scholarly journals The Challenge of Informed Consent and Return of Results in Translational Genomics: Empirical Analysis and Recommendations

2014 ◽  
Vol 42 (3) ◽  
pp. 344-355 ◽  
Author(s):  
Gail E. Henderson ◽  
Susan M. Wolf ◽  
Kristine J. Kuczynski ◽  
Steven Joffe ◽  
Richard R. Sharp ◽  
...  
Keyword(s):  
Genome Sequencing ◽  
Large Scale ◽  
Treatment Options ◽  
Return Of Results ◽  
Clinical Settings ◽  
Exploratory Research ◽  
Whole Exome ◽  
National Human ◽  
Study Participants ◽  
The Impact

Large-scale sequencing tests, including whole-exome and whole-genome sequencing (WES/WGS), are rapidly moving into clinical use. Sequencing is already being used clinically to identify therapeutic opportunities for cancer patients who have run out of conventional treatment options, to help diagnose children with puzzling neurodevelopmental conditions, and to clarify appropriate drug choices and dosing in individuals. To evaluate and support clinical applications of these technologies, the National Human Genome Research Institute (NHGRI) and National Cancer Institute (NCI) have funded studies on clinical and research sequencing under the Clinical Sequencing Exploratory Research (CSER) program as well as studies on return of results (RoR). Most of these studies use sequencing in real-world clinical settings and collect data on both the application of sequencing and the impact of receiving genomic findings on study participants. They are occurring in the context of controversy over how to obtain consent for exome and genome sequencing.

scholarly journals Categorized Genetic Analysis in Childhood-Onset Cardiomyopathy

2020 ◽  
Vol 13 (5) ◽  
pp. 504-514
Author(s):  
Zuhair N. Al-Hassnan ◽  
Abdulrahman Almesned ◽  
Sahar Tulbah ◽  
Ali Alakhfash ◽  
Faten Alhadeq ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Genome Sequencing ◽  
Founder Mutation ◽  
Genetic Test ◽  
Whole Genome ◽  
Childhood Onset ◽  
Whole Exome ◽  
The Impact

Background: Childhood-onset cardiomyopathy is a heterogeneous group of conditions the cause of which is largely unknown. The influence of consanguinity on the genetics of cardiomyopathy has not been addressed at a large scale. Methods: To unravel the genetic cause of childhood-onset cardiomyopathy in a consanguineous population, a categorized approach was adopted. Cases with childhood-onset cardiomyopathy were consecutively recruited. Based on the likelihood of founder mutation and on the clinical diagnosis, genetic test was categorized to either (1) targeted genetic test with targeted mutation test, single-gene test, or multigene panel for Noonan syndrome, or (2) untargeted genetic test with whole-exome sequencing or whole-genome sequencing. Several bioinformatics tools were used to filter the variants. Results: Two-hundred five unrelated probands with various forms of cardiomyopathy were evaluated. The median age of presentation was 10 months. In 30.2% (n=62), targeted genetic test had a yield of 82.7% compared with 33.6% for whole-exome sequencing/whole-genome sequencing (n=143) giving an overall yield of 53.7%. Strikingly, 96.4% of the variants were homozygous, 9% of which were found in 4 dominant genes. Homozygous variants were also detected in 7 novel candidates ( ACACB, AASDH, CASZ1, FLII, RHBDF1, RPL3L, ULK1 ). Conclusions: Our work demonstrates the impact of consanguinity on the genetics of childhood-onset cardiomyopathy, the value of adopting a categorized population-sensitive genetic approach, and the opportunity of uncovering novel genes. Our data suggest that if a founder mutation is not suspected, adopting whole-exome sequencing/whole-genome sequencing as a first-line test should be considered.

scholarly journals Scalable analysis of multi-modal biomedical data

GigaScience ◽  
2021 ◽  
Vol 10 (9) ◽  
Author(s):  
Jaclyn Smith ◽  
Yao Shi ◽  
Michael Benedikt ◽  
Milos Nikolic
Keyword(s):  
Data Integration ◽  
Large Scale ◽  
Treatment Options ◽  
Complex Data ◽  
Biomedical Data ◽  
Data Types ◽  
Large Scale Data Processing ◽  
Scalable Analysis ◽  
Targeted Medicine ◽  
The Impact

Abstract Background Targeted diagnosis and treatment options are dependent on insights drawn from multi-modal analysis of large-scale biomedical datasets. Advances in genomics sequencing, image processing, and medical data management have supported data collection and management within medical institutions. These efforts have produced large-scale datasets and have enabled integrative analyses that provide a more thorough look of the impact of a disease on the underlying system. The integration of large-scale biomedical data commonly involves several complex data transformation steps, such as combining datasets to build feature vectors for learning analysis. Thus, scalable data integration solutions play a key role in the future of targeted medicine. Though large-scale data processing frameworks have shown promising performance for many domains, they fail to support scalable processing of complex datatypes. Solution To address these issues and achieve scalable processing of multi-modal biomedical data, we present TraNCE, a framework that automates the difficulties of designing distributed analyses with complex biomedical data types. Performance We outline research and clinical applications for the platform, including data integration support for building feature sets for classification. We show that the system is capable of outperforming the common alternative, based on “flattening” complex data structures, and runs efficiently when alternative approaches are unable to perform at all.

scholarly journals Competency Mapping Based on Identifying the Impact over the Productivity of SMEs

2019 ◽  
Vol 9 (2) ◽  
pp. 2236-2243
Keyword(s):  
Data Collection ◽  
Performance Appraisal ◽  
Large Scale ◽  
Manufacturing Sector ◽  
Mapping Method ◽  
Primary Data ◽  
Exploratory Research ◽  
Collection Method ◽  
Data Collection Method ◽  
The Impact

This study is based on identifying the applicability and benefits of competency mapping in Small Medium-sized Enterprises with context to Delhi-NCR region. The reason of choosing the manufacturing sector of Small Medium-sized Enterprises is that they don’t like to opt for such types of modern HR practices at their workplace due to many myths like increase of cost to the company, no direct benefit in adopting this practice, wastage of time etc. Ability advancement by Competency mapping is one of the most precise methods used by large-scale size companies. The small firms can also achieve the same result if this HR practice is properly implemented over their employees and their result should be further used for their development. Sometimes companies used the Competency mapping method for the performance appraisal of their employees, handling their conflicts but this is not just the limitation it can be used in other area also like for preparing the customized training schedule for individual employee. The author selected the certain competency factors, which are having the impact over the productivity, and on the basis of these factors a primary data collection method is used. This is an exploratory research design in which both primary and secondary data collection method is used. ANOVA test, Correlation and Chi-square tests is used for analyzing the data, conducted through SPSS version 22. The result of this study is applicable only for Delhi NCR region. The analysis-based recommendation is useful only for the SME’s manufacturing sector. Further research on this topic can be easily done because this research is based on limited sample size, budget and time constraints. The result of this study helps the Small Mediumsized Enterprises by disclosing for them various ways for full utilization of their available resources at workplace, performance evaluation of employees with ease and as per the pre established criterion, setting up of competency framework etc

scholarly journals Translating genotype data of 44,000 biobank participants into clinical pharmacogenetic recommendations: challenges and solutions

2018 ◽  
Author(s):  
Sulev Reisberg ◽  
Kristi Krebs ◽  
Mart Kals ◽  
Reedik Mägi ◽  
Kristjan Metsalu ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Exome Sequencing ◽  
Genome Sequencing ◽  
Drug Prescription ◽  
Cost Effective ◽  
Genotype Data ◽  
Whole Genome ◽  
Whole Exome ◽  
Drug Doses ◽  
The Impact

ABSTRACTPurposeBiomedical databases combining electronic medical records, phenotypic and genomic data constitute a powerful resource for the personalization of treatment. To leverage the wealth of information provided, algorithms are required that systematically translate the contained information into treatment recommendations based on existing genotype-phenotype associations.MethodsWe developed and tested algorithms for translation of pre-existing genotype data of over 44,000 participants of the Estonian biobank into pharmacogenetic recommendations. We compared the results obtained by whole genome sequencing, whole exome sequencing and genotyping using microarrays, and evaluated the impact of pharmacogenetic reporting based on drug prescription statistics in the Nordic countries and Estonia.ResultsOur most striking result was that the performance of genotyping arrays is similar to that of whole genome sequencing, whereas exome sequencing is not suitable for pharmacogenetic predictions. Interestingly, 99.8% of all assessed individuals had a genotype associated with increased risks to at least one medication, and thereby the implementation of pharmacogenetic recommendations based on genotyping affects at least 50 daily drug doses per 1000 inhabitants.ConclusionWe find that microarrays are a cost-effective solution for creating pre-emptive pharmacogenetic reports, and with slight modifications, existing databases can be applied for automated pharmacogenetic decision support for clinicians.

Mutagenesis of human genomes by endogenous mobile elements on a population scale

2021 ◽  
Author(s):  
Nelson T. Chuang ◽  
Eugene J. Gardner ◽  
Diane M. Terry ◽  
Jonathan Crabtree ◽  
Anup A. Mahurkar ◽  
...  
Keyword(s):  
Large Scale ◽  
Mobile Element ◽  
Disease Genes ◽  
Whole Genome ◽  
Computational Costs ◽  
Population Distributions ◽  
Human Genomes ◽  
Whole Exome ◽  
Population Scale ◽  
The Impact

Several large-scale Illumina whole-genome sequencing (WGS) and whole-exome sequencing (WES) projects have emerged recently that have provided exceptional opportunities to discover mobile element insertions (MEIs) and study the impact of these MEIs on human genomes. However, these projects also have presented major challenges with respect to the scalability and computational costs associated with performing MEI discovery on tens or even hundreds of thousands of samples. To meet these challenges, we have developed a more efficient and scalable version of our mobile element locator tool (MELT) called CloudMELT. We then used MELT and CloudMELT to perform MEI discovery in 57,919 human genomes and exomes, leading to the discovery of 104,350 nonredundant MEIs. We leveraged this collection (1) to examine potentially active L1 source elements that drive the mobilization of new Alu, L1, and SVA MEIs in humans; (2) to examine the population distributions and subfamilies of these MEIs; and (3) to examine the mutagenesis of GENCODE genes, ENCODE-annotated features, and disease genes by these MEIs. Our study provides new insights on the L1 source elements that drive MEI mutagenesis and brings forth a better understanding of how this mutagenesis impacts human genomes.

scholarly journals Haplocheck: Phylogeny-based Contamination Detection in Mitochondrial and Whole-Genome Sequencing Studies

2020 ◽  
Author(s):  
Hansi Weissensteiner ◽  
Lukas Forer ◽  
Liane Fendt ◽  
Azin Kheirkhah ◽  
Antonio Salas ◽  
...  
Keyword(s):  
Mitochondrial Genome ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Large Scale ◽  
Nuclear Dna ◽  
Nuclear Genome ◽  
Whole Genome ◽  
Sequencing Studies ◽  
Project Data ◽  
The Impact

AbstractWithin-species contamination is a major issue in sequencing studies, especially for mitochondrial studies. Contamination can be detected by analysing the nuclear genome or by inspecting the heteroplasmic sites in the mitochondrial genome. Existing methods using the nuclear genome are computationally expensive, and no suitable tool for detecting contamination in large-scale mitochondrial datasets is available. Here we present haplocheck, a tool that requires only the mitochondrial genome to detect contamination in both mitochondrial and whole-genome sequencing studies. Haplocheck is able to distinguish between contaminated and real heteroplasmic sites using the mitochondrial phylogeny. By applying haplocheck to the 1000 Genomes Project data, we show (1) high concordance in contamination estimates between mitochondrial and nuclear DNA and (2) quantify the impact of mitochondrial copy numbers on the mitochondrial based contamination results. Haplocheck complements leading nuclear DNA based contamination tools, and can therefore be used as a proxy tool in nuclear genome studies.Haplocheck is available both as a command-line tool at https://github.com/genepi/haplocheck and as a cloud web-service producing interactive reports that facilitates the navigation through the phylogeny of contaminated samples.

scholarly journals Scalable Analysis of Multi-Modal Biomedical Data

2020 ◽  
Author(s):  
Jaclyn M Smith ◽  
Yao Shi ◽  
Michael Benedikt ◽  
Milos Nikolic
Keyword(s):  
Data Integration ◽  
Large Scale ◽  
Treatment Options ◽  
Complex Data ◽  
Biomedical Data ◽  
Data Types ◽  
Large Scale Data Processing ◽  
Scalable Analysis ◽  
Targeted Medicine ◽  
The Impact

Targeted diagnosis and treatment options are dependent on insights drawn from multi-modal analysis of large-scale biomedical datasets. Advances in genomics sequencing, image processing, and medical data management have supported data collection and management within medical institutions. These efforts have produced large-scale datasets and have enabled integrative analyses that provide a more thorough look of the impact of a disease on the underlying system. The integration of large-scale biomedical data commonly involves several complex data transformation steps, such as combining datasets to build feature vectors for learning analysis. Thus, scalable data integration solutions play a key role in the future of targeted medicine. Though large-scale data processing frameworks have shown promising performance for many domains, they fail to support scalable processing of complex datatypes. To address these issues and achieve scalable processing of multi-modal biomedical data, we present TraNCE, a framework that automates the difficulties of designing distributed analyses with complex biomedical data types. We outline research and clinical applications for the platform, including data integration support for building feature sets for classification. We show that the system is capable of outperforming the common alternative, based on flattening complex data structures, and runs efficiently when alternative approaches are unable to perform at all.

scholarly journals Gastric Electrical Stimulation for Treatment of Refractory Gastroparesis: the Current Approach to Management

2021 ◽  
Vol 23 (2) ◽  
Author(s):  
Aaron Shanker ◽  
Mohammad Bashashati ◽  
Ali Rezaie
Keyword(s):  
Electrical Stimulation ◽  
Large Scale ◽  
Treatment Options ◽  
Current Approach ◽  
Gastric Electrical Stimulation ◽  
Effective Management ◽  
Treatment Pathway ◽  
Gastric Emptying Time ◽  
Cardinal Symptoms ◽  
The Impact

Abstract Purpose of Review Gastroparesis is one of the more challenging entities in the landscape of gastroenterology, posing difficulties for both patients and physicians with regard to effective management and therapies. In this article, we reviewed various gastroparesis treatment options, with an emphasis on gastric electrical stimulation (GES). Recent Findings GES has demonstrated a significant reduction of cardinal symptoms in refractory gastroparetic patients, particularly nausea and vomiting, across multiple studies. However, GES has not been shown to conclusively decrease gastric emptying time in these patients. Such finding has led the investigators to analyze the impact of combining GES with pyloroplasty. While this treatment pathway is nascent, its results thus far reveal an amplified improvement of gastroparesis symptomatology in addition to significant reduction of gastric transit, compared to GES by itself. Summary Limited treatment choices are available for refractory gastroparesis. Combining GES with pyloroplasty holds promise but requires further assessment in large-scale trials to fully evaluate the risks and benefits.

Surgery for Lung Cancer and the Consequences for the Swallow

2016 ◽  
Vol 1 (13) ◽  
pp. 162-168
Author(s):  
Pippa Hales ◽  
Corinne Mossey-Gaston
Keyword(s):  
Lung Cancer ◽  
Treatment Options ◽  
Vocal Cord Palsy ◽  
Subsequent Treatment ◽  
Physiological Reserve ◽  
Palliative Phase ◽  
And Function ◽  
The Impact ◽  
Swallow Function

Lung cancer is one of the most commonly diagnosed cancers across Northern America and Europe. Treatment options offered are dependent on the type of cancer, the location of the tumor, the staging, and the overall health of the person. When surgery for lung cancer is offered, difficulty swallowing is a potential complication that can have several influencing factors. Surgical interaction with the recurrent laryngeal nerve (RLN) can lead to unilateral vocal cord palsy, altering swallow function and safety. Understanding whether the RLN has been preserved, damaged, or sacrificed is integral to understanding the effect on the swallow and the subsequent treatment options available. There is also the risk of post-surgical reduction of physiological reserve, which can reduce the strength and function of the swallow in addition to any surgery specific complications. As lung cancer has a limited prognosis, the clinician must also factor in the palliative phase, as this can further increase the burden of an already compromised swallow. By understanding the surgery and the implications this may have for the swallow, there is the potential to reduce the impact of post-surgical complications and so improve quality of life (QOL) for people with lung cancer.

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