Efficient detection of copy‐number variations using exome data: Batch‐ and sex‐based analyses,Human Mutation

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Efficient detection of copy‐number variations using exome data: Batch‐ and sex‐based analyses
Human Mutation ( IF 3.3 ) Pub Date : 2020-11-01 , DOI: 10.1002/humu.24129
Yuri Uchiyama _{1,

2} , Daisuke Yamaguchi ₃ , Kazuhiro Iwama _{2,

4} , Satoko Miyatake _{2,

5} , Kohei Hamanaka ₂ , Naomi Tsuchida _{1,

2} , Hiromi Aoi _{2,

6} , Yoshiteru Azuma ₂ , Toshiyuki Itai ₂ , Ken Saida ₂ , Hiromi Fukuda _{2,

7} , Futoshi Sekiguchi ₂ , Tomohiro Sakaguchi ₂ , Ming Lei ₂ , Sachiko Ohori ₂ , Masamune Sakamoto _{2,

4} , Mitsuhiro Kato ₈ , Takayoshi Koike ₉ , Yukitoshi Takahashi ₉ , Koichi Tanda ₁₀ , Yuki Hyodo ₁₁ , Rachel S Honjo ₁₂ , Debora Romeo Bertola ₁₂ , Chong Ae Kim ₁₂ , Masahide Goto ₁₃ , Tetsuya Okazaki ₁₄ , Hiroyuki Yamada ₁₄ , Yoshihiro Maegaki ₁₄ , Hitoshi Osaka ₁₃ , Lock-Hock Ngu ₁₅ , Ch'ng G Siew ₁₅ , Keng W Teik ₁₅ , Manami Akasaka ₁₆ , Hiroshi Doi ₇ , Fumiaki Tanaka ₇ , Tomohide Goto ₁₇ , Long Guo ₁₈ , Shiro Ikegawa ₁₈ , Kazuhiro Haginoya ₁₉ , Muzhirah Haniffa ₁₅ , Nozomi Hiraishi ₂₀ , Yoko Hiraki ₂₁ , Satoru Ikemoto ₂₂ , Atsuro Daida ₂₂ , Shin-Ichiro Hamano ₂₂ , Masaki Miura _{23,

24} , Akihiko Ishiyama ₂₃ , Osamu Kawano ₂₅ , Akane Kondo ₂₆ , Hiroshi Matsumoto ₂₇ , Nobuhiko Okamoto ₂₈ , Tohru Okanishi _{14,

29} , Yukimi Oyoshi ₂₃ , Eri Takeshita ₂₃ , Toshifumi Suzuki ₆ , Yoshiyuki Ogawa ₃₀ , Hiroshi Handa ₃₀ , Yayoi Miyazono ₃₁ , Eriko Koshimizu ₂ , Atsushi Fujita ₂ , Atsushi Takata ₂ , Noriko Miyake ₂ , Takeshi Mizuguchi ₂ , Naomichi Matsumoto ₂

Affiliation

Department of Rare Disease Genomics, Yokohama City University Hospital, Yokohama, Japan.
Department of Human Genetics, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
BITS Co., Ltd., Tokyo, Japan.
Department of Pediatrics, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
Clinical Genetics Department, Yokohama City University Hospital, Yokohama, Japan.
Department of Obstetrics and Gynecology, Faculty of Medicine Juntendo University, Tokyo, Japan.
Department of Neurology and Stroke Medicine, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
Department of Pediatrics, Showa University School of Medicine, Tokyo, Japan.
National Epilepsy Center, NHO Shizuoka Institute of Epilepsy and Neurological Disorders, Shizuoka, Japan.
Department of Pediatrics, Japanese Red Cross Kyoto Daiichi Hospital, Kyoto, Japan.
Department of Child Neurology, Okayama University Hospital, Okayama, Japan.
Unidade de Genetica do Instituto da Crianca do Hospital das Clinicas da Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, Brazil.
Department of Pediatrics, Jichi Medical University, Shimotsuke, Japan.
Department of Brain and Neurosciences, Division of Child Neurology, Faculty of Medicine, Tottori University, Yonago, Japan.
Department of Genetics, Kuala Lumpur Hospital, Kuala Lumpur, Malaysia.
Department of Pediatrics, Iwate Medical University School of Medicine, Morioka, Japan.
Division of Neurology, Kanagawa Children's Medical Center, Yokohama, Japan.
Laboratory for Bone and Joint Diseases, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan.
Department of Pediatric Neurology, Miyagi Children's Hospital, Sendai, Japan.
Department of Pediatrics, Yokohama City University Medical Center, Yokohama, Japan.
Hiroshima Municipal Center for Child Health and Development, Hiroshima, Japan.
Division of Neurology, Saitama Children's Medical Center, Saitama, Japan.
Department of Child Neurology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo, Japan.
Department of Pediatrics, Nagaoka Red Cross Hospital, Nagaoka, Japan.
Department of Pediatrics, Hokkaido University Hospital, Sapporo, Japan.
Clinical Genetics Center, Shikoku Medical Center for Children and Adults, National Hospital Organization, Kagawa, Japan.
Department of Pediatrics, National Defense Medical College, Saitama, Japan.
Department of Medical Genetics, Osaka Women's and Children's Hospital, Osaka, Japan.
Department of Child Neurology, Comprehensive Epilepsy Center, Seirei Hamamatsu General Hospital, Hamamatsu, Japan.
Department of Hematology, Gunma University Graduate School of Medicine, Gunma, Japan.
Department of Child Health, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.

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.

中文翻译：

使用外显子组数据有效检测拷贝数变异：基于批次和性别的分析

已经报道了许多使用外显子组测序 (ES) 数据检测拷贝数变异 (CNV) 的算法，并评估了它们的灵敏度和特异性、再现性和精确度。然而，尚未完全解决此类算法的操作优化以获得更好的性能。对 1199 个样本进行了 ES，其中包括 763 名具有不同疾病特征的患者。通过 eXome 隐马尔可夫模型 (XHMM) 和改进的 Nord 方法分析 ES 数据以检测 CNV。为了有效检测稀有 CNV，我们旨在通过同时分析在同一流动槽中作为批次测序的所有无关样本的数据来减少测序偏差，并通过分析女性和男性序列分开。我们还应用了几个过滤步骤来更有效地选择 CNV。在一个样本中检测到的 CNV 的平均数量小于 5。这种优化与 Nord 方法的靶向 CNV 分析一起确定了 34 名患者 (4.5%, 34/763) 的致病性/可能致病性 CNV。特别是，在 142 名癫痫患者中，目前的方案在 19 名（13.4%）患者中检测到了临床相关的 CNV，而之前的方案仅在 14 名（9.9%）患者中发现了它们。因此，这种基于批次的 XHMM 分析有效地选择了遗传疾病中罕见的致病性 CNV。而之前的方案仅在 14 名 (9.9%) 患者中发现了它们。因此，这种基于批次的 XHMM 分析有效地选择了遗传疾病中罕见的致病性 CNV。而之前的方案仅在 14 名 (9.9%) 患者中发现了它们。因此，这种基于批次的 XHMM 分析有效地选择了遗传疾病中罕见的致病性 CNV。

更新日期：2020-12-26

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