Efficient detection of copy‐number variations using exome data: Batch‐ and sex‐based analyses
Many algorithms to detect copy number variations (CNVs) using exome sequencing (ES) data have been reported and evaluated on their sensitivity and specificity, reproducibility, and precision. However, operational optimization of such algorithms for a better performance has not been fully addressed....
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| Veröffentlicht in: | Human mutation 2021-01, Vol.42 (1), p.50-65 |
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| creator | Uchiyama, Yuri Yamaguchi, Daisuke Iwama, Kazuhiro Miyatake, Satoko Hamanaka, Kohei Tsuchida, Naomi Aoi, Hiromi Azuma, Yoshiteru Itai, Toshiyuki Saida, Ken Fukuda, Hiromi Sekiguchi, Futoshi Sakaguchi, Tomohiro Lei, Ming Ohori, Sachiko Sakamoto, Masamune Kato, Mitsuhiro Koike, Takayoshi Takahashi, Yukitoshi Tanda, Koichi Hyodo, Yuki Honjo, Rachel S. Bertola, Debora Romeo Kim, Chong Ae Goto, Masahide Okazaki, Tetsuya Yamada, Hiroyuki Maegaki, Yoshihiro Osaka, Hitoshi Ngu, Lock‐Hock Siew, Ch'ng G. Teik, Keng W. Akasaka, Manami Doi, Hiroshi Tanaka, Fumiaki Goto, Tomohide Guo, Long Ikegawa, Shiro Haginoya, Kazuhiro Haniffa, Muzhirah Hiraishi, Nozomi Hiraki, Yoko Ikemoto, Satoru Daida, Atsuro Hamano, Shin‐ichiro Miura, Masaki Ishiyama, Akihiko Kawano, Osamu Kondo, Akane Matsumoto, Hiroshi Okamoto, Nobuhiko Okanishi, Tohru Oyoshi, Yukimi Takeshita, Eri Suzuki, Toshifumi Ogawa, Yoshiyuki Handa, Hiroshi Miyazono, Yayoi Koshimizu, Eriko Fujita, Atsushi Takata, Atsushi Miyake, Noriko Mizuguchi, Takeshi Matsumoto, Naomichi |
| description | Many algorithms to detect copy number variations (CNVs) using exome sequencing (ES) data have been reported and evaluated on their sensitivity and specificity, reproducibility, and precision. However, operational optimization of such algorithms for a better performance has not been fully addressed. ES of 1199 samples including 763 patients with different disease profiles was performed. ES data were analyzed to detect CNVs by both the eXome Hidden Markov Model (XHMM) and modified Nord's method. To efficiently detect rare CNVs, we aimed to decrease sequencing biases by analyzing, at the same time, the data of all unrelated samples sequenced in the same flow cell as a batch, and to eliminate sex effects of X‐linked CNVs by analyzing female and male sequences separately. We also applied several filtering steps for more efficient CNV selection. The average number of CNVs detected in one sample was |
| doi_str_mv | 10.1002/humu.24129 |
| format | Article |
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Using exome sequencing of 1199 samples including 763 from patients, we identified pathogenic/likely pathogenic copy number variations (CNVs) in 34 (4.5%, 34/763) patients using two CNV‐detection algorithms (eXome Hidden Markov Model [XHMM] and Nord's method) with the optimization by batch‐ and sex‐based analyses.</description><identifier>ISSN: 1059-7794</identifier><identifier>EISSN: 1098-1004</identifier><identifier>DOI: 10.1002/humu.24129</identifier><identifier>PMID: 33131168</identifier><language>eng</language><publisher>United States: Hindawi Limited</publisher><subject>Algorithms ; Copy number ; copy number variation ; DNA Copy Number Variations ; Epilepsy ; Exome - genetics ; exome sequencing ; Female ; High-Throughput Nucleotide Sequencing - methods ; Humans ; jNord ; Male ; Markov chains ; mendelian disorder ; Reproducibility of Results ; Sensitivity analysis ; Whole Exome Sequencing ; XHMM</subject><ispartof>Human mutation, 2021-01, Vol.42 (1), p.50-65</ispartof><rights>2020 Wiley Periodicals LLC</rights><rights>2020 Wiley Periodicals LLC.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3939-d27c4f068d66edbe51d2c02c2d31bc0a107811693b4d7b8716ccacb5e76f95ec3</citedby><cites>FETCH-LOGICAL-c3939-d27c4f068d66edbe51d2c02c2d31bc0a107811693b4d7b8716ccacb5e76f95ec3</cites><orcidid>0000-0001-7595-1618 ; 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However, operational optimization of such algorithms for a better performance has not been fully addressed. ES of 1199 samples including 763 patients with different disease profiles was performed. ES data were analyzed to detect CNVs by both the eXome Hidden Markov Model (XHMM) and modified Nord's method. To efficiently detect rare CNVs, we aimed to decrease sequencing biases by analyzing, at the same time, the data of all unrelated samples sequenced in the same flow cell as a batch, and to eliminate sex effects of X‐linked CNVs by analyzing female and male sequences separately. We also applied several filtering steps for more efficient CNV selection. The average number of CNVs detected in one sample was <5. This optimization together with targeted CNV analysis by Nord's method identified pathogenic/likely pathogenic CNVs in 34 patients (4.5%, 34/763). In particular, among 142 patients with epilepsy, the current protocol detected clinically relevant CNVs in 19 (13.4%) patients, whereas the previous protocol identified them in only 14 (9.9%) patients. Thus, this batch‐based XHMM analysis efficiently selected rare pathogenic CNVs in genetic diseases.
Using exome sequencing of 1199 samples including 763 from patients, we identified pathogenic/likely pathogenic copy number variations (CNVs) in 34 (4.5%, 34/763) patients using two CNV‐detection algorithms (eXome Hidden Markov Model [XHMM] and Nord's method) with the optimization by batch‐ and sex‐based analyses.</description><subject>Algorithms</subject><subject>Copy number</subject><subject>copy number variation</subject><subject>DNA Copy Number Variations</subject><subject>Epilepsy</subject><subject>Exome - genetics</subject><subject>exome sequencing</subject><subject>Female</subject><subject>High-Throughput Nucleotide Sequencing - methods</subject><subject>Humans</subject><subject>jNord</subject><subject>Male</subject><subject>Markov chains</subject><subject>mendelian disorder</subject><subject>Reproducibility of Results</subject><subject>Sensitivity analysis</subject><subject>Whole Exome 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detection of copy‐number variations using exome data: Batch‐ and sex‐based analyses</title><author>Uchiyama, Yuri ; Yamaguchi, Daisuke ; Iwama, Kazuhiro ; Miyatake, Satoko ; Hamanaka, Kohei ; Tsuchida, Naomi ; Aoi, Hiromi ; Azuma, Yoshiteru ; Itai, Toshiyuki ; Saida, Ken ; Fukuda, Hiromi ; Sekiguchi, Futoshi ; Sakaguchi, Tomohiro ; Lei, Ming ; Ohori, Sachiko ; Sakamoto, Masamune ; Kato, Mitsuhiro ; Koike, Takayoshi ; Takahashi, Yukitoshi ; Tanda, Koichi ; Hyodo, Yuki ; Honjo, Rachel S. ; Bertola, Debora Romeo ; Kim, Chong Ae ; Goto, Masahide ; Okazaki, Tetsuya ; Yamada, Hiroyuki ; Maegaki, Yoshihiro ; Osaka, Hitoshi ; Ngu, Lock‐Hock ; Siew, Ch'ng G. ; Teik, Keng W. ; Akasaka, Manami ; Doi, Hiroshi ; Tanaka, Fumiaki ; Goto, Tomohide ; Guo, Long ; Ikegawa, Shiro ; Haginoya, Kazuhiro ; Haniffa, Muzhirah ; Hiraishi, Nozomi ; Hiraki, Yoko ; Ikemoto, Satoru ; Daida, Atsuro ; Hamano, Shin‐ichiro ; Miura, Masaki ; Ishiyama, Akihiko ; Kawano, Osamu ; Kondo, Akane ; Matsumoto, Hiroshi ; Okamoto, Nobuhiko ; Okanishi, Tohru ; Oyoshi, Yukimi ; Takeshita, Eri ; Suzuki, Toshifumi ; Ogawa, Yoshiyuki ; Handa, Hiroshi ; Miyazono, Yayoi ; Koshimizu, Eriko ; Fujita, Atsushi ; Takata, Atsushi ; Miyake, Noriko ; Mizuguchi, Takeshi ; Matsumoto, Naomichi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3939-d27c4f068d66edbe51d2c02c2d31bc0a107811693b4d7b8716ccacb5e76f95ec3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Copy number</topic><topic>copy number variation</topic><topic>DNA Copy Number Variations</topic><topic>Epilepsy</topic><topic>Exome - genetics</topic><topic>exome sequencing</topic><topic>Female</topic><topic>High-Throughput Nucleotide Sequencing - methods</topic><topic>Humans</topic><topic>jNord</topic><topic>Male</topic><topic>Markov chains</topic><topic>mendelian disorder</topic><topic>Reproducibility of Results</topic><topic>Sensitivity analysis</topic><topic>Whole Exome Sequencing</topic><topic>XHMM</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Uchiyama, Yuri</creatorcontrib><creatorcontrib>Yamaguchi, Daisuke</creatorcontrib><creatorcontrib>Iwama, Kazuhiro</creatorcontrib><creatorcontrib>Miyatake, Satoko</creatorcontrib><creatorcontrib>Hamanaka, Kohei</creatorcontrib><creatorcontrib>Tsuchida, Naomi</creatorcontrib><creatorcontrib>Aoi, Hiromi</creatorcontrib><creatorcontrib>Azuma, Yoshiteru</creatorcontrib><creatorcontrib>Itai, Toshiyuki</creatorcontrib><creatorcontrib>Saida, Ken</creatorcontrib><creatorcontrib>Fukuda, Hiromi</creatorcontrib><creatorcontrib>Sekiguchi, Futoshi</creatorcontrib><creatorcontrib>Sakaguchi, Tomohiro</creatorcontrib><creatorcontrib>Lei, Ming</creatorcontrib><creatorcontrib>Ohori, Sachiko</creatorcontrib><creatorcontrib>Sakamoto, Masamune</creatorcontrib><creatorcontrib>Kato, Mitsuhiro</creatorcontrib><creatorcontrib>Koike, Takayoshi</creatorcontrib><creatorcontrib>Takahashi, Yukitoshi</creatorcontrib><creatorcontrib>Tanda, Koichi</creatorcontrib><creatorcontrib>Hyodo, Yuki</creatorcontrib><creatorcontrib>Honjo, Rachel S.</creatorcontrib><creatorcontrib>Bertola, Debora Romeo</creatorcontrib><creatorcontrib>Kim, Chong Ae</creatorcontrib><creatorcontrib>Goto, Masahide</creatorcontrib><creatorcontrib>Okazaki, Tetsuya</creatorcontrib><creatorcontrib>Yamada, Hiroyuki</creatorcontrib><creatorcontrib>Maegaki, Yoshihiro</creatorcontrib><creatorcontrib>Osaka, Hitoshi</creatorcontrib><creatorcontrib>Ngu, Lock‐Hock</creatorcontrib><creatorcontrib>Siew, Ch'ng G.</creatorcontrib><creatorcontrib>Teik, Keng W.</creatorcontrib><creatorcontrib>Akasaka, Manami</creatorcontrib><creatorcontrib>Doi, Hiroshi</creatorcontrib><creatorcontrib>Tanaka, Fumiaki</creatorcontrib><creatorcontrib>Goto, Tomohide</creatorcontrib><creatorcontrib>Guo, Long</creatorcontrib><creatorcontrib>Ikegawa, Shiro</creatorcontrib><creatorcontrib>Haginoya, Kazuhiro</creatorcontrib><creatorcontrib>Haniffa, Muzhirah</creatorcontrib><creatorcontrib>Hiraishi, Nozomi</creatorcontrib><creatorcontrib>Hiraki, Yoko</creatorcontrib><creatorcontrib>Ikemoto, Satoru</creatorcontrib><creatorcontrib>Daida, Atsuro</creatorcontrib><creatorcontrib>Hamano, Shin‐ichiro</creatorcontrib><creatorcontrib>Miura, Masaki</creatorcontrib><creatorcontrib>Ishiyama, Akihiko</creatorcontrib><creatorcontrib>Kawano, Osamu</creatorcontrib><creatorcontrib>Kondo, Akane</creatorcontrib><creatorcontrib>Matsumoto, Hiroshi</creatorcontrib><creatorcontrib>Okamoto, Nobuhiko</creatorcontrib><creatorcontrib>Okanishi, Tohru</creatorcontrib><creatorcontrib>Oyoshi, Yukimi</creatorcontrib><creatorcontrib>Takeshita, Eri</creatorcontrib><creatorcontrib>Suzuki, Toshifumi</creatorcontrib><creatorcontrib>Ogawa, Yoshiyuki</creatorcontrib><creatorcontrib>Handa, Hiroshi</creatorcontrib><creatorcontrib>Miyazono, Yayoi</creatorcontrib><creatorcontrib>Koshimizu, Eriko</creatorcontrib><creatorcontrib>Fujita, Atsushi</creatorcontrib><creatorcontrib>Takata, Atsushi</creatorcontrib><creatorcontrib>Miyake, Noriko</creatorcontrib><creatorcontrib>Mizuguchi, Takeshi</creatorcontrib><creatorcontrib>Matsumoto, Naomichi</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Human mutation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Uchiyama, Yuri</au><au>Yamaguchi, Daisuke</au><au>Iwama, Kazuhiro</au><au>Miyatake, Satoko</au><au>Hamanaka, Kohei</au><au>Tsuchida, Naomi</au><au>Aoi, Hiromi</au><au>Azuma, Yoshiteru</au><au>Itai, Toshiyuki</au><au>Saida, Ken</au><au>Fukuda, Hiromi</au><au>Sekiguchi, Futoshi</au><au>Sakaguchi, Tomohiro</au><au>Lei, Ming</au><au>Ohori, Sachiko</au><au>Sakamoto, Masamune</au><au>Kato, Mitsuhiro</au><au>Koike, Takayoshi</au><au>Takahashi, Yukitoshi</au><au>Tanda, Koichi</au><au>Hyodo, Yuki</au><au>Honjo, Rachel S.</au><au>Bertola, Debora Romeo</au><au>Kim, Chong Ae</au><au>Goto, Masahide</au><au>Okazaki, Tetsuya</au><au>Yamada, Hiroyuki</au><au>Maegaki, Yoshihiro</au><au>Osaka, Hitoshi</au><au>Ngu, Lock‐Hock</au><au>Siew, Ch'ng G.</au><au>Teik, Keng W.</au><au>Akasaka, Manami</au><au>Doi, Hiroshi</au><au>Tanaka, Fumiaki</au><au>Goto, Tomohide</au><au>Guo, Long</au><au>Ikegawa, Shiro</au><au>Haginoya, Kazuhiro</au><au>Haniffa, Muzhirah</au><au>Hiraishi, Nozomi</au><au>Hiraki, Yoko</au><au>Ikemoto, Satoru</au><au>Daida, Atsuro</au><au>Hamano, Shin‐ichiro</au><au>Miura, Masaki</au><au>Ishiyama, Akihiko</au><au>Kawano, Osamu</au><au>Kondo, Akane</au><au>Matsumoto, Hiroshi</au><au>Okamoto, Nobuhiko</au><au>Okanishi, Tohru</au><au>Oyoshi, Yukimi</au><au>Takeshita, Eri</au><au>Suzuki, Toshifumi</au><au>Ogawa, Yoshiyuki</au><au>Handa, Hiroshi</au><au>Miyazono, Yayoi</au><au>Koshimizu, Eriko</au><au>Fujita, Atsushi</au><au>Takata, Atsushi</au><au>Miyake, Noriko</au><au>Mizuguchi, Takeshi</au><au>Matsumoto, Naomichi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Efficient detection of copy‐number variations using exome data: Batch‐ and sex‐based analyses</atitle><jtitle>Human mutation</jtitle><addtitle>Hum Mutat</addtitle><date>2021-01</date><risdate>2021</risdate><volume>42</volume><issue>1</issue><spage>50</spage><epage>65</epage><pages>50-65</pages><issn>1059-7794</issn><eissn>1098-1004</eissn><abstract>Many algorithms to detect copy number variations (CNVs) using exome sequencing (ES) data have been reported and evaluated on their sensitivity and specificity, reproducibility, and precision. However, operational optimization of such algorithms for a better performance has not been fully addressed. ES of 1199 samples including 763 patients with different disease profiles was performed. ES data were analyzed to detect CNVs by both the eXome Hidden Markov Model (XHMM) and modified Nord's method. To efficiently detect rare CNVs, we aimed to decrease sequencing biases by analyzing, at the same time, the data of all unrelated samples sequenced in the same flow cell as a batch, and to eliminate sex effects of X‐linked CNVs by analyzing female and male sequences separately. We also applied several filtering steps for more efficient CNV selection. The average number of CNVs detected in one sample was <5. This optimization together with targeted CNV analysis by Nord's method identified pathogenic/likely pathogenic CNVs in 34 patients (4.5%, 34/763). In particular, among 142 patients with epilepsy, the current protocol detected clinically relevant CNVs in 19 (13.4%) patients, whereas the previous protocol identified them in only 14 (9.9%) patients. Thus, this batch‐based XHMM analysis efficiently selected rare pathogenic CNVs in genetic diseases.
Using exome sequencing of 1199 samples including 763 from patients, we identified pathogenic/likely pathogenic copy number variations (CNVs) in 34 (4.5%, 34/763) patients using two CNV‐detection algorithms (eXome Hidden Markov Model [XHMM] and Nord's method) with the optimization by batch‐ and sex‐based analyses.</abstract><cop>United States</cop><pub>Hindawi Limited</pub><pmid>33131168</pmid><doi>10.1002/humu.24129</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0001-7595-1618</orcidid><orcidid>https://orcid.org/0000-0003-3295-2236</orcidid><orcidid>https://orcid.org/0000-0002-4540-5277</orcidid><orcidid>https://orcid.org/0000-0003-3842-9294</orcidid><orcidid>https://orcid.org/0000-0002-0166-3469</orcidid><orcidid>https://orcid.org/0000-0001-7203-884X</orcidid><orcidid>https://orcid.org/0000-0002-9961-2693</orcidid><orcidid>https://orcid.org/0000-0001-5415-656X</orcidid><orcidid>https://orcid.org/0000-0001-5472-5275</orcidid><orcidid>https://orcid.org/0000-0002-1922-8967</orcidid><orcidid>https://orcid.org/0000-0002-5363-5638</orcidid><orcidid>https://orcid.org/0000-0002-0928-4586</orcidid><orcidid>https://orcid.org/0000-0001-9846-6500</orcidid><orcidid>https://orcid.org/0000-0003-0987-310X</orcidid><orcidid>https://orcid.org/0000-0003-1485-8553</orcidid><orcidid>https://orcid.org/0000-0001-5807-2523</orcidid><orcidid>https://orcid.org/0000-0003-3455-5285</orcidid><orcidid>https://orcid.org/0000-0002-4701-6777</orcidid><orcidid>https://orcid.org/0000-0003-0316-2147</orcidid><orcidid>https://orcid.org/0000-0002-2584-9524</orcidid><orcidid>https://orcid.org/0000-0002-5928-0233</orcidid><orcidid>https://orcid.org/0000-0002-9223-7341</orcidid><orcidid>https://orcid.org/0000-0001-5179-331X</orcidid><orcidid>https://orcid.org/0000-0002-1754-1300</orcidid><orcidid>https://orcid.org/0000-0003-3753-9862</orcidid><orcidid>https://orcid.org/0000-0002-0029-6310</orcidid><orcidid>https://orcid.org/0000-0001-6195-0737</orcidid><orcidid>https://orcid.org/0000-0002-9660-6941</orcidid><orcidid>https://orcid.org/0000-0003-4998-1244</orcidid><orcidid>https://orcid.org/0000-0002-2396-1686</orcidid><orcidid>https://orcid.org/0000-0002-4686-6147</orcidid><orcidid>https://orcid.org/0000-0003-4708-577X</orcidid><orcidid>https://orcid.org/0000-0002-1320-1165</orcidid><orcidid>https://orcid.org/0000-0003-1350-7700</orcidid><orcidid>https://orcid.org/0000-0003-0515-0481</orcidid><orcidid>https://orcid.org/0000-0002-7630-2051</orcidid><orcidid>https://orcid.org/0000-0002-0242-7521</orcidid><orcidid>https://orcid.org/0000-0002-9774-0464</orcidid><orcidid>https://orcid.org/0000-0002-6243-2269</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1059-7794 |
| ispartof | Human mutation, 2021-01, Vol.42 (1), p.50-65 |
| issn | 1059-7794 1098-1004 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2456856221 |
| source | MEDLINE; Access via Wiley Online Library |
| subjects | Algorithms Copy number copy number variation DNA Copy Number Variations Epilepsy Exome - genetics exome sequencing Female High-Throughput Nucleotide Sequencing - methods Humans jNord Male Markov chains mendelian disorder Reproducibility of Results Sensitivity analysis Whole Exome Sequencing XHMM |
| title | Efficient detection of copy‐number variations using exome data: Batch‐ and sex‐based analyses |
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