Identifying SNPs frequencies in Genetic Data of De-Identified Myalgic Encephalomyelitis/Chronic Fatigue Syndrome patients for potential diagnostic biomarker establishment

Identifying SNPs frequencies in Genetic Data of De-Identified Myalgic Encephalomyelitis/Chronic Fatigue Syndrome patients for potential diagnostic biomarker establishment

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating disease with unknown causes. It is known that Single Nucleotide Polymorphisms (SNPs) play an important role in gene expression. Changes to that can manifest as phenotypic changes. Prior to this ongoing study, there existed no known databases of SNPs in patients diagnosed with ME/CFS.Our objectives are to create and continually update a novel database of SNPs that are specific for ME/CFS patients, and to analyze the relative frequency significance in our cohort of specific SNPs. A genetic database was created on- site through the use of a secure user-friendly online platform, REDCap©, for participants to upload their raw genetic data, acquired from 23andMe. The uploaded de-identified genetic data acquired from RedCap is modified to a suitable format for Seattle Sequence Annotation 138. The annotated data is then filtered to include only non-synonymous and nonsense SNPs from protein coding regions (exons), microRNAs, and SNPs that are close to splice sites. The frequencies of each SNP have been calculated within our cohort and compared to public databases. Those SNPs frequencies of differing prevalence between our database and the general public have been noted for further analysis. Further analysis will include looking for significant SNPs and the metabolic pathways they play a role in. Ongoing recruitment for submission of de-identified genetic data to our database leads to a constantly increasing sample size for continual application of the aforementioned method. Additional SNP investigation from the larger sample size allows for further validation of SNP trend significance.