Sebastiani Paola, Ramoni Marco F, Nolan Vikki, Baldwin Clinton T, Steinberg Martin H
Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA.
Nat Genet. 2005 Apr;37(4):435-40. doi: 10.1038/ng1533. Epub 2005 Mar 20.
Sickle cell anemia (SCA) is a paradigmatic single gene disorder caused by homozygosity with respect to a unique mutation at the beta-globin locus. SCA is phenotypically complex, with different clinical courses ranging from early childhood mortality to a virtually unrecognized condition. Overt stroke is a severe complication affecting 6-8% of individuals with SCA. Modifier genes might interact to determine the susceptibility to stroke, but such genes have not yet been identified. Using Bayesian networks, we analyzed 108 SNPs in 39 candidate genes in 1,398 individuals with SCA. We found that 31 SNPs in 12 genes interact with fetal hemoglobin to modulate the risk of stroke. This network of interactions includes three genes in the TGF-beta pathway and SELP, which is associated with stroke in the general population. We validated this model in a different population by predicting the occurrence of stroke in 114 individuals with 98.2% accuracy.
镰状细胞贫血(SCA)是一种典型的单基因疾病,由β-珠蛋白基因座上一个独特突变的纯合性引起。SCA的表型复杂,临床病程各异,从幼儿期死亡到几乎未被识别的情况都有。明显的中风是一种严重并发症,影响6%至8%的SCA患者。修饰基因可能相互作用以决定中风易感性,但此类基因尚未被识别。我们使用贝叶斯网络,分析了1398名SCA患者中39个候选基因的108个单核苷酸多态性(SNP)。我们发现12个基因中的31个SNP与胎儿血红蛋白相互作用,以调节中风风险。这种相互作用网络包括转化生长因子-β(TGF-β)途径中的三个基因和SELP(在普通人群中与中风相关)。我们通过预测114名个体中风的发生情况,在另一个人群中验证了该模型,准确率为98.2%。