Benjamin C Fu1, Timothy W Randolph2, Unhee Lim3, Kristine R Monroe4, Iona Cheng5, Lynne R Wilkens3, Loïc Le Marchand3, Meredith A J Hullar2, Johanna W Lampe6. 1. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA; Department of Epidemiology, University of Washington, Seattle. 2. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA. 3. Epidemiology Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu. 4. Department of Preventive Medicine, University of Southern California, Los Angeles. 5. Cancer Prevention Institute of California, Fremont; Stanford Cancer Institute, Stanford, CA. 6. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA; Department of Epidemiology, University of Washington, Seattle. Electronic address: jlampe@fredhutch.org.
Abstract
PURPOSE: The development of next-generation sequencing and accompanying bioinformatics tools has revolutionized characterization of microbial communities. As interest grows in the role of the human microbiome in health and disease, so does the need for well-powered, robustly designed epidemiologic studies. Here, we discuss sources of bias that can arise in gut microbiome research. METHODS: Research comparing methods of specimen collection, preservation, processing, and analysis of gut microbiome samples is reviewed. Although selected studies are primarily based on the gut, many of the same principles are applicable to samples derived from other anatomical sites. Methods for participant recruitment and sampling of the gut microbiome implemented in an ongoing population-based study, the Multiethnic Cohort (MEC), are also described. RESULTS: Variation in methodologies can influence the results of human microbiome studies. To help minimize bias, techniques such as sample homogenization, addition of internal standards, and quality filtering should be adopted in protocols. Within the MEC, participant response rates to stool sample collection were comparable to other studies, and in-home stool sample collection yields sufficient high-quality DNA for gut microbiome analysis. CONCLUSIONS: Application of standardized and quality controlled methods in human microbiome studies is necessary to ensure data quality and comparability among studies.
PURPOSE: The development of next-generation sequencing and accompanying bioinformatics tools has revolutionized characterization of microbial communities. As interest grows in the role of the human microbiome in health and disease, so does the need for well-powered, robustly designed epidemiologic studies. Here, we discuss sources of bias that can arise in gut microbiome research. METHODS: Research comparing methods of specimen collection, preservation, processing, and analysis of gut microbiome samples is reviewed. Although selected studies are primarily based on the gut, many of the same principles are applicable to samples derived from other anatomical sites. Methods for participant recruitment and sampling of the gut microbiome implemented in an ongoing population-based study, the Multiethnic Cohort (MEC), are also described. RESULTS: Variation in methodologies can influence the results of human microbiome studies. To help minimize bias, techniques such as sample homogenization, addition of internal standards, and quality filtering should be adopted in protocols. Within the MEC, participant response rates to stool sample collection were comparable to other studies, and in-home stool sample collection yields sufficient high-quality DNA for gut microbiome analysis. CONCLUSIONS: Application of standardized and quality controlled methods in human microbiome studies is necessary to ensure data quality and comparability among studies.
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