Biobank-wide association scan identifies risk factors for late-onset Alzheimer’s disease and endophenotypes

Yan, Da; Hu, Bing; Bf, Darst; Mukherjee, Shubhabrata; Bw, Kunkle; Deming, Yuetiva; Dumitrescu, Logan; Wang, Y; Naj, Adam C; Kuzma, Amanda B.; Zhao, Yi; Kang, Hyun-Seung; Sc, Johnson; Cruchaga, Carlos; Tj, Hohman; Crane, PK; Engelman, CD

doi:10.1101/468306

Cited by 11 publications

(16 citation statements)

References 61 publications

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“…The average predictive correlation from four-fold cross-validation was used to identify the bestperforming model for each metabolite. The metabolite prediction models were then used to impute and test the associations of the CSF metabolites with 27 brain-related phenotypes from available GWAS summary statistics using the BADGERS approach 14 .…”

Section: Methods Overviewmentioning

confidence: 99%

“…The BADGERS (Biobank-wide Association Discovery using GEnetic Risk Scores) software package 14 was used to test the association of each imputed CSF metabolite with each of the GWAS summary statistics phenotypes ( Supplementary Table 10). A Q-Q plot of all BADGERS association test results was created to assess potential inflation ( Supplementary Figure 26).…”

Section: Metabolite-phenotype Association Testingmentioning

confidence: 99%

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Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

Panyard

Kim

Darst

et al. 2020

Preprint

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Advances in technology have allowed for the study of metabolomics in the context of disease, enabling the discovery of new potential risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular biomedical importance as it is in direct contact with the brain and spinal cord. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid compared to blood or urine studies. Here, we present a metabolome-wide association study (MWAS), an analysis using individual-level genetic and metabolomic data from two cohorts to impute metabolites into large samples with genome-wide association summary statistics. We conducted a metabolome-wide genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations, 6 of which were novel. Using these results, we then built prediction models for all available CSF metabolites and tested for associations with 27 neurological and psychiatric phenotypes in large cohorts, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the potential of MWAS to overcome the logistic challenges inherent in cerebrospinal fluid research to study the role of metabolomics in brain-related phenotypes and the feasibility of this framework for similar studies of omic data in scarce sample types.

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Section: Methods Overviewmentioning

confidence: 99%

Section: Metabolite-phenotype Association Testingmentioning

confidence: 99%

Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

Panyard

Kim

Darst

et al. 2020

Preprint

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“…This provided a first level of validation that the genetic signal in our analysis was correlated with common phenotypes (e.g., cognitive performance and educational attainment) while also providing insight into novel resilience associations. Additionally, we replicated our top genomic correlation results leveraging the BADGERS program (Yan et al, 2018) and quantified correlation across 1,738 traits in the UK Biobank (http://biobank.ndph.ox.ac.uk/showcase/). To aid in interpretation of genetic covariance results, we also quantified heritability estimates using the Genome-wide Complex Trait Analysis (GCTA) tool (Yang et al, 2011).…”

Section: Genetic Markers Of Resilience 12mentioning

confidence: 84%

“…As a second level of validation, we also quantified genetic correlations with phenotypes in the UK Biobank leveraging a recently published method to perform phenome-wide association analyses leveraging summary statistics (Yan et al, 2018).…”

Section: Genetic Covariance Resultsmentioning

confidence: 99%

Genetic Variants and Functional Pathways Associated with Resilience to Alzheimer’s Disease

Dumitrescu

Mahoney

Mukherjee

et al. 2020

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Approximately 30% of older adults exhibit the neuropathologic features of Alzheimer's disease (AD) without signs of cognitive impairment. Yet, little is known about the genetic factors that allow these potentially resilient individuals to remain cognitively normal in the face of substantial neuropathology. We performed a large, genome-wide association study (GWAS) of two previously validated metrics of cognitive resilience quantified using a latent variable modeling approach and representing betterthan-predicted cognitive performance for a given level of neuropathology. Data were harmonized across 5,108 participants from a clinical trial of AD and three longitudinal cohort studies of cognitive aging. All analyses were run across all participants and repeated restricting the sample to individuals with normal cognition to identify variants at the earliest stages of disease. As expected, all resilience metrics were genetically correlated with cognitive performance and education attainment traits (p-values<2.5x10 -20 ), and we observed novel correlations with neuropsychiatric conditions

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“…44 BADGERS software was used to conduct the imaging trait PRS-AD association analysis. 33 Metaanalysis was conducted using the sample size-weighted approach. 45…”

Section: Identifying Neuroimaging Traits Associated With Admentioning

confidence: 99%

Fine-tuning Polygenic Risk Scores with GWAS Summary Statistics

Zhao

et al. 2019

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Polygenic risk scores (PRSs) have wide applications in human genetics research. Notably, most PRS models include tuning parameters which improve predictive performance when properly selected. However, existing model-tuning methods require validation data that is independent with both training and testing samples. These data rarely exist in practice, creating a significant gap between PRS methodology and applications. Here, we introduce PUMAS, a novel method to finetune PRS models using summary statistics from genome-wide association studies (GWASs). Through extensive simulations, external validations, and analysis of 65 GWAS traits, we demonstrate that PUMAS can perform a variety of model-tuning procedures (e.g. cross-validation) using GWAS summary statistics and can effectively benchmark and optimize PRS models under diverse genetic architecture. Applied to 211 neuroimaging traits and Alzheimer's disease, we show that fine-tuned PRSs will improve statistical power in association analysis. We believe our method resolves a fundamental problem without a current solution and will greatly benefit genetic prediction applications.

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Biobank-wide association scan identifies risk factors for late-onset Alzheimer’s disease and endophenotypes

Cited by 11 publications

References 61 publications

Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

Genetic Variants and Functional Pathways Associated with Resilience to Alzheimer’s Disease

Fine-tuning Polygenic Risk Scores with GWAS Summary Statistics

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