{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Hu F"],"funding":["FOCUS on Health and Leadership for Women and Penn PROMOTES Research on Sex and Gender in Health","American Medical Association","National Institutes of Health Medical Scientist Training","NIMH NIH HHS","NINDS NIH HHS","National Institutes of Health","NIH HHS","NIGMS NIH HHS"],"pagination":["btaf625"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12802883"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["42(1)"],"pubmed_abstract":["<h4>Motivation</h4>There is growing interest in estimating population reference ranges across age and sex to better identify atypical clinically-relevant measurements throughout the lifespan. For this task, the World Health Organization recommends using Generalized Additive Models for Location, Scale, and Shape (GAMLSS), which can model non-linear growth trajectories under complex distributions that address the heterogeneity in human populations.Fitting GAMLSS models requires large, generalizable sample sizes, especially for accurate estimation of extreme quantiles, but obtaining such multi-site data can be challenging due to privacy concerns and practical considerations. In settings where patient data cannot be shared, privacy-preserving distributed algorithms for federated learning can b"],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["dGAMLSS: an exact, distributed algorithm to fit Generalized Additive Models for Location, Scale, and Shape for privacy-preserving population reference charts."],"pmcid":["PMC12802883"],"funding_grant_id":["R01 NS112274","R01MH132934","R01 MH133843","R01MH133843","R01MH134896","R01 MH134896","R01 MH123550","R01 MH132934","R01MH112847","R01 MH112847","R01MH123550","R01NS112274","T32 GM07170","R01 MH123563","R01 NS060910","R01MH123563","R01NS060910","T32 GM007170"],"pubmed_authors":["Schaare HL","Hedden T","Paly L","Giedd J","Lewis JD","Pierce K","Crossley N","Zuo XN","Ayub M","Tzourio C","Liston C","Valdes-Sosa MJ","Chetelat G","Vandekar SN","Westwater ML","Raznahan A","Suckling J","Hansson O","Zar HJ","Auyeung B","Galan-Garcia L","Kern S","Gunning FM","Hu F","Westman E","Holmes AJ","Wang YS","Pantelis C","Lifespan Brain Chart Consortium","Calhoun VD","Rowitch DH","Beare R","Alexander-Bloch AF","Cropley VL","Salum GA","Kremen WS","Vasung L","Wagstyl K","Kelley EA","Grant PE","Fonagy P","Zugman A","Zalesky A","Ziauddeen H","Park MTM","Boardman JP","Rollins CK","Vachon-Presseau E","Zimmerman D","Jackowski AP","Rosenberg MD","Anagnostou E","Franz CE","Edwards AD","Gholipour A","Rodrigue A","Bullmore ET","Chertavian C","Pan PM","Whalley HC","Adler S","Chen Y","Dunlop K","Li H","Li J","Warfield SK","Crivello F","Shinohara RT","van Amelsvoort T","Chen C","Ball G","Fair DA","Traut N","Courchesne E","Qiu A","Satterthwaite TD","Turk-Browne NB","Mallard TT","Stolicyn A","Desrivieres S","Sharp D","Chakravarty MM","Heinz A","Sullivan G","Astle DE","Delorme R","Mechelli A","Jia T","Romero-Garcia R","Lombardo MV","Ellis CT","Mothersill D","McGuire P","Meaney MJ","Valdes-Sosa PA","Vertes PE","Chen AA","Ipser J","Palaniyappan L","Paus T","Marcelis M","Skoog I","Jones PB","Devenyi GA","Kahn RS","Hoare J","Crosbie J","Morgan SE","Schultz AP","Schachar RJ","Areces-Gonzalez A","Anderson KM","Ortinau C","Tong J","Eyler L","Seidlitz J","White SR","Elman JA","Mathias SR","Costantino M","Heuer K","Bedford SA","Corvin A","Ossenkoppele R","Ronan L","Delarue M","Binette AP","Henson RN","Bosch-Bayard JF","Hammill CF","Donald KA","Karlsson L","Karlsson H","Blangero J","Victoria LW","Villeneuve S","Jones DT","Glahn DC","Gur RC","Goodyer IM","Gur RE","Zettergren A","Baron-Cohen S","Tuulari JJ","Gardner M","Kawashima R","Lv J","Qian X","Dolan R","Dorfschmidt L","Scholl M","Stein DJ","Sperling RA","Pausova Z","Kitzbichler MG","Misic B","Beyer F","Vogel JW","Huang H","Jack CR","Groenewold NA","Kim KW","Chong YS","Ouyang M","Tsvetanov KA","Zhou JH","Cabez MB","Borzage M","Bae JB","Lalonde F","Fletcher PC","Sun K","Biase MAD","Yang N","Liao W","Thyreau B","Gilmore JH","Donohoe G","Valk SL","Yun HJ","Holla B","Witte AV","Warrier V","Smyser CD","Villringer A","Paz-Linares D","Bourke N","Mazoyer B","Lerch J","Bethlehem RAI","Landeau B","Toro R","Yeo BTT","Rittman T","Elison JT","Benegal V","Alexander-Bloch A","Adamson C"],"additional_accession":[]},"is_claimable":false,"name":"dGAMLSS: an exact, distributed algorithm to fit Generalized Additive Models for Location, Scale, and Shape for privacy-preserving population reference charts.","description":"<h4>Motivation</h4>There is growing interest in estimating population reference ranges across age and sex to better identify atypical clinically-relevant measurements throughout the lifespan. For this task, the World Health Organization recommends using Generalized Additive Models for Location, Scale, and Shape (GAMLSS), which can model non-linear growth trajectories under complex distributions that address the heterogeneity in human populations.Fitting GAMLSS models requires large, generalizable sample sizes, especially for accurate estimation of extreme quantiles, but obtaining such multi-site data can be challenging due to privacy concerns and practical considerations. In settings where patient data cannot be shared, privacy-preserving distributed algorithms for federated learning can b","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Jan","modification":"2026-06-02T03:22:38.57Z","creation":"2026-06-02T03:12:36.601Z"},"accession":"S-EPMC12802883","cross_references":{"pubmed":["41507058"],"doi":["10.1093/bioinformatics/btaf625"]}}