Integrated pathogenicity assessment of clinically actionable genetic variants

临床可行的遗传变异的综合致病性评估

• 批准号: 10443630
• 负责人: Christopher Cassa
• 金额: $ 69.24万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-24 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10443630
• 关键词: Age; Algorithms; American; Bayesian Modeling; Bayesian Prediction; Binding Proteins; Biochemical; Characteristics; Classification; ClinVar; Clinical; Clinical Data; Clinical assessments; Code; Complement; Control Groups; Coupling; Crystallization; Data; Databases; Daughter; Development; Disease; Epidemiology; Etiology; Evaluation; Family; Fathers; Genes; Genetic; Genomics; Hypertrophic Cardiomyopathy; Individual; Knowledge; Laboratories; Malignant Neoplasms; Measures; Medical; Medical Genetics; Methods; Modeling; Molecular Conformation; Mutation; Participant; Pathogenicity; Patients; Pattern; Penetrance; Performance; Phenotype; Population; Positioning Attribute; Predisposition; Protein Region; Proteins; Recurrence; Risk; Role; Screening procedure; Single Nucleotide Polymorphism; Site; Structure; Syndrome; Training; Trans-Omics for Precision Medicine; Variant; Veterans; biobank; clinical application; clinical diagnostics; clinical risk; clinically actionable; cohort; exhaustion; genetic pedigree; genetic variant; health data; improved; insight; medical schools; novel; population health; prospective; protein structure; segregation; standard of care; variant of unknown significance

Integrated pathogenicity assessment of clinically actionable genetic variants ! Project Summary/Abstract Large biobanks such as All of Us and the Million Veteran Project have now collected genetic data from millions of patients, and other population health studies are expanding rapidly. The interpretation of variants in clinically actionable disease genes is becoming increasingly common in such projects. The American College of Medical Genetics and Genomics has recommended that sequence interpretation include a minimum set of 59 genes regardless of the indication for sequencing (ACMG 59). These genes are responsible for a variety of clinical syndromes and have been extensively studied. However, even in well-studied disease genes, the majority of variants are only observed in one or two families. which makes it challenging to be sure of their role in causation of disease. Further, while there may be existing evidence about a variant, it is often inadequate for interpretation, as many variants in databases were originally identified in small, symptomatic cohorts without matched control groups, so their associations can suffer from incorrect estimates of significance or effect size, and a non-trivial fraction are likely to be spurious. For these reasons, a central challenge in clinical genomics is to interpret variants in clinically actionable genes that are identified during sequencing. Because the ACMG 59 genes have been studied intensively due to their clinical applicability, there is a unique abundance of functional and structural data that can be used to improve predictions. Here, we propose to develop new data that can be leveraged in the clinical assessment of variants including novel predictions of structural consequences, regional and structurally-informed selective constraint, and clinical risk from clinical diagnostic and epidemiologic health data. Using these data, we will develop a Bayesian statistical model to predict the effects of mutations that can complement existing assessments made by consortia and clinical laboratories. This will specifically include efforts to intensively improve computational predictions of structural and functional impact using the extensive scientific and medical knowledge in each of these genes. Next, we combine that structural and functional insight with large-scale population data. We will measure statistical aberration of variation for related groups of missense variants, and also identify groups of variant sites which are enriched in recurrent somatic or germline variation associated with cancer. Finally, we will develop a Bayesian prediction framework that integrates the full set of variant observations and characteristics to improve predictions of clinical risk for individual variants, and prospectively measure its performance in a clinical diagnostic laboratory. ! !

临床可操作基因变异的综合致病性评估

项目成果

Estimating clinical risk in gene regions from population sequencing cohort data.

根据群体测序队列数据估计基因区域的临床风险。

• DOI: 10.1101/2023.01.06.23284281
• 发表时间: 2023
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Fife,JamesD;Cassa,ChristopherA
• 通讯作者: Cassa,ChristopherA

Data sharing to improve concordance in variant interpretation across laboratories: results from the Canadian Open Genetics Repository.

• DOI: 10.1136/jmedgenet-2021-107738
• 发表时间: 2022-06
• 期刊: Journal of medical genetics
• 影响因子: 4
• 作者: Mighton C;Smith AC;Mayers J;Tomaszewski R;Taylor S;Hume S;Agatep R;Spriggs E;Feilotter HE;Semenuk L;Wong H;Lazo de la Vega L;Marshall CR;Axford MM;Silver T;Charames GS;Di Gioacchino V;Watkins N;Foulkes WD;Clavier M;Hamel N;Chong G;Lamont RE;Parboosingh J;Karsan A;Bosdet I;Young SS;Tucker T;Akbari MR;Speevak MD;Vaags AK;Lebo MS;Lerner-Ellis J;Canadian Open Genetics Repository Working Group
• 通讯作者: Canadian Open Genetics Repository Working Group

Reply to 'Selective effects of heterozygous protein-truncating variants'.

回复“杂合蛋白质截短变体的选择性效应”。

• DOI: 10.1038/s41588-018-0301-y
• 发表时间: 2019
• 期刊: Nature genetics
• 影响因子: 30.8
• 作者: Cassa,ChristopherA;Weghorn,Donate;Balick,DanielJ;Jordan,DanielM;Nusinow,David;Samocha,KaitlinE;O'Donnell-Luria,Anne;MacArthur,DanielG;Daly,MarkJ;Beier,DavidR;Sunyaev,ShamilR
• 通讯作者: Sunyaev,ShamilR

Revisiting mutagenesis at non-B DNA motifs in the human genome.

• DOI: 10.1038/s41594-023-00936-6
• 发表时间: 2023-04
• 期刊: NATURE STRUCTURAL & MOLECULAR BIOLOGY
• 影响因子: 16.8
• 作者: McGinty, R. J.;Sunyaev, S. R.
• 通讯作者: Sunyaev, S. R.

DeMAG predicts the effects of variants in clinically actionable genes by integrating structural and evolutionary epistatic features.

• DOI: 10.1038/s41467-023-37661-z
• 发表时间: 2023-04-19
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Luppino, Federica;Adzhubei, Ivan A.;Cassa, Christopher A.;Toth-Petroczy, Agnes
• 通讯作者: Toth-Petroczy, Agnes