Genome‐wide expression quantitative trait locus studies facilitate isolation of causal genes controlling panicle structure

2020 ◽  
Vol 103 (1) ◽  
pp. 266-278
Author(s):  
Fanmiao Wang ◽  
Kenji Yano ◽  
Shiro Nagamatsu ◽  
Mayuko Inari‐Ikeda ◽  
Eriko Koketsu ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Expression Quantitative Trait Locus ◽  
Genome Wide ◽  
Causal Genes ◽  
Trait Locus ◽  
Genome Wide Expression

scholarly journals Genome-wide analysis links NFATC2 with asparaginase hypersensitivity

Blood ◽  
2015 ◽  
Vol 126 (1) ◽  
pp. 69-75 ◽  
Author(s):  
Christian A. Fernandez ◽  
Colton Smith ◽  
Wenjian Yang ◽  
Charles G. Mullighan ◽  
Chunxu Qu ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Expression Quantitative Trait Locus ◽  
Genome Wide Analysis ◽  
Genome Wide ◽  
Increased Risk ◽  
Key Points ◽  
Trait Locus

Key Points The rs6021191 variant in NFATC2 is associated with an increased risk of asparaginase hypersensitivity and is an expression quantitative trait locus associated with expression of NFATC2. Exome interrogation confirms the importance of the HLA-DRB1*07:01 allele in asparaginase hypersensitivity.

scholarly journals Epigenome-Wide Study Identified Methylation Sites Associated with the Risk of Obesity

Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1984
Author(s):  
Majid Nikpay ◽  
Sepehr Ravati ◽  
Robert Dent ◽  
Ruth McPherson
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Rare Variants ◽  
Genome Wide ◽  
A Genome ◽  
Genome Wide Search ◽  
Risk Snps ◽  
Trait Locus ◽  
Genomic Regions ◽  
Eqtl Data

Here, we performed a genome-wide search for methylation sites that contribute to the risk of obesity. We integrated methylation quantitative trait locus (mQTL) data with BMI GWAS information through a SNP-based multiomics approach to identify genomic regions where mQTLs for a methylation site co-localize with obesity risk SNPs. We then tested whether the identified site contributed to BMI through Mendelian randomization. We identified multiple methylation sites causally contributing to the risk of obesity. We validated these findings through a replication stage. By integrating expression quantitative trait locus (eQTL) data, we noted that lower methylation at cg21178254 site upstream of CCNL1 contributes to obesity by increasing the expression of this gene. Higher methylation at cg02814054 increases the risk of obesity by lowering the expression of MAST3, whereas lower methylation at cg06028605 contributes to obesity by decreasing the expression of SLC5A11. Finally, we noted that rare variants within 2p23.3 impact obesity by making the cg01884057 site more susceptible to methylation, which consequently lowers the expression of POMC, ADCY3 and DNAJC27. In this study, we identify methylation sites associated with the risk of obesity and reveal the mechanism whereby a number of these sites exert their effects. This study provides a framework to perform an omics-wide association study for a phenotype and to understand the mechanism whereby a rare variant causes a disease.

scholarly journals Optimizing expression quantitative trait locus mapping workflows for single-cell studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Anna S. E. Cuomo ◽  
Giordano Alvari ◽  
Christina B. Azodi ◽  
Davis J. McCarthy ◽  
Marc Jan Bonder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait Locus ◽  
Single Cell ◽  
Quantitative Trait ◽  
Cell Types ◽  
Expression Quantitative Trait Locus ◽  
Eqtl Mapping ◽  
Trait Locus ◽  
The Impact

Abstract Background Single-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease. Results While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches. Conclusion We provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.

Sign in / Sign up

Export Citation Format

Share Document