The rapid development of next generation sequencing technologies and accompanying reduction in cost produce an increasing number of single nucleotide polymorphisms (SNPs) that can be identified across the genome. Analyzing high-dimensional genomic data is a challenge and requires development of new statistical methods. We propose to use the functional analysis of variance (FANOVA) to perform inference for sequencing data. FANOVA is used to test for differences in functional means of k groups over time. We suggest using FANOVA to test for a significant difference among SNPs between levels of a phenotype, such as the presence or absence of a disease. Since increasing numbers of SNPs can be identified across the genome, the position of a genetic variant in a genomic region under consideration can play the role of time in the FANOVA. To investigate performance of the proposed methodology we conduct a simulation study to calculate the empirical Type 1 error rate and the empirical power for different ranges of risk parameters.