{ "_id": "6a101678acfb0bcc41c84688", "Package": "QTLEMM", "Type": "Package", "Title": "QTL EM Algorithm Mapping and Hotspots Detection", "Version": "3.1.0", "Authors@R": "c(\nperson(\"Ping-Yuan\", \"Chung\", email = \"pychung@webmail.stat.sinica.edu.tw\", role = \"cre\"),\nperson(\"Chen-Hung\", \"Kao\", email = \"chkao@webmail.stat.sinica.edu.tw\", role = \"aut\"),\nperson(\"Y.-T.\", \"Guo\", role = \"aut\"),\nperson(\"H.-N.\", \"Ho\", role = \"aut\"),\nperson(\"H.-I.\", \"Lee\", role = \"aut\"),\nperson(\"P.-Y.\", \"Wu\", role = \"aut\"),\nperson(\"M.-H.\", \"Yang\", role = \"aut\"),\nperson(\"M.-H.\", \"Zeng\", role = \"aut\"))", "Description": "For QTL mapping, this package comprises several functions\ndesigned to execute diverse tasks, such as simulating or\nanalyzing data, calculating significance thresholds, and\nvisualizing QTL mapping results. The single-QTL or multiple-QTL\nmethod, which enables the fitting and comparison of various\nstatistical models, is employed to analyze the data for\nestimating QTL parameters. The models encompass linear\nregression, permutation tests, normal mixture models, and\ntruncated normal mixture models. The Gaussian stochastic\nprocess is utilized to compute significance thresholds for QTL\ndetection on a genetic linkage map within experimental\npopulations. Two types of data, complete genotyping, and\nselective genotyping data from various experimental\npopulations, including backcross, F2, recombinant inbred (RI)\npopulations, and advanced intercrossed (AI) populations, are\nconsidered in the QTL mapping analysis. For QTL hotspot\ndetection, statistical methods can be developed based on either\nutilizing individual-level data or summarized data. We have\nproposed a statistical framework capable of handling both\nindividual-level data and summarized QTL data for QTL hotspot\ndetection. Our statistical framework can overcome the\nunderestimation of thresholds resulting from ignoring the\ncorrelation structure among traits. Additionally, it can\nidentify different types of hotspots with minimal computational\ncost during the detection process. Here, we endeavor to furnish\nthe R codes for our QTL mapping and hotspot detection methods,\nintended for general use in genes, genomics, and genetics\nstudies. The QTL mapping methods for the complete and selective\ngenotyping designs are based on the multiple interval mapping\n(MIM) model proposed by Kao, C.-H. , Z.-B. Zeng and R. D.\nTeasdale (1999) and H.-I\nLee, H.-A. Ho and C.-H. Kao (2014) , respectively. The QTL hotspot\ndetection analysis is based on the method by Wu, P.-Y., M.-.H.\nYang, and C.-H. Kao (2021) .", "URL": "https://github.com/py-chung/QTLEMM", "BugReports": "https://github.com/py-chung/QTLEMM/issues", "License": "GPL-2", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-14 07:27:19 UTC", "User": "root" }, "Author": "Ping-Yuan Chung [cre], Chen-Hung Kao [aut], Y.-T. Guo [aut],\nH.-N. Ho [aut], H.-I. Lee [aut], P.-Y. Wu [aut], M.-H. Yang\n[aut], M.-H. Zeng [aut]", "Maintainer": "Ping-Yuan Chung ", "Repository": "https://py-chung.r-universe.dev", "Date/Publication": "2025-09-16 08:04:12 UTC", "RemoteUrl": "https://github.com/py-chung/qtlemm", "RemoteRef": "HEAD", "RemoteSha": "542c6f5b5ade175df87979b511bf9e617edc733f", "MD5sum": "3f62ee1fac07ea3b9d67d83bd9771797", "_user": "py-chung", "_type": "src", "_file": "QTLEMM_3.1.0.tar.gz", "_fileid": "a70b95d12de5f3d44891a0c0d3f3957c796e8fa2b55855a473dd37c46b824cba", "_filesize": 93862876, "_sha256": "a70b95d12de5f3d44891a0c0d3f3957c796e8fa2b55855a473dd37c46b824cba", "_created": "2026-05-14T07:27:19.000Z", "_published": "2026-05-22T08:40:24.927Z", "_distro": "noble", "_jobs": [ { "job": 77345399109, "time": 149, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "6989435127" }, { "job": 77345399295, "time": 148, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "6989432745" }, { "job": 77345399448, "time": 174, "config": "macos-oldrel-arm64", 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