scholarly journals Identifying Nootropic Drug Targets via Large-Scale Cognitive GWAS and Transcriptomics

2020 ◽  
Author(s):  
Max Lam ◽  
Chen Chia-Yen ◽  
Xia Yan ◽  
W. David Hill ◽  
Joey W. Trampush ◽  
...  
Keyword(s):  
Cognitive Ability ◽  
Drug Targets ◽  
Large Scale ◽  
Drug Repurposing ◽  
Gene Identification ◽  
Summary Statistics ◽  
General Cognitive Ability ◽  
Nootropic Drug ◽  
Genome Wide ◽  
Meta Analyses

AbstractBackgroundCognitive traits demonstrate significant genetic correlations with many psychiatric disorders and other health-related traits. Many neuropsychiatric and neurodegenerative disorders are marked by cognitive deficits. Therefore, genome-wide association studies (GWAS) of general cognitive ability might suggest potential targets for nootropic drug repurposing. Our previous effort to identify “druggable genes” (i.e., GWAS-identified genes that produce proteins targeted by known small molecules) was modestly powered due to the small cognitive GWAS sample available at the time. Since then, two large cognitive GWAS meta-analyses have reported 148 and 205 genome-wide significant loci, respectively. Additionally, large-scale gene expression databases, derived from post-mortem human brain, have recently been made available for GWAS annotation. Here, we 1) reconcile results from these two cognitive GWAS meta-analyses to further enhance power for locus discovery; 2) employ several complementary transcriptomic methods to identify genes in these loci with variants that are credibly associated with cognition; and 3) further annotate the resulting genes to identify “druggable” targets.MethodsGWAS summary statistics were harmonized and jointly analysed using Multi-Trait Analysis of GWAS [MTAG], which is optimized for handling sample overlaps. Downstream gene identification was carried out using MAGMA, S-PrediXcan/S-TissueXcan Transcriptomic Wide Analysis, and eQTL mapping, as well as more recently developed methods that integrate GWAS and eQTL data via Summary-statistics Mendelian Randomization [SMR] and linkage methods [HEIDI], Available brain-specific eQTL databases included GTEXv7, BrainEAC, CommonMind, ROSMAP, and PsychENCODE. Intersecting credible genes were then annotated against multiple chemoinformatic databases [DGIdb, KI, and a published review on “druggability”].ResultsUsing our meta-analytic data set (N = 373,617) we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. 26 genes were associated with general cognitive ability via SMR, 16 genes via STissueXcan/S-PrediXcan, 47 genes via eQTL mapping, and 68 genes via MAGMA pathway analysis. The use of the HEIDI test permitted the exclusion of candidate genes that may have been artifactually associated to cognition due to linkage, rather than direct causal or indirect pleiotropic effects. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging on our various transcriptome and pathway analyses, as well as available chemoinformatic databases, we identified 16 putative genes that may suggest drug targets with nootropic properties.DiscussionResults converged on several categories of significant drug targets, including serotonergic and glutamatergic genes, voltage-gated ion channel genes, carbonic anhydrase genes, and phosphodiesterase genes. The current results represent the first efforts to apply a multi-method approach to integrate gene expression and SNP level data to identify credible actionable genes for general cognitive ability.

scholarly journals A Further Comment on ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ by Lam et al.

2018 ◽  
Vol 21 (6) ◽  
pp. 538-545 ◽  
Author(s):  
W. D. Hill
Keyword(s):  
Cognitive Ability ◽  
Drug Targets ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Genome Wide Association ◽  
Tissue Specific ◽  
Nootropic Drug ◽  
Genome Wide ◽  
Quality Control Metrics

Lam et al. (2018) respond to a commentary of their paper entitled ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ Lam et al. (2017). While Lam et al. (2018) have now provided the recommended quality control metrics for their paper, problems remain. Specifically, Lam et al. (2018) do not dispute that the results of their multi-trait analysis of genome-wide association study (MTAG) analysis has produced a phenotype with a genetic correlation of one with three measures of education, but do claim the associations found are specific to the trait of cognitive ability. In this brief paper, it is empirically demonstrated that the phenotype derived by Lam et al. (2017) is more genetically similar to education than cognitive ability. In addition, it is shown that of the genome-wide significant loci identified by Lam et al. (2017) are loci that are associated with education rather than with cognitive ability.

scholarly journals Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics

2021 ◽  
Author(s):  
Max Lam ◽  
Chia-Yen Chen ◽  
Tian Ge ◽  
Yan Xia ◽  
David W. Hill ◽  
...  
Keyword(s):  
Drug Targets ◽  
Large Scale ◽  
Association Studies ◽  
Drug Repurposing ◽  
Genome Wide Association Studies ◽  
Data Set ◽  
Nootropic Drug ◽  
Gene Sets ◽  
Genome Wide ◽  
Novel Drug

AbstractBroad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent cognitive GWAS to further enhance power for locus discovery; (2) employ several complementary transcriptomic methods to identify genes in these loci that are credibly associated with cognition; and (3) further annotate the resulting genes using multiple chemoinformatic databases to identify “druggable” targets. Using our meta-analytic data set (N = 373,617), we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging our transcriptomic and chemoinformatic databases, we identified 16 putative genes targeted by existing drugs potentially available for cognitive repurposing.

scholarly journals Comment on “Large-Scale cognitive GWAS Meta-analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets” by Lam et al.

2017 ◽  
Author(s):  
William David Hill
Keyword(s):  
Cognitive Ability ◽  
Drug Targets ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Genome Wide Association ◽  
Data Sets ◽  
Nootropic Drug ◽  
Genome Wide ◽  
Short Commentary

Lam et al. (2017) reported a large-scale genome-wide association study (GWAS) of cognitive ability. They used the new analytical method of Multi-Trait Analysis of GWAS (MTAG) (Turley et al., 2017) to combine GWAS data sets on the correlated phenotypes of cognitive ability and education, deriving 70 loci that they described as “trait specific” to cognitive ability. The purpose of this short commentary is to examine whether the use of MTAG, in this case (Lam et al., 2017), has resulted in a phenotype more similar to education than cognitive ability.

scholarly journals Comment on ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ by Lam et al.

2018 ◽  
Vol 21 (2) ◽  
pp. 84-88 ◽  
Author(s):  
W. David Hill
Keyword(s):  
Genetic Information ◽  
Drug Targets ◽  
Large Scale ◽  
Association Studies ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Data Sets ◽  
Genome Wide Association Studies ◽  
Nootropic Drug ◽  
Genome Wide

Intelligence and educational attainment are strongly genetically correlated. This relationship can be exploited by Multi-Trait Analysis of GWAS (MTAG) to add power to Genome-wide Association Studies (GWAS) of intelligence. MTAG allows the user to meta-analyze GWASs of different phenotypes, based on their genetic correlations, to identify association's specific to the trait of choice. An MTAG analysis using GWAS data sets on intelligence and education was conducted by Lam et al. (2017). Lam et al. (2017) reported 70 loci that they described as ‘trait specific’ to intelligence. This article examines whether the analysis conducted by Lam et al. (2017) has resulted in genetic information about a phenotype that is more similar to education than intelligence.

scholarly journals Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Daniel J. Panyard ◽  
Kyeong Mo Kim ◽  
Burcu F. Darst ◽  
Yuetiva K. Deming ◽  
Xiaoyuan Zhong ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Drug Targets ◽  
Large Scale ◽  
Prediction Models ◽  
Genome Wide Association ◽  
Large Samples ◽  
Genome Wide ◽  
Metabolomic Data ◽  
Related Phenotype ◽  
Omic Data

AbstractThe study of metabolomics and disease has enabled the discovery of new risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular importance. 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. Here, we present a metabolome-wide association study (MWAS), which uses genetic and metabolomic data to impute metabolites into large samples with genome-wide association summary statistics. We conduct a metabolome-wide, genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations (metabolite quantitative trait loci, or mQTLs). We then build prediction models for all available CSF metabolites and test for associations with 27 neurological and psychiatric phenotypes, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the feasibility of MWAS to study omic data in scarce sample types.

scholarly journals Genomics-driven drug discovery based on disease-susceptibility genes

2021 ◽  
Vol 41 (1) ◽  
Author(s):  
Kyuto Sonehara ◽  
Yukinori Okada
Keyword(s):  
Drug Discovery ◽  
Drug Targets ◽  
Disease Susceptibility ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Drug Repurposing ◽  
Susceptibility Genes ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Disease Associations

AbstractGenome-wide association studies have identified numerous disease-susceptibility genes. As knowledge of gene–disease associations accumulates, it is becoming increasingly important to translate this knowledge into clinical practice. This challenge involves finding effective drug targets and estimating their potential side effects, which often results in failure of promising clinical trials. Here, we review recent advances and future perspectives in genetics-led drug discovery, with a focus on drug repurposing, Mendelian randomization, and the use of multifaceted omics data.

scholarly journals Better estimation of SNP heritability from summary statistics provides a new understanding of the genetic architecture of complex traits

2018 ◽  
Author(s):  
Doug Speed ◽  
David J Balding
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Large Scale ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Confounding Bias ◽  
Conserved Regions ◽  
Genome Wide ◽  
Variation Explained

LD Score Regression (LDSC) has been widely applied to the results of genome-wide association studies. However, its estimates of SNP heritability are derived from an unrealistic model in which each SNP is expected to contribute equal heritability. As a consequence, LDSC tends to over-estimate confounding bias, under-estimate the total phenotypic variation explained by SNPs, and provide misleading estimates of the heritability enrichment of SNP categories. Therefore, we present SumHer, software for estimating SNP heritability from summary statistics using more realistic heritability models. After demonstrating its superiority over LDSC, we apply SumHer to the results of 24 large-scale association studies (average sample size 121 000). First we show that these studies have tended to substantially over-correct for confounding, and as a result the number of genome-wide significant loci has under-reported by about 20%. Next we estimate enrichment for 24 categories of SNPs defined by functional annotations. A previous study using LDSC reported that conserved regions were 13-fold enriched, and found a further twelve categories with above 2-fold enrichment. By contrast, our analysis using SumHer finds that conserved regions are only 1.6-fold (SD 0.06) enriched, and that no category has enrichment above 1.7-fold. SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data.

scholarly journals Large-scale genome-wide meta-analyses provide insights for the development of new disease modifying targets for osteoarthritis

2020 ◽  
Vol 28 ◽  
pp. S54
Author(s):  
K. Hatzikotoulas ◽  
C. Boer ◽  
L. Southam ◽  
L. Stefánsdóttir ◽  
Y. Zhang ◽  
...  
Keyword(s):  
Large Scale ◽  
New Disease ◽  
Genome Wide ◽  
Disease Modifying ◽  
Meta Analyses

The relationship between hand preference, hand performance, and general cognitive ability

2010 ◽  
Vol 16 (4) ◽  
pp. 585-592 ◽  
Author(s):  
MICHAEL E.R. NICHOLLS ◽  
HEIDI L. CHAPMAN ◽  
TOBIAS LOETSCHER ◽  
GINA M. GRIMSHAW
Keyword(s):  
Cognitive Ability ◽  
Performance Measures ◽  
Large Scale ◽  
Hand Preference ◽  
Performance Measure ◽  
General Cognitive Ability ◽  
Preference Data ◽  
Hand Performance ◽  
The Relationship

AbstractThe idea that handedness indicates something about a person’s cognitive ability and personality is a perennial issue. A variety of models have been put forward to explain this relationship and predict a range of outcomes from higher levels of cognitive ability in left-handers or moderate right-handers to lower levels of achievement in left- or mixed-handers. We tested these models using a sample (n = 895) drawn from the BRAINnet database (www.brainnet.net). Participants completed a general cognitive ability (GCA) scale and a test of hand preference/performance. Moderate right-handers, as indexed by their performance measures, had higher GCA scores compared with strong left- or right-handers. The performance measure also showed lower levels of GCA for left-handers compared with right-handers. The hand preference data showed little or no association with cognitive ability—perhaps because this measure clusters individuals toward the extremes of the handedness distribution. While adding support to Annett’s heterozygous advantage model, which predicts a cognitive disadvantage for strong left- or right-handers, the data also confirm recent research showing a GCA disadvantage for left-handers. Although this study demonstrates that handedness is related to cognitive ability, the effects are subtle and might only be identified in large-scale studies with sensitive measures of hand performance. (JINS, 2010, 16, 585–592.)

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