New framework helps estimate sample size for microbiome association studies

Conflicting findings are often reported in microbiome association studies, which could be due to insufficient sample sizes and numerous confounders. A new study, utilizing two large-scale human microbiome datasets with approximately 10,000 individuals, sheds light on this issue by quantifying association effect sizes and reproducibility as a function of sample size through bootstrap sampling.

The research examined the relationship between microbial relative abundance and 43 factors, including demographic characteristics, physiological factors, blood parameters, and lifestyle variables. The researchers noted that microbiome associations were found to be smaller than previously thought, leading to inflated effect sizes, statistically underpowered studies, and replication failures at typically small sample sizes. For strong associations with effect sizes greater than 0.125, around 500 participants were needed to achieve 80% statistical power. However, for weaker associations with effect sizes below 0.092, thousands of samples may be required.

Additionally, the study evaluated microbiome associations with 14 diseases. For conditions like hypertriglyceridemia, obesity, hyperuricemia, hypertension, metabolic syndrome, and hypercholesterolemia, approximately 500 individuals were needed to detect the strongest associations. However, for diseases such as renal calculus, neurosis, diabetes, low HDL cholesterol, rheumatoid arthritis, and gastritis, sample sizes beyond the scope of this study may be necessary.

"Our approach provides a method to estimate the sample size required for microbiome studies, based on effect sizes and analysis methods," said the corresponding author. "We hope our findings could serve as a reminder for researchers conducting their own microbiome data analysis."

The study also offers guidance for future research on rare clinical conditions. When large sample sizes are difficult to obtain, the researchers recommend longitudinal rather than cross-sectional studies, as well as interventional rather than observational studies.

This new framework serves as a valuable tool for microbiome researchers, helping them design studies that are better equipped to detect meaningful associations between microbiome data and various health factors.

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