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Jessica Woo

Publications and source records attributed to Jessica Woo.

2 recordsLinked to original sources

Association of apolipoprotein E4 and haplotypes of the apolipoprotein E gene with lobar intracerebral hemorrhage.

BACKGROUND AND PURPOSE: Conflicting reports in the literature exist with regard to the association of apolipoprotein E (apo E) alleles and lobar intracerebral hemorrhage (ICH). We genotyped 12 single-nucleotide polymorphisms in the 5' upstream regulatory, exonic, and intronic regions of the apo E gene and performed genotype and haplotype association analyses. METHODS: We prospectively enrolled subjects with hemorrhagic stroke and matched them with 2 controls based on age, race, and sex. Each case was reviewed by a physician to determine case status and location of the ICH. Multivariate logistic-regression modeling with backward elimination was used to determine significant risk factors for lobar ICH. Associations at the genotype and haplotype levels and linkage disequilibrium were conducted according to standard statistical methods. RESULTS: Between May 1997 and December 2002, 315 cases of ICH were recruited, of whom 107 were lobar ICH cases matched to 205 controls. No association was found for apo E2, E3, or E4 with nonlobar ICH. Independent, significant risk factors for lobar ICH included apo E4, untreated hypertension, anticoagulant use, a first-degree relative with ICH, and < or =high school education (compared with >high school education). Treated hypercholesterolemia compared with "no history of hypercholesterolemia" was associated with a decreased risk of lobar ICH. Haplotype association analysis demonstrated a significant association of the apo E gene with lobar ICH among whites (P<0.0001) and blacks (P=0.0024). CONCLUSIONS: Apo E4 is independently associated with lobar ICH but not nonlobar ICH. Haplotypes of the apo E gene are associated with lobar ICH. Untreated hypertension is a risk factor for lobar ICH.

Alleles↗

Importance sampling method of correction for multiple testing in affected sib-pair linkage analysis.

Using the Genetic Analysis Workshop 13 simulated data set, we compared the technique of importance sampling to several other methods designed to adjust p-values for multiple testing: the Bonferroni correction, the method proposed by Feingold et al., and naïve Monte Carlo simulation. We performed affected sib-pair linkage analysis for each of the 100 replicates for each of five binary traits and adjusted the derived p-values using each of the correction methods. The type I error rates for each correction method and the ability of each of the methods to detect loci known to influence trait values were compared. All of the methods considered were conservative with respect to type I error, especially the Bonferroni method. The ability of these methods to detect trait loci was also low. However, this may be partially due to a limitation inherent in our binary trait definitions.

Computer Simulation↗