scholarly journals Integrative tissue-specific functional annotations in the human genome provide novel insights on many complex traits and improve signal prioritization in genome wide association studies

2015 ◽  
Author(s):  
Qiongshi Lu ◽  
Ryan Lee Powles ◽  
Qian Wang ◽  
Beixin Julie He ◽  
Hongyu Zhao
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Functional Annotations ◽  
Functional Regions ◽  
Genome Wide ◽  
Multiple Resolutions ◽  
Artery Disease

Extensive efforts have been made to understand genomic function through both experimental and computational approaches, yet proper annotation still remains challenging, especially in non-coding regions. In this manuscript, we introduce GenoSkyline, an unsupervised learning framework to predict tissue-specific functional regions through integrating high-throughput epigenetic annotations. GenoSkyline successfully identified a variety of non-coding regulatory machinery including enhancers, regulatory miRNA, and hypomethylated transposable elements in extensive case studies. Integrative analysis of GenoSkyline annotations and results from genome-wide association studies (GWAS) led to novel biological insights on the etiologies of a number of human complex traits. We also explored using tissue-specific functional annotations to prioritize GWAS signals and predict relevant tissue types for each risk locus. Brain and blood-specific annotations led to better prioritization performance for schizophrenia than standard GWAS p-values and non-tissue-specific annotations. As for coronary artery disease, heart-specific functional regions was highly enriched of GWAS signals, but previously identified risk loci were found to be most functional in other tissues, suggesting a substantial proportion of still undetected heart-related loci. In summary, GenoSkyline annotations can guide genetic studies at multiple resolutions and provide valuable insights in understanding complex diseases. GenoSkyline is available at http://genocanyon.med.yale.edu/GenoSkyline.

Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies

2021 ◽  
Vol 42 (1) ◽  
Author(s):  
Dinesh K. Saini ◽  
Yuvraj Chopra ◽  
Jagmohan Singh ◽  
Karansher S. Sandhu ◽  
Anand Kumar ◽  
...  
Keyword(s):  
Complex Traits ◽  
Comprehensive Evaluation ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Mapping Complex Traits

scholarly journals GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background

2020 ◽  
Author(s):  
Nasa Sinnott-Armstrong ◽  
Sahin Naqvi ◽  
Manuel Rivas ◽  
Jonathan K Pritchard
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Biological Processes ◽  
Uk Biobank ◽  
The Core ◽  
Genome Wide ◽  
Core Genes

SummaryGenome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. However, for most traits it remains difficult to interpret what genes and biological processes are impacted by the top hits. Here, as a contrast, we describe UK Biobank GWAS results for three molecular traits—urate, IGF-1, and testosterone—that are biologically simpler than most diseases, and for which we know a great deal in advance about the core genes and pathways. Unlike most GWAS of complex traits, for all three traits we find that most top hits are readily interpretable. We observe huge enrichment of significant signals near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of variation in each trait, including insights into differences in testosterone regulation between females and males. Meanwhile, in other respects the results are reminiscent of GWAS for more-complex traits. In particular, even these molecular traits are highly polygenic, with most of the variance coming not from core genes, but from thousands to tens of thousands of variants spread across most of the genome. Given that diseases are often impacted by many distinct biological processes, including these three, our results help to illustrate why so many variants can affect risk for any given disease.

scholarly journals A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome

2019 ◽  
Author(s):  
Tom G Richardson ◽  
Gibran Hemani ◽  
Tom R Gaunt ◽  
Caroline L Relton ◽  
George Davey Smith
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Drug Repositioning ◽  
Association Studies ◽  
Thyroid Tissue ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Genome Wide

AbstractBackgroundDeveloping insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. By applying the principles of Mendelian randomization, we have undertaken a systematic analysis to evaluate transcriptome-wide associations between gene expression across 48 different tissue types and 395 complex traits.ResultsOverall, we identified 100,025 gene-trait associations based on conventional genome-wide corrections (P < 5 × 10−08) that also provided evidence of genetic colocalization. These results indicated that genetic variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. We identified many examples of tissue-specific effects, such as genetically-predicted TPO, NR3C2 and SPATA13 expression only associating with thyroid disease in thyroid tissue. Additionally, FBN2 expression was associated with both cardiovascular and lung function traits, but only when analysed in heart and lung tissue respectively.We also demonstrate that conducting phenome-wide evaluations of our results can help flag adverse on-target side effects for therapeutic intervention, as well as propose drug repositioning opportunities. Moreover, we find that exploring the tissue-dependency of associations identified by genome-wide association studies (GWAS) can help elucidate the causal genes and tissues responsible for effects, as well as uncover putative novel associations.ConclusionsThe atlas of tissue-dependent associations we have constructed should prove extremely valuable to future studies investigating the genetic determinants of complex disease. The follow-up analyses we have performed in this study are merely a guide for future research. Conducting similar evaluations can be undertaken systematically at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.

scholarly journals GWAS-Flow: A GPU accelerated framework for efficient permutation based genome-wide association studies

2019 ◽  
Author(s):  
Jan A. Freudenthal ◽  
Markus J. Ankenbrand ◽  
Dominik G. Grimm ◽  
Arthur Korte
Keyword(s):  
Complex Traits ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Large Datasets ◽  
Genome Wide Association ◽  
Small Data ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Non Gaussian

AbstractMotivationGenome-wide association studies (GWAS) are one of the most commonly used methods to detect associations between complex traits and genomic polymorphisms. As both genotyping and phenotyping of large populations has become easier, typical modern GWAS have to cope with massive amounts of data. Thus, the computational demand for these analyses grew remarkably during the last decades. This is especially true, if one wants to implement permutation-based significance thresholds, instead of using the naïve Bonferroni threshold. Permutation-based methods have the advantage to provide an adjusted multiple hypothesis correction threshold that takes the underlying phenotypic distribution into account and will thus remove the need to find the correct transformation for non Gaussian phenotypes. To enable efficient analyses of large datasets and the possibility to compute permutation-based significance thresholds, we used the machine learning framework TensorFlow to develop a linear mixed model (GWAS-Flow) that can make use of the available CPU or GPU infrastructure to decrease the time of the analyses especially for large datasets.ResultsWe were able to show that our application GWAS-Flow outperforms custom GWAS scripts in terms of speed without loosing accuracy. Apart from p-values, GWAS-Flow also computes summary statistics, such as the effect size and its standard error for each individual marker. The CPU-based version is the default choice for small data, while the GPU-based version of GWAS-Flow is especially suited for the analyses of big data.AvailabilityGWAS-Flow is freely available on GitHub (https://github.com/Joyvalley/GWAS_Flow) and is released under the terms of the MIT-License.

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