Dataset Information

Counsellee's experience of cancer genetic counselling with pedigrees that automatically incorporate genealogical and cancer database information.

ABSTRACT: While pedigree drawing software is often utilised in genetic services, the use of genealogical databases in genetic counselling is unusual. This is mainly because of the unavailability of such databases in most countries. Electronically generated pedigrees used for cancer genetic counselling in Iceland create pedigrees that automatically incorporate information from a large, comprehensive genealogy database and nation-wide cancer registry. The aim of this descriptive qualitative study was to explore counsellees' experiences of genetic services, including family history taking, using these electronically generated pedigrees. Four online focus groups with 19 participants were formed, using an asynchronous posting method. Participants were encouraged to discuss their responses to questions posted on the website by the researcher. The main themes arising were motivation, information and trust, impact of testing and emotional responses. Most of the participants expressed trust in the method of using electronically generated pedigrees, although some voiced worries about information safety. Many experienced worry and anxiety while waiting for results of genetic testing, but limited survival guilt was noted. Family communication was either unchanged or improved following genetic counselling. The use of electronically generated pedigrees was well received by participants, and they trusted the information obtained via the databases. Age did not seem to influence responses. These results may be indicative of the particular culture in Iceland, where genealogical information is well known and freely shared. Further studies are needed to determine whether use of similar approaches to genealogical information gathering may be acceptable elsewhere.

SUBMITTER: Stefansdottir V

PROVIDER: S-EPMC4960030 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Counsellee's experience of cancer genetic counselling with pedigrees that automatically incorporate genealogical and cancer database information.

Stefansdottir Vigdis V Johannsson Oskar Th OT Skirton Heather H Jonsson Jon J JJ

Journal of community genetics 20160702 3

While pedigree drawing software is often utilised in genetic services, the use of genealogical databases in genetic counselling is unusual. This is mainly because of the unavailability of such databases in most countries. Electronically generated pedigrees used for cancer genetic counselling in Iceland create pedigrees that automatically incorporate information from a large, comprehensive genealogy database and nation-wide cancer registry. The aim of this descriptive qualitative study was to exp ...[more]

PMID: 27372834

Similar Datasets

Project description:A combined matrix that exploits genealogy together with marker-based information could improve the selection of elite individuals in breeding programs. We present genetic parameters for adaptive and growth traits in beef cattle by exploring linear combinations of pedigree-based (A) and marker-based (G) relationship matrices. We use a data set with 2,111 Brahman (BB) and 2,550 Tropical Composite (TC) cattle with genotypes for 729,068 SNP, and phenotypes for five traits. A weighted relationship matrix (WRM) combining G and A was constructed as WRM = ?G + (1 - ?)A. The weight (?) was explored at values from 0.0 to 1.0, at 0.1 intervals. Additionally, four alternative G matrices, in the WRM, were evaluated according to the selection of SNP used to generate them: 1) Gw: all autosomal SNP with minor allele frequency (MAF) > 1%; 2) Gg: autosomal SNP with MAF > 1% and mapped inside to gene coding regions; 3) Gp: autosomal SNP with MAF > 1% and previously reported to have significant pleiotropic effect in these two populations; and 4) Gc: autosomal SNP with MAF > 1% and with significant correlated effects previously reported in both BB and TC populations. In addition, two A matrices were evaluated: 1) A: all relationships between animals were considered after tracing back known ancestors; and 2) Ad: a distorted A matrix where a random 1% of the off-diagonal nonzero values were set to zero to simulate relationship errors. Five independent Ad matrices were explored each with a different random 1% of relationships masked. Criteria for comparing the resulting WRM included estimates of heritability (h2) and cross-validation accuracy (ACC) of genomic estimated breeding values. The choice of WRM had a greater impact on h2 than on ACC estimates. The 1% errors introduced in pedigree relationships generated large distortion in genetic parameters and ACC estimates. However, employing a ? > 0.7 was an efficient mechanism to compensate for the errors in A. Additionally, although significant (P-value < 0.0001), we found no consistent relationship between the type of SNP used to compute G and h2 or ACC estimates. We devised the optimal value of ? for maximum h2 and ACC at ? = 0.7 suggesting a 70% and 30% weighting to genomic and genealogical information, respectively, as an optimal strategy to compensate for pedigree errors, to improve genetic parameters estimates and lead to more accurate selection decisions.

Project description:BACKGROUND: The amount of available biological information is rapidly increasing and the focus of biological research has moved from single components to networks and even larger projects aiming at the analysis, modelling and simulation of biological networks as well as large scale comparison of cellular properties. It is therefore essential that biological knowledge is easily accessible. However, most information is contained in the written literature in an unstructured way, so that methods for the systematic extraction of knowledge directly from the primary literature have to be deployed. DESCRIPTION: Here we present a text mining algorithm for the extraction of kinetic information such as K(M), K(i), k(cat) etc. as well as associated information such as enzyme names, EC numbers, ligands, organisms, localisations, pH and temperatures. Using this rule- and dictionary-based approach, it was possible to extract 514,394 kinetic parameters of 13 categories (K(M), K(i), k(cat), k(cat)/K(M), V(max), IC(50), S(0.5), K(d), K(a), t(1/2), pI, n(H), specific activity, V(max)/K(M)) from about 17 million PubMed abstracts and combine them with other data in the abstract. A manual verification of approx. 1,000 randomly chosen results yielded a recall between 51% and 84% and a precision ranging from 55% to 96%, depending of the category searched.The results were stored in a database and are available as "KID the KInetic Database" via the internet. CONCLUSIONS: The presented algorithm delivers a considerable amount of information and therefore may aid to accelerate the research and the automated analysis required for today's systems biology approaches. The database obtained by analysing PubMed abstracts may be a valuable help in the field of chemical and biological kinetics. It is completely based upon text mining and therefore complements manually curated databases. The database is available at http://kid.tu-bs.de. The source code of the algorithm is provided under the GNU General Public Licence and available on request from the author.

Dataset Information

Counsellee's experience of cancer genetic counselling with pedigrees that automatically incorporate genealogical and cancer database information.

Publications

Counsellee's experience of cancer genetic counselling with pedigrees that automatically incorporate genealogical and cancer database information.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets