{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T12:24:16Z","timestamp":1778588656799,"version":"3.51.4"},"reference-count":44,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T00:00:00Z","timestamp":1547769600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN\u20102017\u201006672"],"award-info":[{"award-number":["RGPIN\u20102017\u201006672"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["145546"],"award-info":[{"award-number":["145546"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["GET\u2010101831"],"award-info":[{"award-number":["GET\u2010101831"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["CMF108031"],"award-info":[{"award-number":["CMF108031"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Genetic Epidemiology"],"published-print":{"date-parts":[[2019,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Analysis of the X chromosome has been largely neglected in genetic studies mainly because of complex underlying biological mechanisms. On the other hand, the study of human microbiome data (typically over\u2010dispersed counts with an excess of zeros) has generated great interest recently because of advancements in next\u2010generation sequencing technologies. We propose a novel approach to infer the association between host genetic variants in the X\u2010chromosome and microbiome data. The method accounts for random X\u2010chromosome inactivation (XCI), skewed (or nonrandom) XCI (XCI\u2010S), and escape of XCI (XCI\u2010E). The inference is performed through a finite mixture model (FMM), in which an indicator variable denoting the \u201ctrue\u201d biological mechanism is treated as missing data. An expectation\u2010maximization algorithm on zero\u2010inflated and two\u2010part models is implemented to estimate genetic effects. We investigate the performance of the FMM along with strategies that assume XCI and XCI\u2010E mechanisms for all subjects compared with alternative approaches. Briefly, an XCI mechanism codes males\u2019 genotypes as homozygous females, whereas under XCI\u2010E, males are treated as heterozygous females. By comprehensive simulations, we evaluate tests of the hypothesis under a computationally efficient score statistic. In summary, the FMM renders reduced bias and commensurate power compared to XCI, XCI\u2010E, and alternative strategies while maintaining adequate Type 1 error control. The proposed method has far\u2010reaching applications. In particular, we illustrate its usage on a large\u2010scale human microbiome study, the Genetic, Environmental and Microbial (GEM) project, to test for the genetic association on the X chromosome.<\/jats:p>","DOI":"10.1002\/gepi.22190","type":"journal-article","created":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T10:46:21Z","timestamp":1547808381000},"page":"427-439","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A finite mixture model for X\u2010chromosome association with an emphasis on microbiome data analysis"],"prefix":"10.1002","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2052-2626","authenticated-orcid":false,"given":"Osvaldo","family":"Espin\u2010Garcia","sequence":"first","affiliation":[{"name":"Dalla Lana School of Public Health University of Toronto Toronto ON Canada"},{"name":"Lunenfeld\u2010Tanenbaum Research Institute Sinai Health System Toronto ON Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenneth","family":"Croitoru","sequence":"additional","affiliation":[{"name":"Lunenfeld\u2010Tanenbaum Research Institute Sinai Health System Toronto ON Canada"},{"name":"Zane Cohen Centre for Digestive Diseases Mount Sinai Hospital Toronto ON Canada"},{"name":"Division of Gastroenterology, Department of Medicine University of Toronto Toronto ON Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Xu","sequence":"additional","affiliation":[{"name":"Dalla Lana School of Public Health University of Toronto Toronto ON Canada"},{"name":"Department of Biostatistics Princess Margaret Cancer Centre, University Health Network Toronto ON Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2019,1,18]]},"reference":[{"issue":"1","key":"e_1_2_9_1_2_1","first-page":"24","article-title":"X chromosome: X inactivation","volume":"1","author":"Ahn J. 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