-
Mashup Score: 0Genome-wide and transcriptome-wide association studies of mammographic density phenotypes reveal novel loci - PubMed - 1 year(s) ago
Our findings provided novel insight into the genetic background of MD phenotypes, and further demonstrated their shared genetic basis with breast cancer.
Source: PubMedCategories: Genetics, Latest HeadlinesTweet
-
Mashup Score: 1Full expression and splicing QTL summary statistics for three human spinal cord segments - 1 year(s) ago
This dataset is part of the manuscript: “Integrative genetic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes” by Jack Humphrey, et al. The files below contain nominal and permuted quantitative trait loci (QTL) associations between common genetic variants derived from whole genome sequencing and either gene expression or splicing…
Source: ZenodoCategories: Latest Headlines, NeurologyTweet-
RT @JackHumphrey_: For #QTL and #TWAS fans, we have deposited all summary stats up on Zenodo: https://t.co/KUc1KpG0hp https://t.co/TxTdKlHc…
-
-
Mashup Score: 0Meta-imputation of transcriptome from genotypes across multiple datasets by leveraging publicly available summary-level data - 2 year(s) ago
Author summary The gene expression levels within a cell are affected by various factors, including DNA variation, cell type, cellular microenvironment, disease status, and other environmental factors surrounding the individual. The genetic component of gene expression is known to explain a substantial fraction of transcriptional variation among individuals and can be imputed from genotypes in a…
Source: journals.plos.orgCategories: Genetics, Latest HeadlinesTweet-
Liu & Kang present SWAM: a framework to accurately impute gene expression levels from genotypes by integrating multiple imputation models w/o individual-level data SWAM can improve the accuracy of transcriptome imputation or improve the power of #TWAS https://t.co/B9kd6X3qIo
-
-
Mashup Score: 1Meta-imputation of transcriptome from genotypes across multiple datasets by leveraging publicly available summary-level data - 2 year(s) ago
Author summary The gene expression levels within a cell are affected by various factors, including DNA variation, cell type, cellular microenvironment, disease status, and other environmental factors surrounding the individual. The genetic component of gene expression is known to explain a substantial fraction of transcriptional variation among individuals and can be imputed from genotypes in a…
Source: journals.plos.orgCategories: Genetics, Latest HeadlinesTweet-
Liu & Kang present SWAM: a framework to accurately impute gene expression levels from genotypes by integrating multiple imputation models w/o individual-level data SWAM can improve the accuracy of transcriptome imputation or improve the power of #TWAS https://t.co/B9kd6X3qIo
-
-
Mashup Score: 0A transcriptome‐wide association study identifies novel candidate susceptibility genes for prostate cancer risk - 3 year(s) ago
Although an estimated 58 percent of prostate cancers are linked to familial factors, most heritable risk variants for prostate cancer remain unidentified. In this transcriptome-wide association study…
Source: Wiley Online LibraryCategories: Hem/Onc News and Journals, Latest HeadlinesTweet-
NOW ONLINE | Duo Liu et al In this #TWAS, data from the #GTEx Project was used to construct models for investigating associations between genetically determined #GeneExpression & #prostatecancer risk ➡️https://t.co/SweXSDo2pf @UHCancerCenter @VUMC_Medicine @KeckMedUSC @UMNews https://t.co/86hcNYra1P
-
-
Mashup Score: 5
Author summary Transcriptome-wide association studies (TWAS) are a type of bioinformatics methodology for identifying complex trait-associated genes. There have been various TWAS methods, each developed under distinct biological assumptions of how genes contribute to complex traits. It is unclear, however, how powerful different TWAS methods are under a variety of biological scenarios. Here, we…
Source: journals.plos.orgCategories: Genetics, Latest HeadlinesTweet-
Li et al propose a new tissue specificity-aware #TWAS analytic framework that integrates and maximizes the performance of multiple TWAS methods In a demonstration of the tool, they reveal novel aspects of #HIV-related traits #Bioinformatics #Genetics https://t.co/qjIn5WhLcT
-
-
Mashup Score: 3
Author summary Transcriptome-wide association studies (TWAS) can improve the statistical power of genetic association studies by leveraging the relationship between genetically predicted transcript expression levels and an outcome. We propose a new TWAS pipeline that integrates data on the genetic regulation of expression levels across multiple tissues. We generate cross-tissue expression…
Source: journals.plos.orgCategories: Genetics, Latest HeadlinesTweet-
🚨 @FengHelian et al present a #TWAS pipeline to integrate data on #genetic regulation of expression levels across multiple tissues The new approach has substantially higher power than traditional single-tissue TWAS methods @GENES_PK @bpasaniuc @nmancuso_ https://t.co/gBlglpZHFj
-
-
Mashup Score: 6Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer’s dementia - 3 year(s) ago
Author summary Traditional Transcriptome-wide association studies (TWAS) tools make strong assumptions about the relationships among genetic variants, transcriptome, and phenotype that may be violated in practice, thereby substantially reducing the power. Here, we propose a Variance-Component TWAS method (VC-TWAS) that relaxes these assumptions and can be implemented with both individual-level…
Source: journals.plos.orgCategories: Genetics, Latest HeadlinesTweet-
Tang et al. @ShizhenTang @EpsteinStatGen @jjloverp present a new Variance-Component #TWAS method: it relaxes assumptions about the relationships among genetic variants, #transcriptome, and #phenotype They demonstrate the method on #Alzheimers #GWAS data https://t.co/G2dyLAkFn1
-
-
Mashup Score: 0Power analysis of transcriptome-wide association study: Implications for practical protocol choice - 3 year(s) ago
Author summary We compared the effectiveness of three methods for finding genetic effects on disease in order to quantify their strengths and help researchers choose the best protocol for their data. The genome-wide association study (GWAS) is the standard method for identifying how the genetic differences between individuals relate to disease. Recently, the transcriptome-wide association study…
Source: journals.plos.orgCategories: Genetics, Latest HeadlinesTweet-
Cao et al. compared the effectiveness of three methods for finding genetic effects on #disease They found that under pleiotropy, using predicted expressions in #TWAS is superior to actual expressions; and #GWAS outperforms TWAS under certain thresholds https://t.co/eF8meiUVYc
-
A 2022 #GWAS & #TWAS study identified 28 significant loci for breast density phenotypes, including 9 new signals. The results show a shared genetic basis for dense breasts & breast cancer. #DCEGBestOf2022 #NCI_ITEB @GretchenBenson @soothing_sharon https://t.co/DR1q9n5EdH https://t.co/fEPbNnurss