Common genetic variants associated with cognitive performance identified using the proxy-phenotype method.
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ABSTRACT:
SUBMITTER: Rietveld CA
PROVIDER: S-EPMC4183313 | biostudies-literature | 2014 Sep
REPOSITORIES: biostudies-literature
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Project description:One surprising feature of the recently completed waves of genome-wide association studies is the limited impact of common genetic variation in individually detectable polymorphisms on many human traits. This has been particularly pronounced for studies on psychiatric conditions, which have failed to produce clear, replicable associations for common variants. One popular explanation for these negative findings is that many of these traits may be genetically heterogeneous, leading to the idea that relevant endophenotypes may be more genetically tractable. Aspects of cognition may be the most important endophenotypes for psychiatric conditions such as schizophrenia, leading many researchers to pursue large-scale studies on the genetic contributors of cognitive performance in the normal population as a surrogate for aspects of liability to disease. Here, we perform a genome-wide association study with two tests of executive function, Digit Symbol and Stroop Color-Word, in 1086 healthy volunteers and with an expanded cognitive battery in 514 of these volunteers. We show that, consistent with published studies of the psychiatric conditions themselves, no single common variant has a large effect (explaining >4-8% of the population variation) on the performance of healthy individuals on standardized cognitive tests. Given that these are important endophenotypes, our work is consistent with the idea that identifying rare genetic causes of psychiatric conditions may be more important for future research than identifying genetically homogenous endophenotypes.
| S-EPMC2987367 | biostudies-literature
Project description:Better drugs are needed for common epilepsies. Drug repurposing offers the potential of significant savings in the time and cost of developing new treatments. In order to select the best candidate drug(s) to repurpose for a disease, it is desirable to predict the relative clinical efficacy that drugs will have against the disease. Common epilepsy can be divided into different types and syndromes. Different antiseizure medications are most effective for different types and syndromes of common epilepsy. For predictions of antiepileptic efficacy to be clinically translatable, it is essential that the predictions are specific to each form of common epilepsy, and reflect the patterns of drug efficacy observed in clinical studies and practice. These requirements are not fulfilled by previously published drug predictions for epilepsy. We developed a novel method for predicting the relative efficacy of drugs against any common epilepsy, by using its Genome-Wide Association Study summary statistics and drugs' activity data. The methodological advancement in our technique is that the drug predictions for a disease are based upon drugs' effects on the function and abundance of proteins, and the magnitude and direction of those effects, relative to the importance, degree and direction of the proteins' dysregulation in the disease. We used this method to predict the relative efficacy of all drugs, licensed for any condition, against each of the major types and syndromes of common epilepsy. Our predictions are concordant with findings from real-world experience and randomized clinical trials. Our method predicts the efficacy of existing antiseizure medications against common epilepsies; in this prediction, our method outperforms the best alternative existing method: area under receiver operating characteristic curve (mean ± standard deviation) 0.83 ± 0.03 and 0.63 ± 0.04, respectively. Importantly, our method predicts which antiseizure medications are amongst the more efficacious in clinical practice, and which antiseizure medications are amongst the less efficacious in clinical practice, for each of the main syndromes of common epilepsy, and it predicts the distinct order of efficacy of individual antiseizure medications in clinical trials of different common epilepsies. We identify promising candidate drugs for each of the major syndromes of common epilepsy. We screen five promising predicted drugs in an animal model: each exerts a significant dose-dependent effect upon seizures. Our predictions are a novel resource for selecting suitable candidate drugs that could potentially be repurposed for each of the major syndromes of common epilepsy. Our method is potentially generalizable to other complex diseases.
| S-EPMC8710935 | biostudies-literature
Project description:Genome-wide association studies (GWAS) have identified many common single nucleotide polymorphisms (SNPs) associated with colorectal cancer risk. These SNPs may tag correlated variants with biological importance. Fine-mapping around GWAS loci can facilitate detection of functional candidates and additional independent risk variants. We analyzed 11,900 cases and 14,311 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colon Cancer Family Registry. To fine-map genomic regions containing all known common risk variants, we imputed high-density genetic data from the 1000 Genomes Project. We tested single-variant associations with colorectal tumor risk for all variants spanning genomic regions 250-kb upstream or downstream of 31 GWAS-identified SNPs (index SNPs). We queried the University of California, Santa Cruz Genome Browser to examine evidence for biological function. Index SNPs did not show the strongest association signals with colorectal tumor risk in their respective genomic regions. Bioinformatics analysis of SNPs showing smaller P-values in each region revealed 21 functional candidates in 12 loci (5q31.1, 8q24, 11q13.4, 11q23, 12p13.32, 12q24.21, 14q22.2, 15q13, 18q21, 19q13.1, 20p12.3, and 20q13.33). We did not observe evidence of additional independent association signals in GWAS-identified regions. Our results support the utility of integrating data from comprehensive fine-mapping with expanding publicly available genomic databases to help clarify GWAS associations and identify functional candidates that warrant more onerous laboratory follow-up. Such efforts may aid the eventual discovery of disease-causing variant(s).
| S-EPMC4933364 | biostudies-literature
Project description:BackgroundGenomewide association studies have identified multiple genetic variants associated with breast cancer. The extent to which these variants add to existing risk-assessment models is unknown.MethodsWe used information on traditional risk factors and 10 common genetic variants associated with breast cancer in 5590 case subjects and 5998 control subjects, 50 to 79 years of age, from four U.S. cohort studies and one case-control study from Poland to fit models of the absolute risk of breast cancer. With the use of receiver-operating-characteristic curve analysis, we calculated the area under the curve (AUC) as a measure of discrimination. By definition, random classification of case and control subjects provides an AUC of 50%; perfect classification provides an AUC of 100%. We calculated the fraction of case subjects in quintiles of estimated absolute risk after the addition of genetic variants to the traditional risk model.ResultsThe AUC for a risk model with age, study and entry year, and four traditional risk factors was 58.0%; with the addition of 10 genetic variants, the AUC was 61.8%. About half the case subjects (47.2%) were in the same quintile of risk as in a model without genetic variants; 32.5% were in a higher quintile, and 20.4% were in a lower quintile.ConclusionsThe inclusion of newly discovered genetic factors modestly improved the performance of risk models for breast cancer. The level of predicted breast-cancer risk among most women changed little after the addition of currently available genetic information.
| S-EPMC2921181 | biostudies-literature
Project description:Genome-wide association studies are providing new insights into the genetic basis of metabolic and cardiovascular traits. In the past 3 years, common variants in approximately 50 loci have been strongly associated with metabolic and cardiovascular traits. Several of these loci have implicated genes without a previously known connection with metabolism. Further studies will be required to characterize the full impact of these loci on metabolism. Many of the identified loci include multiple independent variants that influence the same metabolic or cardiovascular trait and a few loci harbor independent variants that each influence distinct traits. The total proportion of trait heritability explained by variants identified so far is still modest (typically <10%). Future studies will build on these successes by identifying additional common and rare variants and by determining the functional impact of the underlying alleles and genes.
| S-EPMC2570060 | biostudies-literature
Project description:Genome wide association (GWA) studies, which test for association between common genetic markers and a disease phenotype, have shown varying degrees of success. While many factors could potentially confound GWA studies, we focus on the possibility that multiple, rare variants (RVs) may act in concert to influence disease etiology. Here, we describe an algorithm for RV analysis, RareCover. The algorithm combines a disparate collection of RVs with low effect and modest penetrance. Further, it does not require the rare variants be adjacent in location. Extensive simulations over a range of assumed penetrance and population attributable risk (PAR) values illustrate the power of our approach over other published methods, including the collapsing and weighted-collapsing strategies. To showcase the method, we apply RareCover to re-sequencing data from a cohort of 289 individuals at the extremes of Body Mass Index distribution (NCT00263042). Individual samples were re-sequenced at two genes, FAAH and MGLL, known to be involved in endocannabinoid metabolism (187Kbp for 148 obese and 150 controls). The RareCover analysis identifies exactly one significantly associated region in each gene, each about 5 Kbp in the upstream regulatory regions. The data suggests that the RVs help disrupt the expression of the two genes, leading to lowered metabolism of the corresponding cannabinoids. Overall, our results point to the power of including RVs in measuring genetic associations.
| S-EPMC2954823 | biostudies-literature
Project description:Erythropoietin (EPO) improves cognitive performance in clinical studies and rodent experiments. We hypothesized that an intrinsic role of EPO for cognition exists, with particular relevance in situations of cognitive decline, which is reflected by associations of EPO and EPO receptor (EPOR) genotypes with cognitive functions. To prove this hypothesis, schizophrenic patients (N > 1000) were genotyped for 5' upstream-located gene variants, EPO SNP rs1617640 (T/G) and EPORSTR(GA)(n). Associations of these variants were obtained for cognitive processing speed, fine motor skills and short-term memory readouts, with one particular combination of genotypes superior to all others (p < 0.0001). In an independent healthy control sample (N > 800), these associations were confirmed. A matching preclinical study with mice demonstrated cognitive processing speed and memory enhanced upon transgenic expression of constitutively active EPOR in pyramidal neurons of cortex and hippocampus. We thus predicted that the human genotypes associated with better cognition would reflect gain-of-function effects. Indeed, reporter gene assays and quantitative transcriptional analysis of peripheral blood mononuclear cells showed genotype-dependent EPO/EPOR expression differences. Together, these findings reveal a role of endogenous EPO/EPOR for cognition, at least in schizophrenic patients.
| S-EPMC3459483 | biostudies-literature
Project description:IMPORTANCE:There are genetic influences on memory ability as we age, but no specific genes have been identified. OBJECTIVE:To use a cognitive endophenotype, exceptional episodic memory (EEM) performance, derived from nondemented offspring from the Long Life Family Study (LLFS) to identify genetic variants that may be responsible for the high cognitive performance of LLFS participants and further replicate these variants using an additional 4006 nondemented individuals from 4 independent elderly cohorts. DESIGN, SETTING, AND PARTICIPANTS:A total of 467 LLFS participants from 18 families with 2 or more offspring that exhibited exceptional memory performance were used for genome-wide linkage analysis. Adjusted multivariate linear analyses in the 40-megabase region encompassing the linkage peak were conducted using 4 independent replication data sets that included 4006 nondemented elderly individuals. Results of the individual replication cohorts were combined by meta-analysis. MAIN OUTCOME MEASURE:Episodic memory scores computed as the mean of the 2 standardized measures of Logical Memory IA and IIA. RESULTS:Heritability estimates indicated a significant genetic component for EEM (h2 = 0.21; SE = 0.09). Genome-wide linkage analysis revealed that EEM was linked to the 6q24 region (maximum logarithm of odds score, 3.64). Association analysis in LLFS families identified single-nucleotide polymorphisms (SNPs) nominally associated with EEM in the 40-megabase window encompassing the linkage peak. Replication in one cohort identified a set of 26 SNPs associated with episodic memory (P ? .05). Meta-analysis of the 26 SNPs using the 4 independent replication cohorts found SNPs rs9321334 and rs6902875 to be nominally significantly associated with episodic memory (P = .009 and P = .013, respectively). With meta-analysis restricted to individuals lacking an APOE ?4 allele, SNP rs6902875 became statistically significant (meta-analysis, P = 6.7 × 10-5). Haplotype analysis incorporating the 2 SNPs flanking rs6902875 (rs9321334 and rs4897574) revealed that the A-A-C haplotype was significantly associated with episodic memory performance (P = 2.4 × 10-5). This genomic region harbors monooxygenase dopamine ?-hydroxylase-like 1 gene (MOXD1), implicated in the biosynthesis of norepinephrine, which is prominently involved in cognitive functions. CONCLUSIONS AND RELEVANCE:The results provide strong evidence for potential candidate genes related to EEM on 6q24. Identifying the genes will help in understanding the biological basis of memory performance and allow interventions for enhancement of cognitive function.
| S-EPMC4355405 | biostudies-literature
Project description: Not available
| S-EPMC7546879 | biostudies-literature
Project description:Recently, genetic markers rs10503253 and rs2616984 in the CUB and Sushi multiple domains-1 (CSMD1) gene have been reported to be associated with schizophrenia and cognitive functions in genome-wide association studies. We examined the associations of the above SNPs with cognitive performance evaluated by the Montreal Cognitive Assessment (MoCA) tool in a cohort of the normal elderly from the Russian population. Significant association of rs2616984 genotypes with the MoCA scores was found using nonparametric analysis. No association of rs10503253 with MoCA scores was observed using both parametric and nonparametric statistics. Significant combined effect of two-locus CSMD1 genotypes on MoCA scores was demonstrated by median test. Allele "A" and genotype "AA" of rs2616984 were significantly associated with the lower MoCA scores in comparison of 1st and 4th quartiles of MoCA total score distribution. The results suggest that genetic variants in CSMD1 gene are likely a part of genetic component of cognitive performance in the elderly.
| S-EPMC5742897 | biostudies-literature