The era of big data has opened up new possibilities in predictive, preventive, and personalized medicine, especially in understanding complex genetic traits and disease mechanisms. However, these opportunities come with significant challenges, particularly due to data heterogeneity and the computational demands of analyzing such vast datasets. Traditional statistical methods often struggle with the diverse nature of big data, which can vary across population subgroups, measurement techniques, and study designs. This variability can make it difficult to uncover consistent patterns and draw reliable conclusions. Quantile regression offers a powerful approach to these challenges, providing a flexible and robust framework that can handle the complexities of big data. It is especially useful in biostatistics, where examining the full distribution of health outcomes can provide deeper insights into risk factors, treatment effects, and targeted interventions across different population groups. This chapter reviews recent advances in quantile regression methods with a focus on biostatistical applications, highlighting both the opportunities and challenges for future research in this area.