Loci were excluded if they Ceritinib LDK378 were monoallelic (n=5), had less than 95% genotyping success (n=22), or had Hardy�CWeinberg p values of less than .01 (��2, n=5). In total, only 22 of the 33 SNPs (67%) from the NICSNP high-density association study and 115 of the 137 SNPs (84%) from the candidate gene study provided usable genotyping information. The surviving 137 SNPs were then analyzed using an ordinal regression analysis and an additive genetic model to identify SNPs significantly associated with nicotine dependence. To maintain internal consistency with our previous publications, our primary data analyses were conducted using DSM-IV nicotine dependence counts. However, to make these results more consistent with those conducted by the NICSNP Consortium and more useful to all investigators, where appropriate, we have provided parallel regression analyses using the FTND data.

Both these symptom counts were treated as ordinal variables. Where appropriate, intermarker disequilibrium between SNPs at each candidate gene locus was calculated using Haploview (Stephens, Smith, & Donnelly, 2001). Haplotypes for genes with at least one significantly associated SNP were inferred using PHASE (Stephens et al., 2001), as described previously (Bradley, Dodelzon, Sandhu, & Philibert, 2005). Haplotypes with frequencies greater than 0.10 were then incorporated as additive factors in ordinal regression analyses that sometimes included sex and nicotine exposure data, as described in the text, using JMP Genomics, SAS version 9.1, and the chi-square test. All test results reported are two-tailed.

Results The demographic and clinical characteristics of the sample population are described in Tables 1 and and2.2. The sample is largely White and predominantly female. Consistent with intentional loading of the sample cohorts for the genetic diatheses for substance use, there are high levels of nicotine use. Some 90% of subjects reported smoking at least once in their lifetime and 51% reported smoking at least 100 cigarettes in their lifetime. Table 1. Subject demographics and characteristics Table 2. DSM-IV nicotine dependence symptom counts and Fagerstr?m Test for Nicotine Dependence (FTND) scores As a first step in our analyses, we conducted ordinal regression analyses of nicotine dependence symptom counts with respect to genotype at each of the 137 SNPs that survived quality assurance assessment using DSM-IV nicotine dependence symptom counts and an additive model (Philibert, Ryu, et al.

, 2007; Philibert, Sandhu, et al., 2007). In total, 12 SNPs were nominally associated with nicotine dependence (p < .05 before correction for multiple comparisons); all these were from the candidate gene analysis (Table 3). Six of these SNPs were from CHRNA2, three were from CHRNA7, three were from CHRNB1, and one was from CHRNA1. In general, the correlation between results obtained using DSM-IV symptom counts and our secondary analyses AV-951 of FTND scores was quite good.