[Show abstract][Hide abstract] ABSTRACT: Glycated hemoglobin A1C (HbA1C) level is used as a diagnostic marker for diabetes mellitus and a predictor of diabetes associated complications. Genome-wide association studies have identified genetic variants associated with HbA1C level. Most of these studies have been conducted in populations of European ancestry. Here we report the findings from a meta-analysis of genome-wide association studies of HbA1C levels in 6,682 non-diabetic subjects of Chinese, Malay and South Asian ancestries. We also sought to examine the associations between HbA1C associated SNPs and microvascular complications associated with diabetes mellitus, namely chronic kidney disease and retinopathy. A cluster of 6 SNPs on chromosome 17 showed an association with HbA1C which achieved genome-wide significance in the Malays but not in Chinese and Asian Indians. No other variants achieved genome-wide significance in the individual studies or in the meta-analysis. When we investigated the reproducibility of the findings that emerged from the European studies, six loci out of fifteen were found to be associated with HbA1C with effect sizes similar to those reported in the populations of European ancestry and P-value ≤ 0.05. No convincing associations with chronic kidney disease and retinopathy were identified in this study.
PLoS ONE 11/2013; 8(11):e79767. DOI:10.1371/journal.pone.0079767 · 3.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Whole-genome sequencing across multiple samples in a population provides an unprecedented opportunity for comprehensively characterizing the polymorphic variants in the population. Although the 1000 Genomes Project (1KGP) has offered brief insights into the value of population-level sequencing, the low coverage has compromised the ability to confidently detect rare and low-frequency variants. In addition, the composition of populations in the 1KGP is not complete, despite the fact that the study design has been extended to more than 2,500 samples from more than 20 population groups. The Malays are one of the Austronesian groups predominantly present in Southeast Asia and Oceania, and the Singapore Sequencing Malay Project (SSMP) aims to perform deep whole-genome sequencing of 100 healthy Malays. By sequencing at a minimum of 30× coverage, we have illustrated the higher sensitivity at detecting low-frequency and rare variants and the ability to investigate the presence of hotspots of functional mutations. Compared to the low-pass sequencing in the 1KGP, the deeper coverage allows more functional variants to be identified for each person. A comparison of the fidelity of genotype imputation of Malays indicated that a population-specific reference panel, such as the SSMP, outperforms a cosmopolitan panel with larger number of individuals for common SNPs. For lower-frequency (<5%) markers, a larger number of individuals might have to be whole-genome sequenced so that the accuracy currently afforded by the 1KGP can be achieved. The SSMP data are expected to be the benchmark for evaluating the value of deep population-level sequencing versus low-pass sequencing, especially in populations that are poorly represented in population-genetics studies.
The American Journal of Human Genetics 12/2012; DOI:10.1016/j.ajhg.2012.12.005 · 11.20 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Genome-wide association studies (GWAS) have become the preferred experimental design in exploring the genetic etiology of complex human traits and diseases. Standard SNP-based meta-analytic approaches have been utilized to integrate the results from multiple experiments. This fundamentally assumes that the patterns of linkage disequilibrium (LD) between the underlying causal variants and the directly genotyped SNPs are similar across the populations for the same SNPs to emerge with surrogate evidence of disease association. We introduce a novel strategy for assessing regional evidence of phenotypic association that explicitly incorporates the extent of LD in the region. This provides a natural framework for combining evidence from multi-ethnic studies of both dichotomous and quantitative traits that (i) accommodates different patterns of LD, (ii) integrates different genotyping platforms and (iii) allows for the presence of allelic heterogeneity between the populations. Our method can also be generalized to perform gene-based or pathway-based analyses. Applying this method on real GWAS data in type 2 diabetes (T2D) boosted the association evidence in regions well-established for T2D etiology in three diverse South-East Asian populations, as well as identified two novel gene regions and a biologically convincing pathway that are subsequently validated with data from the Wellcome Trust Case Control Consortium.
European journal of human genetics: EJHG 11/2011; 20(4):469-75. DOI:10.1038/ejhg.2011.219 · 3.56 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Copy number variants (CNVs) extend our understanding of the genetic diversity in humans. However, the distribution and characteristics of CNVs in Asian populations remain largely unexplored, especially for rare CNVs that have emerged as important genetic factors for complex traits. In the present study, we performed an in-depth investigation of common and rare CNVs across 8,148 individuals from the three major Asian ethnic groups: Chinese (n = 1,945), Malays (n = 2,399), and Indians (n = 2,217) in Singapore, making this investigation the most comprehensive genome-wide survey of CNVs outside the European-ancestry populations to date. We detected about 16 CNVs per individual and the ratio of loss to gain events is ∼2:1. The majority of the CNVs are of low frequency (<10%), and 40% are rare (<1%). In each population, ∼20% of the CNVs are not previously catalogued in the Database of Genomic Variants (DGV). Contrary to findings from European studies, the common CNVs (>5%) in our populations are not well tagged by SNPs in Illumina 1M and 610K arrays, and most disease-associated common CNVs previously reported in Caucasians are rare in our populations. We also report noticeable population differentiation in the CNV landscape of these Asian populations, with the greatest diversity seen between the Indians and the Chinese.
Human Mutation 08/2011; 32(12):1341-9. DOI:10.1002/humu.21601 · 5.05 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Recent reports have identified a north-south cline in genetic variation in East and South-East Asia, but these studies have not formally explored the basis of these clinical differences. Understanding the origins of these variations may provide valuable insights in tracking down the functional variants in genomic regions identified by genetic association studies. Here we investigate the genetic basis of these differences with genome-wide data from the HapMap, the Human Genome Diversity Project and the Singapore Genome Variation Project. We implemented four bioinformatic measures to discover genomic regions that are considerably differentiated either between two Han Chinese populations in the north and south of China, or across 22 populations in East and South-East Asia. These measures prioritized genomic stretches with: (i) regional differences in the allelic spectrum for SNPs common to the two Han Chinese populations; (ii) differential evidence of positive selection between the two populations as quantified by integrated haplotype score (iHS) and cross-population extended haplotype homozygosity (XP-EHH); (iii) significant correlation between allele frequencies and geographical latitudes of the 22 populations. We also explored the extent of linkage disequilibrium variations in these regions, which is important in combining genetic association studies from North and South Chinese. Two of the regions that emerged are found in HLA class I and II, suggesting that the HLA imputation panel from the HapMap may not be directly applicable to every Chinese sample. This has important implications to autoimmune studies that plan to impute the classical HLA alleles to fine map the SNP association signals.
European journal of human genetics: EJHG 07/2011; 20(1):102-10. DOI:10.1038/ejhg.2011.139 · 3.56 Impact Factor