Genetic Dissection and Prognostic
Modeling of a Complex Trait


Welcome to the web companion of the article:


Sebastiani P, Ramoni MF, Nolan V, Baldwin CT, Steinberg MH. Genetic dissection and prognostic modeling of overt stroke in sickle cell anemia. Nat Genet. 2005 April; 37(4), pp 435 - 440.


Sickle cell anemia (SCA) is a paradigmatic single gene disorder caused by homozygosity for a unique mutation on the b-globin locus. Phenotypically, SCA is a complex disease with different clinical courses, ranging from early childhood mortality to a virtually unrecognized condition. Overt stroke is a severe complication affecting 6-8% of SCA patients. It has been conjectured that modifier genes interact to determine the susceptibility to stroke, but they remain unknown. Using Bayesian networks (BNs), we analyzed 108 single nucleotide polymorphisms (SNPs) on 39 candidate genes in 1398 SCA subjects. We found that 31 SNPs on 12 genes interact with fetal hemoglobin to modulate the risk of stroke. This network of interactions includes three genes in the TGF- pathway and SELP, already associated with stroke in the general population. We validated this model in a different population by predicting the occurrence of stroke in 114 subjects with 98.2% accuracy.


For a comprehensive introduction to Bayesian networks in genomic research:

Sebastiani P, Abad M, Ramoni, MF. Bayesian networks for genomics research. In Genomic Signal Processing and Statistics, EURASIP Book Series on Signal Processing and Communications, to appear.