Journal: International Journal of Epidemiology

Article Title: Identifying factors contributing to increased susceptibility to COVID-19 risk: a systematic review of Mendelian randomization studies

doi: 10.1093/ije/dyac076

Figure Lengend Snippet: The characteristics of the included Mendelian randomization studies of factors on COVID-19 outcomes [(A) severity, (B) hospitalization, (C) susceptibility]

Article Snippet: Pairo-Castineira E 2021 , Two-sample , Druggable targets , eQTL of IFNAR2 , IFNAR1 , IL6R , JAK1 , CTSL , IFNGR2 , CSF3 (SD) , A , GenOMICC, COVID-19 HGI R2, 23andMe, UKB , European , European.

Techniques: Expressing, Activity Assay, Reflux, Cannabis, Cell Counting, Marker, Plasmid Preparation, Coagulation

Journal: BMC Genomics

Article Title: Applying genome-wide gene-based expression quantitative trait locus mapping to study population ancestry and pharmacogenetics

doi: 10.1186/1471-2164-15-319

Figure Lengend Snippet: Flow chart of data pre-processing and ancestry-informative g-eQTL mapping.

Article Snippet: The command line for this example is written as follows: % macro gbPLS ( Tgene = GE , Sgene = SNP , nProbe = 4 , nSNP = 28 , Out = OUTPUT ) ; Users can also run a SNP-based eQTL mapping using this SAS macro by providing a genotype file that contains the data of a single SNP.

Techniques:

Journal: BMC Genomics

Article Title: Applying genome-wide gene-based expression quantitative trait locus mapping to study population ancestry and pharmacogenetics

doi: 10.1186/1471-2164-15-319

Figure Lengend Snippet: Results of classification analysis for identifying AIMs based on g-eQTL found in “CHB or JPT”, “CHB and JPT”, or “CHB-specific or JPT-specific”.

Article Snippet: The command line for this example is written as follows: % macro gbPLS ( Tgene = GE , Sgene = SNP , nProbe = 4 , nSNP = 28 , Out = OUTPUT ) ; Users can also run a SNP-based eQTL mapping using this SAS macro by providing a genotype file that contains the data of a single SNP.

Techniques:

Journal: BMC Genomics

Article Title: Applying genome-wide gene-based expression quantitative trait locus mapping to study population ancestry and pharmacogenetics

doi: 10.1186/1471-2164-15-319

Figure Lengend Snippet: Results of classification analysis for identifying AIMs based on s-eQTL found in “CHB or JPT”, “CHB and JPT”, or “CHB-specific or JPT-specific”.

Article Snippet: The command line for this example is written as follows: % macro gbPLS ( Tgene = GE , Sgene = SNP , nProbe = 4 , nSNP = 28 , Out = OUTPUT ) ; Users can also run a SNP-based eQTL mapping using this SAS macro by providing a genotype file that contains the data of a single SNP.

Techniques:

Journal: BMC Genomics

Article Title: Applying genome-wide gene-based expression quantitative trait locus mapping to study population ancestry and pharmacogenetics

doi: 10.1186/1471-2164-15-319

Figure Lengend Snippet: Results of classification analysis for identifying AIMs based on the combination of g-eQTL and s-eQTL found in “CHB or JPT”, “CHB and JPT”, or “CHB-specific or JPT-specific”.

Article Snippet: The command line for this example is written as follows: % macro gbPLS ( Tgene = GE , Sgene = SNP , nProbe = 4 , nSNP = 28 , Out = OUTPUT ) ; Users can also run a SNP-based eQTL mapping using this SAS macro by providing a genotype file that contains the data of a single SNP.

Techniques:

Journal: bioRxiv

Article Title: Discovering disease genetic variation impacting gene expression in 103 brain tissues with the Brain Ontology Expression (BRONTE) graph neural network model

doi: 10.1101/2025.05.08.652857

Figure Lengend Snippet: A.B , P-value (A) and Slope (B) comparison between our results and GTEx results on the overlapped eQTLs for GTEx tissue Amygdala-Each point is one eQTL. C , Bar plots showing the number of eQTLs identified in three categories: GTEx expression data eQTLs. BRONTE imputed data eQTLs. and the overlapping eQTLs across 10 GTEx tissues. “GTEx reproduced results” refers to eQTLs obtained by running our eQTL analysis on the original GTEx data, rather than directly using results from the GTEx portal, to ensure consistency.

Article Snippet: We perform cis-eQTL analysis on GTEx data using the eQTL discovery pipeline for the GTEx Consortium introduced by Broad Institute[ ].

Techniques: Comparison, Expressing

Journal: bioRxiv

Article Title: Discovering disease genetic variation impacting gene expression in 103 brain tissues with the Brain Ontology Expression (BRONTE) graph neural network model

doi: 10.1101/2025.05.08.652857

Figure Lengend Snippet: A , B , Histograms showing estimated number of tissues in which lead eQTLs are “shared”, using two different sharing definitions, by sign (A) and by magnitude (B). C , Pairwise sharing of eQTL effects by magnitude across 103 Allen tissues, with tissues grouped by Allen Level 4 categories and distinguished by color. The mapping between the 10 GTEx tissues and the Allen tissues is illustrated in the accompanying diagram.

Article Snippet: We perform cis-eQTL analysis on GTEx data using the eQTL discovery pipeline for the GTEx Consortium introduced by Broad Institute[ ].

Techniques: