Guosheng Su

Review: How to improve genomic predictions in small dairy cattle populations

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Review : How to improve genomic predictions in small dairy cattle populations. / Lund, M S; van den Berg, I; Ma, P; Brøndum, R F; Su, G.

I: Animal, Bind 10, Nr. 6, 06.2016, s. 1042-1049.

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

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Author

Lund, M S ; van den Berg, I ; Ma, P ; Brøndum, R F ; Su, G. / Review : How to improve genomic predictions in small dairy cattle populations. I: Animal. 2016 ; Bind 10, Nr. 6. s. 1042-1049.

Bibtex

@article{c3818d4a30e244b5b754885c6dbe0583,
title = "Review: How to improve genomic predictions in small dairy cattle populations",
abstract = "This paper reviews strategies and methods to improve accuracies of genomic predictions from the perspective of a numerically small population. Improvements are realized by influencing one or both of the main factors: (1) improve or increase genomic connections to phenotypic records in training data. (2) Models and strategies to focus genomic predictions on markers closer to the causative variants. Combining populations into a joint reference population results in high improvements when combining populations of the same breed and diminishes as the genetic distance between populations increases. For distantly related breeds sophisticated Bayesian variable selection models in combination with denser markers sets or functional subsets of markers is needed. This is expected to be further improved by the efficient use of sequence information. In addition predictions can be improved by the use of phenotypes of genotyped and non-genotyped cows directly. For a small population the optimal approach will combine the above components.",
author = "Lund, {M S} and {van den Berg}, I and P Ma and Br{\o}ndum, {R F} and G Su",
year = "2016",
month = jun,
doi = "10.1017/S1751731115003031",
language = "English",
volume = "10",
pages = "1042--1049",
journal = "Animal",
issn = "1751-7311",
publisher = "Cambridge University Press",
number = "6",

}

RIS

TY - JOUR

T1 - Review

T2 - How to improve genomic predictions in small dairy cattle populations

AU - Lund, M S

AU - van den Berg, I

AU - Ma, P

AU - Brøndum, R F

AU - Su, G

PY - 2016/6

Y1 - 2016/6

N2 - This paper reviews strategies and methods to improve accuracies of genomic predictions from the perspective of a numerically small population. Improvements are realized by influencing one or both of the main factors: (1) improve or increase genomic connections to phenotypic records in training data. (2) Models and strategies to focus genomic predictions on markers closer to the causative variants. Combining populations into a joint reference population results in high improvements when combining populations of the same breed and diminishes as the genetic distance between populations increases. For distantly related breeds sophisticated Bayesian variable selection models in combination with denser markers sets or functional subsets of markers is needed. This is expected to be further improved by the efficient use of sequence information. In addition predictions can be improved by the use of phenotypes of genotyped and non-genotyped cows directly. For a small population the optimal approach will combine the above components.

AB - This paper reviews strategies and methods to improve accuracies of genomic predictions from the perspective of a numerically small population. Improvements are realized by influencing one or both of the main factors: (1) improve or increase genomic connections to phenotypic records in training data. (2) Models and strategies to focus genomic predictions on markers closer to the causative variants. Combining populations into a joint reference population results in high improvements when combining populations of the same breed and diminishes as the genetic distance between populations increases. For distantly related breeds sophisticated Bayesian variable selection models in combination with denser markers sets or functional subsets of markers is needed. This is expected to be further improved by the efficient use of sequence information. In addition predictions can be improved by the use of phenotypes of genotyped and non-genotyped cows directly. For a small population the optimal approach will combine the above components.

U2 - 10.1017/S1751731115003031

DO - 10.1017/S1751731115003031

M3 - Journal article

C2 - 26781646

VL - 10

SP - 1042

EP - 1049

JO - Animal

JF - Animal

SN - 1751-7311

IS - 6

ER -