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Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation.

ABSTRACT: We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10^-9), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background.

SUBMITTER: Mahajan A

PROVIDER: S-EPMC9179018 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation.

Mahajan Anubha A Spracklen Cassandra N CN Zhang Weihua W Ng Maggie C Y MCY Petty Lauren E LE Kitajima Hidetoshi H Yu Grace Z GZ Rüeger Sina S Speidel Leo L Kim Young Jin YJ Horikoshi Momoko M Mercader Josep M JM Taliun Daniel D Moon Sanghoon S Kwak Soo-Heon SH Robertson Neil R NR Rayner Nigel W NW Loh Marie M Kim Bong-Jo BJ Chiou Joshua J Miguel-Escalada Irene I Della Briotta Parolo Pietro P Lin Kuang K Bragg Fiona F Preuss Michael H MH Takeuchi Fumihiko F Nano Jana J Guo Xiuqing X Lamri Amel A Nakatochi Masahiro M Scott Robert A RA Lee Jung-Jin JJ Huerta-Chagoya Alicia A Graff Mariaelisa M Chai Jin-Fang JF Parra Esteban J EJ Yao Jie J Bielak Lawrence F LF Tabara Yasuharu Y Hai Yang Y Steinthorsdottir Valgerdur V Cook James P JP Kals Mart M Grarup Niels N Schmidt Ellen M EM Pan Ian I Sofer Tamar T Wuttke Matthias M Sarnowski Chloe C Gieger Christian C Nousome Darryl D Trompet Stella S Long Jirong J Sun Meng M Tong Lin L Chen Wei-Min WM Ahmad Meraj M Noordam Raymond R Lim Victor J Y VJY Tam Claudia H T CHT Joo Yoonjung Yoonie YY Chen Chien-Hsiun CH Raffield Laura M LM Lecoeur Cécile C Prins Bram Peter BP Nicolas Aude A Yanek Lisa R LR Chen Guanjie G Jensen Richard A RA Tajuddin Salman S Kabagambe Edmond K EK An Ping P Xiang Anny H AH Choi Hyeok Sun HS Cade Brian E BE Tan Jingyi J Flanagan Jack J Abaitua Fernando F Adair Linda S LS Adeyemo Adebowale A Aguilar-Salinas Carlos A CA Akiyama Masato M Anand Sonia S SS Bertoni Alain A Bian Zheng Z Bork-Jensen Jette J Brandslund Ivan I Brody Jennifer A JA Brummett Chad M CM Buchanan Thomas A TA Canouil Mickaël M Chan Juliana C N JCN Chang Li-Ching LC Chee Miao-Li ML Chen Ji J Chen Shyh-Huei SH Chen Yuan-Tsong YT Chen Zhengming Z Chuang Lee-Ming LM Cushman Mary M Das Swapan K SK de Silva H Janaka HJ Dedoussis George G Dimitrov Latchezar L Doumatey Ayo P AP Du Shufa S Duan Qing Q Eckardt Kai-Uwe KU Emery Leslie S LS Evans Daniel S DS Evans Michele K MK Fischer Krista K Floyd James S JS Ford Ian I Fornage Myriam M Franco Oscar H OH Frayling Timothy M TM Freedman Barry I BI Fuchsberger Christian C Genter Pauline P Gerstein Hertzel C HC Giedraitis Vilmantas V González-Villalpando Clicerio C González-Villalpando Maria Elena ME Goodarzi Mark O MO Gordon-Larsen Penny P Gorkin David D Gross Myron M Guo Yu Y Hackinger Sophie S Han Sohee S Hattersley Andrew T AT Herder Christian C Howard Annie-Green AG Hsueh Willa W Huang Mengna M Huang Wei W Hung Yi-Jen YJ Hwang Mi Yeong MY Hwu Chii-Min CM Ichihara Sahoko S Ikram Mohammad Arfan MA Ingelsson Martin M Islam Md Tariqul MT Isono Masato M Jang Hye-Mi HM Jasmine Farzana F Jiang Guozhi G Jonas Jost B JB Jørgensen Marit E ME Jørgensen Torben T Kamatani Yoichiro Y Kandeel Fouad R FR Kasturiratne Anuradhani A Katsuya Tomohiro T Kaur Varinderpal V Kawaguchi Takahisa T Keaton Jacob M JM Kho Abel N AN Khor Chiea-Chuen CC Kibriya Muhammad G MG Kim Duk-Hwan DH Kohara Katsuhiko K Kriebel Jennifer J Kronenberg Florian F Kuusisto Johanna J Läll Kristi K Lange Leslie A LA Lee Myung-Shik MS Lee Nanette R NR Leong Aaron A Li Liming L Li Yun Y Li-Gao Ruifang R Ligthart Symen S Lindgren Cecilia M CM Linneberg Allan A Liu Ching-Ti CT Liu Jianjun J Locke Adam E AE Louie Tin T Luan Jian'an J Luk Andrea O AO Luo Xi X Lv Jun J Lyssenko Valeriya V Mamakou Vasiliki V Mani K Radha KR Meitinger Thomas T Metspalu Andres A Morris Andrew D AD Nadkarni Girish N GN Nadler Jerry L JL Nalls Michael A MA Nayak Uma U Nongmaithem Suraj S SS Ntalla Ioanna I Okada Yukinori Y Orozco Lorena L Patel Sanjay R SR Pereira Mark A MA Peters Annette A Pirie Fraser J FJ Porneala Bianca B Prasad Gauri G Preissl Sebastian S Rasmussen-Torvik Laura J LJ Reiner Alexander P AP Roden Michael M Rohde Rebecca R Roll Kathryn K Sabanayagam Charumathi C Sander Maike M Sandow Kevin K Sattar Naveed N Schönherr Sebastian S Schurmann Claudia C Shahriar Mohammad M Shi Jinxiu J Shin Dong Mun DM Shriner Daniel D Smith Jennifer A JA So Wing Yee WY Stančáková Alena A Stilp Adrienne M AM Strauch Konstantin K Suzuki Ken K Takahashi Atsushi A Taylor Kent D KD Thorand Barbara B Thorleifsson Gudmar G Thorsteinsdottir Unnur U Tomlinson Brian B Torres Jason M JM Tsai Fuu-Jen FJ Tuomilehto Jaakko J Tusie-Luna Teresa T Udler Miriam S MS Valladares-Salgado Adan A van Dam Rob M RM van Klinken Jan B JB Varma Rohit R Vujkovic Marijana M Wacher-Rodarte Niels N Wheeler Eleanor E Whitsel Eric A EA Wickremasinghe Ananda R AR van Dijk Ko Willems KW Witte Daniel R DR Yajnik Chittaranjan S CS Yamamoto Ken K Yamauchi Toshimasa T Yengo Loïc L Yoon Kyungheon K Yu Canqing C Yuan Jian-Min JM Yusuf Salim S Zhang Liang L Zheng Wei W Raffel Leslie J LJ Igase Michiya M Ipp Eli E Redline Susan S Cho Yoon Shin YS Lind Lars L Province Michael A MA Hanis Craig L CL Peyser Patricia A PA Ingelsson Erik E Zonderman Alan B AB Psaty Bruce M BM Wang Ya-Xing YX Rotimi Charles N CN Becker Diane M DM Matsuda Fumihiko F Liu Yongmei Y Zeggini Eleftheria E Yokota Mitsuhiro M Rich Stephen S SS Kooperberg Charles C Pankow James S JS Engert James C JC Chen Yii-Der Ida YI Froguel Philippe P Wilson James G JG Sheu Wayne H H WHH Kardia Sharon L R SLR Wu Jer-Yuarn JY Hayes M Geoffrey MG Ma Ronald C W RCW Wong Tien-Yin TY Groop Leif L Mook-Kanamori Dennis O DO Chandak Giriraj R GR Collins Francis S FS Bharadwaj Dwaipayan D Paré Guillaume G Sale Michèle M MM Ahsan Habibul H Motala Ayesha A AA Shu Xiao-Ou XO Park Kyong-Soo KS Jukema J Wouter JW Cruz Miguel M McKean-Cowdin Roberta R Grallert Harald H Cheng Ching-Yu CY Bottinger Erwin P EP Dehghan Abbas A Tai E-Shyong ES Dupuis Josée J Kato Norihiro N Laakso Markku M Köttgen Anna A Koh Woon-Puay WP Palmer Colin N A CNA Liu Simin S Abecasis Goncalo G Kooner Jaspal S JS Loos Ruth J F RJF North Kari E KE Haiman Christopher A CA Florez Jose C JC Saleheen Danish D Hansen Torben T Pedersen Oluf O Mägi Reedik R Langenberg Claudia C Wareham Nicholas J NJ Maeda Shiro S Kadowaki Takashi T Lee Juyoung J Millwood Iona Y IY Walters Robin G RG Stefansson Kari K Myers Simon R SR Ferrer Jorge J Gaulton Kyle J KJ Meigs James B JB Mohlke Karen L KL Gloyn Anna L AL Bowden Donald W DW Below Jennifer E JE Chambers John C JC Sim Xueling X Boehnke Michael M Rotter Jerome I JI McCarthy Mark I MI Morris Andrew P AP

Nature genetics 20220512 5

We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10<sup>-9</sup>), which were delineated to 338 distinct association signals. Fine-mapping of these s ...[more]

PMID: 35551307

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Project description:Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry1-3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific4-10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States-where minority populations have a disproportionately higher burden of chronic conditions13-the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.

Project description:BackgroundHispanic/Latino (HL) populations bear a disproportionately high burden of type 2 diabetes (T2D). The ability to predict T2D genetic risk using polygenic risk scores (PRS) offers great promise for improved screening and prevention. However, there are a number of complications related to the accurate inference of genetic risk across HL populations with distinct ancestry profiles. We investigated how ancestry affects the inference of T2D genetic risk using PRS in diverse HL populations from Colombia and the United States (US). In Colombia, we compared T2D genetic risk for the Mestizo population of Antioquia to the Afro-Colombian population of Chocó, and in the US, we compared European-American versus Mexican-American populations.MethodsWhole genome sequences and genotypes from the 1000 Genomes Project and the ChocoGen Research Project were used for genetic ancestry inference and for T2D polygenic risk score (PRS) calculation. Continental ancestry fractions for HL genomes were inferred via comparison with African, European, and Native American reference genomes, and PRS were calculated using T2D risk variants taken from multiple genome-wide association studies (GWAS) conducted on cohorts with diverse ancestries. A correction for ancestry bias in T2D risk inference based on the frequencies of ancestral versus derived alleles was developed and applied to PRS calculations in the HL populations studied here.ResultsT2D genetic risk in Colombian and US HL populations is positively correlated with African and Native American ancestry and negatively correlated with European ancestry. The Afro-Colombian population of Chocó has higher predicted T2D risk than Antioquia, and the Mexican-American population has higher predicted risk than the European-American population. The inferred relative risk of T2D is robust to differences in the ancestry of the GWAS cohorts used for variant discovery. For trans-ethnic GWAS, population-specific variants and variants with same direction effects across populations yield consistent results. Nevertheless, the control for bias in T2D risk prediction confirms that explicit consideration of genetic ancestry can yield more reliable cross-population genetic risk inferences.ConclusionsT2D associations that replicate across populations provide for more reliable risk inference, and modeling population-specific frequencies of ancestral and derived risk alleles can help control for biases in PRS estimation.

Project description:BackgroundType 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color. Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non-European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations.MethodsWe integrated T2D GWAS in European, African, and East Asian populations to construct a trans-ancestry T2D PRS using a newly developed Bayesian polygenic modeling method, and assessed the prediction accuracy of the PRS in the multi-ethnic Electronic Medical Records and Genomics (eMERGE) study (11,945 cases; 57,694 controls), four Black cohorts (5137 cases; 9657 controls), and the Taiwan Biobank (4570 cases; 84,996 controls). We additionally evaluated a post hoc ancestry adjustment method that can express the polygenic risk on the same scale across ancestrally diverse individuals and facilitate the clinical implementation of the PRS in prospective cohorts.ResultsThe trans-ancestry PRS was significantly associated with T2D status across the ancestral groups examined. The top 2% of the PRS distribution can identify individuals with an approximately 2.5-4.5-fold of increase in T2D risk, which corresponds to the increased risk of T2D for first-degree relatives. The post hoc ancestry adjustment method eliminated major distributional differences in the PRS across ancestries without compromising its predictive performance.ConclusionsBy integrating T2D GWAS from multiple populations, we developed and validated a trans-ancestry PRS, and demonstrated its potential as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare.

Project description:BackgroundAdmixed populations arise when two or more previously isolated populations interbreed. A powerful approach to addressing the genetic complexity in admixed populations is to infer ancestry. Ancestry inference including the proportion of an individual's genome coming from each population and its ancestral origin along the chromosome of an admixed population requires the use of ancestry informative markers (AIMs) from reference ancestral populations. AIMs exhibit substantial differences in allele frequency between ancestral populations. Given the huge amount of human genetic variation data available from diverse populations, a computationally feasible and cost-effective approach is becoming increasingly important to extract or filter AIMs with the maximum information content for ancestry inference, admixture mapping, forensic applications, and detecting genomic regions that have been under recent selection.ResultsTo address this gap, we present MI-MAAP, an easy-to-use web-based bioinformatics tool designed to prioritize informative markers for multi-ancestry admixed populations by utilizing feature selection methods and multiple genomics resources including 1000 Genomes Project and Human Genome Diversity Project. Specifically, this tool implements a novel allele frequency-based feature selection algorithm, Lancaster Estimator of Independence (LEI), as well as other genotype-based methods such as Principal Component Analysis (PCA), Support Vector Machine (SVM), and Random Forest (RF). We demonstrated that MI-MAAP is a useful tool in prioritizing informative markers and accurately classifying ancestral populations. LEI is an efficient feature selection strategy to retrieve ancestry informative variants with different allele frequency/selection pressure among (or between) ancestries without requiring computationally expensive individual-level genotype data.ConclusionsMI-MAAP has a user-friendly interface which provides researchers an easy and fast way to filter and identify AIMs. MI-MAAP can be accessed at https://research.cchmc.org/mershalab/MI-MAAP/login/.

Dataset Information

Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation.

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Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation.

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