{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T17:32:03Z","timestamp":1776706323301,"version":"3.51.2"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2020,12,7]],"date-time":"2020-12-07T00:00:00Z","timestamp":1607299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100003593","name":"National Council for Scientific and Technological Development","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002322","name":"Coordination for the Improvement of Higher Education Personnel","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,6,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Multivariate data are common in biological experiments and using the information on multiple traits is crucial to make better decisions for treatment recommendations or genotype selection. However, identifying genotypes\/treatments that combine high performance across many traits has been a challenger task. Classical linear multi-trait selection indexes are available, but the presence of multicollinearity and the arbitrary choosing of weighting coefficients may erode the genetic gains.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose a novel approach for genotype selection and treatment recommendation based on multiple traits that overcome the fragility of classical linear indexes. Here, we use the distance between the genotypes\/treatment with an ideotype defined a priori as a multi-trait genotype\u2013ideotype distance index (MGIDI) to provide a selection process that is unique, easy-to-interpret, free from weighting coefficients and multicollinearity issues. The performance of the MGIDI index is assessed through a Monte Carlo simulation study where the percentage of success in selecting traits with desired gains is compared with classical and modern indexes under different scenarios. Two real plant datasets are used to illustrate the application of the index from breeders and agronomists\u2019 points of view. Our experimental results indicate that MGIDI can effectively select superior treatments\/genotypes based on multi-trait data, outperforming state-of-the-art methods, and helping practitioners to make better strategic decisions toward an effective multivariate selection in biological experiments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The source code is available in the R package metan (https:\/\/github.com\/TiagoOlivoto\/metan) under the function mgidi().<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa981","type":"journal-article","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T10:45:44Z","timestamp":1605005144000},"page":"1383-1389","source":"Crossref","is-referenced-by-count":276,"title":["MGIDI: toward an effective multivariate selection in biological experiments"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0241-9636","authenticated-orcid":false,"given":"Tiago","family":"Olivoto","sequence":"first","affiliation":[{"name":"Department of Agronomy, Centro Universit\u00e1rio UNIDEAU , Get\u00falio Vargas, RS 99900-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4177-4921","authenticated-orcid":false,"given":"Maicon","family":"Nardino","sequence":"additional","affiliation":[{"name":"Department of Agronomy, Federal University of Vi\u00e7osa , Vi\u00e7osa, MG 36570-900, Brazil"}]}],"member":"286","published-online":{"date-parts":[[2020,12,7]]},"reference":[{"key":"2023051709350780100_btaa981-B1","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1590\/1678-4499.20190380","article-title":"Selection to high productivity and stink bugs resistance by multivariate data analyses in soybean","volume":"79","author":"Bermudez","year":"2020","journal-title":"Bragantia"},{"key":"2023051709350780100_btaa981-B2","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1590\/S0100-204X2012000300012","article-title":"Genetic gains in physic nut using selection indexes","volume":"47","author":"Bhering","year":"2012","journal-title":"Pesquisa Agropecuaria Brasileira"},{"key":"2023051709350780100_btaa981-B3","doi-asserted-by":"crossref","first-page":"110","DOI":"10.5935\/1806-6690.20170012","article-title":"Selection indices for agronomic traits in segregating populations of soybean","volume":"48","author":"Bizari","year":"2017","journal-title":"Revista Ciencia Agronomica"},{"key":"2023051709350780100_btaa981-B4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11295-018-1308-3","article-title":"Genotype\u2013environment interaction involving site differences in expression of genetic variation along with genotypic rank changes: simulations of economic significance","volume":"15","author":"Burdon","year":"2019","journal-title":"Tree Genet. 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