Genotyping Factors
The newest institution away from lymphoblastoid cell contours, quality assurance from genomic DNA, acquisition of genetic data, and you will genotyping quality control metrics have been did centered on practical methods. Excite understand the on the web-simply Studies Complement for those details.
Opinion single-nucleotide polymorphisms (SNPs) one to passed quality assurance in both stages (genome-broad connection and you can friends-founded phase) had been matched for everyone readily available sibships (2239 SNPs have been imputed on probands). Sibships were confirmed whenever pairwise pi_cap opinions were anywhere between 0.thirty five and you will 0.65; samples was taken from a great sibship if estimated pi_hat value was not in this range. This dataset of your mutual genotyping phase stands for the last dataset for all next revealed analyses. The fresh circulate off patients regarding the analysis was found inside Contour step one.
Genetic Research Data
All family-based analyses were conducted with PLINK 1.07 software. 8 The dFam utility within PLINK implements a siblings-based transmission-disequilibrium test and was used to conduct these analyses. The dFam option is a powerful test for sibling-only datasets, incorporating data across sibships as well as using data from estimated parental genotypes to calculate expected allele frequencies for comparison with observed allele frequencies. The association test is based on the Cochran-Mantel-Haenszel test. Bonferroni correction for the number of tested SNPs corresponds to a minimum probability value for a genome-wide significance of P
Additional Statistical Analyses
Frequencies of stroke risk factors (hypertension, hyperlipidemia, and diabetes) between affected and unaffected participants were compared by using ? 2 tests. The correlation between affected sibling age at stroke was estimated by rencontre sapiosexuelle pour sexe using the Pearson test of correlation. These analyses were conducted across all TOAST subtypes as well as after stratification by concordant and discordant subtypes among affected sibling pairs. Linear regression was used to determine the confidence intervals and linear fit of the age association, as shown in Figure 2. Kappa statistics were calculated to quantify concordance of phenotypes of interest within sibling pairs for all ages and stroke subtypes as well as models stratified by age (
Figure 2. Correlation between proband and sibling age at stroke. Correlation coefficient=0.83. P
Results
A total of 312 affected sibling pairs (312 probands) were enrolled at 70 centers across the United States and Canada. After quality control filtering, the final study population consisted of 223 probands, 248 stroke-affected siblings, and 84 stroke-unaffected siblings (total sample size, 555). Ischemic stroke–affected individuals had expected high rates of conventional atherosclerotic risk factors (Table 1). Stroke-affected individuals (probands and affected siblings) were significantly more likely to have hypertension (P
Sibling age at the time of stroke was strongly correlated with proband age at the time of stroke, despite the sibling’s being older. As shown in Figure 2 for all sibling pairs, the correlation coefficient was r=0.83 (95% CI, 0.78–0.86; P