Robust meta-analysis for large-scale genomic experiments based on an empirical approach

Background

Recent high-throughput technologies have opened avenues for simultaneous analyses of thousands of genes. With the availability of a multitude of public databases, one can easily access multiple genomic study results where each study comprises of significance testing results of thousands of genes. Researchers currently tend to combine this genomic information from these multiple studies in the form of a meta-analysis. As the number of genes involved is very large, the classical meta-analysis approaches need to be updated to acknowledge this large-scale aspect of the data.

Methods

In this article, we discuss how application of standard theoretical null distributional assumptions of the classical meta-analysis methods, such as Fisher’s p-value combination and Stouffer’s Z, can lead to incorrect significant testing results, and we propose a robust meta-analysis method that empirically modifies the individual test statistics and p-values before combining them.

Results

Our proposed meta-analysis method performs best in significance testing among several meta-analysis approaches, especially in presence of hidden confounders, as shown through a wide variety of simulation studies and real genomic data analysis.

Conclusion

The proposed meta-analysis method produces superior meta-analysis results compared to the standard p-value combination approaches for large-scale simultaneous testing in genomic experiments. This is particularly useful in studies with large number of genes where the standard meta-analysis approaches can result in gross false discoveries due to the presence of unobserved confounding variables.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01530-y.

Leveraging pleiotropic association using sparse group variable selection in genomics data

Background

Genome-wide association studies (GWAS) have identified genetic variants associated with multiple complex diseases. We can leverage this phenomenon, known as pleiotropy, to integrate multiple data sources in a joint analysis. Often integrating additional information such as gene pathway knowledge can improve statistical efficiency and biological interpretation. In this article, we propose statistical methods which incorporate both gene pathway and pleiotropy knowledge to increase statistical power and identify important risk variants affecting multiple traits.

Methods

We propose novel feature selection methods for the group variable selection in multi-task regression problem. We develop penalised likelihood methods exploiting different penalties to induce structured sparsity at a gene (or pathway) and SNP level across all studies. We implement an alternating direction method of multipliers (ADMM) algorithm for our penalised regression methods. The performance of our approaches are compared to a subset based meta analysis approach on simulated data sets. A bootstrap sampling strategy is provided to explore the stability of the penalised methods.

Results

Our methods are applied to identify potential pleiotropy in an application considering the joint analysis of thyroid and breast cancers. The methods were able to detect eleven potential pleiotropic SNPs and six pathways. A simulation study found that our method was able to detect more true signals than a popular competing method while retaining a similar false discovery rate.

Conclusion

We developed feature selection methods for jointly analysing multiple logistic regression tasks where prior grouping knowledge is available. Our method performed well on both simulation studies and when applied to a real data analysis of multiple cancers.

Are Alzheimer's and coronary artery diseases genetically related to longevity?

INTRODUCTION

In the last decade researchers have attempted to investigate the shared genetic architecture of longevity and age-related diseases and assess whether the increased longevity in certain people is due to protective alleles in the risk genes for a particular condition or whether there are specific "longevity" genes increasing the lifespan independently of age-related conditions' risk genes. The aim of this study was to investigate the shared genetic component between longevity and two age-related conditions.

METHODS

We performed a cross-trait meta-analysis of publicly available genome-wide data for Alzheimer's disease, coronary artery disease and longevity using a subset-based approach provided by the R package ASSET.

RESULTS

Despite the lack of strong genetic correlation between longevity and the two diseases, we identified 38 genome-wide significant lead SNPs across 22 independent genomic loci. Of them 6 were found to be potentially shared among the three traits mapping to genes including DAB2IP, DNM2, FCHO1, CLPTM1, and SNRPD2. We also identified 19 novel genome-wide associations for the individual traits in this study. Functional annotations and biological pathway enrichment analyses suggested that pleiotropic variants are involved in clathrin-mediated endocytosis and plasma lipoprotein and neurotransmitter clearance processes.

DISCUSSION

In summary, we have been able to advance in the knowledge of the genetic overlap existing among longevity and the two most common age-related disorders.

Shared genetic liability between major depressive disorder and osteoarthritis

Aims

Deciphering the genetic relationships between major depressive disorder (MDD) and osteoarthritis (OA) may facilitate an understanding of their biological mechanisms, as well as inform more effective treatment regimens. We aim to investigate the mechanisms underlying relationships between MDD and OA in the context of common genetic variations.

Methods

Linkage disequilibrium score regression was used to test the genetic correlation between MDD and OA. Polygenic analysis was performed to estimate shared genetic variations between the two diseases. Two-sample bidirectional Mendelian randomization analysis was used to investigate causal relationships between MDD and OA. Genomic loci shared between MDD and OA were identified using cross-trait meta-analysis. Fine-mapping of transcriptome-wide associations was used to prioritize putatively causal genes for the two diseases.

Results

MDD has a significant genetic correlation with OA (r_g = 0.29) and the two diseases share a considerable proportion of causal variants. Mendelian randomization analysis indicates that genetic liability to MDD has a causal effect on OA (b_xy = 0.24) and genetic liability to OA conferred a causal effect on MDD (b_xy = 0.20). Cross-trait meta-analyses identified 29 shared genomic loci between MDD and OA. Together with fine-mapping of transcriptome-wide association signals, our results suggest that Estrogen Receptor 1 (ESR1), SRY-Box Transcription Factor 5 (SOX5), and Glutathione Peroxidase 1 (GPX1) may have therapeutic implications for both MDD and OA.

Conclusion

The study reveals substantial shared genetic liability between MDD and OA, which may confer risk for one another. Our findings provide a novel insight into phenotypic relationships between MDD and OA.

Cite this article: Bone Joint Res 2022;11(1):12–22.

Sex-Dependent Shared and Nonshared Genetic Architecture Across Mood and Psychotic Disorders

BACKGROUND

Sex differences in incidence and/or presentation of schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP) are pervasive. Previous evidence for shared genetic risk and sex differences in brain abnormalities across disorders suggest possible shared sex-dependent genetic risk.

METHODS

We conducted the largest to date genome-wide genotype-by-sex (G×S) interaction of risk for these disorders using 85,735 cases (33,403 SCZ, 19,924 BIP, and 32,408 MDD) and 109,946 controls from the PGC (Psychiatric Genomics Consortium) and iPSYCH.

RESULTS

Across disorders, genome-wide significant single nucleotide polymorphism-by-sex interaction was detected for a locus encompassing NKAIN2 (rs117780815, p = 3.2 × 10-8), which interacts with sodium/potassium-transporting ATPase (adenosine triphosphatase) enzymes, implicating neuronal excitability. Three additional loci showed evidence (p < 1 × 10-6) for cross-disorder G×S interaction (rs7302529, p = 1.6 × 10-7; rs73033497, p = 8.8 × 10-7; rs7914279, p = 6.4 × 10-7), implicating various functions. Gene-based analyses identified G×S interaction across disorders (p = 8.97 × 10-7) with transcriptional inhibitor SLTM. Most significant in SCZ was a MOCOS gene locus (rs11665282, p = 1.5 × 10-7), implicating vascular endothelial cells. Secondary analysis of the PGC-SCZ dataset detected an interaction (rs13265509, p = 1.1 × 10-7) in a locus containing IDO2, a kynurenine pathway enzyme with immunoregulatory functions implicated in SCZ, BIP, and MDD. Pathway enrichment analysis detected significant G×S interaction of genes regulating vascular endothelial growth factor receptor signaling in MDD (false discovery rate-corrected p < .05).

CONCLUSIONS

In the largest genome-wide G×S analysis of mood and psychotic disorders to date, there was substantial genetic overlap between the sexes. However, significant sex-dependent effects were enriched for genes related to neuronal development and immune and vascular functions across and within SCZ, BIP, and MDD at the variant, gene, and pathway levels.

Pleiotropic MLLT10 variation confers risk of meningioma and estrogen-mediated cancers

Background

Risk of tumors of the breast, ovary, and meninges has been associated with hormonal factors and with one another. Genome-wide association studies (GWAS) identified a meningioma risk locus on 10p12 near previous GWAS hits for breast and ovarian cancers, raising the possibility of genetic pleiotropy.

Methods

We performed imputation-based fine-mapping in three case-control datasets of meningioma (927 cases, 790 controls), female breast cancer (28 108 cases, 22 209 controls), and ovarian cancer (25 509 cases, 40 941 controls). Analyses were stratified by sex (meningioma), estrogen receptor (ER) status (breast), and histotype (ovarian), then combined using subset-based meta-analysis in ASSET. Lead variants were assessed for association with additional traits in UK Biobank to identify potential effect-mediators.

Results

Two-sided subset-based meta-analysis identified rs7084454, an expression quantitative trait locus (eQTL) near the MLLT10 promoter, as lead variant (5.7 × 10^-14). The minor allele was associated with increased risk of meningioma in females (odds ratio (OR) = 1.42, 95% Confidence Interval (95%CI):1.20-1.69), but not males (OR = 1.19, 95%CI: 0.91-1.57). It was positively associated with ovarian (OR = 1.09, 95%CI:1.06-1.12) and ER+ breast (OR = 1.05, 95%CI: 1.02-1.08) cancers, and negatively associated with ER- breast cancer (OR = 0.91, 95%CI: 0.86-0.96). It was also associated with several adiposity traits (P < 5.0 × 10^-8), but adjusting for body mass index did not attenuate its association with meningioma. MLLT10 and ESR1 expression were positively correlated in normal meninges (P = .058) and meningioma tumors (P = .0065).

Conclusions

We identify a MLLT10 eQTL positively associated with risk of female meningioma, ER+ breast cancer, ovarian cancer, and obesity, and implicate a potential estrogenic mechanism underlying this pleiotropy.

Shared Genetic Liability and Causal Associations Between Major Depressive Disorder and Cardiovascular Diseases

Major depressive disorder (MDD) is phenotypically associated with cardiovascular diseases (CVD). We aim to investigate mechanisms underlying relationships between MDD and CVD in the context of shared genetic variations. Polygenic overlap analysis was used to test genetic correlation and to analyze shared genetic variations between MDD and seven cardiovascular outcomes (coronary artery disease (CAD), heart failure, atrial fibrillation, stroke, systolic blood pressure, diastolic blood pressure, and pulse pressure measurement). Mendelian randomization analysis was used to uncover causal relationships between MDD and cardiovascular traits. By cross-trait meta-analysis, we identified a set of genomic loci shared between the traits of MDD and stroke. Putative causal genes for MDD and stroke were prioritized by fine-mapping of transcriptome-wide associations. Polygenic overlap analysis pointed toward substantial genetic variation overlap between MDD and CVD. Mendelian randomization analysis indicated that genetic liability to MDD has a causal effect on CAD and stroke. Comparison of genome-wide genes shared by MDD and CVD suggests 20q12 as a pleiotropic region conferring risk for both MDD and CVD. Cross-trait meta-analyses and fine-mapping of transcriptome-wide association signals identified novel risk genes for MDD and stroke, including RPL31P12, BORSC7, PNPT11, and PGF. Many genetic variations associated with MDD and CVD outcomes are shared, thus, pointing that genetic liability to MDD may also confer risk for stroke and CAD. Presented results shed light on mechanistic connections between MDD and CVD phenotypes.

Shared genetic liability and causal effects between major depressive disorder and insomnia

Deciphering the genetic relationships between major depressive disorder (MDD) and insomnia may facilitate understanding biological mechanisms as well as inform more effective treatment regimens for these conditions. Here we attempted to investigate mechanisms underlying relationships between MDD and insomnia in the context of shared genetic variations. Shared genetic variation was evaluated by polygenic analysis. In two-sample bidirectional Mendelian randomization analysis, causal relationships between MDD and insomnia were investigated; the list of shared genomic loci was identified using cross-trait meta-analysis. Putatively causal genes for the two diseases were prioritized by fine-mapping of transcriptome-wide associations. Polygenic analysis identified 15.1 thousand variants as causally influencing MDD, and 10.8 thousand variants as influencing insomnia. Among these variants, 8.5 thousand were shared between the two diseases. Mendelian randomization analysis suggests that genetic liability to MDD and to insomnia have mutual causal effects (MDD on insomnia with OR = 1.25 and insomnia on MDD with OR = 2.23). Cross-trait meta-analyses identified 89 genomic loci as being shared between MDD and insomnia, with some of them being prioritized as causal in subsequent fine-mapping of transcriptome-wide association signals. Analysis highlights possible role of endogenous production of nitric oxide in the brain, and the gonadotropic secretion in the pituitary as possibly physiological connectors of MDD and insomnia. Here we show a substantial shared genetic liability and mutual causal links between MDD and insomnia. Presented findings provide novel insight into phenotypic relationship between these two interconnected conditions.

A pleiotropic ATM variant (rs1800057 C>G) is associated with risk of multiple cancers

ATM (ataxia-telangiectasia mutated) is an important cell-cycle checkpoint kinase required for cellular response to DNA damage. Activated by DNA double strand breaks, ATM regulates the activities of many downstream proteins involved in various carcinogenic events. Therefore, ATM or its genetic variants may have a pleiotropic effect in cancer development. We conducted a pleiotropic analysis to evaluate associations between genetic variants of ATM and risk of multiple cancers. With genotyping data extracted from previously published genome-wide association studies of various cancers, we performed multivariate logistic regression analysis, followed by a meta-analysis for each cancer site, to identify cancer risk-associated single-nucleotide polymorphisms (SNPs). In the ASSET two-sided analysis, we found that two ATM SNPs were significantly associated with risk of multiple cancers. One tagging SNP (rs1800057 C>G) was associated with risk of multiple cancers (two-sided P=5.27×10 -7). Because ATM rs1800057 is a missense variant, we also explored the intermediate phenotypes through which this variant may confer risk of multiple cancers and identified a possible immune-mediated effect of this variant. Our findings indicate that genetic variants of ATM may have a pleiotropic effect on cancer risk and thus provide an important insight into common mechanisms of carcinogenesis.

Performing post-genome-wide association study analysis: overview, challenges and recommendations

Genome-wide association studies (GWAS) provide  huge information on statistically significant single-nucleotide polymorphisms (SNPs) associated with various human complex traits and diseases. By performing GWAS studies, scientists have successfully identified the association of hundreds of thousands to  millions of SNPs to a single phenotype. Moreover, the association of some SNPs with rare diseases has been intensively tested. However, classic GWAS studies have not yet provided solid, knowledgeable insight into functional and biological mechanisms underlying phenotypes or mechanisms of diseases. Therefore, several post-GWAS (pGWAS) methods have been recommended. Currently, there is no simple scientific document to provide a quick guide for performing pGWAS analysis. pGWAS is a crucial step for a better understanding of the biological machinery beyond the SNPs. Here, we provide an overview to performing pGWAS analysis and demonstrate the challenges behind each method. Furthermore, we direct readers to key articles for each pGWAS method and present the overall issues in pGWAS analysis.  Finally, we include a custom pGWAS pipeline to guide new users when performing their research.

Shared Genetic Liability Between Major Depressive Disorder and Atopic Diseases

Objectives

Deciphering the genetic relationships between major depressive disorder (MDD) and atopic diseases (asthma, hay fever, and eczema) may facilitate understanding of their biological mechanisms as well as the development of novel treatment regimens. Here we tested the genetic correlation between MDD and atopic diseases by linkage disequilibrium score regression.

Methods

A polygenic overlap analysis was performed to estimate shared genetic variations between the two diseases. Causal relationships between MDD and atopic diseases were investigated using two-sample bidirectional Mendelian randomization analysis. Genomic loci shared between MDD and atopic diseases were identified using cross-trait meta-analysis. Putative functional genes were evaluated by fine-mapping of transcriptome-wide associations.

Results

The polygenic analysis revealed approximately 15.8 thousand variants causally influencing MDD and 0.9 thousand variants influencing atopic diseases. Among these variants, approximately 0.8 thousand were shared between the two diseases. Mendelian randomization analysis indicates that genetic liability to MDD has a causal effect on atopic diseases (b = 0.22, p = 1.76 × 10^-6), while genetic liability to atopic diseases confers a weak causal effect on MDD (b = 0.05, p = 7.57 × 10^-3). Cross-trait meta-analyses of MDD and atopic diseases identified 18 shared genomic loci. Both fine-mapping of transcriptome-wide associations and analysis of existing literature suggest the estrogen receptor β-encoding gene ESR2 as one of the potential risk factors for both MDD and atopic diseases.

Conclusion

Our findings reveal shared genetic liability and causal links between MDD and atopic diseases, which shed light on the phenotypic relationship between MDD and atopic diseases.

Shared Genetic Liability Between Major Depressive Disorder and Atopic Diseases

OBJECTIVES

Deciphering the genetic relationships between major depressive disorder (MDD) and atopic diseases (asthma, hay fever, and eczema) may facilitate understanding of their biological mechanisms as well as the development of novel treatment regimens. Here we tested the genetic correlation between MDD and atopic diseases by linkage disequilibrium score regression.

METHODS

A polygenic overlap analysis was performed to estimate shared genetic variations between the two diseases. Causal relationships between MDD and atopic diseases were investigated using two-sample bidirectional Mendelian randomization analysis. Genomic loci shared between MDD and atopic diseases were identified using cross-trait meta-analysis. Putative functional genes were evaluated by fine-mapping of transcriptome-wide associations.

RESULTS

The polygenic analysis revealed approximately 15.8 thousand variants causally influencing MDD and 0.9 thousand variants influencing atopic diseases. Among these variants, approximately 0.8 thousand were shared between the two diseases. Mendelian randomization analysis indicates that genetic liability to MDD has a causal effect on atopic diseases (b = 0.22, p = 1.76 × 10^-6), while genetic liability to atopic diseases confers a weak causal effect on MDD (b = 0.05, p = 7.57 × 10^-3). Cross-trait meta-analyses of MDD and atopic diseases identified 18 shared genomic loci. Both fine-mapping of transcriptome-wide associations and analysis of existing literature suggest the estrogen receptor β-encoding gene ESR2 as one of the potential risk factors for both MDD and atopic diseases.

CONCLUSION

Our findings reveal shared genetic liability and causal links between MDD and atopic diseases, which shed light on the phenotypic relationship between MDD and atopic diseases.

Investigating Shared Genetic Basis Across Tourette Syndrome and Comorbid Neurodevelopmental Disorders Along the Impulsivity-Compulsivity Spectrum

BACKGROUND

Tourette syndrome (TS) is often found comorbid with other neurodevelopmental disorders across the impulsivity-compulsivity spectrum, with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) as most prevalent. This points to the possibility of a common etiological thread along an impulsivity-compulsivity continuum.

METHODS

Investigating the shared genetic basis across TS, ADHD, ASD, and OCD, we undertook an evaluation of cross-disorder genetic architecture and systematic meta-analysis, integrating summary statistics from the latest genome-wide association studies (93,294 individuals, 6,788,510 markers).

RESULTS

As previously identified, a common unifying factor connects TS, ADHD, and ASD, while TS and OCD show the highest genetic correlation in pairwise testing among these disorders. Thanks to a more homogeneous set of disorders and a targeted approach that is guided by genetic correlations, we were able to identify multiple novel hits and regions that seem to play a pleiotropic role for the specific disorders analyzed here and could not be identified through previous studies. In the TS-ADHD-ASD genome-wide association study single nucleotide polymorphism-based and gene-based meta-analysis, we uncovered 13 genome-wide significant regions that host single nucleotide polymorphisms with a high posterior probability for association with all three studied disorders (m-value > 0.9), 11 of which were not identified in previous cross-disorder analysis. In contrast, we also identified two additional pleiotropic regions in the TS-OCD meta-analysis. Through conditional analysis, we highlighted genes and genetic regions that play a specific role in a TS-ADHD-ASD genetic factor versus TS-OCD. Cross-disorder tissue specificity analysis implicated the hypothalamus-pituitary-adrenal gland axis in TS-ADHD-ASD.

CONCLUSIONS

Our work underlines the value of redefining the framework for research across traditional diagnostic categories.

Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals

Genome-wide association studies (GWASs) have identified thousands of cancer risk loci revealing many risk regions shared across multiple cancers. Characterizing the cross-cancer shared genetic basis can increase our understanding of global mechanisms of cancer development. In this study, we collected GWAS summary statistics based on up to 375,468 cancer cases and 530,521 controls for fourteen types of cancer, including breast (overall, estrogen receptor [ER]-positive, and ER-negative), colorectal, endometrial, esophageal, glioma, head/neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancer, to characterize the shared genetic basis of cancer risk. We identified thirteen pairs of cancers with statistically significant local genetic correlations across eight distinct genomic regions. Specifically, the 5p15.33 region, harboring the TERT and CLPTM1L genes, showed statistically significant local genetic correlations for multiple cancer pairs. We conducted a cross-cancer fine-mapping of the 5p15.33 region based on eight cancers that showed genome-wide significant associations in this region (ER-negative breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, and prostate cancer). We used an iterative analysis pipeline implementing a subset-based meta-analysis approach based on cancer-specific conditional analyses and identified ten independent cross-cancer associations within this region. For each signal, we conducted cross-cancer fine-mapping to prioritize the most plausible causal variants. Our findings provide a more in-depth understanding of the shared inherited basis across human cancers and expand our knowledge of the 5p15.33 region in carcinogenesis.

[Exploratory screening of potential pan-cancer biomarkers based on The Cancer Genome Atlas database]

OBJECTIVE

To screen potential pan-cancer biomarkers based on The Cancer Genome Atlas (TCGA) database, and to provide help for the diagnosis and prognosis assessment of a variety of cancers.

METHODS

"GDC Data Transfer Tool" and "GDCRNATools" packages were used to obtain TCGA database. After data sorting, a total of 13 cancers were selected for further analysis. False disco-very rate (FDR) < 0.05 and fold change (FC) >1.5 were used as the differential expression criteria to screen genes and miRNAs that were up- or down-regulated in all the 13 cancers. In the receiver operating characteristic curve (ROC curve), the area under the curve (AUC), the best cut-off value and the corresponding sensitivity and specificity were used to reflect diagnostic significance. The Kaplan-Meier method was used to calculate the survival probability and then the log-rank test was performed. Hazard ratio (HR) was calculated to reflect prognostic evaluation significance. DAVID tool were used to perform GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis for differentially expressed genes. STRING and TargetScan tools were used to analyze the regulatory network of differentially expressed genes and miRNAs.

RESULTS

A total of 48 genes and 2 miRNAs were differentially expressed in all the 13 cancers. Among them, 25 genes were up-regulated, 23 genes and 2 miRNAs were down-regulated. Most differentially expressed genes and miRNAs had good ability to distinguish between the cases and controls, with AUC, sensitivity and specificity up to 0.8-0.9. Survival analysis results show that differentially expressed genes and miRNAs were significantly associated with patient survival in a variety of cancers. Most up-regulated genes were risk factors for patient survival (HR>1), while most down-regulated genes were protective factors for patient survival (0 < HR < 1). The enrichment analysis of GO and KEGG showed that the differentially expressed genes were mostly enriched in biological events related to cell proliferation. In the regulatory network analysis, a total of 13 differentially expressed genes and 2 differentially expressed miRNAs had regulatory and interaction relationships.

CONCLUSION

The 48 genes and 2 miRNAs that were differentially expressed in 13 cancers may serve as potential pan-cancer biomarkers, providing help for the diagnosis and prognosis evaluation of a variety of cancers, and providing clues for the development of broad-spectrum tumor therapeutic targets.

Genome-Wide Association Analyses Identify Variants in IRF4 Associated With Acute Myeloid Leukemia and Myelodysplastic Syndrome Susceptibility

The role of common genetic variation in susceptibility to acute myeloid leukemia (AML), and myelodysplastic syndrome (MDS), a group of rare clonal hematologic disorders characterized by dysplastic hematopoiesis and high mortality, remains unclear. We performed AML and MDS genome-wide association studies (GWAS) in the DISCOVeRY-BMT cohorts (2,309 cases and 2,814 controls). Association analysis based on subsets (ASSET) was used to conduct a summary statistics SNP-based analysis of MDS and AML subtypes. For each AML and MDS case and control we used PrediXcan to estimate the component of gene expression determined by their genetic profile and correlate this imputed gene expression level with risk of developing disease in a transcriptome-wide association study (TWAS). ASSET identified an increased risk for de novo AML and MDS (OR = 1.38, 95% CI, 1.26-1.51, Pmeta = 2.8 × 10-12) in patients carrying the T allele at s12203592 in Interferon Regulatory Factor 4 (IRF4), a transcription factor which regulates myeloid and lymphoid hematopoietic differentiation. Our TWAS analyses showed increased IRF4 gene expression is associated with increased risk of de novo AML and MDS (OR = 3.90, 95% CI, 2.36-6.44, Pmeta = 1.0 × 10-7). The identification of IRF4 by both GWAS and TWAS contributes valuable insight on the role of genetic variation in AML and MDS susceptibility.

Sequencing at lymphoid neoplasm susceptibility loci maps six myeloma risk genes

Inherited genetic risk factors play a role in multiple myeloma (MM), yet considerable missing heritability exists. Rare risk variants at genome-wide association study (GWAS) loci are a new avenue to explore. Pleiotropy between lymphoid neoplasms (LNs) has been suggested in family history and genetic studies, but no studies have interrogated sequencing for pleiotropic genes or rare risk variants. Sequencing genetically enriched cases can help discover rarer variants. We analyzed exome sequencing in familial or early-onset MM cases to identify rare, functionally relevant variants near GWAS loci for a range of LNs. A total of 149 distinct and significant LN GWAS loci have been published. We identified six recurrent, rare, potentially deleterious variants within 5 kb of significant GWAS single nucleotide polymorphisms in 75 MM cases. Mutations were observed in BTNL2, EOMES, TNFRSF13B, IRF8, ACOXL and TSPAN32. All six genes replicated in an independent set of 255 early-onset MM or familial MM or precursor cases. Expansion of our analyses to the full length of these six genes resulted in a list of 39 rare and deleterious variants, seven of which segregated in MM families. Three genes also had significant rare variant burden in 733 sporadic MM cases compared with 935 control individuals: IRF8 (P = 1.0 × 10-6), EOMES (P = 6.0 × 10-6) and BTNL2 (P = 2.1 × 10-3). Together, our results implicate six genes in MM risk, provide support for genetic pleiotropy between LN subtypes and demonstrate the utility of sequencing genetically enriched cases to identify functionally relevant variants near GWAS loci.

Multitrait transcriptome-wide association study (TWAS) tests

Multitrait tests can improve power to detect associations between individual single-nucleotide polymorphisms (SNPs) and several related traits. Here, we develop methods for multi-SNP transcriptome-wide association (TWAS) tests to test the association between predicted gene expression levels and multiple phenotypes. We show that the correlation in TWAS test statistics for multiple phenotypes has the same form as multitrait statistics for the single-SNP setting. Thus, established methods for combining single-SNP test statistics across multiple traits can be extended directly to the TWAS setting. We performed an extensive evaluation across eight multitrait methods in simulations that varied gene-phenotype effect sizes in addition to the underlying covariance structure among the phenotypes. We found that all multitrait TWAS tests have well-calibrated Type I error (except ASSET, which can have a slightly elevated or depressed Type I error rate). Our results show that multitrait TWAS can improve statistical power compared with multiple single-trait TWAS followed by Bonferroni correction. To illustrate our approach to real data, we conducted a multitrait TWAS of four circulating lipid traits from the Global Lipids Genetics Consortium. We found that our multitrait Wald TWAS approach identified 506 genes associated with lipid levels compared with 87 identified through Bonferroni-corrected single-trait TWAS. Overall, we find that our proposed multitrait TWAS framework outperforms single-trait approaches to identify new genetic associations, especially for functionally correlated phenotypes and phenotypes with overlapping genome-wide association studies samples, leading to insights into the genetic architecture of multiple phenotypes.

Genetic evidence suggests posttraumatic stress disorder as a subtype of major depressive disorder

BACKGROUND

Major depressive disorder (MDD) and posttraumatic stress disorder (PTSD) are highly comorbid and exhibit strong correlations with one another. We aimed to investigate mechanisms of underlying relationships between PTSD and three kinds of depressive phenotypes, namely, MDD, depressed affect (DAF), and depression (DEP, including both MDD and the broad definition of depression).

METHODS

Genetic correlations between PTSD and the depressive phenotypes were tested using linkage disequilibrium score regression. Polygenic overlap analysis was used to estimate shared and trait-specific causal variants across a pair of traits. Causal relationships between PTSD and the depressive phenotypes were investigated using Mendelian randomization. Shared genomic loci between PTSD and MDD were identified using cross-trait meta-analysis.

RESULTS

Genetic correlations of PTSD with the depressive phenotypes were in the range of 0.71~0.80. The estimated numbers of causal variants were 14,565, 12,965, 10,565, and 4,986 for MDD, DEP, DAF, and PTSD, respectively. In each case, causal variants contributing to PTSD were completely or largely covered by causal variants defining each of the depressive phenotypes. Mendelian randomization analysis indicates that the genetically determined depressive phenotypes confer a causal effect on PTSD (b = 0.21~0.31). Notably, genetically determined PTSD confers a causal effect on DEP (b = 0.14) and DAF (b = 0.15), but not MDD. Cross-trait meta-analysis of MDD and PTSD identifies 47 genomic loci, including 29 loci shared between PTSD and MDD.

CONCLUSION

Evidence from shared genetics suggests that PTSD is a subtype of MDD. This study provides support to the efforts in reducing diagnostic heterogeneity in psychiatric nosology.

FUNDING

The National Key Research and Development Program of China (2018YFC1314300) and the National Natural Science Foundation of China (81471364 and 81971255).

Detecting local genetic correlations with scan statistics

Genetic correlation analysis has quickly gained popularity in the past few years and provided insights into the genetic etiology of numerous complex diseases. However, existing approaches oversimplify the shared genetic architecture between different phenotypes and cannot effectively identify precise genetic regions contributing to the genetic correlation. In this work, we introduce LOGODetect, a powerful and efficient statistical method to identify small genome segments harboring local genetic correlation signals. LOGODetect automatically identifies genetic regions showing consistent associations with multiple phenotypes through a scan statistic approach. It uses summary association statistics from genome-wide association studies (GWAS) as input and is robust to sample overlap between studies. Applied to seven phenotypically distinct but genetically correlated neuropsychiatric traits, we identify 227 non-overlapping genome regions associated with multiple traits, including multiple hub regions showing concordant effects on five or more traits. Our method addresses critical limitations in existing analytic strategies and may have wide applications in post-GWAS analysis.

Identifying loci with different allele frequencies among cases of eight psychiatric disorders using CC-GWAS

Psychiatric disorders are highly genetically correlated, but little research has been conducted on the genetic differences between disorders. We developed a new method (case-case genome-wide association study; CC-GWAS) to test for differences in allele frequency between cases of two disorders using summary statistics from the respective case-control GWAS, transcending current methods that require individual-level data. Simulations and analytical computations confirm that CC-GWAS is well powered with effective control of type I error. We applied CC-GWAS to publicly available summary statistics for schizophrenia, bipolar disorder, major depressive disorder and five other psychiatric disorders. CC-GWAS identified 196 independent case-case loci, including 72 CC-GWAS-specific loci that were not significant at the genome-wide level in the input case-control summary statistics; two of the CC-GWAS-specific loci implicate the genes KLF6 and KLF16 (from the Krüppel-like family of transcription factors), which have been linked to neurite outgrowth and axon regeneration. CC-GWAS loci replicated convincingly in applications to datasets with independent replication data.

Epidemiology of Marginal Zone Lymphoma

In 2016 there were an estimated 7,460 newly diagnosed patients with marginal zone lymphoma (MZL) in the US, which comprised 7% of all mature non-Hodgkin lymphomas (NHL). Based on data from the US SEER-18 program from 2001-2017, the age-standardized incidence rate for MZL was 19.6 per 1,000,000 person-years; 9% of MZL cases were splenic MZL (SMZL), 30% nodal MZL (NMZL), and 61% extranodal MZL (EMZL) of mucusa-associated lymphoid tissue (MALT). Incidence rates were slightly higher in men for SMZL and NMZL, but similar for EMZL, and increased steeply with age for all MZL subtypes. The incidence (age-standardized per 1,000,000) of MZL was highest among non-Hispanic whites (20.7), followed by Hispanics of all races (17.6), non-Hispanic blacks (15.4), and Asian/Pacific islanders (15.0). The incidence of MZL increased +1.0% per year in the US from 2001-2017, with increases reported in other countries during this timeframe. The 5-year relative survival rate for MZL in the US was 89.8% and was similar across racial/ethnic groups and by sex; survival rates have been increasing in the US and other countries. Established risk factors for MZL (or MZL subtypes) include family history of NHL, genetic loci in the HLA region, Helicobacter pylori infection (gastric MALT lymphoma), and several autoimmune diseases (Sjögren syndrome, systemic lupus erythematosus and Hashimoto thyroiditis), with strong (but not definitive) evidence for Chlamydia psittaci (ocular adnexal MALT lymphoma), Borrelia burgdorferi (cutaneous MZL), hepatitis C virus, human immunodeficiency virus, and solid organ transplantation. Promising risk factors that require additional study include other infections, other autoimmune conditions, trichloroethylene exposure, certain occupations, hair dye, cigarette smoking, sun exposure (protective), and alcohol use (protective). MZL is a model of an antigen-driven malignancy, where epidemiologic risk factors, tissue-specific factors, and host immune response (including the impact of chronic inflammation and immunosuppression) drive lymphomagenesis with implications for prevention.

Associations between COVID-19 and skin conditions identified through epidemiology and genomic studies

BACKGROUND

Coronavirus disease 2019 (COVID-19) is commonly associated with skin manifestations, and may also exacerbate existing skin diseases, yet the relationship between COVID-19 and skin diseases remains unclear.

OBJECTIVE

By investigating this relationship through a multiomics approach, we sought to ascertain whether patients with skin conditions are more susceptible to COVID-19.

METHODS

We conducted an epidemiological study and then compared gene expression across 9 different inflammatory skin conditions and severe acute respiratory syndrome coronavirus 2-infected bronchial epithelial cell lines, and then performed a genome-wide association study transdisease meta-analysis between COVID-19 susceptibility and 2 skin diseases (psoriasis and atopic dermatitis).

RESULTS

Skin conditions, including psoriasis and atopic dermatitis, increase the risk of COVID-19 (odds ratio, 1.55; P = 1.4 × 10-9) but decrease the risk of mechanical ventilation (odds ratio, 0.22; P = 8.5 × 10-5). We observed significant overlap in gene expression between the infected normal bronchial epithelial cells and inflammatory skin diseases, such as psoriasis and atopic dermatitis. For genes that are commonly induced in both the severe acute respiratory syndrome coronavirus 2 infection and skin diseases, there are 4 S100 family members located in the epidermal differentiation complex, and we also identified the "IL-17 signaling pathway" (P = 4.9 × 10-77) as one of the most significantly enriched pathways. Furthermore, a shared genome-wide significant locus in the epidermal differentiation complex was identified between psoriasis and severe acute respiratory syndrome coronavirus 2 infection, with the lead marker being a significant expression quantitative trait locus for S100A12 (P = 3.3 × 10-7).

CONCLUSIONS

Together our findings suggest association between inflammatory skin conditions and higher risk of COVID-19, but with less severe course, and highlight shared components involved in anti-COVID-19 immune response.

Genome-wide association meta-analysis identifies pleiotropic risk loci for aerodigestive squamous cell cancers

Squamous cell carcinomas (SqCC) of the aerodigestive tract have similar etiological risk factors. Although genetic risk variants for individual cancers have been identified, an agnostic, genome-wide search for shared genetic susceptibility has not been performed. To identify novel and pleotropic SqCC risk variants, we performed a meta-analysis of GWAS data on lung SqCC (LuSqCC), oro/pharyngeal SqCC (OSqCC), laryngeal SqCC (LaSqCC) and esophageal SqCC (ESqCC) cancers, totaling 13,887 cases and 61,961 controls of European ancestry. We identified one novel genome-wide significant (Pmeta<5x10-8) aerodigestive SqCC susceptibility loci in the 2q33.1 region (rs56321285, TMEM273). Additionally, three previously unknown loci reached suggestive significance (Pmeta<5x10-7): 1q32.1 (rs12133735, near MDM4), 5q31.2 (rs13181561, TMEM173) and 19p13.11 (rs61494113, ABHD8). Multiple previously identified loci for aerodigestive SqCC also showed evidence of pleiotropy in at least another SqCC site, these include: 4q23 (ADH1B), 6p21.33 (STK19), 6p21.32 (HLA-DQB1), 9p21.33 (CDKN2B-AS1) and 13q13.1(BRCA2). Gene-based association and gene set enrichment identified a set of 48 SqCC-related genes rel to DNA damage and epigenetic regulation pathways. Our study highlights the importance of cross-cancer analyses to identify pleiotropic risk loci of histology-related cancers arising at distinct anatomical sites.

Penalized partial least squares for pleiotropy

BACKGROUND

The increasing number of genome-wide association studies (GWAS) has revealed several loci that are associated to multiple distinct phenotypes, suggesting the existence of pleiotropic effects. Highlighting these cross-phenotype genetic associations could help to identify and understand common biological mechanisms underlying some diseases. Common approaches test the association between genetic variants and multiple traits at the SNP level. In this paper, we propose a novel gene- and a pathway-level approach in the case where several independent GWAS on independent traits are available. The method is based on a generalization of the sparse group Partial Least Squares (sgPLS) to take into account groups of variables, and a Lasso penalization that links all independent data sets. This method, called joint-sgPLS, is able to convincingly detect signal at the variable level and at the group level.

RESULTS

Our method has the advantage to propose a global readable model while coping with the architecture of data. It can outperform traditional methods and provides a wider insight in terms of a priori information. We compared the performance of the proposed method to other benchmark methods on simulated data and gave an example of application on real data with the aim to highlight common susceptibility variants to breast and thyroid cancers.

CONCLUSION

The joint-sgPLS shows interesting properties for detecting a signal. As an extension of the PLS, the method is suited for data with a large number of variables. The choice of Lasso penalization copes with architectures of groups of variables and observations sets. Furthermore, although the method has been applied to a genetic study, its formulation is adapted to any data with high number of variables and an exposed a priori architecture in other application fields.

Patterns of Human Leukocyte Antigen Class I and Class II Associations and Cancer

Human leukocyte antigen (HLA) gene variation is associated with risk of cancers, particularly those with infectious etiology or hematopoietic origin, given its role in immune presentation. Previous studies focused primarily on HLA allele/haplotype-specific associations. To answer whether associations are driven by HLA class I (essential for T-cell cytotoxicity) or class II (important for T-cell helper responses) genes, we analyzed GWAS from 24 case-control studies and consortia comprising 27 cancers (totaling >71,000 individuals). Associations for most cancers with infectious etiology or of hematopoietic origin were driven by multiple HLA regions, suggesting that both cytotoxic and helper T-cell responses are important. Notable exceptions were observed for nasopharyngeal carcinoma, an EBV-associated cancer, and CLL/SLL forms of non-Hodgkin lymphomas; these cancers were associated with HLA class I region only and HLA class II region only, implying the importance of cytotoxic T-cell responses for the former and CD4+ T-cell helper responses for the latter. Our findings suggest that increased understanding of the pattern of HLA associations for individual cancers could lead to better insights into specific mechanisms involved in cancer pathogenesis. SIGNIFICANCE: GWAS of >71,000 individuals across 27 cancer types suggest that patterns of HLA Class I and Class II associations may provide etiologic insights for cancer.

Associating Multivariate Traits with Genetic Variants Using Collapsing and Kernel Methods with Pedigree- or Population-Based Studies

In genetic association analysis, several relevant phenotypes or multivariate traits with different types of components are usually collected to study complex or multifactorial diseases. Over the past few years, jointly testing for association between multivariate traits and multiple genetic variants has become more popular because it can increase statistical power to identify causal genes in pedigree- or population-based studies. However, most of the existing methods mainly focus on testing genetic variants associated with multiple continuous phenotypes. In this investigation, we develop a framework for identifying the pleiotropic effects of genetic variants on multivariate traits by using collapsing and kernel methods with pedigree- or population-structured data. The proposed framework is applicable to the burden test, the kernel test, and the omnibus test for autosomes and the X chromosome. The proposed multivariate trait association methods can accommodate continuous phenotypes or binary phenotypes and further can adjust for covariates. Simulation studies show that the performance of our methods is satisfactory with respect to the empirical type I error rates and power rates in comparison with the existing methods.

Meta-analysis of SNP-environment interaction with heterogeneity for overlapping data

Meta-analysis is a popular method used in genome-wide association studies, by which the results of multiple studies are combined to identify associations. This process generates heterogeneity. Recently, we proposed a random effect model meta-regression method (MR) to study the effect of single nucleotide polymorphism (SNP)-environment interactions. This method takes heterogeneity into account and produces high power. We also proposed a fixed effect model overlapping MR in which the overlapping data is taken into account. In the present study, a random effect model overlapping MR that simultaneously considers heterogeneity and overlapping data is proposed. This method is based on the random effect model MR and the fixed effect model overlapping MR. A new way of solving the logarithm of the determinant of covariance matrices in likelihood functions is also provided. Tests for the likelihood ratio statistic of the SNP-environment interaction effect and the SNP and SNP-environment joint effects are given. In our simulations, null distributions and type I error rates were proposed to verify the suitability of our method, and powers were applied to evaluate the superiority of our method. Our findings indicate that this method is effective in cases of overlapping data with a high heterogeneity.

A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes

MOTIVATION

While gene-environment (GxE) interactions contribute importantly to many different phenotypes, detecting such interactions requires well-powered studies and has proven difficult. To address this, we combine two approaches to improve GxE power: simultaneously evaluating multiple phenotypes and using a two-step analysis approach. Previous work shows that the power to identify a main genetic effect can be improved by simultaneously analyzing multiple related phenotypes. For a univariate phenotype, two-step methods produce higher power for detecting a GxE interaction compared to single step analysis. Therefore, we propose a two-step approach to test for an overall GxE effect for multiple phenotypes.

RESULTS

Using simulations we demonstrate that, when more than one phenotype has GxE effect (i.e., GxE pleiotropy), our approach offers substantial gain in power (18%-43%) to detect an aggregate-level GxE effect for a multivariate phenotype compared to an analogous two-step method to identify GxE effect for a univariate phenotype. We applied the proposed approach to simultaneously analyze three lipids, LDL, HDL and Triglyceride with the frequency of alcohol consumption as environmental factor in the UK Biobank. The method identified two loci with an overall GxE effect on the vector of lipids, one of which was missed by the competing approaches.

AVAILABILITY

We provide an R package MPGE implementing the proposed approach which is available from CRAN: https://cran.r-project.org/web/packages/MPGE/index.html.

PLEIO: a method to map and interpret pleiotropic loci with GWAS summary statistics

Identifying and interpreting pleiotropic loci is essential to understanding the shared etiology among diseases and complex traits. A common approach to mapping pleiotropic loci is to meta-analyze GWAS summary statistics across multiple traits. However, this strategy does not account for the complex genetic architectures of traits, such as genetic correlations and heritabilities. Furthermore, the interpretation is challenging because phenotypes often have different characteristics and units. We propose PLEIO (Pleiotropic Locus Exploration and Interpretation using Optimal test), a summary-statistic-based framework to map and interpret pleiotropic loci in a joint analysis of multiple diseases and complex traits. Our method maximizes power by systematically accounting for genetic correlations and heritabilities of the traits in the association test. Any set of related phenotypes, binary or quantitative traits with different units, can be combined seamlessly. In addition, our framework offers interpretation and visualization tools to help downstream analyses. Using our method, we combined 18 traits related to cardiovascular disease and identified 13 pleiotropic loci, which showed four different patterns of associations.

Bayesian meta-analysis models for cross cancer genomic investigation of pleiotropic effects using group structure

An increasing number of genome-wide association studies (GWAS) summary statistics is made available to the scientific community. Exploiting these results from multiple phenotypes would permit identification of novel pleiotropic associations. In addition, incorporating prior biological information in GWAS such as group structure information (gene or pathway) has shown some success in classical GWAS approaches. However, this has not been widely explored in the context of pleiotropy. We propose a Bayesian meta-analysis approach (termed GCPBayes) that uses summary-level GWAS data across multiple phenotypes to detect pleiotropy at both group-level (gene or pathway) and within group (eg, at the SNP level). We consider both continuous and Dirac spike and slab priors for group selection. We also use a Bayesian sparse group selection approach with hierarchical spike and slab priors that enables us to select important variables both at the group level and within group. GCPBayes uses a Bayesian statistical framework based on Markov chain Monte Carlo (MCMC) Gibbs sampling. It can be applied to multiple types of phenotypes for studies with overlapping or nonoverlapping subjects, and takes into account heterogeneity in the effect size and allows for the opposite direction of the genetic effects across traits. Simulations show that the proposed methods outperform benchmark approaches such as ASSET and CPBayes in the ability to retrieve pleiotropic associations at both SNP and gene-levels. To illustrate the GCPBayes method, we investigate the shared genetic effects between thyroid cancer and breast cancer in candidate pathways.

A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information

Large-scale genome-wide association (GWAS) studies provide opportunities for developing genetic risk prediction models that have the potential to improve disease prevention, intervention or treatment. The key step is to develop polygenic risk score (PRS) models with high predictive performance for a given disease, which typically requires a large training data set for selecting truly associated single nucleotide polymorphisms (SNPs) and estimating effect sizes accurately. Here, we develop a comprehensive penalized regression for fitting l ₁ regularized regression models to GWAS summary statistics. We propose incorporating Pleiotropy and ANnotation information into PRS (PANPRS) development through suitable formulation of penalty functions and associated tuning parameters. Extensive simulations show that PANPRS performs equally well or better than existing PRS methods when no functional annotation or pleiotropy is incorporated. When functional annotation data and pleiotropy are informative, PANPRS substantially outperforms existing PRS methods in simulations. Finally, we applied our methods to build PRS for type 2 diabetes and melanoma and found that incorporating relevant functional annotations and GWAS of genetically related traits improved prediction of these two complex diseases.

Leveraging correlations between variants in polygenic risk scores to detect heterogeneity in GWAS cohorts

Evidence from both GWAS and clinical observation has suggested that certain psychiatric, metabolic, and autoimmune diseases are heterogeneous, comprising multiple subtypes with distinct genomic etiologies and Polygenic Risk Scores (PRS). However, the presence of subtypes within many phenotypes is frequently unknown. We present CLiP (Correlated Liability Predictors), a method to detect heterogeneity in single GWAS cohorts. CLiP calculates a weighted sum of correlations between SNPs contributing to a PRS on the case/control liability scale. We demonstrate mathematically and through simulation that among i.i.d. homogeneous cases generated by a liability threshold model, significant anti-correlations are expected between otherwise independent predictors due to ascertainment on the hidden liability score. In the presence of heterogeneity from distinct etiologies, confounding by covariates, or mislabeling, these correlation patterns are altered predictably. We further extend our method to two additional association study designs: CLiP-X for quantitative predictors in applications such as transcriptome-wide association, and CLiP-Y for quantitative phenotypes, where there is no clear distinction between cases and controls. Through simulations, we demonstrate that CLiP and its extensions reliably distinguish between homogeneous and heterogeneous cohorts when the PRS explains as low as 3% of variance on the liability scale and cohorts comprise 50, 000 − 100, 000 samples, an increasingly practical size for modern GWAS. We apply CLiP to heterogeneity detection in schizophrenia cohorts totaling > 50, 000 cases and controls collected by the Psychiatric Genomics Consortium. We observe significant heterogeneity in mega-analysis of the combined PGC data (p-value 8.54 × 0⁻⁴), as well as in individual cohorts meta-analyzed using Fisher’s method (p-value 0.03), based on significantly associated variants. We also apply CLiP-Y to detect heterogeneity in neuroticism in over 10, 000 individuals from the UK Biobank and detect heterogeneity with a p-value of 1.68 × 10⁻⁹. Scores were not significantly reduced when partitioning by known subclusters (“Depression” and “Worry”), suggesting that these factors are not the primary source of observed heterogeneity.

Several traits, such as bipolar disease, are known to be heterogeneous and comprise distinct subtypes with unique genomic associations. For other traits such as schizophrenia, heterogeneity may be suspected, but specific subtypes are less well characterized. Furthermore, conventional mixture model-based methods to detect subtypes in genome-wide association data struggle with the high polygenicity of complex traits. We propose CLiP (Correlated Liability Predictors), a method that does not identify subtype-specific effects, but is very well-powered to detect heterogeneity of any kind within the very weak signals of GWAS. CLiP serves as a method to flag particular phenotypes for potential further study into the genomic factors driving heterogeneity, as well as a means to evaluate the transferability of polygenic risk scores. We also develop extensions of CLiP applicable to scoring heterogeneity in quantitative phenotypes and quantitative predictors such as gene expression. We apply CLiP to scoring heterogeneity in schizophrenia cohorts from the Psychiatric Genomics Consortium and neuroticism in individuals in the UK Biobank and find significant heterogeneity in both phenotypes, warranting further study.

Pan-cancer study detects genetic risk variants and shared genetic basis in two large cohorts

Deciphering the shared genetic basis of distinct cancers has the potential to elucidate carcinogenic mechanisms and inform broadly applicable risk assessment efforts. Here, we undertake genome-wide association studies (GWAS) and comprehensive evaluations of heritability and pleiotropy across 18 cancer types in two large, population-based cohorts: the UK Biobank (408,786 European ancestry individuals; 48,961 cancer cases) and the Kaiser Permanente Genetic Epidemiology Research on Adult Health and Aging cohorts (66,526 European ancestry individuals; 16,001 cancer cases). The GWAS detect 21 genome-wide significant associations independent of previously reported results. Investigations of pleiotropy identify 12 cancer pairs exhibiting either positive or negative genetic correlations; 25 pleiotropic loci; and 100 independent pleiotropic variants, many of which are regulatory elements and/or influence cross-tissue gene expression. Our findings demonstrate widespread pleiotropy and offer further insight into the complex genetic architecture of cross-cancer susceptibility.

A framework for pathway knowledge driven prioritization in genome-wide association studies

Many variants with low frequencies or with low to modest effects likely remain unidentified in genome-wide association studies (GWAS) because of stringent genome-wide thresholds for detection. To improve the power of detection, variant prioritization based on their functional annotations and epigenetic landmarks has been used successfully. Here, we propose a novel method of prioritization of a GWAS by exploiting gene-level knowledge (e.g., annotations to pathways and ontologies) and show that it further improves power. Often, disease associated variants are found near genes that are coinvolved in specific biological pathways relevant to disease process. Utilization of this knowledge to conduct a prioritized scan increases the power to detect loci that map to genes clustered in a few specific pathways. We have developed a computationally scalable framework based on penalized logistic regression (termed GKnowMTest-Genomic Knowledge-guided Multiplte Testing) to enable a prioritized pathway-guided GWAS scan with a very large number of gene-level annotations. We demonstrate that the proposed strategy improves overall power and maintains the Type 1 error globally. Our method works on genome-wide summary level data and a user-specified list of pathways (e.g., those extracted from large pathway databases without reference to biology of a specific disease). It automatically reweights the input p values by incorporating the pathway enrichments as "adaptively learned" from the data using a cross-validation technique to avoid overfitting. We used whole-genome simulations and some publicly available GWAS data sets to illustrate the application of our method. The GKnowMTest framework has been implemented as a user-friendly open-source R package.

Collagenous Colitis Is Associated With HLA Signature and Shares Genetic Risks With Other Immune-Mediated Diseases

BACKGROUND & AIMS

Collagenous colitis (CC) is an inflammatory bowel disorder with unknown etiopathogenesis involving HLA-related immune-mediated responses and environmental and genetic risk factors. We carried out an array-based genetic association study in a cohort of patients with CC and investigated the common genetic basis between CC and Crohn's disease (CD), ulcerative colitis (UC), and celiac disease.

METHODS

DNA from 804 CC formalin-fixed, paraffin-embedded tissue samples was genotyped with Illumina Immunochip. Matching genotype data on control samples and CD, UC, and celiac disease cases were provided by the respective consortia. A discovery association study followed by meta-analysis with an independent cohort, polygenic risk score calculation, and cross-phenotype analyses were performed. Enrichment of regulatory expression quantitative trait loci among the CC variants was assessed in hemopoietic and intestinal cells.

RESULTS

Three HLA alleles (HLA-B∗08:01, HLA-DRB1∗03:01, and HLA-DQB1∗02:01), related to the ancestral haplotype 8.1, were significantly associated with increased CC risk. We also identified an independent protective effect of HLA-DRB1∗04:01 on CC risk. Polygenic risk score quantifying the risk across multiple susceptibility loci was strongly associated with CC risk. An enrichment of expression quantitative trait loci was detected among the CC-susceptibility variants in various cell types. The cross-phenotype analysis identified a complex pattern of polygenic pleiotropy between CC and other immune-mediated diseases.

CONCLUSIONS

In this largest genetic study of CC to date with histologically confirmed diagnosis, we strongly implicated the HLA locus and proposed potential non-HLA mechanisms in disease pathogenesis. We also detected a shared genetic risk between CC, celiac disease, CD, and UC, which supports clinical observations of comorbidity.

Insecticide use and risk of non-Hodgkin lymphoma subtypes: A subset meta-analysis of the North American Pooled Project

Insecticide use has been linked to increased risk of non-Hodgkin lymphoma (NHL), however, findings of epidemiologic studies have been inconsistent, particularly for NHL subtypes. We analyzed 1690 NHL cases and 5131 controls in the North American Pooled Project (NAPP) to investigate self-reported insecticide use and risk of NHL overall and by subtypes: follicular lymphoma (FL), diffuse large B-cell lymphoma (DLBCL) and small lymphocytic lymphoma (SLL). Odds ratios (OR) and 95% confidence intervals for each insecticide were estimated using logistic regression. Subtype-specific associations were evaluated using ASSET (Association analysis for SubSETs). Increased risks of multiple NHL subtypes were observed for lindane (OR = 1.60, 1.20-2.10: FL, DLCBL, SLL), chlordane (OR = 1.59, 1.17-2.16: FL, SLL) and DDT (OR = 1.36, 1.06-1.73: DLBCL, SLL). Positive trends were observed, within the subsets with identified associations, for increasing categories of exposure duration for lindane (P_trend = 1.7 × 10^-4 ), chlordane (P_trend = 1.0 × 10^-3 ) and DDT (P_trend = 4.2 × 10^-3 ), however, the exposure-response relationship was nonlinear. Ever use of pyrethrum was associated with an increased risk of FL (OR = 3.65, 1.45-9.15), and the relationship with duration of use appeared monotonic (OR for >10 years: OR = 5.38, 1.75-16.53; P_trend = 3.6 × 10^-3 ). Our analysis identified several novel associations between insecticide use and specific NHL subtypes, suggesting possible etiologic heterogeneity in the context of pesticide exposure.

Investigating asthma heterogeneity through shared and distinct genetics: Insights from genome-wide cross-trait analysis

Asthma is a heterogeneous respiratory disease reflecting distinct pathobiologic mechanisms. These mechanisms are based, at least partly, on different genetic factors shared by many other conditions, such as allergic diseases and obesity. Investigating the shared genetic effects enables better understanding of the mechanisms of phenotypic correlations and is less subject to confounding by environmental factors. The increasing availability of large-scale genome-wide association study (GWAS) for asthma has enabled researchers to examine the genetic contributions to the epidemiologic associations between asthma subtypes and those between coexisting diseases and/or traits and asthma. Studies have found not only shared but also distinct genetic components between asthma subtypes, indicating that the heterogeneity is related to distinct genetics. This review summarizes a recently compiled analytic approach-genome-wide cross-trait analysis-to determine shared and distinct genetic architecture. The genome-wide cross-trait analysis features in several analytic aspects: genetic correlation, cross-trait meta-analysis, Mendelian randomization, polygenic risk score, and functional analysis. In this article, we discuss in detail the scientific goals that can be achieved by these analyses, their advantages, and their limitations. We also make recommendations for future directions: (1) ethnicity-specific asthma GWASs and (2) application of cross-trait methods to multiomics data to dissect the heritability found in GWASs. Finally, these analytic approaches are also applicable to complex and heterogeneous traits beyond asthma.

Polygenic risk and pleiotropy in neurodegenerative diseases

In this paper we explore the phenomenon of pleiotropy in neurodegenerative diseases, focusing on Alzheimer's disease (AD). We summarize the various techniques developed to investigate pleiotropy among traits, elaborating in the polygenic risk scores (PRS) analysis. PRS was designed to assess a cumulative effect of a large number of SNPs for association with a disease and, later for disease risk prediction. Since genetic predictions rely on heritability, we discuss SNP-based heritability from genome-wide association studies and its contribution to the prediction accuracy of PRS. We review work examining pleiotropy in neurodegenerative diseases and related phenotypes and biomarkers. We conclude that the exploitation of pleiotropy may aid in the identification of novel genes and provide further insights in the disease mechanisms, and along with PRS analysis, may be advantageous for precision medicine.