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1. The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits

Author

Costanzo, Maria C, von Grotthuss, Marcin, Massung, Jeffrey, Jang, Dongkeun, Caulkins, Lizz, Koesterer, Ryan, Gilbert, Clint, Welch, Ryan P, Kudtarkar, Parul, Hoang, Quy, Boughton, Andrew P, Singh, Preeti, Sun, Ying, Duby, Marc, Moriondo, Annie, Nguyen, Trang, Smadbeck, Patrick, Alexander, Benjamin R, Brandes, MacKenzie, Carmichael, Mary, Dornbos, Peter, Green, Todd, Huellas-Bruskiewicz, Kenneth C, Ji, Yue, Kluge, Alexandria, McMahon, Aoife C, Mercader, Josep M, Ruebenacker, Oliver, Sengupta, Sebanti, Spalding, Dylan, Taliun, Daniel, Consortium, AMP-T2D, Abecasis, Gonçalo, Akolkar, Beena, Allred, Nicholette D, Altshuler, David, Below, Jennifer E, Bergman, Richard, Beulens, Joline WJ, Blangero, John, Boehnke, Michael, Bokvist, Krister, Bottinger, Erwin, Bowden, Donald, Brosnan, M Julia, Brown, Christopher, Bruskiewicz, Kenneth, Burtt, Noël P, Cebola, Inês, Chambers, John, Chen, Yii-Der Ida, Cherkas, Andriy, Chu, Audrey Y, Clark, Christopher, Claussnitzer, Melina, Cox, Nancy J, Hoed, Marcel den, Dong, Duc, Duggirala, Ravindranath, Dupuis, Josée, Elders, Petra JM, Engreitz, Jesse M, Fauman, Eric, Ferrer, Jorge, Flannick, Jason, Flicek, Paul, Flickinger, Matthew, Florez, Jose C, Fox, Caroline S, Frayling, Timothy M, Frazer, Kelly A, Gaulton, Kyle J, Gloyn, Anna L, Hanis, Craig L, Hanson, Robert, Hattersley, Andrew T, Im, Hae Kyung, Iqbal, Sidra, Jacobs, Suzanne BR, Jang, Dong-Keun, Jordan, Tad, Kamphaus, Tania, Karpe, Fredrik, Keane, Thomas M, Kim, Seung K, Lage, Kasper, Lange, Leslie A, and Lazar, Mitchell

Subjects
Genetics, Diabetes, Human Genome, Metabolic and endocrine, Good Health and Well Being, Humans, Diabetes Mellitus, Type 2, Access to Information, Prospective Studies, Genomics, Phenotype, AMP-T2D Consortium, CMDKP, GWAS, T2DKP, data sharing, diabetes, effector genes, genetic associations, genetic support, genomics, portal, Biochemistry and Cell Biology, Medical Biochemistry and Metabolomics, Endocrinology & Metabolism
Abstract

Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP's comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results.

Published
2023

2. Integrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk

Author

Yin, Xianyong, Bose, Debraj, Kwon, Annie, Hanks, Sarah C, Jackson, Anne U, Stringham, Heather M, Welch, Ryan, Oravilahti, Anniina, Silva, Lilian Fernandes, FinnGen, Locke, Adam E, Fuchsberger, Christian, Service, Susan K, Erdos, Michael R, Bonnycastle, Lori L, Kuusisto, Johanna, Stitziel, Nathan O, Hall, Ira M, Morrison, Jean, Ripatti, Samuli, Palotie, Aarno, Freimer, Nelson B, Collins, Francis S, Mohlke, Karen L, Scott, Laura J, Fauman, Eric B, Burant, Charles, Boehnke, Michael, Laakso, Markku, and Wen, Xiaoquan

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Epidemiology, Health Sciences, Medical Biochemistry and Metabolomics, Nutrition, Biotechnology, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Bilirubin, Carnitine, Genome-Wide Association Study, Glycerophospholipids, Humans, Male, Metabolomics, Quantitative Trait Loci, Solute Carrier Family 22 Member 5, Transcriptome, FinnGen, colocalizataion, genome-wide association study, metabolomics, transcriptome-wide association study, transcriptomics, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Transcriptomics data have been integrated with genome-wide association studies (GWASs) to help understand disease/trait molecular mechanisms. The utility of metabolomics, integrated with transcriptomics and disease GWASs, to understand molecular mechanisms for metabolite levels or diseases has not been thoroughly evaluated. We performed probabilistic transcriptome-wide association and locus-level colocalization analyses to integrate transcriptomics results for 49 tissues in 706 individuals from the GTEx project, metabolomics results for 1,391 plasma metabolites in 6,136 Finnish men from the METSIM study, and GWAS results for 2,861 disease traits in 260,405 Finnish individuals from the FinnGen study. We found that genetic variants that regulate metabolite levels were more likely to influence gene expression and disease risk compared to the ones that do not. Integrating transcriptomics with metabolomics results prioritized 397 genes for 521 metabolites, including 496 previously identified gene-metabolite pairs with strong functional connections and suggested 33.3% of such gene-metabolite pairs shared the same causal variants with genetic associations of gene expression. Integrating transcriptomics and metabolomics individually with FinnGen GWAS results identified 1,597 genes for 790 disease traits. Integrating transcriptomics and metabolomics jointly with FinnGen GWAS results helped pinpoint metabolic pathways from genes to diseases. We identified putative causal effects of UGT1A1/UGT1A4 expression on gallbladder disorders through regulating plasma (E,E)-bilirubin levels, of SLC22A5 expression on nasal polyps and plasma carnitine levels through distinct pathways, and of LIPC expression on age-related macular degeneration through glycerophospholipid metabolic pathways. Our study highlights the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.

Published
2022

3. Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci

Author

Yin, Xianyong, Chan, Lap Sum, Bose, Debraj, Jackson, Anne U, VandeHaar, Peter, Locke, Adam E, Fuchsberger, Christian, Stringham, Heather M, Welch, Ryan, Yu, Ketian, Fernandes Silva, Lilian, Service, Susan K, Zhang, Daiwei, Hector, Emily C, Young, Erica, Ganel, Liron, Das, Indraniel, Abel, Haley, Erdos, Michael R, Bonnycastle, Lori L, Kuusisto, Johanna, Stitziel, Nathan O, Hall, Ira M, Wagner, Gregory R, Kang, Jian, Morrison, Jean, Burant, Charles F, Collins, Francis S, Ripatti, Samuli, Palotie, Aarno, Freimer, Nelson B, Mohlke, Karen L, Scott, Laura J, Wen, Xiaoquan, Fauman, Eric B, Laakso, Markku, and Boehnke, Michael

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Epidemiology, Health Sciences, Medical Biochemistry and Metabolomics, Human Genome, Clinical Research, Aetiology, 2.1 Biological and endogenous factors, Alleles, Finland, Gene Frequency, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Phenotype, Polymorphism, Single Nucleotide, FinnGen
Abstract

Few studies have explored the impact of rare variants (minor allele frequency

Published
2022

4. Intestinal α1-2-Fucosylation Contributes to Obesity and Steatohepatitis in Mice.

Author

Zhou, Rongrong, Llorente, Cristina, Cao, Jinling, Zaramela, Livia S, Zeng, Suling, Gao, Bei, Li, Shang-Zhen, Welch, Ryan D, Huang, Feng-Qing, Qi, Lian-Wen, Pan, Chuyue, Huang, Yan, Zhou, Pengchen, Beussen, Iris, Zhang, Ying, Bryam, Gregory, Fiehn, Oliver, Wang, Lirui, Liu, E-Hu, Yu, Ruth T, Downes, Michael, Evans, Ronald M, Goglin, Karrie, Fouts, Derrick E, Brenner, David A, Bode, Lars, Fan, Xuegong, Zengler, Karsten, and Schnabl, Bernd

Subjects
Bile Acids, Metabolic Syndrome, Metabolome, Microbiota, Nonalcoholic Steatohepatitis, Digestive Diseases, Liver Disease, Nutrition, Obesity, 2.1 Biological and endogenous factors, Metabolic and endocrine, Cardiovascular, Oral and gastrointestinal
Abstract

Background & aimsFucosyltransferase 2 (Fut2)-mediated intestinal α1- 2-fucosylation is important for host-microbe interactions and has been associated with several diseases, but its role in obesity and hepatic steatohepatitis is not known. The aim of this study was to investigate the role of Fut2 in a Western-style diet-induced mouse model of obesity and steatohepatitis.MethodsWild-type (WT) and Fut2-deficient littermate mice were used and features of the metabolic syndrome and steatohepatitis were assessed after 20 weeks of Western diet feeding.ResultsIntestinal α1-2-fucosylation was suppressed in WT mice after Western diet feeding, and supplementation of α1-2-fucosylated glycans exacerbated obesity and steatohepatitis in these mice. Fut2-deficient mice were protected from Western diet-induced features of obesity and steatohepatitis despite an increased caloric intake. These mice have increased energy expenditure and thermogenesis, as evidenced by a higher core body temperature. Protection from obesity and steatohepatitis associated with Fut2 deficiency is transmissible to WT mice via microbiota exchange; phenotypic differences between Western diet-fed WT and Fut2-deficient mice were reduced with antibiotic treatment. Fut2 deficiency attenuated diet-induced bile acid accumulation by altered relative abundance of bacterial enzyme 7-α-hydroxysteroid dehydrogenases metabolizing bile acids and by increased fecal excretion of secondary bile acids. This also was associated with increased intestinal farnesoid X receptor/fibroblast growth factor 15 signaling, which inhibits hepatic synthesis of bile acids. Dietary supplementation of α1-2-fucosylated glycans abrogates the protective effects of Fut2 deficiency.Conclusionsα1-2-fucosylation is an important host-derived regulator of intestinal microbiota and plays an important role for the pathogenesis of obesity and steatohepatitis in mice.

Published
2021

5. Intestinal α1-2-Fucosylation Contributes to Obesity and Steatohepatitis in Mice

Author

Zhou, Rongrong, Llorente, Cristina, Cao, Jinling, Zaramela, Livia S, Zeng, Suling, Gao, Bei, Li, Shang-Zhen, Welch, Ryan D, Huang, Feng-Qing, Qi, Lian-Wen, Pan, Chuyue, Huang, Yan, Zhou, Pengchen, Beussen, Iris, Zhang, Ying, Bryam, Gregory, Fiehn, Oliver, Wang, Lirui, Liu, E-Hu, Yu, Ruth T, Downes, Michael, Evans, Ronald M, Goglin, Karrie, Fouts, Derrick E, Brenner, David A, Bode, Lars, Fan, Xuegong, Zengler, Karsten, and Schnabl, Bernd

Subjects
Nutrition, Obesity, Liver Disease, Digestive Diseases, 2.1 Biological and endogenous factors, Aetiology, Cardiovascular, Oral and gastrointestinal, Metabolic and endocrine, Animals, Diet, Fatty Liver, Fucosyltransferases, Intestines, Mice, Mice, Knockout, Metabolic Syndrome, Nonalcoholic Steatohepatitis, Microbiota, Metabolome, Bile Acids
Abstract

Background & aimsFucosyltransferase 2 (Fut2)-mediated intestinal α1- 2-fucosylation is important for host-microbe interactions and has been associated with several diseases, but its role in obesity and hepatic steatohepatitis is not known. The aim of this study was to investigate the role of Fut2 in a Western-style diet-induced mouse model of obesity and steatohepatitis.MethodsWild-type (WT) and Fut2-deficient littermate mice were used and features of the metabolic syndrome and steatohepatitis were assessed after 20 weeks of Western diet feeding.ResultsIntestinal α1-2-fucosylation was suppressed in WT mice after Western diet feeding, and supplementation of α1-2-fucosylated glycans exacerbated obesity and steatohepatitis in these mice. Fut2-deficient mice were protected from Western diet-induced features of obesity and steatohepatitis despite an increased caloric intake. These mice have increased energy expenditure and thermogenesis, as evidenced by a higher core body temperature. Protection from obesity and steatohepatitis associated with Fut2 deficiency is transmissible to WT mice via microbiota exchange; phenotypic differences between Western diet-fed WT and Fut2-deficient mice were reduced with antibiotic treatment. Fut2 deficiency attenuated diet-induced bile acid accumulation by altered relative abundance of bacterial enzyme 7-α-hydroxysteroid dehydrogenases metabolizing bile acids and by increased fecal excretion of secondary bile acids. This also was associated with increased intestinal farnesoid X receptor/fibroblast growth factor 15 signaling, which inhibits hepatic synthesis of bile acids. Dietary supplementation of α1-2-fucosylated glycans abrogates the protective effects of Fut2 deficiency.Conclusionsα1-2-fucosylation is an important host-derived regulator of intestinal microbiota and plays an important role for the pathogenesis of obesity and steatohepatitis in mice.

Published
2021

6. Global chemical effects of the microbiome include new bile-acid conjugations

Author

Quinn, Robert A, Melnik, Alexey V, Vrbanac, Alison, Fu, Ting, Patras, Kathryn A, Christy, Mitchell P, Bodai, Zsolt, Belda-Ferre, Pedro, Tripathi, Anupriya, Chung, Lawton K, Downes, Michael, Welch, Ryan D, Quinn, Melissa, Humphrey, Greg, Panitchpakdi, Morgan, Weldon, Kelly C, Aksenov, Alexander, da Silva, Ricardo, Avila-Pacheco, Julian, Clish, Clary, Bae, Sena, Mallick, Himel, Franzosa, Eric A, Lloyd-Price, Jason, Bussell, Robert, Thron, Taren, Nelson, Andrew T, Wang, Mingxun, Leszczynski, Eric, Vargas, Fernando, Gauglitz, Julia M, Meehan, Michael J, Gentry, Emily, Arthur, Timothy D, Komor, Alexis C, Poulsen, Orit, Boland, Brigid S, Chang, John T, Sandborn, William J, Lim, Meerana, Garg, Neha, Lumeng, Julie C, Xavier, Ramnik J, Kazmierczak, Barbara I, Jain, Ruchi, Egan, Marie, Rhee, Kyung E, Ferguson, David, Raffatellu, Manuela, Vlamakis, Hera, Haddad, Gabriel G, Siegel, Dionicio, Huttenhower, Curtis, Mazmanian, Sarkis K, Evans, Ronald M, Nizet, Victor, Knight, Rob, and Dorrestein, Pieter C

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Chemical Sciences, Microbiology, Medical Biochemistry and Metabolomics, Genetics, Digestive Diseases, Aetiology, 2.1 Biological and endogenous factors, Oral and gastrointestinal, Animals, Bile Acids and Salts, Cholic Acid, Cystic Fibrosis, Germ-Free Life, Humans, Inflammatory Bowel Diseases, Metabolomics, Mice, Microbiota, Receptors, Cytoplasmic and Nuclear, General Science & Technology
Abstract

A mosaic of cross-phylum chemical interactions occurs between all metazoans and their microbiomes. A number of molecular families that are known to be produced by the microbiome have a marked effect on the balance between health and disease1-9. Considering the diversity of the human microbiome (which numbers over 40,000 operational taxonomic units10), the effect of the microbiome on the chemistry of an entire animal remains underexplored. Here we use mass spectrometry informatics and data visualization approaches11-13 to provide an assessment of the effects of the microbiome on the chemistry of an entire mammal by comparing metabolomics data from germ-free and specific-pathogen-free mice. We found that the microbiota affects the chemistry of all organs. This included the amino acid conjugations of host bile acids that were used to produce phenylalanocholic acid, tyrosocholic acid and leucocholic acid, which have not previously been characterized despite extensive research on bile-acid chemistry14. These bile-acid conjugates were also found in humans, and were enriched in patients with inflammatory bowel disease or cystic fibrosis. These compounds agonized the farnesoid X receptor in vitro, and mice gavaged with the compounds showed reduced expression of bile-acid synthesis genes in vivo. Further studies are required to confirm whether these compounds have a physiological role in the host, and whether they contribute to gut diseases that are associated with microbiome dysbiosis.

Published
2020

7. Adipose Tissue Gene Expression Associations Reveal Hundreds of Candidate Genes for Cardiometabolic Traits.

Author

Raulerson, Chelsea, Ko, Arthur, Kidd, John, Currin, Kevin, Brotman, Sarah, Cannon, Maren, Wu, Ying, Spracklen, Cassandra, Jackson, Anne, Stringham, Heather, Welch, Ryan, Fuchsberger, Christian, Locke, Adam, Narisu, Narisu, Lusis, Aldons, Civelek, Mete, Furey, Terrence, Kuusisto, Johanna, Collins, Francis, Boehnke, Michael, Scott, Laura, Lin, Dan-Yu, Love, Michael, Laakso, Markku, Pajukanta, Päivi, and Mohlke, Karen

Subjects
GWAS, adipocyte, colocalization, diabetes, eQTL, lipid, obesity, secondary signal, trans-ancestry, waist-hip ratio, Adipose Tissue, Alleles, Body Mass Index, Diabetes Mellitus, Type 2, Finland, Gene Expression, Genome-Wide Association Study, Humans, Male, Obesity, Quantitative Trait Loci, Waist-Hip Ratio
Abstract

Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with cardiometabolic traits including type 2 diabetes (T2D), lipid levels, body fat distribution, and adiposity, although most causal genes remain unknown. We used subcutaneous adipose tissue RNA-seq data from 434 Finnish men from the METSIM study to identify 9,687 primary and 2,785 secondary cis-expression quantitative trait loci (eQTL;

Published
2019

8. Exome sequencing of Finnish isolates enhances rare-variant association power

Author

Locke, Adam E, Steinberg, Karyn Meltz, Chiang, Charleston WK, Service, Susan K, Havulinna, Aki S, Stell, Laurel, Pirinen, Matti, Abel, Haley J, Chiang, Colby C, Fulton, Robert S, Jackson, Anne U, Kang, Chul Joo, Kanchi, Krishna L, Koboldt, Daniel C, Larson, David E, Nelson, Joanne, Nicholas, Thomas J, Pietilä, Arto, Ramensky, Vasily, Ray, Debashree, Scott, Laura J, Stringham, Heather M, Vangipurapu, Jagadish, Welch, Ryan, Yajnik, Pranav, Yin, Xianyong, Eriksson, Johan G, Ala-Korpela, Mika, Järvelin, Marjo-Riitta, Männikkö, Minna, Laivuori, Hannele, Dutcher, Susan K, Stitziel, Nathan O, Wilson, Richard K, Hall, Ira M, Sabatti, Chiara, Palotie, Aarno, Salomaa, Veikko, Laakso, Markku, Ripatti, Samuli, Boehnke, Michael, and Freimer, Nelson B

Subjects
Medical Biochemistry and Metabolomics, Biological Sciences, Biomedical and Clinical Sciences, Genetics, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Alleles, Cholesterol, HDL, Cluster Analysis, Endpoint Determination, Finland, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Variation, Geographic Mapping, Humans, Multifactorial Inheritance, Quantitative Trait Loci, Reproducibility of Results, Exome Sequencing, FinnGen Project, General Science & Technology
Abstract

Exome-sequencing studies have generally been underpowered to identify deleterious alleles with a large effect on complex traits as such alleles are mostly rare. Because the population of northern and eastern Finland has expanded considerably and in isolation following a series of bottlenecks, individuals of these populations have numerous deleterious alleles at a relatively high frequency. Here, using exome sequencing of nearly 20,000 individuals from these regions, we investigate the role of rare coding variants in clinically relevant quantitative cardiometabolic traits. Exome-wide association studies for 64 quantitative traits identified 26 newly associated deleterious alleles. Of these 26 alleles, 19 are either unique to or more than 20 times more frequent in Finnish individuals than in other Europeans and show geographical clustering comparable to Mendelian disease mutations that are characteristic of the Finnish population. We estimate that sequencing studies of populations without this unique history would require hundreds of thousands to millions of participants to achieve comparable association power.

Published
2019

9. Exome sequencing of Finnish isolates enhances rare-variant association power.

Author

Locke, Adam E, Steinberg, Karyn Meltz, Chiang, Charleston WK, Service, Susan K, Havulinna, Aki S, Stell, Laurel, Pirinen, Matti, Abel, Haley J, Chiang, Colby C, Fulton, Robert S, Jackson, Anne U, Kang, Chul Joo, Kanchi, Krishna L, Koboldt, Daniel C, Larson, David E, Nelson, Joanne, Nicholas, Thomas J, Pietilä, Arto, Ramensky, Vasily, Ray, Debashree, Scott, Laura J, Stringham, Heather M, Vangipurapu, Jagadish, Welch, Ryan, Yajnik, Pranav, Yin, Xianyong, Eriksson, Johan G, Ala-Korpela, Mika, Järvelin, Marjo-Riitta, Männikkö, Minna, Laivuori, Hannele, FinnGen Project, Dutcher, Susan K, Stitziel, Nathan O, Wilson, Richard K, Hall, Ira M, Sabatti, Chiara, Palotie, Aarno, Salomaa, Veikko, Laakso, Markku, Ripatti, Samuli, Boehnke, Michael, and Freimer, Nelson B

Subjects
FinnGen Project, Humans, Genetic Predisposition to Disease, Endpoint Determination, Cluster Analysis, Reproducibility of Results, Multifactorial Inheritance, Alleles, Quantitative Trait Loci, Finland, Cholesterol, HDL, Genetic Variation, Genetic Association Studies, Geographic Mapping, Whole Exome Sequencing, Cholesterol, HDL, General Science & Technology
Abstract

Exome-sequencing studies have generally been underpowered to identify deleterious alleles with a large effect on complex traits as such alleles are mostly rare. Because the population of northern and eastern Finland has expanded considerably and in isolation following a series of bottlenecks, individuals of these populations have numerous deleterious alleles at a relatively high frequency. Here, using exome sequencing of nearly 20,000 individuals from these regions, we investigate the role of rare coding variants in clinically relevant quantitative cardiometabolic traits. Exome-wide association studies for 64 quantitative traits identified 26 newly associated deleterious alleles. Of these 26 alleles, 19 are either unique to or more than 20 times more frequent in Finnish individuals than in other Europeans and show geographical clustering comparable to Mendelian disease mutations that are characteristic of the Finnish population. We estimate that sequencing studies of populations without this unique history would require hundreds of thousands to millions of participants to achieve comparable association power.

Published
2019

10. Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls

Author

Flannick, Jason, Mercader, Josep M, Fuchsberger, Christian, Udler, Miriam S, Mahajan, Anubha, Wessel, Jennifer, Teslovich, Tanya M, Caulkins, Lizz, Koesterer, Ryan, Barajas-Olmos, Francisco, Blackwell, Thomas W, Boerwinkle, Eric, Brody, Jennifer A, Centeno-Cruz, Federico, Chen, Ling, Chen, Siying, Contreras-Cubas, Cecilia, Córdova, Emilio, Correa, Adolfo, Cortes, Maria, DeFronzo, Ralph A, Dolan, Lawrence, Drews, Kimberly L, Elliott, Amanda, Floyd, James S, Gabriel, Stacey, Garay-Sevilla, Maria Eugenia, García-Ortiz, Humberto, Gross, Myron, Han, Sohee, Heard-Costa, Nancy L, Jackson, Anne U, Jørgensen, Marit E, Kang, Hyun Min, Kelsey, Megan, Kim, Bong-Jo, Koistinen, Heikki A, Kuusisto, Johanna, Leader, Joseph B, Linneberg, Allan, Liu, Ching-Ti, Liu, Jianjun, Lyssenko, Valeriya, Manning, Alisa K, Marcketta, Anthony, Malacara-Hernandez, Juan Manuel, Martínez-Hernández, Angélica, Matsuo, Karen, Mayer-Davis, Elizabeth, Mendoza-Caamal, Elvia, Mohlke, Karen L, Morrison, Alanna C, Ndungu, Anne, Ng, Maggie CY, O’Dushlaine, Colm, Payne, Anthony J, Pihoker, Catherine, Post, Wendy S, Preuss, Michael, Psaty, Bruce M, Vasan, Ramachandran S, Rayner, N William, Reiner, Alexander P, Revilla-Monsalve, Cristina, Robertson, Neil R, Santoro, Nicola, Schurmann, Claudia, So, Wing Yee, Soberón, Xavier, Stringham, Heather M, Strom, Tim M, Tam, Claudia HT, Thameem, Farook, Tomlinson, Brian, Torres, Jason M, Tracy, Russell P, van Dam, Rob M, Vujkovic, Marijana, Wang, Shuai, Welch, Ryan P, Witte, Daniel R, Wong, Tien-Yin, Atzmon, Gil, Barzilai, Nir, Blangero, John, Bonnycastle, Lori L, Bowden, Donald W, Chambers, John C, Chan, Edmund, Cheng, Ching-Yu, Cho, Yoon Shin, Collins, Francis S, de Vries, Paul S, Duggirala, Ravindranath, Glaser, Benjamin, Gonzalez, Clicerio, Gonzalez, Ma Elena, Groop, Leif, Kooner, Jaspal Singh, and Kwak, Soo Heon

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, Prevention, Biotechnology, Diabetes, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Animals, Case-Control Studies, Decision Support Techniques, Diabetes Mellitus, Type 2, Exome, Female, Gene Frequency, Genome-Wide Association Study, Humans, Male, Mice, Mice, Knockout, Exome Sequencing, Broad Genomics Platform, DiscovEHR Collaboration, CHARGE, LuCamp, ProDiGY, GoT2D, ESP, SIGMA-T2D, T2D-GENES, AMP-T2D-GENES, General Science & Technology
Abstract

Protein-coding genetic variants that strongly affect disease risk can yield relevant clues to disease pathogenesis. Here we report exome-sequencing analyses of 20,791 individuals with type 2 diabetes (T2D) and 24,440 non-diabetic control participants from 5 ancestries. We identify gene-level associations of rare variants (with minor allele frequencies of less than 0.5%) in 4 genes at exome-wide significance, including a series of more than 30 SLC30A8 alleles that conveys protection against T2D, and in 12 gene sets, including those corresponding to T2D drug targets (P = 6.1 × 10-3) and candidate genes from knockout mice (P = 5.2 × 10-3). Within our study, the strongest T2D gene-level signals for rare variants explain at most 25% of the heritability of the strongest common single-variant signals, and the gene-level effect sizes of the rare variants that we observed in established T2D drug targets will require 75,000-185,000 sequenced cases to achieve exome-wide significance. We propose a method to interpret these modest rare-variant associations and to incorporate these associations into future target or gene prioritization efforts.

Published
2019

11. Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls.

Author

Flannick, Jason, Mercader, Josep M, Fuchsberger, Christian, Udler, Miriam S, Mahajan, Anubha, Wessel, Jennifer, Teslovich, Tanya M, Caulkins, Lizz, Koesterer, Ryan, Barajas-Olmos, Francisco, Blackwell, Thomas W, Boerwinkle, Eric, Brody, Jennifer A, Centeno-Cruz, Federico, Chen, Ling, Chen, Siying, Contreras-Cubas, Cecilia, Córdova, Emilio, Correa, Adolfo, Cortes, Maria, DeFronzo, Ralph A, Dolan, Lawrence, Drews, Kimberly L, Elliott, Amanda, Floyd, James S, Gabriel, Stacey, Garay-Sevilla, Maria Eugenia, García-Ortiz, Humberto, Gross, Myron, Han, Sohee, Heard-Costa, Nancy L, Jackson, Anne U, Jørgensen, Marit E, Kang, Hyun Min, Kelsey, Megan, Kim, Bong-Jo, Koistinen, Heikki A, Kuusisto, Johanna, Leader, Joseph B, Linneberg, Allan, Liu, Ching-Ti, Liu, Jianjun, Lyssenko, Valeriya, Manning, Alisa K, Marcketta, Anthony, Malacara-Hernandez, Juan Manuel, Martínez-Hernández, Angélica, Matsuo, Karen, Mayer-Davis, Elizabeth, Mendoza-Caamal, Elvia, Mohlke, Karen L, Morrison, Alanna C, Ndungu, Anne, Ng, Maggie CY, O'Dushlaine, Colm, Payne, Anthony J, Pihoker, Catherine, Broad Genomics Platform, Post, Wendy S, Preuss, Michael, Psaty, Bruce M, Vasan, Ramachandran S, Rayner, N William, Reiner, Alexander P, Revilla-Monsalve, Cristina, Robertson, Neil R, Santoro, Nicola, Schurmann, Claudia, So, Wing Yee, Soberón, Xavier, Stringham, Heather M, Strom, Tim M, Tam, Claudia HT, Thameem, Farook, Tomlinson, Brian, Torres, Jason M, Tracy, Russell P, van Dam, Rob M, Vujkovic, Marijana, Wang, Shuai, Welch, Ryan P, Witte, Daniel R, Wong, Tien-Yin, Atzmon, Gil, Barzilai, Nir, Blangero, John, Bonnycastle, Lori L, Bowden, Donald W, Chambers, John C, Chan, Edmund, Cheng, Ching-Yu, Cho, Yoon Shin, Collins, Francis S, de Vries, Paul S, Duggirala, Ravindranath, Glaser, Benjamin, Gonzalez, Clicerio, Gonzalez, Ma Elena, Groop, Leif, and Kooner, Jaspal Singh

Subjects
Broad Genomics Platform, DiscovEHR Collaboration, CHARGE, LuCamp, ProDiGY, GoT2D, ESP, SIGMA-T2D, T2D-GENES, AMP-T2D-GENES, Animals, Mice, Knockout, Humans, Mice, Diabetes Mellitus, Type 2, Case-Control Studies, Gene Frequency, Decision Support Techniques, Female, Male, Genome-Wide Association Study, Exome, Whole Exome Sequencing, Biotechnology, Prevention, Diabetes, Human Genome, Genetics, 2.1 Biological and endogenous factors, Metabolic and endocrine, General Science & Technology
Abstract

Protein-coding genetic variants that strongly affect disease risk can yield relevant clues to disease pathogenesis. Here we report exome-sequencing analyses of 20,791 individuals with type 2 diabetes (T2D) and 24,440 non-diabetic control participants from 5 ancestries. We identify gene-level associations of rare variants (with minor allele frequencies of less than 0.5%) in 4 genes at exome-wide significance, including a series of more than 30 SLC30A8 alleles that conveys protection against T2D, and in 12 gene sets, including those corresponding to T2D drug targets (P = 6.1 × 10-3) and candidate genes from knockout mice (P = 5.2 × 10-3). Within our study, the strongest T2D gene-level signals for rare variants explain at most 25% of the heritability of the strongest common single-variant signals, and the gene-level effect sizes of the rare variants that we observed in established T2D drug targets will require 75,000-185,000 sequenced cases to achieve exome-wide significance. We propose a method to interpret these modest rare-variant associations and to incorporate these associations into future target or gene prioritization efforts.

Published
2019

12. Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

Author

Flannick, Jason, Fuchsberger, Christian, Mahajan, Anubha, Teslovich, Tanya M, Agarwala, Vineeta, Gaulton, Kyle J, Caulkins, Lizz, Koesterer, Ryan, Ma, Clement, Moutsianas, Loukas, McCarthy, Davis J, Rivas, Manuel A, Perry, John RB, Sim, Xueling, Blackwell, Thomas W, Robertson, Neil R, Rayner, N William, Cingolani, Pablo, Locke, Adam E, Tajes, Juan Fernandez, Highland, Heather M, Dupuis, Josee, Chines, Peter S, Lindgren, Cecilia M, Hartl, Christopher, Jackson, Anne U, Chen, Han, Huyghe, Jeroen R, van de Bunt, Martijn, Pearson, Richard D, Kumar, Ashish, Müller-Nurasyid, Martina, Grarup, Niels, Stringham, Heather M, Gamazon, Eric R, Lee, Jaehoon, Chen, Yuhui, Scott, Robert A, Below, Jennifer E, Chen, Peng, Huang, Jinyan, Go, Min Jin, Stitzel, Michael L, Pasko, Dorota, Parker, Stephen CJ, Varga, Tibor V, Green, Todd, Beer, Nicola L, Day-Williams, Aaron G, Ferreira, Teresa, Fingerlin, Tasha, Horikoshi, Momoko, Hu, Cheng, Huh, Iksoo, Ikram, Mohammad Kamran, Kim, Bong-Jo, Kim, Yongkang, Kim, Young Jin, Kwon, Min-Seok, Lee, Juyoung, Lee, Selyeong, Lin, Keng-Han, Maxwell, Taylor J, Nagai, Yoshihiko, Wang, Xu, Welch, Ryan P, Yoon, Joon, Zhang, Weihua, Barzilai, Nir, Voight, Benjamin F, Han, Bok-Ghee, Jenkinson, Christopher P, Kuulasmaa, Teemu, Kuusisto, Johanna, Manning, Alisa, Ng, Maggie CY, Palmer, Nicholette D, Balkau, Beverley, Stančáková, Alena, Abboud, Hanna E, Boeing, Heiner, Giedraitis, Vilmantas, Prabhakaran, Dorairaj, Gottesman, Omri, Scott, James, Carey, Jason, Kwan, Phoenix, Grant, George, Smith, Joshua D, Neale, Benjamin M, Purcell, Shaun, Butterworth, Adam S, Howson, Joanna MM, Lee, Heung Man, Lu, Yingchang, Kwak, Soo-Heon, Zhao, Wei, Danesh, John, and Lam, Vincent KL

Abstract

This corrects the article DOI: 10.1038/sdata.2017.179.

Published
2018

13. Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls

Author

Flannick, Jason, Fuchsberger, Christian, Mahajan, Anubha, Teslovich, Tanya M, Agarwala, Vineeta, Gaulton, Kyle J, Caulkins, Lizz, Koesterer, Ryan, Ma, Clement, Moutsianas, Loukas, McCarthy, Davis J, Rivas, Manuel A, Perry, John RB, Sim, Xueling, Blackwell, Thomas W, Robertson, Neil R, Rayner, N William, Cingolani, Pablo, Locke, Adam E, Tajes, Juan Fernandez, Highland, Heather M, Dupuis, Josee, Chines, Peter S, Lindgren, Cecilia M, Hartl, Christopher, Jackson, Anne U, Chen, Han, Huyghe, Jeroen R, van de Bunt, Martijn, Pearson, Richard D, Kumar, Ashish, Müller-Nurasyid, Martina, Grarup, Niels, Stringham, Heather M, Gamazon, Eric R, Lee, Jaehoon, Chen, Yuhui, Scott, Robert A, Below, Jennifer E, Chen, Peng, Huang, Jinyan, Go, Min Jin, Stitzel, Michael L, Pasko, Dorota, Parker, Stephen CJ, Varga, Tibor V, Green, Todd, Beer, Nicola L, Day-Williams, Aaron G, Ferreira, Teresa, Fingerlin, Tasha, Horikoshi, Momoko, Hu, Cheng, Huh, Iksoo, Ikram, Mohammad Kamran, Kim, Bong-Jo, Kim, Yongkang, Kim, Young Jin, Kwon, Min-Seok, Lee, Juyoung, Lee, Selyeong, Lin, Keng-Han, Maxwell, Taylor J, Nagai, Yoshihiko, Wang, Xu, Welch, Ryan P, Yoon, Joon, Zhang, Weihua, Barzilai, Nir, Voight, Benjamin F, Han, Bok-Ghee, Jenkinson, Christopher P, Kuulasmaa, Teemu, Kuusisto, Johanna, Manning, Alisa, Ng, Maggie CY, Palmer, Nicholette D, Balkau, Beverley, Stančáková, Alena, Abboud, Hanna E, Boeing, Heiner, Giedraitis, Vilmantas, Prabhakaran, Dorairaj, Gottesman, Omri, Scott, James, Carey, Jason, Kwan, Phoenix, Grant, George, Smith, Joshua D, Neale, Benjamin M, Purcell, Shaun, Butterworth, Adam S, Howson, Joanna MM, Lee, Heung Man, Lu, Yingchang, Kwak, Soo-Heon, Zhao, Wei, Danesh, John, Lam, Vincent KL, and Park, Kyong Soo

Abstract

This corrects the article DOI: 10.1038/sdata.2017.179.

Published
2018

14. Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

Author

Flannick, Jason, Fuchsberger, Christian, Mahajan, Anubha, Teslovich, Tanya M, Agarwala, Vineeta, Gaulton, Kyle J, Caulkins, Lizz, Koesterer, Ryan, Ma, Clement, Moutsianas, Loukas, McCarthy, Davis J, Rivas, Manuel A, Perry, John RB, Sim, Xueling, Blackwell, Thomas W, Robertson, Neil R, Rayner, N William, Cingolani, Pablo, Locke, Adam E, Tajes, Juan Fernandez, Highland, Heather M, Dupuis, Josee, Chines, Peter S, Lindgren, Cecilia M, Hartl, Christopher, Jackson, Anne U, Chen, Han, Huyghe, Jeroen R, van de Bunt, Martijn, Pearson, Richard D, Kumar, Ashish, Müller-Nurasyid, Martina, Grarup, Niels, Stringham, Heather M, Gamazon, Eric R, Lee, Jaehoon, Chen, Yuhui, Scott, Robert A, Below, Jennifer E, Chen, Peng, Huang, Jinyan, Go, Min Jin, Stitzel, Michael L, Pasko, Dorota, Parker, Stephen CJ, Varga, Tibor V, Green, Todd, Beer, Nicola L, Day-Williams, Aaron G, Ferreira, Teresa, Fingerlin, Tasha, Horikoshi, Momoko, Hu, Cheng, Huh, Iksoo, Ikram, Mohammad Kamran, Kim, Bong-Jo, Kim, Yongkang, Kim, Young Jin, Kwon, Min-Seok, Lee, Juyoung, Lee, Selyeong, Lin, Keng-Han, Maxwell, Taylor J, Nagai, Yoshihiko, Wang, Xu, Welch, Ryan P, Yoon, Joon, Zhang, Weihua, Barzilai, Nir, Voight, Benjamin F, Han, Bok-Ghee, Jenkinson, Christopher P, Kuulasmaa, Teemu, Kuusisto, Johanna, Manning, Alisa, Ng, Maggie CY, Palmer, Nicholette D, Balkau, Beverley, Stančáková, Alena, Abboud, Hanna E, Boeing, Heiner, Giedraitis, Vilmantas, Prabhakaran, Dorairaj, Gottesman, Omri, Scott, James, Carey, Jason, Kwan, Phoenix, Grant, George, Smith, Joshua D, Neale, Benjamin M, Purcell, Shaun, Butterworth, Adam S, Howson, Joanna MM, Lee, Heung Man, Lu, Yingchang, Kwak, Soo-Heon, Zhao, Wei, Danesh, John, and Lam, Vincent KL

Subjects
Humans, Diabetes Mellitus, Type 2, European Continental Ancestry Group, Genetic Variation, Diabetes, Human Genome, Genetics, 2.1 Biological and endogenous factors, Metabolic and endocrine
Abstract

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

Published
2017

15. Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

Author

Flannick, Jason, Fuchsberger, Christian, Mahajan, Anubha, Teslovich, Tanya M, Agarwala, Vineeta, Gaulton, Kyle J, Caulkins, Lizz, Koesterer, Ryan, Ma, Clement, Moutsianas, Loukas, McCarthy, Davis J, Rivas, Manuel A, Perry, John RB, Sim, Xueling, Blackwell, Thomas W, Robertson, Neil R, Rayner, N William, Cingolani, Pablo, Locke, Adam E, Tajes, Juan Fernandez, Highland, Heather M, Dupuis, Josee, Chines, Peter S, Lindgren, Cecilia M, Hartl, Christopher, Jackson, Anne U, Chen, Han, Huyghe, Jeroen R, van de Bunt, Martijn, Pearson, Richard D, Kumar, Ashish, Müller-Nurasyid, Martina, Grarup, Niels, Stringham, Heather M, Gamazon, Eric R, Lee, Jaehoon, Chen, Yuhui, Scott, Robert A, Below, Jennifer E, Chen, Peng, Huang, Jinyan, Go, Min Jin, Stitzel, Michael L, Pasko, Dorota, Parker, Stephen CJ, Varga, Tibor V, Green, Todd, Beer, Nicola L, Day-Williams, Aaron G, Ferreira, Teresa, Fingerlin, Tasha, Horikoshi, Momoko, Hu, Cheng, Huh, Iksoo, Ikram, Mohammad Kamran, Kim, Bong-Jo, Kim, Yongkang, Kim, Young Jin, Kwon, Min-Seok, Lee, Juyoung, Lee, Selyeong, Lin, Keng-Han, Maxwell, Taylor J, Nagai, Yoshihiko, Wang, Xu, Welch, Ryan P, Yoon, Joon, Zhang, Weihua, Barzilai, Nir, Voight, Benjamin F, Han, Bok-Ghee, Jenkinson, Christopher P, Kuulasmaa, Teemu, Kuusisto, Johanna, Manning, Alisa, Ng, Maggie CY, Palmer, Nicholette D, Balkau, Beverley, Stančáková, Alena, Abboud, Hanna E, Boeing, Heiner, Giedraitis, Vilmantas, Prabhakaran, Dorairaj, Gottesman, Omri, Scott, James, Carey, Jason, Kwan, Phoenix, Grant, George, Smith, Joshua D, Neale, Benjamin M, Purcell, Shaun, Butterworth, Adam S, Howson, Joanna MM, Lee, Heung Man, Lu, Yingchang, Kwak, Soo-Heon, Zhao, Wei, Danesh, John, and Lam, Vincent KL

Subjects
Humans, Diabetes Mellitus, Type 2, Genetic Variation, White People, Diabetes, Human Genome, Genetics, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine
Abstract

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

Published
2017

16. Re-sequencing expands our understanding of the phenotypic impact of variants at GWAS loci.

Author

Service, Susan K, Teslovich, Tanya M, Fuchsberger, Christian, Ramensky, Vasily, Yajnik, Pranav, Koboldt, Daniel C, Larson, David E, Zhang, Qunyuan, Lin, Ling, Welch, Ryan, Ding, Li, McLellan, Michael D, O'Laughlin, Michele, Fronick, Catrina, Fulton, Lucinda L, Magrini, Vincent, Swift, Amy, Elliott, Paul, Jarvelin, Marjo-Riitta, Kaakinen, Marika, McCarthy, Mark I, Peltonen, Leena, Pouta, Anneli, Bonnycastle, Lori L, Collins, Francis S, Narisu, Narisu, Stringham, Heather M, Tuomilehto, Jaakko, Ripatti, Samuli, Fulton, Robert S, Sabatti, Chiara, Wilson, Richard K, Boehnke, Michael, and Freimer, Nelson B

Subjects
Humans, Cholesterol, Genotype, Linkage Disequilibrium, Phenotype, Quantitative Trait Loci, Population Groups, European Continental Ancestry Group, Finland, Cholesterol, HDL, Genome-Wide Association Study, High-Throughput Nucleotide Sequencing, HDL, Genetics, Developmental Biology
Abstract

Genome-wide association studies (GWAS) have identified >500 common variants associated with quantitative metabolic traits, but in aggregate such variants explain at most 20-30% of the heritable component of population variation in these traits. To further investigate the impact of genotypic variation on metabolic traits, we conducted re-sequencing studies in >6,000 members of a Finnish population cohort (The Northern Finland Birth Cohort of 1966 [NFBC]) and a type 2 diabetes case-control sample (The Finland-United States Investigation of NIDDM Genetics [FUSION] study). By sequencing the coding sequence and 5' and 3' untranslated regions of 78 genes at 17 GWAS loci associated with one or more of six metabolic traits (serum levels of fasting HDL-C, LDL-C, total cholesterol, triglycerides, plasma glucose, and insulin), and conducting both single-variant and gene-level association tests, we obtained a more complete understanding of phenotype-genotype associations at eight of these loci. At all eight of these loci, the identification of new associations provides significant evidence for multiple genetic signals to one or more phenotypes, and at two loci, in the genes ABCA1 and CETP, we found significant gene-level evidence of association to non-synonymous variants with MAF

Published
2014

17. Re-sequencing Expands Our Understanding of the Phenotypic Impact of Variants at GWAS Loci

Author

Service, Susan K, Teslovich, Tanya M, Fuchsberger, Christian, Ramensky, Vasily, Yajnik, Pranav, Koboldt, Daniel C, Larson, David E, Zhang, Qunyuan, Lin, Ling, Welch, Ryan, Ding, Li, McLellan, Michael D, O'Laughlin, Michele, Fronick, Catrina, Fulton, Lucinda L, Magrini, Vincent, Swift, Amy, Elliott, Paul, Jarvelin, Marjo-Riitta, Kaakinen, Marika, McCarthy, Mark I, Peltonen, Leena, Pouta, Anneli, Bonnycastle, Lori L, Collins, Francis S, Narisu, Narisu, Stringham, Heather M, Tuomilehto, Jaakko, Ripatti, Samuli, Fulton, Robert S, Sabatti, Chiara, Wilson, Richard K, Boehnke, Michael, and Freimer, Nelson B

Subjects
Clinical Research, Human Genome, Genetics, Prevention, Diabetes, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Cholesterol, Cholesterol, HDL, Finland, Genome-Wide Association Study, Genotype, High-Throughput Nucleotide Sequencing, Humans, Linkage Disequilibrium, Phenotype, Population Groups, Quantitative Trait Loci, White People, Developmental Biology
Abstract

Genome-wide association studies (GWAS) have identified >500 common variants associated with quantitative metabolic traits, but in aggregate such variants explain at most 20-30% of the heritable component of population variation in these traits. To further investigate the impact of genotypic variation on metabolic traits, we conducted re-sequencing studies in >6,000 members of a Finnish population cohort (The Northern Finland Birth Cohort of 1966 [NFBC]) and a type 2 diabetes case-control sample (The Finland-United States Investigation of NIDDM Genetics [FUSION] study). By sequencing the coding sequence and 5' and 3' untranslated regions of 78 genes at 17 GWAS loci associated with one or more of six metabolic traits (serum levels of fasting HDL-C, LDL-C, total cholesterol, triglycerides, plasma glucose, and insulin), and conducting both single-variant and gene-level association tests, we obtained a more complete understanding of phenotype-genotype associations at eight of these loci. At all eight of these loci, the identification of new associations provides significant evidence for multiple genetic signals to one or more phenotypes, and at two loci, in the genes ABCA1 and CETP, we found significant gene-level evidence of association to non-synonymous variants with MAF

Published
2014

18. A genome-wide association search for type 2 diabetes genes in African Americans.

Author

Palmer, Nicholette D, McDonough, Caitrin W, Hicks, Pamela J, Roh, Bong H, Wing, Maria R, An, S Sandy, Hester, Jessica M, Cooke, Jessica N, Bostrom, Meredith A, Rudock, Megan E, Talbert, Matthew E, Lewis, Joshua P, DIAGRAM Consortium, MAGIC Investigators, Ferrara, Assiamira, Lu, Lingyi, Ziegler, Julie T, Sale, Michele M, Divers, Jasmin, Shriner, Daniel, Adeyemo, Adebowale, Rotimi, Charles N, Ng, Maggie CY, Langefeld, Carl D, Freedman, Barry I, Bowden, Donald W, Voight, Benjamin F, Scott, Laura J, Steinthorsdottir, Valgerdur, Morris, Andrew P, Dina, Christian, Welch, Ryan P, Zeggini, Eleftheria, Huth, Cornelia, Aulchenko, Yurii S, Thorleifsson, Gudmar, McCulloch, Laura J, Ferreira, Teresa, Grallert, Harald, Amin, Najaf, Wu, Guanming, Willer, Cristen J, Raychaudhuri, Soumya, McCarroll, Steve A, Langenberg, Claudia, Hofmann, Oliver M, Dupuis, Josée, Qi, Lu, Segrè, Ayellet V, van Hoek, Mandy, Navarro, Pau, Ardlie, Kristin, Balkau, Beverley, Benediktsson, Rafn, Bennett, Amanda J, Blagieva, Roza, Boerwinkle, Eric, Bonnycastle, Lori L, Boström, Kristina Bengtsson, Bravenboer, Bert, Bumpstead, Suzannah, Burtt, Noël P, Charpentier, Guillaume, Chines, Peter S, Cornelis, Marilyn, Couper, David J, Crawford, Gabe, Doney, Alex SF, Elliott, Katherine S, Elliott, Amanda L, Erdos, Michael R, Fox, Caroline S, Franklin, Christopher S, Ganser, Martha, Gieger, Christian, Grarup, Niels, Green, Todd, Griffin, Simon, Groves, Christopher J, Guiducci, Candace, Hadjadj, Samy, Hassanali, Neelam, Herder, Christian, Isomaa, Bo, Jackson, Anne U, Johnson, Paul RV, Jørgensen, Torben, Kao, Wen HL, Klopp, Norman, Kong, Augustine, Kraft, Peter, Kuusisto, Johanna, Lauritzen, Torsten, Li, Man, Lieverse, Aloysius, Lindgren, Cecilia M, Lyssenko, Valeriya, Marre, Michel, Meitinger, Thomas, and Midthjell, Kristian

Subjects
DIAGRAM Consortium, MAGIC Investigators, Humans, Diabetes Mellitus, Type 2, Genetic Predisposition to Disease, Case-Control Studies, Cohort Studies, Genotype, Polymorphism, Single Nucleotide, Adult, Aged, Middle Aged, African Americans, Female, Male, Meta-Analysis as Topic, Validation Studies as Topic, Genome-Wide Association Study, Diabetes Mellitus, Type 2, Polymorphism, Single Nucleotide, General Science & Technology
Abstract

African Americans are disproportionately affected by type 2 diabetes (T2DM) yet few studies have examined T2DM using genome-wide association approaches in this ethnicity. The aim of this study was to identify genes associated with T2DM in the African American population. We performed a Genome Wide Association Study (GWAS) using the Affymetrix 6.0 array in 965 African-American cases with T2DM and end-stage renal disease (T2DM-ESRD) and 1029 population-based controls. The most significant SNPs (n = 550 independent loci) were genotyped in a replication cohort and 122 SNPs (n = 98 independent loci) were further tested through genotyping three additional validation cohorts followed by meta-analysis in all five cohorts totaling 3,132 cases and 3,317 controls. Twelve SNPs had evidence of association in the GWAS (P

Published
2012

19. A Genome-Wide Association Search for Type 2 Diabetes Genes in African Americans

Author

Palmer, Nicholette D, McDonough, Caitrin W, Hicks, Pamela J, Roh, Bong H, Wing, Maria R, An, S Sandy, Hester, Jessica M, Cooke, Jessica N, Bostrom, Meredith A, Rudock, Megan E, Talbert, Matthew E, Lewis, Joshua P, Ferrara, Assiamira, Lu, Lingyi, Ziegler, Julie T, Sale, Michele M, Divers, Jasmin, Shriner, Daniel, Adeyemo, Adebowale, Rotimi, Charles N, Ng, Maggie CY, Langefeld, Carl D, Freedman, Barry I, Bowden, Donald W, Voight, Benjamin F, Scott, Laura J, Steinthorsdottir, Valgerdur, Morris, Andrew P, Dina, Christian, Welch, Ryan P, Zeggini, Eleftheria, Huth, Cornelia, Aulchenko, Yurii S, Thorleifsson, Gudmar, McCulloch, Laura J, Ferreira, Teresa, Grallert, Harald, Amin, Najaf, Wu, Guanming, Willer, Cristen J, Raychaudhuri, Soumya, McCarroll, Steve A, Langenberg, Claudia, Hofmann, Oliver M, Dupuis, Josée, Qi, Lu, Segrè, Ayellet V, van Hoek, Mandy, Navarro, Pau, Ardlie, Kristin, Balkau, Beverley, Benediktsson, Rafn, Bennett, Amanda J, Blagieva, Roza, Boerwinkle, Eric, Bonnycastle, Lori L, Boström, Kristina Bengtsson, Bravenboer, Bert, Bumpstead, Suzannah, Burtt, Noël P, Charpentier, Guillaume, Chines, Peter S, Cornelis, Marilyn, Couper, David J, Crawford, Gabe, Doney, Alex SF, Elliott, Katherine S, Elliott, Amanda L, Erdos, Michael R, Fox, Caroline S, Franklin, Christopher S, Ganser, Martha, Gieger, Christian, Grarup, Niels, Green, Todd, Griffin, Simon, Groves, Christopher J, Guiducci, Candace, Hadjadj, Samy, Hassanali, Neelam, Herder, Christian, Isomaa, Bo, Jackson, Anne U, Johnson, Paul RV, Jørgensen, Torben, Kao, Wen HL, Klopp, Norman, Kong, Augustine, Kraft, Peter, Kuusisto, Johanna, Lauritzen, Torsten, Li, Man, Lieverse, Aloysius, Lindgren, Cecilia M, Lyssenko, Valeriya, Marre, Michel, Meitinger, Thomas, Midthjell, Kristian, Morken, Mario A, and Narisu, Narisu

Subjects
Human Genome, Diabetes, Clinical Research, Genetics, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Adult, Black or African American, Aged, Case-Control Studies, Cohort Studies, Diabetes Mellitus, Type 2, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Male, Meta-Analysis as Topic, Middle Aged, Polymorphism, Single Nucleotide, Validation Studies as Topic, DIAGRAM Consortium, MAGIC Investigators, General Science & Technology
Abstract

African Americans are disproportionately affected by type 2 diabetes (T2DM) yet few studies have examined T2DM using genome-wide association approaches in this ethnicity. The aim of this study was to identify genes associated with T2DM in the African American population. We performed a Genome Wide Association Study (GWAS) using the Affymetrix 6.0 array in 965 African-American cases with T2DM and end-stage renal disease (T2DM-ESRD) and 1029 population-based controls. The most significant SNPs (n = 550 independent loci) were genotyped in a replication cohort and 122 SNPs (n = 98 independent loci) were further tested through genotyping three additional validation cohorts followed by meta-analysis in all five cohorts totaling 3,132 cases and 3,317 controls. Twelve SNPs had evidence of association in the GWAS (P

Published
2012

20. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index

Author

Speliotes, Elizabeth K, Willer, Cristen J, Berndt, Sonja I, Monda, Keri L, Thorleifsson, Gudmar, Jackson, Anne U, Allen, Hana Lango, Lindgren, Cecilia M, Luan, Jian'an, Mägi, Reedik, Randall, Joshua C, Vedantam, Sailaja, Winkler, Thomas W, Qi, Lu, Workalemahu, Tsegaselassie, Heid, Iris M, Steinthorsdottir, Valgerdur, Stringham, Heather M, Weedon, Michael N, Wheeler, Eleanor, Wood, Andrew R, Ferreira, Teresa, Weyant, Robert J, Segrè, Ayellet V, Estrada, Karol, Liang, Liming, Nemesh, James, Park, Ju-Hyun, Gustafsson, Stefan, Kilpeläinen, Tuomas O, Yang, Jian, Bouatia-Naji, Nabila, Esko, Tõnu, Feitosa, Mary F, Kutalik, Zoltán, Mangino, Massimo, Raychaudhuri, Soumya, Scherag, Andre, Smith, Albert Vernon, Welch, Ryan, Zhao, Jing Hua, Aben, Katja K, Absher, Devin M, Amin, Najaf, Dixon, Anna L, Fisher, Eva, Glazer, Nicole L, Goddard, Michael E, Heard-Costa, Nancy L, Hoesel, Volker, Hottenga, Jouke-Jan, Johansson, Åsa, Johnson, Toby, Ketkar, Shamika, Lamina, Claudia, Li, Shengxu, Moffatt, Miriam F, Myers, Richard H, Narisu, Narisu, Perry, John RB, Peters, Marjolein J, Preuss, Michael, Ripatti, Samuli, Rivadeneira, Fernando, Sandholt, Camilla, Scott, Laura J, Timpson, Nicholas J, Tyrer, Jonathan P, van Wingerden, Sophie, Watanabe, Richard M, White, Charles C, Wiklund, Fredrik, Barlassina, Christina, Chasman, Daniel I, Cooper, Matthew N, Jansson, John-Olov, Lawrence, Robert W, Pellikka, Niina, Prokopenko, Inga, Shi, Jianxin, Thiering, Elisabeth, Alavere, Helene, Alibrandi, Maria TS, Almgren, Peter, Arnold, Alice M, Aspelund, Thor, Atwood, Larry D, Balkau, Beverley, Balmforth, Anthony J, Bennett, Amanda J, Ben-Shlomo, Yoav, Bergman, Richard N, Bergmann, Sven, Biebermann, Heike, Blakemore, Alexandra IF, Boes, Tanja, Bonnycastle, Lori L, Bornstein, Stefan R, Brown, Morris J, and Buchanan, Thomas A

Subjects
Biological Sciences, Genetics, Prevention, Human Genome, Body Height, Body Mass Index, Body Size, Body Weight, Chromosome Mapping, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Obesity, Polymorphism, Single Nucleotide, White People, MAGIC, Procardis Consortium, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.

Published
2010

21. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index.

Author

Speliotes, Elizabeth K, Willer, Cristen J, Berndt, Sonja I, Monda, Keri L, Thorleifsson, Gudmar, Jackson, Anne U, Lango Allen, Hana, Lindgren, Cecilia M, Luan, Jian'an, Mägi, Reedik, Randall, Joshua C, Vedantam, Sailaja, Winkler, Thomas W, Qi, Lu, Workalemahu, Tsegaselassie, Heid, Iris M, Steinthorsdottir, Valgerdur, Stringham, Heather M, Weedon, Michael N, Wheeler, Eleanor, Wood, Andrew R, Ferreira, Teresa, Weyant, Robert J, Segrè, Ayellet V, Estrada, Karol, Liang, Liming, Nemesh, James, Park, Ju-Hyun, Gustafsson, Stefan, Kilpeläinen, Tuomas O, Yang, Jian, Bouatia-Naji, Nabila, Esko, Tõnu, Feitosa, Mary F, Kutalik, Zoltán, Mangino, Massimo, Raychaudhuri, Soumya, Scherag, Andre, Smith, Albert Vernon, Welch, Ryan, Zhao, Jing Hua, Aben, Katja K, Absher, Devin M, Amin, Najaf, Dixon, Anna L, Fisher, Eva, Glazer, Nicole L, Goddard, Michael E, Heard-Costa, Nancy L, Hoesel, Volker, Hottenga, Jouke-Jan, Johansson, Asa, Johnson, Toby, Ketkar, Shamika, Lamina, Claudia, Li, Shengxu, Moffatt, Miriam F, Myers, Richard H, Narisu, Narisu, Perry, John RB, Peters, Marjolein J, Preuss, Michael, Ripatti, Samuli, Rivadeneira, Fernando, Sandholt, Camilla, Scott, Laura J, Timpson, Nicholas J, Tyrer, Jonathan P, van Wingerden, Sophie, Watanabe, Richard M, White, Charles C, Wiklund, Fredrik, Barlassina, Christina, Chasman, Daniel I, Cooper, Matthew N, Jansson, John-Olov, Lawrence, Robert W, Pellikka, Niina, Prokopenko, Inga, Shi, Jianxin, Thiering, Elisabeth, Alavere, Helene, Alibrandi, Maria TS, Almgren, Peter, Arnold, Alice M, Aspelund, Thor, Atwood, Larry D, Balkau, Beverley, Balmforth, Anthony J, Bennett, Amanda J, Ben-Shlomo, Yoav, Bergman, Richard N, Bergmann, Sven, Biebermann, Heike, Blakemore, Alexandra IF, Boes, Tanja, Bonnycastle, Lori L, Bornstein, Stefan R, Brown, Morris J, and Buchanan, Thomas A

Subjects
MAGIC, Procardis Consortium, Humans, Obesity, Genetic Predisposition to Disease, Body Weight, Body Mass Index, Body Size, Body Height, Chromosome Mapping, Polymorphism, Single Nucleotide, European Continental Ancestry Group, Genome-Wide Association Study, Polymorphism, Single Nucleotide, Prevention, Genetics, Nutrition, 2.1 Biological and endogenous factors, Stroke, Cardiovascular, Metabolic and Endocrine, Developmental Biology, Medical and Health Sciences, Biological Sciences
Abstract

Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.

Published
2010

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