Associations among multiple markers and complex disease: models, algorithms, and applications.
This chapter is a report on collaborations among its authors and others over many years. It devolves from our goal of understanding genes, their main and epistatic effects combined with interactions involving demographic and environmental features also, as together they predict genetically complex diseases. Thus, our goal is "association." Particular phenotypes of interest to us are hypertension, insulin resistance, angina, and myocardial infarction. Prediction of complex disease is notoriously difficult, though it would be made easier were we given strand-specific information on genotype. Unfortunately, with current technology, genotypic information comes to us "unphased." While obviously we have strand-specific information when genotype is homozygous, we do not have such information when genotype is heterozygous. To summarize, the ultimate goals of approaches we provide is to predict phenotype, typically untoward or not, within a specific window of time. Our approach is neither through linkage nor from finding haplotype frequencies per se.
Assimes
Themistocles L
TL
Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA 94305-5406, USA.
Olshen
Adam B
AB
Narasimhan
Balasubramanian
B
Olshen
Richard A
RA
D000465:Algorithms; D000818:Animals; D005819:Genetic Markers; D020022:Genetic Predisposition to Disease; D006801:Humans; D008957:Models, Genetic; D020412:Multifactorial Inheritance / Q000235:genetics*; D011336:Probability
D005819:Genetic Markers