Maurine Tong

Bio: Maurine Tong is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Single-nucleotide polymorphism & TCF7L2. The author has an hindex of 6, co-authored 10 publications receiving 3130 citations. Previous affiliations of Maurine Tong include National Institutes of Health.

A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants.

Abstract: Identifying the genetic variants that increase the risk of type 2 diabetes (T2D) in humans has been a formidable challenge. Adopting a genome-wide association strategy, we genotyped 1161 Finnish T2D cases and 1174 Finnish normal glucose-tolerant (NGT) controls with >315,000 single-nucleotide polymorphisms (SNPs) and imputed genotypes for an additional >2 million autosomal SNPs. We carried out association analysis with these SNPs to identify genetic variants that predispose to T2D, compared our T2D association results with the results of two similar studies, and genotyped 80 SNPs in an additional 1215 Finnish T2D cases and 1258 Finnish NGT controls. We identify T2D-associated variants in an intergenic region of chromosome 11p12, contribute to the identification of T2D-associated variants near the genes IGF2BP2 and CDKAL1 and the region of CDKN2A and CDKN2B, and confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk. This brings the number of T2D loci now confidently identified to at least 10.

Screening of 134 single nucleotide polymorphisms (SNPs) previously associated with type 2 diabetes replicates association with 12 SNPs in nine genes.

Abstract: More than 120 published reports have described associations between single nucleotide polymorphisms (SNPs) and type 2 diabetes. However, multiple studies of the same variant have often been discordant. From a literature search, we identified previously reported type 2 diabetes-associated SNPs. We initially genotyped 134 SNPs on 786 index case subjects from type 2 diabetes families and 617 control subjects with normal glucose tolerance from Finland and excluded from analysis 20 SNPs in strong linkage disequilibrium (r(2) > 0.8) with another typed SNP. Of the 114 SNPs examined, we followed up the 20 most significant SNPs (P < 0.10) on an additional 384 case subjects and 366 control subjects from a population-based study in Finland. In the combined data, we replicated association (P < 0.05) for 12 SNPs: PPARG Pro12Ala and His447, KCNJ11 Glu23Lys and rs5210, TNF -857, SLC2A2 Ile110Thr, HNF1A/TCF1 rs2701175 and GE117881_360, PCK1 -232, NEUROD1 Thr45Ala, IL6 -598, and ENPP1 Lys121Gln. The replication of 12 SNPs of 114 tested was significantly greater than expected by chance under the null hypothesis of no association (P = 0.012). We observed that SNPs from genes that had three or more previous reports of association were significantly more likely to be replicated in our sample (P = 0.03), although we also replicated 4 of 58 SNPs from genes that had only one previous report of association.

Comprehensive Association Study of Type 2 Diabetes and Related Quantitative Traits With 222 Candidate Genes

Abstract: Objective: Type 2 diabetes (T2D) is a common complex disorder with environmental and genetic components. We used a candidate gene-based approach to identify single nucleotide polymorphism (SNP) variants in 222 candidate genes that influence susceptibility to T2D. Research Design and Method: In a case-control study of 1,161 T2D and 1,174 normal glucose tolerant (NGT) control Finns, we genotyped 3,531 tagSNPs and annotation-based SNPs and imputed an additional 7,498 SNPs, providing 99.9% coverage of common HapMap variants in the 222 candidate genes. Selected SNPs were genotyped in an additional 1,211 T2D cases and 1,259 NGT controls, also from Finland. Results: Using SNP and gene-based analysis methods, we replicated previously reported SNP- T2D associations in PPARG, KCNJ11 , and SLC2A2 , identified significant SNPs in genes with previously reported associations, ENPP1 (rs2021966, p=.00026) and NRF1 (rs1882095, p=.00096), and implicated novel genes in T2D susceptibility including RAPGEF1 (rs4740283, p=.00013) and TP53 (rs1042522; Arg72Pro, p=.00086). Conclusion: Our study provides an effective gene-based approach to association study design and analysis. One or more of the newly implicated genes may contribute to T2D pathogenesis; analysis of additional samples will be necessary to determine their effect on susceptibility.

Tissue-specific alternative splicing of TCF7L2.

Abstract: Common variants in the transcription factor 7-like 2 (TCF7L2) gene have been identified as the strongest genetic risk factors for type 2 diabetes (T2D). However, the mechanisms by which these non-coding variants increase risk for T2D are not well-established. We used 13 expression assays to survey mRNA expression of multiple TCF7L2 splicing forms in up to 380 samples from eight types of human tissue (pancreas, pancreatic islets, colon, liver, monocytes, skeletal muscle, subcutaneous adipose tissue and lymphoblastoid cell lines) and observed a tissue-specific pattern of alternative splicing. We tested whether the expression of TCF7L2 splicing forms was associated with single nucleotide polymorphisms (SNPs), rs7903146 and rs12255372, located within introns 3 and 4 of the gene and most strongly associated with T2D. Expression of two splicing forms was lower in pancreatic islets with increasing counts of T2D-associated alleles of the SNPs: a ubiquitous splicing form (P = 0.018 for rs7903146 and P = 0.020 for rs12255372) and a splicing form found in pancreatic islets, pancreas and colon but not in other tissues tested here (P = 0.009 for rs12255372 and P = 0.053 for rs7903146). Expression of this form in glucose-stimulated pancreatic islets correlated with expression of proinsulin (r(2) = 0.84-0.90, P < 0.00063). In summary, we identified a tissue-specific pattern of alternative splicing of TCF7L2. After adjustment for multiple tests, no association between expression of TCF7L2 in eight types of human tissue samples and T2D-associated genetic variants remained significant. Alternative splicing of TCF7L2 in pancreatic islets warrants future studies. GenBank Accession Numbers: FJ010164-FJ010174.

Discovery-Based Science Education: Functional Genomic Dissection in Drosophila by Undergraduate Researchers

Abstract: The excitement of scientific research and discovery cannot be fully conveyed by didactic lectures alone. Several recent initiatives and proposals, therefore, have supported a more participatory, discovery-based instruction for undergraduate science education [1,2]. In functional genomics, we have found an ideal platform to simultaneously benefit students and contribute to scientific discovery. The sequencing of eukaryotic genomes has facilitated the identification of complete sets of genes in humans and model genetic organisms. This has allowed many forms of high-throughput analyses of transcriptional profiles, protein interactions, structural motifs, and even genome-wide knock-downs in cell lines or in selected organisms. However, one of the best tools to provide functional information about gene action— obtaining in vivo evidence about the phenotype resulting from heritable loss of function—is difficult and less amenable to high-throughput research. We were able to achieve a large-scale in vivo analysis with a significant number of undergraduate students at UCLA, called the UCLA Undergraduate Consortium for Functional Genomics. This work, a practical manifestation of policy positions proposing discoverybased education, is described in summary form here (and in Box 1) and in detail online at http://www.bruinfly.ucla.edu. This effort combines professional-quality research with a strategy for research-based undergraduate education.

Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls

Abstract: There is increasing evidence that genome-wide association ( GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study ( using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined similar to 2,000 individuals for each of 7 major diseases and a shared set of similar to 3,000 controls. Case-control comparisons identified 24 independent association signals at P < 5 X 10(-7): 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn's disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals ( including 58 loci with single-point P values between 10(-5) and 5 X 10(-7)) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research.

A metagenome-wide association study of gut microbiota in type 2 diabetes

Abstract: Assessment and characterization of gut microbiota has become a major research area in human disease, including type 2 diabetes, the most prevalent endocrine disease worldwide. To carry out analysis on gut microbial content in patients with type 2 diabetes, we developed a protocol for a metagenome-wide association study (MGWAS) and undertook a two-stage MGWAS based on deep shotgun sequencing of the gut microbial DNA from 345 Chinese individuals. We identified and validated approximately 60,000 type-2-diabetes-associated markers and established the concept of a metagenomic linkage group, enabling taxonomic species-level analyses. MGWAS analysis showed that patients with type 2 diabetes were characterized by a moderate degree of gut microbial dysbiosis, a decrease in the abundance of some universal butyrate-producing bacteria and an increase in various opportunistic pathogens, as well as an enrichment of other microbial functions conferring sulphate reduction and oxidative stress resistance. An analysis of 23 additional individuals demonstrated that these gut microbial markers might be useful for classifying type 2 diabetes.

A second generation human haplotype map of over 3.1 million SNPs

Abstract: We describe the Phase II HapMap, which characterizes over 3.1 million human single nucleotide polymorphisms (SNPs) genotyped in 270 individuals from four geographically diverse populations and includes 25-35% of common SNP variation in the populations surveyed. The map is estimated to capture untyped common variation with an average maximum r2 of between 0.9 and 0.96 depending on population. We demonstrate that the current generation of commercial genome-wide genotyping products captures common Phase II SNPs with an average maximum r2 of up to 0.8 in African and up to 0.95 in non-African populations, and that potential gains in power in association studies can be obtained through imputation. These data also reveal novel aspects of the structure of linkage disequilibrium. We show that 10-30% of pairs of individuals within a population share at least one region of extended genetic identity arising from recent ancestry and that up to 1% of all common variants are untaggable, primarily because they lie within recombination hotspots. We show that recombination rates vary systematically around genes and between genes of different function. Finally, we demonstrate increased differentiation at non-synonymous, compared to synonymous, SNPs, resulting from systematic differences in the strength or efficacy of natural selection between populations.

METAL: fast and efficient meta-analysis of genomewide association scans.

Abstract: Summary: METAL provides a computationally efficient tool for meta-analysis of genome-wide association scans, which is a commonly used approach for improving power complex traits gene mapping studies. METAL provides a rich scripting interface and implements efficient memory management to allow analyses of very large data sets and to support a variety of input file formats. Availability and implementation: METAL, including source code, documentation, examples, and executables, is available at http://www.sph.umich.edu/csg/abecasis/metal/ Contact: ude.hcimu@olacnog

Genome-wide association studies for complex traits: consensus, uncertainty and challenges

Abstract: The past year has witnessed substantial advances in understanding the genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.