Fast and cheaper next generation sequencing technologies will generate unprecedentedly massive (thousands of individuals) and highly-dimensional (ten millions) genomic and epigenomic variation data that allow nearly complete evaluation of genomic and epigenomic variation including common and rare variants, insertion/deletion, CNVs, RNA-seq, mRNA-seq, methylation-seq and Chip-seq. This is revolutionizing not only biomedical research, but also diagnosis, disease prevention and treatment. This is changing our view of genomics from discrete to hybrid (both discrete and continuous) models and disease models. This is also causing great changes in analytic methods for genomic and epigenomic analysis from standard multivariate data analysis to functional data analysis, from independent sampling to dependent sampling, low dimensional data analysis to high dimensional data analysis, from single genomic or epigenomic variant analysis and integrated genomic and epigenomic analysis. Majority analytic methods for microarray gene expression, miRNA expression and methylation analysis will be replaced by new mathematical methods designed for NGS data analysis. The computer for data analysis will be shifted from individual PC to clusters and cloud computation. We are facing great challenges in NGS data analysis. This talk is to mainly focus on association studies for NGS, and stimulate discussions regarding the study designs and analytical methods for association studies with next-generation sequencing and searching new analytical platforms to use the unified genetic disease model for identifying genetic variants underlying diseases. My goal is to use associ