{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:33:55Z","timestamp":1772138035682,"version":"3.50.1"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"24","license":[{"start":{"date-parts":[[2020,12,26]],"date-time":"2020-12-26T00:00:00Z","timestamp":1608940800000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Oxford Biomedical Research Computing"},{"name":"Wellcome Centre for Human Genetics and the Big Data Institute"},{"DOI":"10.13039\/501100023699","name":"Health Data Research UK","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100023699","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100013373","name":"NIHR Oxford Biomedical Research Centre","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013373","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["203141\/Z\/16\/Z"],"award-info":[{"award-number":["203141\/Z\/16\/Z"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NHS"},{"DOI":"10.13039\/100006662","name":"NIHR","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006662","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003921","name":"Department of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003921","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["109106\/Z\/15\/Z"],"award-info":[{"award-number":["109106\/Z\/15\/Z"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010663","name":"European Research Council","doi-asserted-by":"publisher","award":["617306"],"award-info":[{"award-number":["617306"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,4,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Gene\u2013environment (GxE) interactions are one of the least studied aspects of the genetic architecture of human traits and diseases. The environment of an individual is inherently high dimensional, evolves through time and can be expensive and time consuming to measure. The UK Biobank study, with all 500\u00a0000 participants having undergone an extensive baseline questionnaire, represents a unique opportunity to assess GxE heritability for many traits and diseases in a well powered setting.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We have developed a randomized Haseman\u2013Elston non-linear regression method applicable when many environmental variables have been measured on each individual. The method (GPLEMMA) simultaneously estimates a linear environmental score (ES) and its GxE heritability. We compare the method via simulation to a whole-genome regression approach (LEMMA) for estimating GxE heritability. We show that GPLEMMA is more computationally efficient than LEMMA on large datasets, and produces results highly correlated with those from LEMMA when applied to simulated data and real data from the UK Biobank.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Software implementing the GPLEMMA method is available from https:\/\/jmarchini.org\/gplemma\/.<\/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\/btaa1079","type":"journal-article","created":{"date-parts":[[2020,12,16]],"date-time":"2020-12-16T16:33:39Z","timestamp":1608136419000},"page":"5632-5639","source":"Crossref","is-referenced-by-count":12,"title":["A non-linear regression method for estimation of gene\u2013environment heritability"],"prefix":"10.1093","volume":"36","author":[{"given":"Matthew","family":"Kerin","sequence":"first","affiliation":[{"name":"Wellcome Trust Center for Human Genetics , Oxford, OX3 7BN, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0610-8322","authenticated-orcid":false,"given":"Jonathan","family":"Marchini","sequence":"additional","affiliation":[{"name":"Regeneron Genetics Center , Tarrytown, NY 10591, USA"}]}],"member":"286","published-online":{"date-parts":[[2020,12,26]]},"reference":[{"key":"2023062408140950800_btaa1079-B1","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1038\/ng.3211","article-title":"LD Score regression distinguishes confounding from polygenicity in genome-wide association studies","volume":"47","author":"Bulik-Sullivan","year":"2015","journal-title":"Nat. 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