scholarly journals An integrative analysis of GWAS and intermediate molecular trait data reveals common molecular mechanisms supporting genetic similarity between seemingly unrelated complex traits

2019 ◽  
Author(s):  
Jialiang Gu ◽  
Chris Fuller ◽  
Jiashun Zheng ◽  
Hao Li
Keyword(s):  
Breast Cancer ◽  
Complex Traits ◽  
Genetic Similarity ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Integrative Analysis ◽  
Ros Generation ◽  
Genome Wide Association Studies ◽  
Statistical Framework ◽  
Significant Gene

AbstractThe rapid accumulation of Genome Wide Association Studies (GWAS) and association studies of intermediate molecular traits provides new opportunities for comparative analysis of the genetic basis of complex human phenotypes. Using a newly developed statistical framework called Sherlock-II that integrates GWAS with eQTL (expression Quantitative Trait Loci) and metabolite-QTL data, we systematically analyzed 445 GWAS datasets, and identified 1371 significant gene-phenotype associations and 308 metabolites-phenotype associations (passing a Q value cutoff of 1/3). This integrative analysis allows us to translate SNP-phenotype associations into functionally informative gene-phenotype association profiles. Genetic similarity analyses based on these profiles clustered phenotypes into sub-trees that reveal both expected and unexpected relationships. We employed a statistical approach to delineate sets of functionally related genes that contribute to the similarity between their association profiles. This approach suggested common molecular mechanisms that connect the phenotypes in a subtree. For example, we found that fasting insulin, fasting glucose, breast cancer, prostate cancer, and lung cancer clustered into a subtree, and identified cyclic AMP/GMP signaling that connects breast cancer and insulin, NAPDH oxidase/ROS generation that connects the three cancers, and apoptosis that connects all five phenotypes. Our approach can be used to assess genetic similarity and suggest mechanistic connections between phenotypes. It has the potential to improve the diagnosis and treatment of a disease by mapping mechanistic insights from one phenotype onto others based on common molecular underpinnings.

Brain imaging genomics: influences of genomic variability on the structure and function of the human brain

2020 ◽  
Vol 32 (1) ◽  
pp. 47-56
Author(s):  
Thomas W. Mühleisen ◽  
Andreas J. Forstner ◽  
Per Hoffmann ◽  
Sven Cichon
Keyword(s):  
Brain Imaging ◽  
Complex Traits ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Phenotypic Variability ◽  
Effect Sizes ◽  
Future Research ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Imaging Genomics

Abstract Brain imaging genomics is an emerging discipline in which genomic and brain imaging data are integrated in order to elucidate the molecular mechanisms that underly brain phenotypes and diseases, including neuropsychiatric disorders. As with all genetic analyses of complex traits and diseases, brain imaging genomics has evolved from small, individual candidate gene investigations towards large, collaborative genome-wide association studies. Recent investigations, mostly population-based, have studied well-powered cohorts comprising tens of thousands of individuals and identified multiple robust associations of single-nucleotide polymorphisms and copy number variants with structural and functional brain phenotypes. Such systematic genomic screens of millions of genetic variants have generated initial insights into the genetic architecture of brain phenotypes and demonstrated that their etiology is polygenic in nature, involving multiple common variants with small effect sizes and rare variants with larger effect sizes. Ongoing international collaborative initiatives are now working to obtain a more complete picture of the underlying biology. As in other complex phenotypes, novel approaches – such as gene–gene interaction, gene–environment interaction, and epigenetic analyses – are being implemented in order to investigate their contribution to the observed phenotypic variability. An important consideration for future research will be the translation of brain imaging genomics findings into clinical practice.

scholarly journals Sexual differences in genetic architecture in UK Biobank

2020 ◽  
Author(s):  
Elena Bernabeu ◽  
Oriol Canela-Xandri ◽  
Konrad Rawlik ◽  
Andrea Talenti ◽  
James Prendergast ◽  
...  
Keyword(s):  
Sexual Dimorphism ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Mammalian Species ◽  
Genetic Correlations ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Uk Biobank

ABSTRACTSex is arguably the most important differentiating characteristic in most mammalian species, separating populations into different groups, with varying behaviors, morphologies, and physiologies based on their complement of sex chromosomes. In humans, despite males and females sharing nearly identical genomes, there are differences between the sexes in complex traits and in the risk of a wide array of diseases. Gene by sex interactions (GxS) are thought to account for some of this sexual dimorphism. However, the extent and basis of these interactions are poorly understood.Here we provide insights into both the scope and mechanism of GxS across the genome of circa 450,000 individuals of European ancestry and 530 complex traits in the UK Biobank. We found small yet widespread differences in genetic architecture across traits through the calculation of sex-specific heritability, genetic correlations, and sex-stratified genome-wide association studies (GWAS). We also found that, in some cases, sex-agnostic GWAS efforts might be missing loci of interest, and looked into possible improvements in the prediction of high-level phenotypes. Finally, we studied the potential functional role of the dimorphism observed through sex-biased eQTL and gene-level analyses.This study marks a broad examination of the genetics of sexual dimorphism. Our findings parallel previous reports, suggesting the presence of sexual genetic heterogeneity across complex traits of generally modest magnitude. Our results suggest the need to consider sex-stratified analyses for future studies in order to shed light into possible sex-specific molecular mechanisms.

scholarly journals On the Use of Z-Scores for Fine-Mapping with Related Individuals

2021 ◽  
Author(s):  
Jicai Jiang
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Statistical Framework ◽  
Genome Wide ◽  
Z Scores ◽  
Related Individuals ◽  
Mapping Complex Traits

Using summary statistics from genome-wide association studies (GWAS) has been widely used for fine-mapping complex traits in humans. The statistical framework was largely developed for unrelated samples. Though it is possible to apply the framework to fine-mapping with related individuals, extensive modifications are needed. Unfortunately, this has often been ignored in summary-statistics-based fine-mapping with related individuals. In this paper, we show in theory and simulation what modifications are necessary to extend the use of summary statistics to related individuals. The analysis also demonstrates that though existing summary-statistics-based fine-mapping methods can be adapted for related individuals, they appear to have no computational advantage over individual-data-based methods.

scholarly journals Phenome-wide search for pleiotropic loci highlights key genes and molecular pathways for human complex traits

2019 ◽  
Author(s):  
Anton E. Shikov ◽  
Alexander V. Predeus ◽  
Yury A. Barbitoff
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Human Genetics ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Statistical Framework ◽  
Genome Wide ◽  
Mhc Locus ◽  
High Degree ◽  
Human Complex

AbstractOver recent decades, genome-wide association studies (GWAS) have dramatically changed the understanding of human genetics. A recent genetic data release by UK Biobank has allowed many researchers worldwide to have comprehensive look into the genetic architecture of thousands of human phenotypes. In this study, we developed a novel statistical framework to assess phenome-wide significance and genetic pleiotropy across the human phenome based on GWAS summary statistics. We demonstrate widespread sharing of genetic architecture components between distinct groups of traits. Apart from known multiple associations inside the MHC locus, we discover high degree of pleiotropy for genes involved in immune system function, apoptosis, hemostasis cascades, as well as lipid and xenobiotic metabolism. We find several notable examples of novel pleiotropic loci (e.g., the MIR2113 microRNA broadly associated with cognition), and provide several possible mechanisms for these association signals. Our results allow for a functional phenome-wide look into the shared components of genetic architecture of human complex traits, and highlight crucial genes and pathways for their development.

scholarly journals Testing and controlling for horizontal pleiotropy with the probabilistic Mendelian randomization in transcriptome-wide association studies

2019 ◽  
Author(s):  
Zhongshang Yuan ◽  
Huanhuan Zhu ◽  
Ping Zeng ◽  
Sheng Yang ◽  
Shiquan Sun ◽  
...  
Keyword(s):  
Complex Traits ◽  
Molecular Mechanisms ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Pleiotropic Effects ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Wide Spread ◽  
Disease Etiology

AbstractIntegrating association results from both genome-wide association studies (GWASs) and expression quantitative trait locus (eQTL) mapping studies has the potential to shed light on the molecular mechanisms underlying disease etiology. Several statistical methods have been recently developed to integrate GWASs with eQTL studies in the form of transcriptome-wide association studies (TWASs). These existing methods can all be viewed as a form of two sample Mendelian randomization (MR) analysis, which has been widely applied in various GWASs for inferring the causal relationship among complex traits. Unfortunately, most existing TWAS and MR methods make an unrealistic modeling assumption and assume that instrumental variables do not exhibit horizontal pleiotropic effects. However, horizontal pleiotropic effects have been recently discovered to be wide spread across complex traits, and, as we will show here, are also wide spread across gene expression traits. Therefore, not allowing for horizontal pleiotropic effects can be overly restrictive, and, as we will be show here, can lead to a substantial inflation of test statistics and subsequently false discoveries in TWAS applications. Here, we present a probabilistic MR method, which we refer to as PMR-Egger, for testing and controlling for horizontal pleiotropic effects in TWAS applications. PMR-Egger relies on an MR likelihood framework that unifies many existing TWAS and MR methods, accommodates multiple correlated instruments, tests the causal effect of gene on trait in the presence of horizontal pleiotropy, and, with a newly developed parameter expansion version of the expectation maximization algorithm, is scalable to hundreds of thousands of individuals. With extensive simulations, we show that PMR-Egger provides calibrated type I error control for causal effect testing in the presence of horizontal pleiotropic effects, is reasonably robust for various types of horizontal pleiotropic effect mis-specifications, is more powerful than existing MR approaches, and, as a by-product, can directly test for horizontal pleiotropy. We illustrate the benefits of PMR-Egger in applications to 39 diseases and complex traits obtained from three GWASs including the UK Biobank. In these applications, we show how PMR-Egger can lead to new biological discoveries through integrative analysis.

scholarly journals Functional genomics based on germline genome-wide association studies of endocrine therapy for breast cancer

2020 ◽  
Vol 21 (9) ◽  
pp. 615-625
Author(s):  
Jacqueline Zayas ◽  
Sisi Qin ◽  
Jia Yu ◽  
James N Ingle ◽  
Liewei Wang
Keyword(s):  
Breast Cancer ◽  
Endocrine Therapy ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Therapy Response ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Dependent Manner ◽  
Estrogen Receptor Modulators ◽  
Genome Wide

Breast cancer is the most common invasive cancer in women worldwide. Functional follow-up of breast cancer genome-wide association studies has led to the discovery of genes that regulate endocrine therapy response in a SNP- and drug-dependent manner. Here, we will present four examples in which functional genomic studies from breast cancer clinical trials led to novel pharmacogenomic insights and molecular mechanisms of selective estrogen receptor modulators and aromatase inhibitors. The approach utilized for studying genetic variability described in this review offers substantial potential for meaningful discoveries that move the field toward precision medicine for patients.

scholarly journals SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yiliang Zhang ◽  
Qiongshi Lu ◽  
Yixuan Ye ◽  
Kunling Huang ◽  
Wei Liu ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Correlations ◽  
Autism Spectrum ◽  
Accurate Estimation ◽  
Genome Wide Association Studies ◽  
Statistical Framework ◽  
Genomic Regions ◽  
Genetic Signatures

AbstractLocal genetic correlation quantifies the genetic similarity of complex traits in specific genomic regions. However, accurate estimation of local genetic correlation remains challenging, due to linkage disequilibrium in local genomic regions and sample overlap across studies. We introduce SUPERGNOVA, a statistical framework to estimate local genetic correlations using summary statistics from genome-wide association studies. We demonstrate that SUPERGNOVA outperforms existing methods through simulations and analyses of 30 complex traits. In particular, we show that the positive yet paradoxical genetic correlation between autism spectrum disorder and cognitive performance could be explained by two etiologically distinct genetic signatures with bidirectional local genetic correlations.

scholarly journals Pig genome functional annotation enhances the biological interpretation of complex traits and human disease

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhangyuan Pan ◽  
Yuelin Yao ◽  
Hongwei Yin ◽  
Zexi Cai ◽  
Ying Wang ◽  
...  
Keyword(s):  
Adaptive Evolution ◽  
Complex Traits ◽  
Functional Annotation ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Regulatory Elements ◽  
Neutral Evolution ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Slow Evolution

AbstractThe functional annotation of livestock genomes is crucial for understanding the molecular mechanisms that underpin complex traits of economic importance, adaptive evolution and comparative genomics. Here, we provide the most comprehensive catalogue to date of regulatory elements in the pig (Sus scrofa) by integrating 223 epigenomic and transcriptomic data sets, representing 14 biologically important tissues. We systematically describe the dynamic epigenetic landscape across tissues by functionally annotating 15 different chromatin states and defining their tissue-specific regulatory activities. We demonstrate that genomic variants associated with complex traits and adaptive evolution in pig are significantly enriched in active promoters and enhancers. Furthermore, we reveal distinct tissue-specific regulatory selection between Asian and European pig domestication processes. Compared with human and mouse epigenomes, we show that porcine regulatory elements are more conserved in DNA sequence, under both rapid and slow evolution, than those under neutral evolution across pig, mouse, and human. Finally, we provide biological insights on tissue-specific regulatory conservation, and by integrating 47 human genome-wide association studies, we demonstrate that, depending on the traits, mouse or pig might be more appropriate biomedical models for different complex traits and diseases.

scholarly journals Pig genome functional annotation enhances biological interpretations of complex traits and comparative epigenomics

2021 ◽  
Author(s):  
Huaijun Zhou ◽  
Zhangyuan Pan ◽  
Yuelin Yao ◽  
Hongwei Ying ◽  
Zexi Cai ◽  
...  
Keyword(s):  
Adaptive Evolution ◽  
Complex Traits ◽  
Functional Annotation ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Regulatory Elements ◽  
Neutral Evolution ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Slow Evolution

Abstract The functional annotation of livestock genomes is crucial for understanding the molecular mechanisms that underpin complex traits of economic importance, adaptive evolution and comparative genomics. Here, we provide the most comprehensive catalogue to date of regulatory elements in the pig (Sus scrofa) by integrating 223 epigenomic and transcriptomic data sets, representing 14 biologically important tissues. We systematically describe the dynamic epigenetic landscape across tissues by functionally annotating 15 different chromatin states and defining their tissue-specific regulatory activities. We demonstrate that genomic variants associated with complex traits and adaptive evolution in pig are significantly enriched in active promoters and enhancers. Furthermore, we reveal distinct tissue-specific regulatory selection between Asian and European pig domestication processes. Compared with human and mouse epigenomes, we show that porcine regulatory elements are more conserved in DNA sequence, under both rapid and slow evolution, than those under neutral evolution across pig, mouse, and human. Finally, we provide novel biological insights on tissue-specific regulatory conservation and demonstrate that, depending on the traits, mouse or pig might be more appropriate biomedical models for different complex traits and diseases in humans through integrating comparative epigenomes with 47 human genome-wide association studies.

scholarly journals Quantifying mediation between omics layers and complex traits

2021 ◽  
Author(s):  
Marie C Sadler ◽  
Chiara Marie Paula Auwerx ◽  
Eleonora Porcu ◽  
Zoltan Kutalik
Keyword(s):  
Complex Traits ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Irritable Bowel Disease ◽  
Protein Levels ◽  
Integrative Framework ◽  
Multiple Transcripts ◽  
Irritable Bowel ◽  
In Cis

Background: High-dimensional omics datasets provide valuable resources to determine the causal role of molecular traits in mediating the path from genotype to phenotype. Making use of quantitative trait loci (QTL) and genome-wide association studies (GWASs) summary statistics, we developed a multivariable Mendelian randomization (MVMR) framework to quantify the connectivity between three omics layers (DNA methylome (DNAm), transcriptome and proteome) and their cascading causal impact on complex traits and diseases. Results: Evaluating 50 complex traits, we found that on average 37.8% (95% CI: [36.0%-39.5%]) of DNAm-to-trait effects were mediated through transcripts in the cis-region, while only 15.8% (95% CI: [11.9%-19.6%]) are mediated through proteins in cis. DNAm sites typically regulate multiple transcripts, and while found to predominantly decrease gene expression, this was only the case for 53.4% across ~47,000 significant DNAm-transcript pairs. The average mediation proportion for transcript-to-trait effects through proteins (encoded for by the assessed transcript or located in trans) was estimated to be 5.27% (95%CI: [4.11%-6.43%]). Notable differences in the transcript and protein QTL architectures were detected with only 22% of protein levels being causally driven by their corresponding transcript levels. Several regulatory mechanisms were hypothesized including an example where cg10385390 (chr1:8,022,505) increases the risk of irritable bowel disease by reducing PARK7 transcript and protein expression. Conclusions: The proposed integrative framework identified putative causal chains through omics layers providing a powerful tool to map GWAS signals. Quantification of causal effects between successive layers indicated that molecular mechanisms can be more complex than what the central dogma of biology would suggest.

Sign in / Sign up

Export Citation Format

Share Document