Review of the Quality Control Checks Performed by Current Genome-Wide and Targeted-Genome Association Studies on Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Citation: Grabowska AD, Lacerda EM, Nacul L and Sepúlveda N (2020) Review of the Quality Control Checks Performed by Current Genome-Wide and Targeted-Genome Association Studies on Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Front. Pediatr. 8:293. doi: 10.3389/fped.2020.00293 Review of the Quality Control Checks Performed by Current Genome-Wide and Targeted-Genome Association Studies on Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

[1]  N. Klimas,et al.  Genetic Predisposition for Immune System, Hormone, and Metabolic Dysfunction in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Pilot Study , 2019, Front. Pediatr..

[2]  T. Harrer,et al.  Chronic viral infections in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) , 2018, Journal of Translational Medicine.

[3]  M. Fletcher,et al.  Identification of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome-associated DNA methylation patterns , 2018, PloS one.

[4]  Andries T Marees,et al.  A tutorial on conducting genome‐wide association studies: Quality control and statistical analysis , 2018, International journal of methods in psychiatric research.

[5]  J. Blomberg,et al.  Infection Elicited Autoimmunity and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: An Explanatory Model , 2018, Front. Immunol..

[6]  Patrick O. McGowan,et al.  Genome-Epigenome Interactions Associated with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome , 2017, bioRxiv.

[7]  K. Minogue The Explanatory Model , 2017 .

[8]  U. Reimer,et al.  Serological profiling of the EBV immune response in Chronic Fatigue Syndrome using a peptide microarray , 2017, PloS one.

[9]  L. Nacul,et al.  How have selection bias and disease misclassification undermined the validity of myalgic encephalomyelitis/chronic fatigue syndrome studies? , 2017, Journal of health psychology.

[10]  D. Staines,et al.  A targeted genome association study examining transient receptor potential ion channels, acetylcholine receptors, and adrenergic receptors in Chronic Fatigue Syndrome/Myalgic Encephalomyelitis , 2016, BMC Medical Genetics.

[11]  A. Palotás,et al.  Genome-wide association analysis identifies genetic variations in subjects with myalgic encephalomyelitis/chronic fatigue syndrome , 2016, Translational Psychiatry.

[12]  Sheng Luo,et al.  Binomial regression with a misclassified covariate and outcome , 2016, Statistical methods in medical research.

[13]  Gabor T. Marth,et al.  A global reference for human genetic variation , 2015, Nature.

[14]  Tom R. Gaunt,et al.  The UK10K project identifies rare variants in health and disease , 2015, Nature.

[15]  M. Rajeevan,et al.  Pathway-focused genetic evaluation of immune and inflammation related genes with chronic fatigue syndrome. , 2015, Human immunology.

[16]  M. Rajeevan,et al.  Convergent Genomic Studies Identify Association of GRIK2 and NPAS2 with Chronic Fatigue Syndrome , 2011, Neuropsychobiology.

[17]  T. Kawai,et al.  Identification of Marker Genes for Differential Diagnosis of Chronic Fatigue Syndrome , 2008, Molecular medicine.

[18]  B. Carruthers Definitions and aetiology of myalgic encephalomyelitis: how the Canadian consensus clinical definition of myalgic encephalomyelitis works , 2006, Journal of Clinical Pathology.

[19]  J. Neuhaus,et al.  Binomial Regression with Misclassification , 2003, Biometrics.

[20]  C. Baird The pilot study. , 2000, Orthopedic nursing.

[21]  Ian Hickie,et al.  The Chronic Fatigue Syndrome: A Comprehensive Approach to Its Definition and Study , 1994, Annals of Internal Medicine.

[22]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .