scholarly journals Identification of genetic variants regulating the abundance of clinically relevant plasma proteins using the Diversity Outbred mouse model

2020 ◽  
Author(s):  
Stéphanie Philtjens ◽  
Dominic J. Acri ◽  
Byungwook Kim ◽  
Hyewon Kim ◽  
Jungsu Kim
Keyword(s):  
Quantitative Trait Loci ◽  
Mouse Model ◽  
Drug Development ◽  
Plasma Protein ◽  
Genetic Variants ◽  
Quantitative Trait ◽  
Plasma Proteins ◽  
Genetic Modifiers ◽  
Protein Levels ◽  
Diversity Outbred

AbstractAlthough there have been numerous expression quantitative trait loci (eQTL) studies, the effect of genetic variants on the levels of multiple plasma proteins still warrants more systematic investigation. To identify genetic modifiers that influence the levels of clinically relevant plasma proteins, we performed protein quantitative trait locus (pQTL) mapping on 92 proteins using the Diversity Outbred (DO) mouse population and identified 12 significant cis and 6 trans pQTL. Among them, we discovered coding variants in a cis-pQTL in Ahr and a trans-pQTL in Rfx1 for the IL-17A protein. Our study reports an innovative pipeline for the identification of genetic modifiers that may be targeted for drug development.Author SummaryBlood plasma is a body fluid that can be collected in a noninvasive way to detect diseases, such as autoimmune disease. However, it is known that plasma protein levels are affected by both the environment and genetic background. To determine the effect of genetics on plasma protein levels in human, one needs a rather large sample size. To overcome this critical issue, a mouse model, the Diversity Outbred (DO), was established that is genetically as diverse as the human population. In this study, we used N=140 DO mice and genotyped over 140,000 variants. In addition, we measured the levels of 92 proteins in plasma of these DO mice using Olink Proteomics technology. The proteins detected in this panel are known to be detectable in human plasma, making our study translatable to human. We identified 18 significant protein quantitative trait loci. Furthermore, we describe an analysis pipeline that allows for the detection of a single gene in the locus that is responsible for the differences in protein levels. We identified how variants in the Regulatory Factor X1 (Rfx1) gene regulates Interleukin-17A (IL-17A) plasma levels. Our study reports an innovative approach to identify genetic modifiers that may be targeted for drug development.

scholarly journals Identification of Genetic Variants Regulating the Abundance of Clinically Relevant Plasma Proteins using the Diversity Outbred Mouse Model

2021 ◽  
Author(s):  
Stéphanie Philtjens ◽  
Dominic J. Acri ◽  
Byungwook Kim ◽  
Hyewon Kim ◽  
Jungsu Kim
Keyword(s):  
Genetic Variants ◽  
Quantitative Trait ◽  
Plasma Proteins ◽  
Systematic Investigation ◽  
Genetic Modifiers ◽  
Gene Encoding ◽  
Mouse Population ◽  
Aryl Hydrocarbon ◽  
Diversity Outbred ◽  
Trait Locus

Abstract Background: Levels of plasma proteins are under control of environmental and genetic factors. To use plasma proteins in biomarker studies, we need to understand how genetic modifiers influence their abundance. Although there has been expression quantitative trait loci (eQTL) studies on a few limited numbers of proteins, the effect of genetic variants on the levels of multiple plasma proteins still warrants more systematic investigation.Results: To identify genetic modifiers that influence the levels of clinically relevant plasma proteins, we performed protein quantitative trait locus (pQTL) mapping on the 92 proteins present in the Olink Mouse Exploratory Panel using the Diversity Outbred (DO) mouse population. We identified 12 significant pQTL that were located in cis and 6 that were in trans. Among them, we discovered that the presence of coding variants in the gene encoding for the Aryl Hydrocarbon Receptor (Ahr) had a significant effect on its abundance in plasma. Most interestingly, we identified variants in the Regulatory Factor X1 (Rfx1) gene that influence the abundance of the IL-17A protein in plasma.Conclusion: Our study reports an innovative pipeline for the identification of genetic modifiers that may be targeted for drug development.

scholarly journals Quantitative Trait Loci for Interhemispheric Commissure Development and Social Behaviors in the BTBR T+ tf/J Mouse Model of Autism

PLoS ONE ◽  
2013 ◽  
Vol 8 (4) ◽  
pp. e61829 ◽  
Author(s):  
Dorothy M. Jones-Davis ◽  
Mu Yang ◽  
Eric Rider ◽  
Nathan C. Osbun ◽  
Gilberto J. da Gente ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Mouse Model ◽  
Quantitative Trait ◽  
Social Behaviors ◽  
Trait Loci

Plasma proteome changes in subjects with Type 2 diabetes mellitus with a low or high early insulin response

2008 ◽  
Vol 114 (7) ◽  
pp. 499-507 ◽  
Author(s):  
Tea Sundsten ◽  
Björn Zethelius ◽  
Christian Berne ◽  
Peter Bergsten
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Plasma Protein ◽  
Plasma Proteins ◽  
Insulin Response ◽  
Β Cell ◽  
Protein Levels ◽  
Early Insulin Response

Circulating proteins contribute to the pathogenesis of T2DM (Type 2 diabetes mellitus) in various ways. The aim of the present study was to investigate variations in plasma protein levels in subjects with T2DM and differences in β-cell function, characterized by the EIR (early insulin response), and to compare these protein levels with those observed in individuals with NGT (normal glucose tolerance). Ten subjects with NGT+high EIR, ten with T2DM+high EIR, and ten with T2DM+low EIR were selected from the community-based ULSAM (Uppsala Longitudinal Study of Adult Men) cohort. Plasma protein profiling was performed using SELDI-TOF (surface-enhanced laser-desorption ionization–time-of-flight) MS. In total, nine plasma proteins differed between the three study groups (P<0.05, as determined by ANOVA). The levels of two forms of transthyretin, haemoglobin α-chain and haemoglobin β-chain were decreased in plasma from subjects with T2DM compared with subjects with NGT, irrespective of the EIR of the subjects. Apolipoprotein H was decreased in plasma from individuals with T2DM+high EIR compared with subjects with NGT. Four additional unidentified plasma proteins also varied in different ways between the experimental groups. In conclusion, the proteins detected in the present study may be related to the development of β-cell dysfunction.

scholarly journals Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR

2021 ◽  
Author(s):  
Michael Scherer ◽  
Gilles Gasparoni ◽  
Souad Rahmouni ◽  
Tatiana Shashkova ◽  
Marion Arnoux ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Quantitative Trait Loci ◽  
Genetic Variants ◽  
Quantitative Trait ◽  
Healthy Individuals ◽  
Cell Type ◽  
Systematic Analysis ◽  
Trait Loci ◽  
Cell Type Specific ◽  
Methylation Quantitative Trait Loci

Background: Understanding the influence of genetic variants on DNA methylation is fundamental for the interpretation of epigenomic data in the context of disease. There is a need for systematic approaches not only for determining methylation quantitative trait loci (methQTL) but also for discriminating general from cell-type-specific effects. Results: Here, we present a two-step computational framework MAGAR, which fully supports identification of methQTLs from matched genotyping and DNA methylation data, and additionally the identification of quantitative cell-type-specific methQTL effects. In a pilot analysis, we apply MAGAR on data in four tissues (ileum, rectum, T-cells, B-cells) from healthy individuals and demonstrate the discrimination of common from cell-type-specific methQTLs. We experimentally validate both types of methQTLs in an independent dataset comprising additional cell types and tissues. Finally, we validate selected methQTLs (PON1, ZNF155, NRG2) by ultra-deep local sequencing. In line with previous reports, we find cell-type-specific methQTLs to be preferentially located in enhancer elements. Conclusions: Our analysis demonstrates that a systematic analysis of methQTLs provides important new insights on the influences of genetic variants to cell-type-specific epigenomic variation.

scholarly journals Identifying causal models between genetically regulated methylation patterns and gene expression in healthy colon tissue

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Anna Díez-Villanueva ◽  
Mireia Jordà ◽  
Robert Carreras-Torres ◽  
Henar Alonso ◽  
David Cordero ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Quantitative Trait Loci ◽  
Genetic Variants ◽  
Quantitative Trait ◽  
Regulation Of Gene Expression ◽  
Regulatory Elements ◽  
Colon Tissue ◽  
Tissue Samples ◽  
Trait Loci

Abstract Background DNA methylation is involved in the regulation of gene expression and phenotypic variation, but the inter-relationship between genetic variation, DNA methylation and gene expression remains poorly understood. Here we combine the analysis of genetic variants related to methylation markers (methylation quantitative trait loci: mQTLs) and gene expression (expression quantitative trait loci: eQTLs) with methylation markers related to gene expression (expression quantitative trait methylation: eQTMs), to provide novel insights into the genetic/epigenetic architecture of colocalizing molecular markers. Results Normal mucosa from 100 patients with colon cancer and 50 healthy donors included in the Colonomics project have been analyzed. Linear models have been used to find mQTLs and eQTMs within 1 Mb of the target gene. From 32,446 eQTLs previously detected, we found a total of 6850 SNPs, 114 CpGs and 52 genes interrelated, generating 13,987 significant combinations of co-occurring associations (meQTLs) after Bonferromi correction. Non-redundant meQTLs were 54, enriched in genes involved in metabolism of glucose and xenobiotics and immune system. SNPs in meQTLs were enriched in regulatory elements (enhancers and promoters) compared to random SNPs within 1 Mb of genes. Three colorectal cancer GWAS SNPs were related to methylation changes, and four SNPs were related to chemerin levels. Bayesian networks have been used to identify putative causal relationships among associated SNPs, CpG and gene expression triads. We identified that most of these combinations showed the canonical pathway of methylation markers causes gene expression variation (60.1%) or non-causal relationship between methylation and gene expression (33.9%); however, in up to 6% of these combinations, gene expression was causing variation in methylation markers. Conclusions In this study we provided a characterization of the regulation between genetic variants and inter-dependent methylation markers and gene expression in a set of 150 healthy colon tissue samples. This is an important finding for the understanding of molecular susceptibility on colon-related complex diseases.

HbF-promoting polymorphisms may specifically reduce the residual risk of cerebral vasculopathy in SCA children with alpha-thalassemia

2020 ◽  
pp. 1-6
Author(s):  
Philippe Joly ◽  
Nathalie Bonello-Palot ◽  
Catherine Badens ◽  
Serge Pissard ◽  
Abdourahim Chamouine ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Blood Cell ◽  
Sickle Cell Anemia ◽  
Red Blood Cell ◽  
Quantitative Trait ◽  
Genetic Modifiers ◽  
Cell Deformability ◽  
Cell Rheology ◽  
Alpha Thalassemia ◽  
Hbf Level

Sickle cell anemia (SCA) is a disease characterized by abnormal red blood cell rheology. Because of their effects on HbS polymerization and red blood cell deformability, alpha-thalassemia and the residual HbF level are known genetic modifiers of the disease. The aim of our study was to determine if the number of HbF quantitative trait loci (QTL) would also favor a specific sub-phenotype of SCA as it is the case for alpha-thalassemia. Our results confirmed that alpha-thalassemia protected from cerebral vasculopathy but increased the risk for frequent painful vaso-occlusive crises. We also showed that more HbF-QTL may provide an additional and specific protection against cerebral vasculopathy but only for children with alpha-thalassemia (-α/αα or -α/-α genotypes).

Quantitative trait loci for proteinuria in the focal glomerulosclerosis mouse model

2005 ◽  
Vol 16 (4) ◽  
pp. 242-250 ◽  
Author(s):  
Eun-Hee Kim ◽  
Chul-Ho Lee ◽  
Byung-Hwa Hyun ◽  
Jun-Gyo Suh ◽  
Yang-Seok Oh ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Mouse Model ◽  
Quantitative Trait ◽  
Focal Glomerulosclerosis ◽  
Trait Loci

scholarly journals Integrative genetic and immune cell analysis of plasma proteins in healthy donors identifies novel associations involving primary immune deficiency genes

2021 ◽  
Author(s):  
Barthelemy Caron ◽  
Etienne Patin ◽  
Maxime Rotival ◽  
Bruno Charbit ◽  
Matthew L Albert ◽  
...  
Keyword(s):  
Health Status ◽  
Blood Cell ◽  
Plasma Protein ◽  
Plasma Proteins ◽  
Genetic Factors ◽  
Immune Cell ◽  
Individual Variability ◽  
Genetic Associations ◽  
Protein Levels ◽  
Age And Sex

Blood plasma proteins play an important role in immune defense against pathogens, including cytokine signaling, the complement system and the acute-phase response. Recent large-scale studies have reported genetic (i.e. quantitative trait loci, pQTLs) and non-genetic factors, such as age and sex, as major determinants to inter-individual variability in immune response variation. However, the contribution of blood cell composition to plasma protein heterogeneity has not been fully characterized and may act as a confounding factor in association studies. Here, we evaluated plasma protein levels from 400 unrelated healthy individuals of western European ancestry, who were stratified by sex and two decades of life (20-29 and 60-69 years), from the Milieu Interieur cohort. We quantified 297 proteins by Luminex in a clinically certified laboratory and their levels of variation were analysed together with 5.2M single-nucleotide polymorphisms. With respect to non-genetic variables, we included more than 700 lifestyle and biochemical factors, as well as counts of seven circulating immune cell populations measured by hemogram and standardized flow cytometry. Collectively, we found 152 significant associations involving 49 proteins and 20 non-genetic variables. Consistent with previous studies, age and sex showed a global, pervasive impact on plasma protein heterogeneity, while body mass index and other health status variables were among the non-genetic factors with the highest number of associations. After controlling for these covariates, we identified 100 and 12 pQTLs acting in cis and trans, respectively, collectively associated with 87 plasma proteins and including 30 novel genetic associations. Genetic factors explained the largest fraction of the variability of plasma protein levels, as compared to non-genetic factors. In addition, blood cell fractions, including leukocytes, lymphocytes and three types of polymorphonuclear cells, had a larger contribution to inter-individual variability than age and sex, and appeared as confounders of specific genetic associations. Finally, we identified new genetic associations with plasma protein levels of eight monogenic Mendelian disease genes including three primary immunodeficiency genes (Ficolin-3, Interleukine-2 Receptor alpha and FAS). Our study identified novel genetic and non-genetic factors associated to plasma protein levels which may inform health status and disease management.

scholarly journals Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Michael Scherer ◽  
Gilles Gasparoni ◽  
Souad Rahmouni ◽  
Tatiana Shashkova ◽  
Marion Arnoux ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Quantitative Trait Loci ◽  
Genetic Variants ◽  
Quantitative Trait ◽  
Healthy Individuals ◽  
Cell Type ◽  
Data Set ◽  
Trait Loci ◽  
Cell Type Specific ◽  
Methylation Quantitative Trait Loci

Abstract Background Understanding the influence of genetic variants on DNA methylation is fundamental for the interpretation of epigenomic data in the context of disease. There is a need for systematic approaches not only for determining methylation quantitative trait loci (methQTL), but also for discriminating general from cell type-specific effects. Results Here, we present a two-step computational framework MAGAR (https://bioconductor.org/packages/MAGAR), which fully supports the identification of methQTLs from matched genotyping and DNA methylation data, and additionally allows for illuminating cell type-specific methQTL effects. In a pilot analysis, we apply MAGAR on data in four tissues (ileum, rectum, T cells, B cells) from healthy individuals and demonstrate the discrimination of common from cell type-specific methQTLs. We experimentally validate both types of methQTLs in an independent data set comprising additional cell types and tissues. Finally, we validate selected methQTLs located in the PON1, ZNF155, and NRG2 genes by ultra-deep local sequencing. In line with previous reports, we find cell type-specific methQTLs to be preferentially located in enhancer elements. Conclusions Our analysis demonstrates that a systematic analysis of methQTLs provides important new insights on the influences of genetic variants to cell type-specific epigenomic variation.

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