Controls were selected based on no history of diabetes, and FBG <110 mg/dL. An interviewer-administered questionnaire was used to collect data on medical history, family history, current prescribed medication (verified from the practice computerized records), cardiovascular selleck chemicals Tofacitinib risk factors, alcohol intake, physical activity, and socio-economic status. Country of birth of participants, parents, and grandparents was recorded together with language and religion for assignment of ethnic subgroups. Physical assessments including blood pressure, anthropometric measurements (height, weight, and WHR), fat mass (bio-impedance), urinalysis, and 12 lead ECG. FBG, insulin, total, HDL-C and LDL-C, TG, were measured on all participants as described previously [6].

At the time of this analysis genotype and phenotype data on 6,530 individuals comprising 1,774 T2D cases and 4,756 controls were available from this study. GWAS Genome-wide association scans in LOLIPOP and SDS samples were performed using Illumina Infinium Beadchips genotypes were called using GenCall or Illuminus algorithms. Samples with a SNP call rate <95% were removed, as were SNPs with call rate <97%, minor allele frequency <1%, or HWE p<1.0��10?6. Principal components analysis (PCA) was used in both GWAS datasets to control for population stratification by comparison to reference samples from the Hapmap YRI, CHB, JPT and CEU panels using PLINK (http://pngu.mgh.harvard.edu/~purcell/plink/) and Eigensoft [43], and the Indian samples collected by Reich and colleagues [44].

Samples with eigenvalues inconsistent with Asian Indian ancestry were removed as described previously [45]. Statistical Analysis Data quality for SNP genotyping was checked by establishing reproducibility of control DNA samples. Departure from HWE in controls was tested using the Pearson chi-square test. The genotype and allele frequencies Batimastat in T2D cases were compared to those in control subjects using the chi-square test. Statistical evaluation of genetic effects on T2D risk used multivariate logistic regression analysis with adjustments for age, gender, and other covariates. Continuous traits with skewed sampling distributions (e.g., TG and total cholesterol) were log-transformed before statistical analysis. However, for illustrative purposes, values were re-transformed into the original measurement scale. Supplementary Figure S2 shows the distribution of serum TG levels before and after transformation. General linear models were used to test the impact of genetic variants on transformed continuous traits. Country of birth was used as a covariate when analyzing the combined sample of the Punjabi and US cohorts.