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