scholarly journals A computational framework for the inheritance pattern of genomic imprinting for complex traits

2011 ◽  
Vol 13 (1) ◽  
pp. 34-45 ◽  
Author(s):  
C. Wang ◽  
Z. Wang ◽  
D. R. Prows ◽  
R. Wu
Keyword(s):  
Genomic Imprinting ◽  
Complex Traits ◽  
Inheritance Pattern ◽  
Computational Framework

scholarly journals Genomic imprinting and parent-of-origin effects on complex traits

2013 ◽  
Vol 14 (9) ◽  
pp. 609-617 ◽  
Author(s):  
Heather A. Lawson ◽  
James M. Cheverud ◽  
Jason B. Wolf
Keyword(s):  
Genomic Imprinting ◽  
Complex Traits ◽  
Parent Of Origin ◽  
Origin Effects

scholarly journals PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuhua Zhang ◽  
◽  
Corbin Quick ◽  
Ketian Yu ◽  
Alvaro Barbeira ◽  
...  
Keyword(s):  
Complex Traits ◽  
Molecular Mechanisms ◽  
Specific Gene ◽  
Summary Statistics ◽  
Gene Expressions ◽  
Computational Framework ◽  
Higher Power ◽  
Causal Assumptions ◽  
Cell Type Specific ◽  
Eqtl Data

Abstract We propose a new computational framework, probabilistic transcriptome-wide association study (PTWAS), to investigate causal relationships between gene expressions and complex traits. PTWAS applies the established principles from instrumental variables analysis and takes advantage of probabilistic eQTL annotations to delineate and tackle the unique challenges arising in TWAS. PTWAS not only confers higher power than the existing methods but also provides novel functionalities to evaluate the causal assumptions and estimate tissue- or cell-type-specific gene-to-trait effects. We illustrate the power of PTWAS by analyzing the eQTL data across 49 tissues from GTEx (v8) and GWAS summary statistics from 114 complex traits.

scholarly journals Accurate eQTL prioritization with an ensemble-based framework

2016 ◽  
Author(s):  
Haoyang Zeng ◽  
Matthew D. Edwards ◽  
Yuchun Guo ◽  
David K. Gifford
Keyword(s):  
Complex Traits ◽  
Sequence Variants ◽  
Eqtl Analysis ◽  
Reporter Expression ◽  
Expression Change ◽  
Computational Framework ◽  
Regulatory Variants ◽  
Genome Interpretation ◽  
Allele Specific ◽  
Reporter Assays

AbstractExpression quantitative trait loci (eQTL) analysis links sequence variants with gene expression change and serves as a successful approach to fine-map variants causal for complex traits and understand their pathogenesis. In this work, we present an ensemble-based computational framework, EnsembleExpr, for eQTL prioritization. When trained on data from massively parallel reporter assays (MPRA), EnsembleExpr accurately predicts reporter expression levels from DNA sequence and identifies sequence variants that exhibit significant allele-specific reporter expression. This framework achieved the best performance in the “eQTL-causal SNPs” open challenge in the Fourth Critical Assessment of Genome Interpretation (CAGI 4). We envision EnsembleExpr to be a powerful resource for interpreting non-coding regulatory variants and prioritizing disease-associated mutations for downstream validation.

scholarly journals Genomic imprinting effects on complex traits: A phenotype-based perspective

Epigenetics ◽  
2008 ◽  
Vol 3 (6) ◽  
pp. 295-299 ◽  
Author(s):  
Jason B. Wolf ◽  
Reinmar Hager ◽  
James M. Cheverud
Keyword(s):  
Genomic Imprinting ◽  
Complex Traits

scholarly journals Genomic imprinting effects on complex traits in domesticated animal species

2015 ◽  
Vol 6 ◽  
Author(s):  
Alan M. O’Doherty ◽  
David E. MacHugh ◽  
Charles Spillane ◽  
David A. Magee
Keyword(s):  
Genomic Imprinting ◽  
Complex Traits ◽  
Animal Species

scholarly journals LPM: a latent probit model to characterize the relationship among complex traits using summary statistics from multiple GWASs and functional annotations

2018 ◽  
Author(s):  
Jingsi Ming ◽  
Tao Wang ◽  
Can Yang
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Association Studies ◽  
Probit Model ◽  
Genome Wide Association Studies ◽  
Computational Framework ◽  
Functional Annotations ◽  
Risk Variants ◽  
Genome Wide ◽  
Cell Type Specific

AbstractMuch effort has been made toward understanding the genetic architecture of complex traits and diseases. Recent results from genome-wide association studies (GWASs) suggest the importance of regulatory genetic effects and pervasive pleiotropy among complex traits. In this study, we propose a unified statistical approach, aiming to characterize relationship among complex traits, and prioritize risk variants by leveraging regulatory information collected in functional annotations. Specifically, we consider a latent probit model (LPM) to integrate summary-level GWAS data and functional annotations. The developed computational framework not only makes LPM scalable to hundreds of annotations and phenotypes, but also ensures its statistically guaranteed accuracy. Through comprehensive simulation studies, we evaluated LPM’s performance and compared it with related methods. Then we applied it to analyze 44 GWASs with nine genic category annotations and 127 cell-type specific functional annotations. The results demonstrate the benefits of LPM and gain insights of genetic architecture of complex traits. The LPM package is available at https://github.com/mingjingsi/LPM.

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