Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs
Quantitative or complex traits are controlled by many genes and environmental factors. Most traits in livestock breeding are quantitative traits. Mapping genes and causative mutations generating the genetic variance of these traits is still a very active area of research in livestock genetics. S...
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2017-09-01
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Online Access: | https://www.arch-anim-breed.net/60/335/2017/aab-60-335-2017.pdf |
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doaj-db53496c87034a1bb65b463ca5ca6ed32020-11-25T00:11:59ZengCopernicus PublicationsArchives Animal Breeding0003-94382363-98222017-09-016033534610.5194/aab-60-335-2017Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designsM. Schmid0J. Bennewitz1University Hohenheim, Institute of Animal Science, Garbenstrasse 17, 70599 Stuttgart, GermanyUniversity Hohenheim, Institute of Animal Science, Garbenstrasse 17, 70599 Stuttgart, GermanyQuantitative or complex traits are controlled by many genes and environmental factors. Most traits in livestock breeding are quantitative traits. Mapping genes and causative mutations generating the genetic variance of these traits is still a very active area of research in livestock genetics. Since genome-wide and dense SNP panels are available for most livestock species, genome-wide association studies (GWASs) have become the method of choice in mapping experiments. Different statistical models are used for GWASs. We will review the frequently used single-marker models and additionally describe Bayesian multi-marker models. The importance of nonadditive genetic and genotype-by-environment effects along with GWAS methods to detect them will be briefly discussed. Different mapping populations are used and will also be reviewed. Whenever possible, our own real-data examples are included to illustrate the reviewed methods and designs. Future research directions including post-GWAS strategies are outlined.https://www.arch-anim-breed.net/60/335/2017/aab-60-335-2017.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
M. Schmid J. Bennewitz |
spellingShingle |
M. Schmid J. Bennewitz Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs Archives Animal Breeding |
author_facet |
M. Schmid J. Bennewitz |
author_sort |
M. Schmid |
title |
Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs |
title_short |
Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs |
title_full |
Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs |
title_fullStr |
Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs |
title_full_unstemmed |
Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs |
title_sort |
invited review: genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs |
publisher |
Copernicus Publications |
series |
Archives Animal Breeding |
issn |
0003-9438 2363-9822 |
publishDate |
2017-09-01 |
description |
Quantitative or complex traits
are controlled by many genes and environmental factors. Most traits in
livestock breeding are quantitative traits. Mapping genes and causative
mutations generating the genetic variance of these traits is still a very
active area of research in livestock genetics. Since genome-wide and dense
SNP panels are available for most livestock species, genome-wide association
studies (GWASs) have become the method of choice in mapping experiments.
Different statistical models are used for GWASs. We will review the
frequently used single-marker models and additionally describe Bayesian
multi-marker models. The importance of nonadditive genetic and
genotype-by-environment effects along with GWAS methods to detect them will
be briefly discussed. Different mapping populations are used and will also be
reviewed. Whenever possible, our own real-data examples are included to
illustrate the reviewed methods and designs. Future research directions
including post-GWAS strategies are outlined. |
url |
https://www.arch-anim-breed.net/60/335/2017/aab-60-335-2017.pdf |
work_keys_str_mv |
AT mschmid invitedreviewgenomewideassociationanalysisforquantitativetraitsinlivestockaselectivereviewofstatisticalmodelsandexperimentaldesigns AT jbennewitz invitedreviewgenomewideassociationanalysisforquantitativetraitsinlivestockaselectivereviewofstatisticalmodelsandexperimentaldesigns |
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