Search

We performed genome-wide meta-analysis of lipid traits on three samples of Mexican and Mexican American ancestry comprising 4,383 individuals, and followed up significant and highly suggestive associations in three additional Hispanic samples comprising 7,876 individuals. Genome-wide significant signals were observed in or near CELSR2, ZNF259/APOA5, KANK2/DOCK6 and NCAN/MAU2 for total cholesterol, LPL, ABCA1, ZNF259/APOA5, LIPC and CETP for HDL cholesterol, CELSR2, APOB and NCAN/MAU2 for LDL cholesterol, and GCKR, TRIB1, ZNF259/APOA5 and NCAN/MAU2 for triglycerides. Linkage disequilibrium and conditional analyses indicate that signals observed at ABCA1 and LIPC for HDL cholesterol and NCAN/MAU2 for triglycerides are independent of previously reported lead SNP associations. Analyses of lead SNPs from the European Global Lipids Genetics Consortium (GLGC) dataset in our Hispanic samples show remarkable concordance of direction of effects as well as strong correlation in effect sizes. A meta-analysis of the European GLGC and our Hispanic datasets identified five novel regions reaching genome-wide significance: two for total cholesterol (FN1 and SAMM50), two for HDL cholesterol (LOC100996634 and COPB1) and one for LDL cholesterol (LINC00324/CTC1/PFAS). The top meta-analysis signals were found to be enriched for SNPs associated with gene expression in a tissue-specific fashion, suggesting an enrichment of tissue-specific function in lipid-associated loci.

Background: We explored the imputation performance of the program IMPUTE in an admixed sample from Mexico City. The following issues were evaluated: (a) the impact of different reference panels (HapMap vs. 1000 Genomes) on imputation; (b) potential differences in imputation performance between single-step vs. two-step (phasing and imputation) approaches; (c) the effect of different INFO score thresholds on imputation performance and (d) imputation performance in common vs. rare markers., Methods: The sample from Mexico City comprised 1,310 individuals genotyped with the Affymetrix 5.0 array. We randomly masked 5% of the markers directly genotyped on chromosome 12 (n=1,046) and compared the imputed genotypes with the microarray genotype calls. Imputation was carried out with the program IMPUTE. The concordance rates between the imputed and observed genotypes were used as a measure of imputation accuracy and the proportion of non-missing genotypes as a measure of imputation efficacy., Results: The single-step imputation approach produced slightly higher concordance rates than the two-step strategy (99.1% vs. 98.4% when using the HapMap phase II combined panel), but at the expense of a lower proportion of non-missing genotypes (85.5% vs. 90.1%). The 1,000 Genomes reference sample produced similar concordance rates to the HapMap phase II panel (98.4% for both datasets, using the two-step strategy). However, the 1000 Genomes reference sample increased substantially the proportion of non-missing genotypes (94.7% vs. 90.1%). Rare variants (<1%) had lower imputation accuracy and efficacy than common markers., Conclusions: The program IMPUTE had an excellent imputation performance for common alleles in an admixed sample from Mexico City, which has primarily Native American (62%) and European (33%) contributions. Genotype concordances were higher than 98.4% using all the imputation strategies, in spite of the fact that no Native American samples are present in the HapMap and 1000 Genomes reference panels. The best balance of imputation accuracy and efficiency was obtained with the 1,000 Genomes panel. Rare variants were not captured effectively by any of the available panels, emphasizing the need to be cautious in the interpretation of association results for imputed rare variants.

Aims/hypothesis: We report a genome-wide association study of type 2 diabetes in an admixed sample from Mexico City and describe the results of a meta-analysis of this study and another genome-wide scan in a Mexican-American sample from Starr County, TX, USA. The top signals observed in this meta-analysis were followed up in the Diabetes Genetics Replication and Meta-analysis Consortium (DIAGRAM) and DIAGRAM+ datasets., Methods: We analysed 967 cases and 343 normoglycaemic controls. The samples were genotyped with the Affymetrix Genome-wide Human SNP array 5.0. Associations of genotyped and imputed markers with type 2 diabetes were tested using a missing data likelihood score test. A fixed-effects meta-analysis including 1,804 cases and 780 normoglycaemic controls was carried out by weighting the effect estimates by their inverse variances., Results: In the meta-analysis of the two Hispanic studies, markers showing suggestive associations (p < 10(-5)) were identified in two known diabetes genes, HNF1A and KCNQ1, as well as in several additional regions. Meta-analysis of the two Hispanic studies and the recent DIAGRAM+ dataset identified genome-wide significant signals (p < 5 × 10(-8)) within or near the genes HNF1A and CDKN2A/CDKN2B, as well as suggestive associations in three additional regions, IGF2BP2, KCNQ1 and the previously unreported C14orf70., Conclusions/interpretation: We observed numerous regions with suggestive associations with type 2 diabetes. Some of these signals correspond to regions described in previous studies. However, many of these regions could not be replicated in the DIAGRAM datasets. It is critical to carry out additional studies in Hispanic and American Indian populations, which have a high prevalence of type 2 diabetes.