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Peter Sørensen

Functional validation of candidate genes detected by genomic feature models

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Standard

Functional validation of candidate genes detected by genomic feature models. / Rohde, Palle Duun; Østergaard, Solveig ; Kristensen, Torsten Nygaard; Sørensen, Peter; Loeschcke, Volker; F C Mackay, Trudy; Sarup, Pernille Merete.

I: G3: Genes, Genomes, Genetics (Bethesda), Bind 8, Nr. 5, 05.05.2018, s. 1659-1668.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Harvard

Rohde, PD, Østergaard, S, Kristensen, TN, Sørensen, P, Loeschcke, V, F C Mackay, T & Sarup, PM 2018, 'Functional validation of candidate genes detected by genomic feature models', G3: Genes, Genomes, Genetics (Bethesda), bind 8, nr. 5, s. 1659-1668. https://doi.org/10.1534/g3.118.200082

APA

Rohde, P. D., Østergaard, S., Kristensen, T. N., Sørensen, P., Loeschcke, V., F C Mackay, T., & Sarup, P. M. (2018). Functional validation of candidate genes detected by genomic feature models. G3: Genes, Genomes, Genetics (Bethesda), 8(5), 1659-1668. https://doi.org/10.1534/g3.118.200082

CBE

Rohde PD, Østergaard S, Kristensen TN, Sørensen P, Loeschcke V, F C Mackay T, Sarup PM. 2018. Functional validation of candidate genes detected by genomic feature models. G3: Genes, Genomes, Genetics (Bethesda). 8(5):1659-1668. https://doi.org/10.1534/g3.118.200082

MLA

Rohde, Palle Duun o.a.. "Functional validation of candidate genes detected by genomic feature models". G3: Genes, Genomes, Genetics (Bethesda). 2018, 8(5). 1659-1668. https://doi.org/10.1534/g3.118.200082

Vancouver

Rohde PD, Østergaard S, Kristensen TN, Sørensen P, Loeschcke V, F C Mackay T o.a. Functional validation of candidate genes detected by genomic feature models. G3: Genes, Genomes, Genetics (Bethesda). 2018 maj 5;8(5):1659-1668. https://doi.org/10.1534/g3.118.200082

Author

Rohde, Palle Duun ; Østergaard, Solveig ; Kristensen, Torsten Nygaard ; Sørensen, Peter ; Loeschcke, Volker ; F C Mackay, Trudy ; Sarup, Pernille Merete. / Functional validation of candidate genes detected by genomic feature models. I: G3: Genes, Genomes, Genetics (Bethesda). 2018 ; Bind 8, Nr. 5. s. 1659-1668.

Bibtex

@article{14c8bded57ba495a94c5d258f92ddbcd,
title = "Functional validation of candidate genes detected by genomic feature models",
abstract = "Understanding the genetic underpinnings of complex traits requires knowledge of the genetic variants that contribute to phenotypic variability. Reliable statistical approaches are needed to obtain such knowledge. In genome-wide association studies, variants are tested for association with trait variability to pinpoint loci that contribute to the quantitative trait. Because stringent genome-wide significance thresholds are applied to control the false positive rate, many true causal variants can remain undetected. To ameliorate this problem, many alternative approaches have been developed, such as genomic feature models (GFM). The GFM approach tests for association of set of genomic markers, and predicts genomic values from genomic data utilizing prior biological knowledge. We investigated to what degree the findings from GFM have biological relevance. We used the Drosophila Genetic Reference Panel to investigate locomotor activity, and applied genomic feature prediction models to identify gene ontology (GO) cate- gories predictive of this phenotype. Next, we applied the covariance association test to partition the genomic variance of the predictive GO terms to the genes within these terms. We then functionally assessed whether the identified candidate genes affected locomotor activity by reducing gene expression using RNA interference. In five of the seven candidate genes tested, reduced gene expression altered the phenotype. The ranking of genes within the predictive GO term was highly correlated with the magnitude of the phenotypic consequence of gene knockdown. This study provides evidence for five new candidate genes for locomotor activity, and provides support for the reliability of the GFM approach",
author = "Rohde, {Palle Duun} and Solveig {\O}stergaard and Kristensen, {Torsten Nygaard} and Peter S{\o}rensen and Volker Loeschcke and {F C Mackay}, Trudy and Sarup, {Pernille Merete}",
year = "2018",
month = may,
day = "5",
doi = "10.1534/g3.118.200082",
language = "English",
volume = "8",
pages = "1659--1668",
journal = "G3: Genes, Genomes, Genetics (Bethesda)",
issn = "2160-1836",
publisher = "Genetics Society of America",
number = "5",

}

RIS

TY - JOUR

T1 - Functional validation of candidate genes detected by genomic feature models

AU - Rohde, Palle Duun

AU - Østergaard, Solveig

AU - Kristensen, Torsten Nygaard

AU - Sørensen, Peter

AU - Loeschcke, Volker

AU - F C Mackay, Trudy

AU - Sarup, Pernille Merete

PY - 2018/5/5

Y1 - 2018/5/5

N2 - Understanding the genetic underpinnings of complex traits requires knowledge of the genetic variants that contribute to phenotypic variability. Reliable statistical approaches are needed to obtain such knowledge. In genome-wide association studies, variants are tested for association with trait variability to pinpoint loci that contribute to the quantitative trait. Because stringent genome-wide significance thresholds are applied to control the false positive rate, many true causal variants can remain undetected. To ameliorate this problem, many alternative approaches have been developed, such as genomic feature models (GFM). The GFM approach tests for association of set of genomic markers, and predicts genomic values from genomic data utilizing prior biological knowledge. We investigated to what degree the findings from GFM have biological relevance. We used the Drosophila Genetic Reference Panel to investigate locomotor activity, and applied genomic feature prediction models to identify gene ontology (GO) cate- gories predictive of this phenotype. Next, we applied the covariance association test to partition the genomic variance of the predictive GO terms to the genes within these terms. We then functionally assessed whether the identified candidate genes affected locomotor activity by reducing gene expression using RNA interference. In five of the seven candidate genes tested, reduced gene expression altered the phenotype. The ranking of genes within the predictive GO term was highly correlated with the magnitude of the phenotypic consequence of gene knockdown. This study provides evidence for five new candidate genes for locomotor activity, and provides support for the reliability of the GFM approach

AB - Understanding the genetic underpinnings of complex traits requires knowledge of the genetic variants that contribute to phenotypic variability. Reliable statistical approaches are needed to obtain such knowledge. In genome-wide association studies, variants are tested for association with trait variability to pinpoint loci that contribute to the quantitative trait. Because stringent genome-wide significance thresholds are applied to control the false positive rate, many true causal variants can remain undetected. To ameliorate this problem, many alternative approaches have been developed, such as genomic feature models (GFM). The GFM approach tests for association of set of genomic markers, and predicts genomic values from genomic data utilizing prior biological knowledge. We investigated to what degree the findings from GFM have biological relevance. We used the Drosophila Genetic Reference Panel to investigate locomotor activity, and applied genomic feature prediction models to identify gene ontology (GO) cate- gories predictive of this phenotype. Next, we applied the covariance association test to partition the genomic variance of the predictive GO terms to the genes within these terms. We then functionally assessed whether the identified candidate genes affected locomotor activity by reducing gene expression using RNA interference. In five of the seven candidate genes tested, reduced gene expression altered the phenotype. The ranking of genes within the predictive GO term was highly correlated with the magnitude of the phenotypic consequence of gene knockdown. This study provides evidence for five new candidate genes for locomotor activity, and provides support for the reliability of the GFM approach

U2 - 10.1534/g3.118.200082

DO - 10.1534/g3.118.200082

M3 - Journal article

C2 - 29519937

VL - 8

SP - 1659

EP - 1668

JO - G3: Genes, Genomes, Genetics (Bethesda)

JF - G3: Genes, Genomes, Genetics (Bethesda)

SN - 2160-1836

IS - 5

ER -