title: Methods for analysis and visualization of SNP genotype data for complex diseases.
abstract: SNP markers are becoming central for studying genetic determinants of complex diseases. Large SNP data collected in such studies call for the development of specialized analysis tools. We present methods for selecting sets of SNPs that can be associated to sample properties in case/control studies. We also describe how scoring and selection can be statistically tested. This is done at the single locus as well as at the set level.
authors.affiliation: Agilent Laboratories, 3500 Deer Creek Road, Palo Alto, CA 94304, USA. anya_tsalenko@agilent.com
authors.forename: Anya
authors.initials: A
authors.lastname: Tsalenko
authors.forename: Amir
authors.initials: A
authors.lastname: Ben-Dor
authors.forename: Nancy
authors.initials: N
authors.lastname: Cox
authors.forename: Zohar
authors.initials: Z
authors.lastname: Yakhini
mesh_terms: D001499:Bayes Theorem; D016022:Case-Control Studies; D002884:Chromosomes, Human, Pair 15 / Q000235:genetics; D002889:Chromosomes, Human, Pair 2 / Q000235:genetics; D002897:Chromosomes, Human, Pair 7 / Q000235:genetics; D019295:Computational Biology; D003196:Computer Graphics; D003627:Data Interpretation, Statistical; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics; D005838:Genotype; D006801:Humans; D017058:Mexican Americans / Q000235:genetics; D020641:Polymorphism, Single Nucleotide; D019655:Quantitative Trait, Heritable