ArticleGenetics2016
Covariance Association Test (CVAT) Identifies Genetic Markers Associated with Schizophrenia in Functionally Associated Biological Processes.
Article in Genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 72 citations in OpenAlex.
- Article
- Evaluation of Bayesian Linear Regression derived gene set test methods.BMC genomics · 2024Article
- Wide-ranging genetic variation in sensitivity to rapamycin in Drosophila melanogaster.Aging cell · 2024Article
- Comparative transcriptome analysis of T lymphocyte subpopulations and identification of critical regulators defining porcine thymocyte identity.Frontiers in immunology · 2024Article
- Comparative epigenomics reveals the impact of ruminant-specific regulatory elements on complex traits.BMC biology · 2022Article
- Comparative transcriptome in large-scale human and cattle populations.Genome biology · 2022Article
- Article
- Single-cell transcriptomic and chromatin accessibility analyses of dairy cattle peripheral blood mononuclear cells and their responses to lipopolysaccharide.BMC genomics · 2022Article
- Comprehensive transcriptome and methylome analysis delineates the biological basis of hair follicle development and wool-related traits in Merino sheep.BMC biology · 2021Article
- Integration of a single-step genome-wide association study with a multi-tissue transcriptome analysis provides novel insights into the genetic basis of wool and weight traits in sheep.Genetics, selection, evolution : GSE · 2021Article
- Article
- Integrating RNA-Seq with GWAS reveals novel insights into the molecular mechanism underpinning ketosis in cattle.BMC genomics · 2020Article
- Epigenomics and genotype-phenotype association analyses reveal conserved genetic architecture of complex traits in cattle and human.BMC biology · 2020Article
- Comprehensive analyses of 723 transcriptomes enhance genetic and biological interpretations for complex traits in cattle.Genome research · 2020Article
- Genetic Signatures of Drug Response Variability inGenetics · 2019Article
- Functional annotation of the cattle genome through systematic discovery and characterization of chromatin states and butyrate-induced variations.BMC biology · 2019Article
- Integrating Signals from Sperm Methylome Analysis and Genome-Wide Association Study for a Better Understanding of Male Fertility in Cattle.Epigenomes · 2019Article
- Comparative analyses of sperm DNA methylomes among human, mouse and cattle provide insights into epigenomic evolution and complex traits.Epigenetics · 2019Article
- Genetic and epigenetic architecture of paternal origin contribute to gestation length in cattle.Communications biology · 2019Article
- MicroRNA-guided prioritization of genome-wide association signals reveals the importance of microRNA-target gene networks for complex traits in cattle.Scientific reports · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Schizophrenia is a psychiatric disorder with large personal and social costs, and understanding the genetic etiology is important. Such knowledge can be obtained by testing the association between a disease phenotype and individual genetic markers; however, such single-marker methods have limited power to detect genetic markers with small effects. Instead, aggregating genetic markers based on biological information might increase the power to identify sets of genetic markers of etiological significance. Several set test methods have been proposed: Here we propose a new set test derived from genomic best linear unbiased prediction (GBLUP), the covariance association test (CVAT). We compared the performance of CVAT to other commonly used set tests. The comparison was conducted using a simulated study population having the same genetic parameters as for schizophrenia. We found that CVAT was among the top performers. When extending CVAT to utilize a mixture of SNP effects, we found an increase in power to detect the causal sets. Applying the methods to a Danish schizophrenia case-control data set, we found genomic evidence for association of schizophrenia with vitamin A metabolism and immunological responses, which previously have been implicated with schizophrenia based on experimental and observational studies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.