Genome-wide Association Studies Datasets Research Articles

Using Genetic Variants to Evaluate the Causal Effect of Plasma Phospholipid Fatty Acids on Breast Cancer and Prostate Cancer: A Mendelian Randomization Study.

BackgroundObservational studies indicate that phospholipid fatty acids (FAs) have an impact on the etiology in cancers, but the results are conflicting. We aimed to investigate the causal association of phospholipid FAs with breast cancer and prostate cancer.MethodsFourteen single nucleotide polymorphisms (SNPs) were selected as instrumental variables to predict the level of 10 phospholipid FAs from Genome-wide association studies (GWAS). We obtained the summary statistics for the latest and largest GWAS datasets for breast cancer (113,789 controls and 133,384 cases) and prostate cancer (61,106 controls and 79,148 cases) from the Breast Cancer Association Consortium (BCAC) and Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. Two-sample Mendelian randomization analysis was applied.ResultsThe results demonstrate that the 10 individual plasma phospholipid FAs are not significantly associated with breast cancer risk and prostate cancer risk.ConclusionThe evidence is insufficient to support the causal association of the 10 individual plasma phospholipid FAs with breast cancer and prostate cancer.

Improving the Utility of Polygenic Risk Scores as a Biomarker for Alzheimer's Disease.

The treatment of complex and multifactorial diseases constitutes a big challenge in day-to-day clinical practice. As many parameters influence clinical phenotypes, accurate diagnosis and prompt therapeutic management is often difficult. Significant research and investment focuses on state-of-the-art genomic and metagenomic analyses in the burgeoning field of Precision (or Personalized) Medicine with genome-wide-association-studies (GWAS) helping in this direction by linking patient genotypes at specific polymorphic sites (single-nucleotide polymorphisms, SNPs) to the specific phenotype. The generation of polygenic risk scores (PRSs) is a relatively novel statistical method that associates the collective genotypes at many of a person’s SNPs to a trait or disease. As GWAS sample sizes increase, PRSs may become a powerful tool for prevention, early diagnosis and treatment. However, the complexity and multidimensionality of genetic and environmental contributions to phenotypes continue to pose significant challenges for the clinical, broad-scale use of PRSs. To improve the value of PRS measures, we propose a novel pipeline which might better utilize GWAS results and improve the utility of PRS when applied to Alzheimer’s Disease (AD), as a paradigm of multifactorial disease with existing large GWAS datasets that have not yet achieved significant clinical impact. We propose a refined approach for the construction of AD PRS improved by (1), taking into consideration the genetic loci where the SNPs are located, (2) evaluating the post-translational impact of SNPs on coding and non-coding regions by focusing on overlap with open chromatin data and SNPs that are expression quantitative trait loci (QTLs), and (3) scoring and annotating the severity of the associated clinical phenotype into the PRS. Open chromatin and eQTL data need to be carefully selected based on tissue/cell type of origin (e.g., brain, excitatory neurons). Applying such filters to traditional PRS on GWAS studies of complex diseases like AD, can produce a set of SNPs weighted according to our algorithm and a more useful PRS. Our proposed methodology may pave the way for new applications of genomic machine and deep learning pipelines to GWAS datasets in an effort to identify novel clinically useful genetic biomarkers for complex diseases like AD.

Weak Association Between the Glutamate Decarboxylase 1 Gene (GAD1) and Schizophrenia in Han Chinese Population.

ObjectivesSchizophrenia (SZ) is a complex psychiatric disorder with high heritability, and genetic components are thought to be pivotal risk factors for this illness. The glutamate decarboxylase 1 gene (GAD1) was hypothesized to be a candidate risk locus for SZ given its crucial role in the GABAergic neurotransmission system, and previous studies have examined the associations of single nucleotide polymorphisms (SNPs) spanning the GAD1 gene with SZ. However, inconsistent results were obtained. We hence examined the associations between GAD1 SNPs and SZ in two independent case-control samples of Han Chinese ancestry.Materials and MethodsTwo Han Chinese SZ case-control samples, referred as the discovery sample and the replication sample, respectively, were recruited for the current study. The discovery sample comprised of 528 paranoid SZ cases (with age of first onset ≥ 18) and 528 healthy controls; the independent replication sample contained 1,256 early onset SZ cases (with age of first onset < 18) and 2,661 healthy controls. Logistic regression analysis was performed to examine the associations between GAD1 SNPs and SZ.ResultsTen SNPs covering GAD1 gene were analyzed in the discovery sample, and two SNPs showed nominal associations with SZ (rs2241165, P = 0.0181, OR = 1.261; rs2241164, P = 0.0225, OR = 1.219). SNP rs2241164 was also nominally significant in the independent replication sample (P = 0.0462, OR = 1.110), and the significance became stronger in a subsequent meta-analysis combining both discovery and replication samples (P = 0.00398, OR = 1.138). Nevertheless, such association could not survive multiple corrections, although the effect size of rs2241164 was comparable with other SZ risk loci identified in genome-wide association studies (GWAS) in Han Chinese population. We also examined the associations between GAD1 SNPs and SZ in published datasets of SZ GWAS in East Asians and Europeans, and no significant associations were observed.ConclusionWe observed weak associations between GAD1 SNPs and risk of SZ in Han Chinese populations. Further analyses in larger Han Chinese samples with more detailed phenotyping are necessary to elucidate the genetic correlation between GAD1 SNPs and SZ.

Association of CXCR6 with COVID-19 severity: delineating the host genetic factors in transcriptomic regulation.

The coronavirus disease 2019 (COVID-19) is an infectious disease that mainly affects the host respiratory system with ~ 80% asymptomatic or mild cases and ~ 5% severe cases. Recent genome-wide association studies (GWAS) have identified several genetic loci associated with the severe COVID-19 symptoms. Delineating the genetic variants and genes is important for better understanding its biological mechanisms. We implemented integrative approaches, including transcriptome-wide association studies (TWAS), colocalization analysis, and functional element prediction analysis, to interpret the genetic risks using two independent GWAS datasets in lung and immune cells. To understand the context-specific molecular alteration, we further performed deep learning-based single-cell transcriptomic analyses on a bronchoalveolar lavage fluid (BALF) dataset from moderate and severe COVID-19 patients. We discovered and replicated the genetically regulated expression of CXCR6 and CCR9 genes. These two genes have a protective effect on lung, and a risk effect on whole blood, respectively. The colocalization analysis of GWAS and cis-expression quantitative trait loci highlighted the regulatory effect on CXCR6 expression in lung and immune cells. In the lung-resident memory CD8+ T (TRM) cells, we found a 2.24-fold decrease of cell proportion among CD8+ T cells and lower expression of CXCR6 in the severe patients than moderate patients. Pro-inflammatory transcriptional programs were highlighted in the TRM cellular trajectory from moderate to severe patients. CXCR6 from the 3p21.31 locus is associated with severe COVID-19. CXCR6 tends to have a lower expression in lung TRM cells of severe patients, which aligns with the protective effect of CXCR6 from TWAS analysis.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00439-021-02305-z.

Genomewide alteration of histone H3K4 methylation underlies genetic vulnerability to psychopathology

Dysregulated histone methylation has emerged as a recurring theme in multiple neuropsychiatric disorders. However, it is yet unclear whether the altered histone methylation is associated with aetiologic mechanisms or an outcome of disease manifestation. In this study, we examined the genomewide association studies datasets of three major psychiatric disorders, schizophrenia (SCZ), bipolar disorder (BIP), and major depression disorder (MDD), which represents a total of 231,783 cases and 425,444 controls, to clarify the relationship. Our gene-set enrichment analysis results identified statistically significant association of genes involved in three histone methylation biological processes with the three adult-onset psychiatric disorders, which is mainly driven by the histone H3K4 methylation pathway (GO: 0051568). Further analysis of histone H3K4 methylation pathway genes revealed a widespread role of the genes in brain function and disease; 29 (52%) and 41 genes (73.2%) were associated with at least one brain-related trait or brain disorder, respectively. Spatiotemporal gene expression analysis suggests that these pathway genes play a critical role during the prenatal period and are consistent regulators in the cerebral cortex throughout an individual's life. AUTS2, DNMT1 and TET2 are genes of particular interest due to their pervasive role in various aspects of brain function. Our findings support a critical aetiologic role of H3K4 methylation genes shared across SCZ, BIP and MDD, providing new direction for the development of epigenetically-focussed drugs targeting common causal factors of these devastating disorders.

RápidoPGS: a rapid polygenic score calculator for summary GWAS data without a test dataset.

MotivationPolygenic scores (PGS) aim to genetically predict complex traits at an individual level. PGS are typically trained on genome-wide association summary statistics and require an independent test dataset to tune parameters. More recent methods allow parameters to be tuned on the training data, removing the need for independent test data, but approaches are computationally intensive. Based on fine-mapping principles, we present RápidoPGS, a flexible and fast method to compute PGS requiring summary-level Genome-wide association studies (GWAS) datasets only, with little computational requirements and no test data required for parameter tuning.ResultsWe show that RápidoPGS performs slightly less well than two out of three other widely used PGS methods (LDpred2, PRScs and SBayesR) for case–control datasets, with median r2 difference: -0.0092, -0.0042 and 0.0064, respectively, but up to 17 000-fold faster with reduced computational requirements. RápidoPGS is implemented in R and can work with user-supplied summary statistics or download them from the GWAS catalog.Availability and implementationOur method is available with a GPL license as an R package from CRAN and GitHub.Supplementary information Supplementary data are available at Bioinformatics online.

Transcriptional Alterations in Dorsolateral Prefrontal Cortex and Nucleus Accumbens Implicate Neuroinflammation and Synaptic Remodeling in Opioid Use Disorder

BackgroundPrevalence rates of opioid use disorder (OUD) have increased dramatically, accompanied by a surge of overdose deaths. While opioid dependence has been extensively studied in preclinical models, an understanding of the biological alterations that occur in the brains of people who chronically use opioids and who are diagnosed with OUD remains limited. To address this limitation, RNA sequencing was conducted on the dorsolateral prefrontal cortex and nucleus accumbens, regions heavily implicated in OUD, from postmortem brains in subjects with OUD. MethodsWe performed RNA sequencing on the dorsolateral prefrontal cortex and nucleus accumbens from unaffected comparison subjects (n = 20) and subjects diagnosed with OUD (n = 20). Our transcriptomic analyses identified differentially expressed transcripts and investigated the transcriptional coherence between brain regions using rank-rank hypergeometric orderlap. Weighted gene coexpression analyses identified OUD-specific modules and gene networks. Integrative analyses between differentially expressed transcripts and genome-wide association study datasets using linkage disequilibrium scores assessed the genetic liability of psychiatric-related phenotypes in OUD. ResultsRank-rank hypergeometric overlap analyses revealed extensive overlap in transcripts between the dorsolateral prefrontal cortex and nucleus accumbens in OUD, related to synaptic remodeling and neuroinflammation. Identified transcripts were enriched for factors that control proinflammatory cytokine, chondroitin sulfate, and extracellular matrix signaling. Cell-type deconvolution implicated a role for microglia as a potential driver for opioid-induced neuroplasticity. Linkage disequilibrium score analysis suggested genetic liabilities for risky behavior, attention-deficit/hyperactivity disorder, and depression in subjects with OUD. ConclusionsOverall, our findings suggest connections between the brain’s immune system and opioid dependence in the human brain.

Integrating Transcriptome-Wide Association Study and mRNA Expression Profiling Identifies Novel Genes Associated With Osteonecrosis of the Femoral Head

ObjectiveThis study aims to identify novel candidate genes associated with osteonecrosis of the femoral head (ONFH).MethodsA transcriptome-wide association study (TWAS) was performed by integrating the genome-wide association study dataset of osteonecrosis (ON) in the UK Biobank with pre-computed mRNA expression reference weights of muscle skeleton (MS) and blood. The ON-associated genes identified by TWAS were further subjected to gene ontology (GO) analysis by the DAVID tool. Finally, a trans-omics comparative analysis of TWAS and genome-wide mRNA expression profiling was conducted to identify the common genes and the GO terms shared by both DNA-level TWAS and mRNA-level expression profile for ONFH.ResultsTWAS totally identified 564 genes that were with PTWAS value <0.05 for MS and blood, such as CBX1 (PTWAS = 0.0001 for MS), SRPK2 (PTWAS = 0.0002 for blood), and MYO5A (PTWAS = 0.0005 for blood). After comparing the genes detected by TWAS with the differentially expressed genes identified by mRNA expression profiling, we detected 59 overlapped genes, such as STEAP4 [PTWAS = 0.0270, FC (fold change)mRNA = 7.03], RABEP1 (PTWAS = 0.010, FCmRNA = 2.22), and MORC3 (PTWAS = 0.0053, FCmRNA = 2.92). The GO analysis of TWAS-identified genes discovered 53 GO terms for ON. Further comparing the GO results of TWAS and mRNA expression profiling identified four overlapped GO terms, including cysteine-type endopeptidase activity (PTWAS = 0.0006, PmRNA = 0.0227), extracellular space (PTWAS = 0.0342, PmRNA = 0.0012), protein binding (PTWAS = 0.0112, PmRNA = 0.0106), and ATP binding (PTWAS = 0.0464, PmRNA = 0.0033).ConclusionSeveral ONFH-associated genes and GO terms were identified by integrating TWAS and mRNA expression profiling. It provides novel clues to reveal the pathogenesis of ONFH.

Integrating brain imaging endophenotypes with GWAS for Alzheimer's disease.

Genome wide association studies (GWAS) have identified many genetic variants associated with increased risk of Alzheimer's disease (AD). These susceptibility loci may effect AD indirectly through a combination of physiological brain changes. Many of these neuropathologic features are detectable via magnetic resonance imaging (MRI). In this study, we examine the effects of such brain imaging derived phenotypes (IDPs) with genetic etiology on AD, using and comparing the following methods: two-sample Mendelian randomization (2SMR), generalized summary statistics based Mendelian randomization (GSMR), transcriptome wide association studies (TWAS) and the adaptive sum of powered score (aSPU) test. These methods do not require individual-level genotypic and phenotypic data but instead can rely only on an external reference panel and GWAS summary statistics. Using publicly available GWAS datasets from the International Genomics of Alzheimer's Project (IGAP) and UK Biobank's (UKBB) brain imaging initiatives, we identify 35 IDPs possibly associated with AD, many of which have well established or biologically plausible links to the characteristic cognitive impairments of this neurodegenerative disease. Our results highlight the increased power for detecting genetic associations achieved by multiple correlated SNP-based methods, i.e., aSPU, GSMR and TWAS, over MR methods based on independent SNPs (as instrumental variables).

Identifying a genome-wide QTL interval controlling for ammonia-nitrogen tolerance on chrLG1 of Nile tilapia

Ammonia‑nitrogen in aquaculture environment is one of the important factors affecting survival of tilapia. Understanding the genetic basis of tilapia controlling for the ammonia‑nitrogen tolerance is significant for selection of tilapia elite lines. In this study, we successfully identified a QTL interval across chrLG1 associated with ammonia‑nitrogen tolerance in the GIFT line of Nile tilapia (Oreochromis niloticus) using ddRAD-Seq based genome-wide association studies (GWAS). Most of the associated SNPs are located between 21,578,770–22,661,799 bp on chrLG1. We validated there exists significant correlations between their genotypes and ammonia‑nitrogen tolerance (p < 0.01) by Sanger sequencing. Four differentially expressed genes (usp10, cgnl1, cog4, gnao1b) in the RNA-seq datasets at 6 h post challenge of ammonia‑nitrogen are found within the QTL interval of the GWAS dataset. It is interesting that the SNP mutation (C/T; position: chrLG1:22632460) in the exon of cog4 located at the peak region of the QTL caused an amino acid change from threonine to proline in the mapping populations. To our knowledge, this is the first GWAS based study focusing on ammonia‑nitrogen tolerance in tilapia. Our finding lays a foundation for molecular marker-assisted selection and functional analysis of ammonia‑nitrogen tolerance in tilapia.

Regulatory Noncoding and Predicted Pathogenic Coding Variants of CCR5 Predispose to Severe COVID-19.

Genome-wide association studies (GWAS) found locus 3p21.31 associated with severe COVID-19. CCR5 resides at the same locus and, given its known biological role in other infection diseases, we investigated if common noncoding and rare coding variants, affecting CCR5, can predispose to severe COVID-19. We combined single nucleotide polymorphisms (SNPs) that met the suggestive significance level (P ≤ 1 × 10−5) at the 3p21.31 locus in public GWAS datasets (6406 COVID-19 hospitalized patients and 902,088 controls) with gene expression data from 208 lung tissues, Hi-C, and Chip-seq data. Through whole exome sequencing (WES), we explored rare coding variants in 147 severe COVID-19 patients. We identified three SNPs (rs9845542, rs12639314, and rs35951367) associated with severe COVID-19 whose risk alleles correlated with low CCR5 expression in lung tissues. The rs35951367 resided in a CTFC binding site that interacts with CCR5 gene in lung tissues and was confirmed to be associated with severe COVID-19 in two independent datasets. We also identified a rare coding variant (rs34418657) associated with the risk of developing severe COVID-19. Our results suggest a biological role of CCR5 in the progression of COVID-19 as common and rare genetic variants can increase the risk of developing severe COVID-19 by affecting the functions of CCR5.

Integrative genomics of the mammalian alveolar macrophage response to intracellular mycobacteria

BackgroundBovine TB (bTB), caused by infection with Mycobacterium bovis, is a major endemic disease affecting global cattle production. The key innate immune cell that first encounters the pathogen is the alveolar macrophage, previously shown to be substantially reprogrammed during intracellular infection by the pathogen. Here we use differential expression, and correlation- and interaction-based network approaches to analyse the host response to infection with M. bovis at the transcriptome level to identify core infection response pathways and gene modules. These outputs were then integrated with genome-wide association study (GWAS) data sets to enhance detection of genomic variants for susceptibility/resistance to M. bovis infection.ResultsThe host gene expression data consisted of RNA-seq data from bovine alveolar macrophages (bAM) infected with M. bovis at 24 and 48 h post-infection (hpi) compared to non-infected control bAM. These RNA-seq data were analysed using three distinct computational pipelines to produce six separate gene sets: 1) DE genes filtered using stringent fold-change and P-value thresholds (DEG-24: 378 genes, DEG-48: 390 genes); 2) genes obtained from expression correlation networks (CON-24: 460 genes, CON-48: 416 genes); and 3) genes obtained from differential expression networks (DEN-24: 339 genes, DEN-48: 495 genes). These six gene sets were integrated with three bTB breed GWAS data sets by employing a new genomics data integration tool—gwinteR. Using GWAS summary statistics, this methodology enabled detection of 36, 102 and 921 prioritised SNPs for Charolais, Limousin and Holstein-Friesian, respectively.ConclusionsThe results from the three parallel analyses showed that the three computational approaches could identify genes significantly enriched for SNPs associated with susceptibility/resistance to M. bovis infection. Results indicate distinct and significant overlap in SNP discovery, demonstrating that network-based integration of biologically relevant transcriptomics data can leverage substantial additional information from GWAS data sets. These analyses also demonstrated significant differences among breeds, with the Holstein-Friesian breed GWAS proving most useful for prioritising SNPS through data integration. Because the functional genomics data were generated using bAM from this population, this suggests that the genomic architecture of bTB resilience traits may be more breed-specific than previously assumed.

Gene-Based Tests of a Genome-Wide Association Study Dataset Highlight Novel Multiple Sclerosis Risk Genes.

Multiple sclerosis (MS) is an autoimmune disorder influenced by genetic and environmental factors. Many studies have provided insights into genetic factors’ contribution to MS via large-scale genome-wide association study (GWAS) datasets. However, genetic variants identified to date do not adequately explain genetic risks for MS. This study hypothesized that novel MS risk genes could be identified by analyzing the MS-GWAS dataset using gene-based tests. We analyzed a GWAS dataset consisting of 9,772 MS cases and 17,376 healthy controls of European descent. We performed gene-based tests of 464,357 autosomal single nucleotide polymorphisms (SNPs) using two methods (PLINK and VEGAS2) and identified 28 shared genes satisfied p-value < 4.56 × 10–6. In further gene expression analysis, ten of the 28 genes were significantly differentially expressed in the MS case-control gene expression omnibus (GEO) database. GALC and HLA-DOB showed the most prominent differences in gene expression (two- and three-fold, respectively) between MS patients and healthy controls. In conclusion, our results reveal more information about MS hereditary characteristics and provide a basis for further studies.

Impact of serum calcium levels on total body bone mineral density: A mendelian randomization study in five age strata

Mendelian randomization (MR) studies have reported the causal association between serum calcium levels and bone mineral density (BMD). The results showed that genetically increased serum calcium levels in individuals with normal calcium levels did not increase BMD and could even reduce BMD. However, whether there are differences in the association between serum calcium and BMD in different age strata remains unclear. We selected eight serum calcium genetic variants with genome-wide significance (P<5.00E-08) as the potential instrumental variables. We conducted an MR analysis to evaluate the impact of serum calcium levels on total body BMD in five age strata, 0-15, 15-30, 30-45, 45-60, and ≥60 years, using large-scale serum calcium (61,079 individuals) and total body BMD genome-wide association study (66,628 individuals) datasets. For pleiotropy analysis, we used a manual method and four common statistical methods, namely the MR-Egger intercept, MR-PRESSO, heterogeneity, and Steiger filtering tests. For MR analysis, we selected four MR methods, namely inverse-variance weighted, weighted median, MR-Egger, and MR-PRESSO. In addition to the univariable MR analysis, we conducted a multivariate MR analysis taking into account the effect of serum parathyroid hormone levels. Univariable MR analysis using the inverse-variance weighted method indicated that per 0.5-mg/dL increase (about 1 standard deviation) in serum calcium levels was statistically significantly associated with reduced total body BMD only in the ≥60 years stratum (effect estimate (beta)=-0.545, 95% confidence interval (CI):-0.892 to-0.198, P=0.002). The weighted median regression (beta=-0.446, 95% CI:-0.821 to-0.094, P=1.40E-02) and MR-PRESSO (beta=-0.545, 95% CI:-0.892 to-0.198, P=0.022) MR methods further supported this suggestive association. The multivariable MR analysis also found a significant association between increased serum calcium levels and reduced total body BMD in the ≥60 years stratum (beta=-0.547, 95% CI:-0.934 to-0.16, P=0.006). Our results provide genetic evidence that increased serum calcium levels did not improve BMD in the general population and that the elevated serum calcium levels in generally healthy populations, especially in adults older than 60 years, may even reduce the BMD. Our results are comparable with those of recent MR findings.

Causal associations of waist circumference and waist-to-hip ratio with type II diabetes mellitus: new evidence from Mendelian randomization.

Type II diabetes mellitus (T2DM) is a metabolic disease with high incidence, which has seriously affected human life and health. The associations among waist circumference (WC), waist-to-hip ratio (WHR), and T2DM were discovered in observational studies. However, the causality of these associations still remains unknown. The present study aims to apply two-sample Mendelian randomization (TSMR) using genetic variants as instrumental variables (IVs) to evaluate the causality among WC, WHR, and T2DM. The participants were from three independent studies in genome-wide association studies (GWAS) datasets, which included 127,997 Europeans for WC, 73,137 Europeans for WHR and 659,316 Europeans for T2DM. Furthermore, 16 were associated WC SNPs and eight were associated WHR SNPs as instrument variables were selected for TSMR using P < 5 × 10-8 standard. The pooled odd ratios (ORs) for the assessment of higher WC and WHR on the risk of T2DM for these SNPs were calculated using inverse variance weighted (IVW) method, and validated through extensively complementary analyses. The OR for T2DM per SD (cm) higher WC was 2.623 (95% CI 2.286-3.010, P = 5.000E-43), and the OR for T2DM per SD (cm) higher WHR was 1.751 (95% CI 1.122-2.733, P = 0.014). Consistent results for other methods were obtained. Furthermore, the range of OR fluctuation between WC and T2DM was from 2.623 to 2.986, while that between WHR and T2DM was from 0.990 to 2.931. Overall, these present results provide genetic support that suggests that the use of TSMR, and higher WC and WHR increased the T2DM risk among the European population.

Genome-Wide Association Studies-Based Machine Learning for Prediction of Age-Related Macular Degeneration Risk.

PurposeBecause age-related macular degeneration (AMD) is a progressive disorder and advanced AMD is currently hard to cure, an accurate and informative prediction of a person's AMD risk using genetic information is desirable for early diagnosis and potential individualized clinical management. The objective of this study was to develop and validate novel prediction models for AMD risk using large genome-wide association studies datasets with different machine learning approaches.MethodsGenotype data from 32,215 Caucasian individuals with age of ≥50 years from the International AMD Genomics Consortium in dbGaP were used to establish and test prediction models for AMD risk. Four different machine learning approaches—neural network, lasso regression, support vector machine, and random forest—were implemented. A standard logistic regression model using a genetic risk score was also considered.ResultsAll machine learning–based methods achieved satisfactory performance for predicting advanced AMD cases (vs. normal controls) (area under the curve = 0.81–0.82, Brier score = 0.17–0.18 in a separate test dataset) and any stage AMD (vs. normal controls) (area under the curve = 0.78–0.79, Brier score = 0.18–0.20 in a separate test dataset). The prediction performance was further validated in an independent dataset of 783 subjects from UK Biobank (area under the curve = 0.67).ConclusionsBy applying multiple state-of-art machine learning approaches on large AMD genome-wide association studies datasets, the predictive models we established can provide an accurate estimation of an individual's AMD risk profile based on genetic information along with age. The online prediction interface is available at: https://yanq.shinyapps.io/no_vs_amd_NN/.Translational RelevanceThe accurate and individualized risk prediction model interface will greatly improve early diagnosis and enhance tailored clinical management of AMD.

Coupled mixed model for joint genetic analysis of complex disorders with two independently collected data sets

BackgroundIn the last decade, Genome-wide Association studies (GWASs) have contributed to decoding the human genome by uncovering many genetic variations associated with various diseases. Many follow-up investigations involve joint analysis of multiple independently generated GWAS data sets. While most of the computational approaches developed for joint analysis are based on summary statistics, the joint analysis based on individual-level data with consideration of confounding factors remains to be a challenge.ResultsIn this study, we propose a method, called Coupled Mixed Model (CMM), that enables a joint GWAS analysis on two independently collected sets of GWAS data with different phenotypes. The CMM method does not require the data sets to have the same phenotypes as it aims to infer the unknown phenotypes using a set of multivariate sparse mixed models. Moreover, CMM addresses the confounding variables due to population stratification, family structures, and cryptic relatedness, as well as those arising during data collection such as batch effects that frequently appear in joint genetic studies. We evaluate the performance of CMM using simulation experiments. In real data analysis, we illustrate the utility of CMM by an application to evaluating common genetic associations for Alzheimer’s disease and substance use disorder using datasets independently collected for the two complex human disorders. Comparison of the results with those from previous experiments and analyses supports the utility of our method and provides new insights into the diseases. The software is available at https://github.com/HaohanWang/CMM.

Identifying Thyroid Carcinoma-Related Genes by Integrating GWAS and eQTL Data

Thyroid carcinoma (TC) is the most common endocrine malignancy. The incidence rate of thyroid cancer has increased rapidly in recent years. The occurrence and development of thyroid cancers are highly related to the massive genetic and epigenetic changes. Therefore, it is essential to explore the mechanism of thyroid cancer pathogenesis. Genome-Wide Association Studies (GWAS) have been widely used in various diseases. Researchers have found multiple single nucleotide polymorphisms (SNPs) are significantly related to TC. However, the biological mechanism of these SNPs is still unknown. In this paper, we used one GWAS dataset and two eQTL datasets, and integrated GWAS with expression quantitative trait loci (eQTL) in both thyroid and blood to explore the mechanism of mutations and causal genes of thyroid cancer. Finally, we found rs1912998 regulates the expression of IGFALS (P = 1.70E-06) and HAGH (P = 5.08E-07) in thyroid, which is significantly related to thyroid cancer. In addition, KEGG shows that these genes participate in multiple thyroid cancer-related pathways.

Integrative Analysis of Transcriptome-Wide Association Study and mRNA Expression Profiles Identified Candidate Genes and Pathways Associated With Acute Myocardial Infarction.

BackgroundAcute myocardial infarction (AMI), characterized by an event of myocardial necrosis, is a common cardiac emergency worldwide. However, the genetic mechanisms of AMI remain largely elusive.MethodsA genome-wide association study dataset of AMI was obtained from the CARDIoGRAMplusC4D project. A transcriptome-wide association study (TWAS) was conducted using the FUSION tool with gene expression references of the left ventricle and whole blood. Significant genes detected by TWAS were subjected to Gene Ontology (GO) enrichment analysis. Then the TWAS results of AMI were integrated with mRNA expression profiling to identify common genes and biological processes. Finally, the identified common genes were validated by RT-qPCR analysis.ResultsTWAS identified 1,050 genes for the left ventricle and 1,079 genes for whole blood. Upon comparison with the mRNA expression profile, 4 common genes were detected, including HP (PTWAS = 1.22 × 10–3, PGEO = 4.98 × 10–2); CAMP (PTWAS = 2.48 × 10–2, PGEO = 2.36 × 10–5); TNFAIP6 (PTWAS = 1.90 × 10–2, PGEO = 3.46 × 10–2); and ARG1 (PTWAS = 8.35 × 10–3, PGEO = 4.93 × 10–2). Functional enrichment analysis of the genes identified by TWAS detected multiple AMI-associated biological processes, including autophagy of mitochondrion (GO: 0000422) and mitochondrion disassembly (GO: 0061726).ConclusionThis integrative study of TWAS and mRNA expression profiling identified multiple candidate genes and biological processes for AMI. Our results may provide a fundamental clue for understanding the genetic mechanisms of AMI.

Identification of Candidate Parkinson Disease Genes by Integrating Genome-Wide Association Study, Expression, and Epigenetic Data Sets

Substantial genome-wide association study (GWAS) work in Parkinson disease (PD) has led to the discovery of an increasing number of loci shown reliably to be associated with increased risk of disease. Improved understanding of the underlying genes and mechanisms at these loci will be key to understanding the pathogenesis of PD. To investigate what genes and genomic processes underlie the risk of sporadic PD. This genetic association study used the bioinformatic tools Coloc and transcriptome-wide association study (TWAS) to integrate PD case-control GWAS data published in 2017 with expression data (from Braineac, the Genotype-Tissue Expression [GTEx], and CommonMind) and methylation data (derived from UK Parkinson brain samples) to uncover putative gene expression and splicing mechanisms associated with PD GWAS signals. Candidate genes were further characterized using cell-type specificity, weighted gene coexpression networks, and weighted protein-protein interaction networks. It was hypothesized a priori that some genes underlying PD loci would alter PD risk through changes to expression, splicing, or methylation. Candidate genes are presented whose change in expression, splicing, or methylation are associated with risk of PD as well as the functional pathways and cell types in which these genes have an important role. Gene-level analysis of expression revealed 5 genes (WDR6 [OMIM 606031], CD38 [OMIM 107270], GPNMB [OMIM 604368], RAB29 [OMIM 603949], and TMEM163 [OMIM 618978]) that replicated using both Coloc and TWAS analyses in both the GTEx and Braineac expression data sets. A further 6 genes (ZRANB3 [OMIM 615655], PCGF3 [OMIM 617543], NEK1 [OMIM 604588], NUPL2 [NCBI 11097], GALC [OMIM 606890], and CTSB [OMIM 116810]) showed evidence of disease-associated splicing effects. Cell-type specificity analysis revealed that gene expression was overall more prevalent in glial cell types compared with neurons. The weighted gene coexpression performed on the GTEx data set showed that NUPL2 is a key gene in 3 modules implicated in catabolic processes associated with protein ubiquitination and in the ubiquitin-dependent protein catabolic process in the nucleus accumbens, caudate, and putamen. TMEM163 and ZRANB3 were both important in modules in the frontal cortex and caudate, respectively, indicating regulation of signaling and cell communication. Protein interactor analysis and simulations using random networks demonstrated that the candidate genes interact significantly more with known mendelian PD and parkinsonism proteins than would be expected by chance. Together, these results suggest that several candidate genes and pathways are associated with the findings observed in PD GWAS studies.