scholarly journals Fast Algorithms for Conducting Large-Scale GWAS of Age-at-Onset Traits Using Cox Mixed-Effects Models

Genetics ◽  
2020 ◽  
Vol 215 (1) ◽  
pp. 41-58 ◽  
Author(s):  
Liang He ◽  
Alexander M. Kulminski
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Statistical Power ◽  
Large Scale ◽  
Cox Model ◽  
Association Studies ◽  
Age At Onset ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Genome Wide Association Studies

Age-at-onset is one of the critical traits in cohort studies of age-related diseases. Large-scale genome-wide association studies (GWAS) of age-at-onset traits can provide more insights into genetic effects on disease progression and transitions between stages. Moreover, proportional hazards (or Cox) regression models can achieve higher statistical power in a cohort study than a case-control trait using logistic regression. Although mixed-effects models are widely used in GWAS to correct for sample dependence, application of Cox mixed-effects models (CMEMs) to large-scale GWAS is so far hindered by intractable computational cost. In this work, we propose COXMEG, an efficient R package for conducting GWAS of age-at-onset traits using CMEMs. COXMEG introduces fast estimation algorithms for general sparse relatedness matrices including, but not limited to, block-diagonal pedigree-based matrices. COXMEG also introduces a fast and powerful score test for dense relatedness matrices, accounting for both population stratification and family structure. In addition, COXMEG generalizes existing algorithms to support positive semidefinite relatedness matrices, which are common in twin and family studies. Our simulation studies suggest that COXMEG, depending on the structure of the relatedness matrix, is orders of magnitude computationally more efficient than coxme and coxph with frailty for GWAS. We found that using sparse approximation of relatedness matrices yielded highly comparable results in controlling false-positive rate and retaining statistical power for an ethnically homogeneous family-based sample. By applying COXMEG to a study of Alzheimer’s disease (AD) with a Late-Onset Alzheimer’s Disease Family Study from the National Institute on Aging sample comprising 3456 non-Hispanic whites and 287 African Americans, we identified the APOE ε4 variant with strong statistical power (P = 1e−101), far more significant than that reported in a previous study using a transformed variable and a marginal Cox model. Furthermore, we identified novel SNP rs36051450 (P = 2e−9) near GRAMD1B, the minor allele of which significantly reduced the hazards of AD in both genders. These results demonstrated that COXMEG greatly facilitates the application of CMEMs in GWAS of age-at-onset traits.

scholarly journals BIN1 rs744373 variant shows different association with Alzheimer’s disease in Caucasian and Asian populations

2019 ◽  
Vol 20 (S25) ◽  
Author(s):  
Zhifa Han ◽  
Tao Wang ◽  
Rui Tian ◽  
Wenyang Zhou ◽  
Pingping Wang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Statistical Power ◽  
Large Scale ◽  
Association Studies ◽  
Meta Analysis ◽  
East Asian ◽  
Additive Model ◽  
Genome Wide Association Studies ◽  
Asian Populations

Abstract Background The association between BIN1 rs744373 variant and Alzheimer’s disease (AD) had been identified by genome-wide association studies (GWASs) as well as candidate gene studies in Caucasian populations. But in East Asian populations, both positive and negative results had been identified by association studies. Considering the smaller sample sizes of the studies in East Asian, we believe that the results did not have enough statistical power. Results We conducted a meta-analysis with 71,168 samples (22,395 AD cases and 48,773 controls, from 37 studies of 19 articles). Based on the additive model, we observed significant genetic heterogeneities in pooled populations as well as Caucasians and East Asians. We identified a significant association between rs744373 polymorphism with AD in pooled populations (P = 5 × 10− 07, odds ratio (OR) = 1.12, and 95% confidence interval (CI) 1.07–1.17) and in Caucasian populations (P = 3.38 × 10− 08, OR = 1.16, 95% CI 1.10–1.22). But in the East Asian populations, the association was not identified (P = 0.393, OR = 1.057, and 95% CI 0.95–1.15). Besides, the regression analysis suggested no significant publication bias. The results for sensitivity analysis as well as meta-analysis under the dominant model and recessive model remained consistent, which demonstrated the reliability of our finding. Conclusions The large-scale meta-analysis highlighted the significant association between rs744373 polymorphism and AD risk in Caucasian populations but not in the East Asian populations.

scholarly journals Genome-wide association analysis of age-at-onset traits using Cox mixed-effects models

2019 ◽  
Author(s):  
Liang He ◽  
Alexander M. Kulminski
Keyword(s):  
Family Structure ◽  
Population Stratification ◽  
Statistical Power ◽  
Large Scale ◽  
Age At Onset ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractAge-at-onset is one of the critical phenotypes in cohort studies of age-related diseases. Large-scale genome-wide association studies (GWAS) of age-at-onset can provide more insights into genetic effects on disease progression, and transitions between different stages. Moreover, proportional hazards or Cox regression generally achieves higher statistical power in a cohort study than a binary trait using logistic regression. Although mixed-effects models are widely used in GWAS to correct for population stratification and family structure, application of Cox mixed-effects models (CMEMs) to large-scale GWAS are so far hindered by intractable computational intensity. In this work, we propose COXMEG, an efficient R package for conducting GWAS of age-at-onset using CMEMs. COXMEG introduces fast estimation algorithms for general sparse relatedness matrices including but not limited to block-diagonal pedigree-based matrices. COXMEG also introduces a fast and powerful score test for fully dense relatedness matrices, accounting for both population stratification and family structure. In addition, COXMEG handles positive semidefinite relatedness matrices, which are common in twin and family studies. Our simulation studies suggest that COXMEG, depending on the structure of the relatedness matrix, is 100∼100,000-fold computationally more efficient for GWAS than coxme for a sample consisting of 1000-10,000 individuals. We found that using sparse approximation of relatedness matrices yielded highly comparable performance in controlling false positives and statistical power for an ethnically homogeneous family-based sample. When applying COXMEG to a NIA-LOADFS sample with 3456 Caucasians, we identified the APOE4 variant with strong statistical power (p=1e-101), far more significant than previous studies using a transformed variable and a marginal Cox model. When investigating a multi-ethnic NIA-LOADFS sample including 3456 Caucasians and 287 African Americans, we identified a novel SNP rs36051450 (p=2e-9) near GRAMD1B, the minor allele of which significantly reduced the hazards of AD in both genders. Our results demonstrated that COXMEG greatly facilitates the application of CMEMs in GWAS of age-at-onset phenotypes.

scholarly journals PVRL2 rs6859 Modifies the Effect of Triglyceride on Progression of Mild Cognitive Impairment: The Shanghai Aging Study

2020 ◽  
Author(s):  
Ruru Wang ◽  
Ding Ding ◽  
Abuduaili Atibaike ◽  
Jianxiong Xi ◽  
Qianhua Zhao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cox Model ◽  
Association Studies ◽  
Interactive Effect ◽  
Genome Wide Association Studies ◽  
Increased Risk ◽  
Aging Study

Abstract Background Mild cognitive impairment (MCI) is an intermediate stage between normal cognition and Alzheimer’s disease (AD). Genome-wide association studies (GWAS) have identified many AD-risk variants and indicated the important role of lipid metabolism pathway in AD progression. This study aimed to investigate the effects of triglyceride (TG) and genetic risk factors on progression from MCI to AD (MCI-AD progression).Methods The current study sample comprised of 305 MCI subjects aged 50 and over who were prospectively followed up for average 4.5 years in a sub-cohort of the Shanghai Aging Study. A consensus diagnosis of incident AD was conducted according to Diagnostic and Statistical Manual of Mental Disorders-IV and the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association criteria. Fasting blood samples were obtained at baseline for analyzing serum TG. Single nucleotide polymorphisms (SNPs) genotyping was performed using a MassARRAY system. The effect of TG, genetic variants and their interaction on MCI-AD progression were analyzed using Cox proportional hazards regression model.Results During a mean (±SD) follow-up period of 4.5±1.3 y, 58 subjects developed incident AD. The SNP, rs6859 in the Poliovirus Receptor–Related 2 (PVRL2) gene, was significantly associated with incident AD (false discovery rate (FDR)-adjusted P = 0.018). In multivariate cox model, the PVRL2 rs6859 AG, AA and AG+AA genotypes were associated with significantly increased incident AD, compared with the GG genotype (hazard ratio [HR] = 2.29, P = 0.029, and HR = 2.92, P = 0.013, and HR = 2.47, P =0.012, respectively). In PVRL2 rs6859 AG/AA carriers, higher ln TG was significantly associated with increased risk of incident AD (adjusted HR =2.64, P = 0.034). Ln TG and PVRL2 rs6859 had interactive effect on the MCI-AD progression (P Ln TG × rs6859 = 0.001). Conclusion The present study indicated that PVRL2 rs6859 modified the effect of TG on MCI-AD progression. Precision prevention in MCI population based on genetic information should be considered to avoid progression to AD.

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

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

scholarly journals A novel age-informed approach for genetic association analysis in Alzheimer’s disease

2021 ◽  
Author(s):  
Yann Le Guen ◽  
Michael E. Belloy ◽  
Valerio Napolioni ◽  
Sarah J. Eger ◽  
Gabriel Kennedy ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Logistic Regression ◽  
Genetic Association ◽  
Statistical Power ◽  
Cox Regression ◽  
Association Studies ◽  
Age At Onset ◽  
Simulated Data ◽  
Power Gain

ABSTRACTIntroductionMany Alzheimer’s disease (AD) genetic association studies disregard age or incorrectly account for it, hampering variant discovery.MethodUsing simulated data, we compared the statistical power of several models: logistic regression on AD diagnosis adjusted and not adjusted for age; linear regression on a score integrating case-control status and age; and multivariate Cox regression on age-at-onset. We applied these models to real exome-wide data of 11,127 sequenced individuals (54% cases) and replicated suggestive associations in 21,631 genotype-imputed individuals (51% cases).ResultsModelling variable AD risk across age results in 10-20% statistical power gain compared to logistic regression without age adjustment, while incorrect age adjustment leads to critical power loss. Applying our novel AD-age score and/or Cox regression, we discovered and replicated novel variants associated with AD on KIF21B, USH2A, RAB10, RIN3 and TAOK2 genes.DiscussionOur AD-age score provides a simple means for statistical power gain and is recommended for future AD studies.

scholarly journals A novel age-informed approach for genetic association analysis in Alzheimer’s disease

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Yann Le Guen ◽  
◽  
Michael E. Belloy ◽  
Valerio Napolioni ◽  
Sarah J. Eger ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Logistic Regression ◽  
Genetic Association ◽  
Statistical Power ◽  
Cox Regression ◽  
Association Studies ◽  
Age At Onset ◽  
Simulated Data ◽  
Power Gain

Abstract Background Many Alzheimer’s disease (AD) genetic association studies disregard age or incorrectly account for it, hampering variant discovery. Methods Using simulated data, we compared the statistical power of several models: logistic regression on AD diagnosis adjusted and not adjusted for age; linear regression on a score integrating case-control status and age; and multivariate Cox regression on age-at-onset. We applied these models to real exome-wide data of 11,127 sequenced individuals (54% cases) and replicated suggestive associations in 21,631 genotype-imputed individuals (51% cases). Results Modeling variable AD risk across age results in 5–10% statistical power gain compared to logistic regression without age adjustment, while incorrect age adjustment leads to critical power loss. Applying our novel AD-age score and/or Cox regression, we discovered and replicated novel variants associated with AD on KIF21B, USH2A, RAB10, RIN3, and TAOK2 genes. Conclusion Our AD-age score provides a simple means for statistical power gain and is recommended for future AD studies.

scholarly journals Functional biological paths altered in Alzheimer’s disease: from genes to bile acids

2020 ◽  
Author(s):  
Priyanka Gorijala ◽  
Kwangsik Nho ◽  
Shannon L. Risacher ◽  
Rima Kaddurah-Daouk ◽  
Andrew J. Saykin ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Bile Acids ◽  
Biological Networks ◽  
Large Scale ◽  
Association Studies ◽  
Underlying Mechanism ◽  
Genome Wide Association Studies ◽  
Functional Protein ◽  
Age Related

AbstractLarge-scale genome wide association studies (GWASs) have been performed in search for risk genes for Alzheimer’s disease (AD). Despite the significant progress, replicability of genetic findings and their translation into targetable mechanisms related to the disease pathogenesis remains a challenge. Given that bile acids have been suggested in recent metabolic studies as potential age-related metabolic factors associated with AD, we integrated genomic and metabolomic data together with heterogeneous biological networks and investigated the potential cascade of effect of genetic variations to proteins, bile acids and ultimately AD brain phenotypes. Particularly, we leveraged functional protein interaction networks and metabolic networks and focused on the genes directly interacting with AD-altered bile acids and their functional regulators. We examined the association of all the SNPs located in those candidate genes with AD brain imaging phenotypes, and identified multiple AD risk SNPs whose downstream genes and bile acids were also found to be altered in AD. These AD related markers span from genetics to metabolomics, forming functional biological paths connecting across multiple-omics layers, and give valuable insights into the underlying mechanism of AD.

scholarly journals Alzheimer’s disease risk SNPs show no strong effect on miRNA expression in human lymphoblastoid cell lines

2018 ◽  
Author(s):  
Inken Wohlers ◽  
Colin Schulz ◽  
Fabian Kilpert ◽  
Lars Bertram
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cell Lines ◽  
Mirna Expression ◽  
Large Scale ◽  
Disease Risk ◽  
Association Studies ◽  
Lymphoblastoid Cell Lines ◽  
Eqtl Analysis ◽  
Genome Wide Association Studies

AbstractThe role of microRNAs (miRNAs) in the pathogenesis of Alzheimer’s disease (AD) is currently extensively investigated. In this study, we assessed the potential impact of AD genetic risk variants on miRNA expression by performing large-scale bioinformatic data integration. Our analysis was based on genetic variants from three AD genome-wide association studies (GWAS). Association with miRNA expression was tested by expression quantitative trait loci (eQTL) analysis using next-generation miRNA sequencing data generated in lymphoblastoid cell lines (LCL). While, overall, we did not identify a strong effect of AD GWAS variants on miRNA expression in this cell type we highlight two notable outliers, i.e. miR-29c-5p and miR-6840-5p. MiR-29c-5p was recently reported to be involved in the regulation of BACE1 and SORL1 expression. In conclusion, despite two exceptions our large-scale assessment provides only limited support for the hypothesis that AD GWAS variants act as miRNA eQTLs.

scholarly journals Genomics and Functional Genomics of Alzheimer’s Disease

2021 ◽  
Author(s):  
M. Ilyas Kamboh
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Genomics ◽  
Early Onset ◽  
Late Onset ◽  
Association Studies ◽  
Age At Onset ◽  
Significant Risk ◽  
Major Public Health Problem ◽  
Genome Wide Association Studies

AbstractAlzheimer’s disease (AD) is a complex and multifactorial neurodegenerative disease. Due to its long clinical course and lack of an effective treatment, AD has become a major public health problem in the USA and worldwide. Due to variation in age-at-onset, AD is classified into early-onset (< 60 years) and late-onset (≥ 60 years) forms with early-onset accounting for only 5–10% of all cases. With the exception of a small number of early-onset cases that are afflicted because of high penetrant single gene mutations in APP, PSEN1, and PSEN2 genes, AD is genetically heterogeneous, especially the late-onset form having a polygenic or oligogenic risk inheritance. Since the identification of APOE as the most significant risk factor for late-onset AD in 1993, the path to the discovery of additional AD risk genes had been arduous until 2009 when the use of large genome-wide association studies opened up the discovery gateways that led the identification of ~ 95 additional risk loci from 2009 to early 2022. This article reviews the history of AD genetics followed by the potential molecular pathways and recent application of functional genomics methods to identify the causal AD gene(s) among the many genes that reside within a single locus. The ultimate goal of integrating genomics and functional genomics is to discover novel pathways underlying the AD pathobiology in order to identify drug targets for the therapeutic treatment of this heterogeneous disorder.

scholarly journals Sex differences in the genetic predictors of Alzheimer’s pathology

Brain ◽  
2019 ◽  
Vol 142 (9) ◽  
pp. 2581-2589 ◽  
Author(s):  
Logan Dumitrescu ◽  
Lisa L Barnes ◽  
Madhav Thambisetty ◽  
Gary Beecham ◽  
Brian Kunkle ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sex Differences ◽  
Disease Risk ◽  
Association Studies ◽  
Age At Onset ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Data Repositories ◽  
Genome Wide

Abstract Autopsy measures of Alzheimer’s disease neuropathology have been leveraged as endophenotypes in previous genome-wide association studies (GWAS). However, despite evidence of sex differences in Alzheimer’s disease risk, sex-stratified models have not been incorporated into previous GWAS analyses. We looked for sex-specific genetic associations with Alzheimer’s disease endophenotypes from six brain bank data repositories. The pooled dataset included 2701 males and 3275 females, the majority of whom were diagnosed with Alzheimer’s disease at autopsy (70%). Sex-stratified GWAS were performed within each dataset and then meta-analysed. Loci that reached genome-wide significance (P < 5 × 10−8) in stratified models were further assessed for sex interactions. Additional analyses were performed in independent datasets leveraging cognitive, neuroimaging and CSF endophenotypes, along with age-at-onset data. Outside of the APOE region, one locus on chromosome 7 (rs34331204) showed a sex-specific association with neurofibrillary tangles among males (P = 2.5 × 10−8) but not females (P = 0.85, sex-interaction P = 2.9 × 10−4). In follow-up analyses, rs34331204 was also associated with hippocampal volume, executive function, and age-at-onset only among males. These results implicate a novel locus that confers male-specific protection from tau pathology and highlight the value of assessing genetic associations in a sex-specific manner.

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