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Peter Sørensen

Genomic Feature Models

Publikation: KonferencebidragPaperForskningpeer review

Standard

Genomic Feature Models. / Sørensen, Peter; Edwards, Stefan McKinnon; Rohde, Palle Duun.

2014. Paper præsenteret ved 10th World Congress on Genetics Applied to Livestock Production (WCGALP), Vancouver, Canada.

Publikation: KonferencebidragPaperForskningpeer review

Harvard

Sørensen, P, Edwards, SM & Rohde, PD 2014, 'Genomic Feature Models', Paper fremlagt ved 10th World Congress on Genetics Applied to Livestock Production (WCGALP), Vancouver, Canada, 17/08/2014 - 22/08/2014. <https://asas.org/docs/default-source/wcgalp-proceedings-oral/303_paper_10280_manuscript_1285_0.pdf>

APA

Sørensen, P., Edwards, S. M., & Rohde, P. D. (2014). Genomic Feature Models. Paper præsenteret ved 10th World Congress on Genetics Applied to Livestock Production (WCGALP), Vancouver, Canada. https://asas.org/docs/default-source/wcgalp-proceedings-oral/303_paper_10280_manuscript_1285_0.pdf

CBE

Sørensen P, Edwards SM, Rohde PD. 2014. Genomic Feature Models. Paper præsenteret ved 10th World Congress on Genetics Applied to Livestock Production (WCGALP), Vancouver, Canada.

MLA

Sørensen, Peter, Stefan McKinnon Edwards og Palle Duun Rohde Genomic Feature Models. 10th World Congress on Genetics Applied to Livestock Production (WCGALP), 17 aug. 2014, Vancouver, Canada, Paper, 2014. 5 s.

Vancouver

Sørensen P, Edwards SM, Rohde PD. Genomic Feature Models. 2014. Paper præsenteret ved 10th World Congress on Genetics Applied to Livestock Production (WCGALP), Vancouver, Canada.

Author

Sørensen, Peter ; Edwards, Stefan McKinnon ; Rohde, Palle Duun. / Genomic Feature Models. Paper præsenteret ved 10th World Congress on Genetics Applied to Livestock Production (WCGALP), Vancouver, Canada.5 s.

Bibtex

@conference{7888ea16b7074f998b03daa2166ebec2,
title = "Genomic Feature Models",
abstract = "Whole-genome sequences and multiple trait phenotypes from large numbers of individuals will soon be available in many populations. Well established statistical modeling approaches enable the genetic analyses of complex trait phenotypes while accounting for a variety of additive and non-additive genetic mechanisms. These modeling approaches have proven to be highly useful to determine population genetic parameters as well as prediction of genetic risk or value. We present a series of statistical modelling approaches that use prior biological information for evaluating the collective action of sets of genetic variants. We have applied these approaches to whole genome sequences and a complex trait phenotype resistance to starvation collected on inbred lines from the Drosophila Genome Reference Panel population. We identified a number of genomic features classification schemes (e.g. prior QTL regions and gene ontologies) that provide better model fit and increase predictive ability of the statistical model for this trait.",
keywords = "Genomic Feature Models, Whole-genome sequences, Data integration",
author = "Peter S{\o}rensen and Edwards, {Stefan McKinnon} and Rohde, {Palle Duun}",
year = "2014",
month = aug,
day = "18",
language = "English",
note = "10th World Congress on Genetics Applied to Livestock Production (WCGALP), WCGALP ; Conference date: 17-08-2014 Through 22-08-2014",

}

RIS

TY - CONF

T1 - Genomic Feature Models

AU - Sørensen, Peter

AU - Edwards, Stefan McKinnon

AU - Rohde, Palle Duun

N1 - Conference code: 10th

PY - 2014/8/18

Y1 - 2014/8/18

N2 - Whole-genome sequences and multiple trait phenotypes from large numbers of individuals will soon be available in many populations. Well established statistical modeling approaches enable the genetic analyses of complex trait phenotypes while accounting for a variety of additive and non-additive genetic mechanisms. These modeling approaches have proven to be highly useful to determine population genetic parameters as well as prediction of genetic risk or value. We present a series of statistical modelling approaches that use prior biological information for evaluating the collective action of sets of genetic variants. We have applied these approaches to whole genome sequences and a complex trait phenotype resistance to starvation collected on inbred lines from the Drosophila Genome Reference Panel population. We identified a number of genomic features classification schemes (e.g. prior QTL regions and gene ontologies) that provide better model fit and increase predictive ability of the statistical model for this trait.

AB - Whole-genome sequences and multiple trait phenotypes from large numbers of individuals will soon be available in many populations. Well established statistical modeling approaches enable the genetic analyses of complex trait phenotypes while accounting for a variety of additive and non-additive genetic mechanisms. These modeling approaches have proven to be highly useful to determine population genetic parameters as well as prediction of genetic risk or value. We present a series of statistical modelling approaches that use prior biological information for evaluating the collective action of sets of genetic variants. We have applied these approaches to whole genome sequences and a complex trait phenotype resistance to starvation collected on inbred lines from the Drosophila Genome Reference Panel population. We identified a number of genomic features classification schemes (e.g. prior QTL regions and gene ontologies) that provide better model fit and increase predictive ability of the statistical model for this trait.

KW - Genomic Feature Models

KW - Whole-genome sequences

KW - Data integration

M3 - Paper

T2 - 10th World Congress on Genetics Applied to Livestock Production (WCGALP)

Y2 - 17 August 2014 through 22 August 2014

ER -