Aarhus Universitets segl

English

Du er her: AU » Om AU » Organisation » Genomic prediction with incomplete omics data

Om AU

Genomic prediction with incomplete omics data

Publikation: Bidrag til bog/antologi/rapport/proceeding › Konferencebidrag i proceedings › Forskning › peer review

Standard

Genomic prediction with incomplete omics data. / Karaman, Emre; Milkevych, Viktor; Cai, Zexi et al.

WCGALP 2022 Programme book. Wageningen Academic Publishers, 2022.

Publikation: Bidrag til bog/antologi/rapport/proceeding › Konferencebidrag i proceedings › Forskning › peer review

Harvard

Karaman, E, Milkevych, V, Cai, Z, Janss, L, Sahana, G & Lund, MS 2022, Genomic prediction with incomplete omics data. i WCGALP 2022 Programme book. Wageningen Academic Publishers, World Congress on Genetics Applied to Livestock Production, Rotterdam, Holland, 03/07/2022. <https://www.wageningenacademic.com/pb-assets/wagen/WCGALP2022/13_012.pdf>

APA

Karaman, E., Milkevych, V., Cai, Z., Janss, L., Sahana, G., & Lund, M. S. (2022). Genomic prediction with incomplete omics data. I WCGALP 2022 Programme book Wageningen Academic Publishers. https://www.wageningenacademic.com/pb-assets/wagen/WCGALP2022/13_012.pdf

CBE

Karaman E, Milkevych V, Cai Z, Janss L, Sahana G, Lund MS. 2022. Genomic prediction with incomplete omics data. I WCGALP 2022 Programme book. Wageningen Academic Publishers.

MLA

Karaman, Emre et al. "Genomic prediction with incomplete omics data". WCGALP 2022 Programme book. Wageningen Academic Publishers. 2022.

Vancouver

Karaman E, Milkevych V, Cai Z, Janss L, Sahana G, Lund MS. Genomic prediction with incomplete omics data. I WCGALP 2022 Programme book. Wageningen Academic Publishers. 2022

Author

Karaman, Emre ; Milkevych, Viktor ; Cai, Zexi et al. / Genomic prediction with incomplete omics data. WCGALP 2022 Programme book. Wageningen Academic Publishers, 2022.

Bibtex

@inproceedings{5aa56b50a5f04e7c9427aaf45809b615,
title = "Genomic prediction with incomplete omics data",
abstract = "In animal breeding, there has been an increasing interest in investigating the added value of intermediate omics traits such as transcriptomes, metabolites and methylation patterns in genomic predictions. Such data are available only for small number of animals. The “singlestep genomic prediction” machinery, which was first proposed to combine pedigree information of a large number of individuals, and genomic information of a fraction of the population, can be useful to handle incomplete omics data. Such an approach, when applied to incomplete omics data scenarios, imply a simple linear relationship from genotypes to different omics traits, which in reality may be very complex. Little is known about the accuracy of genetic evaluations when the omics traits are generated for the whole population. Here, we present two different approaches to handle incomplete omics data, and investigate their impact on genomic predictions, using simulations. ",
author = "Emre Karaman and Viktor Milkevych and Zexi Cai and Luc Janss and Goutam Sahana and Lund, {Mogens Sand{\o}}",
year = "2022",
month = jul,
language = "English",
booktitle = "WCGALP 2022 Programme book",
publisher = "Wageningen Academic Publishers",
note = "null ; Conference date: 03-07-2022 Through 08-07-2022",
url = "https://wcgalp.com/",

}

RIS

TY - GEN

T1 - Genomic prediction with incomplete omics data

AU - Karaman, Emre

AU - Milkevych, Viktor

AU - Cai, Zexi

AU - Janss, Luc

AU - Sahana, Goutam

AU - Lund, Mogens Sandø

N1 - Conference code: 12

PY - 2022/7

Y1 - 2022/7

N2 - In animal breeding, there has been an increasing interest in investigating the added value of intermediate omics traits such as transcriptomes, metabolites and methylation patterns in genomic predictions. Such data are available only for small number of animals. The “singlestep genomic prediction” machinery, which was first proposed to combine pedigree information of a large number of individuals, and genomic information of a fraction of the population, can be useful to handle incomplete omics data. Such an approach, when applied to incomplete omics data scenarios, imply a simple linear relationship from genotypes to different omics traits, which in reality may be very complex. Little is known about the accuracy of genetic evaluations when the omics traits are generated for the whole population. Here, we present two different approaches to handle incomplete omics data, and investigate their impact on genomic predictions, using simulations.

AB - In animal breeding, there has been an increasing interest in investigating the added value of intermediate omics traits such as transcriptomes, metabolites and methylation patterns in genomic predictions. Such data are available only for small number of animals. The “singlestep genomic prediction” machinery, which was first proposed to combine pedigree information of a large number of individuals, and genomic information of a fraction of the population, can be useful to handle incomplete omics data. Such an approach, when applied to incomplete omics data scenarios, imply a simple linear relationship from genotypes to different omics traits, which in reality may be very complex. Little is known about the accuracy of genetic evaluations when the omics traits are generated for the whole population. Here, we present two different approaches to handle incomplete omics data, and investigate their impact on genomic predictions, using simulations.

UR - https://www.wageningenacademic.com/pb-assets/wagen/WCGALP2022/13_012.pdf

M3 - Article in proceedings

BT - WCGALP 2022 Programme book

PB - Wageningen Academic Publishers

Y2 - 3 July 2022 through 8 July 2022

ER -