In stepwise fashion, a SNP was added to the model if it had the smallest P-value among all the SNPs not yet included in the model and if it was statistically significant after adjusting for multiple comparisons. SNPs selected by this algorithm were also used in haplotype analysis using WHAP [27], as implemented in PLINK [22]. Validation Sample We sought to validate any novel findings from the WGHS in an independent replication sample comprised of 455 non-diabetic Caucasian participants recruited from the Boston metropolitan area [28]. A history of diabetes, or use of anti-diabetic medications, and/or fasting glucose levels .125 mg/dL, or abnormal oral glucose tolerance test results were used to exclude persons with diabetes in this cohort. Glycated hemoglobin was measured with an HPLC assay that serves as a primary reference assay for the National Glycohemoglobin Standardization Pro- gram, with coefficients of variation ,2.5% for low and high standards. Genotype determination was performed with an ABI fluorescence-based allelic discrimination method (rs7072268) [29] or a single reaction kinetic thermal cycling method (rs2305198) [30]. The study was approved by the Brigham and Women’s Hospital and Massachusetts General Hospital Institutional Review Boards for human subjects research. Results Clinical characteristics of the 14,618 non-diabetic women in the primary WGHS analysis and of the 455 non-diabetic men and women in the external validation cohort are provided in Supplementary Table S1. Results of the genome-wide association analysis of adjusted baseline glycated hemoglobin concentration are presented in Table 1, which includes all SNPs with P-value lower than 1026. Six SNPs at three different loci – G6PC2, GCK and HK1 - had an association P-value lower than our previously defined genome-wide significance threshold of 561028: rs1402837, rs560887 and rs6709087 at G6PC2; rs730497 and rs4607517 at GCK; rs7072268 at HK1. In addition, two SNPs, one at the HK1 locus and one at the SCL30A8 locus, had association P-values just above this threshold level (5.561028 and 9.861028, respectively). Genetic context of all these loci is presented in Figure 1 along with the 2log10 transformed P-values. To define further the extent of genetic associations at these 4 loci, we applied a forward model selection algorithm to each of them, identifying SNPs that were non-redundantly associated with glycated hemoglobin. Briefly, 38 SNPs at GCK, 31 at SLC30A8, 41 at HK1 and 44 at G6PC2 were initially assessed for possible inclusion in a multiple linear regression model. Using a P-value cut-off of 361024 to account for the 154 SNPs considered, 5 SNPs were selected, representing the lead SNP at each of the 4 loci considered plus one SNP (rs2305198; located at 70799 Kb on chromosome 10) at the HK1 locus (see Table 2). Interestingly, this later SNP (rs2305198; HK1) was marginally significant in univariable analysis (p = 0.02), illustrating that its inclusion in the model and significant association are conditional on the genotypes at rs7072268 (HK1). Multiple regression beta coefficients and P- values of the 5 SNPs selected are shown in Table 2 and range from 5.461028 to 1.8610225. Shown in Figure 2 are the quantile- quantile plots of association P-values before and after adjusting glycated hemoglobin concentration for the combined effect of these 5 SNPs. Among these SNPs, there was no strong evidence for non-additive effects of the minor allele as judged by lack of significance for a likelihood ratio test comparing the additive regression model to an alternative genotype model with an Association of HK1 with Glycated Hemoglobin additional degree of freedom. No pairwise gene-gene interaction was observed between any of the model selected SNPs. That is, we tested every possible 2-SNPs combination among these 5 SNPs. We sought to replicate the novel association between glycated hemoglobin level and HK1 SNPs (rs2305198 and rs7072268) in the external validation sample of 455 non-diabetic men and women from the Boston metropolitan area. We included both SNPs (assuming an additive genetic effect) in a multiple regression model that also included age, sex, menopause and BMI as covariates. Both SNPs were successfully replicated, with two-sided P-values of 0.027 (rs2305198) and 0.050 (rs7072268), and both with consistent direction of effect. Furthermore, the effect size (i.e. beta coefficients) was larger than in the WGHS, with changes in glycated hemoglobin of 0.101% (rs2305198) and 0.085% (rs7072268) per allele in the replication sample, as opposed to 0.041% and 0.046% respectively in the WGHS. The association was validated in both men (N = 204) and women (N = 251), without any evidence of interaction with sex (data not shown). Both SNPs were in Hardy-Weinberg equilibrium, had allelic frequencies consistent with the ones observed in WGHS, and were successfully genotyped in over 93% of individuals. Of note, the P- values for rs2305198 and rs7072268 were non-significant (p.0.05) when each SNP was analyzed separately in a linear model. Overall, the P-values for rs2305198 and rs7072268 when both SNPs were included in a multiple regression model that used the combined WGHS and replication sample were 9.9610220 and 1.7610225, respectively. The 2 SNPs at the HK1 locus selected by our algorithm were also used in haplotype analysis in WGHS individuals (Table 3). The estimate of the proportion of variance attributable to haplotypes, as well as their regression coefficients, is consistent with the linear model of these same SNPs, reinforcing the adequacy of the multiple linear model to explain the association. Linkage disequilibrium between these two HK1 SNPs was 0.49 for r2 and 0.90 for D’. The 2 SNPs at HK1 collectively explained 0.7% of the total variance in glycated hemoglobin concentration, whereas the G6PC2 SNP (rs1402837) explained 0.2%, the GCK SNP (rs730497) 0.3% and the SLC30A8 SNP (rs13266634) 0.2%. In comparison, clinical covariates accounted for 9.5% of the variance (Table 4), and together the candidate loci and the clinical variables accounted for 10.9% of total variance. As was the case in the WGHS, haplotype analysis of the replication sample yielded similar results as the multivariable linear regression, albeit with lesser statistical significance (data not shown). Since several SNPs have been unequivocally associated with type 2 diabetes, we tested whether these ‘‘candidate’’ SNPs [1,4,31,32] were associated with glycated hemoglobin as well (Table 5). When a SNP was not directly typed in WGHS, we used HapMap CEU linkage disequilibrium data to find the WGHS SNP with the highest r2 with that SNP. While all SNPs tested had a direction of association consistent with their effect on the risk of diabetes, only 10 SNPs had a (one-sided) P-value lower than 0.05 (Table 5). Overall, the one-sided association P-value for the 19 SNPs taken together (i.e. the P-value obtained by combining all 19 one-sided P-values; this was done using Fisher’s method for combining P-values from independent tests) was 3.0610222. The P-value was 8.561029 when the SLC30A8 SNP rs13266634 and the GCK SNP rs730497 were removed. It has previously been shown that the metabolic effects of rs8050136 (FTO locus) are mediated mainly through increased body weight [33,34]. Adjust- ment of glycated hemoglobin for BMI would therefore be expected to dampen the association of rs8050136 (FTO locus) with glycated hemoglobin level. Indeed, non-adjusted glycated hemoglobin showed stronger association with rs8051136 (p = 0.007). PLoS Genetics | www.plosgenetics.org 3 December 2008 | Volume 4 | Issue 12 | e1000312