scholarly journals Protein QTL analysis of IGF-I and its binding proteins provides insights into growth biology

2020 ◽  
Author(s):  
Eric Bartell ◽  
Masanobu Fujimoto ◽  
Jane C. Khoury ◽  
Philip R. Khoury ◽  
Sailaja Vedantam ◽  
...  
Keyword(s):  
Birth Weight ◽  
Association Studies ◽  
Genetic Regulation ◽  
Human Growth ◽  
Serum Levels ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Protein Levels ◽  
Igf I ◽  
Igfbp 3

AbstractThe growth hormone and insulin-like growth factor (IGF) system is integral to human growth. Genome-wide association studies (GWAS) have identified variants associated with height and located near the genes in this pathway. However, mechanisms underlying these genetic associations are not understood. To investigate the regulation of the genes in this pathway and mechanisms by which regulation could affect growth, we performed GWAS of measured serum protein levels of IGF-I, IGFBP-3, PAPP-A2, IGF-II, and IGFBP-5 in 839 children (3-18 years) from the Cincinnati Genomic Control Cohort. We identified variants associated with protein levels near IGFBP3 and IGFBP5 genes, which contain multiple signals of association with height and other skeletal growth phenotypes. Surprisingly, variants that associate with protein levels at these two loci do not colocalize with height associations, confirmed through conditional analysis. Rather, the IGFBP3 signal (associated with total IGFBP-3 and IGF-II levels) colocalizes with an association with sitting height ratio (SHR); the IGFBP5 signal (associated with IGFBP-5 levels) colocalizes with birth weight. Indeed, height-associated SNPs near genes encoding other proteins in this pathway are not associated with serum levels, possibly excluding PAPP-A2. Mendelian randomization supports a stronger relationship of measured serum levels with SHR (for IGFBP-3) and birth weight (for IGFBP-5) than with height. In conclusion, we begin to characterize the genetic regulation of serum levels of IGF-related proteins in childhood. Furthermore, our data strongly suggest the existence of growth-regulating mechanisms acting through IGF-related genes in ways that are not reflected in measured serum levels of the corresponding proteins.

scholarly journals Protein QTL analysis of IGF-I and its binding proteins provides insights into growth biology

2020 ◽  
Vol 29 (15) ◽  
pp. 2625-2636
Author(s):  
Eric Bartell ◽  
Masanobu Fujimoto ◽  
Jane C Khoury ◽  
Philip R Khoury ◽  
Sailaja Vedantam ◽  
...  
Keyword(s):  
Birth Weight ◽  
Association Studies ◽  
Genetic Regulation ◽  
Serum Levels ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genetic Associations ◽  
Protein Levels ◽  
Igf I ◽  
Igfbp 3

Abstract The growth hormone and insulin-like growth factor (IGF) system is integral to human growth. Genome-wide association studies (GWAS) have identified variants associated with height and located near the genes in this pathway. However, mechanisms underlying these genetic associations are not understood. To investigate the regulation of the genes in this pathway and mechanisms by which regulation could affect growth, we performed GWAS of measured serum protein levels of IGF-I, IGF binding protein-3 (IGFBP-3), pregnancy-associated plasma protein A (PAPP-A2), IGF-II and IGFBP-5 in 838 children (3–18 years) from the Cincinnati Genomic Control Cohort. We identified variants associated with protein levels near IGFBP3 and IGFBP5 genes, which contain multiple signals of association with height and other skeletal growth phenotypes. Surprisingly, variants that associate with protein levels at these two loci do not colocalize with height associations, confirmed through conditional analysis. Rather, the IGFBP3 signal (associated with total IGFBP-3 and IGF-II levels) colocalizes with an association with sitting height ratio (SHR); the IGFBP5 signal (associated with IGFBP-5 levels) colocalizes with birth weight. Indeed, height-associated single nucleotide polymorphisms near genes encoding other proteins in this pathway are not associated with serum levels, possibly excluding PAPP-A2. Mendelian randomization supports a stronger causal relationship of measured serum levels with SHR (for IGFBP-3) and birth weight (for IGFBP-5) than with height. In conclusion, we begin to characterize the genetic regulation of serum levels of IGF-related proteins in childhood. Furthermore, our data strongly suggest the existence of growth-regulating mechanisms acting through IGF-related genes in ways that are not reflected in measured serum levels of the corresponding proteins.

scholarly journals Genetic Regulation of Biomarkers as Stress Proxies in Dairy Cows

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 534
Author(s):  
Marco Milanesi ◽  
Matilde Maria Passamonti ◽  
Katia Cappelli ◽  
Andrea Minuti ◽  
Valentino Palombo ◽  
...  
Keyword(s):  
Inflammatory Responses ◽  
Bos Taurus ◽  
Association Studies ◽  
Meta Analysis ◽  
Genetic Regulation ◽  
Chromosome 1 ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Physiological Level ◽  
Glutamyl Transferase

Stress in livestock reduces productivity and is a welfare concern. At a physiological level, stress is associated with the activation of inflammatory responses and increased levels of harmful reactive oxygen species. Biomarkers that are indicative of stress could facilitate the identification of more stress-resilient animals. We examined twenty-one metabolic, immune response, and liver function biomarkers that have been associated with stress in 416 Italian Simmental and 436 Italian Holstein cows which were genotyped for 150K SNPs. Single-SNP and haplotype-based genome-wide association studies were carried out to assess whether the variation in the levels in these biomarkers is under genetic control and to identify the genomic loci involved. Significant associations were found for the plasma levels of ceruloplasmin (Bos taurus chromosome 1—BTA1), paraoxonase (BTA4) and γ-glutamyl transferase (BTA17) in the individual breed analysis that coincided with the position of the genes coding for these proteins, suggesting that their expression is under cis-regulation. A meta-analysis of both breeds identified additional significant associations with paraoxonase on BTA 16 and 26. Finding genetic associations with variations in the levels of these biomarkers suggests that the selection for high or low levels of expression could be achieved rapidly. Whether the level of expression of the biomarkers correlates with the response to stressful situations has yet to be determined.

scholarly journals SUN-054 Genetic Studies of Height-Associated Protein Expression Levels in Childhood

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Masanobu Fujimoto ◽  
Eric Bartell ◽  
Jane C Khoury ◽  
Philip R Khoury ◽  
Sailaja Vedantam ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Protein Level ◽  
Fixed Effects ◽  
Adult Height ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Protein Levels ◽  
Genome Wide ◽  
Common Genetic Variants ◽  
Igfbp 3

Abstract Background: Genome-wide association studies (GWAS) have identified thousands of common genetic variants associated with human height, implicating hundreds of genes and loci. However, the mechanisms by which many of these genetic variants contribute to human adult height are still unknown. Integrating knowledge of the interaction between genetic background and protein levels in childhood can provide insights into the biology of human growth. Objective: To investigate biological associations at height-associated loci in the GH-IGF signaling pathway. Methods: We used data from the Cincinnati Genomic Control Cohort, a community-based cohort comprised of 1,020 children. The study was approved by the institutional review board at Cincinnati Children’s Hospital Medical Center. Protein levels for free and total IGF-I, intact and total IGFBP-3, PAPP-A2, IGF-II, and IGFBP-5 were measured by ELISA in 839 children (ages 3-18 years) and corrected for age- and sex-effects. We associated protein-level phenotypes using plink qassoc and stratified by sex and population, in ~870 European- and African-descent individuals. Meta-analyses were performed using the METAL fixed-effects model. GWAS of anthropometric traits were performed in the UK Biobank of ~400,000 individuals using Bolt-LMM, or curated from publicly available summary statistics. Results: We identified 17 independent genome-wide significant protein-level-associated loci (p<5x10-8). The most robust associations were previously identified expression quantitative trait loci (eQTLs). The IGFBP3 locus was associated with serum total IGFBP3 and IGF-II levels. Despite falling within a height locus, conditional analyses showed that the effect on IGFBP-3 protein levels was independent of the height signal (p=2.8e-31, post conditioning). However, conditional analyses showed that the protein level signal colocalizes with a known GWAS signal for sitting height ratio (SHR). The IGFBP5 locus was associated with IGFBP-5 protein levels and was also independent of height signals identified in the region (p=3.3e-32, post conditioning). Conclusions: We have identified novel pQTLs for IGF2, IGFBP3, and IGFBP5 that act independently from genetic signals in the same regions associated with adult height but may interact with related anthropometric traits including SHR. Additionally, this suggests that SNPs affecting adult height in these loci do not work via increasing serum levels of these proteins but rather through a different and undetermined mechanism.

scholarly journals Penalized partial least squares for pleiotropy

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Camilo Broc ◽  
Therese Truong ◽  
Benoit Liquet
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Association Studies ◽  
A Priori ◽  
Simulated Data ◽  
Real Data ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Multiple Traits ◽  
Application Fields

Abstract Background The increasing number of genome-wide association studies (GWAS) has revealed several loci that are associated to multiple distinct phenotypes, suggesting the existence of pleiotropic effects. Highlighting these cross-phenotype genetic associations could help to identify and understand common biological mechanisms underlying some diseases. Common approaches test the association between genetic variants and multiple traits at the SNP level. In this paper, we propose a novel gene- and a pathway-level approach in the case where several independent GWAS on independent traits are available. The method is based on a generalization of the sparse group Partial Least Squares (sgPLS) to take into account groups of variables, and a Lasso penalization that links all independent data sets. This method, called joint-sgPLS, is able to convincingly detect signal at the variable level and at the group level. Results Our method has the advantage to propose a global readable model while coping with the architecture of data. It can outperform traditional methods and provides a wider insight in terms of a priori information. We compared the performance of the proposed method to other benchmark methods on simulated data and gave an example of application on real data with the aim to highlight common susceptibility variants to breast and thyroid cancers. Conclusion The joint-sgPLS shows interesting properties for detecting a signal. As an extension of the PLS, the method is suited for data with a large number of variables. The choice of Lasso penalization copes with architectures of groups of variables and observations sets. Furthermore, although the method has been applied to a genetic study, its formulation is adapted to any data with high number of variables and an exposed a priori architecture in other application fields.

scholarly journals Common genetic variants with fetal effects on birth weight are enriched for proximity to genes implicated in rare developmental disorders

2021 ◽  
Author(s):  
Robin N Beaumont ◽  
Isabelle K Mayne ◽  
Rachel M Freathy ◽  
Caroline F Wright
Keyword(s):  
Birth Weight ◽  
Statistical Power ◽  
Developmental Disorders ◽  
Association Studies ◽  
Later Life ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Common Genetic Variants ◽  
Causal Genes

Abstract Birth weight is an important factor in newborn survival; both low and high birth weights are associated with adverse later-life health outcomes. Genome-wide association studies (GWAS) have identified 190 loci associated with maternal or fetal effects on birth weight. Knowledge of the underlying causal genes is crucial to understand how these loci influence birth weight and the links between infant and adult morbidity. Numerous monogenic developmental syndromes are associated with birth weights at the extreme ends of the distribution. Genes implicated in those syndromes may provide valuable information to prioritize candidate genes at the GWAS loci. We examined the proximity of genes implicated in developmental disorders (DDs) to birth weight GWAS loci using simulations to test whether they fall disproportionately close to the GWAS loci. We found birth weight GWAS single nucleotide polymorphisms (SNPs) fall closer to such genes than expected both when the DD gene is the nearest gene to the birth weight SNP and also when examining all genes within 258 kb of the SNP. This enrichment was driven by genes causing monogenic DDs with dominant modes of inheritance. We found examples of SNPs in the intron of one gene marking plausible effects via different nearby genes, highlighting the closest gene to the SNP not necessarily being the functionally relevant gene. This is the first application of this approach to birth weight, which has helped identify GWAS loci likely to have direct fetal effects on birth weight, which could not previously be classified as fetal or maternal owing to insufficient statistical power.

scholarly journals Genetic association studies of alterations in protein function expose recessive effects on cancer predisposition

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nadav Brandes ◽  
Nathan Linial ◽  
Michal Linial
Keyword(s):  
Cancer Risk ◽  
Protein Function ◽  
Association Studies ◽  
Genetic Effects ◽  
Cancer Predisposition ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Functional Interpretation ◽  
Gene Damage ◽  
Genomic Regions

AbstractThe characterization of germline genetic variation affecting cancer risk, known as cancer predisposition, is fundamental to preventive and personalized medicine. Studies of genetic cancer predisposition typically identify significant genomic regions based on family-based cohorts or genome-wide association studies (GWAS). However, the results of such studies rarely provide biological insight or functional interpretation. In this study, we conducted a comprehensive analysis of cancer predisposition in the UK Biobank cohort using a new gene-based method for detecting protein-coding genes that are functionally interpretable. Specifically, we conducted proteome-wide association studies (PWAS) to identify genetic associations mediated by alterations to protein function. With PWAS, we identified 110 significant gene-cancer associations in 70 unique genomic regions across nine cancer types and pan-cancer. In 48 of the 110 PWAS associations (44%), estimated gene damage is associated with reduced rather than elevated cancer risk, suggesting a protective effect. Together with standard GWAS, we implicated 145 unique genomic loci with cancer risk. While most of these genomic regions are supported by external evidence, our results also highlight many novel loci. Based on the capacity of PWAS to detect non-additive genetic effects, we found that 46% of the PWAS-significant cancer regions exhibited exclusive recessive inheritance. These results highlight the importance of recessive genetic effects, without relying on familial studies. Finally, we show that many of the detected genes exert substantial cancer risk in the studied cohort determined by a quantitative functional description, suggesting their relevance for diagnosis and genetic consulting.

scholarly journals A description of large-scale metabolomics studies: increasing value by combining metabolomics with genome-wide SNP genotyping and transcriptional profiling

2012 ◽  
Vol 215 (1) ◽  
pp. 17-28 ◽  
Author(s):  
Georg Homuth ◽  
Alexander Teumer ◽  
Uwe Völker ◽  
Matthias Nauck
Keyword(s):  
Blood Cells ◽  
Large Scale ◽  
Genetic Factors ◽  
Association Studies ◽  
Transcriptional Profiling ◽  
Genome Wide Association Studies ◽  
Protein Levels ◽  
Future Developments ◽  
Genome Wide ◽  
Metabolome Data

The metabolome, defined as the reflection of metabolic dynamics derived from parameters measured primarily in easily accessible body fluids such as serum, plasma, and urine, can be considered as the omics data pool that is closest to the phenotype because it integrates genetic influences as well as nongenetic factors. Metabolic traits can be related to genetic polymorphisms in genome-wide association studies, enabling the identification of underlying genetic factors, as well as to specific phenotypes, resulting in the identification of metabolome signatures primarily caused by nongenetic factors. Similarly, correlation of metabolome data with transcriptional or/and proteome profiles of blood cells also produces valuable data, by revealing associations between metabolic changes and mRNA and protein levels. In the last years, the progress in correlating genetic variation and metabolome profiles was most impressive. This review will therefore try to summarize the most important of these studies and give an outlook on future developments.

scholarly journals Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes

2014 ◽  
Author(s):  
Daniel S Himmelstein ◽  
Sergio E Baranzini
Keyword(s):  
Data Integration ◽  
Protein Interactions ◽  
Heterogeneous Network ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Protein Coding ◽  
Multiple Node ◽  
Pathogenic Variants ◽  
Disease Associated Genes

The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a multiscale understanding of pathogenic variants, and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks—graphs with multiple node and edge types—for accomplishing both tasks. First we constructed a network with 18 node types—genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures database)collections—and 19 edge types from high-throughput publicly-available resources. From this network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the probability of association between each protein-coding gene and each of 29 well-studied complex diseases. The model, which achieved 132-fold enrichment in precision at 10% recall, outperformed any individual domain, highlighting the benefit of integrative approaches. We identified pleiotropy, transcriptional signatures of perturbations, pathways, and protein interactions as fundamental mechanisms explaining pathogenesis. Our method successfully predicted the results (with AUROC = 0.79) from a withheld multiple sclerosis (MS) GWAS despite starting with only 13 previously associated genes. Finally, we combined our network predictions with statistical evidence of association to propose four novel MS genes, three of which (JAK2, REL, RUNX3) validated on the masked GWAS. Furthermore, our predictions provide biological support highlighting REL as the causal gene within its gene-rich locus. Users can browse all predictions online (http://het.io). Heterogeneous network edge prediction effectively prioritized genetic associations and provides a powerful new approach for data integration across multiple domains.

scholarly journals RECENT RESEARCH ON THE GROWTH PLATE: Recent insights into the regulation of the growth plate

2014 ◽  
Vol 53 (1) ◽  
pp. T1-T9 ◽  
Author(s):  
Julian C Lui ◽  
Ola Nilsson ◽  
Jeffrey Baron
Keyword(s):  
Growth Plate ◽  
Bone Morphogenetic Proteins ◽  
Bone Growth ◽  
Association Studies ◽  
Expression Patterns ◽  
Human Growth ◽  
Cytokine Signaling ◽  
Genome Wide Association Studies ◽  
Paracrine Factors ◽  
Large Numbers

For most bones, elongation is driven primarily by chondrogenesis at the growth plates. This process results from chondrocyte proliferation, hypertrophy, and extracellular matrix secretion, and it is carefully orchestrated by complex networks of local paracrine factors and modulated by endocrine factors. We review here recent advances in the understanding of growth plate physiology. These advances include new approaches to study expression patterns of large numbers of genes in the growth plate, using microdissection followed by microarray. This approach has been combined with genome-wide association studies to provide insights into the regulation of the human growth plate. We also review recent studies elucidating the roles of bone morphogenetic proteins, fibroblast growth factors, C-type natriuretic peptide, and suppressor of cytokine signaling in the local regulation of growth plate chondrogenesis and longitudinal bone growth.

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