Publications

Publications                                                         Citation Guidelines

Principles and methods for transferring polygenic risk scores across global populations

Kachuri L, Chatterjee N, Hirbo J, Schaid DJ, Martin I, Kullo IJ, Kenny EE, Pasaniuc B; Polygenic Risk Methods in Diverse Populations (PRIMED) Consortium Methods Working Group; Witte JS, Ge T. Principles and methods for transferring polygenic risk scores across global populations. Nat Rev Genet. 2023 Aug 24. doi: 10.1038/s41576-023-00637-2. Epub ahead of print. PMID: 37620596.

A continuous measure for understanding the accuracy of genetically based predictions

A continuous measure for understanding the accuracy of genetically based predictions. Nature. 2023 May 17. doi: 10.1038/d41586-023-01492-1. Epub ahead of print. PMID: 37198464.

Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity

Tsuo K, Zhou W, Wang Y, Kanai M, Namba S, Gupta R, Majara L, Nkambule LL, Morisaki T, Okada Y, Neale BM; Global Biobank Meta-analysis Initiative; Daly MJ, Martin AR. Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity. Cell Genom. 2022 Nov 8;2(12):100212. doi: 10.1016/j.xgen.2022.100212. PMID: 36778051; PMCID: PMC9903683.

Meta-Analysis Fine-Mapping is often Miscalibrated at Single-variant Resolution

Kanai M, Elzur R, Zhou W; Global Biobank Meta-analysis Initiative; Daly MJ, Finucane HK. Meta-analysis fine-mapping is often miscalibrated at single-variant resolution. Cell Genom. 2022 Dec 14;2(12):100210. doi: 10.1016/j.xgen.2022.100210. Epub 2022 Nov 4. PMID: 36643910; PMCID: PMC9839193.

Exome-wide association study to identify rare variants influencing COVID-19 outcomes: Results from the Host Genetics Initiative

Butler-Laporte G, Povysil G, Kosmicki JA, Cirulli ET, Drivas T, Furini S, Saad C, Schmidt A, Olszewski P, Korotko U, Quinodoz M, Çelik E, Kundu K, Walter K, Jung J, Stockwell AD, Sloofman LG, Jordan DM, Thompson RC, Del Valle D, Simons N, Cheng E, Sebra R, Schadt EE, Kim-Schulze S, Gnjatic S, Merad M, Buxbaum JD, Beckmann ND, Charney AW, Przychodzen B, Chang T, Pottinger TD, Shang N, Brand F, Fava F, Mari F, Chwialkowska K, Niemira M, Pula S, Baillie JK, Stuckey A, Salas A, Bello X, Pardo-Seco J, Gómez-Carballa A, Rivero-Calle I, Martinón-Torres F, Ganna A, Karczewski KJ, Veerapen K, Bourgey M, Bourque G, Eveleigh RJ, Forgetta V, Morrison D, Langlais D, Lathrop M, Mooser V, Nakanishi T, Frithiof R, Hultström M, Lipcsey M, Marincevic-Zuniga Y, Nordlund J, Schiabor Barrett KM, Lee W, Bolze A, White S, Riffle S, Tanudjaja F, Sandoval E, Neveux I, Dabe S, Casadei N, Motameny S, Alaamery M, Massadeh S, Aljawini N, Almutairi MS, Arabi YM, Alqahtani SA, Al Harthi FS, Almutairi A, Alqubaishi F, Alotaibi S, Binowayn A, Alsolm EA, El Bardisy H, Fawzy M, Cai F, Soranzo N, Butterworth A; COVID-19 Host Genetics Initiative; DeCOI Host Genetics Group; GEN-COVID Multicenter Study (Italy); Mount Sinai Clinical Intelligence Center; GEN-COVID consortium (Spain); GenOMICC Consortium; Japan COVID-19 Task Force; Regeneron Genetics Center; Geschwind DH, Arteaga S, Stephens A, Butte MJ, Boutros PC, Yamaguchi TN, Tao S, Eng S, Sanders T, Tung PJ, Broudy ME, Pan Y, Gonzalez A, Chavan N, Johnson R, Pasaniuc B, Yaspan B, Smieszek S, Rivolta C, Bibert S, Bochud PY, Dabrowski M, Zawadzki P, Sypniewski M, Kaja E, Chariyavilaskul P, Nilaratanakul V, Hirankarn N, Shotelersuk V, Pongpanich M, Phokaew C, Chetruengchai W, Tokunaga K, Sugiyama M, Kawai Y, Hasegawa T, Naito T, Namkoong H, Edahiro R, Kimura A, Ogawa S, Kanai T, Fukunaga K, Okada Y, Imoto S, Miyano S, Mangul S, Abedalthagafi MS, Zeberg H, Grzymski JJ, Washington NL, Ossowski S, Ludwig KU, Schulte EC, Riess O, Moniuszko M, Kwasniewski M, Mbarek H, Ismail SI, Verma A, Goldstein DB, Kiryluk K, Renieri A, Ferreira MAR, Richards JB. Exome-wide association study to identify rare variants influencing COVID-19 outcomes: Results from the Host Genetics Initiative. PLoS Genet. 2022 Nov 3;18(11):e1010367. doi: 10.1371/journal.pgen.1010367. PMID: 36327219; PMCID: PMC9632827.

Proteome-Wide Mendelian Randomization in Global Biobank Meta-Analysis Reveals Multi-Ancestry Drug Targets for Common Diseases

Zhao H, Rasheed H, Nøst TH, Cho Y, Liu Y, Bhatta L, Bhattacharya A; Global Biobank Meta-analysis Initiative; Hemani G, Davey Smith G, Brumpton BM, Zhou W, Neale BM, Gaunt TR, Zheng J. Proteome-wide Mendelian randomization in global biobank meta-analysis reveals multi-ancestry drug targets for common diseases. Cell Genom. 2022 Nov 9;2(11):None. doi: 10.1016/j.xgen.2022.100195. PMID: 36388766; PMCID: PMC9646482.

Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease

Zhou W, Kanai M, Wu KH, Rasheed H, Tsuo K, Hirbo JB, Wang Y, Bhattacharya A, Zhao H, Namba S, Surakka I, Wolford BN, Lo Faro V, Lopera-Maya EA, Läll K, Favé MJ, Partanen JJ, Chapman SB, Karjalainen J, Kurki M, Maasha M, Brumpton BM, Chavan S, Chen TT, Daya M, Ding Y, Feng YA, Guare LA, Gignoux CR, Graham SE, Hornsby WE, Ingold N, Ismail SI, Johnson R, Laisk T, Lin K, Lv J, Millwood IY, Moreno-Grau S, Nam K, Palta P, Pandit A, Preuss MH, Saad C, Setia-Verma S, Thorsteinsdottir U, Uzunovic J, Verma A, Zawistowski M, Zhong X, Afifi N, Al-Dabhani KM, Al Thani A, Bradford Y, Campbell A, Crooks K, de Bock GH, Damrauer SM, Douville NJ, Finer S, Fritsche LG, Fthenou E, Gonzalez-Arroyo G, Griffiths CJ, Guo Y, Hunt KA, Ioannidis A, Jansonius NM, Konuma T, Lee MTM, Lopez-Pineda A, Matsuda Y, Marioni RE, Moatamed B, Nava-Aguilar MA, Numakura K, Patil S, Rafaels N, Richmond A, Rojas-Muñoz A, Shortt JA, Straub P, Tao R, Vanderwerff B, Vernekar M, Veturi Y, Barnes KC, Boezen M, Chen Z, Chen CY, Cho J, Smith GD, Finucane HK, Franke L, Gamazon ER, Ganna A, Gaunt TR, Ge T, Huang H, Huffman J, Katsanis N, Koskela JT, Lajonchere C, Law MH, Li L, Lindgren CM, Loos RJF, MacGregor S, Matsuda K, Olsen CM, Porteous DJ, Shavit JA, Snieder H, Takano T, Trembath RC, Vonk JM, Whiteman DC, Wicks SJ, Wijmenga C, Wright J, Zheng J, Zhou X, Awadalla P, Boehnke M, Bustamante CD, Cox NJ, Fatumo S, Geschwind DH, Hayward C, Hveem K, Kenny EE, Lee S, Lin YF, Mbarek H, Mägi R, Martin HC, Medland SE, Okada Y, Palotie AV, Pasaniuc B, Rader DJ, Ritchie MD, Sanna S, Smoller JW, Stefansson K, van Heel DA, Walters RG, Zöllner S; Biobank of the Americas; Biobank Japan Project; BioMe; BioVU; CanPath - Ontario Health Study; China Kadoorie Biobank Collaborative Group; Colorado Center for Personalized Medicine; deCODE Genetics; Estonian Biobank; FinnGen; Generation Scotland; Genes & Health Research Team; LifeLines; Mass General Brigham Biobank; Michigan Genomics Initiative; National Biobank of Korea; Penn Medicine BioBank; Qatar Biobank; QSkin Sun and Health Study; Taiwan Biobank; HUNT Study; UCLA ATLAS Community Health Initiative; Uganda Genome Resource; UK Biobank; Martin AR, Willer CJ, Daly MJ, Neale BM. Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease. Cell Genom. 2022 Oct 12;2(10):100192. doi: 10.1016/j.xgen.2022.100192. PMID: 36777996; PMCID: PMC9903716.

Best Practices for Multi-ancestry, Meta-analytic Transcriptome-wide Association Studies: Lessons from the Global Biobank Meta-analysis Initiative

Bhattacharya A, Hirbo JB, Zhou D, Zhou W, Zheng J, Kanai M; Global Biobank Meta-analysis Initiative; Pasaniuc B, Gamazon ER, Cox NJ. Best practices for multi-ancestry, meta-analytic transcriptome-wide association studies: Lessons from the Global Biobank Meta-analysis Initiative. Cell Genom. 2022 Oct 12;2(10):100180. doi: 10.1016/j.xgen.2022.100180. PMID: 36341024; PMCID: PMC9631681.

Accuracy of Asthma Computable Phenotypes to Identify Pediatric Asthma at an Academic Institution

Ross MK, Zheng H, Zhu B, Lao A, Hong H, Natesan A, Radparvar M, Bui AAT. Accuracy of Asthma Computable Phenotypes to Identify Pediatric Asthma at an Academic Institution. Methods Inf Med. 2020 Dec;59(6):219-226. doi: 10.1055/s-0041-1729951. Epub 2021 Jul 14. PMID: 34261147; PMCID: PMC9113735.

A Practical Guideline of Genomics-Driven Drug Discovery in the Era of Global Biobank Meta-Analysis

Namba S, Konuma T, Wu KH, Zhou W; Global Biobank Meta-analysis Initiative; Okada Y. A practical guideline of genomics-driven drug discovery in the era of global biobank meta-analysis. Cell Genom. 2022 Oct 12;2(10):100190. doi: 10.1016/j.xgen.2022.100190. PMID: 36778001; PMCID: PMC9903693.

Leveraging Global Multi-Ancestry Meta-Analysis in the Study of Idiopathic Pulmonary Fibrosis Genetics

Partanen JJ, Häppölä P, Zhou W, Lehisto AA, Ainola M, Sutinen E, Allen RJ, Stockwell AD, Leavy OC, Oldham JM, Guillen-Guio B, Cox NJ, Hirbo JB, Schwartz DA, Fingerlin TE, Flores C, Noth I, Yaspan BL, Jenkins RG, Wain LV, Ripatti S, Pirinen M; International IPF Genetics Consortium; Global Biobank Meta-Analysis Initiative (GBMI); Laitinen T, Kaarteenaho R, Myllärniemi M, Daly MJ, Koskela JT. Leveraging global multi-ancestry meta-analysis in the study of idiopathic pulmonary fibrosis genetics. Cell Genom. 2022 Oct 12;2(10):100181. doi: 10.1016/j.xgen.2022.100181. PMID: 36777997; PMCID: PMC9903787.

Polygenic Scoring Accuracy Varies Across the Genetic Ancestry Continuum in all Human Populations

Ding Y, Hou K, Xu Z, Pimplaskar A, Petter E, Boulier K, Privé F, Vilhjálmsson BJ, Olde Loohuis LM, Pasaniuc B. Polygenic scoring accuracy varies across the genetic ancestry continuum. Nature. 2023 Jun;618(7966):774-781. doi: 10.1038/s41586-023-06079-4. Epub 2023 May 17. PMID: 37198491; PMCID: PMC10284707.

Leveraging genomic diversity for discovery in an electronic health record linked biobank: the UCLA ATLAS Community Health Initiative

Johnson R, Ding Y, Venkateswaran V, Bhattacharya A, Boulier K, Chiu A, Knyazev S, Schwarz T, Freund M, Zhan L, Burch KS, Caggiano C, Hill B, Rakocz N, Balliu B, Denny CT, Sul JH, Zaitlen N, Arboleda VA, Halperin E, Sankararaman S, Butte MJ; UCLA Precision Health Data Discovery Repository Working Group, UCLA Precision Health ATLAS Working Group; Lajonchere C, Geschwind DH, Pasaniuc B. Leveraging genomic diversity for discovery in an electronic health record linked biobank: the UCLA ATLAS Community Health Initiative. Genome Med. 2022 Sep 9;14(1):104. doi: 10.1186/s13073-022-01106-x. Erratum in: Genome Med. 2022 Nov 16;14(1):128. PMID: 36085083; PMCID: PMC9461263.

Methylation Risk Scores are Associated With a Collection of Phenotypes Within Electronic Health Record Systems

Thompson M, Hill BL, Rakocz N, Chiang JN, Geschwind D, Sankararaman S, Hofer I, Cannesson M, Zaitlen N, Halperin E. Methylation risk scores are associated with a collection of phenotypes within electronic health record systems. NPJ Genom Med. 2022 Aug 25;7(1):50. doi: 10.1038/s41525-022-00320-1. PMID: 36008412; PMCID: PMC9411568.

Electronic Health Record Signatures Identify Undiagnosed Patients with Common Variable Immunodeficiency Disease

Electronic health record signatures identify undiagnosed patients with Common Variable Immunodeficiency Disease Ruth Johnson, Alexis V. Stephens, Sergey Knyazev, Lisa A. Kohn, Malika K. Freund, Leroy Bondhus, Brian L. Hill, Tommer Schwarz, Noah Zaitlen, Valerie A. Arboleda, Manish J. Butte, Bogdan Pasaniuc medRxiv 2022.08.03.22278352; doi: https://doi.org/10.1101/2022.08.03.22278352

Health Care Utilization of Fine-Scale Identity by Descent Clusters in a Los Angeles Biobank

Christa Caggiano, Arya Boudaie, Ruhollah Shemirani, Ella Petter, Alec Chiu, Ruth Johnson, Defne Ercelen, Bogdan Pasaniuc, Eimear Kenny, Jonathan Shortt,Chris Gignoux, Brunilda Balliu, Valerie Arboleda, Gillian Belbin,Noah Zaitlen medRxiv 2022.07.12.22277520; doi: https://doi.org/10.1101/2022.07.12.22277520

Phenome-Wide Association Study of Polygenic Risk Score for Alzheimer's Disease in Electronic Health Records

Fu M; UCLA Precision Health Data Discovery Repository Working Group; UCLA Precision Health ATLAS Working Group; Chang TS. Phenome-Wide Association Study of Polygenic Risk Score for Alzheimer's Disease in Electronic Health Records. Front Aging Neurosci. 2022 Mar 15;14:800375. doi: 10.3389/fnagi.2022.800375. PMID: 35370621; PMCID: PMC8965623.

The UCLA ATLAS Community Health Initiative: Promoting Precision Health Research in a Diverse Biobank

Johnson R, Ding Y, Bhattacharya A, Knyazev S, Chiu A, Lajonchere C, Geschwind DH, Pasaniuc B. The UCLA ATLAS Community Health Initiative: Promoting precision health research in a diverse biobank. Cell Genom. 2023 Jan 11;3(1):100243. doi: 10.1016/j.xgen.2022.100243. PMID: 36777178; PMCID: PMC9903668.

An Integrated, Scalable, Electronic Video Consent Process to Power Precision Health Research: Large, Population-Based, Cohort Implementation and Scalability Study

Lajonchere C, Naeim A, Dry S, Wenger N, Elashoff D, Vangala S, Petruse A, Ariannejad M, Magyar C, Johansen L, Werre G, Kroloff M, Geschwind D. An Integrated, Scalable, Electronic Video Consent Process to Power Precision Health Research: Large, Population-Based, Cohort Implementation and Scalability Study. J Med Internet Res. 2021 Dec 8;23(12):e31121. doi: 10.2196/31121. PMID: 34889741; PMCID: PMC8701720.

Pre-existing Conditions in Hispanics/Latinxs that are COVID-19 Risk Factors

Chang TS, Ding Y, Freund MK, Johnson R, Schwarz T, Yabu JM, Hazlett C, Chiang JN, Wulf DA; UCLA Precision Health Data Discovery Repository Working Group; Geschwind DH, Butte MJ, Pasaniuc B. Pre-existing conditions in Hispanics/Latinxs that are COVID-19 risk factors. iScience. 2021 Mar 19;24(3):102188. doi: 10.1016/j.isci.2021.102188. Epub 2021 Feb 12. PMID: 33615196; PMCID: PMC7879099.