| Popis: |
Functional data analysis has demonstrated significant success in time series analysis. In recent biomedical research, it has also been used to analyze sequence variations in genome-wide association studies (GWAS). The observations of genetic variants, called single-nucleotide polymorphisms (SNPs), of an individual are distributed over the loci of a DNA sequence. Thus, it can be regarded as a realization of a stochastic process, which is no different from a time series. However, SNPs are usually coded as the number of minor alleles, which are categorical. The usual least-square smoothing in FDA only works well when the data is continuous and normally distributed. The normality assumption will be violated for categorical SNP data. In this work, we propose a two-step method for smoothing categorical SNPs using a novel method and constructing haplotypes having strong associations with the disease using functional generalized linear models. We show its effectiveness through a real-world PennCATH dataset. |
