Understanding the impact of pre-analytic variation in haematological and clinical chemistry analytes on the power of association studies

International Journal of Epidemiology

Volumn 43, Issue 5, 2014, Pages 1633-1644

  Gaye, Amadou ^a   Peakman, Tim ^b   Tobin, Martin D ^a   Burton, Paul R ^a  

^a UNIVERSITY OF LEICESTER   (United Kingdom)

^b UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Biobank; Biosamples; Pre analytical variation; Statistical power

Indexed keywords

ALANINE AMINOTRANSFERASE; ALBUMIN; ALKALINE PHOSPHATASE; AMYLASE; ASPARTATE AMINOTRANSFERASE; BICARBONATE; BILIRUBIN; C REACTIVE PROTEIN; CALCIUM; CHLORIDE; CHOLESTEROL; CREATINE KINASE; CREATININE; FIBRINOGEN; GAMMA GLUTAMYLTRANSFERASE; GLUCOSE; HEMOGLOBIN A1C; INSULIN; MAGNESIUM; POTASSIUM; PROTEIN; SODIUM; TRIACYLGLYCEROL; UREA;

DATA SET; HEALTH IMPACT; HEMATOLOGY; PUBLIC HEALTH; SAMPLING;

ADULT; ALANINE AMINOTRANSFERASE BLOOD LEVEL; ALKALINE PHOSPHATASE BLOOD LEVEL; AMYLASE BLOOD LEVEL; ANALYSIS OF VARIANCE; ARTICLE; ASPARTATE AMINOTRANSFERASE BLOOD LEVEL; BASOPHIL; BILIRUBIN BLOOD LEVEL; BIOCHEMISTRY; BLOOD ANALYSIS; BLOOD SAMPLING; CALCIUM URINE LEVEL; CHLORIDE BLOOD LEVEL; CREATINE KINASE BLOOD LEVEL; CREATININE BLOOD LEVEL; ERYTHROCYTE; EXPLANATORY VARIABLE; FEMALE; FIBRINOGEN BLOOD LEVEL; GAMMA GLUTAMYL TRANSFERASE BLOOD LEVEL; GLUCOSE BLOOD LEVEL; HEMATOCRIT; HEMOGLOBIN BLOOD LEVEL; HUMAN; MALE; MEAN CORPUSCULAR HEMOGLOBIN; MEASUREMENT ERROR; POTASSIUM URINE LEVEL; SODIUM BLOOD LEVEL; SODIUM URINE LEVEL; THROMBOCYTE COUNT; TRIACYLGLYCEROL BLOOD LEVEL; UREA NITROGEN BLOOD LEVEL; BLOOD EXAMINATION; CASE CONTROL STUDY; CHEMISTRY; CLINICAL CHEMISTRY; HEALTH CARE FACILITY; LABORATORY DIAGNOSIS; MEDICAL RESEARCH; PROCEDURES; SERUM; STANDARDS; THEORETICAL MODEL; UNCERTAINTY;

UNITED KINGDOM;

ANALYSIS OF VARIANCE; BIOLOGICAL SPECIMEN BANKS; BIOMEDICAL RESEARCH; BLOOD CHEMICAL ANALYSIS; CASE-CONTROL STUDIES; CHEMISTRY, CLINICAL; HEMATOLOGIC TESTS; HUMANS; MODELS, THEORETICAL; SERUM; SPECIMEN HANDLING; UNCERTAINTY;

EID: 84913552987     PISSN: 03005771     EISSN: 14643685     Source Type: Journal    
DOI: 10.1093/ije/dyu127     Document Type: Article

References (29)

1. P3G Lexicon. The Biobank Lexicon. 2009. http://www.p3gob servatory.org/lexicon/list.htm.
2. Wolfson M, Wallace SE, Masca N et al. DataSHIELD: resolving a conflict in contemporary bioscience - performing a pooled analysis of individual-level data without sharing the data. Int J Epidemiol 2010;39:1372-82.
3. Burton PR, Hansell AL, Fortier I et al. Size matters: just how big is BIG? Quantifying realistic sample size requirements for human genome epidemiology. Int J Epidemiol 2009;38:263-73.
4. Wong MY, Day NE, Luan JA, Chan KP, Wareham NJ. The detection of gene-environment interaction for continuous traits: should we deal with measurement error by bigger studies or better measurement? Int J Epidemiol 2003;32:51-57.
5. Holland NT, Smith MT, Eskenazi B, Bastaki M. Biological sample collection and processing for molecular epidemiological studies. Mutat Res 2003;543:217-34.
6. Betsou F, Barnes R, Burke T et al. Human biospecimen research: experimental protocol and quality control tools. Cancer Epidemiol Biomarkers Prev 2009;18:1017-25.
7. Bowen RA, Hortin GL, Csako G, Otanez OH, Remaley AT. Impact of blood collection devices on clinical chemistry assays. Clin Biochem 2008;434-25.
8. Lehmann S, Roche S, Allory Y et al. Preanalytical guidelines for clinical proteomics investigation of biological fluids. Ann Biol Clin 2009;67:629-39.
9. Vaught JB. Blood collection, shipment, processing, and storage. Cancer Epidemiol Biomarkers Prev 2006;15:1582-84.
10. Weikert S, Boeing H, Pischon T et al. Fruits and vegetables and renal cell carcinoma: findings from the European prospective investigation into cancer and nutrition (EPIC). Int J Cancer 2006;118:3133-39.
11. Health NCIatNIo. NCI Home. http://www.cancer.gov/
12. Development OfECa. OECD Home. http://www.oecd.org/
13. International Society for B, Environmental R. ISBER Best Practices For Repositories. 2009.
14. Society PPPiGa. Sample Collection and Processing. 2009.
15. Society PPPiGa. P3G Home. http://p3g.org/
16. Clark S, Youngman LD, Palmer A, Parish S, Peto R, Collins R. Stability of plasma analytes after delayed separation of whole blood: implications for epidemiological studies. Int J Epidemiol 2003;32:125-30.
17. Maelstrom. Maelstrom Research. https://www.maelstromresearch. org/
18. BioSHARE-EU. Biobank Standardisation and Harmonisation for Research Excellence in the European Union. European Union, FP7 Program 2011.
19. Jackson C, Best N, Elliott P. UK Biobank Pilot Study: stability of haematological and clinical chemistry analytes. Int J Epidemiol 2008;37 Suppl 1:i16-22.
20. Peakman TC, Elliott P. The UK Biobank sample handling and storage validation studies. Int J Epidemiol 2008;37 Suppl 1:i2-6.
21. Collins R. What makes UK Biobank special? Lancet 2012;379:1173-74.
22. UK.Biobank. Sex-specific Factors. http://biobank.ctsu.ox.ac.uk/ crystal/label.cgi?id=100068
23. Rasbash J, Charlton C, Browne WJ, Healy M, Cameron B. MLwiN Version 2.1. Bristol, UK: Centre for Multilevel Modelling, University of Bristol, 2009.
24. Burton PR, Hansell A. UK Biobank: the expected distribution of incident and prevalent cases of chronic disease and the statistical power of nested case-control studies. Manchester, UK: UK Biobank Technical Reports, 2005.
25. Gaye A. Key determinants of statistical power in large scale genetic association studies. Thesis. University of Leicester, 2012.
26. Gaye A. ESPRESSO, Software for Statistical Power Analysis and Sample Size Calculation. 2013 www.espresso-research.org
27. Burton P, Tiller K, Gurrin L, Cookson W, Musk A, Palmer L. Genetic variance components analysis for binary phenotypes using generalized linear mixed models (GLMMs) and Gibbs sampling. Genet Epidemiol 1999;17:118-40.
28. Scurrah KJ, Palmer LJ, Burton PR. Variance components analysis for pedigree-based censored survival data using generalized linear mixed models (GLMMs) and Gibbs sampling in BUGS. Genet Epidemiol 2000;19:127-48.
29. Pearson TA, Manolio TA. How to interpret a genome-wide association study. JAMA 2008;299:1335-44.