Fast and cheaper next generation sequencing technologies will generate unprecedentedly massive and highly-dimensional genomic and epigenomic variation data. In the near future, a routine part of medical record will include the sequenced genomes. A fundamental question is how to efficiently extract genomic and epigenomic variants of clinical utility which will provide information for optimal wellness and interference strategies. Traditional paradigm for identifying variants of clinical validity is to test association of the variants. However, significantly associated genetic variants may or may not be usefulness for diagnosis and prognosis of diseases. Alternative to association studies for finding genetic variants of predictive utility is to systematically search variants that contain sufficient information for phenotype prediction. To achieve this, we introduce concepts of sufficient dimension reduction and coordinate hypothesis which project the original high dimensional data to very low dimensional space while preserving all information on response phenotypes. We then formulate clinically significant genetic variant discovery problem into sparse SDR problem and develop algorithms that can select significant genetic variants from up to or even ten millions of predictors with the aid of dividing SDR for whole genome into a number of subSDR problems defined for genomic regions. The sparse SDR is in turn formulated as sparse optimal scoring problem, but with penalty which can remove row vectors from the basis matrix. To speed up computation, we develop the modified alternating direction method for multipliers to solve the sparse optimal scoring problem which can easily be implemented in parallel. To illustrate its application, the proposed method is applied to simulation data and the NHLBI's Exome Sequencing Project dataset
Correspondence on ACMG STATEMENT: ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG) by Miller et al
我们有兴趣阅读最近更新的关于临床测序中报告次要结果的建议1，以及随附的应寻求次要结果的基因更新列表（ACMG SF v3.0）2。虽然作者相当详细地讨论了不完全突变的挑战，但我们担心这些建议并没有完全传达与遗传性心肌病相关的基因变异的不确定性程度，这些基因占该列表的近四分之一。由于突变是不完全的，并且与年龄有关，发现携带变异的个体通常需要监测，而不是一次性的明确诊断评估。目前缺乏证据证明机会性筛查的益处、危害和医疗成本。
GestaltMML: Enhancing Rare Genetic Disease Diagnosis through Multimodal Machine Learning Combining Facial Images and Clinical Text
Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia（雷蒙德·G·佩尔曼细胞与分子治疗中心，费城儿童医院）；Department of Mathematics, University of Pennsylvania（数学系，宾夕法尼亚大学）；Department of Biomedical Informatics, Columbia University Irving Medical Center（生物医学信息学系，哥伦比亚大学伊万斯医疗中心）；Department of Human Genetics, New York State Institute for Basic Research in Developmental Disabilities, Staten Island, NY, USA（人类遗传学系，纽约州发育障碍基础研究机构，纽约州史泰登岛）；Division of Human Genetics, Children’s Hospital of Philadelphia（人类遗传学部，费城儿童医院）；Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School（儿科系，波士顿儿童医院，哈佛医学院）；Biology PhD Program, The Graduate Center, The City University of New York（生物学博士项目，纽约市立大学研究生中心）；Department of Genetics, Perelman School of Medicine, University of Pennsylvania（遗传学系，宾夕法尼亚大学佩尔曼医学学院）；Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania（儿科系，宾夕法尼亚大学佩尔曼医学学院）；Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania（病理学与实验室医学系，宾夕法尼亚大学佩尔曼医学学院）
Comparison of Machine Learning Classifiers to Predict Patient Survival and Genetics of GBM: Towards a Standardized Model for Clinical Implementation
Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）；Sant’Andrea Hospital, La Sapienza University（圣安德烈医院，罗马第一大学）；Bambino Gesù Children's Hospital, IRCCS（耶稣圣婴儿童医院，IRCCS）；Regina Elena National Cancer Institute, IRCCS（雷吉娜·埃琳娜国家癌症研究所，IRCCS）；Castelli Romani Hospital（卡斯特利罗马尼医院）
GV-Rep: A Large-Scale Dataset for Genetic Variant Representation Learning
Imperial College London（帝国理工学院）；Vector Institute（向量研究院）；University Health Network（大学健康网络）；Hospital for Sick Children（病童医院）；University of Toronto（多伦多大学）
Genomics-guided drawing of malignant regulatory signatures revealed a pivotal role of human stem cell-associated retroviral sequences (SCARS) and functionally-active hESC enhancers
Institute of Engineering in Medicine University of California, San Diego（加州大学圣地亚哥分校工程医学院）；OncoSCAR, Inc.（OncoSCAR公司）
Preparing to Integrate Generative Pretrained Transformer Series 4 models into Genetic Variant Assessment Workflows: Assessing Performance, Drift, and Nondeterminism Characteristics Relative to Classifying Functional Evidence in Literature
Mass General Brigham Personalized Medicine（麻省总医院布里格姆个性化医学）；Brigham and Women’s Hospital（布莱根妇女医院）；Microsoft Corporation（微软公司）
