Ditte Demontis

Improved prediction of genetic predisposition to psychiatric disorders using genomic feature best linear unbiased prediction models

Publikation: KonferencebidragPosterForskning

Standard

Improved prediction of genetic predisposition to psychiatric disorders using genomic feature best linear unbiased prediction models. / Rohde, Palle Duun; Demontis, Ditte; Børglum, Anders; iPSYCH-Broad Consortium ; Sørensen, Peter.

2017. Poster session præsenteret ved ESHG 2017: European Society of Human Genetics Annual Meeting, Copenhagen, Danmark.

Publikation: KonferencebidragPosterForskning

Harvard

Rohde, PD, Demontis, D, Børglum, A, iPSYCH-Broad Consortium & Sørensen, P 2017, 'Improved prediction of genetic predisposition to psychiatric disorders using genomic feature best linear unbiased prediction models', ESHG 2017: European Society of Human Genetics Annual Meeting, Copenhagen, Danmark, 27/05/2017 - 30/05/2017.

APA

Rohde, P. D., Demontis, D., Børglum, A., iPSYCH-Broad Consortium, & Sørensen, P. (2017). Improved prediction of genetic predisposition to psychiatric disorders using genomic feature best linear unbiased prediction models. Poster session præsenteret ved ESHG 2017: European Society of Human Genetics Annual Meeting, Copenhagen, Danmark.

CBE

Rohde PD, Demontis D, Børglum A, iPSYCH-Broad Consortium, Sørensen P. 2017. Improved prediction of genetic predisposition to psychiatric disorders using genomic feature best linear unbiased prediction models. Poster session præsenteret ved ESHG 2017: European Society of Human Genetics Annual Meeting, Copenhagen, Danmark.

MLA

Rohde, Palle Duun o.a.. Improved prediction of genetic predisposition to psychiatric disorders using genomic feature best linear unbiased prediction models. ESHG 2017: European Society of Human Genetics Annual Meeting, 27 maj 2017, Copenhagen, Danmark, Poster, 2017.

Vancouver

Rohde PD, Demontis D, Børglum A, iPSYCH-Broad Consortium, Sørensen P. Improved prediction of genetic predisposition to psychiatric disorders using genomic feature best linear unbiased prediction models. 2017. Poster session præsenteret ved ESHG 2017: European Society of Human Genetics Annual Meeting, Copenhagen, Danmark.

Author

Rohde, Palle Duun ; Demontis, Ditte ; Børglum, Anders ; iPSYCH-Broad Consortium ; Sørensen, Peter. / Improved prediction of genetic predisposition to psychiatric disorders using genomic feature best linear unbiased prediction models. Poster session præsenteret ved ESHG 2017: European Society of Human Genetics Annual Meeting, Copenhagen, Danmark.

Bibtex

@conference{99e87f145aac41b1a0a81cae39751c5b,
title = "Improved prediction of genetic predisposition to psychiatric disorders using genomic feature best linear unbiased prediction models",
abstract = "Introduction: Accurate prediction of unobserved phenotypes from observed genotypes is essential for the success in predicting disease risk from genotypes. However, the performance is somewhat limited. Genomic feature best linear unbiased prediction (GFBLUP) models separate the total genomic variance into components capturing the variance by a genomic feature (e.g. GO term) and the remaining genomic variance by differential weighting of the genetic variants within the two groups. Previously we have demonstrated (on pigs and fruit flies) increased predictive ability when the genomic feature is enriched for causal variants. Here we apply the GFBLUP model to a small schizophrenia case-control study to test the promise of this model on psychiatric disorders, and hypothesize that the performance will be increased when applying the model to a larger ADHD case-control study if the genomic feature contains the causal variants.Materials and Methods: The schizophrenia study consisted of 882 controls and 888 schizophrenia cases genotyped for 520,000 SNPs. The ADHD study contained 25,954 controls and 16,663 ADHD cases with 8,4 million imputed genotypes. Results: The predictive ability for schizophrenia for the null model (all SNPs weighted equally) was low (0.07). Few GO terms did show a tendency of increased predictive ability; e.g. GO:0008645 had a predictive ability of 0.11 (unadjusted t-test p-value = 7.4x10-5), and explained 9% of the genomic variance, and 1.5% of the total phenotypic variance (0.6% for the null model).Conclusion: The improvement in predictive ability for schizophrenia was marginal, however, greater improvement is expected for the larger ADHD data. ",
author = "Rohde, {Palle Duun} and Ditte Demontis and Anders B{\o}rglum and {iPSYCH-Broad Consortium} and Peter S{\o}rensen",
year = "2017",
month = may,
day = "27",
language = "English",
note = "null ; Conference date: 27-05-2017 Through 30-05-2017",

}

RIS

TY - CONF

T1 - Improved prediction of genetic predisposition to psychiatric disorders using genomic feature best linear unbiased prediction models

AU - Rohde, Palle Duun

AU - Demontis, Ditte

AU - Børglum, Anders

AU - iPSYCH-Broad Consortium

AU - Sørensen, Peter

PY - 2017/5/27

Y1 - 2017/5/27

N2 - Introduction: Accurate prediction of unobserved phenotypes from observed genotypes is essential for the success in predicting disease risk from genotypes. However, the performance is somewhat limited. Genomic feature best linear unbiased prediction (GFBLUP) models separate the total genomic variance into components capturing the variance by a genomic feature (e.g. GO term) and the remaining genomic variance by differential weighting of the genetic variants within the two groups. Previously we have demonstrated (on pigs and fruit flies) increased predictive ability when the genomic feature is enriched for causal variants. Here we apply the GFBLUP model to a small schizophrenia case-control study to test the promise of this model on psychiatric disorders, and hypothesize that the performance will be increased when applying the model to a larger ADHD case-control study if the genomic feature contains the causal variants.Materials and Methods: The schizophrenia study consisted of 882 controls and 888 schizophrenia cases genotyped for 520,000 SNPs. The ADHD study contained 25,954 controls and 16,663 ADHD cases with 8,4 million imputed genotypes. Results: The predictive ability for schizophrenia for the null model (all SNPs weighted equally) was low (0.07). Few GO terms did show a tendency of increased predictive ability; e.g. GO:0008645 had a predictive ability of 0.11 (unadjusted t-test p-value = 7.4x10-5), and explained 9% of the genomic variance, and 1.5% of the total phenotypic variance (0.6% for the null model).Conclusion: The improvement in predictive ability for schizophrenia was marginal, however, greater improvement is expected for the larger ADHD data.

AB - Introduction: Accurate prediction of unobserved phenotypes from observed genotypes is essential for the success in predicting disease risk from genotypes. However, the performance is somewhat limited. Genomic feature best linear unbiased prediction (GFBLUP) models separate the total genomic variance into components capturing the variance by a genomic feature (e.g. GO term) and the remaining genomic variance by differential weighting of the genetic variants within the two groups. Previously we have demonstrated (on pigs and fruit flies) increased predictive ability when the genomic feature is enriched for causal variants. Here we apply the GFBLUP model to a small schizophrenia case-control study to test the promise of this model on psychiatric disorders, and hypothesize that the performance will be increased when applying the model to a larger ADHD case-control study if the genomic feature contains the causal variants.Materials and Methods: The schizophrenia study consisted of 882 controls and 888 schizophrenia cases genotyped for 520,000 SNPs. The ADHD study contained 25,954 controls and 16,663 ADHD cases with 8,4 million imputed genotypes. Results: The predictive ability for schizophrenia for the null model (all SNPs weighted equally) was low (0.07). Few GO terms did show a tendency of increased predictive ability; e.g. GO:0008645 had a predictive ability of 0.11 (unadjusted t-test p-value = 7.4x10-5), and explained 9% of the genomic variance, and 1.5% of the total phenotypic variance (0.6% for the null model).Conclusion: The improvement in predictive ability for schizophrenia was marginal, however, greater improvement is expected for the larger ADHD data.

M3 - Poster

Y2 - 27 May 2017 through 30 May 2017

ER -