scholarly journals ColocQuiaL: A QTL-GWAS colocalization pipeline

2021 ◽  
Author(s):  
Brian Y Chen ◽  
William Paul Bone ◽  
Kimberly Lorenz ◽  
Michael Levin ◽  
Marylyn D Ritchie ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Genome Wide Association Study ◽  
Tissue Expression ◽  
Data Availability ◽  
Genome Wide ◽  
Trait Loci ◽  
Causal Genes ◽  
Difficult Challenge

Summary: Identifying genomic features responsible for genome-wide association study (GWAS) signals has proven to be a difficult challenge; many researchers have turned to colocalization analysis of GWAS signals with expression quantitative trait loci (eQTL) and splicing quantitative trait loci (sQTL) to connect GWAS signals to candidate causal genes. The ColocQuiaL pipeline provides a framework to perform these colocalization analyses at scale across the genome and returns summary files and locus visualization plots to allow for detailed review of the results. As an example, we used ColocQuiaL to perform colocalization between the latest type 2 diabetes GWAS data and Genotype-Tissue Expression (GTEx) v8 single-tissue eQTL and sQTL data. Availability and Implementation: ColocQuiaL is primarily written in R and is freely available at github: https://github.com/bychen9/eQTL_colocalizer. Contact: [email protected] Availability and Implementation: ColocQuiaL is primarily written in R and is freely available at github: https://github.com/bychen9/eQTL_colocalizer. Contact: [email protected]

scholarly journals A Genome-Wide Association Study Identifies Protein Quantitative Trait Loci (pQTLs)

PLoS Genetics ◽  
2008 ◽  
Vol 4 (5) ◽  
pp. e1000072 ◽  
Author(s):  
David Melzer ◽  
John R. B. Perry ◽  
Dena Hernandez ◽  
Anna-Maria Corsi ◽  
Kara Stevens ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Association Study ◽  
Quantitative Trait ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Genome Wide ◽  
A Genome ◽  
Trait Loci

scholarly journals Mapping adipose and muscle tissue expression quantitative trait loci in African Americans to identify genes for type 2 diabetes and obesity

2016 ◽  
Vol 135 (8) ◽  
pp. 869-880 ◽  
Author(s):  
Satria P. Sajuthi ◽  
Neeraj K. Sharma ◽  
Jeff W. Chou ◽  
Nicholette D. Palmer ◽  
David R. McWilliams ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Quantitative Trait Loci ◽  
African Americans ◽  
Muscle Tissue ◽  
Quantitative Trait ◽  
Tissue Expression ◽  
Expression Quantitative Trait Loci ◽  
Trait Loci ◽  
Diabetes And Obesity

scholarly journals Common Expression Quantitative Trait Loci Shared by Histone Genes

2017 ◽  
Vol 2017 ◽  
pp. 1-14
Author(s):  
Hanseol Kim ◽  
Yujin Suh ◽  
Chaeyoung Lee
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Genome Wide Association Study ◽  
Histone Genes ◽  
Expression Quantitative Trait Loci ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
A Genome ◽  
Trait Loci ◽  
Cycle Dependent

A genome-wide association study (GWAS) was conducted to examine expression quantitative trait loci (eQTLs) for histone genes. We examined common eQTLs for multiple histone genes in 373 European lymphoblastoid cell lines (LCLs). A linear regression model was employed to identify single-nucleotide polymorphisms (SNPs) associated with expression of the histone genes, and the number of eQTLs was determined by linkage disequilibrium analysis. Additional associations of the identified eQTLs with other genes were also examined. We identified 31 eQTLs for 29 histone genes through genome-wide analysis using 29 histone genes (P<2.97×10−10). Among them, 12 eQTLs were associated with the expression of multiple histone genes. Transcriptome-wide association analysis using the identified eQTLs showed their associations with additional 80 genes (P<4.75×10−6). In particular, expression of RPPH1, SCARNA2, and SCARNA7 genes was associated with 26, 25, and 23 eQTLs, respectively. This study suggests that histone genes shared 12 common eQTLs that might regulate cell cycle-dependent transcription of histone and other genes. Further investigations are needed to elucidate the transcriptional mechanisms of these genes.

scholarly journals Genome-wide association study identifies quantitative trait loci regions involved in muscle acidic profile in Large White heavy pigs

animal ◽  
2020 ◽  
Vol 14 (7) ◽  
pp. 1342-1350 ◽  
Author(s):  
G. Catillo ◽  
M. Zappaterra ◽  
P. Zambonelli ◽  
L. Buttazzoni ◽  
R. Steri ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Association Study ◽  
Quantitative Trait ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Large White ◽  
Genome Wide ◽  
Trait Loci

scholarly journals Identification of Quantitative Trait Loci for Mesocotyl Length of Rice Seedling in Different Sowing Depths using Genome-Wide Association Study

2020 ◽  
Author(s):  
Chuanhang Yang ◽  
Hao Wang ◽  
Xiaojuan Tang ◽  
Qing Guo ◽  
Youhui Li ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Association Study ◽  
Quantitative Trait ◽  
Genome Wide Association Study ◽  
Rice Seedling ◽  
Mapping Method ◽  
Genome Wide Association ◽  
Sand Culture ◽  
Genome Wide ◽  
Trait Loci

Abstract Background: The mesocotyl length of rice seedling is a complex trait associated with the ability of seedling emerging and controlled by quantitative trait loci (QTLs). Genome-wide association study (GWAS) is a high-through QTLs mapping method and widely used to identify QTLs in the field of plant genetics.Results: A series of rice accessions including 290 varieties from all over the world, which harbored widely genetic background, were finished whole genome re-sequencing and investigated the mesocotyl length of rice seedlings in three different sowing depths (0cm, 4cm and 6cm). The analysis of coefficient of variation (CV) showed that the rice mesocotyl length variability in the three different treatments with 0cm, 4cm and 6cm sand culture are 114.43%, 146.65% and 152.55%, respectively. The mesocotyl length of the three groups are positively correlated with each other, indicating that the variation trend is consistent despite the differences in experimental treatment conditions, which led to the different performances of the mesocotyl lengths of the same accession. After GWAS, we found There are 922 single nucleotide polymorphism (SNP) makers associated with rice seedling mesocotyl length across 20 QTL regions and covered 1246 genes.Conclusions: In this study, we found that there are 922 SNP makers associated with rice seedling mesocotyl length across 20 QTL regions and covered 1246 genes. And we detected 23 candidate genes which maybe have effect to the mesocotyl length of rice seedling at different sowing depths

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