GWAS-Top Polymorphisms Associated With Late-Onset Alzheimer Disease in Brazil: Pointing Out Possible New Culprits Among Non-Coding RNAs

Several genome-wide association studies (GWAS) have been carried out with late-onset Alzheimer’s disease (LOAD), mainly in European and Asian populations. Different polymorphisms were associated, but several of them without a functional explanation. GWAS are fundamental for identifying loci associated with diseases, although they often do not point to causal polymorphisms. In this sense, functional investigations are a fundamental tool for discovering causality, although the failure of this validation does not necessarily indicate a non-causality. Furthermore, the allele frequency of associated genetic variants may vary widely between populations, requiring replication of these associations in other ethnicities. In this sense, our study sought to replicate in 150 AD patients and 114 elderly controls from the South Brazilian population 18 single-nucleotide polymorphisms (SNPs) associated with AD in European GWAS, with further functional investigation using bioinformatic tools for the associated SNPs. Of the 18 SNPs investigated, only four were associated in our population: rs769449 (APOE), rs10838725 (CELF1), rs6733839, and rs744373 (BIN1–CYP27C1). We identified 54 variants in linkage disequilibrium (LD) with the associated SNPs, most of which act as expression or splicing quantitative trait loci (eQTLs/sQTLs) in genes previously associated with AD or with a possible functional role in the disease, such as CELF1, MADD, MYBPC3, NR1H3, NUP160, SPI1, and TOMM40. Interestingly, eight of these variants are located within long non-coding RNA (lncRNA) genes that have not been previously investigated regarding AD. Some of these polymorphisms can result in changes in these lncRNAs’ secondary structures, leading to either loss or gain of microRNA (miRNA)-binding sites, deregulating downstream pathways. Our pioneering work not only replicated LOAD association with polymorphisms not yet associated in the Brazilian population but also identified six possible lncRNAs that may interfere in LOAD development. The results lead us to emphasize the importance of functional exploration of associations found in large-scale association studies in different populations to base personalized and inclusive medicine in the future.

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Secure large-scale genome-wide association studies using homomorphic encryption

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1918257117 ◽

2020 ◽

Vol 117 (21) ◽

pp. 11608-11613 ◽

Cited By ~ 1

Author(s):

Marcelo Blatt ◽

Alexander Gusev ◽

Yuriy Polyakov ◽

Shafi Goldwasser

Keyword(s):

Large Scale ◽

Homomorphic Encryption ◽

Association Studies ◽

Genome Wide Association ◽

Single Server ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

User Interactions ◽

Individual Level ◽

Genome Wide

Genome-wide association studies (GWASs) seek to identify genetic variants associated with a trait, and have been a powerful approach for understanding complex diseases. A critical challenge for GWASs has been the dependence on individual-level data that typically have strict privacy requirements, creating an urgent need for methods that preserve the individual-level privacy of participants. Here, we present a privacy-preserving framework based on several advances in homomorphic encryption and demonstrate that it can perform an accurate GWAS analysis for a real dataset of more than 25,000 individuals, keeping all individual data encrypted and requiring no user interactions. Our extrapolations show that it can evaluate GWASs of 100,000 individuals and 500,000 single-nucleotide polymorphisms (SNPs) in 5.6 h on a single server node (or in 11 min on 31 server nodes running in parallel). Our performance results are more than one order of magnitude faster than prior state-of-the-art results using secure multiparty computation, which requires continuous user interactions, with the accuracy of both solutions being similar. Our homomorphic encryption advances can also be applied to other domains where large-scale statistical analyses over encrypted data are needed.

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Animal-ImputeDB: a comprehensive database with multiple animal reference panels for genotype imputation

Nucleic Acids Research ◽

10.1093/nar/gkz854 ◽

2019 ◽

Vol 48 (D1) ◽

pp. D659-D667 ◽

Cited By ~ 2

Author(s):

Wenqian Yang ◽

Yanbo Yang ◽

Cecheng Zhao ◽

Kun Yang ◽

Dongyang Wang ◽

...

Keyword(s):

Large Scale ◽

Association Studies ◽

Genotype Imputation ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

High Quality ◽

Single Nucleotide ◽

Genome Wide ◽

Whole Genome Resequencing ◽

Missing Genotypes

Abstract Animal-ImputeDB (http://gong_lab.hzau.edu.cn/Animal_ImputeDB/) is a public database with genomic reference panels of 13 animal species for online genotype imputation, genetic variant search, and free download. Genotype imputation is a process of estimating missing genotypes in terms of the haplotypes and genotypes in a reference panel. It can effectively increase the density of single nucleotide polymorphisms (SNPs) and thus can be widely used in large-scale genome-wide association studies (GWASs) using relatively inexpensive and low-density SNP arrays. However, most animals except humans lack high-quality reference panels, which greatly limits the application of genotype imputation in animals. To overcome this limitation, we developed Animal-ImputeDB, which is dedicated to collecting genotype data and whole-genome resequencing data of nonhuman animals from various studies and databases. A computational pipeline was developed to process different types of raw data to construct reference panels. Finally, 13 high-quality reference panels including ∼400 million SNPs from 2265 samples were constructed. In Animal-ImputeDB, an easy-to-use online tool consisting of two popular imputation tools was designed for the purpose of genotype imputation. Collectively, Animal-ImputeDB serves as an important resource for animal genotype imputation and will greatly facilitate research on animal genomic selection and genetic improvement.

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Genetically Predicted Circulating Concentrations of Micronutrients and Risk of Amyotrophic Lateral Sclerosis: A Mendelian Randomization Study

Frontiers in Genetics ◽

10.3389/fgene.2021.811699 ◽

2022 ◽

Vol 12 ◽

Author(s):

Changqing Mu ◽

Yating Zhao ◽

Chen Han ◽

Dandan Tian ◽

Na Guo ◽

...

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Large Scale ◽

Mendelian Randomization ◽

Association Studies ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Inverse Variance ◽

Effective Role ◽

Copper Zinc ◽

Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive and devastating neurodegenerative disease with increasing incidence and high mortality, resulting in a considerable socio-economic burden. Till now, plenty of studies have explored the potential relationship between circulating levels of various micronutrients and ALS risk. However, the observations remain equivocal and controversial. Thus, we conducted a two-sample Mendelian randomization (MR) study to investigate the causality between circulating concentrations of 9 micronutrients, including retinol, folate acid, vitamin B12, B6 and C, calcium, copper, zinc as well as magnesium, and ALS susceptibility. In our analysis, several single nucleotide polymorphisms were collected as instrumental variables from large-scale genome-wide association studies of these 9 micronutrients. Then, inverse variance weighted (IVW) approach as well as alternative MR-Egger regression, weighted median and MR-pleiotropy residual sum and outlier (MR-PRESSO) analyses were performed to evaluate causal estimates. The results from IVW analysis showed that there was no causal relationship of 9 micronutrients with ALS risk. Meanwhile, the three complementary approaches obtained similar results. Thus, our findings indicated that supplementation of these 9 micronutrients may not play a clinically effective role in preventing the occurrence of ALS.

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Identifying Genetic Interactions Associated with Late-Onset Alzheimer’s Disease

10.7287/peerj.preprints.123 ◽

2013 ◽

Author(s):

Charalampos S Floudas ◽

Nara Um ◽

M. Ilyas Kamboh ◽

Michael M Barmada ◽

Shyam Visweswaran

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Late Onset ◽

Association Studies ◽

Univariate Analysis ◽

Genetic Interactions ◽

Supporting Evidence ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Genome Wide

Background Identifying genetic interactions in data obtained from genome-wide association studies (GWASs) can help in understanding the genetic basis of complex diseases. The large number of single nucleotide polymorphisms (SNPs) in GWASs however makes the identification of genetic interactions computationally challenging. We developed the Bayesian Combinatorial Method (BCM) that can identify pairs of SNPs that in combination have high statistical association with disease. Results We applied BCM to two late-onset Alzheimer’s disease (LOAD) GWAS datasets to identify SNP-SNP interactions between a set of known SNP associations and the dataset SNPs. For evaluation we compared our results with those from logistic regression, as implemented in PLINK. Gene Ontology analysis of genes from the top 200 dataset SNPs for both GWAS datasets showed overrepresentation of LOAD-related terms. Four genes were common to both datasets: APOE and APOC1, which have well established associations with LOAD, and CAMK1D and FBXL13, not previously linked to LOAD but having evidence of involvement in LOAD. Supporting evidence was also found for additional genes from the top 30 dataset SNPs. Conclusion BCM performed well in identifying several SNPs having evidence of involvement in the pathogenesis of LOAD that would not have been identified by univariate analysis due to small main effect. These results provide support for applying BCM to identify potential genetic variants such as SNPs from high dimensional GWAS datasets.

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Pleiotropy of Alzheimer’s Disease and Educational Attainment: Insights from the Summary Statistics

Innovation in Aging ◽

10.1093/geroni/igab046.3513 ◽

2021 ◽

Vol 5 (Supplement_1) ◽

pp. 986-986

Author(s):

Yury Loika ◽

Elena Loiko ◽

Irina Culminskaya ◽

Alexander Kulminski

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Educational Attainment ◽

Large Scale ◽

Association Studies ◽

Epidemiological Studies ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Nucleotide Polymorphisms ◽

Omnibus Test

Abstract Epidemiological studies report beneficial associations of higher educational attainment (EDU) with Alzheimer’s disease (AD). Prior genome-wide association studies (GWAS) also reported variants associated with AD and EDU separately. The analysis of pleiotropic predisposition to these phenotypes may shed light on EDU-related protection against AD. We examined pleiotropic predisposition to AD and EDU using Fisher’s method and omnibus test applied to summary statistics for single nucleotide polymorphisms (SNPs) associated with AD and EDU in large-scale univariate GWAS at suggestive-effect (5×10-8

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Cigarette smoking and schizophrenia: Mendelian randomisation study

The British Journal of Psychiatry ◽

10.1192/bjp.2020.116 ◽

2020 ◽

pp. 1-6

Author(s):

Jianhua Chen ◽

Ruirui Chen ◽

Siying Xiang ◽

Ningning Li ◽

Chengwen Gao ◽

...

Keyword(s):

Cigarette Smoking ◽

Association Studies ◽

Smoking Initiation ◽

Sensitivity Analyses ◽

Mendelian Randomisation ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Complex Behaviour ◽

Asian Populations ◽

Smoking Behaviours

Background The link between schizophrenia and cigarette smoking has been well established through observational studies. However, the cause–effect relationship remains unclear. Aims We conducted Mendelian randomisation analyses to assess any causal relationship between genetic variants related to four smoking-related traits and the risk of schizophrenia. Method We performed a two-sample Mendelian randomisation using summary statistics from genome-wide association studies (GWAS) of smoking-related traits and schizophrenia (7711 cases, 18 327 controls) in East Asian populations. Single nucleotide polymorphisms (SNPs) correlated with smoking behaviours (smoking initiation, smoking cessation, age at smoking initiation and quantity of smoking) were investigated in relation to schizophrenia using the inverse-variance weighted (IVW) method. Further sensitivity analyses, including Mendelian randomisation-Egger (MR-Egger), weighted median estimates and leave-one-out analysis, were used to test the consistency of the results. Results The associated SNPs for the four smoking behaviours were not significantly associated with schizophrenia status. Pleiotropy did not inappropriately affect the results. Conclusions Cigarette smoking is a complex behaviour in people with schizophrenia. Understanding factors underlying the observed association remains important; however, our findings do not support a causal role of smoking in influencing risk of schizophrenia.

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Large-Scale Phenomic and Genomic Analysis of Brain Asymmetrical Skew

Cerebral Cortex ◽

10.1093/cercor/bhab075 ◽

2021 ◽

Author(s):

Xiang-Zhen Kong ◽

Merel Postema ◽

Dick Schijven ◽

Amaia Carrión Castillo ◽

Antonietta Pepe ◽

...

Keyword(s):

Large Scale ◽

Right Hemisphere ◽

Association Studies ◽

Genomic Analysis ◽

Population Level ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Imaging Data ◽

Physical And Mental Health ◽

Human Connectome Project

Abstract The human cerebral hemispheres show a left–right asymmetrical torque pattern, which has been claimed to be absent in chimpanzees. The functional significance and developmental mechanisms are unknown. Here, we carried out the largest-ever analysis of global brain shape asymmetry in magnetic resonance imaging data. Three population datasets were used, UK Biobank (N = 39 678), Human Connectome Project (N = 1113), and BIL&GIN (N = 453). At the population level, there was an anterior and dorsal skew of the right hemisphere, relative to the left. Both skews were associated independently with handedness, and various regional gray and white matter metrics oppositely in the two hemispheres, as well as other variables related to cognitive functions, sociodemographic factors, and physical and mental health. The two skews showed single nucleotide polymorphisms-based heritabilities of 4–13%, but also substantial polygenicity in causal mixture model analysis, and no individually significant loci were found in genome-wide association studies for either skew. There was evidence for a significant genetic correlation between horizontal brain skew and autism, which requires future replication. These results provide the first large-scale description of population-average brain skews and their inter-individual variations, their replicable associations with handedness, and insights into biological and other factors which associate with human brain asymmetry.

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A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics

BMC Medicine ◽

10.1186/s12916-021-02186-z ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Haojie Lu ◽

Jiahao Qiao ◽

Zhonghe Shao ◽

Ting Wang ◽

Shuiping Huang ◽

...

Keyword(s):

Psychiatric Disorders ◽

Genetic Correlation ◽

Large Scale ◽

Association Studies ◽

Single Gene ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Nucleotide Polymorphisms ◽

Entire Genome ◽

Gene Level

Abstract Background Recent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear. Methods We analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders. Results We confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders. Conclusions Our study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.

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Genetic combination risk for schizophrenia

10.1101/478958 ◽

2018 ◽

Cited By ~ 1

Author(s):

Kengo Oishi ◽

Tomihisa Niitsu ◽

Nobuhisa Kanahara ◽

Tasuku Hashimoto ◽

Hideki Komatsu ◽

...

Keyword(s):

Large Scale ◽

Disease Risk ◽

Mental Disease ◽

Association Studies ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Potential Candidate ◽

Nucleotide Polymorphisms ◽

Functional Snps ◽

Dopamine Signaling

Summary ParagraphSchizophrenia is a highly hereditary mental disease1 related to abnormal dopaminergic activities.2,3 To elucidate the mechanisms underlying schizophrenia’s development, genomic studies have sought to identify the pathogenic genetic polymorphisms. Large-scale genome-wide association studies (GWAS) have reported potential candidate loci that contribute to schizophrenia’s development.4,5 The risk genetic profiles are not yet established. Here we show that the combination of three functional single nucleotide polymorphisms (SNPs) related to the key factors of dopaminergic signaling can be used to predict the risk of schizophrenia’s development, though none of the SNPs is known to be associated by itself. These functional SNPs were reported to demonstrate directional influences in their parent gene activity, perhaps characterizing the integrated properties of dopaminergic signaling. Interestingly, the risk combination presented here included the major genotype as well as the minor polymorphisms, suggesting a possible association of unaffected activities of some dopamine-related genes with the disease development. The phenotype speculated based on the allelic status seemed consistent with the conventional pathophysiological hypotheses, although recently developed predictive methods, such as the polygenic risk score, could miss this potent pathogenic role of carrying a normal genotype by evaluating only minor polymorphisms. Our results demonstrate the presence of a subtype in schizophrenia with the favored genetic background related to dopamine signaling. Our findings indicate the possibility that the combinations could characterize integrated biological functions (including neurotransmission) and therefore identify individuals with a disease risk. The biological microenvironment indicated by the functional SNPs could bring an insight to elucidate the pathogenic mechanisms of developing schizophrenia. Furthermore, we believe that our approach will contribute to the development of innovative means to predict disease risks even for other multi-factorial diseases and then, the following preventive medicine.

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Association between PRDM16, MEF2D, TRPM8, LRP1 gene polymorphisms and migraine susceptibility in the She ethnic population in China

Clinical & Investigative Medicine ◽

10.25011/cim.v42i1.32389 ◽

2019 ◽

Vol 42 (1) ◽

pp. E21-E30 ◽

Cited By ~ 3

Author(s):

Xianguo Fu ◽

Jing Yang ◽

Xiaoyang Wu ◽

Qifang Lin ◽

Yuli Zeng ◽

...

Keyword(s):

Association Studies ◽

Direct Sequencing ◽

Significant Risk ◽

Significant Risk Factor ◽

Han Chinese ◽

Small Sample ◽

Genome Wide Association Studies ◽

Healthy Controls ◽

Nucleotide Polymorphisms ◽

Asian Populations

Background: The prevalence of migraines in the She population, a minority in China, is significantly higher than that in Han Chinese and other Asian populations. Two single nucleotide polymorphisms (SNPs) have been found to be associated with migraine susceptibility in the She population. Purpose: This study investigated four SNPs, identified in genome-wide association studies, within migraine-susceptible loci in Han Chinese for their association with migraine susceptibility in the She population. Methods: Two-hundred unrelated migraine patients and 200 healthy controls were recruited. The SNPs examined included rs2651899 (PRDM16 ), rs2274316 (MEF2D ), rs7577262 (TRPM8) and rs11172113 (LRP1). Genotyping of the SNPs was performed by allele-specific polymerase chain reaction and direct sequencing. Results: No significant differences between the participants with migraines and controls (participants without migraines) were demonstrated in genotypes, alleles and allele carriage frequencies for the four SNPs. A subgroup analysis found that migraine with aura had a lower frequency of C allele positivity in rs2651899 than in healthy controls (59.6% vs. 74.5%, respectively; P < 0.034). Univariate analyses indicated that no genotype of the four SNPs had a significant association with migraines. Males had a lower risk of migraines, and advanced age was a significant risk factor for migraines in females. Conclusion: The SNPs in four migraine susceptible loci in Han Chinese were not risk factors for migraines in a relatively small sample of the She population.

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