scholarly journals Using synthetic datasets to better understand and explain health outcomes associated with common single nucleotide polymorphisms

2019 ◽  
Author(s):  
Thomas R. Wood ◽  
Nathan Owens
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Disease Risk ◽  
Personalised Medicine ◽  
Methylation Status ◽  
Fasting Blood Glucose ◽  
Significant Heterogeneity ◽  
Common Snps ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Synthetic Datasets

ABSTRACTDue to decreasing costs and a move towards “personalised medicine”, the use of direct-to-consumer genetic analyses is increasing. Both consumers and healthcare practitioners must therefore be able to understand the true disease risks associated with common genetic single nucleotide polymorphisms (SNPs). However, most population studies of common SNPs only provide average (+/−error) phenotypic or risk descriptions for a given genotype, which hides the true heterogeneity of the population and reduces the ability of an individual to determine how they themselves might truly be effected. Here, we describe the use of synthetic datasets generated from descriptive phenotypic data published on common SNPs associated with obesity, elevated fasting blood glucose, and methylation status. Using both simple statistical theory and full graphical representation of the generated data, we show that single common SNPs are associated with a less than 10% likelihood of effecting final phenotype, even in homozygotes. The significant heterogeneity in the data, as well as the baseline disease risk of Western populations suggests that most disease risk is dominated by the effect of the modern environment.

scholarly journals Using synthetic datasets to bridge the gap between the promise and reality of basing health-related decisions on common single nucleotide polymorphisms

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 2147
Author(s):  
Thomas R. Wood ◽  
Nathan Owens
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Disease Risk ◽  
Methylation Status ◽  
Fasting Blood Glucose ◽  
Risk Scores ◽  
Individual Risk ◽  
Common Snps ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Synthetic Datasets

Background: While the academic genetic literature has clearly shown that common genetic single nucleotide polymorphisms (SNPs), and even large polygenic SNP risk scores, cannot reliably be used to determine risk of disease or to personalize interventions, a significant industry of companies providing SNP-based recommendations still exists. Healthcare practitioners must therefore be able to navigate between the promise and reality of these tools, including being able to interpret the literature that is associated with a given risk or suggested intervention. One significant hurdle to this process is the fact that most population studies of common SNPs only provide average (+/- error) phenotypic or risk descriptions for a given genotype, which hides the true heterogeneity of the population and reduces the ability of an individual to determine how they themselves or their patients might truly be affected. Methods: We generated synthetic datasets generated from descriptive phenotypic data published on common SNPs associated with obesity, elevated fasting blood glucose, and methylation status. Using simple statistical theory and full graphical representation of the generated data, we developed a method by which anybody can better understand phenotypic heterogeneity in a population, as well as the degree to which common SNPs truly drive disease risk. Results: Individual risk SNPs had a <10% likelihood of effecting the associated phenotype (bodyweight, fasting glucose, or homocysteine levels). Example polygenic risk scores including the SNPs most associated with obesity and type 2 diabetes only explained 2% and 5% of the final phenotype, respectively. Conclusions: The data suggest that most disease risk is dominated by the effect of the modern environment, providing further evidence to support the pursuit of lifestyle-based interventions that are likely to be beneficial regardless of genetics.

Association between Single-Nucleotide Polymorphisms of the hOGG1,NEIL1,APEX1, FEN1,LIG1, and LIG3 Genes and Alzheimer's Disease Risk

2016 ◽  
Vol 73 (2) ◽  
pp. 98-107 ◽  
Author(s):  
Dominik Kwiatkowski ◽  
Piotr Czarny ◽  
Monika Toma ◽  
Anna Korycinska ◽  
Katarzyna Sowinska ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Single Nucleotide Polymorphisms ◽  
Disease Risk ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide

scholarly journals An Approach to Identify Individual Functional Single Nucleotide Polymorphisms and Isoform MicroRNAs

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Ying Wang ◽  
Jidong Ru ◽  
Tao Jin ◽  
Ming Sun ◽  
Lizhu Jia ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Single Nucleotide Polymorphisms ◽  
Disease Risk ◽  
Software Tool ◽  
Mature Mirnas ◽  
Nucleotide Polymorphisms ◽  
Sequencing Data ◽  
Single Nucleotide ◽  
1000 Genomes ◽  
Mirna Sequencing

MicroRNAs (miRNAs) and single nucleotide polymorphisms (SNPs) play important roles in disease risk and development, especially cancer. Importantly, when SNPs are located in pre-miRNAs, they affect their splicing mechanism and change the function of miRNAs. To improve disease risk assessment, we propose an approach and developed a software tool, IsomiR_Find, to identify disease/phenotype-related SNPs and isomiRs in individuals. Our approach is based on the individual’s samples, with SNP information extracted from the 1000 Genomes Project. SNPs were mapped to pre-miRNAs based on whole-genome coordinates and then SNP-pre-miRNA sequences were constructed. Moreover, we developed matpred2, a software tool to identify the four splicing sites of mature miRNAs. Using matpred2, we identified isomiRs and then verified them by searching within individual miRNA sequencing data. Our approach yielded biomarkers for biological experiments, mined functions of miRNAs and SNPs, improved disease risk assessment, and provided a way to achieve individualized precision medicine.

Single Nucleotide Polymorphisms Influence the Phenotype of Mutations P618A and R1336W found in the ADAMTS13 Gene of Congenital TTP Patients.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 2977-2977
Author(s):  
Barbara Plaimauer ◽  
Gabriele Mohr ◽  
Waltraud Wernhart ◽  
Katharina Bruno ◽  
Gerhard Antoine ◽  
...  
Keyword(s):  
Amino Acid ◽  
Single Nucleotide Polymorphisms ◽  
Single Amino Acid ◽  
Paternal Allele ◽  
Amino Acid Substitutions ◽  
Common Snps ◽  
Nucleotide Polymorphisms ◽  
Adamts13 Activity ◽  
Maternal Allele ◽  
Single Nucleotide

Abstract ADAMTS13 cleaves plasmatic von Willebrand factor (VWF) between Tyr1605 and Met1606 and regulates thereby the hemostatic activity of VWF. Mutations in the ADAMTS13 gene leading to severe ADAMTS13 deficiency have been found in patients with congenital thrombotic thrombocytopenic purpura (TTP). We have analyzed the ADAMTS13 gene defects in two brothers with hereditary TTP [Antoine et al, Brit. J. Hematol., 2003] where we detected a total of six nucleotide exchanges causing point mutations. On the maternal allele we found an accumulation of five amino acid substitutions (R7W, Q448E, P618A, A732V, R1336W) and on the paternal allele a stop mutation (Q44X) leading to premature protein termination in the propeptide region. Both brothers were double heterozygotes with < 3% of ADAMTS13 activity, whereas their asymptomatic parents have ~ 50% activity. Four (R7W, Q448E, P618A, A732V) of the five maternal mutations constitute single nucleotide polymorphisms (SNP) but R1336W was identified as novel rare mutation in the second cub domain. To evaluate the biologic phenotype of a given haplotype, e.g. the functional significance of the presence of the various SNPs, we analyzed the functional impact of the individual mutations on ADAMTS13 antigen levels and ADAMTS13 activity. A series of mutant ADAMTS13 molecules was expressed which contained either single amino acid substitutions or combinations of mutations with each other. We found that the common SNPs R7W, Q448E and A732V, as single mutations, had either no or only a minor impact on ADAMTS13 secretion and ADAMTS13 activity, whereas P618A and R1336W conferred a dominant adverse effect on ADAMTS13 secretion levels. Co-expression of SNPs R7W or Q448E with SNP P618A lead to improved ADAMTS13 secretion levels and could therefore partly attenuate the detrimental effect of P618A. Concomitant expression of all four SNPs reconstituted secretion levels similar to wild-type implicating a complex synergistically interaction of SNPs located in different ADAMTS13 domain regions, however, functional activity was impaired to 50%. Mutation R1336W was shown to be, as a single amino acid exchange, responsible for reduced ADAMTS13 antigen levels, but in contrast to P618A, the negative effect of R1336W was rather enhanced by the co-expression of R7W and Q448E, than rescued, leading to the total absence of ADAMTS13 secretion from the maternal allele. Our findings provide for the first time evidence that fairly common SNPs, dependent on the presence or absence of other mutations, may differently modulate functional ADAMTS13 protease levels.

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