Dataset Information

Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy.

ABSTRACT: Rapid methodological advances in statistical and computational genomics have enabled researchers to better identify and interpret both rare and common variants responsible for complex human diseases. As we continue to see an expansion of these advances in the field, it is now imperative for researchers to understand the resources and methodologies available for various data types and study designs. In this review, we provide an overview of recent methods for identifying rare and common variants and understanding their roles in disease etiology. Additionally, we discuss the strategy, challenge, and promise of gene therapy. As computational and statistical approaches continue to improve, we will have an opportunity to translate human genetic findings into personalized health care.

SUBMITTER: Wang YC

PROVIDER: S-EPMC8878256 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Publications

Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy.

Wang Yung-Chun YC Wu Yuchang Y Choi Julie J Allington Garrett G Zhao Shujuan S Khanfar Mariam M Yang Kuangying K Fu Po-Ying PY Wrubel Max M Yu Xiaobing X Mekbib Kedous Y KY Ocken Jack J Smith Hannah H Shohfi John J Kahle Kristopher T KT Lu Qiongshi Q Jin Sheng Chih SC

Journal of personalized medicine 20220127 2

Rapid methodological advances in statistical and computational genomics have enabled researchers to better identify and interpret both rare and common variants responsible for complex human diseases. As we continue to see an expansion of these advances in the field, it is now imperative for researchers to understand the resources and methodologies available for various data types and study designs. In this review, we provide an overview of recent methods for identifying rare and common variants ...[more]

PMID: 35207663

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Project description:BackgroundFamily history has traditionally been an essential part of clinical care to assess health risks. However, declining sequencing costs have precipitated a shift towards genomics-first approaches in population screening programs rendering the value of family history unknown. We evaluated the utility of incorporating family history information for genomic sequencing selection.MethodsTo ascertain the relationship between family histories on such population-level initiatives, we analysed whole genome sequences of 1750 research participants with no known pre-existing conditions, of which half received comprehensive family history assessment of up to four generations, focusing on 95 cancer genes.ResultsAmongst the 1750 participants, 866 (49.5%) had high-quality standardised family history available. Within this group, 73 (8.4%) participants had an increased family history risk of cancer (increased FH risk cohort) and 1 in 7 participants (n = 10/73) carried a clinically actionable variant inferring a sixfold increase compared with 1 in 47 participants (n = 17/793) assessed at average family history cancer risk (average FH risk cohort) (p = 0.00001) and a sevenfold increase compared to 1 in 52 participants (n = 17/884) where family history was not available (FH not available cohort) (p = 0.00001). The enrichment was further pronounced (up to 18-fold) when assessing only the 25 cancer genes in the American College of Medical Genetics (ACMG) Secondary Findings (SF) genes. Furthermore, 63 (7.3%) participants had an increased family history cancer risk in the absence of an apparent clinically actionable variant.ConclusionsThese findings demonstrate that the collection and analysis of comprehensive family history and genomic data are complementary and in combination can prioritise individuals for genomic analysis. Thus, family history remains a critical component of health risk assessment, providing important actionable data when implementing genomics screening programs.Trial registrationClinicalTrials.gov NCT02791152 . Retrospectively registered on May 31, 2016.

Dataset Information

Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy.

Publications

Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy.

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