Environment

Author(s):  
Christopher R. Holroyd ◽  
Nicholas C. Harvey ◽  
Mark H. Edwards ◽  
Cyrus Cooper

Musculoskeletal disease covers a broad spectrum of conditions whose aetiology comprises variable genetic and environmental contributions. More recently it has become clear that, particularly early in life, the interaction of gene and environment is critical to the development of later disease. Additionally, only a small proportion of the variation in adult traits such as bone mineral density has been explained by specific genes in genome-wide association studies, suggesting that gene-environment interaction may explain a much larger part of the inheritance of disease risk than previously thought. It is therefore critically important to evaluate the environmental factors which may predispose to diseases such as osteorthritis, osteoporosis, and rheumatoid arthritis both at the individual and at the population level. In this chapter we describe the environmental contributors, across the whole life course, to osteoarthritis, osteoporosis and rheumatoid arthritis, as exemplar conditions. We consider factors such as age, gender, nutrition (including the role of vitamin D), geography, occupation, and the clues that secular changes of disease pattern may yield. We describe the accumulating evidence that conditions such as osteoporosis may be partly determined by the early interplay of environment and genotype, through aetiological mechanisms such as DNA methylation and other epigenetic phenomena. Such studies, and those examining the role of environmental influences across other stages of the life course, suggest that these issues should be addressed at all ages, starting from before conception, in order to optimally reduce the burden of musculoskeletal disorders in future generations.

Author(s):  
Christopher R. Holroyd ◽  
Nicholas C. Harvey ◽  
Mark H. Edwards ◽  
Cyrus Cooper

Musculoskeletal disease covers a broad spectrum of conditions whose aetiology comprises variable genetic and environmental contributions. More recently it has become clear that, particularly early in life, the interaction of gene and environment is critical to the development of later disease. Additionally, only a small proportion of the variation in adult traits such as bone mineral density has been explained by specific genes in genome-wide association studies, suggesting that gene-environment interaction may explain a much larger part of the inheritance of disease risk than previously thought. It is therefore critically important to evaluate the environmental factors which may predispose to diseases such as osteorthritis, osteoporosis, and rheumatoid arthritis both at the individual and at the population level. In this chapter we describe the environmental contributors, across the whole life course, to osteoarthritis, osteoporosis and rheumatoid arthritis, as exemplar conditions. We consider factors such as age, gender, nutrition (including the role of vitamin D), geography, occupation, and the clues that secular changes of disease pattern may yield. We describe the accumulating evidence that conditions such as osteoporosis may be partly determined by the early interplay of environment and genotype, through aetiological mechanisms such as DNA methylation and other epigenetic phenomena. Such studies, and those examining the role of environmental influences across other stages of the life course, suggest that these issues should be addressed at all ages, starting from before conception, in order to optimally reduce the burden of musculoskeletal disorders in future generations.


Author(s):  
Christopher R. Holroyd ◽  
Nicholas C. Harvey ◽  
Mark H. Edwards ◽  
Cyrus Cooper

Musculoskeletal disease covers a broad spectrum of conditions whose aetiology comprises variable genetic and environmental contributions. More recently it has become clear that, particularly early in life, the interaction of gene and environment is critical to the development of later disease. Additionally, only a small proportion of the variation in adult traits such as bone mineral density has been explained by specific genes in genome-wide association studies, suggesting that gene-environment interaction may explain a much larger part of the inheritance of disease risk than previously thought. It is therefore critically important to evaluate the environmental factors which may predispose to diseases such as osteorthritis, osteoporosis, and rheumatoid arthritis both at the individual and at the population level. In this chapter we describe the environmental contributors, across the whole life course, to osteoarthritis, osteoporosis and rheumatoid arthritis, as exemplar conditions. We consider factors such as age, gender, nutrition (including the role of vitamin D), geography, occupation, and the clues that secular changes of disease pattern may yield. We describe the accumulating evidence that conditions such as osteoporosis may be partly determined by the early interplay of environment and genotype, through aetiological mechanisms such as DNA methylation and other epigenetic phenomena. Such studies, and those examining the role of environmental influences across other stages of the life course, suggest that these issues should be addressed at all ages, starting from before conception, in order to optimally reduce the burden of musculoskeletal disorders in future generations.


2019 ◽  
Author(s):  
Jing Yang ◽  
Amanda McGovern ◽  
Paul Martin ◽  
Kate Duffus ◽  
Xiangyu Ge ◽  
...  

AbstractGenome-wide association studies have identified genetic variation contributing to complex disease risk. However, assigning causal genes and mechanisms has been more challenging because disease-associated variants are often found in distal regulatory regions with cell-type specific behaviours. Here, we collect ATAC-seq, Hi-C, Capture Hi-C and nuclear RNA-seq data in stimulated CD4+ T-cells over 24 hours, to identify functional enhancers regulating gene expression. We characterise changes in DNA interaction and activity dynamics that correlate with changes gene expression, and find that the strongest correlations are observed within 200 kb of promoters. Using rheumatoid arthritis as an example of T-cell mediated disease, we demonstrate interactions of expression quantitative trait loci with target genes, and confirm assigned genes or show complex interactions for 20% of disease associated loci, including FOXO1, which we confirm using CRISPR/Cas9.


2020 ◽  
Vol 21 (12) ◽  
pp. 4269 ◽  
Author(s):  
Victoria L. Halperin Kuhns ◽  
Owen M. Woodward

Hyperuricemia, or elevated serum urate, causes urate kidney stones and gout and also increases the incidence of many other conditions including renal disease, cardiovascular disease, and metabolic syndrome. As we gain mechanistic insight into how urate contributes to human disease, a clear sex difference has emerged in the physiological regulation of urate homeostasis. This review summarizes our current understanding of urate as a disease risk factor and how being of the female sex appears protective. Further, we review the mechanisms of renal handling of urate and the significant contributions from powerful genome-wide association studies of serum urate. We also explore the role of sex in the regulation of specific renal urate transporters and the power of new animal models of hyperuricemia to inform on the role of sex and hyperuricemia in disease pathogenesis. Finally, we advocate the use of sex differences in urate handling as a potent tool in gaining a further understanding of physiological regulation of urate homeostasis and for presenting new avenues for treating the constellation of urate related pathologies.


2015 ◽  
Author(s):  
Oriol Canela-Xandri ◽  
Konrad Rawlik ◽  
John A. Woolliams ◽  
Albert Tenesa

Genome-wide association studies (GWAS) promised to translate their findings into clinically beneficial improvements of patient management by tailoring disease management to the individual through the prediction of disease risk. However, the ability to translate genetic findings from GWAS into predictive tools that are of clinical utility and which may inform clinical practice has, so far, been encouraging but limited. Here we propose to use a more powerful statistical approach that enables the prediction of multiple medically relevant phenotypes without the costs associated with developing a genetic test for each of them. As a proof of principle, we used a common panel of 319,038 SNPs to train the prediction models in 114,264 unrelated White-British for height and four obesity related traits (body mass index, basal metabolic rate, body fat percentage, and waist-to-hip ratio). We obtained prediction accuracies that ranged between 46% and 75% of the maximum achievable given their explained heritable component. This represents an improvement of up to 75% over the phenotypic variance explained by the predictors developed through large collaborations, which used more than twice as many training samples. Across-population predictions in White non-British individuals were similar to those of White-British whilst those in Asian and Black individuals were informative but less accurate. The genotyping of circa 500,000 UK Biobank participants will yield predictions ranging between 66% and 83% of the maximum. We anticipate that our models and a common panel of genetic markers, which can be used across multiple traits and diseases, will be the starting point to tailor disease management to the individual. Ultimately, we will be able to capitalise on whole-genome sequence and environmental risk factors to realise the full potential of genomic medicine.


2019 ◽  
Author(s):  
Zhe Wang ◽  
Han Chen ◽  
Traci M. Bartz ◽  
Lawrence F. Bielak ◽  
Daniel I. Chasman ◽  
...  

AbstractBackgroundAlcohol intake influences plasma lipid levels and such effects may be modulated by genetic variants.ObjectiveWe aimed to characterize the role of aggregated rare and low-frequency variants in gene by alcohol consumption interactions associated with fasting plasma lipid levels.DesignIn the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, fasting plasma triglycerides (TG), and high- and low-density lipoprotein cholesterol (HDL-c and LDL-c) were measured in 34,153 European Americans from five discovery studies and 32,275 individuals from six replication studies. Rare and low-frequency protein coding variants (minor allele frequency ≤ 5%) measured by an exome array were aggregated by genes and evaluated by a gene-environment interaction (GxE) test and a joint test of genetic main and GxE interaction effects. Two dichotomous self-reported alcohol consumption variables, current drinker, defined as any recurrent drinking behavior, and regular drinker, defined as the subset of current drinkers who consume at least two drinks per week, were considered.ResultsWe discovered and replicated 21 gene-lipid associations at 13 known lipid loci through the joint test. Eight loci (PCSK9, LPA, LPL, LIPG, ANGPTL4, APOB, APOC3 and CD300LG) remained significant after conditioning on the common index single nucleotide polymorphism (SNP) identified by previous genome-wide association studies, suggesting an independent role for rare and low-frequency variants at these loci. One significant gene-alcohol interaction on TG was discovered at a Bonferroni corrected significance level (p-value <5×10−5) and replicated (p-value <0.013 for the interaction test) inSMC5.ConclusionsIn conclusion, this study applied new gene-based statistical approaches to uncover the role of rare and low-frequency variants in gene-alcohol consumption interactions on lipid levels.


2018 ◽  
Author(s):  
Inken Wohlers ◽  
Lars Bertram ◽  
Christina M. Lill

AbstractGenome-wide association studies (GWAS) have identified a large number of genetic risk loci for autoimmune diseases. However, the functional variants underlying these disease associations remain largely unknown. There is evidence that microRNA-mediated regulation may play an important role in this context. Therefore, we assessed whether autoimmune disease loci unfold their effects via altering microRNA expression in relevant immune cells.To this end, we performed microRNA expression quantitative trait loci (eQTL) analyses across 115 GWAS regions associated with 12 autoimmune diseases using next-generation sequencing data of 345 lymphoblastoid cell lines. Statistical analyses included the application and extension of a recently proposed framework (joint likelihood mapping), to microRNA expression data and microRNA target gene enrichment analyses of relevant GWAS data.Overall, only a minority of autoimmune disease risk loci may exert their pathophysiologic effects by altering miRNA expression based on JLIM. However, detailed functional fine-mapping revealed two independent GWAS regions harboring autoimmune disease risk SNPs with significant effects on microRNA expression. These relate to SNPs associated with Crohn’s disease (CD; rs102275) and rheumatoid arthritis (RA; rs968567), which affect the expression of miR-1908-5p (prs102275=1.44e-20, prs968567=2.54e-14). In addition, an independent CD risk SNP, rs3853824, was found to alter the expression of miR-3614-5p (p=5.70e-7). To support these findings, we demonstrate that GWAS signals for RA and CD were enriched in genes predicted to be targeted by both miRNAs (all with p<0.05).In summary, our study points towards a pathophysiological role of miR-1908-5p and miR- 3614-5p in autoimmunity.


2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Muhammad Muaaz Aslam ◽  
Peter John ◽  
Kang-Hsien Fan ◽  
Attya Bhatti ◽  
Wajahat Aziz ◽  
...  

Rheumatoid arthritis (RA) is a complex and multifactorial autoimmune disorder with the involvement of multiple genetic and environmental factors. Genome-wide association studies (GWAS) have identified more than 50 RA genetic loci in European populations. Given the anticipated overlap of RA-relevant genes and pathways across different ethnic groups, we sought to replicate 58 GWAS-implicated SNPs reported in Europeans in Pakistani subjects. 1,959 unrelated subjects comprising 1,222 RA cases and 737 controls were collected from three rheumatology facilities in Pakistan. Genotyping was performed using iPLEX or TaqMan® methods. A total of 50 SNPs were included in the final association analysis after excluding those that failed assay design/run or postrun QC analysis. Fourteen SNPs (LINC00824/rs1516971, PADI4/rs2240336, CEP57/rs4409785, CTLA4/rs3087243, STAT4/rs13426947, HLA-B/MICA/rs2596565, C5orf30/rs26232, CCL21/rs951005, GATA3/rs2275806, VPS37C/rs595158, HLA-DRB1/rs660895, EOMES/rs3806624, SPRED2/rs934734, and RUNX1/rs9979383) were replicated in our Pakistani sample at false discovery rate (FDR) of <0.20 with nominal p values ranging from 4.73E-06 to 3.48E-02. Our results indicate that several RA susceptibility loci are shared between Pakistani and European populations, supporting the role of common genes/pathways.


2016 ◽  
Author(s):  
Farhad Hormozdiari ◽  
Martijn van de Bunt ◽  
Ayellet V. Segrè ◽  
Xiao Li ◽  
Jong Wha J Joo ◽  
...  

AbstractThe vast majority of genome-wide association studies (GWAS) risk loci fall in non-coding regions of the genome. One possible hypothesis is that these GWAS risk loci alter the individual’s disease risk through their effect on gene expression in different tissues. In order to understand the mechanisms driving a GWAS risk locus, it is helpful to determine which gene is affected in specific tissue types. For example, the relevant gene and tissue may play a role in the disease mechanism if the same variant responsible for a GWAS locus also affects gene expression. Identifying whether or not the same variant is causal in both GWAS and eQTL studies is challenging due to the uncertainty induced by linkage disequilibrium (LD) and the fact that some loci harbor multiple causal variants. However, current methods that address this problem assume that each locus contains a single causal variant. In this paper, we present a new method, eCAVIAR, that is capable of accounting for LD while computing the quantity we refer to as the colocalization posterior probability (CLPP). The CLPP is the probability that the same variant is responsible for both the GWAS and eQTL signal. eCAVIAR has several key advantages. First, our method can account for more than one causal variant in any loci. Second, it can leverage summary statistics without accessing the individual genotype data. We use both simulated and real datasets to demonstrate the utility of our method. Utilizing publicly available eQTL data on 45 different tissues, we demonstrate that computing CLPP can prioritize likely relevant tissues and target genes for a set of Glucose and Insulin-related traits loci. eCAVIAR is available at http://genetics.cs.ucla.edu/caviar/


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