Mendelian randomization analysis reveals systematic bias when genetic variants with smaller effect sizes are used as instruments for causal inference. The research demonstrates that weaker genetic variants are more susceptible to heritable confounding, producing biased estimates that often exceed the magnitude of traditional observational study biases. Using C-reactive protein and type 2 diabetes as an example, investigators show this bias affects multiple common MR estimation methods in the same direction, potentially creating false confidence in spurious causal relationships. This finding challenges the growing reliance on weaker genetic signals from increasingly large genome-wide association studies. The implications extend far beyond statistical methodology to health research validity. Many recent studies claiming causal relationships between biomarkers, lifestyle factors, and disease outcomes may need reexamination. The research suggests multivariable MR approaches and Steiger filtering can help mitigate these biases, but standard pleiotropy correction methods prove insufficient. As this is a preprint awaiting peer review, these methodological concerns require confirmation before widespread adoption of the proposed solutions. The work represents a crucial but incremental advance in genetic epidemiology, potentially reshaping how researchers interpret causal claims from genetic data across nutrition, longevity, and disease prevention studies.