0000000000633836
AUTHOR
Klaus Golka
Cluster-Localized Sparse Logistic Regression for SNP Data
The task of analyzing high-dimensional single nucleotide polymorphism (SNP) data in a case-control design using multivariable techniques has only recently been tackled. While many available approaches investigate only main effects in a high-dimensional setting, we propose a more flexible technique, cluster-localized regression (CLR), based on localized logistic regression models, that allows different SNPs to have an effect for different groups of individuals. Separate multivariable regression models are fitted for the different groups of individuals by incorporating weights into componentwise boosting, which provides simultaneous variable selection, hence sparse fits. For model fitting, th…
Genotyping NAT2 with only two SNPs (rs1041983 and rs1801280) outperforms the tagging SNP rs1495741 and is equivalent to the conventional 7-SNP NAT2 genotype
Genotyping N-acetyltransferase 2 (NAT2) is of high relevance for individualized dosing of antituberculosis drugs and bladder cancer epidemiology. In this study we compared a recently published tagging single nucleotide polymorphism (SNP) (rs1495741) to the conventional 7-SNP genotype (G191A, C282T, T341C, C481T, G590A, A803G and G857A haplotype pairs) and systematically analysed if novel SNP combinations outperform the latter. For this purpose, we studied 3177 individuals by PCR and phenotyped 344 individuals by the caffeine test. Although the tagSNP and the 7-SNP genotype showed a high degree of correlation (R=0.933, P0.0001) the 7-SNP genotype nevertheless outperformed the tagging SNP wit…